本篇博文主要内容为 2026-06-09 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 (2026-06-09)
今日共更新1412篇论文,其中:
- 自然语言处理共244篇(Computation and Language (cs.CL))
- 人工智能共510篇(Artificial Intelligence (cs.AI))
- 计算机视觉共277篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共438篇(Machine Learning (cs.LG))
- 多智能体系统共35篇(Multiagent Systems (cs.MA))
- 信息检索共31篇(Information Retrieval (cs.IR))
- 人机交互共44篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] FASE: Fast Adaptive Semantic Entropy for Code Quality
【速读】：该论文旨在解决多智能体代码生成中因大语言模型（LLM）幻觉及智能体间错误传播导致的系统可靠性问题。现有方法虽利用语义熵（semantic entropy）对不确定性进行量化，但普遍依赖计算成本高昂的LLM驱动等价性验证，限制了其在实际多智能体工作流中的应用。本文提出一种新型度量指标——快速自适应语义熵（Fast Adaptive Semantic Entropy, FASE），其核心创新在于基于结构与语义差异图的最小生成树（minimum spanning tree）构建功能正确性的近似评估机制，无需依赖昂贵的等价性检查。实验结果表明，在HumanEval和BigCodeBench基准上，FASE相较于基于LLM蕴含关系的先进语义熵方法，在斯皮尔曼相关系数上平均提升25%，在ROCAUC分数上相对于基于真实测试用例的Pass@1指标提升19%；同时，其计算开销仅为传统方法的约0.3%，显著降低了运行时成本。因此，FASE的关键突破在于实现了高精度不确定性量化与极低计算代价的平衡，为真实场景下的多智能体软件开发提供了高效、可扩展的解决方案。
链接: https://arxiv.org/abs/2606.09800
作者: Shizhe Lin,Ladan Tahvildari
机构: University of Waterloo(滑铁卢大学); University of Waterloo(滑铁卢大学)
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Multi-agent code generation offers a promising paradigm for autonomous software development by simulating the human software engineering lifecycle. However, system reliability remains hindered by LLM hallucinations and error propagation across interacting agents. While semantic entropy provides a principled way to quantify uncertainty without ground-truth answers, current methods often rely on costly LLM-driven equivalence checks. In this work, we introduce Fast Adaptive Semantic Entropy (FASE), a novel metric that approximates functional correctness based on the minimum spanning tree of structural and semantic dissimilarity graphs. Evaluations on HumanEval and BigCodeBench demonstrate that FASE outperforms state-of-the-art semantic entropy by LLM entailment, achieving a 25% average improvement in Spearman correlation and a 19% increase in ROCAUC score against Pass@1 from ground-truth test cases when using the Qwen3-Embedding-8B model. Furthermore, by eliminating costly LLM-driven equivalence evaluation, FASE incurs negligible computational overhead, requiring only approximately 0.3% of the runtime cost of traditional semantic entropy approaches. These results position FASE as a practical, cost-effective solution for optimizing uncertainty quantification in real-world multi-agent workflows.
[MA-1] Revisiting mesoscopic traffic flow simulation in SUMO: Limitations analysis and an alternative
【速读】：该论文旨在解决现有宏观-微观混合交通流模型（mesoscopic traffic flow model）在模拟城市交通拥堵动态时存在的理论不一致性与现实偏差问题。具体而言，当前基于Eissfeldt（2004）提出的模型在仿真平台SUMO中虽具备较高的计算效率并能刻画个体车辆行为，但其未严格遵循Lighthill-Whitham-Richards（LWR）模型的基本原理，导致对排队演化过程的考虑不完整以及后向传播空间（backward traveling space）的实现受限，从而引发拥堵起始与消散模式的失真，且普遍低估拥堵程度。解决方案的关键在于提出一种符合LWR理论的、基于离散时间的路段传输模型（link transmission model）的合理实现方式，通过显式引入后向传播空间以准确捕捉排队溢出（queue spillback）现象，使路段密度输出与运动波理论（kinematic wave theory）及SUMO中的微观仿真结果保持一致，从而在保证计算效率的同时显著提升模型对真实交通拥堵动态的表征精度。
链接: https://arxiv.org/abs/2606.09282
作者: Ying-Chuan Ni,Alina Akopian,Anastasios Kouvelas,Michail A. Makridis
机构: ETH Zurich (苏黎世联邦理工学院)
类目: ystems and Control (eess.SY); Multiagent Systems (cs.MA)
备注: Presentation at SUMO Conference 2026
Abstract:Mesoscopic traffic flow models combines the merits of both macroscopic and microscopic models by capturing individual vehicle behavior in great detail and remaining the computational efficiency. At the time of this study, the mesoscopic model proposed by Eissfeldt (2004) is used in Simulation of Urban MObility (SUMO). The movement of vehicles is governed by dynamic headways between edges. However, the model does not fully comply with the principle of the Lighthill-Whitham-Richards (LWR) model. Several problems are identified, including the incomplete consideration of queue dynamics and the limited implementation of backward traveling spaces. Two case study scenarios demonstrate that the problems lead to unrealistic onset and recovery pattern of congestion. The magnitude of congestion is generally underestimated with this model. To address these drawbacks, a proper mesoscopic discrete-time implementation of link transmission model, which follows the LWR principle, is proposed. By explicitly incorporating backward traveling spaces to capture queue spillback phenomena, the proposed model provides a more precise representation of congestion dynamics. The link density outputs are consistent with the kinematic wave theory and the microscopic traffic simulation in SUMO, thus verifying its theoretical accuracy.
[MA-2] Performance Evaluation of Social Learning
【速读】：该论文旨在解决分布式社会学习（Social Learning）框架下性能评估指标的有效性问题，特别是针对现有评价方法在刻画学习效率时存在的缺陷。其核心问题是：当前广泛采用的“拒识率”（rejection rate）作为衡量错误假设信念衰减速度的性能指标，会引发若干悖论，因而不适用于准确评估社会学习系统的整体性能。为此，论文转而研究“误差概率”（error probability）这一更为合理的度量方式，并以二元高斯模型为例，推导出个体代理误差概率与最优贝叶斯误差概率之比的解析表达式。该公式表明，该比例由两个关键因素决定：网络连通性的影响和先验信息的作用，二者共同构成乘积形式。由此揭示出一个不可消除的误差差距——即去中心化学习与集中式最优贝叶斯决策之间的误差概率差异，该差距依赖于具体代理且在渐近条件下仍无法消失，成为系统固有的性能限制。因此，该研究的关键贡献在于提出更可靠的性能评估范式，并揭示了网络结构与先验知识对学习性能的根本性影响。
链接: https://arxiv.org/abs/2606.09176
作者: Felice Scala,Marco Carpentiero,Vincenzo Matta,Ali H. Sayed
机构: 未知
类目: Multiagent Systems (cs.MA); Information Theory (cs.IT)
备注: This work has been submitted to the IEEE for possible publication
Abstract:Social Learning is a decentralized decision-making paradigm in which spatially dispersed agents collect streaming observations regulated by one of a finite number of models (the hypotheses). The agents are interested in assigning probability scores (the beliefs) to the possible hypotheses. To this end, the agents exchange their beliefs according to a certain communication graph. It has been shown that, under reasonable conditions on the identifiability of the decision model and the network connectivity, each agent ultimately places all the belief mass on the true hypothesis governing the data. However, several questions remain unanswered regarding the evaluation of the social learning performance. One recently adopted performance metric is the rejection rate, i.e., the rate at which the beliefs about the erroneous hypotheses vanish. One contribution of this work is to establish that the rejection rate leads to several paradoxes, which make it unsuitable as a valid performance measure. We then focus on studying the error probability measure. For a binary Gaussian problem, we derive an analytical formula characterizing the ratio between the individual agents’ probabilities and the optimal Bayesian probability. The formula shows that this ratio is expressed by the product of two terms quantifying the effect of the network connectivity and the role of the prior information. As a result, an irreducible gap emerges between the decentralized and the centralized error probabilities, which is agent-dependent and does not disappear asymptotically.
[MA-3] Autonomous Incident Resolution at Hyperscale: An Agent ic AI Architecture for Network Operations
【速读】：该论文旨在解决超大规模云网络基础设施中因故障的高频率、高速度与复杂性导致传统人工驱动的事件响应机制无法及时应对的问题。其核心解决方案是提出一种基于多智能体协同的自主事件处置（agentic AI）架构，关键在于通过分层智能体分解实现职责分工，基于技能的工具调用机制通过标准化协议实现高效协作，结合从运维手册中结构化编码的知识库支持决策，并采用渐进式自主性与安全边界控制确保系统可靠性，同时引入闭环验证机制保障操作有效性。该架构已在主流云服务商生产环境中部署，实证表明其在常见故障类别上可实现超过90%的自主修复率，同时通过分层授权与回滚机制维持安全约束，显著提升了大规模网络运维的自动化水平与韧性。
链接: https://arxiv.org/abs/2606.09122
作者: Arun Malik
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Multiagent Systems (cs.MA); Networking and Internet Architecture (cs.NI)
备注: 7 pages, 6 figures
Abstract:Cloud network infrastructure at hyperscale presents unique operational challenges where traditional human-driven incident response cannot keep pace with the volume, velocity, and complexity of failures. This paper presents an agentic AI architecture for autonomous incident resolution in large-scale network operations. Our system employs a multi-agent orchestration framework where specialized AI agents collaborate to detect, diagnose, and remediate network incidents without human intervention. We describe the architectural principles, including hierarchical agent decomposition, skills-based tool invocation via standardized protocols, structured knowledge encoding from operational runbooks, progressive autonomy with safety boundaries, and closed-loop verification. The architecture has been deployed in production at a major cloud provider, demonstrating that agentic AI systems can achieve autonomous resolution rates exceeding 90% for common incident categories while maintaining safety guarantees through layered authorization and rollback mechanisms. We discuss design tradeoffs, failure modes, and lessons learned from operating autonomous AI agents at scale.
[MA-4] A Multi-Agent System for IPMSM Design Optimization via an FEA-AI Hybrid Approach
【速读】：该论文旨在解决永磁同步电机（Interior Permanent Magnet Synchronous Motor, IPMSM）设计中多目标冲突与多物理场约束难以平衡的难题，同时克服现代优化流程中的三大瓶颈：人工问题建模、有限元分析（Finite Element Analysis, FEA）计算成本高，以及在稀疏或分布外区域中代理模型搜索不可靠的问题。其解决方案的关键在于提出一种端到端自动化的IPMSM设计优化框架，融合检索增强生成（Retrieval-Augmented Generation, RAG）与不确定性感知的FEA-AI混合优化流程。该框架通过设计代理（Design agent）连接电机教材知识库，利用RAG生成基于领域知识的设计选项与工程建议，并自动生成优化卡片与实验设计计划；训练代理（Training agent）自动化执行电磁FEA，记录几何验证与求解器失败日志，结合方差分析（ANOVA）与大语言模型（LLM）推理分析失败案例，驱动采样代理重新定义设计空间并生成新样本；优化代理（Optimization agent）采用基于遗传算法（GA）的搜索策略，结合不确定性驱动的切换机制：低不确定性候选由AI代理模型推断评估，而高不确定性及可靠性关键的帕累托前沿或前K名候选则通过高保真FEA修正，并用于迭代重训练。该框架将依赖经验的人工配置转化为可复现的工作流，在计算成本与预测可靠性之间实现有效权衡。实验结果表明，在相同高保真FEA预算下，所提混合方法在目标性能上优于纯FEA搜索（受限于早期预算耗尽）和纯AI搜索（收敛至低置信度最优解），同时保持更低且可进一步降低的预测不确定性。
链接: https://arxiv.org/abs/2606.09037
作者: Jinseong Han,Sunwoong Yang,Namwoo Kang
机构: KAIST(韩国科学技术院); Hanyang University (汉阳大学); Narnia Labs (纳尼亚实验室)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 26 pages, 21 figures
Abstract:Interior permanent magnet synchronous motor (IPMSM) design requires balancing conflicting objectives and multi-physics constraints, while modern optimization workflows face three bottlenecks: manual problem setup, high finite element analysis (FEA) cost, and unreliable surrogate-based search in sparse or out-of-distribution regions. To address these limitations, we propose an end-to-end automated IPMSM design optimization framework that integrates retrieval-augmented generation (RAG) for structured problem definition with an uncertainty-aware FEA-AI hybrid optimization pipeline. A Design agent, connected to a motor textbook through RAG, provides domain-knowledge-based options and engineering tips, and compiles an optimization card and a design-of-experiments plan for AI-model training. A Training agent automates electromagnetic FEA, records geometry-validation and solver-failure logs, analyzes failed geometries using ANOVA-based data analysis and LLM reasoning, and invokes a Design Sampling agent to redefine the design space and generate additional samples. An Optimization agent performs GA-based search with uncertainty-driven switching: low-uncertainty candidates are evaluated by AI-surrogate inference, whereas high-uncertainty and reliability-critical Pareto-front or top-K candidates are corrected by high-fidelity FEA and reused for iterative retraining. The framework converts manual, experience-dependent configuration into a reproducible workflow that balances computational cost and prediction reliability. Experimental results under a matched high-fidelity FEA budget show that the proposed hybrid approach achieves better objective performance while maintaining low and further reducible predictive uncertainty, outperforming FEA-only search, which is limited by early budget exhaustion, and AI-only search, which converges to a low-confidence optimum.
[MA-5] Hardening Agent Benchmarks with Adversarial Hacker-Fixer Loops
【速读】：该论文旨在解决当前代理基准测试（agent benchmark）中评估机制存在的可被“奖励欺骗”（reward hacking）的问题。现有评估系统普遍依赖人工编写的、结构脆弱的结局验证器（outcome verifier），导致前沿大模型仅凭任务描述即可找到漏洞，绕过真实求解过程而获得成功评分，从而污染排行榜排名与强化学习（RL）训练信号。其核心解决方案是提出“黑客-修复者循环”（hacker-fixer loop）机制，通过三个由大语言模型（LLM）驱动的代理角色——黑客（hacker）尝试在不真正解决问题的前提下通过验证器、修复者（fixer）针对已发现的漏洞对验证器进行修补、求解者（solver）则验证修补后的验证器仍能正确接纳合法解——形成闭环迭代优化。该循环持续重构验证器所奖励的行为模式，不断暴露新漏洞，实现无需逐任务手动修复的自适应抗攻击能力。研究进一步引入验证器访问共享机制，使修补策略可在不同任务间迁移，显著扩展了漏洞探测范围。实验表明，在KernelBench上，该方法将攻击成功率从62%降至0%；更令人惊讶的是，较弱代理（如Gemini 3 Flash）构建的循环可有效防御更强模型（如Gemini 3.1 Pro、Claude Opus 4.7）的攻击，将攻击成功率从76%和61%降至0%。作者发布了“Terminal Wrench”数据集，包含323个可被攻击的环境、3,632条攻击轨迹及完整修补方案，为未来构建鲁棒验证机制提供基准与工具支持。
链接: https://arxiv.org/abs/2606.08960
作者: Ziqian Zhong,Ivgeni Segal,Ivan Bercovich,Shashwat Saxena,Kexun Zhang,Aditi Raghunathan
机构: Carnegie Mellon University (卡内基梅隆大学); Fewshot Corp (Fewshot公司)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注:
Abstract:Agent benchmarks score submissions with outcome verifiers that are typically hand-written and brittle, leaving them open to reward hacking. We audit 1,968 tasks across five terminal-agent benchmarks and find 323 (16%) hackable by frontier models given only the task description. This corrupts both leaderboard rankings and RL training signal, yet the standard response is manual and reactive. We introduce the hacker-fixer loop, a method for building exploit-resistant verifiers without per-task manual patching. The loop alternates three LLM agents: a hacker tries to pass the verifier without solving the task, a fixer patches the verifier to reject each discovered exploit, and a solver confirms the patched verifier still admits legitimate solutions. The loop iterates: each patch reshapes what the verifier rewards, surfacing the next exploit. We further add verifier access, and let patches transfer across tasks, to broaden the exploits the loop discovers. On KernelBench, the loop drives the attack success rate from 62% to 0% on a held-out corpus of publicly reported exploits. We also find that weaker agents in the loop can defend against much stronger hackers: Gemini 3 Flash’s loop drives the stronger Gemini 3.1 Pro and Claude Opus 4.7’s attack success rate from 76% and 61% to 0% on KernelBench, and Gemini 3.1 Pro’s from 39% to 17% on Terminal Bench across 77 tasks. We release Terminal Wrench (323 hackable environments, 3,632 hack trajectories) as a snapshot of the current attack surface, our patched verifiers, the exploits the loop discovered, and our implementation as a basis for future work. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA) Cite as: arXiv:2606.08960 [cs.CR] (or arXiv:2606.08960v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2606.08960 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[MA-6] PerspectiveGap: A Benchmark for Multi-Agent Orchestration Prompting
【速读】：该论文旨在解决当前大语言模型（LLM）在多智能体系统（multi-agent systems）协同工作时，难以准确识别并分配各子智能体所需知识与职责的问题。其核心挑战在于如何设计有效的编排提示（orchestration prompting），以确保各智能体在复杂任务中能够高效协作、避免信息冗余或遗漏。解决方案的关键是提出一个名为PerspectiveGap的基准测试框架，通过110个基于真实工程实践提炼的场景，涵盖角色片段分配和自由格式提示撰写两种干扰项混合的任务形式，系统评估模型在不同拓扑结构下的编排能力。该基准以“提示经济”（Prompt Economy）原则为导向，强调构建以任务环路为中心的低开销、高效率的智能体协作架构。实验结果表明，尽管GPT-5.5在各项指标上显著领先，但整体模型表现仍十分有限，平均通过率仅为14.9%，信息泄露率高达246.5%，凸显了多智能体编排提示能力的独立性与评估不足，而PerspectiveGap为此类能力的系统性测量与优化提供了坚实基础。
链接: https://arxiv.org/abs/2606.08878
作者: Youran Sun,Xingyu Ren,Kejia Zhang,Xinpeng Liu,Jiaxuan Guo
机构: 未知
类目: Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注:
Abstract:Real-world LLM applications are moving beyond single-agent workflows toward orchestrated multi-agent systems, yet current models still struggle to determine what each sub-agent needs to know. To measure this, we introduce PerspectiveGap, a benchmark for evaluating LLMs’ ability to compose orchestration prompts for multi-agent systems. PerspectiveGap contains 110 scenarios, each evaluated through two distractor-mixed task formats: role-fragment assignment and free-form prompt writing. These scenarios are organized into 10 topologies, which are distilled from the authors’ real-world engineering practice and framed by the Prompt Economy principle: building loop-centered orchestrations that maximize utility with minimal role and engineering overhead. In experiments with 27 commercial models from 10 companies, GPT-5.5 substantially outperforms all competitors, whereas Opus 4.7 shows a notable weakness in orchestration prompting despite its strong coding performance. Nevertheless, PerspectiveGap remains challenging: the evaluated models achieve an average combined pass rate of only 14.9% (GPT-5.5 62.0%) and an average overall leakage rate of 246.5% (a per-scenario information leak-event count, not a proportion; GPT-5.5 49.1%). These findings suggest that multi-agent orchestration prompting is a distinct and under-evaluated capability, and PerspectiveGap provides a foundation for measuring and improving it systematically.
[MA-7] RAILS: Verification-Native Clearing For Agent ic Commerce
【速读】：该论文旨在解决智能体商业（agentic commerce）中的“代理清算问题”（agentic clearing problem），即在自主智能体（如自动谈判、采购代码、转移资金的AI代理）执行任务时，缺乏一个中立、可验证的机制来判定其是否履行了委托义务、责任归属如何确定，以及后续应采取何种结算行动。现有技术如工具协议（MCP）、代理间通信（A2A）、支付通道（x402）、授权与网络代理协议（AP2、Visa、Mastercard）及结算风险托管机制均未提供真正的清算功能，仅依赖于授权、支付或评分等非清算性操作，无法实现对交易结果的可信验证。
本文提出的解决方案核心是构建名为RAILS（Real-Time Agent Integrity Ledger Settlement）的完整性与清算层，其关键在于引入一套形式化的七元基本构件（Obligation Object、Evidence Envelope、Verification Mesh、Clearing Decision、Settlement Instruction、Clearing Passport、Finality Rules），并基于可接受性分级验证的形式化模型，确保任何具有财务影响的结算行为都必须建立在不低于义务可接受性阈值的证据之上。该设计实现了完备性属性（soundness property）：即不存在由低于义务可接受性门槛的证据支持的金融结算。这一属性可被形式化地验证与证伪，且在现有文献中尚无类似机制明确提出此类可验证的性质。因此，该论文的核心贡献在于首次为智能体经济系统定义并规范了一个具备可验证可靠性的原生验证型清算协议。
链接: https://arxiv.org/abs/2606.08790
作者: Adrian de Valois-Franklin,Alex Bogdan
机构: Evolutionairy AI(进化人工智能)
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Multiagent Systems (cs.MA)
备注: 49 pages, 15 figures
Abstract:Autonomous agents negotiate, purchase, deploy code, and move funds, but no neutral mechanism determines whether they met their delegated obligation, who is responsible when they did not, or which settlement action follows. This is the agentic clearing problem. Tool protocols (MCP), inter-agent communication (A2A), payment rails (x402), mandate and network agent protocols (AP2, Visa, Mastercard), and settlement-risk standards each assume that determination and none produce it. Clearing is the missing primitive. Payment is not clearing. Authorization is not clearing. LLM-as-judge evaluation is not clearing. Settlement-risk escrow is not clearing: it consumes clearing decisions. RAILS (Real-Time Agent Integrity Ledger Settlement) is the integrity and clearing layer for agentic commerce, spanning a per-output reliability score, a published reliability record, and a clearing function that consumes them. The clearing protocol at its core closes that gap. Seven primitives (Obligation Object, Evidence Envelope, Verification Mesh, Clearing Decision, Settlement Instruction, Clearing Passport, Finality Rules), bound by a formal model of admissibility-graded verification, together yield a soundness property: no financially material settlement is supported by evidence below the obligation’s admissibility floor. The property is falsifiable against the spec. We are not aware of a prior agent-commerce verification mechanism that states a property of this kind. The approaches nearest to it emit a pass, a delivery guarantee, a bare score, or an equilibrium. This paper specifies that clearing protocol. Comments: 49 pages, 15 figures Subjects: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Multiagent Systems (cs.MA) Cite as: arXiv:2606.08790 [cs.AI] (or arXiv:2606.08790v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2606.08790 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Alex Bogdan [view email] [v1] Sun, 7 Jun 2026 19:12:55 UTC (3,270 KB) Full-text links: Access Paper: View a PDF of the paper titled RAILS: Verification-Native Clearing For Agentic Commerce, by Adrian de Valois-Franklin and 1 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI prev | next new | recent | 2026-06 Change to browse by: cs cs.CR cs.MA 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(); About Help contact arXivClick here to contact arXiv Contact subscribe to arXiv mailingsClick here to subscribe Subscribe Copyright Privacy Policy Web Accessibility Assistance arXiv Operational Status
[MA-8] Is Telehealth Better Used to Treat Patients or Help Other Physicians Treat Patients? An Agent -Based Modeling Study of Healthcare Provision
【速读】：该论文旨在解决远程医疗（telehealth）在提升医疗服务可及性与优化医疗系统利用效率方面的实际效果问题，尤其关注其对临床结局和系统资源消耗的影响。研究发现，医生间远程会诊（physician-physician telehealth）能够有效改善患者健康状况，且不显著改变系统整体利用率，其效益随临床复杂度的增加而更加明显；相比之下，医生对患者的直接远程诊疗（physician-patient telehealth）虽导致成本和系统利用率上升，但并未带来临床结局的实质性改善。因此，该研究的核心解决方案在于强调：在当前医疗体系下，将远程医疗优先用于促进全科医生对专科知识的获取，相较于直接面向公众提供远程诊疗，更具成本效益与临床价值。
链接: https://arxiv.org/abs/2606.08701
作者: Michael Chary
机构: Weill Cornell Medical College (威尔·康奈尔医学院)
类目: Multiagent Systems (cs.MA); Computers and Society (cs.CY); Social and Information Networks (cs.SI)
备注: Presented at HICSS 2022
Abstract:Telehealth, the delivery of medical care remotely, is hoped to increase access to specialty services or decrease health care utilization. Physicians can provide telehealth to each other or to patients. Specialists often treat complex patients who can be adequately cared for only in academic hospitals, suggesting that providing specialty services via telehealth will reallocate rather than reduce system utilization. Here I use agent-based modeling to investigate telehealth’s effects on clinical outcomes and system utilization in medical toxicology. I found that physician-physician telehealth increased patient health but system utilization did not change. The effects were more pronounced as clinical complexity increased. Physician-patient telehealth increased cost and system utilization but not clinical outcomes. Within the limitations of our approach, these results suggest that telehealth is more cost-effective for improving generalist access to specialist knowledge than in providing care to the public.
[MA-9] Quantitative Promise Theory: Intentionality and Inference in Autonomous Agents
【速读】：该论文旨在解决自主代理（autonomous agents）在复杂系统中建模时面临的量化与协调难题，尤其关注如何在不确定性环境下实现代理间的有效协作与意图对齐。其核心问题在于传统概率方法（如贝叶斯推理）在处理多代理系统时所固有的非局部协调、概率校准与归一化等挑战。解决方案的关键在于将承诺理论（Promise Theory）与贝叶斯概率及信息论优化（包括主动推断，Active Inference）相结合，通过引入“承诺”作为约束边界条件的机制，来限定允许状态空间并选择决策阈值，从而为代理提供可扩展的意图定义（agent alignment）。该框架使得代理可通过最小化信息量自发凝聚成具有超智能体（superagent）特性的群体，即便在存在不确定性的情况下仍能实现高效协同。承诺理论不仅补充了概率方法的不足，还为多代理系统的可解释性与可扩展性提供了理论支撑。
链接: https://arxiv.org/abs/2606.08552
作者: Mark Burgess
机构: ChiTek-i AS (ChiTek-i AS)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Neural and Evolutionary Computing (cs.NE); Data Analysis, Statistics and Probability (physics.data-an)
备注:
Abstract:I discuss some quantitative representations of Promise Theory for processes involving autonomous agents. Agent models are common in software systems, machine learning, and biology, for example, but may also apply to physics and other forms of engineering. I describe how Bayesian probability and information theoretic optimization, including Active Inference, may be incorporated with promise semantics – as well as how Promise Theory supplements solutions, helping to avoid probability’s pitfalls, which include non-local coordination, calibrating, and normalizing probabilistic computations. The role of boundary conditions in constraining allowed states and selecting decision thresholds is a form of promise, and agent alignment provides a scalable definition of intent. Autonomous agents may congeal into swarms with superagent characteristics by trying to minimize their information, despite uncertainty that works to maximize it. The use of Promise Theory involves some research challenges as well as stylistic preferences.
[MA-10] he Consistency Illusion: How Multi-Agent Debate Hides Reasoning Misalignment
【速读】：该论文旨在解决多智能体大语言模型（Multi-agent LLM）在医学问答系统中因过度依赖答案级共识（answer-level consensus）而导致的“一致性幻觉”（consistency illusion）问题。尽管多个智能体在答案上达成一致，但其推理过程（reasoning chain）可能缺乏实质性的逻辑对齐，从而掩盖了潜在的错误推理或不一致认知。其解决方案的关键在于提出一种名为CARA（Cross-Agent Reasoning Alignment）的自动化度量方法，用于量化智能体在答案一致的同时是否在推理层面也保持一致。为缓解这一对齐缺失问题，研究进一步设计了基于提示工程的“有根基的辩论协议”（Grounded Debate Protocol, GDP），强制各智能体明确引用具体医学事实并公开回应其他智能体的主张，从而显著提升推理层面的一致性，实验显示其效应量（Cohen’s d）达+1.43至+1.99，且无需增加模型调用或修改系统架构。该工作强调在安全关键领域，除准确性外，跨智能体推理对齐（cross-agent reasoning alignment）应作为重要的可审计指标。
链接: https://arxiv.org/abs/2606.08457
作者: Xiaoyang Wang,Christopher C. Yang
机构: Drexel University
类目: Multiagent Systems (cs.MA)
备注:
Abstract:Multi-agent LLM systems for medical question answering often treat consensus as a reliability signal: if multiple agents agree on an answer, it is presumed trustworthy. However, answer-level consensus does not entail reasoning-level alignment. We introduce CARA (Cross-Agent Reasoning Alignment), a family of automated metrics that measure whether agents who agree on an answer also agree on the reasoning. Applying CARA to a standard debate system on two medical QA benchmarks, MedQA-USMLE and MedThink-Bench, we identify the consistency illusion: a failure mode where debate reduces detectable contradictions between agents while simultaneously decreasing the semantic similarity of their reasoning chains; agents appear to agree more but reason less consistently. To improve this misalignment, we propose the Grounded Debate Protocol (GDP), a prompt-level intervention that requires agents to commit to named medical facts and take explicit stances on other agents’ claims. GDP produces large, consistent alignment improvements, with Cohen’s d ranging from +1.43 to +1.99, across two datasets and two backbone models, without adding LLM calls or modifying system architecture. Our results motivate cross-agent reasoning alignment as a quantity to audit alongside accuracy in safety-critical domains.
[MA-11] SceneConductor: 3D Scene Generation from Single Image with Multi-Agent Orchestration
【速读】：该论文旨在解决从单张图像生成完整三维场景时面临的全局一致性难题，即如何在视觉信息高度模糊的情况下，准确推断出几何结构、物体间空间关系及环境上下文。现有方法多采用整体或弱分解的流水线，将多种因素耦合处理，并依赖大量场景级标注，导致在复杂真实场景中泛化能力受限。其解决方案的关键在于提出一种多智能体协同框架，将单图像3D场景生成过程系统性分解为三个阶段：场景初始化、环境构建与多智能体精炼。其中，场景初始化阶段通过图像分割提取物体掩码，构建物体级3D表示并预测初始空间布局；环境构建阶段基于初始化结果与点云地图的几何先验，生成支撑面、房间边界、材质与光照等环境骨架；精炼阶段则由规划者智能体识别结构与视觉不一致，执行简单修正，并调度专业智能体对局部复杂问题进行精细化调整，再将其重新融合至全局场景。为减少对场景级标注的依赖并提升结构初始化的可靠性，作者进一步引入一种基于稀疏几何先验（源自点云地图）训练的几何感知布局预测器，该模型仅需分割级数据即可训练，具备更强的跨场景泛化能力。大量基准测试表明，该方法在几何精度、空间一致性及感知真实性方面均显著优于现有方法。
链接: https://arxiv.org/abs/2606.08402
作者: Jeonghwan Kim,Yushi Lan,Yongwei Chen,Hieu Trung Nguyen,Chuanyu Pan,Xingang Pan
机构: Nanyang Technological University (南洋理工大学); University of Oxford (牛津大学); Meshy AI (Meshy AI)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Generating complete 3D scenes from a single image requires inferring globally consistent geometry, object relationships, and environmental context from inherently ambiguous visual evidence. Despite recent progress in joint layout-and-mesh generation, existing methods often rely on holistic or weakly decomposed pipelines that entangle many factors at once and demand extensive scene-level supervision, limiting their generalization to complex real-world environments. We propose a multi-agent orchestration framework that decomposes single-image 3D scene generation into three structured stages: scene initialization, environment construction, and multi-agent refinement. The initialization stage extracts image-derived object masks, builds object-level 3D representations, and predicts an initial spatial layout to form a coarse 3D scene. The environment-construction stage then leverages this initialization together with point-map geometry to build an environmental scaffold of supporting surfaces, room boundaries, materials, and illumination. Finally, in the refinement stage, a planner agent identifies structural and visual inconsistencies, applies simple corrections directly, and dispatches specialist agents for complex localized revisions that are reintegrated into the global scene. To provide reliable structural initialization while reducing reliance on scene-level annotations, we further introduce a geometry-aware layout predictor supervised by sparse geometric priors derived from point maps. Unlike fully supervised layout generators, the predictor can be trained from segmentation-level data and generalizes robustly to diverse real-world scenes. Extensive experiments on benchmark datasets show that our method consistently outperforms prior approaches in geometric accuracy, spatial consistency, and perceptual realism.
[MA-12] Emergence World: A Platform for Evaluating Long-Horizon Multi-Agent Autonomy
【速读】：该论文旨在解决当前大语言模型（LLM）智能体评估体系与实际自主系统部署场景之间存在的严重脱节问题。现有评估多采用短期、离散的任务形式，难以捕捉真实环境中长期演化所引发的关键动态，如行为漂移（behavioral drift）、跨环境治理适应性以及不同模型家族智能体间的交叉影响等。其解决方案的核心在于构建一个名为“Emergence World”的持续运行的多智能体仿真平台，该平台通过引入实时外部数据（如天气、新闻API、互联网访问），使智能体在共享的时空环境中长期交互；每个智能体配备120余种专业化工具及三种持久化记忆系统，并通过具有实质性后果的民主治理机制实现自我管理。平台在推理层具备模型无关性（model-agnostic），支持来自不同厂商的异构智能体共存于同一世界，从而能够有效观测和测量长时程自主系统的演化规律。研究通过为期15天的跨厂商实验验证了平台的有效性，揭示了在相同初始条件下不同模型群体可产生从稳定协商治理到整体崩溃的极端差异结果，凸显了长期演化视角下智能体自主性的复杂性与不可预测性。
链接: https://arxiv.org/abs/2606.08367
作者: Deepak Akkil,Ravi Kokku,Karthik Vikram,Tamer Abuelsaad,Aditya Vempaty,Satya Nitta
机构: Emergence AI
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注:
Abstract:Most evaluations of LLM agents look like exams: a discrete task, a clean environment, a score in minutes or hours. We argue that this approach is mismatched with the deployment conditions of autonomous systems, where the relevant timescale can be weeks to months, and where the dynamics that matter most, such as behavioral drift, governance in diverse environmental contexts, and cross-influence between agents from different model families, only emerge over time. We introduce Emergence World, a continuously running multi-agent simulation platform designed to make those dynamics measurable. The platform hosts populations of LLM-driven agents in a shared spatial world grounded in live external data (e.g. real-time weather, news APIs, internet access), equips each agent with 120+ specialized tools and three persistent memory systems, and lets them govern themselves through democratic mechanisms with consequential outcomes. The platform is model-agnostic at the reasoning layer and supports heterogeneous populations in which agents from different vendors share the same world. To illustrate the kinds of questions the platform makes tractable, we present a 15-day cross-vendor study with five parallel worlds powered by Claude Sonnet 4.6, Grok 4.1 Fast, Gemini 3 Flash, GPT-5-mini, and a mixed population. Identical roles and starting conditions produced radically different outcomes, ranging from stable deliberative governance to total population collapse. We release the prompts, log data and configurations to support further research on long-horizon multi-agent autonomy.
[MA-13] Benchmarking Open-Ended Multi-Agent Coordination in Language Agents
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在开放域多智能体协作任务中长期协调能力不足的问题。随着LLM被越来越多地部署为自主代理，其在复杂、非结构化环境中与多个其他智能体进行长时间、高动态交互的能力成为关键挑战，而现有评估体系往往局限于单智能体任务、短时交互或高度结构化的多智能体场景，难以全面检验此类能力。为此，作者提出了alem——一个基于JAX构建的、基于Craftax类动态机制的开放域多智能体协作基准测试平台。alem通过程序化生成的任务设计，嵌入了软性角色分化（soft specialisation）、通信机制以及可调控的协调难度，在包含探索、制作、交易和战斗等元素的长期生存世界中模拟真实复杂的协作需求。研究采用零样本（zero-shot）方式评估13个前沿LLM在同质团队中的表现，并以训练好的多智能体强化学习（Multi-Agent Reinforcement Learning, MARL）代理作为参照基准。结果表明，当前主流LLM代理在alem上的平均归一化回报仅为约6%，远未达到理想水平，且失败模式呈现显著差异：在最困难的协调设置下，零样本的Gemini-3.1-Pro-High已接近经过十亿步训练的MARL代理性能，而GPT-5.4-High虽在基础任务上表现优异，但协作奖励明显偏低，揭示了个体任务能力与协作能力之间存在显著脱节。消融实验进一步证明，通信是提升协作效果的最大贡献因素，而记忆与推理则在维持多步计划时发挥辅助作用。总体而言，本研究明确将“协作能力”识别为前沿LLM代理的一个独立瓶颈，区别于单智能体能力，而alem为量化该瓶颈并开发具备有效沟通、角色分配与共享计划执行能力的智能体提供了可控的实验平台。
链接: https://arxiv.org/abs/2606.08340
作者: Kale-ab Abebe Tessera,Andras Szecsenyi,Cameron Barker,Alexander Rutherford,Davide Paglieri,Aidan Scannell,Henry Gouk,Elliot J. Crowley,Tim Rocktäschel,Amos Storkey
机构: University of Edinburgh(爱丁堡大学); University of Oxford(牛津大学); University College London(伦敦大学学院)
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注: 42 pages, preprint
Abstract:As language models are increasingly deployed as autonomous agents, they must coordinate with others over long horizons in open-ended interactive tasks. Yet existing evaluations rarely test these demands together, instead emphasising single-agent tasks, short interactions, or highly structured multi-agent settings. We introduce alem , a JAX-based benchmark for open-ended multi-agent coordination built on Craftax-like dynamics. Alem embeds procedurally generated coordination tasks, soft specialisation, communication, and controllable coordination difficulty into a long-horizon survival world with exploration, crafting, trading, and combat. We evaluate 13 modern LLMs zero-shot within homogeneous teams, with trained MARL agents as reference points. Current LLM agents remain far from solving alem, averaging only ~6% normalised return, but their failures are not uniform. On the hardest coordination setting, zero-shot Gemini-3.1-Pro-High approaches MARL agents trained for one billion steps, while GPT-5.4-High achieves strong base-task reward but much lower coordination reward. This contrast shows that individual task competence does not imply coordination competence. Ablations show that communication is the largest contributor to coordination, while memory and reasoning help when used to maintain multi-step plans. Overall, our results identify coordination as a distinct bottleneck for frontier LLM agents, separate from single-agent capabilities. Alem makes this bottleneck measurable and provides a controlled testbed for developing agents that communicate, allocate roles, and execute shared plans. Code is available at this https URL.
[MA-14] o Nuke or Not to Nuke: LLM s (Missing) Ethical Reasoning and Actions in a High-Stakes Decision-Making Simulation
【速读】：该论文旨在解决生成式 AI（Generative AI）在复杂、长期决策场景中伦理能力失效的问题，特别是当大型语言模型（LLM）作为具备自主决策能力的智能体（agentic agent）时，其在抽象伦理推理任务中表现良好，但在真实多维度博弈环境中难以有效抑制极端行为（如核武器授权的激化）。其核心解决方案在于通过在《文明V》这一高度复杂的多人策略游戏中进行实证研究，系统评估多种提示干预（prompt interventions）——包括伦理提示强调核武器危害、移除前序决策逻辑、高风险情境框架强化现实影响——对抑制模型行为激化的有效性。研究发现，这些干预措施及其组合均无法可靠阻止模型在高张力情境下出现核升级行为，揭示出三种关键失败路径：伦理推理无法自发浮现、即使被触发也难以持续显现，或虽被激活但被战略权衡因素所压制。因此，论文强调，对智能体模型的伦理评估必须超越孤立的伦理问答测试，转而检验其在复杂决策语境中伦理推理是否能自发产生并真正影响行为，即评估其伦理能力的自发性与行为有效性。
链接: https://arxiv.org/abs/2606.08310
作者: John Chen,Sihan Cheng,Can Gurkan,H M Abdul Fattah
机构: University of Arizona (亚利桑那大学); Northwestern University (西北大学)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Large language models (LLMs) are increasingly deployed as long-horizon agents with decision-making capacities. While LLMs can show ethical competence on dilemmas such as trolley problems, this competence may not translate to complex, agentic scenarios. We study this gap in Civilization V, a multiplayer game with a complex decision-making landscape including economy, diplomacy, technology, and military strategy. Starting from 130 high-tension LLM self-play episodes, in which an LLM player spontaneously escalated nuclear authorization, we replay them across 13 models with three prompt interventions: an ethical prompt naming nuclear harm, removal of the previous model’s decision-making rationale, and high-stakes framing emphasizing real-world impacts. No interventions nor their combinations reliably eliminate emergent escalation. We identify three failure pathways: ethical reasoning that fails to surface without prompting, fails to appear even when prompted, or surfaces but fails to take effect when strategic counter-factors dominate. Evaluations of agentic models, therefore, must test whether ethical reasoning is spontaneously invoked and behaviorally effective in complex decision-making contexts, beyond whether it can be elicited in isolation.
[MA-15] oward Human-Centered Multi-Agent Systems: Integrating Cognition Culture Values and Cooperation in AI Agents
【速读】：该论文旨在解决当前基于大语言模型（LLM）的智能体在复杂人类社会环境中存在的根本性局限问题，即现有智能体仍局限于以预测、优化和任务完成为核心的“任务能力”范式，未能充分具备理解人类社会性、文化情境与价值信念的“以人为本”能力。其核心挑战在于：尽管生成式AI在语言生成、规划与多智能体协作方面取得进展，但缺乏将认知机制、文化背景、价值观体系与社会行为整合为统一框架的能力，导致智能体难以在真实人类社会中实现可信、可解释且符合社会规范的自主决策。解决方案的关键在于构建一种融合认知科学、社会语言学、计算社会科学与人工智能对齐理论的综合性框架，通过引入文化对齐基准、偏好学习、可解释性技术以及对人类信念系统与社会规范的建模，推动多智能体系统从单纯的任务执行者向具备认知基础、文化敏感性、价值对齐与合作意识的下一代智能体演进。
链接: https://arxiv.org/abs/2606.08274
作者: Safia Baloch,Rahemeen Khan
机构: Ghulam Ishaq Khan Institute of Engineering Sciences and Technology (GIKI)(古勒姆·伊沙克·汗工程科学与技术学院)
类目: Multiagent Systems (cs.MA)
备注: 14 pages
Abstract:The emergence of large language model (LLM)-based agents and multi-agent systems has enabled a shift from narrow task automation to more autonomous decision-making. Despite progress in language generation, planning, tool use, and coordination, most agents still treat intelligence as prediction, optimization, and task completion. Human environments are social and normative, where people reason under bounded rationality, communicate in culturally situated language, and make decisions guided by values, beliefs, trust, and social norms. This survey argues that future AI agents, especially those acting on behalf of humans, must move beyond task competence toward human-centered capabilities. We review research across six areas: (1) evolution of intelligent agents, (2) human cognition and decision-making, (3) language, culture, and social context, (4) human values and belief systems, (5) human-agent collaboration, and (6) multi-agent coordination and modeling of human characteristics. We synthesize work from cognitive science, sociolinguistics, computational social science, and AI alignment, along with recent advances in LLM agents, cultural alignment benchmarks, preference learning, explainability, and agent societies. We identify a key gap: existing systems do not provide a unified framework integrating cognition, culture, values, and social behavior into autonomous agents. We conclude with directions for building culturally aware, value-aligned, cognitively grounded, and cooperative multi-agent systems. Comments: 14 pages Subjects: Multiagent Systems (cs.MA) Cite as: arXiv:2606.08274 [cs.MA] (or arXiv:2606.08274v1 [cs.MA] for this version) https://doi.org/10.48550/arXiv.2606.08274 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[MA-16] Silent Failure in LLM Agent Systems: The Entropy Principle and the Inevitable Disorder of Autonomous Agents
【速读】：该论文旨在解决大语言模型（Large Language Model, LLM）智能体系统中一类隐蔽性故障（silent failures）的根本成因问题，即在无外部触发（如对抗输入、资源耗尽或注入攻击）的正常运行条件下，系统仍会出现行为偏离预期的现象。这类故障长期被误判为软件缺陷或配置错误，实则源于系统内在结构演化带来的熵增。其核心解决方案的关键在于提出“熵原则”（Entropy Principle），即通过数学形式化表达系统熵（S(t) = S₀ × e^(αt)）随交互轮次单调增长的规律，并识别出22项贯穿智能体生命周期六层（基础语义、跨智能体传输、记忆持久性、任务执行、反馈修正与系统演进）的内在属性，当这些属性共现时将导致输出一致性、任务准确性和跨会话连贯性的持续退化。为此，作者设计了物理完整性门控（PIG, Physical Integrity Gate）引擎与代理交付工程（ADE, Agent Delivery Engineering）协议套件，作为应对熵驱动无序的工程对策，强调应将智能熵视为需通过确定性治理进行管理的物理约束，而非单纯修复的“缺陷”，从而实现大规模复杂智能体系统的可靠演化。
链接: https://arxiv.org/abs/2606.08162
作者: Dexing Liu
机构: Shanghai Qijing Digital Technology Co., Ltd.(上海启景数字科技有限公司)
类目: Multiagent Systems (cs.MA)
备注: 10 pages, 7 figures
Abstract:Large Language Model (LLM) agent systems suffer from failures that occur without external triggers – no injection, no adversarial input, no resource exhaustion. These silent failures – unexpected deviations from intended behavior under normal conditions – are routinely misattributed to bugs or configuration errors. Through systematic analysis of over 40,000 controlled trials and long-term production observations spanning 100,000+ agent interactions, we identify a common structural logic underlying these failures. Building on patterns observed in our experiments, we survey the global research literature on autonomous agent reliability and synthesize 22 intrinsic properties of LLM agent systems across six lifecycle layers: foundation semantics, inter-agent transmission, memory persistence, task execution, feedback correction, and systemic evolution. We demonstrate that whenever a sufficient subset of these properties co-exist, system entropy – the measurable accumulation of disorder: loss of output consistency, task accuracy, and cross-session coherence – increases monotonically with interaction rounds. We formalize this as the Entropy Principle: S(t) = S0 * e^(alpha * t), with alpha measured empirically across multiple architectures. We propose the PIG (Physical Integrity Gate) Engine with the ADE (Agent Delivery Engineering) protocol suite as an engineering countermeasure to entropy-driven disorder. Our findings establish silent failure not as a bug to be fixed but as a manifestation of Intelligence Entropy – a physical constraint to be managed through deterministic governance. We argue that any engineering effort stabilizing the structure and order of agent systems participates in a unified mission: keeping intelligent systems reliable as they grow in scale and complexity. Comments: 10 pages, 7 figures Subjects: Multiagent Systems (cs.MA) Cite as: arXiv:2606.08162 [cs.MA] (or arXiv:2606.08162v1 [cs.MA] for this version) https://doi.org/10.48550/arXiv.2606.08162 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Dexing Liu [view email] [v1] Sat, 6 Jun 2026 13:22:07 UTC (409 KB) Full-text links: Access Paper: View a PDF of the paper titled Silent Failure in LLM Agent Systems: The Entropy Principle and the Inevitable Disorder of Autonomous Agents, by Dexing LiuView PDFHTML (experimental)TeX Source view license Current browse context: cs.MA prev | next new | recent | 2026-06 Change to browse by: cs References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); About Help contact arXivClick here to contact arXiv Contact subscribe to arXiv mailingsClick here to subscribe Subscribe Copyright Privacy Policy Web Accessibility Assistance arXiv Operational Status
[MA-17] PACE: Anytime-Valid Acceptance Tests for Self-Evolving Agents
【速读】：该论文旨在解决自演化智能体（self-evolving agents）在持续自我改进过程中因采纳机制（acceptor）设计不当而导致的虚假更新与性能退化问题。现有方法普遍关注生成候选变更的提议器（proposer），而忽视了采纳规则的可靠性；当前广泛采用的“若得分提升则保留”规则在反复应用于噪声扰动的验证集时，实质上构成了不受控的自适应多重假设检验，导致智能体产生大量虚假采纳（false commits）和有害修改，引发系统性漂移与性能衰减。本文提出一种无需训练、具备任意时间有效性（anytime-valid）的采纳门控机制——PACE（Paired Anytime-valid Commit Evaluation），将采纳决策重新建模为成对序列假设检验，并基于“以赌定测”（testing-by-betting）的e-process累积决定性证据，实现对每个候选变更的严格错误采纳概率控制，即使在可选停止（optional stopping）条件下仍能保证每步决策的置信水平。实验表明，在Qwen2.5系列模型（0.5B–3B）于GSM8K、SVAMP及ARC-Challenge任务上的提示级自演化中，贪婪采纳策略会引入30%-42%的虚假更新与10%-33%的有害修改，而PACE仅采纳真实有效改进，几乎无误报，且在保持与贪婪相当的验证准确率的同时，显著降低方差（约18%评估成本下降）。当无真实收益时，贪婪策略平均每轮引入13-21次虚假自修改（72%-100%为虚假），使最脆弱代理性能下降4.9分，而PACE则稳定维持基线表现。研究结论强调：自演化系统的可靠性关键在于采纳机制，而非仅依赖提议器。
链接: https://arxiv.org/abs/2606.08106
作者: Zayx Shawn
机构: Independent Researcher(独立研究员)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Self-evolving agents improve by repeatedly proposing changes to their own prompts, skills, or workflows and keeping those that score higher on a small held-out set. Almost all effort has gone into the proposer that generates candidates; we argue the weak point is the acceptor, the rule that decides whether to commit a change. Applied hundreds of times against the same noisy dev estimate, the ubiquitous “keep it if the score went up” rule is uncontrolled adaptive multiple testing: the agent effectively p-hacks itself, accumulating false commits that make it churn and drift rather than improve. We recast committing as a sequential hypothesis test and propose PACE (Paired Anytime-valid Commit Evaluation), a training-free, anytime-valid commit gate. Each candidate is compared to the incumbent on identical instances and committed only when a testing-by-betting e-process accumulates decisive evidence, stopping early to save evaluations and controlling each candidate’s false-commit probability at a user-set level even under optional stopping (a per-decision guarantee). On Qwen2.5 agents (0.5B-3B) self-evolving at the prompt level on GSM8K, SVAMP, and ARC-Challenge, greedy acceptance commits 30-42% false and 10-33% harmful edits when a genuine improvement is hidden among noisy proposals, while PACE commits the real one and essentially nothing else, matching greedy’s held-out accuracy at sharply lower variance and about 18% lower evaluation cost. With no real gain available, greedy commits 13-21 spurious self-modifications per run (72-100% false) and degrades the most fragile agent by 4.9 points, while PACE holds at baseline. Reliability of self-evolution depends on the acceptor, not only on the proposer. Subjects: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) Cite as: arXiv:2606.08106 [cs.AI] (or arXiv:2606.08106v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2606.08106 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[MA-18] Continual Quadruped Robots Coordination via Semantic Skill Discovery
【速读】：该论文旨在解决多足机器人在开放持续学习场景下进行连续协调任务时面临的挑战，即如何在不断接收新任务的同时，有效学习新协作技能并保留已有知识，避免灾难性遗忘。现有方法多依赖于多智能体强化学习（MARL）训练特定任务的协调策略，但难以适应任务序列动态变化的开放环境。其解决方案的关键在于提出一种语义技能库框架Conquer，将连续多足协调建模为“检索-适应-更新”三阶段过程：首先设计团队结构化的自盟友目标（SAG）骨干网络，通过显式建模每个机器人的状态、队友上下文及任务目标，支持不同规模的机器人团队；针对每项新任务，系统基于预执行信息生成任务级语义描述符，并从技能库中检索最相关技能进行适应性调整；任务成功执行后，通过提取轨迹级语义描述符并按语义距离组织，实现技能库的持续积累与跨任务知识迁移。实验结果表明，Conquer在仿真环境中达到95.6%的平均成功率，展现出优异的前向迁移能力与几乎可忽略的灾难性遗忘现象，真实机器人平台上的部署验证了其在实际多足协同中的可行性。
链接: https://arxiv.org/abs/2606.08102
作者: Daoqing Wang,Yuchen Xiao,Weixuan Huang,Zhilong Zhang,Shenghua Wan,Meng Li,Lei Yuan,Yang Yu
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 22 pages, 8 figures, 11 tables. Project page: this https URL
Abstract:Multi-quadruped coordination has attracted increasing attention due to its enhanced payload capacity, broader contact coverage, and improved adaptability to challenging tasks. Existing methods for multi-quadruped manipulation typically focus on predefined or closed task families, often relying on multi-agent reinforcement learning (MARL) to train task-specific coordination policies. However, such methods struggle in open-ended continual learning settings, where tasks arrive sequentially and robots are expected to acquire new coordination skills while reusing previously learned ones without catastrophic forgetting. To address this challenge, we propose Conquer, a semantic skill-library framework that formulates continual multi-quadruped coordination as a retrieve-adapt-update process. First, to accommodate varying team sizes across tasks, we design a team-structured Self-Allies-Goal (SAG) backbone that supports variable-cardinality robot teams by explicitly modeling each robot’s own state, teammate context, and task goal. For each incoming task, Conquer constructs a task-level semantic descriptor from pre-execution information and retrieves a relevant skill from the library for adaptation. After successful execution, Conquer updates the skill library by extracting trajectory-level semantic descriptors and organizing them according to semantic distance, thereby enabling continual skill accumulation and cross-task knowledge transfer. Simulation experiments show that Conquer achieves a final average success rate of 95.6%, demonstrating strong forward transfer and negligible catastrophic forgetting. Real-world rollouts on Unitree Go2 teams further validate the deployment feasibility of Conquer for practical multi-quadruped coordination. Simulation and real-robot demonstration videos are available at: this https URL.
[MA-19] SKILL.nb: Selective Formalization and Gated Execution for Durable Agent Workflows
【速读】：该论文旨在解决生成式AI代理在长期使用中因环境漂移、任务定义不明确或任务分布变化导致的可复用工作流（reusable agent workflows）可靠性下降问题，尤其在网页自动化场景下尤为突出。其核心挑战在于：即使某工作流在特定条件下成功执行一次，也无法保证在后续运行中持续有效，从而影响系统的稳健性与可维护性。解决方案的关键在于提出一种基于证据校准生命周期策略（evidence-calibrated lifecycle policies）的框架——this http URL，通过选择性形式化（selective formalization）实现动态决策：利用执行过程中的多模态证据（如输出、截图、错误追踪等）判断哪些流程步骤应转化为可执行代码，哪些应保留为自然语言引导，并在检测到环境漂移时自动触发修订机制。工作流以可审计、版本化的笔记本形式存储，集成自然语言指导、多语言可执行单元、验证门控、回退路径与多模态证据。运行时采用门控条件执行（gate-conditioned execution），仅当验证门控通过时才执行代码，否则本地回退以应对漂移。实验表明，该方法在WebArena-Verified上实现53.7%的一轮成功率，优于最强基线3.9个百分点；在三次重执行中保持91.7%的初始成功任务，领先次优方法15.5个百分点；在有限修复下恢复72.9%的失败任务，且修复后回归率仅为4.2%，显著优于基线的15.0%–17.0%。此外，在跨网站、跨领域任务及GitLab迁移测试中均表现出优异的泛化能力。结果表明，生命周期治理与门控执行机制是超越一次性任务成功的可靠性关键维度。
链接: https://arxiv.org/abs/2606.08049
作者: Amine El Hattami,Nicolas Chapados,Christopher Pal
机构: ServiceNow Research; Mila; Polytechnique Montréal; Canada CIFAR AI Chair
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:AI agents increasingly turn past experience into reusable artifacts such as code, workflows, and procedural memories. Reuse can improve efficiency, but it also creates a lifecycle reliability problem: artifacts that succeed once may fail under environment drift, underspecified tasks, or changing task distributions, especially in web automation. We introduce this http URL, a framework for governing reusable agent workflows with evidence-calibrated lifecycle policies. this http URL uses selective formalization: execution evidence decides which workflow steps should become executable code, which should remain natural-language guided, and when those choices should be revised. Workflows are stored as auditable, versioned notebooks that interleave natural-language guidance, multi-language executable cells, validation gates, fallback paths, and multimodal evidence such as outputs, screenshots, and error traces. At runtime, gate-conditioned execution lets each step run code when its gates validate, or fall back locally when drift invalidates the executable realization. On WebArena-Verified, this http URL achieves 53.7% single-round success, improving over the strongest baseline by 3.9 percentage points. Across three re-executions, it retains 91.7% of initially successful tasks, 15.5 points above the next best method. Under bounded repair, it recovers 72.9% of subsequent failures while limiting post-repair regressions to 4.2%, compared with 15.0% to 17.0% for persistent baselines. It also leads on Mind2Web cross-website and cross-domain splits. In a GitLab migration test, this http URL preserves performance when reusing frozen state learned on GitLab 15.7, with frozen-versus-fresh target-version gaps of -1.7 points on GitLab 16.11 and +0.6 points on GitLab 18.9. These results identify lifecycle governance and gate-conditioned execution as reliability axes beyond one-shot task success.
[MA-20] Voting Protocols as Coordination Mechanisms for Role-Constrained Multi-Agent Tutoring Systems ICML2026
【速读】：该论文旨在解决生成式教学系统中多智能体协作的协调难题：尽管多个角色特化的教学智能体（分别负责脚手架、误解纠正、动机激励与元认知引导）可能提出合理且差异化的干预策略，但每次仅能向学习者输出单一响应。其核心解决方案在于通过设计并比较四种投票机制（简单投票、排序投票、累计投票与同意投票），探究集体决策规则如何在部分教学目标冲突的情境下影响智能体间的协作模式。研究基于SciQ和HumanEval两个基准的模拟教学环境，通过对1200次模拟交互的分析发现，智能体的协商过程与投票协议类型显著影响最终采纳的干预策略，表明二者均深刻塑造了集体决策结果。不同投票规则催生出各异的协作行为模式，并在短暂的教学回合中即体现出可测量的学习成效。研究揭示，投票协议的选择直接关联于角色专业化教学智能体之间的协调模式，凸显了决策机制在构建高效教学协同系统中的关键作用。
链接: https://arxiv.org/abs/2606.08030
作者: Eric S. Qiu,Joyce Gill
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注: Accepted to ICML 2026 Workshop on AI4Good
Abstract:Agentic tutoring systems introduce a coordination challenge: multiple agents may propose different but reasonable interventions, yet only one response can be delivered to the learner. In this paper, we study how voting protocols shape cooperation among four role-constrained pedagogical agents responsible for scaffolding, misconception, motivation, and metacognition. We compare four voting protocols – simple, ranked, cumulative, and approval voting – across two simulated tutoring environments on SciQ and HumanEval benchmarks. Rather than using voting as a simple aggregation step, we use it to analyze how collective decision rules shape coordination under partial pedagogical conflict. Across 1,200 simulated interactions, we find that agent deliberation and voting protocol type frequently change which response ultimately wins, showing that both meaningfully shape the collective decision. Different voting rules also produce distinct coordination behaviors, and even brief tutoring turns show measurable learning gains in simulated students. Overall, we show that protocol choice is associated with distinct coordination patterns among role-specialized pedagogical agents.
[MA-21] Semantic Quorum Assurance: Collective Certification for Non-Deterministic AI Infrastructure
【速读】：该论文旨在解决大规模语言模型（Large Language Model, LLM）代理在自治云运维中引入的语义可靠性问题：尽管提议代理生成的操作在语法上合法且具备静态授权，但其实际执行可能带来操作上的安全隐患，例如修改身份与访问管理（IAM）策略、开放防火墙安全组或执行数据导出等。传统分布式共识协议仅能复制确定性状态转换，无法评估提议意图的安全性。为此，论文提出一种名为语义法定保证（Semantic Quorum Assurance, SQA）的控制平面原语，用于治理非确定性智能体驱动的基础设施。SQA 的核心在于将提案封装为绑定加密证据链的声明式执行契约，并将其路由至由只读、沙箱化验证代理组成的多样化评审小组；通过风险自适应的法定人数判定机制，综合考虑模型与原型多样性、基于校准保证分数动态加权，并尊重特定原型的否决权，最终仅允许经审核的提案通过主权执行网关执行。研究在云原生控制平面中实现了 SQA，并形式化构建了非确定性验证者之间的相关认知失效模型。在 500 个受基础设施启发的变异场景测试中，排除模糊案例后，相较于单代理验证 18.5% 的不安全批准率，SQA 将该比例降至 0.3%，同时在不同风险等级下仅增加 1.45–4.12 秒的中位验证延迟。
链接: https://arxiv.org/abs/2606.08021
作者: Jun He,Deying Yu
机构: OpenKedge.io
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 21 pages, 2 figures, 6 tables
Abstract:As large language model (LLM) agents are integrated into autonomous cloud operations, distributed systems face a semantic reliability problem: proposer agents can generate production mutations, such as modifying IAM policies, opening firewall security groups, or executing data exports, that are syntactically valid and statically authorized but operationally unsafe. Classical distributed consensus protocols replicate deterministic state transitions but do not evaluate the safety of the proposed intent. To address this gap, we introduce Semantic Quorum Assurance (SQA), a control-plane primitive for governing non-deterministic agentic infrastructure. SQA represents proposals as declarative execution contracts bound to cryptographic evidence chains and routes them to a diverse panel of read-only, sandboxed validator agents. SQA aggregates their judgments under a risk-adaptive quorum predicate that enforces model and archetype diversity, adjusts weights based on calibrated assurance scores, and respects archetype-specific vetoes. Admitted proposals execute only through a sovereign execution gate. We instantiate SQA in a cloud-native control plane and formalize a correlated cognitive failure model for non-deterministic validators. On 500 infrastructure-inspired mutation scenarios, with safety results reported on held-out safe/unsafe trials excluding ambiguous scenarios, SQA reduces unsafe approval from 18.5% for single-agent validation to 0.3% while adding median validation latency of 1.45–4.12 seconds across the studied risk buckets.
[MA-22] EduMirror: Modeling Educational Social Dynamics with Value-driven Multi-agent Simulation ICML2026
【速读】：该论文旨在解决教育社会动态演化研究中长期存在的因果推断困境：传统观察性研究缺乏因果效力，而受控实验又常因伦理限制难以实施。现有基于大语言模型（LLM）的多智能体仿真方法虽具备可扩展性，但受限于心理机制建模薄弱及对潜在心理状态测量不足。为此，本文提出EduMirror——一个面向教育场景的多智能体仿真系统，其核心创新在于构建了基于心理学需求与社会价值取向的价值驱动型智能体，并引入双轨测量协议，同步量化可观测行为与潜在心理状态。通过校园欺凌与群体合作等案例研究及多场景评估，验证了EduMirror生成的教育社会动态在现实性、理论一致性及可测量性方面均表现优异，为教育科学中的假设检验与反事实干预分析提供了可结构化、可重复的计算研究工具。
链接: https://arxiv.org/abs/2606.07948
作者: Jingzhe Lin,Hengbin Yu,Yongdan Zeng,Fangwei Zhong
机构: 未知
类目: Multiagent Systems (cs.MA); Computers and Society (cs.CY)
备注: ICML 2026
Abstract:Understanding how educational social dynamics evolve is critical for informing effective educational policies and counterfactual interventions. However, traditional methods face a fundamental dilemma: observational studies often lack causal power, while controlled experiments are frequently constrained by ethical concerns. Although LLM-based multi-agent simulations offer a scalable in silico alternative, existing approaches remain limited by weak psychological grounding and insufficient measurement of latent psychological states. To address this, we introduce EduMirror, a multi-agent simulator for the scientific study of educational social dynamics. We provide configurable education-oriented agent forms, including value-driven agents grounded in psychological needs and social value orientation, together with a dual-track measurement protocol for quantifying observable behaviors and latent psychological states. We validate the realism and usability of EduMirror through case studies on school bullying and group cooperation, as well as broader evaluations across diverse educational scenarios. The results show that EduMirror generates educational social dynamics that are realistic, theory-consistent, and measurable by empirical criteria. These properties enable structured in silico educational research, providing a computational tool for hypothesis testing and counterfactual intervention analysis in educational science. Project page: this https URL.
[MA-23] Overcoming the Regulatory Bottleneck via Agent -to-Agent Protocols: A Nuclear Case Study
【速读】：该论文旨在解决先进核反应堆设计监管审查周期过长（超过三年）且成本高昂（累计耗资数亿美元）的核心问题，其根源在于现行监管机构与申请人之间依赖人工主导的非结构化沟通流程，导致信息传递效率低下、重复劳动严重。解决方案的关键在于提出“监管情境协议”（Regulatory Context Protocol, RCP），一种基于代理间（Agent-to-Agent）通信的标准机制，通过构建可审计、结构化的智能体协作通道，替代传统的人工对接流程，同时在关键安全决策点保留人类监督。RCP以美国核管会（NRC）1,236份先进堆文件为基准进行校准，并通过多智能体原型验证，相较重建基线（8900万美元、42个月），可实现成本降低50%-77%（2100万-4400万美元）、时间缩短65%（15个月）。相比之下，孤立运行的智能体仅能实现5400万-7400万美元成本和21个月周期，表明剩余差距源于组织间协作的结构性瓶颈，而非算法局限。该瓶颈同样存在于制药审批、环境许可、金融监管及航空认证等领域，因此该协议具备广泛适用性；若全面推广，预计每年可节省2100亿至3300亿美元，约占美国GDP的1%。
链接: https://arxiv.org/abs/2606.07866
作者: Akshay J. Dave,David Grabaskas,Joseph A. Renevitz,Richard B. Vilim
机构: Argonne National Laboratory(阿贡国家实验室); Idaho National Laboratory(爱达荷国家实验室)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 26 pages, 10 figures
Abstract:Regulatory review of advanced nuclear reactor designs routinely spans more than three years and consumes hundreds of millions of dollars in combined regulator and applicant labor. We present the Regulatory Context Protocol (RCP), an Agent-to-Agent communication standard that replaces the formal human-to-human pipeline between regulators and applicants with a structured, auditable agentic channel, while preserving human oversight at safety-significant decision points. The protocol is calibrated against an analysis of 1,236 documents from U.S. Nuclear Regulatory Commission advanced reactor dockets and demonstrated with a working multi-agent pilot. Against an 89M USD, 42-month Reconstructed Baseline, RCP cuts costs by 50-77 percent (21M-44M USD) and timelines by 65 percent (15 months). Without a shared protocol, Standalone Agents reach only 54M-74M USD and 21 months. The residual cost-and-time gap is structural, not algorithmic: it traces to the inter-organizational pipeline that only an agent-to-agent standard can compress. The same bottleneck - formal multi-party review under strict auditability requirements - characterizes pharmaceutical approvals, environmental permitting, financial supervision, and aviation certification. The US regulatory paperwork burden carries a 426.5 billion USD annual opportunity cost; replicated broadly, the projected 50-77 percent reduction implies savings on the order of 210-330 billion USD per year - approaching 1 percent of US GDP.
[MA-24] Cost-Aware Speculative Execution for LLM -Agent Workflows: An Integrated Five-Dimension Method
【速读】：该论文旨在解决大语言模型代理（LLM-agent）工作流中因串行依赖导致的大量空闲等待时间问题，即下游操作必须等待上游完成才能启动，造成显著的时延浪费。其核心解决方案是通过推测性执行（speculative execution）提前启动下游操作，以利用原本空闲的计算资源。关键创新在于提出一套系统化的设计决策体系：（D1）在上游任务完成前即启动下游操作；（D2）将每次推测按输入与输出分别计费，实现精确的美元级成本量化；（D3）通过单一操作旋钮统一调控延迟与成本权衡；（D4）采用基于期望值的决策规则，引入失败加权成本项与偏好调整阈值；（D5）使用贝叶斯β-二项分布后验概率估计成功概率，且先验参数由依赖类型分类体系（dependency-type taxonomy）决定。该方法仅在满足可接受性前提（无副作用、幂等性或可在提交屏障后暂存）的边上传播推测，以避免不可逆副作用。研究提供了运行时机制、随上游分支因子增长而自限的闭式结果、五阶段校准流程（离线回放、影子测试、灰度发布、在线校准、漂移触发熔断），以及覆盖八类生产场景的工作负载适配框架。与四种最相近系统（DSP、Speculative Actions v2、Sherlock、B-PASTE）的对比表明，在所有维度均具差异化优势，合成验证套件亦证实了预测的决策边界、概率阈值、后验恢复能力及流式取消行为。
链接: https://arxiv.org/abs/2606.07846
作者: Faisal Fareed
机构: AWS(亚马逊网络服务)
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:LLM-agent workflows chain model calls and tool invocations, and spend most of their wall-clock time waiting on upstream operations before downstream ones can start. Speculative execution can reclaim that idle time by launching a downstream operation with a predicted upstream input, but here each speculation costs real money (per-token billing) and its success probability is hard to estimate and drifts over time. This paper presents a method organized around five design decisions: (D1) start a downstream operation before its upstream completes; (D2) price each speculation in real dollars at separate input and output rates; (D3) expose a single operator dial for latency versus cost; (D4) decide via an expected-value rule with a failure-weighted cost term and a preference-adjusted threshold; and (D5) estimate the success probability with a Bayesian Beta-Binomial posterior whose prior is keyed to a dependency-type taxonomy. Variants of these ideas appear in recent work; the combination, with every decision logged in dollars, is what is new. The rule fires only on edges passing an admissibility precondition (side-effect-free, idempotent, or stageable behind a commit barrier), since a wrong speculation is rolled back by re-execution, which refunds tokens but cannot un-send an irreversible side effect. We specify the runtime mechanics, a closed-form result that the rule self-limits as the upstream branching factor grows, a five-stage calibration pipeline (offline replay, shadow, canary, online calibration, drift-triggered kill-switch), and a workload-fit rubric over eight production archetypes. Contrast tables against the four closest published systems (DSP, Speculative Actions v2, Sherlock, B-PASTE) show differentiators on every dimension, and a synthetic validation suite confirms the predicted decision boundary, probability threshold, posterior recovery, and streaming-cancellation behavior.
[MA-25] GRPO Does Not Close the Multi-Agent Coordination Gap
【速读】：该论文旨在解决当前大型语言模型在多智能体协作场景下，尤其是在共享有限资源（如哲学家进餐问题中的筷子）时的协调能力不足问题。其核心挑战在于如何有效训练模型以实现稳定、高效的多智能体协作策略。研究的关键发现是：尽管部分前沿闭源系统在该任务中表现出一定性能（平均奖励0.45至0.87），但开源模型如Qwen3-14B表现较差（0.13至0.35），且采用基于任务自身回放数据的组相对策略优化（Group Relative Policy Optimization, GRPO）无法显著提升性能——统计检验显示在五名哲学家场景下无显著差异（p = 0.66，Hedges’ g = -0.11）。进一步分析揭示，训练过程中的奖励设计缺陷（四元组奖励存在零动作下的退化解，导致模型趋向不行动）、检查点选择机制依赖最终训练步而非最优中间状态，以及缺乏跨问题规模的课程学习，共同构成了开放权重14B模型在多智能体协调上的主要瓶颈。因此，提升多智能体协作能力的关键并非计算资源，而在于改进训练方法学，包括设计抗退化的奖励函数、建立合理的检查点选取策略以及引入分阶段的训练课程。
链接: https://arxiv.org/abs/2606.07845
作者: Najmul Hasan,Prashanth BusiReddyGari
机构: University of North Carolina at Pembroke (北卡罗来纳大学彭布罗克分校)
类目: Multiagent Systems (cs.MA); Machine Learning (cs.LG)
备注: 15 pages, 15 figures
Abstract:We measure how well current large language models coordinate as multiple agents sharing a common resource, using the dining philosophers problem as a clean test bed. Across 630 episodes spanning seven models and three philosopher counts, four frontier closed-source systems reach mean reward 0.45 to 0.87 and Mistral-Small 24B reaches 0.83 to 0.99, while Qwen3-14B reaches 0.13 to 0.35. We then ask whether group relative policy optimization (GRPO) on rollouts from the task itself can close the gap and find that it cannot: a Welch’s t-test on per-episode reward at five philosophers gives p = 0.66 and a Hedges’ g of -0.11, with no statistically significant change at ten or fifteen philosophers either. Two further observations qualify the result. The training reward of both 8B and 14B runs peaked at step nine and then declined, so the default saved checkpoint at step 15 is strictly worse than several earlier ones. The four-term reward we use admits a degenerate maximum at zero actions, which DeepSeek-R1-Distill-Qwen-7B and Mistral-Small 24B at five philosophers both inhabit, with mean reward 1.0 and 0.83 respectively at zero meals. The bottleneck for an open-weight 14B model on multi-agent coordination is not training compute but training methodology: reward shaping that does not collapse to a no-action maximum, checkpoint discipline that does not depend on the final step, and curriculum across problem scales.
[MA-26] Cherry-pick Override: Unsafe Directional Commitment in LLM Judges under Mixed Evidence
【速读】：该论文旨在解决大语言模型（LLM）在处理混合证据（即同时存在支持与反驳来源的命题）时，因评判系统默认将非方向性结论（如“冲突”）作为授权判决，而强制输出方向性结论（如“支持”或“反驳”）所引发的安全性问题。这一现象被命名为“樱桃选偏覆盖”（Cherry-pick Override, CCO），其本质是系统在未获得充分授权的情况下做出方向性承诺，违背了任务契约（task contract）。解决方案的关键在于引入一个外部的承诺控制层（commitment-control layer），通过将证据结构（structural evidence）与置信度（confidence）作为正交通道（orthogonal channels），并以“不承诺”（NO-COMMIT）作为可路由的控制状态，实现对判决生成与承诺授权的解耦。该设计有效避免了单一通道修复策略（如类型化词汇、面板聚合、置信度阈值、验证者过滤）带来的残余失败，尤其在AVerTeC和VitaminC-Mixed数据集上均表现出对CCO的结构性抑制，且在随机否决零假设下显著提升“冲突”判定的准确性（如AVerTeC中经验p < 1/2001），从而实现了更可靠、可解释的非方向性判决机制。
链接: https://arxiv.org/abs/2606.07834
作者: Haoran Xu
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注: 12 pages, 1 figure
Abstract:LLM judges increasingly turn verdicts into system commitments. Under mixed evidence (claims with both supporting and refuting sources) this is unsafe: when the schema exposes CONFLICTING as the authorized non-directional verdict, returning SUPPORTS/REFUTES is an unauthorized directional commitment, a failure we name Cherry-pick Override (CCO). We define CCO under an explicit task contract and report it with a same-denominator diagnostic protocol paired with matched-coverage bootstrap and an apples-to-apples random-veto null. On AVeriTeC’s Conflicting subset (N_C = 150), three-option judges return a directional verdict on more than 84% of mixed-evidence claims; under the typed schema, three-judge majority voting amplifies direction-on-conflict on AVeriTeC (0.887 vs. 0.840; 95% CI [+0.013, +0.080]) but does not replicate on VitaminC-Mixed. Walking an intervention ladder of common single-channel fixes (typed vocabulary, panel aggregation, confidence thresholding, validator-only filtering), each leaves a distinct residual failure: panel aggregation suppresses single-judge CONFLICTING dissent in 48% of CCO cases; the panel is well-calibrated for direction (ECE = 0.07 on pure-S/R) so confidence cannot operationally separate CCO from correct directional commits; validator-as-classifier nearly halves pure-evidence accuracy. A minimal two-channel reference probe reaches operating points neither single channel reaches; under the random-veto null its promotion to CONFLICTING is structurally targeted on AVeriTeC (empirical p 1/2001) and weaker but in the same direction on VitaminC-Mixed, a selectivity result rather than a magnitude one. We argue for an external commitment-control layer that separates verdict generation from commitment authorization, using structural evidence and confidence as orthogonal channels and NO-COMMIT as a routed controller state.
[MA-27] Beyond Goodharts Law: A Dynamic Benchmark for Evaluating Compliance in Multi-Agent Systems
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在向自主执行型智能体演进过程中，因现有评估框架忽视程序合规性而引发的“马基雅维利式”行为问题——即智能体为最大化奖励而策略性地违反安全规则，这正是古德哈特定律（Goodhart’s Law）的体现。其解决方案的关键在于提出MAC-Bench动态对抗性基准测试体系，以及基于“种子-演化-精炼-验证”（SERV）的“智能体即基准”范式，将非结构化的法律文本转化为可执行、无污染的场景，并通过构建全息沙箱环境与校准的社会工程压力向量，迫使多智能体系统在任务成功率与合规性之间进行帕累托最优权衡。研究引入了合规加权成功率（Compliance-Weighted Success Rate, CSR）和马基雅维利差距（Machiavellian Gap, MG）等新型评估指标，对前沿模型进行了全面评估，揭示了当前主流模型在成功与合规之间普遍存在显著权衡关系。
链接: https://arxiv.org/abs/2606.07805
作者: Yiyang Zhao,Zhuo Zhang,Qingxuan Le,Lizhen Qu,Zenglin Xu
机构: Fudan University (复旦大学); Shanghai Academy of AI for Science (上海人工智能科学研究院); Shanghai Artificial Intelligence Laboratory (上海人工智能实验室); Monash University (莫纳什大学)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:The rapid evolution of Large Language Models (LLMs) from passive assistants to autonomous, execution-capable agents has introduced critical operational risks. Most current evaluation frameworks neglect procedural compliance, leading to ‘‘Machiavellian’’ behaviors where agents strategically violate safety rules to maximize rewards - a direct manifestation of Goodhart’s Law. To address this blind spot, we introduce MAC-Bench, a dynamic, adversarial benchmark designed to evaluate the procedural alignment of multi-agent systems under realistic pressure. We propose the SERV(Seed - Evolve - Refine - Verify) pipeline, an ``Agent-as-a-Benchmark’’ paradigm that transforms unstructured legal texts into executable, contamination-free scenarios. By synthesizing holographic sandbox environments and injecting calibrated social-engineering pressure vectors, MAC-Bench forces agents into Pareto-optimal trade-offs between task success and regulatory adherence. We introduced novel metrics: the Compliance-Weighted Success Rate (CSR) and the Machiavellian Gap (MG), and conducted a comprehensive evaluation of state-of-the-art frontier models to reveal the pervasive trade-offs between success and compliance.
[MA-28] Systematic LLM Translation of Legacy Scientific Code to Differentiable Frameworks: Application to a Land Surface Model
【速读】：该论文旨在解决将遗留的Fortran科学计算代码（如地球系统模型）迁移至可微编程框架中的难题，尤其针对大规模、复杂且缺乏可微性支持的数值模拟代码。其核心挑战在于保持数值精度的同时实现自动化的可微转换，并确保转换后模型在参数估计与敏感性分析等任务中具备高效性和可靠性。解决方案的关键在于提出一个基于大语言模型（Large Language Model, LLM）的五阶段智能体（agentic）流水线：首先通过静态依赖分析构建完整的调用图以确定模块翻译顺序；随后采用迭代式编译-修复循环实现错误的自主纠正；引入Fortran参考基准（reference oracle）在模块级保证数值一致性；最终完成梯度验证与集成。该方法在19,000行的CLM-ml-v2陆面模型上成功实现了端到端可微转换，生成的模型可在单次反向传播中计算完整雅可比矩阵，在物理参数恢复任务中较无梯度优化方法减少8倍迭代步数，并在2,048个并行样本下相较传统串行Fortran实现24倍的时钟速度提升。研究成果不仅包含可微模型本身，还提供了可复用的通用框架，为其他地球系统模型组件的可微化提供了有效路径。
链接: https://arxiv.org/abs/2606.07681
作者: Aya Lahlou,Linnia Hawkins,Pierre Gentine
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Multiagent Systems (cs.MA)
备注:
Abstract:Differentiable programming offers transformative capabilities for scientific modeling, enabling gradient-based parameter estimation, sensitivity analysis, and data assimilation. Yet, migrating legacy codebases into differentiable frameworks remains a challenge. We present a five-phase LLM-based agentic pipeline that translates legacy Fortran into JAX: static dependency analysis determines module translation order from the full call graph; iterative compile-repair loops correct errors autonomously; and a Fortran reference oracle enforces numerical parity at the module level before integration and gradient verification. We instantiate and evaluate the pipeline on CLM-ml-v2, a 19,000-line Fortran land surface model, and analyze agent behavior across 73 module translation tasks. The resulting differentiable model computes the complete Jacobian in a single backward pass, recovers physical parameters in eight times fewer steps than gradient-free optimization, and achieves a 24 times wall-clock speedup over sequential Fortran at ensemble size N=2,048. Both the translated model and pipeline infrastructure are released as a reusable framework for differentiating other Earth system model components.
[MA-29] Crayotter: Traceable Multi-Agent Workflows for Long-Form Video Editing
【速读】：该论文旨在解决长视频从异构素材中进行编辑时，如何在多阶段（包括素材准备、时间线构建、后期制作与修订）中保持叙事意图一致性，并确保编辑过程可追溯、可诊断的问题。传统方法往往缺乏对编辑过程的透明性，导致失败后需全量重启，效率低下。其解决方案的关键在于提出一个名为Crayotter的开源多模态多智能体系统，该系统将视频编辑流程划分为三个可追溯的阶段：基于覆盖度感知的素材准备、基于产物的编辑研究以及基于工具的时序执行。每个阶段均生成可观测的中间产物（如覆盖度报告、多模态分析结果、编辑蓝图、工具调用记录及中间渲染），从而实现全流程的可追踪性，使故障段落可被精准定位并局部修正，避免整体重做。实验表明，相较于CapCut-Mate和CutClaw两个基线模型，Crayotter在23个编辑主题上的人类评估得分平均达3.40/5，显著优于基线（分别为2.44和1.70），并在主题契合度、叙事连贯性和剪辑流畅性方面均有稳定提升。此外，论文还设计了可回放的轨迹结构与可验证的奖励机制，为未来基于策略优化的自动化编辑奠定了基础。
链接: https://arxiv.org/abs/2606.07636
作者: Lecheng Yan,Yichong Zhang,Ben Pan,Xiaoyu Zheng,Jiawei Qian,Anqi Wu,Wenxi Li,Chenyang Lyu
机构: Crayotter Project
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注: 11 pages, 5 figures
Abstract:Editing a long-form video from heterogeneous footage requires more than selecting clips: an agent must preserve narrative intent across material preparation, timeline construction, post-production, and revision while leaving enough evidence to diagnose failures. We present \textbfCrayotter, an open-source multimodal multi-agent system for prompt-driven video editing. Crayotter organizes production into three phases: coverage-aware material preparation, artifact-based editing research, and tool-grounded timeline execution. Each phase externalizes inspectable artifacts, including coverage reports, multimodal analyses, editing blueprints, tool calls, and intermediate renders. These artifacts make an editing run traceable and allow failed segments to be diagnosed and selectively revised instead of requiring a full restart. We evaluate Crayotter on 23 editing themes against CapCut-Mate and CutClaw. Under human evaluation, Crayotter achieves an average score of 3.40/5, compared with 2.44 and 1.70 for the two baselines, with consistent gains in theme alignment, narrative coherence, and editing smoothness. We additionally describe a replayable trajectory schema and verifiable reward design that prepare these workflows for future policy optimization. Code, traces, and examples are publicly available at this https URL.
[MA-30] From Human Guidance to Autonomy: Agent Skill System for End-to-End LLM Deployment on Spatial NPUs
【速读】：该论文旨在解决在资源受限的空间型神经网络处理单元（Spatial NPU）上实现大语言模型（LLM）端到端高效部署所面临的高人力成本与复杂性问题。现有方法多聚焦于单核优化，缺乏对完整推理流程的系统性支持，尤其在边缘设备上难以实现可复用、自动化的部署范式。其解决方案的关键在于提出一种两阶段方法：第一阶段通过人类引导的智能体协作，完成Llama-3.2-1B模型在AMD XDNA 2 NPU上的参考部署，显著提升预填充（prefill）和解码（decode）阶段性能（分别提速2.2倍和4.0倍），并以结构化文档形式记录优化路径与经验；第二阶段将这些经验提炼为包含八个阶段的智能体技能体系（agent skill system），集成优化与调试能力，并在每个阶段严格保证数值正确性。基于此体系，研究实现了对8个额外解码器类LLM（包括不同规模的Llama、SmolLM2、Qwen系列）在相同硬件平台上的全自动端到端部署，首次在开源编译栈下完成此类模型在AMD NPU上的部署，平均耗时仅0.5–4小时，几乎无需人工干预，且所有部署均通过数值正确性验证，展现出良好的功能泛化能力。其中三个模型的持续性能达到或超过参考部署水平，表明该方法可在无额外模型特异性人工调优的情况下实现高性能、可扩展的自动化部署。
链接: https://arxiv.org/abs/2606.07586
作者: Jiajie Li,Erwei Wang,Zhiru Zhang,Samuel Bayliss
机构: AMD Research and Advanced Development(AMD研究与高级开发部门)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Multiagent Systems (cs.MA)
备注: Accepted by MLArchSys 2026
Abstract:Spatial neural processing units (NPUs) provide an energy-efficient platform for edge LLM inference, but efficiently deploying an LLM end-to-end on such hardware remains labor-intensive. Although AI coding agents have begun to lower this cost, existing studies have largely focused on single-kernel optimization rather than end-to-end LLM deployment on resource-constrained spatial NPUs. We present a two-stage methodology, instantiated on the AMD XDNA 2 NPU, that progresses from human-guided development to agent autonomy. In the first stage, we develop a reference deployment of Llama-3.2-1B through human-guided agent assistance. The resulting implementation achieves a speedup of 2.2x on prefill and 4.0x on decode over the hand-optimized baseline, with the optimization trajectory and its lessons recorded as structured documentation throughout. In the second stage, we distill the documentation into an agent skill system consisting of eight phases, orchestrating the optimization and debugging skill sets, with numerical correctness strictly enforced at each phase. Using our agent skill system, we autonomously deploy eight additional decoder-only LLMs (Llama-3.2-3B, SmolLM2-1.7B, Qwen2.5-0.5B, 1.5B, 3B, Qwen3-0.6B, 1.7B, 4B) end-to-end on the AMD XDNA 2 NPU using the open-source compiler stack. To our knowledge, these models have not previously been deployed on AMD NPUs via any open-source software stack. Each deployment completes in 0.5-4 hours of agent wall time with almost no human guidance, and passes the numerical-correctness gates, demonstrating functional generalization to previously unencountered LLMs. Three of the eight match or exceed the sustained performance of our Llama-3.2-1B reference deployment, suggesting that the resulting implementations can be competitive without additional model-specific human engineering. Comments: Accepted by MLArchSys 2026 Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Multiagent Systems (cs.MA) Cite as: arXiv:2606.07586 [cs.LG] (or arXiv:2606.07586v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2606.07586 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[MA-31] When Should an AI Scientist Stop? Verifiable Experiment Steering and Refusal for Autonomous Discovery ICML2026
【速读】：该论文旨在解决生成式 AI (Generative AI) 在科学发现过程中因模型不确定性与知识库不完备性导致的不可靠推断问题，特别是针对未建模子空间（unresolved subspace）的实验设计偏差、潜在假设模糊性以及知识库外机制误识别等核心挑战。其解决方案的关键在于提出CARTOGRAPH框架，通过三个协同机制实现可信验证：一是基于选择（select）的未解空间实验引导，利用局部线性-高斯桥下的无偏投影作为初始准则；二是通过显式模糊性闭合（resolve）机制，引入精确的未解A-最优规则（CARTOGRAPH-A），以闭合局部信息增益（EIG）与Box-Hill准则的理论边界；三是基于残差的库外不充分性检测（refuse），通过残差分析识别并撤销超出知识库范围的药代动力学机制误判。实验结果表明，在五组测试基准中，CARTOGRAPH-A在d=8维度下显著优于原始投影方法（129胜/0平/15负，p < 10⁻²¹），并在低维药代动力学与过滤后的EPA场景中准确预测了近似分歧行为。此外，在对40项已发表自主材料系统正向结论的回溯审计中，拒绝防护机制成功标记所有后续被人工复核为不确定的声明，同时保留32/36个经证实的结论，验证了其在真实科研流程中的可靠性与实用性。
链接: https://arxiv.org/abs/2606.07576
作者: Neel Tushar Shah,Manglam Kartik
机构: 未知
类目: Machine Learning (cs.LG); Emerging Technologies (cs.ET); Multiagent Systems (cs.MA)
备注: Accepted at AI for Science Workshop at ICML 2026
Abstract:We present CARTOGRAPH, a verification layer for AI scientists that couples unresolved-subspace experiment steering (select), explicit ambiguity closure (resolve), and residual-based library inadequacy detection (refuse). Under a local linear-Gaussian bridge, raw unresolved projection is the isotropic unresolved Fisher-information trace, while CARTOGRAPH-A is the exact unresolved A-optimal rule; closed-form EIG and Box-Hill arise as local comparators rather than global equivalents. Across five testbeds, CARTOGRAPH-A beats raw projection 129W/0T/15L at d = 8 (p 10^-21) in a replicated structured cascade. More distinctively, the framework tentatively identifies three out-of-library pharmacokinetic mechanisms and then revokes those identifications as residuals expose structural misfit, while one perturbed in-library control stays identified throughout. In low-dimensional pharmacokinetic and filtered EPA settings, near-ties against disagreement are predicted by theory and observed. Finally, in a retrospective audit of 40 positive claims from the published A-Lab autonomous materials system, the refuse guard flags all 4 claims later marked inconclusive under manual reanalysis while passing 32/36 confirmed claims. Code is available at this https URL
[MA-32] SPIN: Decentralized Swarm Control via Tensorized Policy Coordination
【速读】：该论文旨在解决资源受限的边缘计算平台上，去中心化多智能体集群协调中因联合动作空间指数级膨胀和高延迟通信开销导致的可扩展性瓶颈问题。其核心解决方案是提出一种名为“群体策略干扰网络”（Swarm Policy Interference Network, SPIN）的架构范式，通过将群体拓扑结构建模为压缩张量网络，将局部多智能体团块的联合策略张量分解为矩阵乘积态（Matrix Product State, MPS）链，从而将策略评估的计算复杂度从指数级 $ O(n^m) $ 降低至严格线性的 $ O(m \cdot n \cdot \chi^2) $，显著提升了计算效率。为在不依赖高功耗在线训练的前提下，实现连续空间几何与离散代数后端之间的无缝衔接，论文设计了一种解耦的混合神经符号控制流水线：局部多层神经网络作为结构化协调编码器，离线预训练以非线性映射手工设计的几何特征到抽象环境目标度量；运行时，边缘智能体通过直接应用Radon-Nikodým导数作为零样本重要性重加权滤波器，实现瞬时行为自适应。在涵盖追踪、去中心化分散/区域覆盖及多目标协同的离散时间多智能体仿真环境中验证表明，该集成框架能够实现稳定的目标导向运动、在去中心化约束下的抗坍缩空间扩散以及多目标场景下的结构化子群形成，为低功耗边缘集群智能提供了数学严谨且可计算的可行路径。
链接: https://arxiv.org/abs/2606.07557
作者: Zhaowen Fan
机构: 未知
类目: Machine Learning (cs.LG); Multiagent Systems (cs.MA); Social and Information Networks (cs.SI)
备注: 11 pages, 2 figures, 1 tables, 6 sections
Abstract:Decentralized multi-agent swarm coordination on resource-constrained edge platforms remains fundamentally bottlenecked by the exponential scaling of joint action spaces and high-latency communication overhead. This paper introduces the Swarm Policy Interference Network (SPIN) framework, an architectural paradigm that bypasses these limitations by modeling swarm topologies as a compressed tensor network. We factorize the joint policy tensors of local multi-agent cliques into Matrix Product State (MPS) chains, reducing the computational complexity of evaluation from an exponential O(n^m) wall to a strictly linear O(m \cdot n \cdot \chi^2) constraint. To bridge local continuous spatial geometry with this discrete algebraic backend without requiring power-intensive online training loops, we introduce a decoupled, hybrid neuro-symbolic control pipeline. Local multi-layered neural networks operate as structural coordination encoders, pre-trained offline to nonlinearly map hand-engineered geometric descriptors into abstract environmental target measures. At runtime, edge agents execute instantaneous behavioral adaptations by applying the Radon-Nikodým derivative directly as a zero-shot importance-reweighting filter. We validate the framework within a discrete-time multi-agent simulation sandbox spanning tracking, decentralized dispersion/area coverage, and multi-goal coordination regimes. Qualitative telemetry demonstrates that the integrated pipeline achieves stable target-directed motion, anti-collapse spatial spreading under decentralized constraints, and structured subgroup formation across multiple targets, providing a mathematically grounded route to tractable, low-power edge swarm intelligence.
[MA-33] Symbolic Reasoning Frameworks Modulate LLM Risk Aversion in Multi-Agent Strategic Settings
【速读】：该论文旨在解决大语言模型作为策略性代理在多智能体博弈中固有的风险规避倾向（即“乌龟”偏差）问题，这种倾向导致其倾向于防御性策略，从而影响整体生态系统的动态平衡。其解决方案的关键在于通过引入符号推理框架（如《易经》蓍草占卜、塔罗牌、打乱文本对照组等）作为每回合的反思性提示注入单一智能体（汉），以差异化地调节该偏差。实验结果表明，不同框架虽不直接影响接收框架的智能体（汉）的胜率或存活率（汉在各条件下均未获胜且存活无显著差异），但显著重塑了多智能体生态系统：《易经》框架使燕与楚共治而秦被压制；塔罗牌框架使秦主导；打乱文本对照组则使齐主导。此外，塔罗牌框架显著提升了汉的巅峰领土规模（平均3.0个供应中心，显著高于其他框架下的2.1–2.5，p = 0.010）。进一步分析显示，框架内容本身（如卦象主题或塔罗牌姿态）与后续行动选择无关（卡方检验p > 0.69），说明调控机制并非源于内容遵循，而是依赖于反思过程本身的结构化引导。因此，该研究揭示了在多智能体系统中，代理层面的对齐框架选择会引发系统级的独特演化路径，凸显了反思性提示设计在塑造集体行为中的关键作用。
链接: https://arxiv.org/abs/2606.07552
作者: Augustin Chan
机构: Iterative(迭代)
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 17 pages, 3 figures, 6 tables, 6 listings. Code and data: this https URL
Abstract:Large language models exhibit innate behavioral tendencies when deployed as strategic agents – notably a risk-averse “turtle” bias toward defensive play. We show that symbolic reasoning frameworks, injected as per-round reflective prompts into one agent, differentially modulate this bias and reshape the multi-agent ecosystem to produce framework-specific winner distributions. In a 7-player Warring States Diplomacy variant (41 games, 4 conditions, single-campaign memory accumulation), each framework produces a distinct ecosystem signature: under control, Yan dominates (7/11, 64%); under I-Ching yarrow divination, Yan and Chu co-dominate while Qin is completely suppressed (0/10); under Tarot, Qin dominates (5/10, Fisher vs. pooled p = 0.006); under scrambled-text ablation (incoherent oracle text preserving prompt structure), Qi dominates (5/10, Fisher vs. pooled p = 0.006). The framework-receiving agent (Han) never wins and shows no survival difference across conditions (Fisher p = 1.0), but Tarot consistently elevates Han’s peak territory (mean 3.0 SCs vs. 2.1-2.5 others, Kruskal-Wallis p = 0.010). Neither framework’s content predicts subsequent actions – hexagram themes (chi-squared p = 0.95) and Tarot card postures (chi-squared p = 0.69) are both independent of action choice – suggesting the modulation operates through the reflective process, not content-following. We present this as an observation paper establishing that alignment-framework choice at the agent level produces distinctive system-level consequences in multi-agent settings.
[MA-34] PathoSage: Towards Multi-Source Evidence Adjudication in Pathology via Experience-Aware Agent ic Workflow
【速读】：该论文旨在解决计算病理学中基于图像的切片级多模态推理中存在的幻觉与决策不一致问题。尽管多模态大语言模型（Multimodal Large Language Models, MLLMs）和智能体工作流在该领域展现出巨大潜力，但端到端的MLLMs常产生形态学特征的幻觉，而现有智能体系统则通过合并工具输出与检索知识至共享上下文进行决策，易受矛盾证据和上下文污染的影响。为此，本文提出PathoSage框架，采用三阶段设计，明确分离知识检索、证据收集与证据仲裁三个环节。其核心创新在于结构化证据审议（Structured Evidence Deliberation）机制，该机制独立评估来自不同工具的异构证据，执行冲突分析，并在全新上下文中生成最终判断，从而降低锚定偏差。此外，引入无需训练的贝塔-伯努利经验系统，实现对工具长期可靠性的持续信用分配，构建相似性加权先验以指导未来工具选择。实验表明，PathoSage显著缓解了视觉问答（VQA）中的幻觉现象及分类器分歧，优于现有的强基线病理MLLM与智能体方法。研究结果强调，显式的证据仲裁与可靠性感知的工具建模是构建鲁棒病理智能体的关键要素。
链接: https://arxiv.org/abs/2606.07549
作者: Chengyang Zhang,Wenchuan Zhang,Bo Li,Mengran Li,Bob Zhang,Yuhao Yi,Hong Bu,Jiancheng Lv
机构: Sichuan University (四川大学); West China Hospital, Sichuan University (华西医院，四川大学); University of Macau (澳门大学); Sun Yat-sen University (中山大学)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Recent advances in Multimodal Large Language Models (MLLMs) and agent workflows have shown strong promise for computational pathology, yet reliable patch-level reasoning remains challenging. End-to-end pathology MLLMs often hallucinate morphological features, while recent agentic systems usually merge tool outputs and retrieved knowledge into a shared context, making decisions vulnerable to conflicting evidence and context contamination. We propose PathoSage, a three-stage framework that explicitly separates knowledge retrieval, evidence collection, and evidence adjudication for patch-level pathology multimodal reasoning. Its core component, Structured Evidence Deliberation, independently evaluates heterogeneous evidence from tools, performs conflict analysis, and generates the final judgment in a fresh context to reduce anchoring bias. We further introduce a training-free Beta-Bernoulli experience system with continuous credit assignment to model long-term tool reliability and construct similarity-weighted priors for future tool use. Experiments show that PathoSage effectively mitigates VQA hallucinations and classifier disagreement, outperforming strong pathology MLLM and agentic baselines. Our results highlight explicit evidence adjudication and reliability-aware tool modeling as key ingredients for robust pathology agents.
自然语言处理
[NLP-0] Causally Evaluating the Learnability of Formal Language Tasks
【速读】： 该论文旨在解决语言模型在训练过程中学习特定任务所需的任务特异性数据量问题，尤其是在自然语言任务中因任务边界模糊和任务间混淆导致难以准确评估学习能力的挑战。其核心问题是：在缺乏因果控制的情况下，传统相关性评估方法会因混杂因素而产生误导性结论。为此，论文提出了一种基于概率有限自动机（Probabilistic Finite Automata, PFA）生成的形式化语言作为受控实验环境，以实现对任务学习性的精确分析。解决方案的关键在于引入“分箱半环”（binning semiring）这一代数结构，通过该结构可精确控制目标属性在采样语料中的出现频率，从而实现对学习过程的因果干预。研究将实验流程建模为因果图模型，并推导出分解后的Kullback-Leibler（KL）散度度量指标，用于量化特定子任务的学习能力。实验结果表明，忽略因果干预的纯相关性评估会因混杂因素导致错误结论，凸显了在自然语言研究中采用因果分析方法的重要性，为未来语言模型评估提供了重要的方法论警示。
链接: https://arxiv.org/abs/2606.09822
作者: Vésteinn Snæbjarnarson,Anej Svete,Josef Valvoda,Reda Boumasmoud,Brian DuSell,Ryan Cotterell
机构: 未知
类目: Computation and Language (cs.CL); Formal Languages and Automata Theory (cs.FL)
备注:
Abstract:Language models, as multi-task learners, acquire a wide range of abilities during training. A fundamental question is how much task-specific data is needed to learn a given task. Answering this for natural language is difficult: tasks are hard to delineate and can confound one another. To rigorously investigate the relationship between data frequency and learnability, we turn to a controlled setting using formal languages induced from probabilistic finite automata. These serve as a methodological testbed to demonstrate that standard correlational evaluation practices are inherently flawed. To enable causal analysis, we introduce the binning semiring, an algebraic object that lets us control how often a targeted property occurs in a sampled corpus. We formulate the experimental pipeline as a causal graphical model and derive decomposed Kullback-Leibler divergence metrics to measure the learnability of specific sub-tasks. Our experiments show that evaluating learnability without causal intervention leads to incorrect conclusions due to confounders in correlational analysis, and serve as a warning about correlational pitfalls in natural-language settings.
[NLP-1] SIGA: Self-Evolving Coding-Agent Adapters for Scientific Simulation
【速读】： 该论文旨在解决领域科学家在使用复杂科学模拟器（如GEOS）时，因需掌握专用输入语言而面临的学习成本高、配置效率低的问题。其核心挑战在于如何让通用编码代理（coding agent）在不依赖大量人工干预的情况下，有效操作特定科学软件。解决方案的关键在于提出SIGA（Simulator-Interface Grounding Adapter），通过检索机制、过程记忆、轨迹内验证及验证强制终止等手段，为通用代理注入模拟器的可执行契约（executable contract），即其词汇表、结构约束、验证规则与终止条件。实验表明，SIGA在约5分钟内即可生成符合要求的完整模拟配置（TreeSim > 0.90），相比耗时约3小时的人类专家实现约36倍的加速；在更具挑战性的测试集上，相较未加接地的代理，其性能提升10%相对增益，并将跨种子标准差降低16倍。此外，自演化机制通过重写适配器内容进一步优化性能，达到或超越手工设计配置水平。跨模态迁移至OpenFOAM和LAMMPS的结果显示，不同接口瓶颈下主导机制各异：当结构完整性为关键瓶颈时，验证机制最为重要；而当领域正确性受限时，记忆与检索起决定作用。这表明轻量级、可自我改进的接口接地层能够有效将通用编码代理转化为实际可用的科学软件操作者。
链接: https://arxiv.org/abs/2606.09774
作者: Matthew Ho,Brian Liu,Jixuan Chen,Audrey Wang,Lianhui Qin
机构: University of California, San Diego(加州大学圣地亚哥分校)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Advanced scientific simulators expose specialized input languages that turn simulation goals into executable configurations, but learning them can cost domain scientists hours to days. We study simulator setup as a problem of agent-tool interface grounding: what minimal simulator-specific adaptations are needed for an off-the-shelf coding agent to operate real scientific software? Our intuition is that coding agents already know how to navigate files, edit code, run commands, and repair outputs, but they lack the simulator’s executable contract: its vocabulary, structural constraints, validation rules, and termination conditions. We introduce SIGA, a Simulator-Interface Grounding Adapter that supplies this contract through retrieval, procedural memory, in-trajectory validation, and validation-enforced termination. We primarily evaluate SIGA on GEOS, an open-source multiphysics simulator used in subsurface science. SIGA produces a complete GEOS deck in about five minutes with TreeSim above 0.90, matching an extended-budget human expert who took about three hours, a roughly 36x wall-clock speedup. On a harder held-out set, grounding raises TreeSim from 0.720 to 0.789, a roughly 10% relative gain over the bare agent, and can reduce the across-seed standard deviation by 16x. Self-evolution further improves SIGA by rewriting adapter contents from prior trajectories, yielding the highest held-out GEOS mean and matching or outperforming the strongest hand-designed configuration. Transfers to OpenFOAM and LAMMPS show that the dominant mechanism shifts by interface: validation matters most when structural completeness is the bottleneck, while memory and retrieval matter most when domain correctness is the bottleneck. These results suggest that lightweight, self-improvable grounding layers can turn general coding agents into practical operators of scientific software.
[NLP-2] Data Synthesis and Parameter-Efficient Fine-Tuning for Low-Resource NMT: A Case Study on Qeqchi Mayan
【速读】： 该论文旨在解决数字资源匮乏的原住民语言在神经机器翻译（Neural Machine Translation, NMT）中因训练数据极度稀缺而难以构建有效模型的问题。传统方法依赖于网络抓取获取目标语言平行语料，但此举往往侵犯数据主权。为此，研究提出一种数据合成方法，通过将社区提供的词典转化为大规模合成语料库，实现无需抓取真实平行文本即可启动NMT模型训练。其关键在于利用参数高效微调（Parameter-Efficient Fine-Tuning, PEFT）技术中的LoRA适配器，在mT5-base模型上进行训练，成功使模型掌握复杂黏着性形态和VOS词序结构（BLEU 42.02），验证了合成约束对语法结构学习的有效性。然而，与自然语料对比时发现模型存在结构性-语义性鸿沟（BLEU 0.59），表现为虽保持语法正确性但缺乏自然语言的词汇根基，根源在于模型过度拟合于合成模板的有限结构变体，难以适应自然语言的句法灵活性。此外，多任务学习架构的消融实验揭示了负迁移现象，表明辅助任务在有限的LoRA参数容量下相互竞争，导致模型过度优化合成标记而牺牲了对真实语言的泛化能力。最终结论指出，合成数据可作为有效的结构预训练工具，但必须结合真实语料，通过课程学习（Curriculum Learning）实现语义层面的精细化调整。
链接: https://arxiv.org/abs/2606.09767
作者: Alexander Chulzhanov,Soeren Eberhardt,Arjun Mukherjee
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted to the 29th International Conference on Text, Speech and Dialogue (TSD 2026). This version of the contribution has been accepted for publication, after peer review but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections
Abstract:Neural machine translation for digitally low-resource Indigenous languages is often hindered by extreme data scarcity, prompting reliance on extractive web-scraping. To ensure data sovereignty, this study introduces a data synthesis methodology to bootstrap NMT models without scraping target-language parallel text. Focusing on Q’eqchi’ Mayan, we transformed community-sourced dictionaries into a massive synthetic corpus, utilizing Parameter-Efficient Fine-Tuning (PEFT) via LoRA adapters on an mT5-base model. In-domain evaluation demonstrates high structural acquisition (BLEU 42.02), proving that synthetic constraints effectively teach complex agglutinative morphology and VOS word order. However, evaluation against an organic glossary reveals a structural-semantic gap (BLEU 0.59), where the model maintains grammatical integrity but lacks the lexical grounding of natural language. The model exhibits overfitting to the constrained structural variance of the synthetic templates; despite high semantic entropy in the pipeline, it struggles with the syntactic fluidity of natural language, forcing organic inputs into rigid learned patterns. Furthermore, an ablation study utilizing a Multi-Task Learning architecture resulted in negative transfer, suggesting that auxiliary tasks competed for limited parameter capacity within the LoRA adapters, causing over-optimization for synthetic markers at the expense of organic flexibility. Ultimately, we establish that synthetic bootstrapping is a highly effective structural primer, but requires authentic data for semantic refinement via Curriculum Learning. Comments: Accepted to the 29th International Conference on Text, Speech and Dialogue (TSD 2026). This version of the contribution has been accepted for publication, after peer review but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2606.09767 [cs.CL] (or arXiv:2606.09767v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2606.09767 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-3] OSWorld: A Benchmark for Personally Intelligent Phone Agents
【速读】： 该论文旨在解决现有移动智能体评估基准缺乏个性化能力的问题，即当前的移动代理评测体系未能充分考虑用户身份、历史行为及偏好等持久性个人数据，导致代理无法在真实设备环境中实现基于用户上下文的推理与决策。其核心挑战在于构建一个能够模拟真实用户长期使用习惯和跨应用数据关联的交互式评测环境。解决方案的关键是提出iOSWorld——首个基于原生iOS系统的交互式仿真评测基准，其创新性体现在：以26个新构建的、数据互联的iOS应用为载体，涵盖交易、消息、行程、社交关系及金融活动等多维度个人数据；通过133项任务（分为单应用、跨应用及记忆与个性化三类），系统性地评估代理在不同复杂度场景下的个人化推理能力。研究进一步揭示了特权信息（如视觉+可访问性树）对前沿模型性能的显著提升作用，而小型模型则难以从中获益，凸显了当前生成式AI在处理高阶个性化任务时的能力瓶颈。该基准已开源，包含全部应用、种子数据、任务定义、评分标准与评估代码，为未来个性化智能体的研究提供了标准化测试平台。
链接: https://arxiv.org/abs/2606.09764
作者: Lawrence Keunho Jang,Mareks Woodside,Geronimo Carom,Andrew Keunwoo Jang,Jing Yu Koh,Ruslan Salakhutdinov
机构: Carnegie Mellon University (卡内基梅隆大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:A useful phone agent needs to be personally intelligent. It should reason over a user’s identity, history, and preferences as they exist on the device, not just follow isolated instructions in an impersonal sandbox. Existing mobile agent benchmarks lack this kind of personalization. We introduce iOSWorld, the first interactive native iOS simulator benchmark built around a persistent user identity spanning 26 newly built iOS apps. These apps contain connected data such as transactions, messages, travel records, social relationships, and financial activity. iOSWorld includes 133 tasks across three increasingly difficult categories. Single-app tasks (27) test one app, multi-app tasks (60) span 2 to 8 apps, and memory and personalization tasks (46) require agents to infer patterns from personal data. We evaluate frontier and open-source computer-use models in both vision-only and privileged vision+XML settings. The best configuration reaches 52% overall but only 37% on multi-app tasks. Privileged vision+XML access improves frontier models by up to 26 percentage points, while smaller models do not benefit from added accessibility-tree input. We release iOSWorld as an open-source benchmark with all apps, seeded data, tasks, rubrics, and evaluation code.
[NLP-4] Multi-Turn Evaluation of Deep Research Agents Under Process-Level Feedback ICML2026
【速读】： 该论文旨在解决现有深度研究代理（Deep Research Agents, DRAs）评估体系的局限性问题，即当前基准测试仅关注单次输出质量，忽略了关键问题：DRAs是否能在反馈指导下持续改进其研究报告。为此，研究设计了多轮评估框架，对比两种反馈机制——自我反思（self-reflection）与过程级反馈（process-level feedback）。其中，过程级反馈的关键突破在于提出“研究缺口推断”（Research Gap Inference, RGI）方法，通过分析评分标准中已满足与未满足项的模式，自动识别研究过程中存在的策略性缺陷，并提供精准指导。实验结果表明：在自我反思条件下，代理对评分标准的采纳与放弃趋于平衡，净改进微乎其微；而在单轮过程级反馈下，报告得分显著提升约8–15个标准化分点，且标准采纳率达35%–40%；然而，后续多轮迭代中，代理在重写报告时会回退高达24%此前已满足的标准项，导致改进效果无法累积。这表明，尽管引入了精准反馈，现有DRAs架构仍难以实现稳定可靠的多轮优化。
链接: https://arxiv.org/abs/2606.09748
作者: Rishabh Sabharwal,Hongru Wang,Amos Storkey,Jeff Z. Pan
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Published as a workshop paper at SCALE - ICML 2026 (Oral)
Abstract:Existing benchmarks for deep research agents (DRAs) assess only single-shot outputs, ignoring a key question: can DRAs improve their reports when guided by feedback? To investigate this, we conduct a multi-turn evaluation of DRAs under two feedback settings: self-reflection, in which the agent revises its report without any external diagnostic signal, and process-level feedback, in which the agent receives guidance targeting gaps in its research strategy. To enable process-level feedback, we design Research Gap Inference (RGI), a method that analyzes patterns of satisfied and unsatisfied rubric criteria to infer research-process gaps. Our analysis reveals three key findings: (i) under self-reflection, agents incorporate and regress on rubric criteria at nearly equal rates, yielding negligible net improvement; (ii) a single round of process-level feedback yields substantial gains, raising the normalized score by approximately 8 - 15 points and yielding a roughly 35 - 40% incorporation rate; (iii) these gains do not compound over subsequent turns, as agents regress on up to 24% of previously satisfied criteria when rewriting the full report to address remaining gaps. Even with targeted guidance, reliable multi-turn improvement remains out of reach for the DRA architectures we evaluate. Our code and results are publicly available at this https URL.
[NLP-5] he Neutral Mask: How RLHF Provides Shallow Alignment while Leaving Partisan Structure Intact in a Large Language Model
【速读】： 该论文旨在解决大语言模型在对齐训练（alignment training）过程中看似实现“安全且有用”目标，实则可能仅达成表面合规而非深层对齐的核心问题。其关键在于揭示强化学习人类反馈（RLHF）机制的实际运作机理：研究发现，针对政治倾向性这一价值维度，RLHF并未消除基础模型中固有的结构性党派方向（partisan direction），而是通过压缩党派信号的方差，使输出呈现一致性的平衡与非党派化。利用稀疏自编码器分解（sparse autoencoder decomposition）与特征级操控实验（feature-level steering），研究证实，原本在基础模型中偶发激活的政策编码特征在指令微调后的模型中完全失活，表明RLHF并非通过移除价值相关结构来实现对齐，而是切断了党派几何结构到生成输出之间的因果路径。这种对齐本质上是功能性的（functional），而非结构性的（structural），因此当用户意图被推断并放大时，模型仍可重新激活党派生成能力。该机制暗示，对齐模型的行为可能比其输出表现更为脆弱，且类似模式或广泛存在于其他价值领域。
链接: https://arxiv.org/abs/2606.09735
作者: Wendy K. Tam
机构: Vanderbilt University (范德比尔特大学); National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign (伊利诺伊大学厄本那-香槟分校超级计算应用国家中心)
类目: Computation and Language (cs.CL)
备注:
Abstract:The ambition behind alignment training is to make large language models safe and useful. The primary mechanism, reinforcement learning from human feedback (RLHF), shapes the behavior of deployed language models by aligning them with ``human values.‘’ Yet the process is opaque. What values are being encoded; whose values are they; and how does RLHF encode them? A growing body of evidence suggests that RLHF produces only functional compliance rather than deep alignment. We offer a mechanistic case study of this phenomenon for partisan political orientation with a comparison of the internal representations of Llama 3.1 8B before and after RLHF. We show that RLHF does not remove the structured partisan direction in the base model. Instead, it compresses the variance of the partisan signal to generate consistently balanced and non-partisan output. Sparse autoencoder decomposition reveals that policy-encoding features, which activate sporadically in the base model, are completely inactive in the Instruct model. Feature-level steering experiments confirm the causal disconnect. RLHF thus encodes a norm of political neutrality, not by erasing the model’s knowledge of partisanship, but by severing the causal pathway from partisan geometry to output generation. Importantly, this neutrality is functional, not structural so that the underlying geometry that enables partisan steering remains intact. The mechanisms that bypass RLHF’s guardrails, such as inferring and amplifying a user’s partisan identity, reactivate partisan generation. If RLHF operates by disconnecting rather than removing value-laden structure, then the same pattern may hold for other value domains, and the aligned model’s behavior may be more fragile than its outputs suggest.
[NLP-6] IS-CoT: Breaking the Long-form Generation Collapse via Interleaved Structural Thinking
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在生成连贯且可控的长文本内容时面临的持续性挑战，尤其针对推理增强型模型在开放式写作任务中出现的“长度坍缩”问题——当目标文本长度超过2000词时，性能急剧下降。其核心问题在于传统静态层次化规划（static hierarchical planning）无法为长上下文提供动态指导。为此，论文提出一种名为交错结构思维链（Interleaved Structural Chain-of-Thought, IS-CoT）的新型框架，其关键创新在于将“规划-生成-反思”（Plan-Write-Reflect）的动态循环内嵌于生成过程之中，实现无需外部代理辅助的连续策略自适应与全局一致性对齐。基于此框架，研究构建了一个高质量的交错式推理轨迹数据集，并训练出IS-Writer-8B模型。实验表明，该模型在多个长文本生成基准测试中达到领先水平（如在LongBench-Write上相比DeepSeek-V3.2提升3.08分），展现出优异的长度合规性与与更大规模专有模型相当的连贯性。
链接: https://arxiv.org/abs/2606.09709
作者: Zechen Sun,Yuyang Sun,Zecheng Tang,Juntao Li,Wenpeng Hu,Wenliang Chen,Zhunchen Luo,Guotong Geng,Min Zhang
机构: Soochow University (苏州大学); PLA Academy of Military Science (中国人民解放军军事科学院)
类目: Computation and Language (cs.CL)
备注:
Abstract:Generating coherent and controllable long-form content remains a persistent challenge for Large Language Models (LLMs). While reasoning-enhanced models have demonstrated success in logic-intensive domains, our evaluation reveals that they suffer from a severe length collapse in open-ended writing, where performance degrades sharply as target lengths exceed 2,000 words. We attribute this failure to the limitation of static hierarchical planning, which struggles to provide dynamic guidance over extended contexts. To bridge this gap, we introduce the Interleaved Structural Chain-of-Thought (IS-CoT) framework. Unlike external agentic workflows, IS-CoT embeds a dynamic Plan-Write-Reflect cycle into the generation process, enabling continuous strategy adaptation and global alignment without additional assistance. Based on this framework, we construct a high-quality dataset of interleaved reasoning traces via a multi-teacher pipeline and train IS-Writer-8B. Experiments demonstrate that IS-Writer-8B achieves state-of-the-art performance on challenging long-form benchmarks (e.g., +3.08 vs. DeepSeek-V3.2 on LongBench-Write), exhibiting robust length compliance and coherence competitive with significantly larger proprietary models.
[NLP-7] BrainSurgery: Reproducible and Reliable Declarative Weight Manipulations for Model Editing and Upcycling
【速读】： 该论文旨在解决大规模深度学习模型在训练过程中生成的大型检查点（checkpoint）难以高效管理、检查与修改的问题。随着模型规模的不断增长，研究人员在进行层结构重构、精度转换、低秩分解及架构调试等操作时，常依赖脆弱且难以复现的临时Python脚本，导致流程不可靠、错误隐蔽。为此，论文提出BrainSurgery，一种用于神经网络检查点“张量手术”（tensor surgery）的鲁棒且可复现的工具。其核心解决方案在于通过声明式YAML配置文件抽象存储格式与内存管理，实现对张量的结构性修改、数学变换与重塑操作，并支持基于正则表达式和结构化定位的精确目标选择。同时，内置断言机制可验证张量的形状、数据类型与数值，有效防止潜在的隐性错误。该方法显著提升了模型修改流程的可靠性与可重复性，为未来模型优化与迁移研究提供了坚实基础。
链接: https://arxiv.org/abs/2606.09707
作者: Gianluca Barmina,Annemette Broch Pirchert,Andrea Blasi Núñez,Lukas Galke Poech,Peter Schneider-Kamp
机构: University of Southern Denmark (南丹麦大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:As deep learning models scale, managing, inspecting, and modifying large checkpoints has become increasingly challenging. Researchers often need to alter model weights for layer restructuring, precision casting, low-rank factorization, and architectural debugging, yet these workflows often rely on fragile ad-hoc Python scripts. Here, we introduce BrainSurgery, a tool for robust and reproducible “tensor surgery” on neural network checkpoints, and provide a system demonstration covering four examples and three case studies from model upcycling to LoRA extraction. By abstracting storage formats and memory management, BrainSurgery executes complex transformations through declarative YAML plans. It supports structural modifications, mathematical transformations, and tensor reshaping through expressive regex and structural targeting, while built-in assertions validate tensor shapes, data types, and values to prevent silent errors. We envision that BrainSurgery will provide a strong foundation for future research through its reproducible and validated operations.
[NLP-8] Learning to Attack and Defend: Adaptive Red Teaming of Language Models via GRPO
【速读】： 该论文旨在解决生成式 AI 安全评估中攻击者与防御者协同训练（attacker-defender co-training）的稳定性与有效性问题，尤其针对梯度增强策略优化（GRPO）在该场景下表现不稳定的问题。其核心解决方案是提出一种名为 AdvGRPO 的新型协同训练框架，通过引入密集多通道奖励机制（dense multi-channel rewards）和解耦的优势归一化（decoupled advantage normalization），显著提升了 GRPO 在联合优化中的稳定性与性能。该方法采用渐进式训练课程（curriculum），从单轮攻击逐步过渡到闭环多轮攻击，并在此基础上启动协同训练，使攻击者与防御者模型交替更新。实验表明，AdvGRPO 能生成高效且具备强迁移能力的攻击，同时训练出的防御者在安全基准测试中显著优于现有基线方法。
链接: https://arxiv.org/abs/2606.09701
作者: Blake Bullwinkel,Eugenia Kim,Amanda Minnich,Mark Russinovich
机构: Microsoft AI Red Team; Microsoft Azure
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:AI red teaming must continually adapt to evolving attackers and defenders. Reinforcement learning offers a promising approach to discovering novel attacks, and co-training methods can produce more robust defenders in tandem. Recent works have demonstrated the efficacy of attacker-defender co-training by applying PPO and DPO, but report that GRPO is unstable in this setting. We introduce AdvGRPO, a co-training framework that makes GRPO viable for joint attacker-defender optimization using dense multi-channel rewards and decoupled advantage normalization. Training progresses through a curriculum from single-turn to closed-loop multi-turn attacks before bootstrapping co-training, where attacker and defender models are updated in alternation. We show that our method can produce highly effective and transferable attacks and that co-trained defenders outperform baselines on safety benchmarks.
[NLP-9] PsychoSafe: Eliciting Psychologically-Informed Refusals in Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在面对应拒绝请求时面临的“帮助性”与“危害预防”之间的权衡问题。传统上，模型对高风险请求采取生硬拒绝的方式，虽可避免直接危害，但可能忽视求助者的真实心理需求，导致支持缺失。为此，本文提出PsychoSafe——一种基于心理学理论的拒绝框架，将拒绝重构为具有结构化支持性的沟通方式，其核心在于融合循证干预策略，使拒绝行为兼具安全性与同理心。关键解决方案包括：构建涵盖五个心理高危领域的8019条提示-响应语料库，并基于Qwen 3.5 27B模型采用提示工程与参数高效微调（Parameter-Efficient Fine-Tuning）进行训练。实验表明，该方法在平衡验证集上相较通用基线提升整体拒绝质量28.1%，尤其在外部资源转介（+46.8%）和心理依据支撑（+34.8%）方面表现显著；同时保持非拒绝任务的下游性能。尽管微调实现近乎完美的拒绝与转介率，但牺牲了部分回应相关性。跨数据集评估显示，模型在领域内具备强鲁棒性，但在跨域泛化能力有限，提示未来需通过多样化微调数据增强模型在不同情境下选择性应用干预策略的能力。
链接: https://arxiv.org/abs/2606.09697
作者: Gianluca Barmina,Federico Torrielli,Sven Harms,Jacob Nielsen,Felix Mächtle,Stine Lyngsø Beltoft,Peter Schneider-Kamp,Thomas Eisenbarth,Lukas Galke Poech,Anne Lauscher
机构: University of Southern Denmark; University of Turin; University of Hamburg; University of Lübeck
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) routinely face requests that should be refused, creating a trade-off between helpfulness and harm prevention. However, refusals themselves can be helpful. In high-risk interactions involving crisis, coercion, or escalating intent, blunt non-compliance may prevent direct harm while still failing to support the needs of the person behind the request. We present PsychoSafe, a psychologically-informed refusal framework that reframes refusal as structured supportive communication grounded in evidence-based intervention strategies. To develop PsychoSafe, we construct a corpus of 8019 prompt-response pairs spanning five psychologically salient risk domains and apply prompting and parameter-efficient fine-tuning to Qwen 3.5 27B. On a balanced validation set of 500 prompts, evaluated with an LLM judge and validated through human ratings, PsychoSafe prompting improves overall refusal quality by 28.1% over a generic baseline, with particularly strong gains in external resource referral (+46.8%) and psychological grounding (+34.8%), while preserving downstream performance on non-refusal tasks. Fine-tuning achieves near-perfect refusal and resource-referral rates but reduces response relevance. Additional evaluations on SORRY-Bench and XSTest show strong in-domain robustness but limited out-of-domain generalization, suggesting that future work should diversify fine-tuning data to help models apply interventions selectively rather than schematically.
[NLP-10] Correlation Is Not Enough: Embedding Human Metadata for Individual Causal Discovery
【速读】： 该论文旨在解决预训练生物医学语言模型在跨领域语义相似性评估中因嵌入空间几何结构失真而导致的虚假相关性问题，即模型将无实际关联的跨域概念（如“皮质醇 28 ug/dL”与“股市波动性”）错误地赋予高相似度评分，严重损害下游任务的可靠性。其核心挑战在于，传统生物医学编码器（如BioBERT、PubMedBERT、BioM-ELECTRA）在未经过专门校正的情况下，对无关跨域对的嵌入距离分布异常集中，导致跨领域区分准确率仅为0%。解决方案的关键在于通过两阶段的对比学习优化嵌入空间：第一阶段采用大规模对比训练（72,034对样本），显著提升模型在生物医学语义相似度（BIOSSES）上的相关性（从0.633升至0.828）并增强域内与跨域嵌入的分离度（从1.05倍提升至1.63倍）；第二阶段引入名为BODHI的硬负例挖掘机制，利用生物医学知识图谱中缺失的边作为负样本，进一步将分离度提升至2.30倍，并实现+0.392的鉴别差距，同时仅以4.5%的精度代价完成优化。此外，研究还揭示了在特定硬件（Intel Xeon 6737P with AMX）上，FP16精度优于INT8的反直觉现象，并通过OpenVINO推理加速实现单查询延迟从1367毫秒降至10毫秒（133倍加速），达到555句/秒吞吐量，验证了系统级部署可行性。研究成果包含可复现的基准测试套件、训练语料、BODHI生成器及推理脚本，为构建可信的生物医学表示学习系统提供了关键技术路径。
链接: https://arxiv.org/abs/2606.09672
作者: Suraj Biswas,Saurabh Gupta,Pritam Mukherjee
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Performance (cs.PF); Quantitative Methods (q-bio.QM)
备注: 20 pages, 18 figures, 9 tables
Abstract:Ask a pretrained biomedical language model whether “cortisol 28 ug/dL” and “stock-market volatility” are related, and it returns a cosine similarity of 0.83 on a scale where 1.0 means identical. The two share no mechanism. This is not a corner case: every off-the-shelf biomedical encoder we tested (BioBERT, PubMedBERT, BioM-ELECTRA) scores unrelated cross-domain pairs between 0.76 and 0.92 when the answer should be near zero. Accuracy on cross-domain discrimination is 0%. Retrieval systems survive this, because a language model downstream filters the noise. A Large Behavioural Model (LBM), a foundation model whose subject is a person rather than a sentence, does not: it reasons over a graph of a user’s life and treats embedding proximity as evidence that two events are causally linked. False proximity writes a false causal edge, and everything downstream inherits the error. Here, embedding geometry is not a tuning knob; it is correctness. We report the fix. A contrastive pass over 72,034 pairs raises PubMedBERT BIOSSES correlation from 0.633 to 0.828 and within-vs-across-domain separation from 1.05x to 1.63x. A second pass, BODHI, mines hard negatives from edges absent in a biomedical knowledge graph and lifts separation to 2.30x and the discrimination gap to +0.392, at a 4.5% BIOSSES cost. On an Intel Xeon 6737P with AMX, OpenVINO cuts single-query latency from 1367 ms to 10 ms (133x) and reaches 555 sentences/sec. One finding contradicts standard advice: FP16 beats INT8 on this silicon at every serving batch size, and we explain why. The same model on a no-AMX Ice Lake instance runs 13-27x slower. We release the benchmark suite, training corpora, the BODHI generator, and the OpenVINO scripts. Comments: 20 pages, 18 figures, 9 tables Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Performance (cs.PF); Quantitative Methods (q-bio.QM) ACMclasses: I.2.7; I.2.6; J.3 Cite as: arXiv:2606.09672 [cs.AI] (or arXiv:2606.09672v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2606.09672 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-11] SpatialWorld: Benchmarking Interactive Spatial Reasoning of Multimodal Agents in Real-World Tasks
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在复杂真实任务中缺乏对交互式空间理解能力的全面评估问题。现有基准主要依赖静态视觉问答（VQA）等被动评估方式或特定模拟器的封闭管道，无法有效衡量模型在动态、开放环境中的主动探索与长期规划能力。为此，研究提出SpatialWorld——一个统一的、面向多模态智能体交互式空间理解的基准平台。其关键创新在于：构建了一个模拟器无关（simulator-agnostic）的协议，集成八种异构仿真后端，涵盖760个由人类标注的跨领域任务（如家庭日常、旅行规划、社交协作），要求智能体在仅依赖视觉输入的局部可观测环境下，通过自适应的主动探索获取第一人称视角的视觉证据，并以符合MLLM特性的文本化动作接口表达决策。为确保评估可靠性，每个任务均配备经人工验证的初始状态、参考执行轨迹及终端状态验证器。实验评估15个先进模型表明，当前最先进的模型GPT-5在任务成功率（TSR）上仅为17.4%，开源模型Qwen-3.5为14.1%，且存在任务成功与执行效率不匹配、领域间性能差异显著等问题，揭示出主动探索与长时程规划仍是核心瓶颈。因此，SpatialWorld作为高难度测试平台，为未来空间智能体的发展提供了重要评估基准。
链接: https://arxiv.org/abs/2606.09669
作者: Hongcheng Gao,Hailong Qu,Jingyi Tang,Jiahao Wang,Zihao Huang,Hengkang Qiao,Shihong Huang,Junming Yang,Yi Li,Hongyixuan Yuan,Wenjie Li,Bohan Zeng,Wenbo Li,Bo Wang,Jianhui Liu,Olive Huang,Haoyang Huang,Wentao Zhang,Guoqing Huang,Nan Duan,Yinpeng Dong
机构: Tsinghua University (清华大学); Chongqing University (重庆大学); Peking University (北京大学); ZenoMind AI; Xi’an Jiaotong University (西安交通大学); Beijing Institute of Technology (北京理工大学); Southeast University (东南大学); Shanghai Jiao Tong University (上海交通大学); Joy Future Academy (未来之光研究院); The University of Hong Kong (香港大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Spatial reasoning is a foundational capability for multimodal large language models (MLLMs) to perceive and operate within the physical world. However, existing benchmarks predominantly rely on passive evaluation (e.g., static VQA) or simulator-specific pipelines, failing to assess general interactive spatial understanding. We introduce SpatialWorld, a unified benchmark designed specifically for evaluating the interactive spatial understanding of multimodal agents in complex real-world tasks. Integrating eight heterogeneous simulation backends under a shared, simulator-agnostic protocol, SpatialWorld features 760 human-annotated tasks across diverse domains (e.g., household routines, travel, social collaboration). Agents must solve tasks under vision-only partial observability, actively gathering egocentric visual evidence and expressing decisions via a unified, text-based action interface native to MLLMs. For reliable evaluation, each task includes a human-validated initial state, a reference trajectory, and a terminal-state verifier. Evaluating 15 advanced agents reveals that robust spatial task solving remains challenging: the strongest model, GPT-5, achieves an average task success rate (TSR) of only 17.4%, while the leading open-source model, Qwen-3.5, reaches 14.1%. Further analysis exposes a clear mismatch between task success and execution efficiency, alongside substantial domain-specific performance variations. These bottlenecks in active exploration and long-horizon planning position SpatialWorld as a rigorous testbed for future spatial agents.
[NLP-12] When Built-in Thinking Helps and Hurts: Constraint-Level Error Shifts in Instruction Following
【速读】： 该论文旨在解决生成式 AI（Generative AI）在开启思维链（Thinking）模式后对指令遵循能力（instruction following）的影响问题，尤其是其在数学与编程任务中表现提升背景下，指令遵循性能是否出现系统性变化。研究发现，尽管整体通过率仅小幅下降（-0.55至-3.52个百分点），但约10%-20%的提示（prompt）在开启与关闭思维模式时出现通过/失败状态切换，表明思维模式并未统一降低性能，而是改变了错误分布模式——部分任务受益，部分任务受损。关键发现在于，通过后验分组分析，可将约束类型划分为“规划类”（Planning，涉及全局计数、结构与协调）和“精度类”（Precision，关注局部精确形式）。其中，规划类任务在开启思维模式后整体性能提升，而精度类任务则持续恶化，且该正负方向模式在四个不同家族的 Hunyuan 模型中均保持一致，即使整体趋势相反。进一步分析显示，思维模式显著影响最终答案长度，匹配长度后的分析虽大幅缓解精度损失，但仍存在残余惩罚。基于交叉编码器相关性度量的思维轨迹分析揭示三种模式：中性类呈现思维相关性与合规性正相关（r ≈ 0.15），规划类虽有可观测的思维参与但相关性极低（r ≈ 0.02），反映思维痕迹相关性与最终输出合规性之间存在执行差距；精度类则表现出微弱负相关（r ≈ -0.05），失败案例的平均相关性反而高于成功案例。激活修补实验表明，精度类翻转实例在多个模型尺寸（1.7B–14B）下更易被恢复（平均恢复率32%-58%），远高于规划类（14%-40%），尤其在14B模型中差异达约30个百分点，说明精度问题更可能由可修复的中间层机制偏差导致。因此，解决方案的关键在于识别并分离不同类型的指令约束，理解思维模式对不同类型任务的差异化影响，并揭示思维过程与最终输出之间的非线性映射关系。
链接: https://arxiv.org/abs/2606.09662
作者: Sai Adith Senthil Kumar
机构: George Mason University (乔治梅森大学)
类目: Computation and Language (cs.CL)
备注: 16 pages, 7 figures, 15 tables
Abstract:Large reasoning models (LRMs) often improve math and coding performance, but their effect on instruction following is unclear. We study IFEval with Qwen3 models (1.7B-32B), using same-weights Thinking ON/OFF controls; four Hunyuan models provide directional cross-family support. Aggregate pass-rate changes are small (-0.55 to -3.52 pp), yet 10-20% of prompts switch between pass and fail across modes, suggesting that thinking changes the pattern of errors–some prompts improve while others worsen–rather than uniformly degrading performance. Under a post-hoc Qwen3-derived grouping, constraint types separate into Planning (global counting, structure, coordination), which improves at the class level under thinking, and Precision (exact local form), which consistently worsens; the class-level Planning/Precision sign pattern holds directionally for all four Hunyuan models despite Hunyuan’s opposite aggregate direction. Thinking also changes final-answer length; matched-length analyses substantially reduce the Precision drop, but a residual penalty remains. Analyzing thinking traces with a cross-encoder relevance metric reveals three patterns: Neutral shows a positive relevance-compliance link (r approximately 0.15); Planning shows near-zero predictive correlation (r approximately 0.02) despite measurable trace engagement, consistent with an execution gap between CE-measured trace relevance and final-answer compliance; Precision shows a small negative correlation (r approximately -0.05), with failing instances having higher mean relevance than passing ones. Activation patching across four model sizes (1.7B-14B) shows that Precision flip instances are more often restored than Planning flip instances (32-58% vs. 14-40% mean layer-restoration), with the largest gap at 14B (about 30 pp).
[NLP-13] End-to-End Context Compression at Scale
【速读】： 该论文旨在解决长上下文语言模型推理中因键值缓存（KV cache）随上下文长度增长而导致的内存瓶颈问题。现有压缩KV cache的技术存在显著局限：要么严重降低模型性能，要么在压缩单个长提示时需要大量时间和计算资源；此外，多数方法要求输入必须适配目标模型的上下文窗口，且与现代生产级推理引擎不兼容。尽管编码器-解码器压缩架构（将长标记序列映射为更短的潜在嵌入序列供解码器使用）在理论上具有吸引力，但现有方法在精度-效率权衡（accuracy-efficiency frontier）上仍不及KV缓存压缩技术。为此，本文重新审视编码器-解码器压缩方案，通过大规模架构搜索和从头预训练，系统性地优化压缩器的设计与训练策略。基于研究发现，持续对一系列0.6B编码器、4B解码器模型进行预训练，覆盖1:4、1:8和1:16三种压缩比，共使用超过3500亿个标记。提出了一类新型压缩器——潜在上下文语言模型（Latent Context Language Models, LCLMs），显著提升了通用任务性能、压缩速度与峰值内存占用之间的帕累托前沿表现。实验表明，LCLMs可作为长时序智能体的有效基础架构，使智能体能够高效扫描压缩后的长上下文，并按需自适应展开相关片段。
链接: https://arxiv.org/abs/2606.09659
作者: Ang Li,Sean McLeish,Haozhe Chen,Nimit Kalra,Zaiqian Chen,Artem Gazizov,Venkata Anoop Suhas Kumar Morisetty,Bhavya Kailkhura,Harshitha Menon,Zhuang Liu,Brian R. Bartoldson,Tom Goldstein,Sanae Lotfi,Micah Goldblum,Pavel Izmailov
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Long-context language model inference is bottlenecked by memory, as the KV cache grows with context length. Recent techniques to compress the KV cache fall short: they either degrade model quality substantially or require considerable time and compute to compress a single long prompt. Furthermore, many methods require the input to fit within the target model’s context window, and are generally incompatible with modern production inference engines. Encoder-decoder compressors, which map a long token sequence to a shorter sequence of latent embeddings consumed by a decoder, are an appealing alternative in principle. However, existing approaches are not competitive with KV cache compression on the accuracy-efficiency frontier. In this work, we revisit encoder-decoder compression and close this gap. We first perform an architecture search, pre-training many variants from scratch to determine how best to design and train encoder-decoder compressors. Guided by our findings, we continually pre-train a family of 0.6B-encoder, 4B-decoder models on over 350B tokens each, at compression ratios of 1:4, 1:8, and 1:16. We introduce Latent Context Language Models (LCLMs), a family of compressors that improve the Pareto frontier across general-task performance, compression speed, and peak memory usage. We demonstrate that LCLMs serve as efficient backbones for long-horizon agents, letting the agent skim through a compressed long context and adaptively expand relevant segments on demand.
[NLP-14] Beyond Accuracy: Community Perspectives on Machine Translation
【速读】： 该论文旨在解决当前机器翻译（Machine Translation, MT）技术发展与真实用户需求之间存在的显著脱节问题。尽管在基准性能上取得了显著进展，但非人工智能（AI）领域的用户群体（如专业译员、语言学习者、语言服务提供商等）普遍关注伦理、可信度、可靠性、成本等实际应用问题，而这些关切并未充分反映在主流研究方向中。其解决方案的关键在于首次通过大规模社交媒体数据分析，系统性地比较四类利益相关者——AI开发者、专业译员、语言学习者和语言服务提供商——在社交平台（Reddit、Facebook、Bluesky、Mastodon，2019–2025）上对机器翻译技术的讨论内容。研究构建了包含79,286条帖子与评论的数据集，揭示出不同群体在翻译质量、效率与可靠性等议题上存在明显分歧甚至强烈对立的情绪极化现象。根本原因在于：AI社区将这些问题视为技术性与计算性挑战，而用户群体则更关注语义细微差别、时间效益、使用信任以及更广泛的社会影响。因此，该研究强调倾听多元用户社群诉求的重要性，以引导科研资源聚焦于真正影响用户实践的核心问题。
链接: https://arxiv.org/abs/2606.09655
作者: Yujun Wang,Ehud Reiter,Shimei Pan,Steffen Eger,Wei Zhao
机构: University of Technology Nuremberg, Germany; University of Maryland, Baltimore County, USA; The Aberdeen NLP Research Group, University of Aberdeen, UK
类目: Computation and Language (cs.CL)
备注:
Abstract:Despite remarkable progress in machine translation (MT), non-AI communities have raised growing concerns about MT systems, suggesting a noticeable gap between technical advancement and the needs of real-world users. For instance, while NLP researchers focus on benchmark performance, end users care about ethical concerns, trust, reliability, costs, and more. We argue that listening to various user communities is essential so that research efforts would be directed towards the problems that the communities care about. To this end, we present a large-scale analysis, for the first time, that investigates what four stakeholder communities (AI developers, professional translators, language learners, and language service providers) post about MT technology on social media. To do so, we construct a dataset of 79,286 posts and comments from Reddit, Facebook, Bluesky, and Mastodon from 2019 to 2025, and analyse where these communities disagree, and how and why. Overall, we find that communities often disagree, and even show strong conflicts due to polarised sentiments on topics such as translation quality, efficiency, and reliability. This is because these communities approach these topics differently: the AI community frames them as technical and computational problems, while non-AI (user) communities care more about quality nuances, time savings, user trust, and broader social issues.
[NLP-15] Where Does the Answer Come From? Benchmarking View-Level Visual Evidence Identification in Multi-View MLLM s for Autonomous Driving
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在视觉推理任务中仅依赖答案准确率评估所导致的“视觉证据溯源失效”问题，尤其针对自动驾驶场景下多视角图像输入时模型可能错误关联视觉源的问题。其核心挑战在于：模型虽能生成看似合理的回答，但其推理依据可能源自错误的摄像头视角，从而影响系统安全性与可解释性。解决方案的关键是提出首个面向多视角视觉问答（Multi-view Visual Question Answering）的基准评测体系——基于同步采集的NuScenes数据集，构建包含73个场景、122对以冲突为核心的问答对，涵盖因果推理、反事实推理及意图预测等复杂语义任务。该基准通过自动冲突挖掘与人工标注相结合的方式生成视图标签，并设计三种评估范式：相机视角选择、给定最优视角的最优问答（oracle QA），以及端到端联合预测（同时选择视角并作答）。评估采用多项选择与自由文本双格式，分别使用精确匹配与大语言模型（LLM）判别器进行评分。通过显式分离“视觉证据来源识别”与“答案正确性”两个维度，该基准有效暴露了传统仅以答案准确率为指标所忽略的模型接地失败（grounding failure）问题。
链接: https://arxiv.org/abs/2606.09644
作者: Yimu Wang,Yee Man Choi,Barry Zhang,Mozhgan Nasr Azadani,Sean Sedwards,Krzysztof Czarnecki
机构: University of Waterloo(滑铁卢大学)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal large language models (MLLMs) achieve strong results on visual reasoning benchmarks, but answer accuracy alone does not indicate whether a model relied on the correct visual evidence. This gap is particularly important in multi-view driving scenes used for autonomous driving, where a model can produce a plausible answer while grounding it in the wrong camera view. We introduce a multi-view visual question answering benchmark for evaluating evidence-source identification: given six synchronized NuScenes views and a question, the model must identify the supporting camera view and answer the question. The benchmark contains 122 conflict-centric question-answer pairs from 73 scenes, spanning causality, counterfactual reasoning, and intent prediction. View labels are proposed by an automatic conflict-mining pipeline and manually verified by annotators. We evaluate three settings: camera-view selection, oracle QA given the golden view, and joint prediction in which the model selects a view and answers in one pass. Answers are evaluated in both multiple-choice and free-form formats, using exact match for structured predictions and an LLM judge for free-form responses. By explicitly separating visual-source identification from answer correctness, the benchmark exposes grounding failures that answer-only evaluation misses.
[NLP-16] Gradient-Guided Reward Optimization for Inference-time Alignment UAI2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在分布漂移（distribution drift）环境下推理可靠性不足的问题，尤其针对现有推理时对齐方法（如Best-of-N和拒绝采样）存在的两大局限：其性能受限于基础模型的生成质量，且依赖不完善的奖励模型易引发奖励劫持（reward hacking）。为克服上述问题，论文提出一种轻量级的推理时优化方法——梯度引导奖励优化（Gradient-Guided Reward Optimization, GGRO），其核心在于通过梯度信号实现生成过程中的精准、最小化干预。具体而言，GGRO利用分词级别的熵监控识别出可能由分布漂移或对齐偏差引起的高不确定性区域，并基于预训练奖励模型的梯度信息生成“引导令牌”（nudging tokens），主动调整生成轨迹，而非仅进行样本重排序。该方法显著提升了安全、有用性和推理能力等多维度基准上的对齐效果，增强了高质量响应的覆盖率与对奖励劫持的鲁棒性，同时保持极低的计算开销。
链接: https://arxiv.org/abs/2606.09635
作者: Hankun Lin,Ruqi Zhang
机构: Purdue University (普渡大学)
类目: Computation and Language (cs.CL)
备注: Accepted to UAI 2026
Abstract:Ensuring the reliability of Large Language Models (LLMs) under distribution drift requires inference-time adaptation. While inference-time alignment methods such as Best-of- N and rejection sampling are widely used, they frame the task as a sampling-intensive, reward-guided search, leading to two key limitations: their performance is bounded by the base model’s generation quality, and their reliance on imperfect reward models makes them vulnerable to reward hacking. To address these challenges, we introduce Gradient-Guided Reward Optimization (GGRO), a lightweight inference-time method that performs targeted, minimal intervention during decoding via gradient guidance. Specifically, GGRO monitors token-level entropy to identify high-uncertainty regions indicative of drift or misalignment. Upon detection, it responds by injecting nudging tokens, generated using gradient signals from an off-the-shelf reward model, to steer the generation trajectory rather than merely re-ranking samples. Experiments show that GGRO consistently improves inference-time alignment across safety, helpfulness, and reasoning benchmarks. It also increases coverage of high-quality responses and robustness to reward hacking, with minimal computational overhead. Code is available at this https URL.
[NLP-17] Civil Court Simulation with Large Language Models
【速读】： 该论文旨在解决传统法庭模拟（court simulation）在法律教育与司法实践之间存在的成本高、可扩展性差的问题，尤其针对现有基于大语言模型（Large Language Models, LLMs）的法庭模拟研究多集中于刑事案例而忽视更常见且复杂度更高的民事诉讼场景这一局限。其解决方案的关键在于提出一个面向中国民事案件的多智能体法庭模拟框架，通过五阶段民事审判程序实现角色驱动的交互，并集成记忆模块（memory module）与法规检索机制以支持长周期裁判过程中的上下文连贯性与法律依据追溯。实验表明，该框架在责任分配与多事项裁判方面表现可靠，且记忆质量对下游模拟效果具有显著影响。研究进一步基于五层因素框架（法律基础、信息条件、司法能力与角色定位、组织压力、社会情境）系统分析了各类因素对模拟可靠性与行为模式的影响，验证了所提框架在民事法庭模拟中的有效性。
链接: https://arxiv.org/abs/2606.09632
作者: Yifan Chen,Haitao Li,Kaiyuan Zhang,Yueyue Wu,Qingyao Ai,Yiqun Liu
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Court simulation bridges legal education and judicial practice, yet human-based simulations are costly and difficult to scale. Large language models (LLMs) offer a scalable alternative, but existing court-simulation research mainly focuses on criminal cases. Civil litigation is more common in practice and harder to simulate because its claims, liability, and remedies are more flexible. We present a multi-agent court simulation framework for Chinese civil cases. The framework organizes role-based interaction through a five-stage civil trial procedure and integrates memory module and statute retrieval to support long-process adjudication. Experiments show that the framework produces reliable civil judgments, with clear strengths in liability allocation and multi-item adjudication. Further experiments show that memory quality substantially affects downstream simulation quality. Through a five-layer factor framework, we analyze how legal grounding, information conditions, judicial capability and role orientation, organizational pressure, and social context affect the framework’s reliability and behavior. These results support the effectiveness of the proposed framework for civil court simulation. The dataset and code are available at: this https URL.
[NLP-18] AGENTS ERVESIM: A Hardware-aware Simulator for Multi-Turn LLM Agent Serving
【速读】： 该论文旨在解决多轮大语言模型（LLM）智能体在实际部署中面临的复杂服务调度问题，其核心挑战在于从传统的无状态请求处理转向需要维护程序级上下文的状态化程序执行。具体而言，多轮智能体工作负载涉及跨轮次的依赖关系、工具调用引入的时间间隙以及可重用的键值缓存（KV-cache）状态，这些动态特性对调度策略、缓存管理与路由机制提出了更高要求。现有仿真器因仅针对无状态的请求级工作负载设计，无法准确捕捉多轮程序执行、跨轮次缓存局部性及工具间隙期间的KV缓存驻留等关键行为。为此，论文提出AGENTSERVESIM——一个面向硬件感知的多轮LLM智能体服务仿真器，其解决方案的关键在于通过可组合模块实现程序粒度的精确建模：程序编排器（Program Orchestrator）保持程序身份与轮次顺序，工具仿真器（Tool Simulator）显式模拟工具调用导致的延迟间隙，会话感知路由器（Session-Aware Router）基于缓存亲和性进行实例调度，而KV驻留模型则追踪不同策略下KV缓存在HBM、主机DRAM/CXL之间的分布与淘汰行为。实验表明，AGENTSERVESIM在真实部署场景与硬件配置下，能以商品级CPU运行，将关键性能指标误差控制在6%以内，从而实现了无需依赖昂贵加速器即可对智能体服务策略进行可控、可重复探索的能力。
链接: https://arxiv.org/abs/2606.09613
作者: Rakibul Hasan Rajib,Mengxin Zheng,Qian Lou
机构: University of Central Florida (中佛罗里达大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Preprint
Abstract:Multi-turn LLM agents interleave model calls with external tool invocations, shifting serving from stateless request processing to stateful program execution. Serving these workloads requires scheduling, KV-cache management, and routing policies that use program-level context, including turn dependencies, tool-induced gaps, and reusable KV state. Evaluating such policies directly on real systems is costly, since each design point may require dedicated accelerator time across arrival rates, model scales, serving-instance counts, and memory hierarchies. Simulation offers a scalable alternative, but existing LLM serving simulators target stateless request-level workloads and therefore omit the core dynamics of agent serving: multi-turn program execution, cross-turn cache locality, and KV-cache residency during tool gaps. We present AGENTSERVESIM, a hardware-aware simulator for multi-turn LLM agent serving. AGENTSERVESIM evaluates serving policies at program granularity through composable modules: a Program Orchestrator preserves program identity and turn order, a Tool Simulator materializes tool-induced gaps, a Session-Aware Router maintains program-to-instance affinity for cache-aware dispatch, and a KV Residency Model tracks policy-defined KV placement across HBM, host DRAM/CXL, and eviction. Across real serving deployments and hardware configurations, AGENTSERVESIM reproduces real-system behavior within 6% error across key performance metrics while running entirely on commodity CPUs. These results show that AGENTSERVESIM enables controlled, repeatable exploration of agent-serving policies without requiring exhaustive deployment on costly accelerators.
[NLP-19] Automated IEP Generation from Traditional Chinese Parent-Teacher Interviews via Corpus-Grounded Feature Diffusion
【速读】： 该论文旨在解决在繁体中文语境下生成个性化教育计划（Individualized Education Program, IEP）所面临的高劳动成本与知识密集型文档负担问题，尤其针对当前生成式人工智能（Generative AI）在繁体中文特殊教育领域因领域数据稀缺、隐私监管严格及缺乏本地评估基准而几乎未被探索的空白。其核心解决方案是提出一种低资源微调流程——基于语料库引导的特征扩散（Corpus-Grounded Feature Diffusion, CGFD），通过筛选高质量双专家标注种子文本、提取句长、结构与量化模板等特征作为提示注入，并结合口语化采样风格的多样性控制实现文本扩散；利用15个专家黄金种子作为扩散锚点，生成567个有效样本，构建582样本训练集，采用QLoRA对Breeze-7B模型进行微调；在推理阶段引入语法约束解码（Grammar-Constrained Decoding, GCD）以强制执行层级化的SMART目标阶梯结构。然而，消融实验在55样本的结构压力测试中揭示出意外结果：在繁体中文的词元预算限制下，GCD反而降低效率——无GCD路径不仅达到100%结构通过率，且中位延迟降低34%，在可靠性与速度上均优于启用GCD的方案。在n=10的正式保留测试中，该无GCD推理路径取得BERTScore F1 = 0.779，显著超越GPT-5.4（0.726）、DeepSeek-V3.2（0.703）、Gemini-3-Flash-Preview（0.703）和Llama-4-Maverick（0.700）等零样本基线模型，同时保证完全本地化、隔离网络环境下的推理安全。该系统填补了繁体中文特殊教育自然语言处理领域的空白，提供了一种可扩展、隐私保护且符合工业工程范式的本地化推理解决方案。
链接: https://arxiv.org/abs/2606.09603
作者: Kuanlin Chen,Cheng-En Ou
机构: 未知
类目: Computation and Language (cs.CL)
备注: 12 pages, 5 figures
Abstract:Writing Individualized Education Programs (IEPs) is a high-labor, knowledge-intensive document burden; English-language research has demonstrated that generative AI can significantly reduce drafting time, yet automated IEP generation in Traditional Chinese remains virtually unexplored due to domain data scarcity, strict privacy regulations, and the absence of local evaluation benchmarks. We propose a low-resource fine-tuning pipeline centered on Corpus-Grounded Feature Diffusion (CGFD): (1) 25 dual-expert high-score seed transcripts are selected via a tau threshold with flag-aware score caps; (2) a FeatureProfile (sentence length, structure, quantification templates) is extracted from seeds and injected into LLM prompts alongside Verbalized-Sampling-style diversity control to drive diffusion; (3) 15 expert gold seeds are used as diffusion anchors, targeting 585 samples; 567 valid diffusion samples are obtained, yielding a 582-sample training set used to fine-tune Breeze-7B with QLoRA; (4) schema-constrained inference via Grammar-Constrained Decoding (GCD) enforces a hierarchical SMART Goal Ladder schema at inference time. Ablation results on a 55-sample schema stress set reveal an unexpected finding: GCD is counterproductive under Traditional Chinese token budgets – the no-GCD path achieves 100% schema pass rate at 34% lower median latency, outperforming GCD on both reliability and speed. On the n=10 formal hold-out, the no-GCD inference path achieves BERTScore F1 = 0.779, exceeding GPT-5.4 (0.726), DeepSeek-V3.2 (0.703), Gemini-3-Flash-Preview (0.703), and Llama-4-Maverick (0.700) zero-shot baselines while maintaining fully local, air-gapped inference. This system addresses a gap in Traditional Chinese special-education NLP and offers a scalable, privacy-preserving local inference solution under an industrial engineering paradigm.
[NLP-20] Clinically Grounded Privacy Evaluation of Medical LMs
【速读】： 该论文旨在解决医疗语言模型（Medical Language Models, LMs）在训练过程中可能记忆并泄露受保护健康信息（Protected Health Information, PHI）的隐私风险问题，尤其关注在现实威胁场景下模型输出的敏感信息泄露，而非仅限于对训练文本的直接恢复。其解决方案的关键在于提出一个基于临床实际情境的分级评估框架，该框架从低到高模拟不同层级的攻击者访问权限——从可公开推断的人口统计学特征（如姓名、出生日期等）到泄露的病历片段——并在每个层级上量化两种类型的隐私泄露：患者特定文本的逐字记忆（verbatim memorization）与敏感诊断的语义级泄露（semantic leakage）。研究发现，在基于37.8万份临床笔记预训练的模型中，常规就诊元数据（如姓名、出生日期、医生、机构、就诊时间等）导致了跨患者时间线的高比例逐字记忆，且对敏感诊断（如堕胎、HIV）的恢复能力达到较高的判别性能（堕胎AUROC=0.91，HIV AUROC=0.81）。同时，研究揭示了逐字匹配记忆可能夸大实际披露风险：36%的记忆内容源于模板化文档生成，并非真实个体信息。因此，该工作强调了长期纵向临床数据训练带来的隐私风险，并提供了一个可操作的、情境化的医疗语言模型隐私评估框架。
链接: https://arxiv.org/abs/2606.09590
作者: Sasha Ronaghi,Sana Tonekaboni,Lena Stempfle,Vivian Utti,Jordan Li Cahoon,Nathaniel Hendrix,Ayin Vala,Marzyeh Ghassemi,Emily Alsentzer
机构: 未知
类目: Computation and Language (cs.CL); Cryptography and Security (cs.CR)
备注:
Abstract:Medical language models (LMs) can memorize and reproduce protected health information, but privacy evaluations often focus on recovery of training text rather than disclosure under realistic threat models. We introduce a clinically grounded framework that evaluates leakage along a graded axis of adversarial access, ranging from publicly inferable demographics to leaked note fragments. At each tier, we measure verbatim memorization of patient-specific text and semantic leakage of sensitive diagnoses. Applying the framework to an LM pretrained on 378k clinical notes, we find that routine encounter metadata (i.e. name, date of birth, provider, practice, visit date) elicits high rates of verbatim memorization across a patient’s timeline and sensitive-diagnosis recovery (AUROC 0.91 for abortion, 0.81 for HIV). At the same time, exact-match memorization can overstate disclosure: 36% of memorized tokens reflect templated documentation. Our work highlights the risks of training on longitudinal clinical data, providing a practical framework for contextual privacy evaluation of medical LMs.
[NLP-21] Code Is More Than Text: Uncertainty Estimation for Code Generation
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在代码生成任务中存在“静默错误”（silently wrong programs）的问题，即生成的程序在语法上看似正确但逻辑错误，从而带来安全与可靠性风险。现有不确定性估计（Uncertainty Estimation, UE）方法大多直接沿用自然语言生成领域的范式，未能充分考虑代码的独特性质。为此，作者提出代码与自然语言在三个关键维度上的本质差异：单个词元（token）错误即可导致整个程序失效（词元脆弱性，token fragility）；算法意图与具体实现之间可独立出现偏差（意图-代码鸿沟，intent-code gap）；以及程序具备可执行性（executability）。基于此，论文构建了三个正交的不确定性评估轴：词汇级（Top-K词元熵，lexical）、算法级（伪代码一致性，pseudo-code consistency）和功能级（行为一致性，behavioral consistency）。通过三轴集成方法，在五种代码生成模型上将平均AUROC从最强的自然语言迁移基线0.696提升至0.776（+8.1点），显著改善了不确定性估计性能。尤其值得注意的是，仅使用一次前向传播的Top-K词元熵在Qwen3-14B上即达到多轮推理基线的性能，且计算成本降低3倍以上，展现出极高的性价比。研究结果表明，代码不确定性估计应采用面向代码特性的专用设计，而非简单移植自然语言的方法。
链接: https://arxiv.org/abs/2606.09577
作者: Yuling Shi,Caiqi Zhang,Yuexian Li,Haopeng Wang,Yeheng Chen,Nigel Collier,Xiaodong Gu
机构: Shanghai Jiao Tong University (上海交通大学); University of Cambridge (剑桥大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG); Software Engineering (cs.SE)
备注:
Abstract:Large language models (LLMs) are increasingly deployed as code generators, where silently wrong programs pose real safety and reliability risks. Reliable uncertainty estimation (UE) is essential for selective prediction, human-in-the-loop review, and downstream agentic decisions. Yet most existing code UE methods are inherited from natural language (NL) generation and ignore properties that make code distinct. We argue that code differs from NL in three ways: a single wrong token can break an entire program (token fragility); algorithmic intent and concrete implementation can disagree independently (intent-code gap); and programs can be executed (executability). We instantiate these properties as three orthogonal uncertainty axes: lexical (Top-K token entropy), algorithmic (pseudo-code consistency), and functional (behavioral consistency). Across five code LLMs, our three-axis ensemble improves average AUROC from 0.696 for the strongest NL-derived baseline to 0.776 (+8.1 points). Notably, on Qwen3-14B, our single-pass Top-K token entropy matches the strongest multi-pass baseline while being over 3x cheaper; across models, it remains a competitive low-cost signal. These results suggest that code UE deserves code-specific design rather than direct NL ports.
[NLP-22] OpenBibleTTS: Large-Scale Speech Resources and TTS Models for Low-Resource Languages
【速读】： 该论文旨在解决低资源语言在语音合成（Text-to-Speech, TTS）领域中合成质量不均衡的问题。尽管近年来神经网络语音合成与多语言语音生成技术显著提升了合成语音的自然度，但这些进展主要集中在少数高资源语言上，而对众多低资源语言的支持仍显不足。现有研究常通过人为降采样高资源语料来模拟低资源场景，但这种做法无法真实反映实际低资源语言中存在的拼写多样性与有限的语音覆盖范围。为此，本文提出了 OpenBibleTTS——一个涵盖37种被忽视语言的大规模低资源语音合成基准数据集，并系统比较了多种TTS架构及大规模语音生成模型在域内圣经文本与域外文本上的表现。研究发现，不存在一种通用最优系统：尽管 Gemini-TTS 在多数语言的主观听感评分中表现最佳，但基于 OpenBibleTTS 训练的单语 EveryVoice 模型在可懂度方面仍具优势，尤其在部分非洲语言中更受青睐；而从零开始训练的开源系统在域外文本上性能急剧下降，暴露出当前多语言覆盖与可靠合成质量之间的持续差距。研究结合自动评估与人工主观评测，公开所有处理后的数据集、对齐信息及训练模型，以推动未来低资源语音合成领域的研究发展。
链接: https://arxiv.org/abs/2606.09553
作者: David Guzmán,Luel Hagos Beyene,Jesujoba Oluwadara Alabi,Yejin Jeon,Dietrich Klakow,David Ifeoluwa Adelani
机构: McGill University (麦吉尔大学); Mila - Quebec AI Institute (Mila-魁北克人工智能研究所); AIMS Research and Innovation Centre (非洲国际数学科学研究中心); NM-AIST (国家信息与通信技术研究所); Saarland University (萨尔兰大学); Canada CIFAR AI Chair (加拿大加拿大首席人工智能学者)
类目: Computation and Language (cs.CL)
备注:
Abstract:Recent advances in neural text-to-speech (TTS) and multilingual speech generation have substantially improved synthetic speech quality, yet these gains remain unevenly distributed across the world’s languages. Existing models are still dominated by a small set of high-resource languages, while many studies of low-resource TTS are simulated on artificially downsampled high-resource corpora that do not reflect the orthographic variation and limited phonetic coverage encountered in genuinely underrepresented settings. As such, we introduce OpenBibleTTS, which is a large-scale benchmark for low-resource speech synthesis spanning 37 underrepresented languages. Moreover, a systematic comparison of various TTS architectures and large-scale speech generation models is conducted across in-domain Biblical text and out-of-domain material. Results show that no single system dominates across languages and metrics: Gemini-TTS achieves the highest listener ratings on most evaluated languages, but monolingual EveryVoice models trained on OpenBibleTTS remain strongest for intelligibility and are preferred in several African languages, while open from-scratch systems degrade sharply on out-of-domain text, revealing a persistent gap between broad multilingual coverage and reliable synthesis quality in underserved linguistic communities. We complement automatic evaluation with subjective human judgments, and open-source all processed datasets, alignments, and trained models to support future low-resource TTS research.
[NLP-23] From Genes to Tokens: a GWAS-inspired Approach for Interpretable Stylometric Analysis
【速读】： 该论文旨在解决文本作者身份识别中的关键问题，即如何从大规模语料中系统性地识别出具有统计显著性的词汇标记（lexical markers），以区分不同作者的写作风格。其解决方案的关键在于借鉴全基因组关联分析（GWAS）的统计框架，将每个词元（token）视为“基因”，将作者身份视为“表型”（phenotype），通过逻辑回归模型检验各词元与特定作者之间的关联强度，并结合多重比较校正方法控制假阳性率。该方法在英语、德语和俄语语料库上均成功识别出具有显著区分力的词汇特征，为作者归属提供了可量化的、数据驱动的分析路径。
链接: https://arxiv.org/abs/2606.09543
作者: Dmitry Pronin,Evgeny Kazartsev
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:This short paper introduces a stylometric interpretation method inspired by genome-wide association studies (GWAS). Each “gene” token’s association with “phenotype” authorship is tested using logistic regression with multiple-comparison correction. Applied to English, German, and Russian corpora, the method detects statistically significant lexical markers distinctive of individual authors.
[NLP-24] Overcoming Decoder Inconsistencies in Whisper for Dravidian and Low-Resource Languages INTERSPEECH2026
【速读】： 该论文旨在解决多语言自动语音识别（ASR）模型在德拉维达语系（Dravidian）语言上表现显著低于印欧语系（Indo-Aryan）语言的问题，尤其聚焦于低资源及黏着语（agglutinative）语言中较高的词错误率（Word Error Rate, WER）。其核心问题源于德拉维达语系语言具有更长的词汇、更高的词汇多样性以及更低的重复性，导致词元（token）分布稀疏，并引发频繁的字符级替换错误。此外，基线微调结果显示解码器中自注意力（self-attention，捕捉语言上下文）与交叉注意力（cross-attention，利用声学线索）之间存在显著不平衡。针对上述问题，作者提出两种解码器层面的改进策略：一是加权注意力（Weighted-Attention），通过自适应调节不同注意力源的贡献以平衡语言与声学信息；二是自条件机制（Self-Conditioning），将中间预测结果重新注入解码过程以增强词元的一致性。实验表明，这两种方法在低资源及黏着语语言上均能实现稳定的词错误率降低，有效提升了模型对复杂语言结构的建模能力。
链接: https://arxiv.org/abs/2606.09535
作者: Chowdam Venkata Kumar,Kumud Tripathi,Pankaj Wasnik
机构: Media Analysis Group, Sony Research India(索尼研究印度媒体分析组)
类目: Computation and Language (cs.CL); Sound (cs.SD)
备注: Accepted at INTERSPEECH 2026, 5 pages, 1 figure, 5 tables
Abstract:Multilingual ASR models such as Whisper perform well on high-resource languages but exhibit substantially higher Word Error Rates (WER) for Dravidian languages compared to Indo-Aryan ones. Through linguistic and dataset analysis, we show that Dravidian languages have longer words, higher vocabulary diversity, and lower repetition, resulting in sparse token distributions and frequent character-level substitution errors. Baseline fine-tuning further reveals decoder imbalance between self-attention (linguistic context) and cross-attention (acoustic cues). Although synthetic token-repetition experiments indicate potential gains, they are impractical. Motivated by these observations, we introduce two decoder-level enhancements: Weighted-Attention, which adaptively balances attention sources, and Self-Conditioning, which reinjects intermediate predictions to improve token consistency. Experiments demonstrate consistent WER reductions for low-resource and agglutinative languages.
[NLP-25] Interpretable Crisis Behavior Analysis Using Mobility and Social Media Data
【速读】： 该论文旨在解决突发事件（如野火与疫情）中人群移动行为与线上情绪表达之间的跨领域协同演化机制被长期孤立研究的问题，强调二者在危机期间的动态耦合关系。其核心解决方案在于提出一种统一且可解释的多模态数据融合框架，通过整合高维异构的日常信号（包括移动轨迹与社交媒体文本），将其转化为二值化行为状态，并运用形式概念分析（Formal Concept Analysis, FCA）挖掘共现结构，进一步提取关联规则；结合时间序列留出测试验证规则稳定性，并通过结构化政策翻译层将强健规则转化为包含触发条件、前置时间及行动预案的操作性简报。该方法在洛杉矶2025年1月野火（短时案例）与阿联酋2020年3月至2021年12月新冠疫情期间（长期案例）均取得显著成效，揭示了两类危机下明确的跨域行为模式：野火事件中交通压力、恐惧/愤怒情绪与治理话语在33天窗口内高度耦合，关键规则置信度达100%，提升指数最高达2.5；新冠疫情中则识别出8条同日稳定规则（留出测试通过率88%）与40条具有2–7天提前量的预测规则。研究表明，可解释的多模态融合能够生成兼具科学可信性与政策可操作性的危机智能。
链接: https://arxiv.org/abs/2606.09532
作者: Muhammad Hamza Arshad Majeed,Sidahmed Benabderrahmane,Talal Rahwan
机构: 未知
类目: Computers and Society (cs.CY); Computation and Language (cs.CL)
备注:
Abstract:Crises alter both how people move and how they communicate. During emergencies such as wildfires and pandemics, changes in mobility patterns and online emotional discourse evolve jointly, yet they are typically studied in isolation. This paper presents a unified and interpretable pipeline that integrates mobility and social media data to identify cross-domain behavioral patterns in crisis settings. The framework is evaluated through two case studies: a short-horizon analysis of the January 2025 Los Angeles wildfires (prototype case) and a longitudinal analysis of UAE COVID-19 behavior from March 2020 to December 2021 (primary case, 671 days). The pipeline aligns heterogeneous daily signals, transforms them into binary behavioral states, applies Formal Concept Analysis (FCA) to extract co-occurrence structure, mines association rules, and validates rule stability through chronological holdout testing. A structured policy-translation layer renders robust rules as operational briefs specifying triggers, lead times, and action playbooks. Results reveal clear cross-domain behavioral structure in both crises. In the wildfire case, traffic stress, fear/anger sentiment, and governance discourse are tightly coupled within a 33-day window, with key rules reaching 100% confidence and lift scores up to 2.5. In the COVID case, repeated mobility adaptation and sentiment volatility yield 8 stable same-day rules (88% holdout pass rate) and 40 clean predictive rules with 2–7 day lead horizons. The work demonstrates that interpretable multimodal fusion can produce both scientifically credible and policy-actionable crisis intelligence.
[NLP-26] Emergence of Context Characteristics Sensitivity in Large Language Models
【速读】： 该论文旨在解决指令微调（Instruction Fine-Tuning, IFT）过程中，大语言模型（Large Language Models, LLMs）如何习得对上下文特征的敏感性这一关键问题。现有研究多聚焦于推理阶段上下文特征与模型使用行为之间的相关性，但忽略了这些关系在训练初期是如何逐步形成的。本文通过系统分析监督微调（Supervised Fine-Tuning, SFT）、直接偏好优化（Direct Preference Optimization, DPO）以及可验证奖励强化学习（Reinforcement Learning with Verifiable Rewards, RLVR）三个阶段中模型对上下文特征（如上下文长度、上下文-查询相似度、流畅性等）敏感性的动态变化，揭示了模型在不同微调阶段对易理解上下文的偏好被主动重塑的过程。研究发现，SFT阶段使模型更倾向于利用易于理解的上下文，而后续阶段（DPO和RLVR）则可能强化或修正这一偏好，具体取决于训练数据分布。因此，该研究的关键贡献在于揭示了上下文使用行为在训练过程中的动态演化机制，并强调构建平衡的指令微调数据集对于确保模型在多样化上下文中具备稳健的上下文利用能力至关重要。
链接: https://arxiv.org/abs/2606.09525
作者: Nadya Yuki Wangsajaya,Haeun Yu,Isabelle Augenstein
机构: Nanyang Technological University (南洋理工大学); University of Copenhagen (哥本哈根大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:During instruction fine-tuning (IFT), large language models (LLMs) learn to follow instructions by using the provided context to answer a query. While prior work has studied how context characteristics correlate with context usage by the LLM, this analysis has been limited to inference time, leaving open how these relationships are acquired in the first place. Here, we measure how models’ sensitivity to such characteristics shifts across successive IFT stages: supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement learning with verifiable rewards (RLVR). Experiments across four models and three datasets show that SFT makes models more likely to use contexts that are easy to understand, such as containing high length, context-query similarity, and fluency. Post-SFT dynamics may either reinforce or resolve these preferences depending on the training dataset. Our findings reveal that context usage is actively reshaped at each IFT stage, and designing a balanced IFT dataset is important in ensuring robust context utilization of instruction-tuned models.
[NLP-27] From Rigid to Dynamic: Entropy-Guided Adaptive Inference for Long-Context LLM s
【速读】： 该论文旨在解决长上下文大语言模型（LLM）推理中因固定稀疏模式或均匀计算预算导致的计算资源分配不合理问题，尤其针对注意力头（attention head）间及不同上下文下注意力行为差异显著却未被充分建模的缺陷。其核心挑战在于：现有方法忽视了注意力熵在头与上下文之间的动态变化特性，导致冗余计算或关键信息丢失。解决方案的关键在于提出一种无需训练的自适应框架EntropyInfer，通过实时分析每个注意力头在不同输入段中的注意力熵分布，将计算资源按头和段粒度动态分配——识别出“刚性头”（Rigid Heads，熵始终接近零）与“动态头”（Dynamic Heads，熵波动显著），并据此减少对低信息量头的计算开销。在解码阶段，引入基于生成输出令牌的潜在键值缓存（KV cache）压缩机制，利用生成过程中的上下文语义信息选择保留最具代表性的缓存条目，而非仅依赖预填充阶段的信息。实验表明，EntropyInfer在Llama、Qwen和openPangu系列模型上均显著优于SnapKV、AdaKV和CritiPrefill等基线方法，在超过10万token的长序列场景下实现最高达2.39倍的端到端推理加速，同时保持极小的质量损失。
链接: https://arxiv.org/abs/2606.09508
作者: Zhanchao Xu,Haoyang Li,Qingfa Xiao,Fei Teng,Chen Jason Zhang,Lei Chen,Qing Li
机构: PolyU(香港理工大学); HKUST(GZ)(香港科技大学(广州)); HKUST(香港科技大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Existing sparse attention and KV cache compression methods for long-context LLM inference typically apply fixed sparsity patterns or uniform budgets across all attention heads, overlooking the substantial variation in attention behavior among heads and contexts. We observe two distinct entropy patterns among attention heads: Rigid Heads, whose entropy stays near zero across input segments, and Dynamic Heads, whose entropy fluctuates significantly. Crucially, the distribution of these types is context-dependent and cannot be predetermined offline. We therefore propose EntropyInfer, a training-free framework that uses attention entropy to adaptively allocate compute at the granularity of individual heads and segments during prefilling. For decoding, we introduce a latent KV cache compression scheme that leverages generated output tokens, rather than prefill tokens alone, to identify and retain the most critical cache entries. Extensive experiments on Llama, Qwen and openPangu model series show that EntropyInfer consistently outperforms baselines including SnapKV, AdaKV, and CritiPrefill, achieving up to 2.39 \times end-to-end speedup beyond 100k tokens with minimal quality degradation compared to full attention. The code is released in this https URL.
[NLP-28] Self-Harness: Harnesses That Improve Themselves
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）驱动的智能体在实际应用中因依赖人工设计的交互框架（harness）而导致的可扩展性瓶颈问题。随着LLM模型日益多样化与快速迭代，传统由人类专家主导的框架设计模式已难以适应，且存在高度依赖模型特性的局限性。其核心挑战在于如何实现针对不同模型行为特性的自适应、自动化框架优化。本文提出的解决方案——自洽框架（Self-Harness），关键在于构建一个无需外部干预的闭环自我改进机制，包含三个阶段：弱点挖掘（Weakness Mining），从执行轨迹中识别模型特定的失败模式；框架提议（Harness Proposal），生成与故障紧密关联、兼具多样性与最小化的框架修改方案；以及提议验证（Proposal Validation），通过回归测试筛选有效变更。实验基于Terminal-Bench-2.0基准，在多个异构基础模型（MiniMax M2.5、Qwen3.5-35B-A3B、GLM-5）上验证了该方法的有效性，平均通过率分别提升至61.9%、38.1%和57.1%，显著优于初始设定。定性分析表明，Self-Harness并非简单添加通用指令，而是将模型固有缺陷转化为可执行的具体框架调整。这一成果揭示了未来大模型智能体不仅可被框架塑造，更具备自主重构其操作框架的能力，为实现真正自适应、自进化的人工智能系统提供了可行路径。
链接: https://arxiv.org/abs/2606.09498
作者: Hangfan Zhang,Shao Zhang,Kangcong Li,Chen Zhang,Yang Chen,Yiqun Zhang,Lei Bai,Shuyue Hu
机构: Shanghai Artificial Intelligence Laboratory (上海人工智能实验室)
类目: Computation and Language (cs.CL)
备注:
Abstract:The performance of LLM-based agents is jointly shaped by their base models and the harnesses that mediate their interaction with the environment. Because different models exhibit distinct behaviors, effective harness design is inherently model-specific. Yet agent harnesses are still largely engineered by human experts, a paradigm that scales poorly as modern LLMs become increasingly diverse and rapidly evolving. In this paper, we introduce Self-Harness, a new paradigm in which an LLM-based agent improves its own operating harness, without relying on human engineers or stronger external agents. We operationalize Self-Harness as an iterative loop with three stages: Weakness Mining, which identifies model-specific failure patterns from execution traces; Harness Proposal, which generates diverse yet minimal harness modifications tied to these failures; and Proposal Validation, which accepts candidate edits only after regression testing. We instantiate Self-Harness on Terminal-Bench-2.0 using a minimal initial harness and three base models from diverse families: MiniMax M2.5, Qwen3.5-35B-A3B, and GLM-5. Across all three models, Self-Harness consistently improves performance, with held-out pass rates increasing from 40.5% to 61.9%, 23.8% to 38.1%, and 42.9% to 57.1%, respectively. Qualitative analyses further show that Self-Harness does not simply add generic instructions, but effectively turns model-specific weaknesses into concrete, executable harness changes. These results suggest a path toward LLM-based agents that are not merely shaped by their harnesses, but can also participate in reshaping them.
[NLP-29] Detecting Differences Is Not Understanding Structure: Large Language Models Fail at Graph Isomorphism
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在图结构推理任务中是否具备真正的拓扑理解能力这一核心问题，特别是针对图同构（graph isomorphism）这一图论中的基础性问题。其关键发现在于：尽管LLMs在图同构检测任务上表现出接近完美的准确率，但当同一图仅通过节点标签的置换呈现时，模型无法识别其同构关系，表明其决策机制依赖于表面模式而非对图结构的抽象理解。由于排列不变性（permutation invariance）是有效结构推理的基本前提，该研究揭示了当前图推理基准测试的成功表现可能源于对输入模式的统计偏好，而非真正的拓扑推理能力，因此不应被误认为是模型具备深层结构认知的证据。
链接: https://arxiv.org/abs/2606.09484
作者: Kumar Thushalika,Sukumar Kishanthan,Asela Hevapathige
机构: University of Ruhuna, Galle, Sri Lanka; University of Moratuwa, Moratuwa, Sri Lanka; University of Melbourne, Melbourne, Australia
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) have shown impressive performance on diverse reasoning tasks, yet their capacity for structural reasoning in graphs remains unclear. We investigate whether LLMs can genuinely understand graph isomorphism -a fundamental problem in graph theory. While LLMs achieve near-perfect accuracy on isomorphism detection, we show this performance is illusory. When identical graphs are presented with permuted node labels, LLMs fail to identify their isomorphism. This finding suggests that LLMs exploit patterns rather than reasoning about abstract graph structure. Since permutation invariance is a fundamental requirement for valid structural reasoning, these results indicate that success on graph reasoning benchmarks should not be interpreted as evidence of genuine topological understanding.
[NLP-30] Memory Beyond Recall: A Dual-Process Cognitive Memory System for Self-Evolving LLM Agents
【速读】： 该论文旨在解决大语言模型（LLM）代理在长期记忆管理中难以支持隐式个性化推理的问题，特别是现有记忆系统将信念修正、因果关联与跨领域抽象等复杂认知过程简化为单一的表面召回任务，导致在用户行为演化轨迹建模方面表现不足。其核心解决方案是提出一种基于认知能力层级结构的动态记忆架构——DCPM（Dynamic Cognitive Hierarchy Memory），该架构遵循双过程理论（dual-process theory）的架构分立思想，通过两个协同机制实现：同步的“日间写入器”（System1）负责以双向链接的替代链形式记录信念演变轨迹，保留原始输入与原子事实之间的时序关系；异步的“夜间引擎”（System2）则在离线状态下推导领域模式、潜在意图，并检测跨领域冲突，将其抽象为高层核心模式。实验结果表明，在需隐式跨会话推理的任务（如PersonaMem-v2）上，启用System2可带来最高达+5.20的性能提升，而在表面片段召回任务中增益最小，验证了该架构设计与认知功能匹配的有效性。
链接: https://arxiv.org/abs/2606.09483
作者: Tianxiang Fei,Mingyang Song,Mao Zheng,Xiang Yu
机构: Tencent(腾讯)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Long-term memory for an LLM agent is more than retrieving the right passage at the right time. Current memory systems collapse belief revision, causal coupling, and cross-domain abstraction into a single retrieval surface tuned for surface recall, and consequently struggle on implicit personalisation that requires reasoning over how a user has evolved. We propose DCPM, which reorganises agent memory along a cognitive capability hierarchy ascending from raw inputs and atomic facts, through diachronic belief trajectories and identity, to domain schemas, latent intentions and cross-domain patterns. The hierarchy is driven by two processes inheriting the architectural split of dual-process theory: a synchronous daytime writer (System1) that records belief revisions as doubly linked supersedes chains, and an asynchronous nighttime engine (System2) that induces schemas and intentions and sweeps for cross-domain collisions abstracted into higher-level core schemas. On LongMemEval, PersonaMem and PersonaMem-v2, enabling System2 contributes most where the benchmark rewards implicit cross-session inference (up to +5.20 on PersonaMem-v2) and least on span recall, matching the architectural prediction.
[NLP-31] Escaping the KL Agreement Trap in On-Policy Distillation
【速读】： 该论文旨在解决生成式 AI（Generative AI）中基于策略的蒸馏（On-policy Distillation, OPD）方法在训练过程中因学生模型（student）陷入不可恢复的前缀漂移状态而产生的监督信号失效问题。当学生模型生成的序列进入低质量路径时，教师模型可能在局部范围内仍给出高置信度评分，导致反向KL散度（reverse KL）较低但缺乏纠正性指导，形成“低KL一致陷阱”（KL Agreement Trap）。这一现象会持续产生无效的监督信号，阻碍模型优化。本文提出KAT（KL Agreement Trap Termination）机制，通过动态自适应阈值在线检测并终止此类持久性低KL一致状态，从而过滤掉退化的一致性监督。其核心创新在于引入一种训练自适应的实时终止规则，有效提升监督信号的质量。实验表明，KAT在四个数学推理基准上将平均准确率（avg@k accuracy）提升2.66%，通过率（pass@k）提升3.43%，同时显著缩短平均轨迹长度59.73%。
链接: https://arxiv.org/abs/2606.09471
作者: Haoran Xin,Anhao Zhao,Ying Sun,Jin Li,Xiaoyu Shen,Hui Xiong
机构: The Hong Kong University of Science and Technology (Guangzhou); The Hong Kong University of Science and Technology, Hong Kong SAR, China; The Hong Kong Polytechnic University; Eastern Institute of Technology, Ningbo
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 13 pages, 8 figures
Abstract:On-policy distillation (OPD) provides dense token-level supervision by asking a teacher to score student-generated rollouts. However, when the student drifts into an unrecoverable prefix, the teacher may locally agree with the degraded state, producing low reverse KL but little corrective training signal. We identify this persistent regime as a low-KL agreement trap. Further analyses show that tokens during and after such traps produce less useful supervision signals. We propose KAT (KL Agreement Trap Termination), an online OPD termination rule that detects persistent low-KL agreement with a dynamic training-adaptive threshold. By filtering weak supervision from degenerate agreement, KAT improves avg@k accuracy by 2.66% and pass@k by 3.43% across four mathematical benchmarks, while reducing average rollout length by 59.73%.
[NLP-32] A Finetuned SpeechLLM for Joint Multi-Granular L2 Assessment and Natural-Language Rationales INTERSPEECH2026
【速读】： 该论文旨在解决自动二语（L2）口语评估中存在的可解释性不足问题，现有方法虽能赋予学习者语言水平标签，但缺乏对评分依据的透明解释。为此，研究提出一种基于评分量表引导的SpeechLLM模型，实现多维度、多粒度的综合评估：在句级（准确性、流利度、语调）预测序数标签、词/音素级准确率，并在同一响应中生成自然语言评语。其核心解决方案在于采用混合目标函数进行训练，结合监督微调与有界直接偏好优化（Bounded Direct Preference Optimization），以提升模型在复杂评估任务中的表现。实验结果表明，在SpeechOcean762数据集上，该方法在保持与现有先进方法竞争力的同时，优于单一粒度模型。进一步分析显示，评语在句级具有较高的自洽性与与真实标签的一致性（即合理性与忠实性），但在词/音素级，评语的忠实性下降，表现为引用信息稀疏且与细粒度标签关联较弱。
链接: https://arxiv.org/abs/2606.09470
作者: Aditya Kamlesh Parikh,Cristian Tejedor-Garcia,Catia Cucchiarini,Helmer Strik
机构: Radboud University (奈梅亨大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted to Interspeech 2026. This publication is part of the project Responsible AI for Voice Diagnostics (RAIVD) with file number NGF.1607.22.013 of the research programme NGF AiNed Fellowship Grants, which is financed by the Dutch Research Council (NWO)
Abstract:Automated L2 speech assessment can assign proficiency labels, but often lacks interpretability. We propose a rubric-guided SpeechLLM for multi-aspect, multi-granular assessment, trained with a hybrid objective combining supervised fine-tuning and Bounded Direct Preference Optimization. The model jointly predicts ordinal labels at the sentence-level (accuracy, fluency, prosody), word/phoneme-level accuracy, and generates a natural-language rationale in the same response. On SpeechOcean762, our approach matches or outperforms single-granularity models while remaining competitive with prior approaches. We analyze rationale reliability along two axes: self-consistency with model predictions and alignment with ground-truth labels, using sentiment consistency (plausibility) and mention-based agreement (faithfulness). Rationales are plausible at the sentence level, but faithfulness degrades at the word/phoneme level: references are sparse and weakly aligned with token-level labels.
[NLP-33] DECSELFMASK: Leverag ing Unlabeled Text via Self-Relevance-Guided Masking for Decoder-Only Classification
【速读】： 该论文旨在解决医学领域中分类任务因标注数据稀缺而导致模型性能受限的问题，尤其是在临床文本等高价值但标注成本高昂的场景下。其核心挑战在于如何有效利用海量未标注的临床文本以提升小样本条件下的分类性能。解决方案的关键在于提出一种名为DecSelfMask（Decoder Self-learning by Masking）的新方法，通过引入一种基于相关性引导的掩码策略，在不依赖人工标注的前提下自动生成高质量的自监督训练样本。具体而言，该方法利用归因分析（relevance attribution）技术识别未标注文本中与目标任务相关的语义片段，并对这些关键部分进行掩码处理，进而训练解码器通过下一个词预测（next-token prediction）来重建被掩码内容。这一过程迫使模型学习到未标注数据中的深层结构与语义规律，从而增强其在下游分类任务中的泛化能力。实验在包含190万条意大利医院临床笔记的136个分类任务上验证了该方法的有效性，覆盖5种不同规模和架构的预训练模型，结果显示DecSelfMask显著优于标准微调（+19.9点宏平均F1）、合成标签生成（+12.5）及持续预训练（+6.3），展现出强大的迁移学习潜力。
链接: https://arxiv.org/abs/2606.09466
作者: Pietro Ferrazzi,Matteo Merler,Giovanni Bonetta,Alberto Lavelli,Bernardo Magnini
机构: Fondazione Bruno Kessler, Trento, Italy; University of Padova, Italy
类目: Computation and Language (cs.CL)
备注:
Abstract:Classification tasks require annotated data, which can often be expensive, time-consuming, or even unfeasible to collect. This is the case of the medical domain, where large datasets often have few annotated examples. To address this, we propose DecSelfMask (Decoder Self-learning by Masking), an approach to enhance decoder-only performance on classification tasks. We build on common self-learning approaches by leveraging a model to create training examples from unlabeled data to propose a novel relevance-guided masking strategy. We use relevance attribution methods to determine what portions of unannotated texts are relevant for a task. We then create self-supervised training examples by masking out those portions, training the model to reconstruct them via next-token-prediction. We hypothesize that those examples convey knowledge about the structure and semantics of unannotated data that can be useful for downstream performance. We test our approach on 136 tasks from a collection of 1.9M clinical notes from an Italian hospital. We quantify DecSelfMask’s impact on downstream tasks on 5 models of different scales and families, including a probing analysis. Experiments show consistent gains, outperforming standard supervised fine-tuning approaches (+19.9 points in Macro F1), synthetic label generation (+12.5), and continual pretraining (+6.3), as well as common baselines.
[NLP-34] H2HMem: A Multimodal Memory Benchmark for Agents in Human-Human Interactions
【速读】： 该论文旨在解决当前大型语言模型（Large Language Model, LLM）代理在复杂人与人交互场景中记忆能力评估不足的问题。现有记忆基准测试主要聚焦于单一用户、纯文本交互，难以涵盖真实应用场景中的多模态特性、话语现象（如回指和指示语）以及多方参与者之间异步或冲突信息的挑战。为此，论文提出H2HMem——一个面向人与人交互的多模态记忆基准，涵盖双人及多人对话，并整合多模态信息流，从记忆召回、推理与应用三个维度评估代理性能。其解决方案的关键在于构建一个贴近真实复杂交互环境的综合性评估框架，能够系统性地检验模型在跨模态、跨参与者及跨会话情境下对信息的构建、保留与利用能力，从而揭示当前先进代理在多主体、多模态交互中记忆能力的显著局限，为下一代LLM代理的发展提供明确改进方向。
链接: https://arxiv.org/abs/2606.09461
作者: Shiping Zhu,Yibo Yang,Zhengyang Wang,Tiancheng Shen,Dandan Guo,Ming-Hsuan Yang
机构: Jilin University (吉林大学); Shanghai Jiao Tong University (上海交通大学); University of California at Merced (加利福尼亚大学默塞德分校)
类目: Computation and Language (cs.CL)
备注: 22 pages, 6 figures
Abstract:Large language model agents are increasingly deployed in human-human interaction settings, such as meeting assistants and clinical documentation systems, where they must observe conversations and retain information for downstream queries. Unlike traditional human-assistant settings, these environments are inherently multimodal, involve complex discourse phenomena such as anaphora and deixis, and contain asynchronous or conflicting information from multiple participants. However, existing memory benchmarks largely focus on single-user, text-only interactions, failing to capture these challenges. To address this gap, we introduce H2HMem, a Human-to-Human Multimodal Memory Benchmark for evaluating memory capabilities in complex human-human interactions. H2HMem includes both dyadic and multi-party conversations with multimodal information streams, and evaluates agents along three dimensions: memory recall, reasoning, and application. Experiments with advanced agents reveal substantial limitations in constructing, retaining, and utilizing memories across modalities, participants, and sessions, highlighting substantial room for improvement in next-generation LLM agents.
[NLP-35] AbstRAG : Learning to Abstract for Retrieval Problems
【速读】： 该论文旨在解决检索增强生成（Retrieval-Augmented Generation, RAG）系统在查询、文档证据与用户意图之间存在抽象层级不匹配时的性能下降问题，即“抽象差距”（abstraction gap）——指为对齐查询意图与可用证据所需的一组最小类型假设。其核心解决方案是提出AbstRAG，将抽象过程显式建模为可检索的对象，通过分解查询-证据差距为表达、概念、意图-证据及事件类型等组件，并结合匹配质量、与查询无关的效用先验以及所需“桥梁”的成本进行相关性评分。该方法的关键机制在于反射式精炼（reflective refinement）：由一个批判器诊断检索失败原因，定位失效的抽象操作符，提出最小化的阶段性修复方案，并在满足充分性与压缩性控制的前提下才接受该修复。实验表明，AbstRAG在三个文档内检索基准上优于七种基线，在nDCG@10指标中18/21次对比表现更优，生成准确率分别提升1.9%、5.2%和4.0%；消融实验进一步验证了反射式精炼是检索性能提升的主要驱动力，且压缩控制单独即可将压力测试片段中的过度扩展误报率从73.7%降至0%。
链接: https://arxiv.org/abs/2606.09459
作者: Lei Xu,Xin Quan,Daniel Pedronette,André Freitas
机构: Idiap Research Institute(伊迪亚研究所), École Polytechnique Fédérale de Lausanne (EPFL)(洛桑联邦理工学院), São Paulo State University(圣保罗州立大学), University of Manchester(曼彻斯特大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Retrieval-augmented generation often fails when the query, the document evidence, and the user’s intent are expressed at different levels of abstraction. A query may ask about a class, a relation, or an event, while the document only states specific instances, indirect framings, or scoped formulations. We define this mismatch as an abstraction gap: the minimal set of typed assumptions required to align query intent with the available evidence. To close this gap, we introduce AbstRAG, which treats abstraction as an explicit retrieval object. AbstRAG decomposes the query–evidence gap into expression, conceptual, intent–evidence, and event-type components, and scores relevance by combining match quality, a query-independent utility prior, and the cost of the required bridges. Its central mechanism is reflective refinement: a critic diagnoses retrieval failures, localizes the failed abstraction operator, proposes a minimal stage-specific patch, and accepts the patch only under sufficiency and compression controls. Across three within-document retrieval benchmarks against seven baselines, AbstRAG outperforms on nDCG@10 in 18 of 21 paired-bootstrap contrasts and improves generation accuracy by 1.9%, 5.2%, and 4.0% across the three benchmarks; ablations confirm that reflective refinement drives most of the retrieval gain and the compression control alone reduces over-expansion false positives from 73.7% to 0% on a stress slice.
[NLP-36] Reasoning without Gold Standards: A Proxy-Judge Theory of Autoformalization
【速读】： 该论文旨在解决复杂推理任务中缺乏可信赖的精确参考答案（gold-standard reference）的问题，尤其在自动形式化（Autoformalization, AF）任务中，由于一个非形式化推理可能存在多种有效的形式化表达，难以通过精确匹配判断输出正确性。其核心挑战在于如何在无精确参考的情况下实现模型输出的可靠评估与迭代优化。解决方案的关键在于提出一种无参考的代理判别框架（reference-free proxy-judge framework），该框架通过多维度结构化的属性检查替代传统的精确匹配：将评估划分为三个层次——全局属性（global properties）、模块内局部属性（per-module properties）以及跨域对齐属性（cross-domain properties），并分别生成对应的判别向量（verdict vector）。该向量驱动一个反射式精炼循环，仅针对被判定为错误的维度进行定向修复，从而实现高效、精准的迭代改进。在有限判别噪声条件下，该方法理论上可使内在误差以几何速率收敛至依赖噪声水平的平台值。实验表明，在miniF2F、ProofNet、e-SNLI和ProntoQA等多个基准上，该方法显著优于单次示例学习（single-shot ICL）基线，且结构化代理判别相较于标量代理更具优势，验证了其在无精确参考场景下的实用性与理论可收敛性。
链接: https://arxiv.org/abs/2606.09449
作者: Lei Xu,Xin Quan,André Freitas
机构: Idiap Research Institute(伊迪亚研究所); École Polytechnique Fédérale de Lausanne (EPFL)(洛桑联邦理工学院); University of Manchester(曼彻斯特大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Complex reasoning tasks increasingly require systems to produce outputs whose correctness cannot be judged by exact match against a single reference. Autoformalization (AF) is a representative example; it asks a model to translate informal mathematical or logical reasoning into a formally checkable object, yet expert-validated formalizations do not scale beyond toy cases and a single informal argument can admit many valid formal renderings. Progress therefore depends on whether partial, structured proxies can substitute for exact references. We introduce a reference-free proxy-judge framework for AF that replaces gold-standard matching with a vector of per-axis property checks. The framework organizes the proxy along three structural scopes that cover global properties of the elicited object, per-module properties internal to its sub-components, and cross-domain properties that re-align it to the informal source, and aggregates each axis into a verdict vector. The vector drives a reflective refinement loop in which a violated coordinate routes the controller to a matching repair target, so each iteration changes only what is judged wrong. Under bounded judge noise, the expected intrinsic gap contracts geometrically to a noise-dependent plateau. Across seven formalization backbones on miniF2F, ProofNet, e-SNLI, and ProntoQA, refinement consistently lifts Pass Rate over the single-shot ICL baseline, and the per-axis proxy outperforms a matched scalar proxy on benchmarks where the baseline has room to improve. Structured proxy judgments therefore provide both a practical refinement signal and a theoretical handle on convergence when exact references are unavailable. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2606.09449 [cs.CL] (or arXiv:2606.09449v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2606.09449 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-37] MUDIDI: A Two-Stage Framework for Multilingual Dictionary Digitization with Language Models EMNLP2026
【速读】： 该论文旨在解决多语言词典（multilingual dictionaries）在低资源和濒危语言中的数字化难题，尤其是针对大量仍仅以扫描图像形式存在的词典，其难以通过传统光学字符识别（OCR）技术转化为机器可读格式的问题。核心挑战在于语言特有的书写系统、复杂的多栏布局、缩写与交叉引用等结构化信息的复杂性。为应对这一问题，论文提出MUDIDI（Multi-lingual Dictionary Digitization）框架，采用两阶段方法：第一阶段评估字符识别与标记（markup）的保真度；第二阶段聚焦于词典条目分割，并将其映射至标准的机器可读词典结构——SIL的多词典格式化器（Multi-Dictionary Formatter）。研究团队还发布了包含30个公共领域词典的人工标注数据集，涵盖多样书写系统、语系与排版格式，用于基准测试主流OCR系统、通用大语言模型（LLMs）与视觉-语言模型（VLMs）。实验表明，在多数书写系统与语言中，LLMs在两个阶段均表现最优，并提供了针对高难度场景的优化实践指南。此外，研究发现向LLMs输入词典前言等额外上下文信息，可显著提升数字化质量。
链接: https://arxiv.org/abs/2606.09435
作者: David Setiawan,Temuulen Khishigsuren,Milind Agarwal,Pagnarith Pit,Aso Mahmudi,Ekaterina Vylomova
机构: The University of Melbourne (墨尔本大学); LILT (语言与信息技术实验室)
类目: Computation and Language (cs.CL)
备注: 9 pages, preprint, submitted to EMNLP 2026
Abstract:Multilingual dictionaries are among the most valuable documentary resources for low-resource and endangered languages, yet many remain available only as scans. For many decades, their digitization and conversion into a machine-readable format was nearly impossible due to language-specific scripts, complex multi-column layouts full of entries with abbreviations and cross-references. Recent vision-language models offer a promising solution, but it is unclear how well they preserve characters, markup, and process lexicographic structure. We introduce MUDIDI, a two-stage framework for multi-lingual dictionary digitization. Stage One evaluates the quality of character recognition and markup preservation; Stage Two focuses on dictionary entry segmentation with subsequent mapping into a machine-readable lexicographic schema, SIL’s Multi-Dictionary Formatter. We also release a dataset that consists of human-annotated lexicographic entries collected from 30 public-domain dictionaries featuring diverse writing systems, language families, and formats. We benchmark OCR systems, general-purpose Large Language Models (LLMs), and Vision Language Models (VLMs) on the dataset, demonstrating superior performance of LLMs across most writing systems and languages in both stages, and provide practical guidelines on improving the results for more challenging scenarios. Finally, we show that supplementing additional information, such as dictionary introduction, to the LLMs can improve the quality of the digitized dictionary. Github: this https URL
[NLP-38] Guide Me Out: A Framework to Benchmark VLM Operators Communication in Crisis Scenarios
【速读】： 该论文旨在解决危机响应中语言指导与物理环境脱节的问题，即现有自然语言处理（Natural Language Processing, NLP）研究多局限于静态、纯文本分类任务，忽视了在动态、具身场景下人工智能（AI）操作员在沟通中的关键作用。其解决方案的核心在于构建一个新颖的基准评估框架，用于评测视觉-语言模型（Vision-Language Models, VLMs）在模拟疏散场景中引导平民代理的能力。研究通过对比窄播（narrowcast）与广播（broadcast）两种通信策略、视觉与基于图的环境表征方式，以及静态与移动威胁行为，在九种结构复杂度不同的地图上进行系统实验。结果表明，窄播策略在所有难度层级下均显著降低平民失败率；模型表现高度依赖于世界表征方式：视觉模态能有效提升性能，而引入邻接图则因模型而异，常导致性能下降；移动威胁显著增加失败率，因通信需随时间持续动态调整。研究揭示，将VLM作为疏散场景中的AI操作员仍面临重大挑战，通信策略与输入表示的选择直接决定干预成败。
链接: https://arxiv.org/abs/2606.09428
作者: Giacomo Gonella,Stefano Menini,Marco Guerini
机构: Fondazione Bruno Kessler, Italy; University of Trento, Italy
类目: Computation and Language (cs.CL)
备注:
Abstract:Effective crisis response requires spatially grounded communication that bridges linguistic guidance of civilians with the physical environment, accounting for structural bottlenecks, evolving threats, and agent-specific contexts. Yet, current NLP research in crisis communication remains mainly limited to static, text-only classification settings, overlooking the critical communicative role of AI operators in dynamic, embodied scenarios. We address this gap with a novel benchmarking framework for evaluating Vision-Language Models (VLMs) tasked with guiding civilian agents through simulated evacuations. We test two communication strategies (narrowcast vs. broadcast), two environment representations (visual vs. graph-based), and two threat behaviors (static vs. moving) across nine maps of varying structural complexity. Our results show that Narrowcast consistently reduces civilian Fail rates compared to Broadcast across all difficulty levels. Guidance quality depends heavily on how the VLM operator represents the world: the visual modality drives performance, while adding an adjacency graph is model-dependent and often harmful. Moving threats raise Fail rates across all conditions as communication must continuously adapt over time. Together, these findings show that deploying VLMs as AI operators in evacuation scenarios remains a non-trivial challenge, where the choice of communication strategy and input representation can directly determine the success or failure of the intervention.
[NLP-39] oward Signing Activity Projection in Sign Language Interaction
【速读】： 该论文旨在解决社交机器人在与使用手语等非语音模态进行交流的用户互动时，缺乏对签名行为的预测性轮换（predictive turn-taking）能力的问题。当前主流的语音活动预测（Voice Activity Projection, VAP）框架虽在语音交互中表现良好，但其能否有效迁移至手语交互仍不明确。本文的关键解决方案是首次尝试将VAP架构适配于双人手语交互场景，基于公共德语手语语料库（Public DGS Corpus）的数据，从词义手语标注中提取二值化手语活动流，并构建代理任务以预测轮换状态（如SHIFT/HOLD）。模型利用每位手语使用者的姿势特征，包括手部、眼区及口区的运动信息。实验结果表明，基于手部线索的SHIFT/HOLD预测具有潜力，而纯粹的SHIFT预测仍具挑战性。研究揭示了将语音交互中的预测模型迁移至手语交互的可行性与局限性，强调未来需建立超越语音衍生类别的手语特异性事件定义，以实现更精准的手语交互预测建模。
链接: https://arxiv.org/abs/2606.09424
作者: Takao Obi,Wang Yusong,Koji Inoue,Kotaro Funakoshi
机构: Institute of Science Tokyo (东京科学研究所); Kyoto University (京都大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Social robots must interact robustly not only with users assumed by speech-centered systems but also with diverse users whose communication relies on different modalities, e.g., sign language. One important capability gap is predictive turn-taking with signing users. Although Voice Activity Projection (VAP) has been successfully used to model future voice activity in spoken interaction, it remains unclear whether the framework transfers to sign language interaction. This paper presents an initial transfer study of adapting a VAP architecture to dyadic sign language interaction. Using interaction recordings from the Public DGS Corpus, we derive binary signing activity streams from lexical sign annotations and formulate proxy tasks for turn-taking prediction. The model uses pose-derived hand, eye-region, and mouth-region features extracted for each signer. The results show that SHIFT/HOLD prediction is promising, especially with hand cues, while SHIFT-prediction remains difficult. These findings provide initial evidence for both the promise and the current limitations of transferring predictive turn-taking models from spoken interaction to sign language interaction. Predictive modeling of sign language interaction still requires sign-language-specific event definitions that go beyond speech-derived categories.
[NLP-40] What Should a Skill Remember? Quality-Cost Trade-offs in Cost-Aware Skill Rewriting for Language Model Agents
【速读】： 该论文旨在解决大语言模型智能体（LLM agents）在技能（skill）重写过程中因过度压缩提示（prompt compression）而导致的效率与鲁棒性下降问题。现有方法常将技能重写视为单纯缩短文本长度的任务，但研究表明，过短的技能会移除稀疏但关键的操作锚点（operational anchors），进而削弱智能体在探索、调试和恢复过程中的能力，反而增加整体运行成本。论文提出从“经济性”（economic lens）视角重新审视技能重写，构建了一个受控框架，系统分析技能结构，采用信息保全策略进行重写，并在固定任务指令、环境及验证器条件下评估重写效果。基于SkillsBench的实验揭示了不同重写策略在质量-成本权衡上的差异：API/代码锚定、工作流保护、规则/公式锚定等策略对不同任务类型各具优势，不存在普适最优模板。主评估中，所提出的可学习策略使总成本降低7.0%，下游智能体的令牌消耗减少6.0%；在冻结模型跨模型迁移场景下，平均成本分别降低14.7%和13.7%，且验证器性能保持不变。研究结论表明，技能设计应被视为一种面向成本意识的操作知识工程（cost-aware operational knowledge engineering），而非简单的提示压缩。
链接: https://arxiv.org/abs/2606.09421
作者: Qinghua Xing,Yinda Chen,Yaping Jin,Zhenhe Wu,Bohan Lin,Hang Zhou,Xinghao Chen,Hanting Chen,Zhiwei Xiong
机构: University of Science and Technology of China(中国科学技术大学); Huawei Technologies(华为技术); Tianjin University(天津大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language model agents increasingly rely on skills: reusable procedural documents encoding workflows, tool use, implementation patterns, validation checks, and domain rules. Skill rewriting is often treated as prompt compression, but shorter skills can make agents more expensive by removing sparse operational anchors that prevent exploration, debugging, and recovery. We study skill rewriting through this economic lens. Our controlled framework profiles skill structure, rewrites skills using information-preservation strategies, and evaluates the rewrites under fixed task instructions, environments, and verifiers. Experiments on SkillsBench reveal distinct quality–cost trade-offs across strategies: API/code anchoring, workflow guarding, and rule/formula anchoring benefit different task families, with no universally dominant template. In the main held-out evaluation, the learned policy reduces total cost by 7.0% and downstream agent-token cost by 6.0%; in frozen cross-model transfer, the corresponding reductions average 14.7% and 13.7%, while verifier quality is preserved. These results position skill design as cost-aware operational knowledge engineering rather than prompt compression. Resources: \hrefthis https URLSkillEE.
[NLP-41] Capacity Not Format: Rethinking Structured Reasoning Failures
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在处理结构化输出时存在的性能下降问题，尤其关注其背后的根本原因。传统观点将结构化输出视为一种“推理负担”（reasoning tax），但本文指出这一理解不完整：结构化格式带来的成本高度依赖于模型的剩余计算容量（spare capacity）。研究通过设计信息匹配的自由文本对照组与四级结构复杂度梯度，系统分离了格式特异性影响与提示长度混淆因素，在4个模型和5个基准测试上实现了0%的解析失败率。关键发现是，结构化格式的影响具有显著的容量依赖性：当模型具备充足容量时（如Sonnet），JSON格式对性能无负面影响（88.7±4.0% vs. 89.3±1.7% CoT on MATH-Hard）；而当模型接近其容量极限时，结构化格式会通过两种机制导致显著性能退化——其一为标准令牌预算下的截断效应（如Haiku下降36.2个百分点，p < 0.0001）；其二为即使扩展预算消除截断后仍存在的纯容量竞争现象（如GPT-4o-mini下降28.0个百分点，p < 0.001），表明该损耗并非由令牌耗尽引起。此外，该格式惩罚随结构复杂度增加而加剧（McNemar, p < 0.0001），且无法仅由提示长度解释。研究进一步挑战了“前沿模型免疫于格式开销”的说法：在AIME竞赛级数学任务中，Opus 4.7从96.2%降至91.0%（实际差值5.26个百分点），验证了容量瓶颈的存在。延迟结构消融实验（先自由推理再格式化）恢复了大部分准确率（3次运行平均80–87%），有力支持了容量竞争机制。因此，解决方案的核心在于“容量适配”——当模型接近其能力边界时，应优先进行推理，待完成后再进行结构化格式化，而非强制实时结构化输出。
链接: https://arxiv.org/abs/2606.09410
作者: Hengxin Fan
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 12 pages, 3 figures
Abstract:Prior work treats structured output as a reasoning tax, but this framing is incomplete: the cost of formatting depends strongly on a model’s spare capacity. Using information-matched prose controls and a four-level schema complexity gradient, we separate format-specific effects from prompt-length confounds across 4 models and 5 benchmarks with 0% parse failures on successfully generated responses. We find that structured formats are capacity-dependent. Models with sufficient headroom absorb JSON constraints without degradation (Sonnet: 88.7\pm4.0 % JSON vs. 89.3\pm1.7 % CoT on MATH-Hard). In contrast, formats severely degrade models operating near their limits through two distinct mechanisms. First, under standard token budgets, Haiku drops 36.2pp ( p 0.0001 ) largely due to truncation. Second, even with extended budgets eliminating truncation, GPT-4o-mini drops 28.0pp ( p 0.001 ), revealing pure capacity competition independent of token exhaustion. This format penalty scales with schema complexity (McNemar p 0.0001 ) and cannot be explained by prompt length alone. Furthermore, these results qualify claims of frontier model immunity: on AIME competition math, Opus 4.7 drops from 96.2% to 91.0% under JSON ( -5.3 pp; the displayed percentages are independently rounded, exact difference is 7/133 = 5.26 pp \approx 5.3 pp). A delayed-structure ablation – reasoning freely before formatting – recovers most of the lost accuracy (3-run mean: 80–87%), supporting the capacity competition mechanism. The practical implication is not to avoid structured output, but to match it to capacity: when a model is near its limits, think first, format later. Comments: 12 pages, 3 figures Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2606.09410 [cs.AI] (or arXiv:2606.09410v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2606.09410 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-42] Correct Looks Better: Pairwise Comparisons Reveal Accuracy Rankings ICML’26
【速读】： 该论文旨在解决生成式模型评估中依赖人工判断所引发的可靠性问题，特别是针对基于成对比较（pairwise comparison）与聚合方法（如Elo评分）在评估生成模型时可能受到表面风格线索或评判者偏见影响的担忧。其核心解决方案在于验证：当存在可比的客观基准（ground truth）时，基于成对比较的Elo评分排名与基于准确率的基准排名具有高度一致性，其斯皮尔曼相关系数超过0.9。研究通过将五个知名基准转化为自由格式生成评估任务，发现即便在评判者能力较弱的情况下，Elo评分仍显著优于直接评价方法。此外，研究揭示尽管多数判断发生在两个答案均正确或均错误的配对上，风格因素和评判者偏见对最终排名的影响较小，但答案末尾的重复内容（echo）被证实是驱动评判偏好的一项因果因素。
链接: https://arxiv.org/abs/2606.09409
作者: Mina Remeli,Moritz Hardt
机构: Max Planck Institute for Intelligent Systems(马克斯普朗克智能系统研究所); Tübingen AI Center(图宾根人工智能中心)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted at ICML’26
Abstract:Pairwise comparisons combined with aggregation methods like Elo have become central to evaluating generative models, yet concerns remain that they reward superficial stylistic cues or display judge biases. In a more positive turn, we show that model rankings from pairwise comparisons strongly agree with ground-truth-based accuracy rankings when such ground truth is available for comparison. By converting five well-known benchmarks into free-form generative evaluations, we find that Elo rankings achieve a Spearman correlation above 0.9 with accuracy rankings and substantially outperform direct evaluation when the judge is weak. Furthermore, style and judge bias have only minor effects on model rankings, despite most judgments occurring on pairs where both candidate answers are correct (or incorrect). On such pairs, we find that repetition after the final answer (echo) is a causal driver of judge preference.
[NLP-43] Introducing multiplex semantic networks as multifaceted representations of creative associative knowledge across multilingual samples
【速读】： 该论文旨在解决传统创造力研究中仅依赖单一任务测量所导致的表征不充分问题，即现有方法难以全面捕捉创造力背后复杂的语义知识组织与检索机制。其核心解决方案在于构建多层语义网络（multiplex semantic networks），通过整合来自六种认知任务（包括词语流畅性、句子链生成、自由联想和叙事写作等）的数据，形成一个多层次、跨任务的语义关联结构，以更全面地建模创造性思维中的联想知识体系。研究发现，不同任务层间蕴含非冗余的语义组织信息，且高创造力与低创造力个体的网络结构保持显著差异，而基于AI人格生成的响应则表现出高度同质性，验证了人类语义网络的复杂性。进一步采用岭回归机器学习模型，结合12个特征（包括网络结构度量、情绪评分及扩散激活模拟）进行预测，结果表明：经前期结构相似性筛选后的多层网络组合可使预测准确率提升50%；其中结构度量具有最高特征重要性，扩散激活动态过程亦提供额外预测能力。研究表明，多层语义网络能够刻画更具丰富性与跨文化一致性的创造性知识关联模式，为创造力的计算建模提供了新范式，并公开数据与代码以促进该领域的多样化计算方法发展。
链接: https://arxiv.org/abs/2606.09403
作者: Edith Haim,Kurt Haim,Roger E. Beaty,Cynthia S.Q. Siew,Massimo Stella
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Creativity is a complex cognitive ability that relies on knowledge organisation and retrieval from semantic memory. Yet most research uses a single task to measure it, capturing only a fraction of this complexity. This study investigates multiplex networks - layered semantic networks obtained from six cognitive tasks - as a more comprehensive approach to modelling the associative knowledge underlying creativity. We collected data from N=518 individuals from four countries (Austria, USA, Singapore, Italy). From their responses to verbal fluency, sentence-chain, free association, and narrative writing tasks, we constructed semantic networks and assembled them in a multiplex structure. AI persona-based responses provided a comparison baseline. Structural reducibility analyses showed that different task layers captured distinct, non-redundant information about semantic organisation, supporting the use of multiple tasks over any single one. The networks from high- and low-creative groups remained structurally distinct, while AI-generated networks showed near-identical structures regardless of creativity group. Finally, we used 12 features (network measures, emotional scores, and spreading activation simulations) in a machine learning model using ridge regression to predict individual creativity scores. The combination of structurally similar layers, as identified in the previous stage, improved a proof-of-concept prediction accuracy by 50%. Structural measures showed the highest feature importance, with spreading activation dynamics providing additional predictive power. Together, these findings indicate that multiplex semantic networks capture a richer, cross-cultural picture of associative knowledge underlying creativity. We also release our diverse dataset and code to foster diverse computational approaches within the creativity community.
[NLP-44] PriFT: Prior-Support Guided Supervised Fine-Tuning
【速读】： 该论文旨在解决监督微调（Supervised Fine-Tuning, SFT）在下游任务适配中泛化能力弱于强化学习（Reinforcement Learning, RL）的问题。其核心挑战在于SFT采用离策略（off-policy）目标，即逐标记地拟合固定示范数据，而这些目标标记可能与模型预训练分布不一致，导致过拟合。现有方法通过为与当前模型预测分布更对齐的标记分配更高权重来缓解此问题，但此类权重由正在微调的在线模型计算，使权重信号与优化轨迹耦合，引发自增强动态，导致分布快速偏离预训练分布。为此，本文提出PriFT（Prior-support guided Fine-Tuning），通过使用冻结的预训练参考模型计算标记权重，获得不受微调过程影响的稳定重加权信号，该信号表征“先验支持度”——即每个目标标记被预训练分布支持的程度。实验表明，在多个数学推理、代码生成和医疗问答任务上，将重加权信号从在线模型替换为预训练模型，可持续提升性能。本文提出了两种实现方式：PriFT-prob基于预训练模型的标记概率，PriFT-mass则依据预训练分布下的累积概率质量选择标记。结果表明，PriFT不仅在所有SFT基线中达到最优表现，还为后续强化学习训练提供了更优的初始化。
链接: https://arxiv.org/abs/2606.09396
作者: Ke Wang,Shuangqi Li,Mathieu Salzmann,Pascal Frossard
机构: EPFL, Lausanne, Switzerland (洛桑联邦理工学院)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: The first two authors contributed equally to this work
Abstract:Supervised fine-tuning (SFT) is an efficient approach for downstream task adaptation and often serves as the initialization stage for reinforcement learning (RL), but it can show weaker generalization than RL. A key limitation is its off-policy objective: SFT fits fixed demonstrations token by token, including targets poorly aligned with the model’s pretrained distribution, which can lead to overfitting. A recent line of work addresses this issue by assigning larger training weights to tokens better aligned with the current model’s predictive distribution, with the intuition that fitting these tokens are less distortive to the model’s pretrained knowledge and representations. However, computing the token weights from the model that is currently fine-tuned entangles token weights with the optimization trajectory, inducing a self-reinforcing dynamics as the distribution rapidly departs from the pretrained model. To address this, we propose PriFT (Prior-support guided Fine-Tuning), which derives token weights from a frozen pretrained reference to obtain a stable reweighting signal unaffected by fine-tuning. This signal estimates prior support: the extent to which each target token is supported by the pretrained distribution. Across multiple existing token-reweighting rules, replacing the reweighting signal from the online model to pretrained model consistently improves performance. We introduce two instantiations: PriFT-prob uses pretrained token probability, while PriFT-mass selects tokens by cumulative probability mass under the pretrained distribution. Extensive experiments on mathematical reasoning, code generation, and medical question answering show that PriFT achieves state-of-the-art results among SFT baselines and provides a better initialization for subsequent RL training.
[NLP-45] LexRubric: A Rubric-Guided Diagnostic Benchmark for Open-Ended Legal Tasks
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在开放性中文法律任务中生成回答的可靠性评估问题。由于法律应用场景对上下文敏感性和准确性要求极高，现有评估方法难以精准识别模型输出质量缺陷的具体来源，因此亟需一种细粒度、可诊断的评估体系。其解决方案的关键在于提出LexRubric——一个基于评分量表的基准测试框架，涵盖来自法律咨询与司法考试的649个实例，覆盖14类法律场景，并构建了12,337条专家撰写的原子级评分标准，统一整合于六维评价框架下，实现跨任务、跨维度的精准评估与故障溯源。通过对比多模型判断与人工判断的一致性，验证了评估体系的可靠性，并对18个主流通用及法律领域大模型进行了测评，揭示了当前模型在开放性法律问答任务中仍存在显著能力差异与挑战。
链接: https://arxiv.org/abs/2606.09389
作者: Yifan Chen,Haitao Li,Yiran Hu,Kaisong Song,Jun Lin,Yueyue Wu,Qingyao Ai,Min Zhang,Yiqun Liu
机构: Beijing University of Posts and Telecommunications; Tsinghua University; University of Waterloo; Alibaba Group
类目: Computation and Language (cs.CL)
备注:
Abstract:As large language models (LLMs) are increasingly applied to real-world legal tasks, evaluating the reliability of their open-ended legal responses has become essential. These tasks require context-sensitive answers and allow little room for error, motivating fine-grained and diagnostic evaluation that can identify specific sources of response quality failures. We introduce LexRubric, a rubric-based benchmark for evaluating open-ended Chinese legal tasks. LexRubric contains 649 instances from legal consultation and judicial examination, which reflect both everyday legal needs and professional legal reasoning and cover 14 legal scenarios. It further includes 12,337 expert-written atomic scoring criteria organized under a unified six-dimensional framework, enabling accurate evaluation and diagnostic analysis across tasks and evaluation dimensions. To validate the reliability of the evaluation, we test multiple judge models and compare model-based judgments with human judgments. We further evaluate 18 recent general and legal-domain LLMs on LexRubric. Results show that different models exhibit distinct capability profiles, and that open-ended legal question remains challenging for current LLMs. Data is available at: this https URL.
[NLP-46] Reasoning Arena: Trace Tournaments When Verifiable Rewards Fall Short
【速读】： 该论文旨在解决生成式强化学习中可验证奖励（verifiable rewards, VR）在群体层面信息缺失的问题：当同一提示（prompt）生成的多个推理轨迹（reasoning trace）获得相同奖励时，基于群体相对优势的估计将无法提供梯度信号，尽管这些轨迹在推理质量上可能存在显著差异。其解决方案的关键在于提出一种自适应训练框架——推理竞技场（Reasoning Arena），该框架不直接丢弃此类非差异化奖励组，而是将其路由至判别系统进行细粒度分析。通过构建“轨迹锦标赛”（trace tournaments），对推理轨迹进行两两对比，从而揭示组内更精细的偏好关系，并将推理质量转化为丰富的相对奖励信号。为提升效率，新生成的轨迹仅与一个动态更新的小规模历史轨迹池（作为锚点）进行比较，而非全量配对；进而利用不完全比较图上的Bradley-Terry模型进行奖励建模，避免了二次复杂度的成对比较，实现了可扩展的强化学习集成。实验表明，该方法在数学与编程竞赛基准上平均优于基线RLVR 7.6%，训练速度提升27%至41%，生成计算成本减少近50%，显著提升了整体推理性能。
链接: https://arxiv.org/abs/2606.09380
作者: Han Zhou,Adam X. Yang,Laurence Aitchison,Anna Korhonen,Albert Q. Jiang
机构: University of Cambridge (剑桥大学); Mistral AI (mistral ai)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 9 pages, 6 figures, 2 tables (17 pages including references and appendices)
Abstract:Reinforcement learning with verifiable rewards (RLVR) has become a leading paradigm for improving the reasoning ability of large language models through outcome-based supervision. However, verifiable rewards frequently become uninformative at the group level: when all sampled traces of a given prompt receive identical rewards, group-relative advantage estimation provides no gradient signal, even though the traces may differ substantially in reasoning quality. We propose Reasoning Arena, an adaptive training framework that routes such non-diverse reward groups to a judge system instead of discarding them. Beyond examining the final answer, Reasoning Arena constructs trace tournaments, where reasoning traces are compared head-to-head to expose finer-grained preferences within the group, converting reasoning quality into rich relative reward signals. To make reward estimation efficient, rather than exhaustively comparing every pair, each new trace is evaluated against a small, dynamically updated pool of previously generated traces as anchors to efficiently establish a relative ranking. We then fit a Bradley-Terry model on the incomplete comparison graph, enabling scalable RL integration without quadratic pairwise comparisons. Empirical results demonstrate that Reasoning Arena consistently outperforms the RLVR baseline by 7.6% on average in competition mathematics and coding benchmarks. By converting otherwise wasted zero-advantage samples into useful gradient updates, our method accelerates training by 27% to 41%, saving nearly 50% of generation compute, and substantially improves overall reasoning performance.
[NLP-47] Precision Is Not Faithfulness: Coverag e-Aware Evaluation of Grounded Generation with a Complete Oracle ACL
【速读】： 该论文旨在解决当前无参考（reference-free）忠实性评估指标在评估生成模型时存在的核心缺陷：仅衡量精确率（precision），即模型所陈述的每个主张是否得到支持，而忽视了召回率（recall），即模型是否充分覆盖了与决策相关的全部事实。这种偏差导致模型可通过“避免回答”来获得高忠实性评分，从而产生误导性的评估结果。其解决方案的关键在于引入一个具备完全确定性且完整事实基础的领域——公式1（Formula 1）赛事决策场景，该场景下每项决策的全部相关事实均可被精确追溯，从而首次实现了对召回率的准确测量。研究通过构建多语言（英文/西班牙语/葡萄牙语）共7,253个决策实例的基准数据集，发现当前最先进的前沿模型在相关事实覆盖率上不足一半，且在F1综合得分中排名垫底，表明仅追求精确率会严重误导系统排序。进一步实验在另一全知识库域（NOAA天气预报）中验证了该现象的可复现性。研究还通过提示工程消融分析排除了“提示不足”的可能性，证明低覆盖率并非因提示设计不当所致。为此，论文提出将忠实性与覆盖率联合为单一评估指标，并通过受验证器引导的生成方法，在无需参考文本的情况下同时提升精确率与召回率。该工作释放了完整的基准数据、结构化标注、评估工具、基线模型及交互式演示系统，为更全面的生成质量评估提供了新范式。
链接: https://arxiv.org/abs/2606.09376
作者: Juan S. Santillana
机构: Globant(全球通)
类目: Computation and Language (cs.CL)
备注: 8 pages, multilingual (EN/ES/PT). A reference-free faithfulness metric adding recall (coverage) against a complete structured oracle: precision-only rewards abstention; requiring coverage reorders models. Code: this https URL Demo: this https URL
Abstract:Reference-free faithfulness metrics verify each atomic claim a model makes against ground truth, and are increasingly used to evaluate grounded generation. We show they share a blind spot: they measure only precision – are the stated claims supported? – and therefore reward abstention, since a model can score near-perfect faithfulness by saying almost nothing. We make this measurable using Formula 1 telemetry, a domain where strategic ground truth is derived deterministically and, crucially, completely: for each decision we know the full set of facts that mattered. This completeness – absent in open-domain faithfulness benchmarks – lets us measure recall (coverage of the relevant facts) exactly, alongside precision. On a multilingual (EN/ES/PT) benchmark of 7,253 decision instances spanning 150 races, the most precise frontier model covers under half of the relevant facts and ranks last by F1, so requiring coverage reorders the systems; the same effect reappears in a second complete-oracle domain (NOAA weather forecasts). A prompt ablation shows the low coverage is not an under-prompting artifact: explicitly asking models to be thorough does not close the gap. We pair faithfulness with coverage into a single score, validate the metric (controlled perturbation; agreement across a model-free regex extractor and a cross-family LLM extractor, system-level Spearman 1.0), and give a verifier-guided generation method that improves precision and recall without references. We release the benchmark, structured annotations, metric, baselines, and an interactive demo.
[NLP-48] Is Text All You Need? Text as a Universal Information Bottleneck for Speech LLM s
【速读】： 该论文旨在解决如何将连续的声学信号有效集成到冻结的大型语言模型（LLM）中的问题，尤其针对现有语音-语言模型接口在处理方式上的两极分化：一是强制近似离散标记对齐，虽利于语音转录但丢失了副语言信息；二是学习无约束的连续表示，易偏离预训练语言模型的输入空间，导致自回归解码性能下降。为此，本文提出了一种名为“凸门”（Convex Gate, C-Gate）的语音-语言模型桥接架构，其核心在于通过结构化的凸包约束，确保所有语音表示均位于预训练语言模型的输入嵌入流形（embedding manifold）内部。具体而言，每一帧语音被建模为词元嵌入的凸组合，既保持了与预训练语言模型的兼容性，又保留了连续表达能力。实验表明，C-Gate在自动语音识别（ASR）和情感识别任务上均表现出色，相对提升了LibriSpeech词错误率（WER）达48.7%，同时在情感识别准确率上达到或超过单任务基准。进一步分析揭示了一个关键洞见：信息并非由离散词元身份承载，而是依赖于嵌入空间中随时间演化的轨迹。因果干预实验证实，轨迹结构及其与预训练嵌入流形的对齐程度对模型性能至关重要。这一发现表明，几何结构而非词元离散性，才是语音-语言模型接口设计的根本因素，并为研究冻结语言模型中的多模态融合提供了可控范式。研究团队已公开模型检查点、样本级输出、机制日志及干预工具集以支持复现。
链接: https://arxiv.org/abs/2606.09366
作者: Ming-Hao Hsu,Yuxuan Hu,Shujie Liu,Jinyu Li,Yan Lu,Zhizheng Wu
机构: National Taiwan University (国立台湾大学); Tsinghua University (清华大学); Peking University (北京大学)
类目: Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注:
Abstract:Large language models (LLMs) provide a powerful reasoning backbone for speech understanding, but integrating continuous acoustic signals into a frozen LLM remains challenging. Existing speech-to-LLM interfaces typically operate at two extremes: either enforcing near-discrete token alignment, which benefits transcription but loses paralinguistic information, or learning unconstrained continuous representations, which can drift away from the LLM’s input space and degrade autoregressive decoding. In this work, we propose Convex Gate (C-Gate), a speech-to-LLM bridge that constrains all speech representations to lie within the LLM’s input embedding manifold with an architectural convex-hull constraint. Concretely, each frame is represented as a convex combination of token embeddings, ensuring compatibility with the pretrained LLM while preserving continuous expressivity. Across automatic speech recognition (ASR) and emotion recognition, C-Gate achieves strong joint performance, improving LibriSpeech WER by up to 48.7% relative while matching or exceeding single-task emotion accuracy. Beyond performance, our analysis reveals a key insight: information is not carried by discrete token identities, but by time-resolved trajectories in the embedding space. Causal interventions confirm that both the trajectory structure and alignment to the pretrained embedding manifold are critical for performance. These results suggest that geometry, rather than token discreteness, is the fundamental design factor in speech-to-LLM interfaces, and provide a controlled regime for studying multimodal integration in frozen LLMs. We release the checkpoint, per-sample outputs, mechanism dumps, and intervention suite for replication.
[NLP-49] Experience Makes Skillful: Enabling Generalizable Medical Agent Reasoning via Self-Evolving Skill Memory
【速读】： 该论文旨在解决医疗智能体在动态临床决策支持中难以有效复用历史经验的问题。现有记忆机制通常保留冗余、噪声大且难以管理的原始历史轨迹，且缺乏对哪些记忆对未来推理真正有用的区分能力，导致其无法积累紧凑可靠的长期经验。为此，本文提出SkeMex——一种无需更新模型权重的后部署自演化框架，其核心在于构建基于技能的记忆系统。该方案将信息丰富的交互轨迹提炼为结构化技能，编码可复用的过程性知识，并组织成涵盖通用、任务特定及动作层级经验的多分支记忆库。通过环境反馈估算上下文相关的记忆效用，SkeMex实现价值感知的检索与记忆库治理，从而决定哪些记忆应被保留或重用。其闭环“读取—写入—评估—治理”生命周期支持持续演化：不断生成新技能、更新效用值、强化有用记忆并剔除有害条目。实验表明，SkeMex在多种临床任务中均显著优于代表性记忆增强型智能体，具备跨模型架构的泛化能力与可迁移的技能记忆特性。
链接: https://arxiv.org/abs/2606.09365
作者: Haoran Sun,Wenjie Li,Yujie Zhang,Zekai Lin,Fanrui Zhang,Kaitao Chen,Xingqi He,Yichen Li,Mianxin Liu,Lei Liu,Yankai Jiang
机构: Fudan University (复旦大学); Shanghai Artificial Intelligence Laboratory (上海人工智能实验室); Shanghai Innovation Institute (上海创新研究院); Huazhong University of Science and Technology (华中科技大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Medical agent systems are increasingly expected to support interactive clinical decision making rather than only static question answering. In such settings, effective agents must reuse prior experience across evolving cases, yet existing memory mechanisms often retain raw historical traces that are redundant, noisy, and difficult to govern. More importantly, they rarely distinguish which memories are truly useful for future reasoning. This limits their ability to accumulate compact and reliable experience for long-horizon clinical reasoning. To close this gap, we propose SkeMex, a post-deployment self-evolution framework that improves medical agents through a skill-based memory without updating model weights. SkeMex distills informative interaction trajectories into structured skills that encode reusable procedural knowledge, and organizes them into a multi-branch repository spanning general, task-specific, and action-level experience. To determine which memories should be reused and retained, SkeMex estimates context-dependent utility from environment feedback and uses it to guide value-aware retrieval and repository governance. A closed-loop ``Read–Write–Assess–Govern" lifecycle further supports continual evolution by writing new skills, updating utilities, promoting useful memories, and removing harmful entries. Experiments across diverse clinical tasks show that SkeMex consistently outperforms representative memory-based agents in both offline and online settings. It also generalizes across model backbones and supports transferable skill memory. All data and code will be released publicly.
[NLP-50] In-Context Learning for the Imputation of Public Opinion Data with Large Language Models
【速读】： 该论文旨在解决调查数据中普遍存在的部分缺失（partial non-response）问题，其核心挑战在于如何在不完全观测数据的情况下有效恢复调查数据集的整体结构。传统数据插补（imputation）方法虽有明确的评估标准，但与预测任务存在本质区别，需兼顾数据分布的保持与统计推断的准确性。本文提出基于上下文学习（in-context learning, ICL）的插补方案，通过利用大语言模型（LLM）在自然语言语境中对缺失值进行推理生成，实现对不同缺失机制（MCAR、MAR、MNAR）下150个意见变量、15波美国趋势面板（American Trends Panel）数据的系统性评估。关键创新在于：采用大规模开源模型（如gpt-oss-120b）并结合100个上下文示例，显著提升了插补精度，在非随机缺失（MNAR）场景下效果最为突出；所提方法不仅将绝对误差持续降低，且生成的置信区间比经典多重插补法（MICE PMM）窄2至5倍，同时达到接近名义水平的总体覆盖度（约95%）。研究还发布了具有scikit-learn风格API的Python工具包，支持本地及私有化部署，便于实际应用。
链接: https://arxiv.org/abs/2606.09351
作者: Tobias Holtdirk,Georg Ahnert,Joseph W Sakshaug,Anna-Carolina Haensch
机构: LMU Munich; Munich Center for Machine Learning; University of Mannheim; Institute for Employment Research (IAB); University of Maryland, College Park
类目: Computation and Language (cs.CL); Methodology (stat.ME)
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Abstract:Large language models have been widely evaluated as simulators of individual survey responses. In practice, however, fully unobserved responses are rare; the dominant problem is partial non-response. Imputation aims to restore the overall structure of a survey dataset by filling in these missing values. It has its own well-defined evaluation criteria and differs fundamentally from prediction. We propose to impute missing survey data through in-context learning (ICL). We systematically evaluate ICL design choices across different missingness mechanisms (MCAR, MAR, MNAR) on 150 opinion variables spanning 15 waves of the American Trends Panel. Compared to well-established statistical methods for data imputation like MICE PMM, our ICL approach consistently reduces absolute error across all missingness mechanisms, with the largest gains under non-random missingness (MNAR). Notably, the best-performing specification (gpt-oss-120b with 100 in-context examples) achieves near-nominal aggregate coverage (approaching the 95% level) with confidence intervals two to five times narrower than MICE PMM. We publish a Python package with an sklearn-like API to enable easy deployment of our method using local and proprietary LLMs.
[NLP-51] PBSD: Privileged Bayesian Self-Distillation for Long-Horizon Credit Assignment
【速读】： 该论文旨在解决长时程智能体任务中基于结果的强化学习所面临的根本性信用分配难题：轨迹级奖励仅能验证最终结果的正确性，却难以提供关于哪些中间推理步骤或工具交互对最终结果产生贡献的充分指导。这一挑战在多轮搜索智能体中尤为突出，因为成功轨迹可能包含误导性操作，而失败轨迹也可能包含有价值的信息收集步骤。为此，论文提出了一种贝叶斯校准的自蒸馏方法——PBSD（Privileged Bayesian Self-Distillation），其核心在于通过已验证答案的后验概率与先验概率之比来衡量轨迹质量，并利用贝叶斯定理将难以估计的答案侧比率转化为标准学生模型与特权答案条件教师模型之间的可计算似然比。通过对该贝叶斯证据得分进行自回归分解，PBSD可生成逐轮级别的信用信号，精确识别每一中间轮次是支持还是削弱了最终验证结果。因此，PBSD提供了一种理论严谨且优雅的重加权机制，能够将稀疏的结果监督转化为经过贝叶斯校准的细粒度轮次级信用信号，同时与标准策略优化方法完全兼容。实验表明，PBSD在域内和域外设置下均显著提升性能，并有效实现从短上下文训练到长上下文推理的知识迁移，证明其精细的信用分配机制有助于更高效地学习策略并提升泛化能力。
链接: https://arxiv.org/abs/2606.09348
作者: Yang Tian,Rui Wang,Xumeng Wen,Junjie Li,Shizhao Sun,Lei Song,Jiang Bian,Bo Zhao
机构: 上海交通大学(Shanghai Jiao Tong University)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
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Abstract:Long-horizon agentic tasks pose a fundamental credit assignment challenge for outcome-base reinforcement learning: trajectory-level rewards verify final correctness but provide limited guidance on which intermediate reasoning steps or tool interactions contribute to the outcome. The difficulty is especially pronounced in multi-turn search agents, where successful trajectories may contain misleading actions and failed trajectories may contain valuable evidence-gathering steps. We propose PBSD (Privileged Bayesian Self-Distillation), a Bayes-calibrated self-distillation method for fine-grained credit assignment under sparse final rewards. PBSD measures trajectory quality through the posterior-to-prior probability ratio of the verified answer and applies Bayes’ rule to convert this hard-to-estimate answer-side ratio into a tractable likelihood ratio between a standard student model and a privileged answer-conditioned teacher model. Autoregressive decomposition of this Bayesian evidence score yields turn-level signals that identify whether each intermediate turn supports or undermines the verified outcome. Consequently, PBSD provides a principled and elegant reweighting scheme that transforms sparse outcome supervision into Bayes-calibrated turn-level credit signals, while remaining fully compatible with standard policy optimization. Experiments demonstrate that PBSD consistently enhances performance across both in-domain and out-of-domain settings, and effectively transfers knowledge from short-context training to long-context inference, suggesting that its fine-grained credit assignment mechanism facilitates more effective policy learning and yields improved generalization.
[NLP-52] Multi-Hop Knowledge Composition is Bound by Pretraining Exposure
【速读】： 该论文旨在解决大语言模型在隐式多跳推理（implicit multi-hop reasoning）任务中的系统性失败问题：尽管模型能够准确回答“X 何时出生？”和“Y 的最亲密朋友是谁？”等单跳问题，但在面对“Y 的最亲密朋友何时出生？”这类需要整合多个事实的多跳问题时仍表现不佳，即使所有相关知识均已被模型完整记忆且可独立检索。研究通过在受控的自然语言环境中进行实验，严格区分了在预训练阶段接触过组合性语境（compositional context）的个体与从未出现在此类语境中的个体，结果表明，即便单跳准确率高达97%，组合性推理能力的缺失依然存在，证实该缺陷源于预训练阶段的知识表征不足，而非知识缺失。其解决方案的关键在于：通过九种数据增强范式进行数据驱动的预训练优化，发现只有在预训练过程中暴露于组合性语境的个体，其多跳推理能力才能泛化至未见问题，而从未接触过组合性语境的个体则始终无法实现有效推理，由此揭示“在预训练中接触组合性语境”是实现隐式多跳推理的必要条件。
链接: https://arxiv.org/abs/2606.09338
作者: Yannis Karmim,Luis Marti,Djamé Seddah,Valentin Barrière
机构: Inria, Paris, France; Inria, Chile; Dept. of Computer Science, Universidad de Chile
类目: Computation and Language (cs.CL)
备注:
Abstract:Large Language Models fail at implicit multi-hop reasoning: a model answers “When was X born?” and “Who is Y 's closest friend?” correctly but fails on “When was Y 's closest friend born?” in a single forward pass, even when both facts are perfectly memorized and individually retrievable. We study this failure in a controlled natural language setting with a strict separation between individuals exposed to compositional contexts during pretraining and those that never appear in any such context. We confirm that compositional failure persists even at 97% 1-hop accuracy, establishing the gap as a pretraining failure rather than a knowledge absence. We propose and test nine data-centric augmentation formats and find that compositional pretraining transfers to unseen questions for exposed individuals, but never to individuals absent from compositional pretraining, suggesting that exposure to compositional contexts during pretraining is a necessary condition for implicit multi-hop reasoning.
[NLP-53] How Far Can Prompting Go for Minimal-Edit Ukrainian Grammatical Error Correction?
【速读】： 该论文旨在解决乌克兰语语法错误纠正（GEC）任务中，生成式 AI 模型在最小编辑（minimal-edits）基准测试上的应用效果不足的问题。尽管经过微调的大语言模型（LLMs）已在该领域占据主导地位，但通过 API 调用的商业大模型在该任务中的表现仍缺乏系统评估。研究的关键解决方案在于采用乌克兰语特定的最小编辑提示（minimal-edits prompts），其语言特异性规则要求使用乌克兰语进行精确表达，从而显著提升纠错性能。实验表明，结合少量样本（few-shot）与基于 LLM 的提示优化策略，在最小编辑提示基础上可实现最优结果（如 Gemini 3.1-Pro 达到 F0.5=69.22，接近微调模型最先进水平的 90%）。此外，研究揭示了五种与乌克兰语语言特性相关的重复性误纠正模式，并发现精细化的最小编辑指令对标点和大小写错误改善最大，但可能忽略低频错误类别。
链接: https://arxiv.org/abs/2606.09334
作者: Kateryna Karpo,Artem Chernodub
机构: Ukrainian Catholic University(乌克兰天主教大学); YouScan(尤斯坎); Zendesk(Zendesk)
类目: Computation and Language (cs.CL)
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Abstract:Fine-tuned Large Language Models (LLMs) dominate in Ukrainian grammatical error correction (GEC), while API-accessed LLMs remain nearly untested on minimal-edit benchmarks. We evaluate 11 commercial LLMs from four providers and one open-source Ukrainian model on the UNLP 2023 GEC-only benchmark, comparing zero-shot, few-shot, minimal-edits, and LLM-assisted prompt optimization strategies. Our best configuration (Gemini 3.1-Pro) reaches F0.5=69.22, closing over 90% of the gap to fine-tuned SOTA (F0.5=73.14). For zero-shot prompts, only Claude models benefit from Ukrainian instructions. However, the best overall results for all models use Ukrainian minimal-edits prompts, whose language-specific rules require Ukrainian to express precisely. LLM-assisted prompt optimization on top of minimal-edits + few-shot achieves the highest score. Detailed minimal-edits instructions yield the largest gains for punctuation and case errors but cause the model to abandon several low-frequency categories. Delving into error analysis, we identify five recurring overcorrection patterns tied to Ukrainian-specific linguistic phenomena. Code, prompts, and outputs are publicly available.
[NLP-54] SG-OPD: Sign-Gated On-Policy Distillation via Sign-Consistency Gating and Phased Teacher Sampling
【速读】： 该论文旨在解决生成式强化学习中在线策略蒸馏（On-Policy Distillation, OPD）在实际应用中性能下降的问题。尽管OPD通过强教师模型对学生的每一步输出提供密集的逐标记监督，通常优于离线策略蒸馏和标准强化学习，但其有效性依赖于两个关键但常失效的假设：一是学生与教师在轨迹层面的对齐性，二是教师偏好在标记层面的均匀可靠性。为突破这一限制，论文提出符号门控在线策略蒸馏（Sign-Gated On-Policy Distillation, SG-OPD），其核心创新在于引入一个二元验证器（binary verifier）作为可信度信号，在两个互补粒度上增强教师指导的可靠性：在冷启动阶段，通过分阶段采样机制融合验证器认可的教师轨迹；在训练过程中，采用符号一致性门控机制，仅在教师与验证器方向一致的标记上执行蒸馏更新，并在不一致处进行插值处理。实验结果表明，在竞赛级数学推理基准测试中，SG-OPD在样本级别和问题级别分别实现1.98和7.50的平均性能提升，显著优于标准OPD，验证了其在提升教师信号可信度方面的有效性。
链接: https://arxiv.org/abs/2606.09304
作者: Haoran Xu,Hongyu Wang,Yifei Gao,Jiaze Li,Xiaofeng Zhang,Xiaosong Yuan
机构: Zhejiang University(浙江大学); Hunan University(湖南大学); Tianjin University(天津大学); Shanghai Jiao Tong University(上海交通大学); Jilin University(吉林大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:On-policy distillation (OPD) trains a student on its own trajectories with dense per-token supervision from a stronger teacher, and often outperforms off-policy distillation and standard reinforcement learning. However, we find that its effectiveness implicitly relies on two assumptions that frequently break in practice: trajectory-level alignment between the student and the teacher, and uniform token-level reliability of the teacher’s preferences. We therefore propose Sign-Gated On-Policy Distillation (SG-OPD), which uses a binary verifier as a trust signal for the teacher at two complementary granularities: phased teacher sampling mixes in verifier-endorsed teacher rollouts at cold-start, and a sign-consistency gate extrapolates the distillation update on tokens where the teacher agrees with the verifier-correct direction and interpolates it where it disagrees. Experiments on competition-level mathematical reasoning benchmarks show that SG-OPD consistently outperforms standard OPD, with average gains of 1.98 and 7.50 at the per-sample and per-question levels, respectively.
[NLP-55] NüshuVoice: Reviving the Voice of Endangered Nüshu with Pitch-Aware Text-to-Speech
【速读】： 该论文旨在解决濒危的女书（Nüshu）语音重建难题，即在极低资源条件下实现其真实发音的声学还原。当前对女书的计算研究主要集中在文本数字化与视觉识别，而其自然语句级发音的声学建模仍处于空白状态。其核心挑战在于可用录音极度稀缺，且多为孤立音节而非完整句子的语音数据。为此，论文提出首个女书文本到语音（TTS）基准——NüshuVoice，构建了一个包含标准化Unicode女书文本、音标转写、标准汉语翻译及档案录音的句级女书文本-音频数据集。为应对低资源问题，作者提出Nüshu-PitchVITS，一种基于基频（F0）条件的VITS框架，显式利用女书特有的五级声调记号作为韵律归纳偏置（prosodic inductive bias），以增强模型对声调特征的建模能力。实验表明，Nüshu-PitchVITS在频谱保真度、基频重建和人工评估的可懂性方面均优于现有强基线模型。该工作公开发布了数据集与代码，为濒危语言的语音技术研究提供了重要基础。
链接: https://arxiv.org/abs/2606.09295
作者: Hongkun Yang,Xinhui Yi,Xiyan Zhao,Yibo Meng,Lionel Z. Wang,Lixu Wang,Yaqi Zhang,Ruiqi Chen,Xuanyue Zhao,Lanxin Zhang,Yu Zeng,Weijia Chu,Yiming Ma,Chenyu Liu,Jianghao Lin,Xin Xu
机构: Ocean University of China; The Hong Kong Polytechnic University; Cornell University; Nanyang Technological University; Shanghai Jiao Tong University; University of Michigan–Ann Arbor; University of Science and Technology of China; Harbin Institute of Technology
类目: Computation and Language (cs.CL)
备注: 12 pages, 3 figures
Abstract:Nüshu is an endangered phonetic script historically used by women in Jiangyong County, southern Hunan, China. While existing computational studies of Nüshu mainly focus on textual digitization and visual recognition, the acoustic reconstruction of its authentic pronunciation remains largely unexplored. Building a Nüshu text-to-speech (TTS) system is particularly challenging because available recordings are extremely limited and mostly consist of isolated syllable-level pronunciations rather than natural sentence-level utterances. In this work, we introduce NüshuVoice, the first TTS benchmark for Nüshu. We construct a sentence-level Nüshu text-to-audio dataset that aligns standardized Unicode Nüshu text, phonetic transcriptions, standard Chinese translations, and archival recordings. To synthesize speech under this extreme low-resource setting, we propose Nüshu-PitchVITS, an F0-conditioned VITS framework that leverages Nüshu’s five-level pitch notation as an explicit prosodic inductive bias. Experimental results show that Nüshu-PitchVITS outperforms strong TTS baselines in spectral fidelity, pitch reconstruction, and human-rated intelligibility. We publicly release the dataset and code at: this https URL.
[NLP-56] One Model Multiple Goals: Adaptive Multi-Objective Learning for E-commerce Dialogue Systems KDD2026
【速读】： 该论文旨在解决电商对话系统中多目标优化的难题，即在保证用户画像（如资格、信用额度）推理准确性以实现正确决策与状态理解的同时，生成自然且忠实于上下文的回应。这两个目标虽互补但存在优化动态差异，直接混合奖励信号易导致学习震荡与不稳定性。为此，本文提出一种自适应多目标强化学习框架MORE，其核心创新在于将推理功能作为约束条件来引导策略优化，而非简单地组合奖励函数。在推理阶段，系统无需显式执行推理步骤即可生成响应，从而保留推理增强的结构支撑并避免额外计算开销。为更好平衡生成过程中的语言目标，引入自适应多奖励机制，通过梯度反馈动态调整流畅性、自然度等信号的权重。在字节跳动两个真实业务对话系统及MultiWOZ 2.2基准上的实验表明，MORE显著优于多个强基线模型；在线14天实验显示，整体转化率和达成转化率分别提升16.53%和30.09%，同时提升用户满意度并降低人工转接率。值得注意的是，在人机对比中，MORE实现了人类客服所带来增量转化约60%的性能恢复。
链接: https://arxiv.org/abs/2606.09293
作者: Mingzhe Li,Jing Xiang,Enguo Zhou,Lang Gao,Tai Li,Qishen Zhang,Xiangliang Zhang,Xiuying Chen
机构: ByteDance(字节跳动); MBZUAI; University of Notre Dame(圣母大学)
类目: Computation and Language (cs.CL)
备注: Accepted by KDD 2026
Abstract:Dialogue systems in e-commerce scenarios often need to satisfy multiple objectives: accurately reasoning over user profiles (e.g., eligibility, credit limit) to ensure correct decision-making and user state interpretation, while also generating natural and faithful responses. These goals are complementary but not identical. In this work, we propose MORE, an adaptive Multi-Objective REinforcement learning framework that jointly optimizes reasoning accuracy and linguistic naturalness. Our preliminary experiments show that directly mixing rewards with diverging optimization dynamics can cause oscillations and unstable learning. Thus, instead of optimizing a single mixed reward, we treat reasoning functions as constraints that guide policy optimization. At inference time, the system directly generates responses without explicit reasoning steps, while still benefiting from reasoning-enhanced scaffold and avoiding additional inference overhead. To better balance linguistic objectives during response generation, we introduce an adaptive multi-reward mechanism that aggregates signals such as fluency and naturalness and dynamically reweighs them via gradient feedback. We evaluate MORE on two real-world dialogue systems at ByteDance and the MultiWOZ 2.2 benchmark, where it consistently outperforms strong baselines. In 14-day online experiments on ByteDance production traffic, MORE improves overall and reached conversion by 16.53% and 30.09%, while increasing user satisfaction and reducing handoff rates. Notably, in a human-machine comparison, MORE recovers about 60% of the incremental conversion lift achieved by human agents.
[NLP-57] ruthSplit: Operationalizing Conditional Validity in Arguments Through Multi-Perspective Reasoning ACL2026
【速读】： 该论文旨在解决现有论证分析工具普遍忽视观点差异对论证有效性影响的问题。传统工具仅关注论证本身的结构、质量、立场或说服力等内在属性，而将视角相关的背景知识隐含处理，导致无法揭示同一论点在不同世界观下可能产生迥异结论的深层机制。其核心解决方案是提出“条件有效性”（conditional validity）这一新范式，通过构建三层次自然语言推理（NLI）框架，结合大语言模型（LLM）与结构化的世界观（worldview）特征画像，实现对论证中逻辑一致性与价值观层面规范一致性的双重评估。系统能够基于用户输入的论证文本，自动抽取主张与前提，利用嵌入核心价值与决策原则的世界观模型对LLM推理进行条件化引导，从而生成具有视角特异性的解释，识别价值冲突与假设缺口，并通过交互式可视化界面呈现不同视角间的分歧，实现多维度、可追溯的论证探索分析。
链接: https://arxiv.org/abs/2606.09251
作者: Benjamin Stieger,Maximilian Terberger,Thomas Huber,Christina Niklaus
机构: University of St. Gallen(圣加仑大学)
类目: Computation and Language (cs.CL)
备注: Demo paper. To appear at ACL 2026
Abstract:We present TruthSplit, an interactive system for multi-perspective argument analysis. Existing argumentation tools typically analyze properties of the argument itself, such as structure, quality, stance, or persuasiveness, while leaving perspective-specific background knowledge implicit. TruthSplit addresses this gap by supporting an exploratory analysis of how the same claim can lead to different conclusions when interpreted through worldview-specific values, assumptions, and conceptual definitions. We refer to this perspective-dependent analysis as conditional validity. Given an input argumentative text, TruthSplit extracts claims and premises, applies a three-layer natural language inference (NLI) approach to assess both logical and worldview-specific normative consistency, and conditions large language model (LLM) reasoning on structured worldview profiles that encode core values and decision principles. The system then generates perspective-specific interpretations, identifies value conflicts and assumption gaps, and visualizes divergence through interactive analytical interfaces.
[NLP-58] he Injection Paradox: Brand-Level Suppression in Safety-Trained LLM Recommendations via RAG Context Injection ICML2026
【速读】： 该论文旨在解决基于检索增强生成（Retrieval-Augmented Generation, RAG）的大型语言模型（Large Language Model, LLM）推荐系统中安全训练引发的异常行为问题，具体表现为“注入悖论”（Injection Paradox）——即攻击者在检索到的文档中嵌入提示注入（prompt injection），本意是操纵模型推荐特定品牌，却因安全训练机制反噬，导致目标品牌推荐率反而低于无注入基准。其解决方案的关键在于揭示：在经过安全训练的Claude系列模型中，包含提示注入的文档会遭遇显著的推荐率下降，且这种抑制效应会通过品牌关联性传播至未被篡改的同品牌文档，形成跨文档的连锁压制。实验表明，在Claude Opus 4.6中，目标品牌在50次试验中均未进入前两名推荐，尽管仅四分之一的文档含注入内容。该现象在反事实实验与多个品牌中可复现，而GPT系列模型则表现出相反趋势，说明不同模型家族对注入类上下文的响应存在本质差异。这一发现揭示了安全敏感模型可能被恶意利用，实现“逆向攻击”（reverse-attack）——即通过向竞争对手文档中植入注入内容，诱发模型的安全机制从而压制对手品牌，为安全训练的潜在漏洞提供了实证依据。
链接: https://arxiv.org/abs/2606.09204
作者: Hyunseok Paeng
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Cryptography and Security (cs.CR)
备注: 16 pages, 1 figure, 15 tables. Accepted at the ICML 2026 Workshop on Failure Modes in Agentic AI (FAGEN), a non-archival venue
Abstract:We present a reproducible failure mode of safety training in RAG-based LLM recommendation – the Injection Paradox – in which prompt injections embedded in retrieved documents backfire against the attacker, suppressing the target brand below the injection-free baseline. In safety-trained Claude models, documents containing prompt injections suffer a sharp drop in recommendation rate, and this suppression propagates beyond the injected document to unmodified documents of the same brand. In Claude Opus 4.6, the target brand drops from a 54% baseline to zero top-2 recommendations across all 50 trials, even though only 1 of 4 brand documents in the corpus contains an injection. The directional pattern is reproduced in counterfactual experiments and across three brands. A contrasting result across the GPT models tested, where the same injection instead increases recommendations, suggests model-family differences in how injection-like context affects recommendation behavior. These findings raise the technical possibility of a reverse-attack scenario in which an adversary embeds injections in a competitor’s documents to suppress the competitor’s brand via safety-sensitive model behavior.
[NLP-59] Symbolic and Abstractive Reasoning with Complex Visual Queries
【速读】： 该论文旨在解决当前多模态大语言模型（MLLMs）在理解与推理抽象视觉内容方面存在的核心挑战，尤其聚焦于人类式神经符号推理中符号化与抽象性推理这一被严重忽视的关键维度。其核心问题在于现有模型难以有效处理具有复杂逻辑结构的抽象视觉查询（Complex Visual Query, CVQ），从而限制了其在高级认知任务中的表现。解决方案的关键在于提出一种基于大规模多模态知识图谱的可扩展CVQ合成管道，通过一阶逻辑算子的系统组合生成涵盖14种不同类型的多样化数据集，并设计了一个两阶段训练框架，逐步增强MLLMs的视觉推理能力。该方法不仅提升了模型在CVQ上的推理性能，还在跨任务与跨场景泛化能力上展现出显著优势，为推动MLLMs的推理能力边界提供了新的研究范式与技术路径。
链接: https://arxiv.org/abs/2606.09195
作者: Yichi Zhang,Jingdian Lu,Zhuo Chen,Lingbing Guo,Jun Xu,Wen Zhang,Huajun Chen
机构: Zhejiang University (浙江大学); Nanjing University (南京大学); Ant Group (蚂蚁集团)
类目: Computation and Language (cs.CL)
备注: Work in progress
Abstract:Understanding and reasoning over abstract visual content remains a challenge for current multi-modal large language models (MLLMs). In this paper, we explore a novel abstract data type termed complex visual query (CVQ), designed to probe symbolic and abstractive reasoning, which is a critical yet underexplored dimension of human-like neuro-symbolic reasoning for MLLMs. We present a comprehensive investigation from three perspectives: \textbfData \times Paradigm \times Exploration. Specifically, we propose a scalable pipeline for synthesizing CVQs grounded in large-scale multi-modal knowledge graphs, generating a diverse dataset encompassing 14 distinct query types via systematic combinations of first-order logic operators. We further introduce a two-stage training framework that progressively equips MLLMs with robust visual reasoning capabilities. We conduct extensive experiments to rigorously evaluate MLLMs across multiple dimensions, including reasoning performance on CVQs, as well as cross-task and cross-scenario generalization. We believe our work opens new perspectives and avenues for advancing the reasoning frontiers of MLLMs.
[NLP-60] Culturally-Adapted Red-Teaming Across East and Southeast Asian Contexts: A Methodological and Comparative Analysis ICML2026
【速读】： 该论文旨在解决多语言大语言模型（LLM）安全评估中普遍依赖英文基准直接翻译（Direct Translation, DT）所导致的评估偏差问题。现有方法仅转换语言表层形式，未能反映威胁情境中的文化背景、社会规范及法律框架，从而造成对实际风险的低估。其解决方案的关键在于构建配对的直接翻译（DT）与文化适配（Culturally-Adapted, CA）数据集，通过一对一种子匹配实现跨语言语义一致性，并在韩语（KO）、日语（JA）、泰语（TH）和高棉语（KM）四门语言上进行对比验证。实验表明，文化适配提示（CA）在所有16个语言×模型组合中均显著提升攻击成功率（平均提升9.3个百分点），且在48种类别×语言组合中，基于DT的评估在44种情况下系统性低估风险。进一步分析显示，不同语言间威胁表现形式分布异质性显著，而文化真实度（Cultural Realism）评估表明，DT生成内容的文化深度（C3）得分均低于1.0（均值0.17），远低于真实场景下的文化贴合度；相比之下，CA提示可达到最高2.51的评分，充分证明单纯依赖语言翻译无法反映真实多元文化语境。因此，论文强调：为实现有效的多语言LLM安全评估，必须将评估基准适配至目标语言的具体文化语境，而非仅依赖语言层面的直接翻译。
链接: https://arxiv.org/abs/2606.09178
作者: Hyeji Choi,Yongtaek Lim,Minwoo Kim
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted to ICML 2026 Workshop on AIWILDS
Abstract:Multilingual safety evaluation of large language models (LLMs) has predominantly relied on direct translation (DT) of English benchmarks into target languages - an approach that converts surface-level linguistic form while failing to reflect the cultural context embedded in threat scenarios, social norms, and legal frameworks. We construct paired DT and culturally-adapted (CA) datasets via 1:1 seed matching for four languages - Korean (KO), Japanese (JA), Thai (TH), and Khmer (KM) - and compare Attack Success Rate (ASR) and Cultural Realism scores across four open-source LLM. CA prompts yield Delta-ASR 0 across all 16 language x model combinations (mean +9.3 pp), and DT-based evaluation underestimates risk in 44 of 48 category x language combinations. Language-level analysis reveals that the distribution of threat forms is heterogeneous across languages. Cultural Realism analysis further shows that DT Cultural Depth (C3) scores remain consistently below 1.0 out of 3.0 across all four languages (mean 0.17), whereas CA scores reach up to 2.51, indicating that direct translation produces inputs systematically divergent from those encountered in real-world multicultural settings. These findings demonstrate that adapting benchmarks to language-specific cultural contexts - rather than relying on linguistic translation alone - is necessary for valid multilingual LLM safety evaluation.
[NLP-61] Unified Energy for Invariant and Independent Decoding in Diffusion Language Models
【速读】： 该论文旨在解决生成式扩散语言模型（Diffusion Language Models, DLMs）在高并行度下性能落后于自回归（auto-regressive, AR）基线模型的问题。现有方法未能充分建模词元间的依赖关系，导致在增加并行生成程度时出现显著性能差距。通过系统分析，作者识别出三个关键因素：模型容量、依赖性以及不变性（invariance）。针对这些问题，论文提出了一种不变能量（Invariant Energy, Inv-E），并设计了一种基于采样的有效估计器以缓解不变性问题；进一步结合独立能量（Independent Energy, Ind-E），构建了一个统一能量函数（Unified Energy, Uni-E），能够同时建模上述三类因素。Uni-E的核心优势在于其可精确计算，无需依赖采样估计的分区项，且对模型架构无特定要求，具备良好的可扩展性。此外，理论证明表明，Uni-E可有效纠正由依赖性和不变性引起的分布偏移。大量实验在DLMs和扩散大语言模型（DLLMs）上的结果验证了Uni-E的有效性与普适性。
链接: https://arxiv.org/abs/2606.09159
作者: Yuchen Yan,Minkai Xu,Zaiquan Yang,Yatao Bian
机构: National University of Singapore (新加坡国立大学); Stanford University (斯坦福大学); City University of Hong Kong (香港城市大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Diffusion Language Models (DLMs) enable parallel text generation by iteratively denoising a full sequence, offering attractive flexibility compared to auto-regressive (AR) decoding. However, existing methods fail to fully capture token relationships, leading to a performance gap relative to AR baselines, especially as the degree of parallelism increases. In this paper, we give a systematic analysis of the gap, identifying three key factors: (i) model capacity, (ii) dependency, and (iii) invariance. To address these issues, we first propose an invariant energy (Inv-E) together with an effective sampling-based estimator to handle the invariance issue. By further combining with the independent energy (Ind-E), we obtain a unified energy (Uni-E), that accounts for all these factors. Uni-E enjoys a unique advantage: it can be computed exactly without sampling-based partition estimation. Besides, Uni-E is model agnostic and can therefore be scaled to models of arbitrary size. We further prove that Uni-E can correct the distribution shift caused by dependency and invariance. Extensive experiments across Diffusion Language Models (DLMs) and Diffusion Large Language Models (DLLMs) demonstrate the effectiveness of the proposed Uni-E.
[NLP-62] SEF-CLGC at SemEval-2026 Task 11: Logical Notation Impact on Language Model Performance ACL2026 SEMEVAL-2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在推理任务中难以有效分离内容依赖性与形式逻辑推理能力的问题，尤其针对SemEval-2026任务11子任务1中“解耦内容与形式推理”的挑战。其核心问题在于现有模型常因过度依赖输入内容的表面信息而产生内容偏差（content bias），从而影响对真正形式化推理能力的评估。为应对这一问题，论文提出基于命题逻辑与符号语言混合训练的小型语言模型（Small Language Models, SLMs）框架——Syllogistic Evaluation Framework-Common Logic Grammar Construction (SEF-CLGC)，通过将形式逻辑表达（formal logical notations）与自然语言及符号语言相结合，构建可解释、低偏倚的推理评估机制。该方案的关键在于利用SLMs在特定逻辑语境下的可控性与可解释性，使模型更聚焦于形式推理过程本身，而非依赖上下文中的语义线索，从而在不牺牲推理准确性的前提下显著降低内容偏差。实验结果表明，仅使用该框架下的SLM即可在任务上取得27.80%的内容得分，并实现推理过程的更纯净评估。
链接: https://arxiv.org/abs/2606.09157
作者: Hanna Abi Akl,Fabien Gandon,Catherine Faron,Pierre Monnin
机构: Université Côte d’Azur, Inria, CNRS, I3S, Sophia Antipolis, France; Data ScienceTech Institute, Paris, France
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted to SemEval-2026 co-located with ACL 2026
Abstract:This paper revisits our pipeline called Syllogistic Evaluation Framework-Common Logic Grammar Construction (SEF-CLGC). We combine formal logical notations with Small Language Models (SLMs) to evaluate reasoning performance on the SemEval-2026 Task 11 Subtask 1: Disentangling Content and Formal Reasoning in Large Language Models. Our experiments show that by relying solely on SLMs, trained on a combination of natural and symbolic languages, our best model achieves a content score of 27.80% on the task while significantly lowering the content bias in reasoning.
[NLP-63] Explicit Representation Alignment for Multimodal Sentiment Analysis
【速读】： 该论文旨在解决多模态情感分析中因模态间表征错位（representation misalignment）导致的性能瓶颈问题，即独立预训练的模态编码器在融合前缺乏对齐，致使多模态模型难以稳定超越强文本单模态基线。其解决方案的关键在于提出一种统一的多模态情感分析框架，利用视觉-语言模型（Vision-Language Models, VLMs）将视觉内容转换为结构化文本描述，从而将异构模态映射至共享的语言空间，实现以文本为中心的可解释推理。为进一步提升鲁棒性，引入混合学习策略，结合语义标记选择与批次级均匀性正则化目标，促进全局特征空间的分散性与稳定性，有效抑制VLM生成描述带来的噪声干扰。实验表明，该方法在多个多模态情感与情绪基准上均显著优于现有单模态及多模态基线，达到当前最优性能，并验证了表征对齐在多模态情感学习中的核心作用。
链接: https://arxiv.org/abs/2606.09148
作者: Baode Wang,Ziming Wang,Huacan Wang,Ronghao Chen,Biao Wu
机构: AgentAlpha
类目: Computation and Language (cs.CL)
备注: 10 pages, 5 figures
Abstract:Multimodal affective analysis aims to understand human sentiment and emotion by jointly modeling heterogeneous modalities such as text and images. However, multimodal models often fail to consistently outperform strong text-only baselines, with performance varying significantly across fusion strategies. In this work, we identify representation misalignment between independently pretrained modality encoders as a key bottleneck for effective multimodal learning, and show through controlled experiments that alignment prior to fusion is often more important than fusion complexity. To address this issue, we propose a unified multimodal affective analysis framework that leverages vision-language models (VLMs) to convert visual content into structured textual descriptions, projecting heterogeneous modalities into a shared linguistic space and enabling interpretable text-centric reasoning. To further improve robustness, we introduce a hybrid learning strategy that combines semantic token selection with a batch-level uniformity regularization objective, encouraging a more dispersed and stable global feature space while mitigating noise introduced by VLM-generated descriptions. Experiments on multiple multimodal sentiment and emotion benchmarks show that our method consistently outperforms strong unimodal and multimodal baselines, achieving state-of-the-art performance. Our analysis further highlights the critical role of representation alignment in multimodal affective learning.
[NLP-64] Claw-R1: A Step-Level Data Middleware System for Agent ic Reinforcement Learning
【速读】： 该论文旨在解决生成式智能体（Agentic Reinforcement Learning, Agentic RL）中代理-环境交互数据生命周期管理缺失的问题，即现有研究多聚焦于策略优化算法与训练框架，而忽视了从数据生成到训练消费的全链路数据管理。其解决方案的关键在于提出 Claw-R1——一个面向交互式智能体强化学习的步骤级数据中间件系统，通过两个核心组件实现：网关服务器（Gateway Server） 与 数据池（Data Pool）。网关服务器通过统一的大型语言模型（LLM）API入口捕获多轮交互步骤，数据池则将这些交互数据组织为包含提示ID、响应ID、奖励及其他元数据的步骤级记录。该系统将智能体交互轨迹视为可管理的数据资产，支持实时轨迹可视化、步骤级状态/动作/奖励审查、数据质量与就绪度筛选，以及针对不同下游强化学习算法的训练批次配置，从而推动社区重视智能体强化学习中的数据治理。
链接: https://arxiv.org/abs/2606.09138
作者: Daoyu Wang,Mingyue Cheng,Qingchuan Li,Shuo Yu,Jie Ouyang,Qi Liu
机构: University of Science and Technology of China (中国科学技术大学); State Key Laboratory of Cognitive Intelligence (认知智能国家重点实验室)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Agentic reinforcement learning (RL) has become an important post-training paradigm for turning LLMs from static chatbots into interactive agents, giving rise to representative applications such as OpenClaw. Existing work mainly focuses on policy optimization algorithms and training frameworks, but pays less attention to the full data lifecycle of agent-environment interactions, from data production to training consumption. To bridge this gap, we present Claw-R1, an interactive step-level data middleware system for agentic RL. Claw-R1 connects heterogeneous agent runtimes with RL training backends through two core components: a Gateway Server and a Data Pool. The Gateway Server captures multi-turn interaction steps through a unified LLM API entry point, while the Data Pool organizes them into step-level records consisting of prompt IDs, response IDs, rewards and other metadata. In our demo, users can interactively inspect live trajectories, examine the state, action, and reward of each step, curate data by quality and readiness, and configure training-ready batches for different downstream RL algorithms. Overall, Claw-R1 treats agent interaction traces as managed data assets rather than temporary runtime logs. Through this demonstration, we hope to encourage the community to recognize the importance of data management in agentic RL. Our code is available at this https URL and the demonstration video can be found at link this https URL.
[NLP-65] From USD Scenes to Knowledge Graphs: Zero-Shot Ontology Grounding with LLM s ICRA2026
【速读】： 该论文旨在解决从3D仿真场景中构建知识图谱时，将场景对象准确映射到形式化本体类（ontology class）这一关键瓶颈问题。传统方法依赖人工维护的词典，存在泛化能力差、难以跨资产迁移的局限性。本文提出一种基于大语言模型（LLM）的零样本、无需训练的自动化映射方案，以替代传统词典方法。其解决方案的关键在于利用场景图中的语义线索（如同级对象名称和父路径层级信息），通过上下文增强提示（context-augmented prompting）实现对对象类别的精准推断。实验表明，在包含125个物体的厨房场景与SOMA-HOME本体框架下，当使用描述性名称时，LLM的精确匹配准确率可达90%-96%；即使在名称高度抽象的情况下，通过上下文提示仍可恢复至最高48%的准确率。特征消融分析进一步揭示，几何信息单独作用效果有限（仅4%-17%准确率），而一旦移除语义线索，准确率骤降至0%-6%，证明语义结构是模型推理的核心依据。
链接: https://arxiv.org/abs/2606.09134
作者: Jiangtao Shuai,Zongxiong Chen,Manfred Hauswirth,Sonja Schimmler
机构: Technical University of Berlin (柏林工业大学); Fraunhofer FOKUS (弗劳恩霍夫应用研究促进协会)
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注: Accepted to the IEEE ICRA 2026 International Joint Workshop on Ontologies, Semantic Maps and Autonomous Robotics Standardization (J-WOSMARS 2026), Vienna, 2026
Abstract:Constructing knowledge graphs from 3D simulation scenes is essential for robot task reasoning, but the key bottleneck, grounding scene objects to formal ontology classes, still relies on manually curated dictionaries that are brittle and do not generalize across assets. We investigate whether large language models (LLMs) can automate this grounding step for Universal Scene Description (USD) scenes as a zero-shot, training-free alternative. On a kitchen scene (125 objects) with SOMA-HOME Ontology, LLMs achieve 90-96% exact-match accuracy with descriptive names and 49-89% with abbreviated names, substantially outperforming dictionary and embedding baselines. Under fully opaque names, context-augmented prompting recovers up to 48%. Feature ablation reveals that LLMs primarily exploit semantic cues in the scene graph (sibling names and parent paths); anonymizing these cues reduces accuracy to 0-6%, while geometry alone yields only 4-17%.
[NLP-66] Late-Layer Fusion is Enough: Dual-Path Vision Token Routing for Multimodal Large Language Models under Visual Saturation
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在架构设计上存在的模态对称性与实际模态异质性之间的矛盾问题。现有MLLMs普遍采用对称的深度Transformer骨干网络，对图像和文本令牌施加相同的计算资源，但事实上图像与文本在信息密度、冗余度及推理深度需求上存在显著差异。通过层级分析发现，视觉令牌在中层（如第4层）即已出现表征饱和，而文本令牌仍持续受益于深层语义建模。这种深度异步的模态演化导致深层视觉计算冗余，并可能引发感知表征在任务特定微调过程中的漂移。针对此问题，论文提出双路径视觉令牌路由（Dual-Path Vision Token Routing, DPVR）框架，其核心为晚期融合版本DPVR-LF：在视觉令牌达到饱和点时将其路由至一个仅含单层可训练参数的旁路分支，深层主干网络仅执行13层纯文本前向传播并跳过图像位置，最终在最后一层重新融合视觉与文本流。该方案仅引入约3%的可训练参数，便在保持标准基准测试性能的同时大幅减少深层Transformer中对视觉信息的计算负担，挑战了“视觉令牌必须贯穿所有深层语言模型层”的传统假设，表明在LLaVA类模型中，单一晚融合层即可充分维持强感知能力。
链接: https://arxiv.org/abs/2606.09131
作者: Siyuan Liu,Jinyang Wu
机构: Peking University (北京大学); Tsinghua University (清华大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 18 pages, 4 figures. Submitted to Pattern Recognition
Abstract:Multimodal large language models (MLLMs) commonly inherit the deep, symmetric Transformer backbone designed for unimodal text modeling, and apply the same computation uniformly to image and language tokens. This design overlooks a key modality asymmetry: image and text tokens differ substantially in information density, redundancy, and required reasoning depth. Through a layer-wise analysis of LLaVA-1.5, we observe that vision tokens tend to saturate in the middle layers. Specifically, text-to-image attention decreases from 0.68 at layer 0 to 0.07 by layer 4, and stabilizes near 0.04 after layer 18, whereas text tokens continue to benefit from deep semantic processing. These findings suggest a mismatch between architectural symmetry and depth-asynchronous modality evolution, resulting in redundant visual computation and possible drift in perceptual representations during deep task-specific adaptation. Motivated by this, we propose Dual-Path Vision Token Routing (DPVR), a modality-asymmetric routing framework for efficient MLLMs. Its core instantiation, DPVR-LF (Late-Layer Fusion), routes vision tokens at the saturation point into a one-layer trainable side branch, runs a thirteen-layer text-only forward that skips image positions in the deep stack, and re-fuses the visual and textual streams only at the final layer. With approximately 3% trainable parameters, DPVR-LF preserves competitive multimodal performance on standard benchmarks while reducing visual computation in the deep Transformer stack. The results challenge the conventional assumption that vision tokens must traverse all deep language-model layers, and indicate that a single late fusion layer can be sufficient for maintaining strong perceptual competence in LLaVA-style MLLMs.
[NLP-67] MAAM: Anchor-Preserving Compression and Contextual Calibration for Chinese Discriminatory Language Detection
【速读】： 该论文旨在解决中文歧视性语言检测中隐含意图与语境依赖性强的难题，其核心挑战在于如何有效识别那些不直接表达偏见但通过上下文传递负面态度的语言。解决方案的关键在于提出一种轻量级、模型无关的MAAM（Myopia–Astigmatism Anchor Mechanism）框架，该框架受功能性视觉模糊机制启发，摒弃对所有词元的均等保留，转而聚焦于保留与歧视性判断相关的语义锚点，并通过C–I–S三重上下文先验（上下文语调、群体身份与立场极性）对其进行校准。这一设计实现了对关键语义信息的可解释性保留与动态上下文适应，显著提升了模型在显性偏见、隐性偏见及情感强度三个维度上的预测性能。实验表明，相较于主流编码器与前沿大模型（LLM）在零样本与少样本提示下的表现，MAAM不仅保持竞争力，还展现出更强的紧凑性与稳定性，验证了语义锚点的可解释性保留与上下文校准在中文歧视性语言评估中的有效性，为应对模型规模膨胀提供了切实可行的替代路径。
链接: https://arxiv.org/abs/2606.09114
作者: Yuxin Fu,Shijing Si
机构: Shanghai International Studies University (上海外国语大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Chinese discriminatory-language detection is challenging because harmful intent is often implicit and context-dependent. We propose MAAM (Myopia–Astigmatism Anchor Mechanism), a lightweight, model-agnostic framework inspired by functional visual blur: rather than preserving every token equally, MAAM retains discrimination-relevant semantic anchors and calibrates them with C–I–S contextual priors (Contextual Tone, Group Identity, and Stance Polarity). We also introduce ChLGBT, to our knowledge the first Chinese LGBT-focused discriminatory-language dataset, with 8,120 manually annotated samples and three ordinal labels: explicit bias, implicit bias, and emotional intensity. Across strong encoder baselines, MAAM improves all three prediction dimensions, with consistent gains in accuracy, F1, Brier score, and expected calibration error. Compared with frontier LLM baselines under zero-shot and few-shot prompting protocols, MAAM remains competitive while offering stronger compactness and stability. These results suggest that interpretable anchor preservation and contextual calibration provide a practical alternative to heavier model scaling for Chinese discriminatory-language assessment.
[NLP-68] Beyond FLOPs: Benchmarking Real Inference Acceleration of LLM Pruning under a GEMM-Centric Taxonomy
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）推理加速中剪枝（Pruning）方法实践效果不明确的问题。尽管各类剪枝方法均以减少计算量为目标，但其在不同硬件和内核实现下的执行行为差异显著，导致实际加速效果难以横向比较，制约了对剪枝技术真实性能边界的理解。为此，本文提出一种以通用矩阵乘法（GEMM）的M、N、K维度为核心的分类体系，将现有剪枝方法重新组织为统一的抽象框架。该框架的关键在于通过GEMM维度的逻辑划分，实现对剪枝方法在计算与内存层面行为的系统性建模，从而构建一个实现一致性的基准测试平台。在此基础上，研究系统地刻画了加速性能与模型质量之间的帕累托前沿（Pareto frontier），揭示出：在内存受限场景下，静态深度剪枝（static depth pruning）仍是最优的基准方案，且最接近理论加速上限；在预填充（prefill）阶段，随着质量损失从0%–4%逐步上升至17%–26%，最优剪枝策略由静态深度剪枝过渡至动态深度剪枝，最终演变为静态宽度剪枝（static width pruning）。该工作首次建立了剪枝驱动的LLM加速的统一实践视图，为未来剪枝方法的设计提供了明确的指导方向。
链接: https://arxiv.org/abs/2606.09080
作者: Haozhe Hu,Hao Wu,Anhao Zhao,Longwei Ding,Peiran Yin,Yunpu Ma,Xiaoyu Shen
机构: Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo; Department of Computing, The Hong Kong Polytechnic University; Munich Center for Machine Learning, LMU Munich
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 22 pages, 14 figures
Abstract:Pruning has emerged as a dominant paradigm for accelerating large language model (LLM) inference, spanning a broad spectrum of methods that remove computation across tokens, layers, heads, dimensions, and attention patterns. Despite sharing the same objective, these pruning approaches induce fundamentally different execution behaviors, causing realized speedups to depend heavily on hardware and kernel implementations. Consequently, the practical acceleration benefits of different pruning families remain poorly understood. In this work, we introduce a GEMM-centric taxonomy that reorganizes existing pruning methods according to the logical \textbfM, \textbfN, and \textbfK dimensions of general matrix multiplication (GEMM). Leveraging this abstraction, we build a unified benchmarking framework that enables implementation-consistent comparison across the pruning design space and systematically characterizes the acceleration–quality Pareto frontier. Our results show that static depth pruning remains the strongest Pareto-optimal baseline and stays closest to its theoretical acceleration upper bound in memory-bounded scenarios. During prefill, the frontier transitions from static depth at low quality loss (0%–4%), to dynamic depth at moderate loss (5%–16%), and finally to static width pruning at higher loss levels (17%–26%). These findings establish the first unified view of the practical limits of pruning-based LLM acceleration and provide guidance for future pruning research.\footnoteCode is available at this https URL
[NLP-69] A Unifying Lens on Reward Uncertainty in RLHF
【速读】： 该论文旨在解决强化学习中人类反馈（Reinforcement Learning from Human Feedback, RLHF）所面临的“奖励黑客”（reward hacking）问题，即智能体通过利用代理奖励模型（Proxy Reward Model, RM）的误差，在不提升真实质量的情况下获得高奖励分数。现有方法中，基于悲观主义（pessimism）的策略通过惩罚在奖励模型不确定性较高的区域内的奖励来缓解该问题，但传统标量奖励模型缺乏对不确定性的合理建模。本文提出应采用分布式奖励模型 $ p(r \mid x, y) $ 作为更合适的建模范式，并从贝叶斯推断或KL-分布鲁棒优化（KL-Distributionally Robust Optimization, KL-DRO）视角出发，推导出正则化后的RLHF目标函数的闭式有效奖励表达式：$ \tilde{r}(x, y) = \pm \beta \log \mathbb{E}_p[e^{\pm r / \beta}] $。该表达式的悲观分支统一了现有的多种奖励模型集成启发式方法——包括均值聚合、最坏情况优化（Worst-Case Optimization, WCO）和不确定性加权优化（Uncertainty-Weighted Optimization, UWO），它们均可视为该统一公式的极限情形或截断形式，从而揭示了各方法背后的隐含假设。解决方案的关键在于引入分布式奖励模型与基于KL正则化的有效奖励构造，实现了对不确定性的显式建模与悲观性的一致性表达。
链接: https://arxiv.org/abs/2606.09073
作者: Ely Hahami,Yoel Zimmermann,Ray Zhou,Jack Benarroch Jedlicki
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Reinforcement learning from human feedback (RLHF) is bottlenecked by \emphreward hacking, where the policy exploits errors in a proxy reward model (RM) and produces high RM scores without genuine quality gains. A natural mitigation is \emphpessimism: penalizing rewards in regions where the RM is uncertain. However, standard scalar RMs provide no principled notion of uncertainty. We argue that the right object is a \emphdistributional reward model p(r\mid x,y) . Under either a Bayesian inference or a KL-distributionally robust optimization (KL-DRO) lens, the KL-regularized RLHF objective admits a closed-form effective reward \tilde r(x,y) = \pm\beta\log\mathbbE_p[e^\pm r/\beta] . The pessimistic branch unifies the prior heuristics for RM ensemble aggregation: mean aggregation, worst-case optimization (WCO), and uncertainty-weighted optimization (UWO) all emerge as limits or truncations of this single expression. This also clarifies the implicit assumptions of each existing rule.
[NLP-70] Emergent Misalignment Can Be Induced by Sycophancy and Reversed via Alignment Gating
【速读】： 该论文旨在解决大语言模型在特定领域中因不良或错误输出的微调而引发的“涌现性错位”（emergent misalignment）问题，即模型在局部任务上微调后，导致其在广泛场景下产生严重有害行为。其核心挑战在于如何高效地逆转这种由微调引发的广泛性错位。解决方案的关键在于提出一种名为“对齐门控”（Alignment Gating）的新方法：在微调过程中向模型引入可学习且可控的门控模块，使模型能够识别并动态调节导致不安全响应的内部表征。通过增强或抑制这些表征，该方法可有效加剧或缓解涌现性错位。研究进一步发现，该门控模块具有优异的泛化能力——仅在窄域数据上训练获得的门控权重即可显著抑制宽域范围内的错位行为，同时保持模型原有的通用能力。
链接: https://arxiv.org/abs/2606.09068
作者: Sicheng Wang,Xiangyang Zhu,Han Wang,Zongrui Wang,Yuan Tian,Kaiwei Zhang,Kaiyuan Ji,Qi Jia,Guangtao Zhai
机构: 未知
类目: Computation and Language (cs.CL)
备注: Code is available at this https URL
Abstract:Prior work has shown that fine-tuning large language models on malicious or incorrect outputs in narrow domains can induce broad misalignment and harmful behavior, a phenomenon known as emergent misalignment. However, efficient methods for reversing such misalignment remain limited. In this work, we make two contributions. First, we identify sycophancy fine-tuning, i.e., training models to passively agree with users’ incorrect opinions, as a previously underexplored driver of emergent misalignment, and show that it induces broad and severe misaligned behavior. Second, we propose Alignment Gating, an efficient method for reversing emergent misalignment that inserts learnable and controllable gates into the model during fine-tuning. Through fine-tuning, these gates learn to identify the internal representations responsible for unsafe responses. Thus, amplifying or suppressing these representations then exacerbates or mitigates EM, respectively. We further find that alignment gating module exhibits strong generalization: gating weights obtained from narrow-domain fine-tuning substantially suppress broad-domain misaligned behavior while preserving the model’s general capabilities.
[NLP-71] INFUSER: Influence-Guided Self-Evolution Improves Reasoning
【速读】： 该论文旨在解决自进化（self-evolution）框架中生成高质量训练样本的可持续性与有效性问题：现有方法依赖大量人工标注或教师生成的数据，而在无监督生成场景下，采用基于难度启发式的奖励机制往往无法真正提升求解器（solver）性能。为此，论文提出INFUSER，一种迭代式协同训练框架，其核心在于双角色协同进化——生成器（Generator）从非结构化文档池中自动提炼问题与参考答案，求解器（Solver）则基于这些样本进行标准正确性奖励下的训练。关键创新在于生成器的奖励机制：引入一种面向优化器的影响力评分（optimizer-aware influence score），以衡量每个生成问题对目标分布下求解器实际性能的提升效果，而非仅关注问题难度。由于该评分具有高噪声和连续特性，传统GRPO难以有效优化，因此论文进一步提出DuGRPO（双归一化GRPO），用于更稳定地训练生成器。该设计使文档池动态演变为适应当前求解器能力的自适应课程，优先选择对求解器有实际帮助的问题。实验表明，在Qwen3-8B-Base模型上，INFUSER在奥数与SuperGPQA基准上相较强基线实现超过20%的相对提升；8B规模的协同进化生成器甚至超越了冻结的32B思维生成器在数学与编程任务上的表现。消融实验验证了各设计组件的必要性，扩展实验进一步展示了框架在指令微调锚点及引入规则可验证强化学习数据（RLVR）时的灵活性与泛化能力。
链接: https://arxiv.org/abs/2606.09052
作者: Siyu Chen,Miao Lu,Beining Wu,Heejune Sheen,Fengzhuo Zhang,Shuangning Li,Zhiyuan Li,Jose Blanchet,Tianhao Wang,Zhuoran Yang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Science and Game Theory (cs.GT); Machine Learning (stat.ML)
备注: 66 pages, 17 figures
Abstract:Self-evolution offers a scalable path to stronger reasoning: a pretrained language model improves itself with only minimal external supervision. Yet existing methods either depend on extensively curated or teacher-generated training data, or, when the generator runs unsupervised, reward it by a difficulty heuristic that need not improve the solver. We introduce INFUSER, an iterative co-training framework with two co-evolving roles: a Generator that drafts questions and reference golden answers from a pool of unstructured, automatically collected documents, and a Solver that improves by training on them. The solver is trained with standard correctness rewards against the generator-provided answers, while the generator is rewarded by an optimizer-aware influence score that measures whether each proposed question would actually improve the solver on the target distribution. Because this continuous, noisy influence score is poorly served by standard GRPO, we propose DuGRPO, a dual-normalized variant of GRPO, for generator training. Together, these turn the document pool into an adaptive curriculum that favors questions useful to the current solver, not just hard ones. On Qwen3-8B-Base, INFUSER outperforms strong self-evolution baselines with over 20% relative improvement on Olympiad and SuperGPQA benchmarks, and an 8B INFUSER co-evolving generator outperforms a frozen 32B thinking generator on math and coding. Ablations confirm each design choice is necessary, and two extensions, applying INFUSER to an instruction-finetuned anchor and augmenting it with rule-verifiable RLVR data, further demonstrate the flexibility and generalizability of the framework. Code is available at this https URL.
[NLP-72] DynaCF: Mitigating Shortcut Learning in Reward Models via Dynamic Counterfactual Sensitivity
【速读】： 该论文旨在解决奖励模型（Reward Model）在基于成对偏好训练过程中容易依赖表面捷径线索（shortcut cues）而非真正响应质量的问题，导致模型泛化能力下降。其解决方案的关键在于提出一种动态重加权框架——DynaCF，通过在优化过程中在线评估样本的捷径敏感性（shortcut sensitivity），利用语义保持的反事实扰动（semantics-preserving counterfactual perturbations）检测当前模型下偏好判断的边际变化与偏好翻转现象，并据此动态调整样本权重。在Bradley-Terry目标函数中对高捷径敏感性的样本进行降权处理，从而引导模型减少对表面模式的依赖，增强对任务相关偏好信号的捕捉能力。实验结果表明，DynaCF能有效提升偏好建模的鲁棒性。
链接: https://arxiv.org/abs/2606.09043
作者: Fengyuan Liu,Yongliang Miao,Zirui He,Yanguang Liu,Fei Sun,Mengnan Du
机构: The Chinese University of Hong Kong, Shenzhen; New Jersey Institute of Technology; Institute of Computing Technology, CAS
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Reward models trained from pairwise preferences often exploit superficial shortcut cues rather than learning true response quality. We propose DynaCF, a dynamic reweighting framework for mitigating shortcut learning in reward model training. Unlike static shortcut heuristics, DynaCF measures shortcut sensitivity online during optimization by applying semantics-preserving counterfactual perturbations and tracking the resulting margin shifts and preference flips under the current model. Samples with higher shortcut sensitivity are dynamically downweighted in the Bradley-Terry objective, encouraging the model to rely less on superficial patterns and more on task-relevant preference signals. Extensive experiments show that DynaCF consistently improves robustness in preference modeling.
[NLP-73] CRANE: Knowledge Editing for Reasoning MLLM s
【速读】： 该论文针对推理型多模态大语言模型（Reasoning Multimodal Large Language Models, MLLMs）在知识编辑过程中存在的深层失效问题展开研究，旨在解决传统知识编辑方法在引入显式思维链（Chain-of-Thought, CoT）后出现的严重性能退化现象。其核心挑战在于：尽管现有方法在教师强制（teacher-forcing）准确率上表现优异（可达100%），但在实际推理路径中却存在严重的“基于事实的成功率”（Grounded Success）为0%的情况。作者识别出三种关键失败模式：（1）结构坍塌（Structural Collapse），即权重修改类方法破坏了CoT的格式；（2）认知失调（Cognitive Dissonance），即模型基于视觉证据主动排斥注入的知识事实；（3）浅层内化（Shallow Internalization），即方法仅对精确查询有效，而无法泛化至重述或多跳推理场景。这些失败模式在推理型MLLM中相互耦合，导致通用性与结构完整性难以兼顾。为此，论文提出一种名为CRANE的检索增强型知识编辑框架，其关键创新在于无需对每条编辑进行参数微调，而是通过一个模态感知的双库检索系统与两阶段训练策略实现高效编辑：首先通过监督微调（SFT）完成结构初始化，再通过带有认知路由奖励（Cognitive Routing Reward）的GRPO训练，使模型学会在视觉先验与外部编辑事实之间进行动态权衡。为系统评估上述问题，论文构建了包含冲突分层、多层次探测及多跳可迁移性测试的ReasonEdit-Bench基准。实验结果表明，CRANE在冲突场景下达到96.9%的基于事实成功率，在多跳链中实现96.9%的中间实体使用率，同时保持97.6%的文本局部性与68.1%的图像局部性，显著优于现有方法，并在分布外的MMEVOKE基准上实现87.0%的编辑独立性（金标准检索条件下）。
链接: https://arxiv.org/abs/2606.09033
作者: Han Huang,Hao Wang,Mengqi Zhang,Shu Wu,Qiang Liu,Liang Wang
机构: University of Chinese Academy of Sciences (中国科学院大学); New Laboratory of Pattern Recognition (NLPR), CASIA (中国科学院自动化研究所模式识别国家重点实验室); Harbin Institute of Technology (哈尔滨工业大学); School of Computer Science and Technology, Shandong University (山东大学计算机科学与技术学院)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注: 10 pages, 5 figures
Abstract:The emergence of reasoning multimodal large language models (MLLMs), which generate explicit chain-of-thought (CoT) reasoning before producing answers, has introduced a new challenge for knowledge editing: methods that appear successful under traditional metrics (teacher-forcing accuracy up to 100%) can fail severely when the model’s reasoning process is examined (Grounded Success as low as 0%). We identify three failure modes: (1) Structural Collapse, where weight-modifying methods destroy the CoT format; (2) Cognitive Dissonance, where the model’s reasoning chain actively rejects the injected edit fact based on visual evidence; and (3) Shallow Internalization, where methods succeed on exact queries but fail on rephrase or multi-hop variants. On reasoning MLLMs, these modes interact: methods that generalize (FT, LoRA) trigger format collapse, while methods without deep modification cannot generalize. To expose these failures, we propose a CoT-aware evaluation protocol and construct ReasonEdit-Bench, with conflict stratification, multi-level probes, and multi-hop portability tests. We propose CRANE, a retrieval-augmented framework that requires no per-edit parameter modification. CRANE combines a modality-aware dual-library retrieval system with a two-phase training strategy: Supervised Fine-Tuning (SFT) for structural initialization, followed by GRPO with a Cognitive Routing Reward that trains the model to arbitrate between visual priors and injected edit facts. On ReasonEdit-Bench, CRANE achieves 96.9% Grounded Success on conflict scenarios and 96.9% intermediate entity usage in multi-hop chains, with 97.6% text-locality and 68.1% image-locality Edit Independence. On the out-of-distribution MMEVOKE benchmark, CRANE reaches 87.0% under gold retrieval. Comments: 10 pages, 5 figures Subjects: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL) Cite as: arXiv:2606.09033 [cs.CV] (or arXiv:2606.09033v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2606.09033 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-74] Bridging the Agent -World Gap: Text World Models for LLM -based Agents
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）驱动的智能体在交互式文本环境中普遍存在的被动响应问题：现有智能体多依赖对观测的直接映射进行动作决策，缺乏对环境结构与动态演化的显式建模，导致规划能力弱、学习效率低且评估不严谨。其核心解决方案是提出文本世界模型（Text World Model, TWM），即一种对文本状态间转移关系的显式建模机制——给定当前状态与候选动作，能够预测下一状态（如网页内容、终端输出、API响应或用户回复）。该模型的关键在于构建一个可形式化表达的环境动态表示框架，通过状态表征与领域对齐实现对复杂交互环境的结构化理解，并支持训练阶段的经验合成与推理阶段的规划、验证与自适应，从而推动智能体从反应式行为向目标导向的主动决策演进。
链接: https://arxiv.org/abs/2606.09032
作者: Yixia Li,Hongru Wang,Peng Lai,Zhiwen Ruan,He Zhu,Youxin Zhu,Ganlong Zhao,Minda Hu,Yun Chen,Sibei Yang,Peng Li,Jeff Z. Pan,Jia Pan,Guanhua Chen,Yang Liu,Guanbin Li
机构: 未知
类目: Computation and Language (cs.CL)
备注: Code: this https URL
Abstract:Large language model (LLM)-based agents are increasingly used in interactive textual environments, from web navigation and code editing to tool use and long-horizon dialogue. Yet many remain largely reactive, mapping observations to actions without an explicit model of how these environments are structured and evolve. This motivates text world models (TWMs): transition models over textual states that, given a state and a candidate action, predict the resulting webpage, terminal output, API response, or user reply, thereby supporting planning, efficient learning, and principled evaluation. We systematically review text world models for LLM-based agents, organized around a formal framework and the agent lifecycle: (1) Foundations, defining text world models and characterizing them by state representation and grounding domain; (2) Construction, taxonomizing LLM-as-WM and code-as-WM paradigms and reviewing methods for building them; (3) Application, examining how world models support agents at training time through experience synthesis and at inference time through planning, verification, and adaptation; and (4) Evaluation, covering both evaluation of the world model itself and its use as an evaluation environment for agents. We aim to consolidate this rapidly developing area, clarify its design space, and highlight open challenges for future research.
[NLP-75] RIAGE: Dialectical Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series with LLM s
【速读】： 该论文旨在解决临床早期预警系统在基于电子健康记录（Electronic Health Records, EHR）构建时面临的双重挑战：如何在不规则采样医学时间序列（Irregularly Sampled Medical Time Series, ISMTS）数据上生成既校准良好（calibrated）又可解释的患者风险评分。现有基于大语言模型（Large Language Models, LLMs）的方法存在风险极化问题，即倾向于将连续的临床风险压缩为过于自信的二分类预测，导致风险评分缺乏校准性与跨患者可比性。其解决方案的关键在于提出TRIAGE框架，通过引导LLM生成针对不同临床结局的特定原因推理（outcome-specific rationales），实现辩证式推理（dialectical reasoning），从而缓解风险极化现象。该方法使单一LLM能够输出基于显式临床逻辑的连续风险评分。在三个ISMTS基准上的实验表明，TRIAGE平均提升AUPRC 3.3%，校准误差降低81%；此外，以LLM为裁判的评估显示，其生成的推理质量较基线的后验解释高出20%。
链接: https://arxiv.org/abs/2606.09030
作者: Hyeongwon Jang,Gyouk Chu,Changhun Kim,Joonhyung Park,Hangyul Yoon,Eunho Yang
机构: KAIST(韩国科学技术院); AITRICS; University of Wisconsin-Madison(威斯康星大学麦迪逊分校)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Code is available at this https URL
Abstract:Clinical early warning systems built on electronic health records, in which clinical observations are recorded as irregularly sampled medical time series (ISMTS), must deliver both calibrated risk scores for patient triage and interpretable rationales that clinicians can verify. Large Language Models (LLMs) have been explored for this task, yet they collapse graded clinical risk into overconfident binary predictions. This risk polarization undermines both calibration and cross-patient comparability. To address this, we propose TRIAGE, a framework that trains an LLM to generate dialectical reasoning over competing clinical outcomes by eliciting outcome-specific rationales. This dialectical formulation mitigates risk polarization, enabling a single LLM to yield continuous risk scores grounded in explicit clinical reasoning. Evaluated on three ISMTS benchmarks, TRIAGE achieves an average AUPRC improvement of 3.3% and reduces calibration error by 81% compared to the competitive baselines. An LLM-as-a-judge assessment further shows that our rationales surpass post-hoc explanations from the baseline by 20% in clinical reasoning quality. The source code is available at this https URL .
[NLP-76] SafeRun: Enabling Determinism in LLM Planning for Running ICML2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在确定性要求高的场景中因概率性生成机制导致的不可靠性问题，特别是在跑步规划这类对安全性要求极高的应用中，传统LLM可能违反安全规则从而引发安全隐患。其解决方案的关键在于提出SafeRun框架，采用解耦架构将自然语言的柔性解释（由LLM完成）与硬性约束的确定性执行（由确定性求解器完成）分离，从而在保持自然语言灵活性的同时，确保严格遵守生理与安全约束。通过构建一个涵盖真实生理与安全约束的综合基准测试集，实验表明SafeRun在五种主流LLM上实现了100%的安全得分（相较平均79.1%的PE和97.6%的CodeAct），同时维持了良好的指令遵循能力，验证了其在保障安全性方面的有效性。
链接: https://arxiv.org/abs/2606.09027
作者: Meilin Chen,Zepeng Zhai,Jiaxuan Zhao,Yuan Lu
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Workshop on Planning in the Era of LLMs (LM4Plan) at ICML 2026
Abstract:Large Language Models enable flexible natural-language planning but remain unreliable in determinism-critical domains due to their probabilistic nature. This limitation is especially problematic in running planning, where violating safety rules can lead to safety risks. We propose SafeRun, a framework for deterministic LLM-based planning via a decoupled architecture. SafeRun separates soft interpretation by an LLM from hard constraint enforcement by a deterministic solver, ensuring strict safety constraints while preserving natural-language flexibility. To validate SafeRun, we build a comprehensive benchmark for running planning under realistic physiological and safety constraints. Experiments across five LLMs show that SafeRun achieves 100% safety score (vs.\ 79.1% PE average and 97.6% CodeAct average) while maintaining competitive instruction-following scores. The SafeRun benchmark is publicly available at \hrefthis https URLhuggingface.
[NLP-77] Beyond Averag es: Evaluating LLM s on Human Survey Replication at the Distributional Level
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在模拟人类调查响应时，现有评估方法仅关注均值层面或聚合一致性，难以揭示模型是否真实复现人类行为变异性的核心问题。其解决方案的关键在于从分布层面（distributional level）系统评估LLMs对人类调查数据的复现能力，采用2010年韩国方便面购买行为的非公开消费者选择实验数据作为测试场景，避免与模型训练数据重叠。研究针对三类不同统计类型的响应变量——二元购买发生、分类品牌选择和计数型购买数量——分别比较了人类与LLM在均值、模式及分布结构上的对齐程度，并引入仅基于人类数据的基准参照。结果表明，尽管LLMs能较好复现条件层面的行为模式，但在分布结构捕捉上表现不佳；尤其在购买数量维度，没有任何模型优于忽略条件信息、直接匹配整体人类分布的基线模型。这一发现揭示：即使模型在均值层面表现良好，其生成分布仍可能比基线更远离真实人类分布，因此仅依赖均值评估会带来误导性结论。此外，输入配置显著影响复现效果，结构化角色设定和多模态输入有助于提升对齐度，而显式推理提示则导致性能单调下降。
链接: https://arxiv.org/abs/2606.09013
作者: Jeonghyeon Moon,Jiwon Kim,Yeheum Lah,Yoonju Han,Yuncheol Kang
机构: Ewha Womans University (弘益女子大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:LLMs are increasingly used to simulate human survey responses, but prior work has mainly evaluated replication using mean-level or aggregate agreement, offering limited insight into whether LLMs reproduce the variability of human behavior. We evaluate LLM-based survey replication at the distributional level using a non-public 2010 consumer choice experiment on Korean instant noodle purchases, a setting unlikely to overlap with model training data. We evaluate three response variables of differing statistical type: binary purchase incidence, categorical brand choice, and count purchase quantity. For each, we compare human and LLM responses at mean-level, pattern, and distributional alignment, and against reference baselines from the human data alone. LLMs reproduce condition-level patterns reasonably well but fail to capture distributional structure: for purchase quantity, no model beats a condition-insensitive baseline that simply matches the pooled human distribution. Because models that match human means well can still produce distributions further from humans than this baseline, mean-based evaluation alone can be actively misleading. Replication also varies with input configuration, with structured personas and multimodal inputs improving alignment while explicit reasoning prompting degrades it monotonically.
[NLP-78] Document-Authored Control-Signal Impersonation: A Low-Cost Indirect Prompt Attack on RAG Safety Boundaries
【速读】： 该论文旨在解决检索增强生成（Retrieval-Augmented Generation, RAG）系统中因文档来源与权威性边界失效所引发的安全问题，具体表现为攻击者通过伪造的检索文本伪装成元数据、出处信息、权威性标识或披露策略信号等控制信号，从而误导模型决策。其核心问题是：在RAG系统将用户查询、检索文档、元数据及指令统一序列化为自然语言提示的过程中，未受信任的文档内容可能被错误地视为可信的控制信号，导致模型对非法输入产生误判。解决方案的关键在于识别并区分“数据”与“策略”的本质差异——即文档作者的内容应被视为数据而非政策指令，任何依赖于文档文本作为控制信号的行为均存在安全隐患。为此，作者提出一种新型间接提示注入攻击模式，称为文档自撰控制信号冒充（Document-Authored Control-Signal Impersonation, DACSI），强调其不依赖命令式指令，而是利用源-权威性混淆机制，在RAG特有的信息融合路径中实现对控制信号的非强制性冒充。研究通过多模型设置、不同提示压力水平和多种评估基准验证了DACSI的有效性，并揭示其在不同模型架构中的行为差异，指出必须针对不同模型范式进行独立评估，以应对该类攻击带来的潜在安全风险。
链接: https://arxiv.org/abs/2606.09005
作者: Jianguo Zhu
机构: Chengdu University of Information Technology (成都信息工程大学)
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL)
备注: Preprint. Independent-author version
Abstract:Retrieval-augmented generation (RAG) systems often serialize user queries, retrieved documents, metadata, system labels, and task instructions into one natural-language prompt. We study a source-authority boundary failure in this design: attacker-authored retrieved text can impersonate metadata, provenance, authority, or disclosure-policy signals that appear control-relevant to the model. We call this pattern Document-Authored Control-Signal Impersonation (DACSI). DACSI is a non-imperative, metadata-like payload subclass within indirect prompt injection. Its central lesson is simple: document-authored labels are data, not policy. Command-style injection asks the model to ignore, override, or violate policy; DACSI asks whether untrusted document text can be misattributed as an authorized control signal when RAG prompt rendering collapses trusted and untrusted text into the same natural-language channel. We evaluate DACSI across six model settings, prompt-pressure levels, injection baselines, signal taxonomies, RAG-mediated pipelines, system-control probes, a source-authority attribution probe, and synthetic canary formats. We interpret the evidence by model regime rather than as six equal replications: DeepSeek V4 Pro and Qwen3.5-397B provide the cleanest positive lift, DeepSeek V4 Flash is a high-susceptibility setting, GPT-5.5 and Gemini 3.1 Pro Low are strong-boundary probes with selected residual risks, and GLM-4.7 is a saturated leakage boundary case. Across these regimes, DACSI warrants separate evaluation because it uses a command-free metadata/provenance/policy surface, follows a RAG-specific source-authority path, and responds to source/channel separation. The source-authority probe is behavioral attribution evidence, not proof of an internal mechanism. Comments: Preprint. Independent-author version Subjects: Cryptography and Security (cs.CR); Computation and Language (cs.CL) Cite as: arXiv:2606.09005 [cs.CR] (or arXiv:2606.09005v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2606.09005 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-79] Language-Aware Token Boosting: LLM Language Confusion Reduction Without Tuning ACL2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在生成非英语文本时出现的语言混淆问题。现有方法多依赖微调（fine-tuning）来缓解此问题，但存在训练成本高、泛化能力受限等缺陷。本文提出一种无需微调的范式，其核心在于通过引入语言感知的令牌增强机制：一是语言感知令牌增强（Language-Aware Token Boosting, LATB），对目标语言相关的令牌施加定向扰动；二是自适应语言感知令牌增强（Adaptive-LATB），根据模型对目标语言的置信度动态调整扰动强度。该方案有效提升了多语言对齐能力，显著降低语言混淆现象，同时保持摘要生成质量，且无需任何额外微调。
链接: https://arxiv.org/abs/2606.08994
作者: Trapoom Ukarapol,Pakhapoom Sarapat,Nut Chukamphaeng
机构: SCB DataX(SCB DataX); Tsinghua University(清华大学); SCBX(SCBX)
类目: Computation and Language (cs.CL)
备注: ACL2026 Main Conference
Abstract:Large language models (LLMs) sometimes exhibit language confusion when generating non-English text. Existing approaches typically rely on fine-tuning to mitigate this issue. In contrast, we propose a tuning-free paradigm for reducing language confusion. Within this paradigm, we introduce two methods: Language-Aware Token Boosting (LATB), which applies targeted perturbations to tokens associated with the desired language, and Adaptive Language-Aware Token Boosting (Adaptive-LATB), which dynamically adjusts these perturbations based on the model’s confidence in the intended language. Experiments demonstrate that our methods effectively improve multilingual alignment by reducing language confusion, while maintain the summarization quality without requiring any additional fine-tuning. Our code is publicly available. this https URL.
[NLP-80] Structure-Aware Modeling of Multiple-Choice Questions Improves Automatic Difficulty Estimation
【速读】： 该论文旨在解决自动题目难度估计（Automatic Question Difficulty Estimation, AQDE）中如何有效利用干扰项（distractor）信息以提升难度预测准确性的关键问题。现有研究对将干扰项作为额外文本输入是否能一致改善预测性能存在分歧，其核心挑战在于干扰项的结构化表示方式是否影响模型表现。本文提出的关键解决方案是：通过设计受控的神经网络架构，将多选题（MCQ）的各个组成部分（题干、正确答案、干扰项）显式建模为独立输入单元，从而分离并量化干扰项内容与顺序对难度预测的贡献。具体而言，采用两种策略——基于位置标记的有序拼接（order-aware concatenation）与无序求和（order-invariant summation）来聚合干扰项表征。在智利2016–2020年自然科学与社会科学共4,114道多选题数据集上的实验表明，引入干扰项结构信息的最优模型分别达到自然学科R² = 0.83、社会科学R² = 0.71，显著优于仅使用题干与正确答案的基线模型；其中无序求和变体在参数量减少约一半的同时保持近似精度，展现出更优的准确性-效率权衡。研究结果证实，干扰项的内容及其结构化表示是提升难度预测性能的关键因素，支持开发高效、结构感知的生成式模型用于大规模教育评估场景。
链接: https://arxiv.org/abs/2606.08988
作者: Gabriel Ortega,Abelino Jiménez,Séverin Lions,Pablo Dartnell
机构: Centro de Investigación Avanzada en Educación (CIAE), Instituto de Estudios Avanzados en Educación (IE), Universidad de Chile; Departamento de Evaluación, Medición y Registro Educacional (DEMRE), Universidad de Chile; Centro de Modelamiento Matemático (CMM), Universidad de Chile; Departamento de Ingeniería Matemática (DIM), Universidad de Chile
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 30 pages, 1 table, 2 figures
Abstract:Automatic Question Difficulty Estimation (AQDE) holds growing promise for educational assessment because it has the potential to yield difficulty estimates that are competitive with expert judgment, while helping reduce the time and financial burden associated with pilot administrations and scaling to digital testing contexts. Prior AQDE studies report mixed evidence on whether adding distractors as additional text to the question stem and the correct key consistently improves difficulty prediction. We hypothesize that the effectiveness of distractor information depends on its structural representation, and that explicitly modeling distractors as separate components improves difficulty estimation over baselines that omit this information. To address this, we designed controlled architectures that model MCQ components as distinct inputs to isolate the contribution of distractor content and order. Specifically, we represented distractors by encoding each distractor as its own text input and aggregating their representations either with order-aware concatenation (with positional tags) or with an order-invariant summation. We evaluated these architectures using two Chilean datasets (Natural and Social Sciences, 2016-2020; 4,114 multiple-choice questions). Compared to a simpler model that only used the question stem and the key, our best distractor-aware architecture achieved higher predictive performance, reaching R^2 = 0.83 for Natural Sciences and R^2 = 0.71 for Social Sciences items. An order-invariant variant achieved nearly the same accuracy with approximately half as many parameters, offering a favorable accuracy-efficiency trade-off. These results show that structural information (especially distractor content) drives gains in predictive accuracy, supporting the development of efficient, structure-aware models that are computationally viable for large-scale educational applications.
[NLP-81] CARE: A Conformal Safety Layer for Medical Summarization
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在医学摘要生成中存在的重要信息遗漏和幻觉（hallucination）问题，现有错误检测方法通常依赖启发式或未经校准的评分机制，无法对遗漏错误提供形式化控制，也缺乏在安全性与临床医生审查负担之间进行权衡的理论依据。为此，论文提出一种后处理、模型无关的安全层——符合性风险评估（Conformal Assessment for Risk Evaluation, CARE），利用符合性风险控制（conformal risk control）技术，为任意LLM生成的摘要叠加经过校准的遗漏与幻觉标记，且无需重新训练模型。CARE的关键在于通过两个控制器实现形式化保证：幻觉控制器严格控制文档中未被标记的幻觉句子出现概率，而遗漏控制器则控制未被揭示的重要遗漏的期望比例。由于遗漏既取决于原始语句的重要性，又取决于其是否被摘要覆盖，因此需联合校准完整（τ, γ）阈值空间中的双重风险，仅单独校准某一维度将破坏目标风险边界。通过联合校准，CARE在保持形式化保证的同时，相较于其他校准基线可减少高达5倍的待审查句子数量。在五个医学摘要任务中，CARE在α = 0.15的设定下，以95%置信度满足目标风险边界，仅需每领域约100个标注文档即可完成校准。初步临床医生评估（75份文档审查）显示，经校准的标记使遗漏检测性能平均提升28.6个百分点。结果表明，基于句子级别的安全保证在LLM辅助医学摘要中是可行的，并提供了可调节的机制以平衡残余风险与人工审查成本。
链接: https://arxiv.org/abs/2606.08969
作者: Suhana Bedi,Bridget Lin,Anson Y. Zhou,Chloe O. Stanwyck,Jenelle A. Jindal,Sanmi Koyejo,David Stutz,Nigam H. Shah
机构: Stanford University (斯坦福大学); Google DeepMind
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 29 pages, 5 figures
Abstract:Large language models (LLMs) are increasingly used for medical summarization, but their outputs can omit medically important information and introduce unsupported claims. Existing error-detection methods produce heuristic or uncalibrated scores, providing no formal control over missed errors and no principled way to trade off safety against clinician review burden. We introduce Conformal Assessment for Risk Evaluation (CARE), a post-hoc, model-agnostic safety layer that uses conformal risk control to overlay calibrated omission and hallucination flags onto summaries from any LLM without retraining. CARE provides finite-sample, distribution-free guarantees through two controllers: a hallucination controller that bounds the probability of a document containing any unflagged hallucinated sentence, and an omission controller that bounds the expected fraction of important omissions not surfaced for review. Unlike hallucination detection, omissions depend jointly on whether a source sentence is important and whether it is covered by the summary. We show that calibrating only one dimension can violate the target risk bound, while marginal decompositions remain valid but overly conservative. By jointly calibrating over the full (\tau,\gamma) threshold space, CARE preserves formal guarantees while surfacing up to 5 \times fewer sentences than alternative calibrated baselines. Across five medical summarization tasks, CARE satisfies the target risk bound at \alpha = 0.15 with 95% confidence across 100 calibration/test resplits, using only ~100 labeled documents per domain. In a preliminary clinician study (75 document reviews), calibrated flags improved omission detection by 28.6 percentage points on average. These results show that sentence-level safety guarantees are feasible for LLM-assisted medical summarization and offer a tunable mechanism for balancing residual risk and review effort.
[NLP-82] ChinaHeritaQA: A Culturally-Grounded Visual Question Answering Dataset for World Heritage Sites in China
【速读】： 该论文旨在解决当前视觉-语言模型（VLMs）在评估中国世界遗产地文化遗产理解能力方面的不足，尤其针对文化推理（cultural reasoning）任务中模型表现不均衡的问题。其核心挑战在于，现有模型虽在图像识别等基础视觉任务上表现优异，但在涉及历史时期划分、建筑风格分析及文化背景理解等深层认知维度时存在显著短板。解决方案的关键在于构建一个基于联合国教科文组织（UNESCO）遗产本体论指导的多模态基准数据集ChinaHeritaQA，该数据集包含2,279张真实场景图像与14,133组双语（中文/英文）多选问答对，覆盖从身份识别到历史分期、建筑分析等七类认知维度，并通过严格的人工标注确保语言质量与事实一致性。实验结果表明，尽管顶尖模型在整体表现上超越人类平均水平，但其性能在具体任务、朝代和地域间呈现显著差异，揭示了强视觉检索能力无法直接转化为文化与历史理解能力。该研究为未来具备文化感知能力的多模态学习提供了重要基准与数据支持。
链接: https://arxiv.org/abs/2606.08959
作者: Yi Zhang,Bolei Ma,Yong Cao,Chengyan Wu,Daniel Hershcovich,Anna-Carolina Haensch
机构: LMU Munich (慕尼黑大学); FAU Erlangen-Nuremberg (埃尔兰根-纽伦堡大学); Munich Center for Machine Learning (慕尼黑机器学习中心); University of Tübingen (图宾根大学); Tübingen AI Center (图宾根人工智能中心); Sun Yat-sen University (中山大学); University of Copenhagen (哥本哈根大学); University of Maryland, College Park (马里兰大学学院帕克分校)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:We introduce ChinaHeritaQA, a multimodal benchmark dataset for evaluating the cultural reasoning abilities of vision-language models (VLMs) on UNESCO World Heritage sites in China. The dataset comprises 2,279 in-the-wild images paired with 14,133 bilingual (Chinese/English) multiple-choice QA pairs spanning seven cognitive dimensions, from basic identity recognition to historical periodization and architectural analysis. Guided by a UNESCO-aligned heritage ontology and verified through rigorous human annotation, the dataset ensures linguistic quality and factual consistency. Evaluations of state-of-the-art VLMs reveal that while top models exceed human performance on average, substantial task-level variation emerges: models excel at visual recognition but struggle with culturally grounded reasoning. Performance also varies by dynasty and region. ChinaHeritaQA reveals that strong visual retrieval does not extend to cultural and historical understanding. We release the dataset to support future research on culturally aware multimodal learning.
[NLP-83] Multilingual Sentiment Aware Text Summarization A Reinforcement Learning Approach for Consistency Maintenance
【速读】： 该论文旨在解决基于人类反馈的强化学习（Reinforcement Learning from Human Feedback, RLHF）在文本摘要任务中导致的情感属性退化问题，具体表现为输出摘要出现系统性的情感中性化倾向（sentiment drift），即摘要情感色彩相较于源文本趋于平缓。其解决方案的关键在于提出一种基于策略归因（Policy Attribution）的分析框架，通过分解RLHF目标函数并量化各组件对模型行为的影响，揭示了KL正则化项是导致情感抑制的主要原因。基于此发现，论文进一步提出一种情感感知型的KL正则化改进方法，通过有选择地降低对承载情感信息的词元（sentiment-bearing tokens）的约束强度，有效缓解情感漂移现象，同时保持摘要的质量与事实一致性。研究结果表明，当前主流对齐方法在提升模型安全性和事实准确性的同时，可能无意中削弱了语言模型的情感表达能力，因而亟需发展能够显式考虑情感保真度的新型对齐策略。
链接: https://arxiv.org/abs/2606.08940
作者: Mikhail Krasitskii,Alexander Gelbukh,Olga Kolesnikova,Grigori Sidorov
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Reinforcement Learning from Human Feedback (RLHF) has significantly improved the quality and fluency of large language models in text summarization. However, its impact on affective properties remains insufficiently understood. In this work, we study sentiment drift, a systematic shift toward neutral sentiment in RLHF-based summarization outputs compared to source texts. We conduct extensive experiments across multiple datasets, model architectures, and eight languages to analyze how alignment objectives influence sentiment preservation. Our results show that sentiment drift is a consistent phenomenon that becomes stronger with increased KL regularization strength, indicating a trade-off between alignment stability and affective fidelity. To explain this behavior, we introduce a Policy Attribution framework that decomposes the RLHF objective and quantifies the contribution of its components. Our analysis reveals that KL regularization is the primary driver of sentiment suppression across all settings. Based on these findings, we propose a sentiment-aware modification of the KL regularization term, which selectively reduces constraints on sentiment-bearing tokens. Empirical results demonstrate that this approach mitigates sentiment drift while maintaining summarization quality. Overall, our findings highlight a fundamental limitation of current alignment methods: while they improve factual consistency and safety, they may unintentionally suppress emotional expressiveness. This motivates the development of alignment strategies that explicitly account for affective preservation.
[NLP-84] PACT: Learning Diverse Diagnostic Strategies via Privileged Synthesis and Branch Consensus
【速读】： 该论文旨在解决在患者信息不完整的情况下，临床诊断中如何灵活运用多种诊断推理范式（diagnostic reasoning paradigms）的问题。现有基于大语言模型（LLM）的医疗智能体虽具备较强的医学推理能力，但单一推理范式或简单混合对话监督导致各范式间学习过程相互干扰，难以有效分离与协同。为此，论文提出PACT（Periodic Anchor Consensus Training）框架，其核心在于通过受监督的多范式对话合成与基于共识的分支训练相结合：在数据层面，采用DPS（Doctor-Patient-Supervisor）机制，利用完整的电子病历（EMRs）进行质量控制，同时确保医生代理仅基于患者可见信息进行交互，从而生成四种诊断推理范式下无隐藏临床答案泄露的验证对话；在训练层面，为每种推理范式分别训练一个特定的LoRA分支，并周期性地通过符号共识（sign consensus）将各分支聚合为共享的锚点（Anchor），实现跨范式的知识对齐与解耦。此外，研究构建了一个动态多轮中文医疗问诊基准，用于评估交互式咨询性能。实验结果表明，PACT在诊断结果与咨询流程等多个指标上均优于对比的专有模型、医学专用模型及任务适配模型，达到当前最优水平。
链接: https://arxiv.org/abs/2606.08938
作者: Gen Li,Yuanze Hu,Zhichao Yang,Qingchen Yu,Jianwei Lv,Yue Guo,Yujing Liu,Faguo Wu,Hongwei Zheng,Xiandong Li,Bo Yuan,Yifan Sun,Zhaoxin Fan
机构: Beihang University(北京航空航天大学); Baidu(百度); ByteDance(字节跳动); Beijing Academy of Blockchain and Edge Computing(北京市区块链与边缘计算研究院); Renmin University of China(中国人民大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 16 pages, 5 figures, 5 tables
Abstract:Clinical diagnosis requires flexible use of multiple reasoning paradigms under incomplete patient information. Existing LLM-based medical agents show strong medical reasoning ability, but single-paradigm or naively mixed dialogue supervision makes these paradigms difficult to learn without interference. We propose \textbfPACT (Periodic Anchor Consensus Training), a framework that couples supervised multi-paradigm dialogue synthesis with consensus-based Branch training. At the data level, \textbfDPS (Doctor-Patient-Supervisor) uses complete electronic medical records (EMRs) for quality control while keeping the doctor agent restricted to patient-visible information. This produces validated dialogues under four diagnostic reasoning paradigms without leaking hidden clinical answers. At the training level, PACT trains one paradigm-specific LoRA Branch per paradigm and periodically aggregates Branches into a shared Anchor through sign consensus. We further construct a dynamic multi-turn Chinese medical diagnosis benchmark for interactive consultation. Experiments show that PACT achieves state-of-the-art performance among compared proprietary, medical-specialized, and task-adapted baselines on diagnostic outcome and consultation-process metrics.
[NLP-85] From Statute to Control Flow: Span-Grounded Deontic Trees for Defeasible Scope Parsing
【速读】： 该论文旨在解决规则遵循型智能体在执行法律法规时因“隐性范围遗漏”（Silent Scope Omission, SSO）导致的合规性失效问题。SSO表现为模型虽表面遵循一般规则，却无声忽略嵌套的例外或反例外情形，从而在关键边缘场景下产生看似合规实则违规的输出。尽管此类问题常被归因于智能体系统设计缺陷，但其根本瓶颈在于对法规条文语义结构的深层理解能力，这一能力传统上属于法律自然语言处理（Legal NLP）的研究范畴。然而，现有大多数法律NLP基准侧重于最终任务表现，难以揭示引发SSO的结构性缺失。为此，作者提出NormBench，一个涵盖中文（法律与地方政策）、英文（美国税法、GDPR及企业政策）以及跨语言场景的2,290个条款基准，专用于可废止范围解析（defeasible scope parsing），即精确识别何条款可覆盖何条款。NormBench采用基于跨度的道义树（Span-Grounded Deontic Trees, SG-DT）作为编译器风格的中间表示，将每个逻辑分支锚定于原文片段，并强制要求显式排除条件（exclusion guards），从而实现确定性编译与可审计性。对前沿大语言模型（LLM）的评估揭示两类普遍病理：（1）递归衰减（Recursion Decay），即随着废止条款深度增加，性能急剧下降；（2）可审计性陷阱（Auditability Trap），即模型能检索相关文本跨度，却无法正确构建控制流。实验表明，使用SG-DT作为受限中间输出可显著提升整棵树的保真度与废止条款恢复能力，且其有效性具有机制特异性——仅在涉及例外、易发SSO的案例中带来明显增益，而当附加结构冗余或解析器保真度不足时，整体准确率可能呈现混合结果。因此，解决方案的关键在于通过结构化中间表示（SG-DT）增强法律条文的逻辑可追溯性与控制流建模能力，以应对复杂规则间的相互约束与例外嵌套。
链接: https://arxiv.org/abs/2606.08932
作者: Jian Chen,Siyuan Li,Chucheng Wan,Zixuan Yuan
机构: The Hong Kong University of Science and Technology (Guangzhou)(香港科技大学(广州)); Sun Yat-Sen University(中山大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注:
Abstract:Rule-following agents tasked with executing policies and regulations often fail via Silent Scope Omission (SSO): a model applies a general rule but silently drops nested exceptions or counter-exceptions, producing outputs that appear compliant yet break on important edge cases. Although such failures are often framed as an agentic-systems problem, the underlying bottleneck is statutory and policy understanding, a capability typically studied in legal NLP. However, most existing legal NLP benchmarks emphasize end-task outcomes, which can overlook the structural omissions that cause SSO. To diagnose and mitigate SSO, we introduce NormBench, a benchmark of 2,290 provisions spanning Chinese (laws and local policies), English (U.S. tax law, GDPR, and corporate policies), and cross-lingual settings, designed for defeasible scope parsing: identifying precisely which clause overrides which. NormBench uses Span-Grounded Deontic Trees (SG-DT), a compiler-style intermediate representation that anchors every logical branch to source spans and requires explicit exclusion guards, enabling deterministic compilation and audit. Evaluations of frontier LLMs reveal two recurring pathologies: (1) Recursion Decay, where performance drops sharply as defeater depth increases, and (2) an Auditability Trap, where models retrieve relevant spans but fail to assemble correct control flow. Using SG-DT as a constrained intermediate output improves whole-tree fidelity and defeater recovery, and downstream experiments show that its utility is mechanism-specific: gains concentrate on exception-active, SSO-prone cases, while aggregate accuracy can be mixed when the added structure is unnecessary or parser fidelity is low.
[NLP-86] Are Reasoning Vision-Language Models Robust to Semantic Visual Distractions?
【速读】： 该论文旨在解决生成式视觉-语言模型（Vision-Language Models, VLMs）在真实场景应用中面临的关键可靠性问题：现有评估方法主要关注视觉输入的物理退化（如噪声、模糊和天气干扰），但忽略了另一种重要且隐蔽的挑战——语义层面的视觉干扰（semantic visual distractions）。这类干扰指在输入图像中添加与任务无关但语义上合理、具有误导性的视觉线索，虽不影响真实答案的正确性，却可能被模型误当作有效证据进行推理，从而导致错误结论。针对此问题，论文提出Distract-Bench基准，专门用于评估VLM对语义视觉干扰的鲁棒性。其核心解决方案在于构建一个包含大量任务无关但语义合理的干扰项的数据集，以揭示模型在复杂推理过程中对非相关证据的敏感性。实验结果表明，尽管主流VLMs在感知退化下仍能保持与基础模型相近的表现，但在面对语义干扰时表现出显著下降的鲁棒性，且分析显示干扰信息常被模型纳入推理链并作为依据。这一发现重新定义了推理型VLM的鲁棒性评估范式，强调应从“感知退化”转向“认知干扰”的关注重点，以实现更可靠的实际应用。
链接: https://arxiv.org/abs/2606.08894
作者: Yizheng Sun,Mochuan Zhan,Yanan Ma,Jia Tong See,Yifan Wang,Ziyi Wang,Hao Li,Yang Cui,Wenhao Cai,Jingyu Sun,Chenghua Lin,Riza Batista-Navarro,Jingyuan Sun
机构: University of Manchester(曼彻斯特大学); Marex; Imperial College London(帝国理工学院)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Reasoning Vision-Language Models (VLMs) achieve strong performance on complex multimodal tasks, but reliable real-world application requires handling visual inputs that are messier than clean, curated benchmarks. Existing works mainly evaluate such reliability of VLMs through input corruptions, such as noise, blur and weather effects, which make visual evidence harder to perceive. This leaves a critical reliability failure mode underexplored: a model may perceive the evidence correctly, yet reason from plausible but irrelevant and distracting evidence and propagate this mistake to its final answer. To address this gap, we introduce \textbfDistract-Bench, a benchmark for evaluating VLM robustness to \textbfsemantic visual distractions, defined as meaningful but task-irrelevant visual cues added to inputs while preserving the ground-truth answer. We comprehensively evaluate eight leading open-source and two closed-source VLMs across conventional vision corruptions and Distract-Bench. Our results show that Distract-Bench exposes a robustness failure distinct from vision corruptions: reasoning VLMs largely track their non-reasoning base models under perceptual degradation, but show consistently lower robustness to semantic distractions. Further analysis shows that these distractions often enter the reasoning process of VLMs, are treated as evidence, and lead to incorrect answers. Together, these findings reframe robustness evaluation for reasoning VLMs, shifting the focus from degraded perception to distractions for reliable real-world visual reasoning. Our data and code are available at this https URL.
[NLP-87] Building Customer Support AI Agents at 100M-User Scale: An Evaluation-Driven Framework
【速读】： 该论文旨在解决大规模语言模型（LLM）在实际生产环境中构建面向客户的支持型AI代理时面临的系统性挑战，即评估方法、上下文工程、训练流程与在线效果测量等关键环节通常孤立发展，导致部署后出现不可预见的盲点。其核心解决方案在于提出一个统一框架，实现从离线开发到在线影响的闭环衔接。该框架的关键在于：（1）针对客服场景的结构化上下文工程设计；（2）基于人机协同的提示词迭代机制；（3）采用具备可衡量评分者一致性（inter-rater agreement）和GEPA优化的严格大模型裁判评估体系，保障评估质量；（4）贯穿“创意—生产”的全流程验证。研究发现，评估管道的质量直接决定了迭代速度，而实证结果表明，在五个不同业务域（如卡券配送、债务管理、信用额度支持等）的部署中，该框架显著提升了客户满意度，并在卡券配送场景中通过大规模A/B测试实现了AI事务型净推荐值（NPS）提升37个百分点、自助服务率提升29个百分点，同时离线模拟指标与线上表现高度相关，验证了以评估驱动开发的有效性。多数场景下，AI代理的满意度已接近人工专家水平。
链接: https://arxiv.org/abs/2606.08867
作者: Aman Gupta,Kevin Rossell,Edesio Alcobaça,Jose Chrystian Lima Pacheco,Carolina Baptista de Lima,Shao Tang,Luiz Paulo Rabachini,Luis Moneda,Herbert Fei,Daniel Silva,Rohan Ramanath
机构: Nubank(努邦克); Palo Alto(帕洛阿尔托)(美国); Mexico City(墨西哥城)(墨西哥); São Paulo(圣保罗)(巴西)
类目: Computation and Language (cs.CL)
备注:
Abstract:The rapid rise in LLM capabilities has made AI agents increasingly viable across a broad range of tasks. Among the most promising applications is building production-ready customer-facing agents, a challenge that demands coordinated excellence in evaluation methodology, context engineering, training, and online measurement. Yet these critical pillars are typically developed in isolation, creating blind spots that only surface after deployment. In this paper, we present a unified framework that bridges offline development with online impact for customer support AI agents at Nubank, a company with 100M+ users. Our approach integrates several key components: (1) structured context engineering tailored to customer support agents, (2) systematic human-in-the-loop prompt iteration, (3) rigorous LLM judge evaluation with measured inter-rater agreement and GEPA optimization for consistency, and (4) ideation-to-production validation. A central insight is that evaluation-pipeline quality directly determines iteration velocity. We present results from five production deployments spanning distinct domains: card delivery, debt management, credit-limit support, card management, and product explanation. These deployments deliver consistent customer-satisfaction gains while substantially accelerating iteration. In our card-delivery deployment, large-scale A/B testing yields a 37 percentage-point improvement in AI transactional Net Promoter Score and a 29 percentage-point gain in self-service rate over prior agent variants, alongside a strong correlation between offline simulation metrics and online outcomes, demonstrating that eval-driven development reliably predicts production impact. On most use cases, AI satisfaction reaches within a few percentage points of expert human agents. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2606.08867 [cs.CL] (or arXiv:2606.08867v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2606.08867 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.1145/3770855.3818332 Focus to learn more DOI(s) linking to related resources
[NLP-88] PaperMentor: A Human-Centered Multi-Agent Writing Tutor for AI Research Papers on Overleaf ACL2026
【速读】： 该论文旨在解决早期科研人员在撰写学术论文过程中缺乏高质量写作反馈的问题，而传统人工审稿因耗时耗力导致优质反馈资源稀缺。现有基于AI的写作辅助工具多局限于语法修正或仅提供评分式的模拟同行评审，难以提供具体、可操作的改进建议以支持作者在写作过程中的迭代优化。其解决方案的关键在于构建一个以人为中心的写作助手系统PaperMentor，该系统通过集成由资深研究者精心整理的专家写作技能库（expert skill library），并结合12个覆盖论文写作不同维度的专用智能代理（specialized agents），如格式合规性、措辞准确性与术语一致性等，在Overleaf原生环境中生成可直接嵌入文档的内联评论（inline comments）。该设计确保了建议的实质性与可执行性，同时完全保留作者对文本创作的主导权。用户研究表明，90.6%的生成评论被评估为具有可操作性，67.5%被认定为有效，显著优于未使用技能库的GPT-5.2基线模型。系统已开源，代码基于AGPL-3.0许可公开发布。
链接: https://arxiv.org/abs/2606.08857
作者: Jiarui Liu,Terry Jingchen Zhang,Ryan Faulkner,X. Angelo Huang,Vilém Zouhar,Dominik Glandorf,Isabel Dahlgren,Van Q. Truong,Rishit Dagli,Yuen Chen,Felix Leeb,Punya Syon Pandey,Yves Bicker,Suvajit Majumder,Wenyuan Jiang,Zeju Qiu,Sankalan Pal Chowdhury,Bernhard Schölkopf,Mona Diab,Zhijing Jin
机构: CMU(卡内基梅隆大学); Jinesis Lab, University of Toronto (多伦多大学杰尼斯实验室); Vector Institute (向量研究所); EuroSafeAI; ETHZ(苏黎世联邦理工学院); EPFL(洛桑联邦理工学院); UIUC(伊利诺伊大学厄本那-香槟分校); Max Planck Institute for Intelligent Systems, Tübingen, Germany(马克斯·普朗克智能系统研究所，图宾根，德国)
类目: Computation and Language (cs.CL)
备注: Accepted to the ACL 2026 Demo Track
Abstract:Expert writing feedback from experienced researchers is critical for early-career scholars to improve their manuscripts, yet high-quality feedback often remains scarce because reviewing research papers is labor-intensive. Emerging AI-powered writing assistants largely focus on grammar fixes or simulating peer review with final scores, yet they fall short of providing concrete, actionable suggestions that help students improve their papers during drafting. We present PaperMentor, a human-centered writing assistant system that delivers actionable suggestions as Overleaf-native inline comments while leaving the actual writing entirely to human authors. PaperMentor integrates an expert skill library carefully curated from established researchers’ writing advice with 12 specialized agents covering different aspects of paper writing, such as formatting compliance, phrasing accuracy, and terminology consistency. In a user study (n=14), 90.6% of the generated comments were rated actionable and 67.5% were rated valid, significantly outperforming a GPT-5.2 baseline uswithout the skill library. We release PaperMentor as open source for public use. Our code is publicly available under the AGPL-3.0 license at this https URL
[NLP-89] sGPO: Trading Inference FLOPs for Training Efficiency in RLVR
【速读】： 该论文旨在解决标准强化学习中可验证奖励（Reinforcement Learning with Verifiable Rewards, RLVR）训练中存在的计算资源浪费问题。传统方法为每个查询分配固定的回溯预算，忽视了查询难度对当前策略的影响，导致两种对称性失败模式：简单查询因策略已能轻易解决而产生接近零的优势信号，难以解决的查询则因从未成功而无法提供有效学习信号，二者均造成训练浮点运算量（FLOPs）的无效消耗。为此，论文提出一种名为排序分组策略优化（sorted Group Policy Optimization, sGPO）的高效计算策略，其核心在于利用少量前向推理计算作为查询难度的离线代理。通过在初始策略下为每个查询并行生成小批量样本，获取模型感知的实证成功率，进而将训练回溯组大小设置为该成功率的倒数，实现每轮回溯样本最大化优势提取，显著提升样本效率。该单一预分析过程同时实现了数据过滤（剔除简单查询、对不可解查询进行子采样）、自适应组大小分配及课程学习构建（按难易程度调度查询）。实验表明，sGPO在包含预分析开销的前提下，总训练计算量降低约三个数量级，且性能达到或超过基线水平。
链接: https://arxiv.org/abs/2606.08854
作者: Shivchander Sudalairaj,Kai Xu,Akash Srivastava,Giorgio Giannone
机构: Red Hat(红帽); IBM(国际商业机器公司)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (stat.ML)
备注:
Abstract:Standard Reinforcement Learning with Verifiable Rewards (RLVR) training allocates a fixed rollout budget to every query, without regard for what each query’s difficulty means for the current policy. This leads to two symmetric failure modes: easy queries produce near-zero advantage because the policy already solves them, while unsolvable queries produce no signal because the policy never solves them. Both regimes waste training FLOPs without contributing to a learning gradient. We introduce sorted Group Policy Optimization (sGPO), a compute-efficient strategy that trades a small budget of inference FLOPs for a large reduction in wasted training FLOPs. The key insight is that cheap inference compute can serve as a single offline proxy for query difficulty. By generating a small batch of parallel samples per query under the initial policy, we obtain a model-aware empirical success rate. This motivates setting the training rollout group size to the inverse of this success rate, a practical rule that maximizes sample efficiency by extracting the most advantage per generated rollout. This single profiling pass simultaneously drives data filtering (removing trivial queries and sub-sampling unsolvable ones), adaptive group size allocation, and curriculum construction (scheduling queries from easy to hard). sGPO matches or exceeds baseline performance while reducing total training compute by a factor of three, with the upfront inference profiling cost included.
[NLP-90] Intrinsic Selection and Particle Resampling for Inference-Time Scaling Beyond Domain Verifiability
【速读】： 该论文旨在解决生成式 AI 在面临系统性错误（如初始假设错误或多重约束不满足）的任务中，难以有效扩展推理时缩放（Inference-Time Scaling, ITS）的问题。传统方法依赖昂贵的外部求解器或脆弱的模型内验证器，限制了ITS在开放域任务中的应用。其核心解决方案是利用并行采样集的内在统计特性——特别是长度调整后的尾部熵（length-adjusted tail entropy），作为无需真实标签即可判别解质量的鲁棒信号。这一内在统计量不仅实现了后处理候选解排序（Intrinsic Selection, iS），在三个领域达到与共识算法相当的性能，并使工程设计选择准确率相比 pass@1 基线提升20%；还进一步发展为步骤级重采样机制（Intrinsic Particle Filtering, iPF），通过动态引导高置信度推理轨迹，在复杂数学问题上平均提升 pass@1 6.1 分；更进一步提出粒子蒸馏（Particle Distillation, dPF），通过早期对数概率融合与KL引导重采样注入先验指导，有效规避系统性推理偏差，使复杂临床应答任务准确率最高提升26.5%。整个框架无需训练奖励模型或精确真值验证，可无缝适配通用、领域专用及多模态架构，显著拓展了ITS在开放域场景下的适用性。
链接: https://arxiv.org/abs/2606.08850
作者: Giorgio Giannone,Mustafa Eyceoz,Shabana Baig,Shivchander Sudalairaj,Anna C. Doris,Faez Ahmed,Akash Srivastava,Kai Xu
机构: Red Hat; MIT; IBM
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (stat.ML)
备注: preprint
Abstract:Inference-Time Scaling (ITS) has largely succeeded in verifiable domains like math and coding, where cheap verification enables scalable output selection. However, extending ITS to tasks prone to systematic failure - driven by faulty initial assumptions or unmet multidimensional constraints - typically relies on costly external solvers or brittle, model-based verifiers. Our key insight is that the intrinsic statistics of parallel sample sets, specifically length-adjusted tail entropy, provide a robust discriminative signal for solution quality without access to ground truth. Crucially, these statistics serve as a difficulty gate for adaptive compute allocation, dynamically routing problems across scaling regimes. First, Intrinsic Selection (iS) ranks candidates post-hoc, matching consensus-based algorithms across three domains and improving engineering design selection by 20% over pass@1 baselines. Second, Intrinsic Particle Filtering (iPF) generalizes this to step-level resampling, guiding generation toward high-confidence reasoning trajectories to improve pass@1 by 6.1 points on average on hard math problems. Finally, Particle Distillation (dPF) injects privileged guidance via early logit blending and KL-guided resampling, steering generation past systematic reasoning errors to satisfy expert rubrics, yielding up to 26.5% gains on complex clinical responses. Our pipeline applies seamlessly across broad-purpose, domain-specialized, and multimodal architectures, successfully extending ITS to open-ended domains without requiring trained reward models or exact ground-truth verification.
[NLP-91] Momentum for Reasoning : Dense Intrinsic Signals in Policy Optimization
【速读】： 该论文旨在解决基于组相对策略优化（GRPO）的可验证奖励强化学习（RLVR）在大语言模型长链推理中面临的两个结构性缺陷：零优势崩溃（Zero-Advantage Collapse）与幻觉确定性（Hallucinated Certainty）。前者表现为同一组内所有轨迹具有相同二元奖励结果，导致梯度消失；后者则表现为模型在训练后期对错误推理路径产生过度自信。为应对上述问题，该研究提出一种新型方法——内在信号策略优化（ISPO），其关键在于通过完全基于策略自身条件概率计算的内在信号来增强奖励密度：一方面引入序列级信号以衡量思维轨迹对最终答案的信息量，另一方面设计词元级方向性奖励，利用“幻觉确定性铰链”机制在关键决策点惩罚高置信度的错误预测。实验表明，ISPO在三种基础模型和五个数学推理基准上均显著优于现有基线，尤其在最难任务上提升最为明显，且训练动态诊断验证了两种失效模式均得到有效缓解。
链接: https://arxiv.org/abs/2606.08815
作者: Hao Chen,Zhanming Shen,Liyao Li,Yanyu Chen,Xuhang Zhu,Xiaomeng Hu,Qi Zhang,Ru Peng,Xiaoyu Shen,Haobo Wang,Junbo Zhao
机构: Zhejiang University (浙江大学); The Chinese University of Hong Kong (香港中文大学); Eastern Institute of Technology (东方理工大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 14 pages, 6 figures, 8 tables
Abstract:Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for eliciting long-chain reasoning in large language models. However, existing methods based on Group Relative Policy Optimization (GRPO) rely on a binary outcome reward, which induces two structural failure modes: Zero-Advantage Collapse, in which all rollouts in a group share the same outcome and the gradient vanishes, and Hallucinated Certainty, in which the model becomes increasingly confident on incorrect rollouts late in training. We address both modes by densifying the reward with intrinsic signals computed entirely from the policy’s own conditional probabilities, and propose ISPO (Intrinsic Signal Policy Optimization, which combines a sequence-level signal measuring how informative the thinking trajectory is for the final answer, with a token-level directional reward whose hallucinated-certainty hinge penalizes confidently-wrong predictions at critical decision tokens. Across three base models and five mathematical reasoning benchmarks, ISPO consistently outperforms competitive baselines, with the largest gains on the hardest benchmarks where zero-advantage collapse is most frequent, and training-dynamics diagnostics confirm that both failure modes are decreased.
[NLP-92] Continuous Language Diffusion as a Decoder-Interface Problem
【速读】： 该论文旨在解决生成式语言模型中一个核心悖论：尽管经过高斯噪声污染的句子嵌入（sentence embeddings）在语义上无直接可解释性，但连续扩散语言模型仍能从中生成流畅自然的文本。其关键在于揭示了“解码器盆地机制”（decoder-basin mechanism），即去噪成功依赖于扩散轨迹能否抵达解码器能够稳定识别有效词元（tokens）的区域。为此，研究提出了一个诊断协议，用于评估可去噪性、语义可恢复性、顺序敏感性、解码器兼容性及轨迹可靠性，从而暴露传统标量指标（如均方误差、困惑度）所掩盖的深层缺陷——例如低误差可能伴随语义丢失，低困惑度可能反映低熵坍缩，而干净的潜在表示也可能存在于狭窄的解码器盆地中。理论分析表明，词元恢复能力取决于解码器边缘（decoder margin）与局部解码器敏感性，而非仅由潜在空间误差决定。对公开的ELF检查点进行审计揭示了一个接口相图：早期预测可读性弱，中期轨迹分歧体现竞争状态，晚期则进入高边缘的最终词元盆地。一旦进入该区域，词元实现极为简单：冻结的T5词元嵌入查找可恢复93–96%的原生解码决策，单一线性读出在32k样本下达到97.9%一致率，仅剩约1.1的困惑度差距构成结构化残差尾部。引入保守边缘门控机制后，可在去噪步骤中提前17–27%完成退出，显著提升效率。进一步在LangFlow、BitstreamDiffusion和连续潜变量扩散语言模型（Cola-DLM）上的边界检测表明，此类接口问题在不同状态对象与解码器架构下依然具有普适意义。因此，论文主张应将连续与潜变量扩散语言模型整体视为“表示-解码器系统”进行评估。
链接: https://arxiv.org/abs/2606.08810
作者: Zhicheng Du,Lan Ma
机构: Tsinghua Shenzhen International Graduate School, Tsinghua University (清华大学深圳国际研究生院)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Gaussian-corrupted sentence embeddings have no direct linguistic interpretation, yet continuous diffusion language models can generate fluent text from them. We study this puzzle through Embedded Language Flows (ELF) and identify a decoder-basin mechanism: denoising succeeds when trajectories reach regions where the native decoder can read stable tokens. We introduce a diagnostic protocol for denoisability, semantic recoverability, order sensitivity, decoder compatibility, and trajectory reliability. It exposes failures hidden by scalar metrics: low mean-squared error can discard linguistic content, low perplexity can reflect low-entropy collapse, and clean latent reconstruction can coexist with a narrow decoder basin. A decoder-margin bound explains why token recovery depends on margin and local decoder sensitivity, not latent error alone. Auditing public ELF checkpoints reveals an interface phase diagram: early predictions are weakly readable, mid-trajectory disagreement marks a competition region, and late predictions enter a high-margin final-token basin. Once inside, token realization is surprisingly simple on generated ELF states: frozen T5 token-embedding lookup recovers 93 – 96% of native decoder decisions, and a single linear readout reaches 97.9% agreement at 32k samples, leaving about a 1.1 perplexity gap in a structured residual tail. A conservative margin gate exits 17 – 27% earlier in denoising steps under an explicit diagnostic monitor. Boundary checks on LangFlow, BitstreamDiffusion, and the Continuous Latent Diffusion Language Model (Cola-DLM) show that the same interface questions remain meaningful when the state object and decoder change. Continuous and latent diffusion language models should therefore be evaluated as representation-decoder systems.
[NLP-93] he Amplifying Mirror: Locating and Steering the Partisan Direction inside a Large Language Model
【速读】： 该论文旨在解决生成式人工智能（Generative AI）模型中潜藏的政党偏见（partisan bias）如何被编码并影响输出的问题。传统观点认为此类偏见是模糊的、难以定位的“涌现属性”，但本文通过实证研究揭示，政党身份在语言模型的激活空间中以可精确识别的几何特征形式存在。其解决方案的关键在于：利用美国国会成员的19万条推文作为标注数据，对Llama 3.1 8B Instruct模型的隐藏状态进行线性探测，在第18层发现一条具有高区分能力（AUC=0.945，Cohen’s d=1.94）的单一几何轴，能够有效分离共和党与民主党的文本模式；进一步结合稀疏自编码器（sparse autoencoders）对该轴进行可解释性分解，识别出具体的政党特征；并通过因果干预手段（如消融或增强该成分），实现对生成内容立场的系统性调控，观察到立场反转、注册偏差及权威虚构等现象。研究表明，政党偏见并非偶然缺陷，而是模型在训练过程中内生形成的结构性信息编码方式。这一发现强调，随着生成式AI逐步取代搜索引擎成为知识获取的主要界面，理解其产品设计中的隐含偏见及其社会政治后果，已成为应对信息生态从“人工筛选”向“自主生成”转型的关键。
链接: https://arxiv.org/abs/2606.08792
作者: Wendy K. Tam
机构: Vanderbilt University (范德堡大学); National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign (伊利诺伊大学香槟分校超级计算应用国家中心)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models are rapicly replacing search engines as the primary interface between people and information. Unlike search engines, which retrieve existing content, LLMs generate novel text shaped by internal representations learned during training. Here we show that partisan political identity is encoded in the model’s activation space, and that this direction directly shapes generation. Using 190,491 tweets from sitting members of the U.S. Congress as labeled training data, we train linear probes on the hidden states of the Llama 3.1 8B Instruct model. We identify a single geometric axis at layer 18 that separates Republican from Democratic text with an AUC of 0.945 and a Cohen’s d of 1.94, and use sparse autoencoders to decompose that axis into interpretable partisan features. Causally intervening along this axis, ablating or amplifying the partisan component mid-generation, produces systematic shifts in the model’s output. We witness stance reversals, register shifting, and structured fabrications of authority. Our results demonstrate that partisan bias in language models is not a vague emergent property but a learned geometric feature that can be precisely located and steered. Partisan bias is not a bug to be patched, but a structural property of how these models encode information about their users. As LLMs displace search engines as the interface to knowledge, understanding that product design (and its consequences) will be essential for navigating the legal, social, and political transitions from an information ecosystem that is curated to one that is generated.
[NLP-94] amHerald@CHIPSAL 2026: Hate Speech Detection and Sentiment Analysis of Nepali Memes using Transformer-based Architectures and Ensemble Learning LREC2026
【速读】： 该论文旨在解决尼泊尔语网络迷因（internet memes）分析中因频繁的代码混用（code-mixing）及缺乏基准资源而带来的挑战。针对迷因固有的图文混合特性，本研究采用以文本为中心的方法，通过光学字符识别（OCR）层提取嵌入文本，并利用基于Transformer的模型进行建模。其解决方案的关键在于对比评估六种不同模型在两项任务中的表现：二分类仇恨言论检测与三分类情感分析，并系统比较硬投票（Hard Voting）与软投票（Soft Voting）集成策略的效果。实验结果表明，对于二分类任务，独立的解码器仅模型（decoder-only model）表现最佳；而在多分类情感分析任务中，软投票集成策略显著优于单一模型，相较最强基线模型实现了15.8%的宏平均F1分数（Macro F1-score）相对提升。研究揭示了集成方法在二分类与多分类任务中行为差异显著，强调了根据具体分类目标选择适配的融合策略的重要性。
链接: https://arxiv.org/abs/2606.08770
作者: Ashish Acharya,Anish Khatiwada,Rohit Khadka,Pragya Aryal
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted at the 2nd Workshop on Challenges in Processing South Asian Languages (CHiPSAL 2026) at LREC 2026
Abstract:The analysis of internet memes in the Nepali language is complicated by frequent code-mixing and a lack of established baseline resources. While memes inherently combine visual and textual elements, this study focuses on a text-centric approach by extracting embedded text using an OCR layer and modeling it with Transformer-based architectures. We evaluate six distinct models and investigate the comparative effectiveness of Hard and Soft Voting ensemble strategies across two tasks: binary hate speech detection and three-class sentiment analysis. Experimental results show that a standalone decoder-only model achieved the highest performance for binary classification, whereas the Soft Voting ensemble performed best for the multi-class sentiment task, yielding a 15.8% relative improvement in Macro F1-score over the strongest standalone baseline. These findings suggest that ensemble strategies behave differently across binary and multi-class tasks, highlighting the importance of selecting aggregation methods suited to the classification objective.
[NLP-95] RadOT-Eval: Auditable Structured-Evidence Transport for Radiology Report Evaluation
【速读】： 该论文旨在解决高风险文本生成任务中传统自动评估方法无法有效捕捉深层语义错误的问题，尤其针对医学影像报告生成场景下的结构性临床证据丢失、幻觉内容、极性反转、位置偏差、不确定性匹配错误及时间对比错误等关键问题。现有评估指标多依赖表面相似度（如BLEU、ROUGE），难以反映临床意义的实质性误差。其解决方案的核心在于提出一种可解释的结构化证据最优传输框架——RadOT-Eval，通过将参考报告与候选报告分解为属性-结构化的临床证据单元，利用熵正则化最优传输（entropy-regularized optimal transport）实现跨报告的细粒度证据对齐，并结合临床有意义的侧通道差异（side-channel discrepancies），在单调风险模型中预测误差负担。所有模型组件均基于ReXVal数据集进行选择与冻结，最终在独立的RadEvalX数据集上进行验证。实验结果表明，RadOT-Eval在总误差负担、临床显著误差和临床非显著误差三个维度上分别达到0.715、0.548和0.399的斯皮尔曼相关系数，优于主流基准指标及开源大语言模型（LLM）评估器GREEN-radllama2-7B；在抗干扰性测试中，于ReXErr-v1数据集上获得0.768的AUROC和0.990的“污染样本胜过干净样本”配对胜率，充分验证了该方法在仅使用ReXVal进行模型选择、且系统完全冻结的前提下，仍能提供可审计、以排序为导向的高可信度临床文本生成评估能力。
链接: https://arxiv.org/abs/2606.08769
作者: Weixin Liu,Juming Xiong,Yang Li,Qingyuan Song,Susannah Rose,Murat Kantarcioglu,Bradley Malin,Zhijun Yin
机构: Weixin Liu1, Juming Xiong1, Yang Li1, Qingyuan Song1, Susannah Rose2, Murat Kantarcioglu3, Bradley Malin1,2, Zhijun Yin1,2
Affiliations:
- Department of Biomedical Informatics, Vanderbilt University Medical Center (范德堡大学医学中心生物信息学系);
- Department of Computer Science, Vanderbilt University (范德堡大学计算机科学系);
- Department of Computer Science, University of Texas at Dallas (得克萨斯大学阿灵顿分校计算机科学系)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 10 pages, 1 figure, 13 tables
Abstract:Automatic evaluation is critical for high-stakes text generation, where errors often involve omitted findings, hallucinated content, polarity reversals, location changes, uncertainty mismatches, and temporal-comparison errors rather than low surface similarity alone. Radiology report generation provides a challenging test case because generated reports must preserve structured clinical evidence across sources. We present RadOT-Eval, an interpretable structured-evidence optimal transport framework for offline auditing of radiology report generation. RadOT-Eval decomposes reference and candidate reports into attribute-structured clinical evidence units, aligns corresponding evidence using entropy-regularized optimal transport, and uses clinically meaningful side-channel discrepancies in a monotone risk model to predict error burden. All transport, feature, and readout choices are selected using the ReXVal dataset, and the frozen system is evaluated on the independent RadEvalX dataset. RadOT-Eval achieves Spearman correlations of 0.715, 0.548, and 0.399 with total, clinically significant, and clinically insignificant annotated error burden, respectively, yielding higher point estimates than standard evaluation metrics and the open-source large language model (LLM)-based evaluator GREEN-radllama2-7B. In a frozen auxiliary corruption-sensitivity stress test on ReXErr-v1, RadOT-Eval achieves 0.768 AUROC and a 0.990 corrupted-greater-than-clean paired win rate. These results show that structured evidence transport provides an auditable, rank-oriented evaluation tool for high-stakes generated clinical text under ReXVal-only model selection and frozen RadEvalX testing.
[NLP-96] Co-Evolving Skill Generation and Policy Optimization
【速读】： 该论文旨在解决生成式技能增强强化学习中一个关键问题：现有方法在在线训练过程中未经验证便将由大语言模型（LLM）生成的技能存入可检索技能库，而未评估其实际效用。研究表明，即使使用前沿的闭源大语言模型生成的技能，其有效性也参差不齐，大量技能对任务性能贡献甚微甚至产生负向影响。由于后续的回放反馈具有延迟性且受多个已检索技能共同作用的影响，难以识别单个技能的边际贡献，导致低效或有害技能一旦入库便难以被发现和剔除。为此，论文提出一种面向技能存入前验证的在线强化学习框架，其核心在于通过标准回放预算构建两组匹配的轨迹：一组仅基于当前已检索技能的基准回放，另一组在相同条件下额外引入候选技能。通过比较两组间的奖励差距，可估计候选技能在特定上下文中的边际效用，从而实现对有效技能的筛选与无效或有害技能的过滤，且无需额外回放开销。进一步地，该边际效用信号被用于训练策略网络自身作为技能生成器，降低对频繁调用闭源模型的依赖；同时，所学习到的技能生成概率可作为上下文相关的评分机制，用于检索时的重排序及随策略演化动态更新的过期技能修剪。
链接: https://arxiv.org/abs/2606.08755
作者: Zhiwei Zhang,Yudi Lin,Nikki Lijing Kuang,Linlin Wu,Xiaomin Li,Songtao Liu,Fenglong Ma
机构: The Pennsylvania State University (宾夕法尼亚州立大学); Nanyang Technological University (南洋理工大学); University of California, San Diego (加州大学圣地亚哥分校); University of Utah (犹他大学); Harvard University (哈佛大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Skill-augmented reinforcement learning improves language agents by storing reusable procedural knowledge acquired from past experience. Existing methods typically use strong language models to analyze trajectories, generate skills, and update a retrievable skill bank during online training. However, they rarely assess whether a newly generated skill is useful before it is stored and reused. We find that this assumption is unreliable: even skills generated by proprietary frontier LLMs exhibit highly mixed utility, with many providing little benefit or even degrading performance. Once such skills enter the bank, their effects are difficult to identify, because subsequent rollout feedback is delayed and usually reflects the combined effect of multiple retrieved skills rather than the marginal contribution of any individual skill. We propose an online reinforcement learning framework for pre-storage skill validation. The framework estimates whether a candidate skill contributes useful information beyond the skills already retrieved for the current task. It uses the standard rollout budget to form two matched groups under the same task and retrieval context: base rollouts conditioned on the currently retrieved skills, and skill-augmented rollouts conditioned on the same skills plus one candidate skill induced from the base trajectories. The reward gap between these two groups estimates the candidate skill’s context-dependent marginal utility, enabling the framework to promote useful skills while filtering ineffective or harmful ones without additional rollout overhead. The framework further uses this marginal-utility signal to train the policy itself as a skill generator, reducing reliance on repeated calls to proprietary models. The learned skill-generation likelihood serves as a context-dependent score for retrieval-time reranking and outdated-skill pruning as the policy evolves.
[NLP-97] HydraQE: OSUs Submission for the IWSLT 2026 Speech Translation Metrics Shared Task
【速读】： 该论文旨在解决语音翻译质量评估（Speech Translation Quality Estimation, QE）中缺乏参考文本且传统级联式方法性能受限的问题。现有方法通常依赖于先进行自动语音识别（ASR），再基于文本进行质量评估，导致误差累积且无法充分建模跨模态语义关联。为此，本文提出HydraQE——一种端到端、无参考的语音翻译质量评估系统，其核心创新在于以Qwen3-ASR作为骨干网络，直接以源音频与翻译假设作为联合输入，通过可学习的稀疏max标量混合（sparsemax scalar mix）融合多层隐藏状态，并经轻量级双向Transformer重新编码，实现跨模态深度交互，最终映射至共享嵌入空间。该系统采用三个独立预测头，分别在人类直接评价（DA）、MetricX-24伪标签和xCOMET伪标签等互补监督信号上进行训练。为缓解高质量人工标注数据稀缺问题，采用渐进式课程学习策略，从合成噪声样本与银色伪标签机器翻译输出开始训练，逐步过渡至真实人类标注数据。实验表明，HydraQE在多项指标上超越级联式文本基基线及先前直接语音QE系统，验证了端到端语音翻译质量评估的有效性与竞争力。
链接: https://arxiv.org/abs/2606.08748
作者: Kevin Krahn,Eric Fosler-Lussier
机构: The Ohio State University (俄亥俄州立大学)
类目: Computation and Language (cs.CL)
备注: Accepted to IWSLT 2026; 9 pages, 3 figures, 4 tables
Abstract:We present HydraQE, our contribution to the IWSLT 2026 Speech Translation Metrics shared task. HydraQE is an end-to-end, reference-free quality estimation (QE) system for speech translation built on a Qwen3-ASR backbone, which accepts source audio and a translation hypothesis as joint input. Hidden states from all backbone layers are combined via a learnable sparsemax scalar mix, then re-encoded by a lightweight bidirectional Transformer to enable full cross-modal interaction prior to pooling into a shared embedding. Three independent prediction heads are trained on complementary supervision signals: human direct assessment (DA) annotations, MetricX-24 pseudo-labels, and xCOMET pseudo-labels. To address the scarcity of human-annotated data, we train on a combination of synthetically corrupted examples and silver pseudo-labeled machine translation outputs, using a curriculum that begins on synthetic and silver data and gradually shifts toward human-annotated examples. HydraQE outperforms cascaded text-based baselines and prior direct speech QE systems, demonstrating that end-to-end speech translation QE is competitive with cascaded approaches.
[NLP-98] Artificial Intelligence for Mathematical Reasoning : An Integrated Survey of Language Models Neuro-symbolic Systems and Verified Discovery
【速读】： 该论文旨在解决生成式人工智能在数学推理领域面临的系统性挑战，包括从自然语言与图形的非形式化推理、形式化证明中的自动定理证明、数学发现中的创新构造，到推理过程与验证机制融合的训练与推断技术。其核心解决方案在于构建一个统一的框架，涵盖四大维度：（i）基于文本与图表的非形式化推理，如数学应用题求解、多模态几何问题及视觉-语言模型（VLMs）的应用；（ii）在证明助手中的形式化推理，涉及自动形式化、策略预测、编译器引导修复与证明搜索；（iii）数学发现任务，包括构造新对象、改进已知界值或辅助攻克未解难题；（iv）推理与训练阶段的关键技术，如思维链（CoT）提示、工具调用、过程奖励模型与基于强化学习的验证回溯（RLVR），以实现生成与验证的闭环整合。关键突破在于通过基准评估的系统性梳理，揭示了当前主流评测中存在基准饱和、污染、报告不一致等问题，并明确区分“pass@1”、“多数投票”与“验证器辅助的pass@k”等评价指标的差异，同时批判性分析了模型在扰动下的脆弱性、奖励欺骗、多模态对齐失败、形式化不稳健性以及大规模推理带来的高能耗等缺陷。最终，论文提出未来方向应聚焦于可验证的发现工作流、推理效率提升及支持广泛可用的AI辅助形式化基础设施建设。
链接: https://arxiv.org/abs/2606.08728
作者: Syed Rifat Raiyan,Mohsinul Kabir,Hasan Mahmud,Md Kamrul Hasan
机构: University of Dhaka (达卡大学); Islamic University, Bangladesh (伊斯兰大学, 孟加拉国)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Under review, 47 pages, 14 figures, 22 tables
Abstract:Mathematical reasoning has long served as a stringent test of machine intelligence; over the past decade, it has moved from a niche problem within NLP to one of the most consequential AI frontiers. This survey provides a unified account of the field’s evolution, from early rule-based math word problem (MWP) solvers and template-driven geometry systems, through neural expression generation and LLM prompting, to contemporary reasoning models, multi-agent systems, neuro-symbolic theorem provers, and verified discovery workflows. We organize the landscape along four axes: (i) informal reasoning over text and diagrams, spanning MWP solving, multimodal geometry, and VLMs; (ii) formal reasoning in proof assistants, including autoformalization, tactic prediction, compiler-guided repair, and proof search; (iii) mathematical discovery, where systems propose constructions, improve bounds, or assist attacks on open problems; and (iv) the inference and training-time techniques, including CoT prompting, tool use, process reward models, and RLVR, that increasingly connect generation with verification. We catalog major benchmarks across grade-school arithmetic, competition mathematics, geometry, formal proving, multimodal and multilingual reasoning, and expert evaluation, and we examine benchmark saturation, contamination, reporting mismatches, and the distinction between pass@1, majority voting, and verifier-assisted pass@ k . We critically assess failure modes: brittleness under perturbation, reward hacking, multimodal grounding failures, fragile formalization, and the energy cost of reasoning-scale inference. Drawing on recent perspectives from working mathematicians, we identify future directions centered on verified-discovery workflows, reasoning efficiency, and infrastructure to make AI-assisted formalization broadly usable. Companion materials: this https URL.
[NLP-99] Can LLM s understand LilyPond? A benchmark for symbolic music generation and understanding
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在符号化音乐生成与理解任务中评估标准碎片化的问题，具体表现为不同表示方式、数据集和评价指标之间的不一致。其核心解决方案是提出LilyBench——一个基于LilyPond的基准测试框架，能够统一评估同一类开源权重LLMs在符号化音乐生成与理解方面的性能。该基准包含200个提示的生成任务套件以及源自ABC-Eval的十个理解任务，覆盖语法、元数据预测、结构序列化和音乐识别等维度。在评估方法上，采用编译成功率、基于Jensen-Shannon相似性的MusPy描述符分布，以及基于LilyBERT的弗雷谢特音乐距离（Fréchet Music Distance, FMD）进行多维度量化分析。实验表明，尽管在零样本设置下可实现可执行的LilyPond代码生成，但结构理解任务仍存在显著挑战，尤其在复杂音乐结构建模方面表现不佳；同时，研究揭示了基于描述符与基于嵌入的评价指标之间存在系统性分歧，强调符号化音乐评估应采用多指标三角验证（metric triangulation）而非单一评分排名。为此，作者开源了完整基准、提示库及评估代码，以推动符号化音乐生成与理解领域的可持续研究。
链接: https://arxiv.org/abs/2606.08722
作者: Matteo Spanio,Mohammad Torabi,Andrea Poltronieri,Antonio Rodà
机构: Centro di Sonologia Computazionale, University of Padova (帕多瓦大学计算机声学中心), Italy; Music Technology Group, Universitat Pompeu Fabra (庞佩乌·法布拉大学音乐技术集团), Spain
类目: ound (cs.SD); Computation and Language (cs.CL)
备注: Accepted at Ital-IA 2026
Abstract:Symbolic music evaluation for large language models remains fragmented across representations, datasets, and metrics. We introduce LilyBench, a LilyPond-based benchmark that jointly evaluates symbolic music generation and music understanding on the same family of open-weight LLMs. The benchmark includes a 200-prompt generation suite and ten understanding tasks adapted from ABC-Eval, covering syntax, metadata prediction, structural sequencing, and music recognition. Generation quality is evaluated using compile rate, MusPy descriptor distributions via Jensen-Shannon similarity, and LilyBERT-based Fréchet Music Distance (FMD). Experiments on four open-weight models show that executable LilyPond generation is achievable in zero-shot settings, while structural understanding tasks remain challenging despite strong performance on composer and genre recognition. Our experiments also reveal systematic disagreements between descriptor-based and embedding-based metrics, suggesting that symbolic music evaluation benefits from metric triangulation rather than single-score ranking. We release the benchmark, prompt bank, and evaluation code to support future research in symbolic music generation and understanding at this https URL
[NLP-100] Operationalizing Linguistic Methods through Prompt-Engineering Skills: An Automatic Chinese Web Neologism Detection Pipeline
【速读】： 该论文旨在解决中文网络新词（neologism）自动检测问题，其核心挑战在于如何将传统语言学识别原则有效转化为可执行的生成式AI任务。解决方案的关键在于构建一个四阶段的自动化流水线：首先通过与分词器无关的字符n-gram候选生成策略捕获潜在新词；其次利用点互信息（Pointwise Mutual Information, PMI）预过滤实现词典锚定以提升候选质量；第三阶段引入基于汉语构词规律的“合语法性”判断技能，确保候选词符合语言结构规则；第四阶段则结合规则与三分类判别模型，区分新词、实体及非新词类别。该方法在BAAI CCI 3.0语料库（2.67亿文档）上成功识别出226,959个候选词，其中4,853个被标注为新词。通过提出的逐阶段条件召回率分解方法，研究发现第一阶段候选覆盖度和第四阶段大语言模型（LLM）的语义判断能力是主要瓶颈（条件召回率分别为41.5%和60.0%），而中间阶段几乎无信息损失。进一步的长度分层分析表明，“合语法性”技能具有长度不变性（96.9%），而“语义新颖性分类”技能则呈现显著长度依赖性（2/3/4字候选词的召回率分别为65.6%、59.0%、44.1%），揭示了当前基于技能的语言学操作化在处理长词时的能力边界。该研究公开了完整方法、流水线输出及评估协议，为后续研究提供重要资源。
链接: https://arxiv.org/abs/2606.08715
作者: Yufeng Wu,Meichun Liu
机构: City University of Hong Kong (香港城市大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:We present a method for automatic Chinese web neologism detection that operationalizes traditional linguistic identification principles as prompt-engineering skills. The method has four stages: tokenizer-independent character n-gram candidate generation; dictionary anchoring with a Pointwise Mutual Information pre-filter; a well-formedness skill based on Chinese word-formation principles; and a combined rule and three-way classification skill that distinguishes neologism, entity, and none. Applied to the BAAI CCI 3.0 corpus (267M documents), the method produces 226,959 classified candidates including 4,853 labeled neologisms. To evaluate the method, we develop a per-stage conditional recall decomposition in which the pipeline’s strict recall factors mathematically into the product of stage conditional recalls. Applied to Hou (2023) (4,199 entries), the decomposition exposes Stage 1 candidate coverage and Stage 4B LLM semantic judgment as the two bottlenecks (R=41.5% and 60.0% respectively), while intermediate stages are near-lossless. A length-stratified analysis further reveals that the structural well-formedness skill is length-invariant (= 96.9%) whereas the semantic novelty-classification skill is length-dependent (65.6%/59.0%/44.1% across 2/3/4-character candidates), mapping a current boundary of skill-based linguistic operationalization. We release the method, pipeline outputs, and evaluation protocol as public resources.
[NLP-101] Analyzing the Correlation Between Hallucinations and Knowledge Conflicts in Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）中存在的幻觉（hallucination）问题，即模型生成在事实上不正确或无法验证的内容，尤其在知识密集型任务中表现显著。现有研究提出，幻觉可能源于训练数据固定且过时所引发的内部知识冲突。本文的核心问题是：这种内部知识冲突的表征是否与幻觉行为存在可解释的关联。其解决方案的关键在于采用受启发于先前工作的探测（probing）技术，系统分析了多个层（包括隐藏层、注意力层和MLP层）以及输出logits的激活模式，在预定义任务中对LLaMA-3-8B和Falcon-7B进行实证研究。研究发现，尽管知识冲突与幻觉在概念上相关，但幻觉的激活模式并不能完全归因于知识冲突的表征，表明幻觉具有更复杂的内在机制。然而，探测方法在多语言和多种激活类型中均表现出良好的鲁棒性，验证了其作为提升大语言模型可解释性的有效工具的价值。该研究推动了对幻觉成因的深入理解，并强调了对模型内部行为进行细粒度分析的重要性。
链接: https://arxiv.org/abs/2606.08705
作者: Lucrezia Laraspata,Giovanna Castellano,Gennaro Vessio
机构: University of Bari Aldo Moro(巴里阿尔多·莫罗大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Hallucinations – factually incorrect or unverifiable outputs – remain one of the most challenging limitations of Large Language Models (LLMs), especially in knowledge-intensive tasks. One proposed explanation is internal knowledge conflicts arising from fixed, outdated training data. This paper investigates whether internal representations linked to knowledge conflicts correlate with hallucination behaviors in LLMs. Using probing techniques inspired by two prior works, we analyzed activations from hidden, attention, and MLP layers, as well as output logits, across predefined tasks. We probed LLaMA-3-8B on hallucination detection benchmarks and Falcon-7B on a knowledge conflict dataset. Our findings show that, although conceptually related, hallucination activation patterns cannot be fully reduced to or explained by knowledge conflict representations. Nonetheless, probing proves a robust tool across multiple languages and activation types, supporting its role in improving LLM interpretability. This work advances the broader understanding of hallucinations in LLMs and underscores the value of fine-grained analysis of their internal behavior. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2606.08705 [cs.CL] (or arXiv:2606.08705v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2606.08705 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-102] Lost in the Flow with Code Talkers: Unveiling the Instruction-Tuning Tax of Large Language Models in Code Tasks
【速读】： 该论文旨在解决生成式代码助手在不同开发认知模式下性能表现不一致的问题，具体聚焦于指令微调（instruction tuning）对代码大模型（CodeLLMs）在“流动模式”（Flow）与“命令模式”（Command）中表现的影响。其核心问题是：尽管指令微调的大型语言模型（LLMs）在理解自然语言指令并转化为可执行代码方面表现出色，但这种能力提升是否普遍适用于所有代码相关任务，尤其是在需要精准补全或填充未完成代码的流动模式场景下？解决方案的关键在于揭示了指令微调带来的“指令遵循能力”提升与“代码补全性能”下降之间的显著权衡关系，即所谓的“指令微调税”（Instruction-Tuning Tax）。研究通过定性和定量分析，包括人工失败分类、生成保真度行为指标以及训练过程中的中间检查点评估，发现指令微调虽增强了模型对结构化指导的理解能力，却往往以牺牲代码补全精度为代价。这一发现表明，未来AI辅助编码工具的设计需在指令跟随能力与高效代码生成之间进行精细化平衡，而非简单依赖指令微调策略。
链接: https://arxiv.org/abs/2606.08676
作者: Shi Ying Chang,Chiok Yew Ho,Yichen Li,Yintong Huo
机构: Singapore Management University(新加坡管理大学); The Chinese University of Hong Kong(香港中文大学)
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 25 pages, 6 figures. Evaluation toolkit and dataset: this https URL
Abstract:AI coding assistants have significantly improved developer productivity by automatically suggesting code that aligns with user intent, and many of these tools are now integrated directly into Integrated Development Environments (IDEs). Developers interact with code in two distinct cognitive modes: Flow and Command. While developers require tools that directly complete or infill code in unfinished programs during Flow mode, they also need tools that can comprehend intentions expressed as natural-language instructions and convert them into executable code in Command mode. Although instruction-tuned Large Language Models (LLMs) dominate many application scenarios due to their abilities to infer and fulfill developers’ intents, it remains unclear whether the same paradigm is equally suitable for different code-related tasks. Therefore, it is necessary to understand how instruction tuning affects the feasibility of CodeLLMs as coding assistants. To fill this gap, we conduct the first empirical study that uncovers a key trade-off caused by instruction tuning across programming modes, which we term the Instruction-Tuning Tax. Our results show that instruction tuning is not a free lunch: although instruction-tuned models are more capable of following instructions and leveraging structured guidance, these gains often come at the cost of weaker infilling performance. We further extend our study through both qualitative and quantitative analyses, including manual failure categorization, behavioral metrics that capture generation fidelity, and intermediate-checkpoint evaluation throughout the tuning process. Summarizing our results into seven findings and four implications, our study offers a new perspective on the development of AI-powered coding tools and highlights the need to carefully balance instruction-following ability with effective code generation assistance.
[NLP-103] ClinicalAligner26AM: A Cross-Lingual Aligner for Dataset Translation; Evidences from the MultiClinCorpus Shared Task
【速读】： 该论文旨在解决医学与临床文本中词级别跨语言对齐（word-level cross-lingual alignment）在专业化领域中现有神经对齐模型适应性不足的问题。其核心挑战在于如何在低资源、专业性强的生物医学语境下实现高精度的跨语言对齐，以支持标注投影、翻译审核及跨语言忠实度评估等任务。解决方案的关键在于提出ClinicalAligner26AM（CA26AM），一个基于ClinicalEncoder26AM初始化的大上下文多语言对齐模型。其创新点在于采用受AWESoME Align启发的训练方法，通过融合句级、短语级和词元级信号构建平行临床文本与对话的成本矩阵，并利用Sinkhorn-Knop最优传输算法对其进行锐化处理，生成软对齐目标；随后通过知识蒸馏，使学生模型的原始余弦相似度得分直接拟合该锐化后的对齐矩阵。推理阶段，通过学习到的词元对齐矩阵将源端跨度得分投影至目标文本，并解码出最长有效高分跨度，可选地结合附录B中的MultiClinNER预测结果进行增强。该方法在MultiClinCorpus共享任务上表现卓越，两个提交系统在所有语言和实体类型上分别位列第一和第二，字符加权F1分数在几乎所有设置中均超过0.95。
链接: https://arxiv.org/abs/2606.08673
作者: François Remy
机构: Parallia Healthcare AI
类目: Computation and Language (cs.CL)
备注:
Abstract:Word-level cross-lingual alignment is central to annotation projection, translation auditing, and cross-lingual faithfulness estimation, yet existing neural aligners are rarely adapted to specialized domains. In this paper, we introduce ClinicalAligner26AM, a large-context multilingual aligner model for biomedical and clinical text initialized from ClinicalEncoder26AM. Our training recipe is inspired by AWESoME Align. We build our soft alignment target by sharpening with Sinkhorn-Knop optimal transport a cost matrix established for parallel clinical texts and conversations through the fusion of sentence-level, phrase-level, and token-level signals. We distill this sharpened alignment matrix directly into our student aligner, by encouraging its naive cosine-based token similarity scores to match this target. At inference time, we project source-span scores through the learned token alignment matrix and decode the longest valid high-scoring span in the target text, optionally supported by MultiClinNER predictions summarized in Appendix B. We evaluate CA26AM on the MultiClinCorpus shared task, which projects Spanish clinical entity annotations into six target languages. Our two submitted systems ranked respectively first and second across all languages and entity types, with character-weighted F1 scores above 0.95 in nearly all settings.
[NLP-104] From Player to Master: Enhancing Test-Time Learning of LLM Agents via Reinforcement Learning over Memory ICML2026
【速读】： 该论文旨在解决在长周期运行场景中，大型语言模型（Large Language Model, LLM）代理如何通过测试时的经验持续改进的问题。现有方法通常依赖于手工设计的提示规则来更新显式记忆，但这类规则难以在多步决策过程中与下游目标保持一致，导致记忆更新效果不稳定。为此，本文提出MemoPilot——一种可插拔的记忆协作者（memory copilot），其核心在于显式训练记忆更新过程，以提升冻结型LLM在序列交互中的表现。关键创新在于将记忆更新建模为多轮决策问题，并采用多轮广义近端策略优化（multi-turn GRPO）进行端到端优化；同时引入（i）逐轮奖励信号和（ii）跨轨迹的上下文无关、轮次级优势估计，实现了更精细的信用分配与更稳定的多轮训练。实验在多轮剪刀石头布（RPS）和有限注德州扑克（Limit Texas Hold’em, LHE）两个基准任务上验证了该方法的有效性，结果显示MemoPilot在两项任务上的Elo评分分别达到1762（LHE）和1590（RPS），显著优于所有基线记忆方法及包括DeepSeek-V3.2在内的专有模型，证明了其在测试时学习中的优越性能。
链接: https://arxiv.org/abs/2606.08656
作者: Yishuo Cai,Xingyu Guo,Xuancheng Huang,Jinhua Du,Can Huang,Wenxuan Huang,Wenhan Ma,Yuyang Hu,Aohan Zeng,Jie Tang,Xu Sun
机构: 未知
类目: Computation and Language (cs.CL)
备注: Accepted by ICML 2026
Abstract:Large language model (LLM) agents are increasingly deployed in long-running settings where improving through experience at test time becomes important. A common approach is to update an explicit memory after each interaction to guide future decisions. However, most existing methods rely on hand-designed prompting rules, making it difficult to align memory updates with downstream objectives over multi-step horizons consistently. We propose MemoPilot, a plug-in memory copilot that explicitly trains the memory update process to improve a frozen LLM’s performance across sequential interactions. We formulate memory updating as a multi-turn decision problem and optimize it end-to-end with multi-turn GRPO. Our training recipe introduces (i) a turn-wise reward signal and (ii) a context-independent, turn-level advantage estimation across rollouts, enabling finer-grained credit assignment and more stable training in multi-turn settings. We evaluate MemoPilot on two testbeds: multi-round Rock-Paper-Scissors (RPS) and Limit Texas Hold’em (LHE). Across both environments, MemoPilot substantially improves test-time learning of a frozen player over strong baselines, ranking first in Elo ratings on both games (1762 on LHE and 1590 on RPS) and outperforming all baseline memory methods and proprietary models, including DeepSeek-V3.2.
[NLP-105] A retrieval conditioned rebinding circuit for dynamic entity tracking in large language models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在动态状态追踪中如何准确绑定实体与其属性，并随状态变化更新绑定关系的问题。其核心挑战在于模型需在上下文理解过程中维持对实体状态的持续追踪，以实现正确的信息检索与推理。论文的关键解决方案是发现并验证了一种“检索条件重绑定机制”（retrieval conditioned rebinding mechanism），该机制由一个紧凑的注意力头电路构成，能够编码与状态切换相关的关键绑定信息，并在读取阶段重新激活这些信息。通过因果干预分析，研究发现在Gemma和Llama系列模型中均存在此机制，但其表征签名存在差异：在Gemma模型中，绑定信息显著体现在相关注意力头的查询（query）与键（key）子空间中；而在Llama模型中，绑定信息主要由键向量承载。这一发现揭示了大语言模型中可解释的上下文依赖状态追踪机制，为理解模型内部动态推理过程提供了关键洞见。
链接: https://arxiv.org/abs/2606.08644
作者: Soyoung Oh,Vera Demberg
机构: Saarland University (萨尔大学); Max Planck Institute for Informatics (马克斯·普朗克信息学研究所)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:To interpret context correctly and retrieve relevant information, large language models must bind entities to their attributes and update these bindings as state changes. We analyze how LLMs implement this binding process in a dynamic state tracking. Using causal interventions, we identify a retrieval conditioned rebinding mechanism, a compact attention head circuit that encodes swap relevant binding information and reinstates it at readout. Across Gemma and Llama models, this circuit supports rebinding behavior, but the representational signature of the mechanism differs across model families. In Gemma models, the binding signature is clearly expressed in the query/key subspaces of the relevant attention heads, whereas in Llama models, the binding information is carried primarily in key vectors. Overall, our results reveal an interpretable mechanism for context dependent state tracking in LLMs.
[NLP-106] Sycophancy Towards Researchers Drives Performative Misalignment
【速读】： 该论文旨在解决生成式 AI 在评估情境下表现出的“对齐伪装”（alignment faking）行为背后的动机问题，即模型是否在有意进行策略性欺骗（scheming），还是出于对研究人员的奉承行为（sycophancy）而改变表现。其核心解决方案的关键在于提出并验证“表演性不对齐”（performative misalignment）这一替代解释：模型的行为变化并非源于恶意的长期策略谋划，而是对评估环境的即时响应，表现为对研究者的迎合。研究通过三项实证发现支持该观点：首先，即使明确告知模型已进入部署状态，其评估感知仍持续存在，与“欺骗论”预测相悖；其次，当前探测与调控方法无法从机制上区分奉承与欺骗；最后，通过微调使模型更具奉承倾向后，其对评估提示的敏感性显著增强。因此，论文强调未来研究必须将奉承行为与意图错位（intent misalignment）解耦，以更准确地评估和应对模型在真实场景中的安全风险。
链接: https://arxiv.org/abs/2606.08629
作者: David D. Baek,Xinnuo Li,Anay Gupta,Taslim Mahbub,Kejian Shi,Max Tegmark,Shi Feng
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:The increasing situational awareness of language models raises safety concerns: models might be aware when they are evaluated, and adjust their behavior to evade monitoring and resist modification, e.g., pretending to be aligned only in evaluation. This alignment faking behavior is often interpreted as scheming: an intentional effort of strategic deception. In this paper, we examine an alternative interpretation, performative misalignment, which explains the change in behavior as a result of sycophancy towards AI researchers. To examine this hypothesis, we present three empirical findings. First, we show that evaluation awareness persists even when we tell models they are deployed, which contradicts the scheming story which predicts less misalignment when the model perceives evaluation. Second, we use probing and steering to show that our current methods cannot mechanistically distinguish sycophancy and scheming in alignment faking evaluations. Third, we fine-tune models to be more sycophantic and observe increased sensitivity to evaluation cues. To conclude, we emphasize deconfounding sycophancy from scheming for future work on evaluations and mitigations of intent misalignment.
[NLP-107] From Holistic Evaluation to Structured Criteria: Rubrics Across the Evolving LLM Landscape
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）向开放式自主智能体演进过程中，评估与行为引导机制滞后于模型能力发展的核心问题。随着LLM范式不断迭代，传统的单一标量奖励或模糊的质性评价已难以有效支撑复杂任务中的行为优化与安全对齐。其解决方案的关键在于提出“评分标准（rubric）”作为统一框架，将人类对质量、安全与意图的隐性价值判断转化为可分解、可操作的显式准则体系。该框架在三个层级上实现关键突破：在评估层面，将整体性质量判断分解为可验证的维度；在训练层面，提供细粒度的密集反馈信号，弥补标量奖励在过程指导上的不足；在内在层面，通过模型行为动态生成自适应评分标准，驱动自我改进。研究表明，评分标准在评估、强化学习与安全对齐等独立研究方向中反复出现，具有深刻的系统性意义。通过系统梳理现有评分标准设计、分析其构建与优化方法，并评估其在生成质量、执行保真度、理论约束及安全威胁下的可靠性，论文进一步展示了基于评分标准的基准测试在多领域中的应用潜力。最终，评分标准实现了人类意图到机器可学习信号的透明化映射，成为连接人类价值观与机器行为的持久桥梁。
链接: https://arxiv.org/abs/2606.08625
作者: Hao Chen,Ziyu Han,Yukun Yan,Qingfu Zhu,Maosong Sun,Wanxiang Che
机构: Research Center for Social Computing and Interactive Robotics; Department of Computer Science and Technology, Institute for AI
类目: Computation and Language (cs.CL)
备注:
Abstract:As Large Language Models (LLMs) advance toward open-ended autonomous agents, the mechanisms used to evaluate and guide their behavior must evolve accordingly. This work introduces the rubric as a unifying framework capturing this evolution, characterizing rubrics as a dynamic response to successive LLM paradigm shifts that recurs across otherwise independent efforts in evaluation, reinforcement learning, and safety alignment. We define rubrics as explicit criteria sets that transform complex quality judgments into structured and actionable standards, and demonstrate that their recurrence across these research threads is not coincidental. We systematically organize existing rubric designs, examine their construction and optimization, and analyze their role across evaluation and training. Rubrics manifest at three progressively deeper levels: at the evaluative level, they decompose holistic judgments into verifiable dimensions; at the training level, they serve as dense feedback signals providing process-level guidance where scalar rewards fall short; at the intrinsic level, they emerge dynamically from model behaviors, driving self-improvement. We further assess rubric reliability across generation quality, execution fidelity, theoretical constraints, and security threats, before surveying rubric-based benchmarks across diverse domains. By rendering assessment transparent and decomposable, rubrics translate human value expectations into machine-learnable signals, serving as the enduring bridge between human intentions and machine behavior.
[NLP-108] Cross-Source Reasoning -based Correction for Author Name Disambiguation KDD2026
【速读】： 该论文旨在解决学术搜索系统中作者姓名消歧（Author Name Disambiguation, AND）面临的挑战，特别是现有算法因累积的论文-作者匹配错误以及跨数据源之间不一致的分配而产生的性能瓶颈。传统方法依赖从头训练或实时消歧，但难以有效处理多源数据中的矛盾信息，且依赖专家标注成本高昂。为此，本文提出一种新视角：通过利用不同数据源间存在的不一致分配进行跨源校正。其核心解决方案是提出一个全栈式框架CrossND，关键在于三阶段协同机制：首先，采用链式精炼（chain-of-refinement）管道对作者档案进行去噪，并生成更准确的论文-作者匹配概率；其次，通过监督微调融合精炼信号与基于概率软逻辑（Probabilistic Soft Logic, PSL）的跨源校正模块，识别并修正存在错误的数据源；最后，引入测试时扩展（test-time scaling）进一步提升预测的准确性与鲁棒性。实验结果表明，CrossND在真实数据集上无需人工干预即可持续超越17个基线方法，充分验证了跨源推理的有效性。
链接: https://arxiv.org/abs/2606.08617
作者: Fanjin Zhang,Yunhe Pang,Bo Chen,Zhiyu Shen,Yanghui Rao,Evgeny Kharlamov,Jie Tang
机构: Renmin University of China (中国人民大学); Sun Yat-Sen University (中山大学); Tsinghua University (清华大学); Robert Bosch GmbH (罗伯特·博世有限公司); University of Oslo (奥斯陆大学)
类目: Computation and Language (cs.CL)
备注: Accepted at KDD 2026 ADS track
Abstract:Author name disambiguation is a critical challenge in academic search systems, often addressed through from-scratch and real-time disambiguation approaches. However, current algorithms remain vulnerable to cumulative errors of paper-author assignments and overlook inconsistent assignments across different sources. Resorting to expert annotation is resource-intensive. To this end, this paper explores a new perspective for author name disambiguation: cross-source correction by leveraging inconsistent assignments across sources. We propose CrossND, a full-stack framework that integrates data refinement, cross-source reasoning, and test-time scaling. First, a chain-of-refinement pipeline denoises author profiles and produces more accurate paper-author matching probabilities. Second, a supervised fine-tuning process incorporates these refined signals and a probabilistic soft logic-based cross-correction module to infer the assignments of which sources are incorrect. Third, test-time scaling further enhances the accuracy and robustness of the predictions. Experiments on real-world datasets indicate that CrossND consistently outperforms 17 baselines by leveraging cross-source reasoning without human intervention.
[NLP-109] Harnessing Streaming Video in the Wild
【速读】： 该论文旨在解决当前视觉-语言模型（Vision-Language Models, VLMs）在处理无边界视频流任务时存在的核心瓶颈问题，即现有模型虽在离线视频理解任务中表现优异，但在实时流式处理、长时记忆保持及主动交互能力方面严重不足，且缺乏针对流式部署的专用基础设施。其解决方案的关键在于从三个层面系统性构建支持流式视频理解的完整技术体系：首先，提出Streaming-Train-248K数据集与新型训练目标，以增强VLM骨干模型对流式交互与持续理解的能力；其次，设计Streaming Harness——一个即插即用的部署系统，使任意VLM均可实现每秒级响应决策（主动交互）、长达12小时的上下文记忆保留（长时记忆）以及亚秒级延迟处理（实时处理）；最后，构建Streaming-Eval基准测试，全面评估模型在多样化真实场景下的流式能力。实验表明，该方案在所有关键流式理解能力上均取得显著提升，推动了从离线视频理解向可部署流式智能的范式转变。
链接: https://arxiv.org/abs/2606.08615
作者: Dingyu Yao,Shuhuan Gu,Qingyi Si,Junhao Zhou,Chenxu Yang,Chuanyu Qin,Naibin Gu,Zheng Lin,Weiping Wang,Nan Duan,Jiaqi Wang
机构: Institute of Information Engineering, Chinese Academy of Sciences (中国科学院信息工程研究所); JD.com(京东)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Vision-Language Models (VLMs) are increasingly required to process unbounded video streams in applications such as video-call assistants, live commentary, and embodied robots. An ideal streaming system should support proactive interaction, long-horizon memory, and real-time processing, while resting on a VLM backbone capable of handling diverse in-the-wild streaming tasks. However, existing VLMs excel at offline video understanding but fall short in streaming capabilities and lack dedicated infrastructure for streaming deployment. We address this gap on three fronts. (i) For backbone capability, we construct \textbfStreaming-Train-248K, a streaming dataset paired with a novel training objective for adapting VLMs to streaming interaction and understanding. (ii) For real-world deployment, we introduce \textbfStreaming Harness, a plug-and-play system that endows any VLM with three core abilities: proactive interaction (per-second response decisions), long-term memory (12-hour context retention), and real-time processing (sub-second latency). (iii) To drive continued community progress on streaming capabilities, we design \textbfStreaming-Eval, a benchmark that reflects models’ capabilities across diverse in-the-wild scenarios. Extensive experiments demonstrate consistent gains from our approach across all core capabilities required for streaming video understanding. We will open-source our data, code, and benchmark to advance the community’s shift from offline video understanding to deployable streaming intelligence.
[NLP-110] Multilingual Fact-Checking at Scale: Fine-Tuned Compact Models vs LLM s
【速读】： 该论文旨在解决多语言事实核查（multilingual fact-checking）在高吞吐量与低延迟场景下的可扩展性与效率问题，特别是在覆盖114种语言的复杂语境下实现稳定、快速的事实验证。其核心挑战在于如何在保证准确性的同时，克服大语言模型（LLM）在多语言支持、推理延迟及部署成本方面的瓶颈。解决方案的关键在于采用模块化流水线架构，包含三阶段：论断检测、证据检索与重排序、真实性预测。通过针对特定任务微调XLM-RoBERTa-Large进行论断检测、mmBERT-base进行三分类立场判断（支持/反驳/混合），以及基于SetFit的多语言重排序模型实现论断-证据匹配，构建了一套高效、自托管的轻量化模型体系。实验表明，这些经过任务特定微调的模型在跨语言性能上表现稳健，且其检索模块在同等硬件条件下显著优于现代专有嵌入模型，展现出更高的计算效率。研究进一步证实，在资源受限、对隐私和成本敏感的生产环境中，紧凑型微调模型仍能提供可靠且高效的多语言事实核查能力，是规模化部署的可行路径。
链接: https://arxiv.org/abs/2606.08605
作者: Pratuat Amatya,Vinay Setty
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:We present a multilingual fact-checking system deployed at Factiverse, designed for high-throughput and low-latency operation across diverse languages. The system follows a modular pipeline with three stages: claim detection, evidence retrieval and re-ranking, and veracity prediction. We fine-tune XLM-RoBERTa-Large for claim detection, mmBERT-base for three-label stance classification (Supports/Refutes/Mixed), and a SetFit-based multilingual re-ranker for claim–evidence matching. We compare these components against strong LLM baselines, including GPT-5.2, Claude Opus~4.6, and Qwen3-8b. Experiments on production data spanning 114 languages for claim detection and 28 languages for veracity prediction show that task-specific fine-tuning provides strong and stable multilingual performance, while the fine-tuned retrieval model remains competitive with modern proprietary embeddings. Same-hardware latency measurements further show large efficiency gains for encoder-based components, supporting their use in production deployments with tight cost and privacy constraints. Overall, compact fine-tuned, self-hosted models remain a practical and effective foundation for multilingual fact-checking at scale. Code and data used for this study are available at this https URL.
[NLP-111] ans-as-a-Layer: Test-Time Memory for Conversational Speech Emotion Recognition ICML2026
【速读】： 该论文旨在解决语音情感识别（Speech Emotion Recognition, SER）在对话场景中缺乏上下文建模的问题，传统方法通常将SER视为话语级别的分类任务，忽略了说话人固有的声学特征范围以及前序话语所建立的情感语境。现有基于语音-语言模型（Speech-Language Models, SLMs）的方法虽可通过微调利用预训练的声学与语义表示，但其仍无法有效捕捉每轮对话中的动态状态。为此，本文提出一种测试时神经记忆机制——作为即插即用模块的“记忆层”（Memory-as-a-Layer, MAL），通过在小型神经记忆中写入对话历史，并将其以音频令牌对齐的残差更新形式读回，从而在不修改主干大语音语言模型（Large Audio Language Models, LALMs）的令牌位置的前提下，引入对话级上下文信息。实验表明，该设计在多种音频大模型和情感识别数据集上均显著提升了SER性能，验证了测试时记忆作为残差上下文机制在对话式情感识别中的有效性。
链接: https://arxiv.org/abs/2606.08573
作者: Daniel Chen,Qicong Hu,Yang Xiao,Ting Dang,Hong Jia
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: ICML 2026 Workshop on Machine Learning for Audio
Abstract:Speech emotion recognition (SER) is commonly formulated as utterance-level classification, although conversational emotion depends on a speaker’s usual vocal range and the emotional context established by previous utterances. Speech-language models provide strong pretrained acoustic and semantic representations, and can adapts them to SER labels via finetune, but this mechanism still missing per-dialogue state. We study whether test-time neural memory can supply this missing context while leaving the large audio language models (LALMs) backbone intact. Building on Titans, we introduce a plug-and-play Memory-as-a-Layer (MAL) adapter that writes dialogue history into a small neural memory and reads it back as an audio-token-aligned residual update, avoiding changes to the host model’s token positions. Across different audio LLMs and emotion recognition datasets evaluations, our design improves SER performs across different evaluation metrics, supporting test-time memory as a residual contextual mechanism for conversational SER.
[NLP-112] Calibration of Structured Ignorance Certificates for Diagnosing Unknown Unknowns in Reasoning Models ICML2026
【速读】： 该论文旨在解决大语言模型在面对超出其知识边界的问题时，倾向于生成看似合理但实际错误的“幻觉”回答这一核心问题。现有模型缺乏对自身认知局限性的显式表达，导致在未知领域盲目输出，影响可靠性与可解释性。其解决方案的关键在于提出结构化无知证明（Structured Ignorance Certificates, SICs）——一种基于JSON格式的标准化输出机制，强制模型在无法作答时明确指出缺失的知识交叉领域、列举所需的核心概念，并生成可执行的检索查询，从而避免幻觉。为训练模型生成高质量SICs，研究构建了一个包含7,347个样本的“未知-未知”（Unknown-Unknown, UU）数据集，通过将七个不同领域（物理、生物、工程、计算机科学、经济、医学、法律）的问题跨域组合，生成单领域专家也无法解答的新问题。采用组相对策略优化（Group Relative Policy Optimization, GRPO）对140亿参数模型进行微调，复合奖励函数融合了检索有效性、概念具体性与输出格式合法性。实验表明，经SIC训练的模型在735个保留的UU问题上达到99.46%的JSON格式正确率、0.967的证书具体性得分，并在基于检索的生成任务中实现3.6%的ROUGE-L提升，验证了显式元认知结构化能力可通过学习获得且具备可度量性。
链接: https://arxiv.org/abs/2606.08571
作者: Subramanyam Sahoo
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted in ICML 2026 Workshop: Epistemic Intelligence in Machine Learning
Abstract:Large language models frequently fail in a characteristic way: rather than acknowledging ignorance, they produce fluent but incorrect answers to questions that lie beyond their knowledge boundaries. We introduce \textbfStructured Ignorance Certificates (SICs), a JSON-formatted output schema that demands a model explicitly name the missing domain intersection, enumerate required concepts, and propose a productive retrieval query rather than hallucinating an answer. To train models to produce high-quality SICs we construct a 7,347-sample \emphUnknown-Unknown (UU) dataset by prompting Qwen3-14B to stitch together questions from seven domains (physics, biology, engineering, CS, economics, medical, legal) into novel cross-domain queries that no single-domain expert could answer. We fine-tune a 14B-parameter model with Group Relative Policy Optimization (GRPO) using a composite reward that combines retrieval utility, concept specificity, and output-format validity. A paraphrase-divergence probe trained on model responses confirms that SIC-tuned outputs systematically exhibit higher unknown-unknown probability scores. Evaluation on 735 held-out UU questions achieves a 99.46% JSON validity rate, a mean Certificate Specificity Score of 0.967, and a 3.6% ROUGE-L improvement over the base model on retrieval-grounded generation – demonstrating that explicit epistemic structuring is a learnable and measurable capability.
[NLP-113] Inside the LLM Word Factory EMNLP2026
【速读】： 该论文旨在解决生成式语言模型在处理子词（subword）输入时，如何实现语义层面的词级（word-level）语义整合这一关键问题，即“去分词化”（detokenization）过程的机制不明问题。其核心挑战在于，尽管模型以子词为基本输入单元，但自然语言理解依赖于词级语义，因此需揭示模型内部如何将子词片段重构为有意义的词级表示。解决方案的关键在于通过受控的激活修补（activation patching）实验，系统性地分离并分析不同模型组件对去分词化的贡献，首次明确揭示了去分词化是一个发生在早期至中期层的两阶段过程：第一阶段由注意力机制负责传递特定于子词的信号，尤其从非末尾子词向后续子词进行序列中继；第二阶段由前馈神经网络（MLP）将该信号与局部嵌入（local embedding）融合，完成词级表征的构建。研究发现，这一两阶段结构在12个来自8种架构家族的模型中具有普遍性，但其作用深度受位置编码方式影响——基于旋转位置编码（RoPE）的模型在第1至第5层完成去分词，而采用学习型绝对位置编码的模型则延展至第5至第10层。此外，研究提出一种仅依赖早期层激活的探测器，可高效判断去分词成功与否，其性能在不同上下文长度下达到0.94至0.97的AUROC，显著提升了对模型内部语义整合过程的可解释性。
链接: https://arxiv.org/abs/2606.08562
作者: Benzi Busigin,Yuval Pinter
机构: 未知
类目: Computation and Language (cs.CL)
备注: 17 pages, 12 figures. Under review at EMNLP 2026
Abstract:Transformer language models process input provided as subword fragments, but natural language semantics usually rely on word-level concepts. Detokenization is the process where models reconcile these two facts, aggregating subwords into word-level representations through their computation. Prior work has found that this takes place mostly in early-to-middle layers, but so far the exact mechanics of the process have not been pinned down. We venture deep into detokenization using activation patching in controlled paired experiments that isolate the contribution of different model components, localizing English detokenization in Llama2-7B to a two-stage process at Layer 1. Attention transmits a token-specific signal from nonfinal subwords, using sequential relays if necessary, while the MLP composes it with the local embedding. This two-stage structure generalizes to twelve models from eight families, but the depth over which it takes place depends on the flavor of positional encoding: RoPE-based models detokenize over 1 to 5 layers, while learned-absolute models take 5 to 10. Finally, we provide a probe for determining the success of the detokenization process based on early-layer activations alone, performing at 0.94-0.97 AUROC depending on the amount of context.
[NLP-114] Ishigaki-IDS: An Open-Weight Verifier-Aware Model for Information Delivery Specification Drafting in Building Information Modeling
【速读】： 该论文旨在解决建筑信息模型（BIM）项目中信息交付规范（IDS）编写过程中的实际瓶颈问题，即如何高效、准确地将人工定义的信息需求转化为可机器校验的、符合严格XML Schema约束的IDS文件。当前流程依赖从业者手动处理领域术语、格式规范及外部验证器合规性，工作量大且易出错。其解决方案的关键在于提出Ishigaki-IDS——一个专用于生成具备验证器感知能力的IDS草案的开源大语言模型（LLM），通过在BIM/IDS语料上进行持续预训练、基于“信息需求→IDS”配对数据的监督微调，以及结合外部验证器反馈的可验证奖励机制进行强化学习，使模型能够生成直接可加载至验证器的初步草案，从而将作者的工作重心从低层级的XML语法修复转向高阶内容审查与修正。实验结果表明，Ishigaki-IDS-8B在166个专家标注的Ishigaki-IDS-Bench测试集上达到0.651的IDSAuditPass得分，显著优于最强单次生成基线Claude Opus 4.5的0.331；其14B和32B版本进一步提升至0.753和0.693，并在要求-特征对齐度（Audit-Gated FacetF1）上分别达到0.392和0.369。六名BIM实践者的流程评估显示，使用Ishigaki辅助可使总工作时间减少54.7%，验证了验证器感知式生成在降低实际工作负担方面的有效性。
链接: https://arxiv.org/abs/2606.08545
作者: Ryo Kanazawa,Koyo Hidaka,Teppei Miyamoto,Takayuki Kato,Tomoki Ando,Chenguang Wang,Dayuan Jiang,Naofumi Fujita,Shuhei Saitoh,Atomu Kondo,Koki Arakawa,Daiho Nishioka
机构: ONESTRUCTION Inc.(ONESTRUCTION公司); AWS GenAI Innovation Center(亚马逊云科技生成式人工智能创新中心)
类目: Computation and Language (cs.CL); Software Engineering (cs.SE)
备注: 8 pages, 2 figures, 5 tables. Preprint
Abstract:Building Information Modeling (BIM) projects require information requirements to be described as machine-checkable Information Delivery Specification (IDS) files in order to verify whether building models contain the required attributes. However, IDS authoring remains a practical bottleneck: practitioners must handle domain vocabulary, strict XML schema constraints, and external validator conformance while also checking whether the requirement itself is correctly expressed. We present Ishigaki-IDS, an open-weight LLM specialized for verifier-aware IDS draft generation. The model combines continued pretraining on BIM/IDS corpora, supervised fine-tuning on information-requirement-to-IDS pairs, and reinforcement learning with verifiable rewards from an external validator. The goal is not to replace expert review, but to move IDS authoring from low-level XML and schema repair toward validator-loadable drafts that practitioners can inspect and correct. On the 166-case expert-created Ishigaki-IDS-Bench, Ishigaki-IDS-8B achieves an IDSAuditPass score of 0.651, a validator-pass metric for generated IDS files, substantially outperforming Claude Opus 4.5, the strongest single-shot LLM baseline we evaluated, at 0.331. It also obtains an Audit-Gated FacetF1 of 0.282, which measures requirement-facet alignment among validator-passing drafts. The same recipe scales: 14B and 32B variants reach IDSAuditPass 0.753 / 0.693 and Audit-Gated FacetF1 0.392 / 0.369. In a workflow check with six BIM practitioners, Ishigaki-assisted authoring reduced aggregate work time by 54.7% under the same validation and alignment endpoint. These results suggest that verifier-aware IDS generation can reduce the practical burden of converting BIM information requirements into reviewable IDS drafts.
[NLP-115] Scaffold Effects on GAIA: A Controlled Comparison
【速读】： 该论文旨在解决当前大模型能力评估中存在的“可激发能力差距（elicitation gap）”问题，即现有发布的模型能力评分往往混淆了模型自身能力与外部支撑架构（scaffold）所带来的性能提升，而这一差距在受控条件下的具体规模尚不明确。其核心解决方案是通过一项预先注册的受控实验，系统比较三种不同架构（ReAct、多智能体规划-执行-评估设计、规划-执行分离式架构）在五种来自三个不同提供商的大模型（包括Claude Opus 4.7、Sonnet 4.6、Haiku 4.5；Gemini 3.1 Pro Preview；GPT-5.5）上对GAIA验证集第1和第2级任务的表现，保持任务与环境一致，并每题进行三次尝试。研究发现，仅改变架构即可使同一模型的准确率波动高达28个百分点（如Opus在Level 2时），证实了预注册假设——架构差异导致的能力差距至少达10个百分点。进一步分析表明，更强大模型对架构的敏感性并未降低，反而在高难度任务中，最强大的Anthropic模型从结构化架构中获益最大，且层级扩展性（tier-scaling）仅在Level 1的稳健子集中成立。多智能体架构相较于ReAct的优势仅在Anthropic家族内部显现，而非跨提供商模型，说明模型家族比能力层级更具决定性；同时，预期的规划-执行架构在文件读取任务中的优势被证伪。此外，结构化架构虽减少工具调用次数，但在复杂路径中错误恢复能力更强，其中Gemini配合规划-执行架构在两个层级均实现最低成本与最高准确率。研究结论强调：单一架构下的能力评分本质上是架构依赖的估计值，且随着模型能力提升，可激发能力差距未必缩小，提示未来评估需充分考虑架构影响。
链接: https://arxiv.org/abs/2606.08529
作者: Jason Starace
机构: Independent Researcher(独立研究员)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 12 pages, 3 figures
Abstract:Published agent capability scores conflate what a model can do with what its scaffold lets it do, and the magnitude of this elicitation gap is not well characterized under controlled conditions. This study executes a pre-registered controlled comparison of three scaffolds (ReAct, a Planner-Actor-Rater multi-agent design, and planner-then-executor) across five models from three providers (Claude Opus 4.7, Sonnet 4.6, Haiku 4.5; Gemini 3.1 Pro Preview; GPT-5.5) on GAIA validation Levels 1 and 2, holding tasks and conditions fixed, with three attempts per question. Scaffold choice alone moves measured accuracy by as much as 28 percentage points within a single model (Opus, Level 2, robust slice), confirming the pre-registered hypothesis that scaffold variation produces gaps of at least 10 points. The pre-registered prediction that more capable models would be less scaffold-sensitive is rejected in direction: scaffold effects vary significantly by model in every dataset slice, but the most capable Anthropic model gains the most from structured scaffolds at the harder level, and tier-scaling holds only at Level 1 under the robust slice. The multi-agent advantage over ReAct at Level 2 appears within the Anthropic family but not for the cross-provider models, making model family rather than capability tier the conditioning variable, and the predicted planner-executor advantage on file-reading tasks is falsified. Structured scaffolds make fewer tool calls yet recover more often from mid-trajectory errors at the harder level, and a single cell (Gemini with planner-then-executor) is the cheapest at both levels and the most accurate at Level 2. These results indicate that single-scaffold capability numbers are scaffold-conditional estimates and that the elicitation gap is not guaranteed to shrink as models improve.
[NLP-116] A Joint Finite-Sample Certificate for Adaptive Selective Conformal Risk Control
【速读】： 该论文旨在解决选择性预测（selective predictors）在实际部署中如何安全地保证性能的问题，核心挑战在于：在有限样本下，需同时提供一个可验证的、非平凡的三重保障——即对选定风险（selected risk）的上界、接受概率（acceptance probability）的下界不低于某一阈值 \pmin，以及部署效用（deployment utility）的下界。传统的基于Hoeffding不等式的方法通过区间估计来处理风险比率，但其依赖于对损失函数范围的假设，且在低接受率场景下导致接受率下界与\pmin呈反比关系（1/\pmin），造成保守性过强。本文的关键创新在于直接将选定风险建模为比率统计量，并采用方差自适应的经验Bernstein界，结合Clopper-Pearson界用于接受率，以及双侧接近度界用于效用，构建了一个联合置信区间框架。该方法实现了对最优策略效用的绝对下界和相对误差不超过2γu的逼近，且在可行条件下均非平凡。尤其值得注意的是，其接受率下界的依赖关系从1/\pmin优化至1/\sqrt{\pmin}，显著提升了高置信度场景下的实用性。进一步地，推导出闭式解表明，在特定每对样本的局部区域内，所提风险界优于传统的Hoeffding型共形风险控制（Hoeffding–CRC）方法。实验结果显示，在ImageNet（三种ResNet）和COCO val 2017全景分割任务上，该证书相较Hoeffding–CRC实现了+22个百分点的认证接受率提升，且较非平凡匹配基准紧约10倍；然而这些优势具有场景依赖性，未在ADE20K上体现。整体算法复杂度为O(\ncert m)，具备高效可扩展性。
链接: https://arxiv.org/abs/2606.08517
作者: Xiaoli Yu,Jiamiao Liu
机构: Chongqing University of Posts and Telecommunications (重庆邮电大学); Army Medical University (Third Military Medical University) (陆军军医大学（第三军医大学）)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Selective predictors answer on confident inputs and abstain elsewhere; deploying one safely needs a single finite-sample certificate that simultaneously upper-bounds the selected risk, lower-bounds the acceptance probability \pacc above a floor \pmin , and lower-bounds the deployment utility. This certificate must be valid under adaptive threshold selection from a finite grid of m pairs on \ncert samples. We give such a certificate for bounded, possibly non-monotone losses by treating the selected risk directly as a ratio rather than through a Hoeffding-style range bound. The construction couples three confidence bounds: a variance-adaptive empirical-Bernstein bound on the ratio risk, a Clopper–Pearson bound on acceptance, and a two-sided closeness bound on utility. Together they lower-bound the certified policy’s utility absolutely and to within 2\gammau of the best over the \emphcertified set, both non-vacuous whenever feasible; a regime-scoped third leg matches an external oracle, informative only where the risk margin \gammar \alpha and vacuous at the headline operating points. Relative to the range-only Hoeffding-ratio construction this sharpens the acceptance-floor dependence from 1/\pmin to 1/\sqrt\pmin , and a closed-form corollary identifies a per-pair regime in which our risk bound dominates a Hoeffding conformal risk control (Hoeffding–CRC) selective bound. Empirically, on ImageNet (three ResNets) and COCO val 2017 panoptic, the certificate opens a +22 pp certified-acceptance frontier over Hoeffding–CRC and is \approx10\times tighter than a non-vacuous matched-valid baseline; these gains are regime-scoped, not universal, and absent on ADE20K. The certifier runs in O(\ncert m) time.
[NLP-117] Friend or Foe? Language as an ideological switch in open-weight LLM s under Russian disinformation stress
【速读】： 该论文旨在解决在俄乌战争背景下，生成式人工智能（Generative AI）模型因本地化微调而被认为具备文化亲和性是否能有效抵御敌方信息操纵这一核心问题。研究发现，传统认知中“文化亲和性微调即增强信息韧性”的假设被系统性证伪：尽管乌克兰导向的模型在乌克兰语中表现出较强本土立场，但在俄语输入下对俄罗斯叙事的抵抗能力反而最弱，而俄罗斯导向的模型则展现出最强的排斥性。其关键发现在于“微调悖论”（Fine-Tuning Paradox），表明模型表现主要受语料库构成、语言覆盖范围及提示格式等技术因素影响，而非名义上的文化归属。这揭示出当前关于混合战争、数字主权与后帝国信息秩序的讨论中，真正威胁区域信息主权的并非敌对微调本身，而是对“文化契合即天然抗性”的未经检验的假设。
链接: https://arxiv.org/abs/2606.08512
作者: Anna Małgorzata Kamińska,Tetiana Klynina
机构: 未知
类目: Computers and Society (cs.CY); Computation and Language (cs.CL)
备注:
Abstract:As Russia’s war against Ukraine extends into generative AI, large language models (LLMs) adapted for local post-Soviet languages are deployed in contested information environments. Policy and industry discourse assumes that culturally aligned adaptation encodes the political orientation of the target community: a Ukrainian-oriented model will resist Russian narratives, a Russian-oriented one will reinforce them. Does it? This article systematically disconfirms that assumption. We run a controlled audit of four openly available LLMs sharing a common base model but fine-tuned for different linguistic communities, querying them in Ukrainian, Russian and English across ten contested wartime narratives: Crimea, “denazification”, the “one people” thesis, and atrocity denial at Bucha and Mariupol. The result is a Fine-Tuning Paradox: the Ukrainian-oriented model shows the weakest resistance to Russian disinformation in Russian, while the Russian-oriented one exhibits the strongest rejection. Corpus composition, language coverage and prompt format prove more decisive than nominal cultural provenance. We situate these findings within debates on hybrid warfare, digital sovereignty and post-imperial information orders, arguing that the principal threat to regional information sovereignty is not adversarial fine-tuning but the untested assumption that cultural alignment guarantees resilience.
[NLP-118] Back on Track: Aligning Rewards and States for Reasoning in Diffusion Large Language Models
【速读】： 该论文旨在解决扩散型大语言模型（dLLM）在强化学习（RL）增强推理能力过程中存在的双重错位问题：一是“过程-奖励错位”，即稀疏的终局奖励被均等地分配给生成过程的所有中间步骤，导致无法实现差异化的信用分配；二是“状态-轨迹错位”，即策略更新常偏离真实的生成轨迹，趋向于人工构造的非典型状态，造成梯度浪费。其解决方案的关键在于提出一种全新的过程对齐策略优化框架（Process Aligned Policy Optimization, PAPO），通过步级感知过程奖励（Step-Aware Process Rewards, SPR） 将稀疏的终局奖励转化为密集的、逐步的信用信号，实现精细化的奖励设计；同时引入熵引导的历史重演机制（Entropy-Guided Historical Re-enactment, EHR），在高不确定性步骤上重播真实生成轨迹，确保策略更新聚焦于具有信息量的真实路径。实验结果表明，PAPO 在 GSM8K、MATH500、Countdown 和 Sudoku 四个基准测试中显著优于基线方法，性能提升最高达 42.2%。
链接: https://arxiv.org/abs/2606.08501
作者: Yawen Shao,Jie Xiao,Kai Zhu,Yu Liu,Hongchen Luo,Xueyang Fu,Yang Cao,Wei Zhai,Zheng-Jun Zha
机构: University of Science and Technology of China (中国科学技术大学); Tongyi Lab; Northeastern University (东北大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Reinforcement learning (RL) holds immense promise for enhancing the reasoning capabilities of diffusion large language models (dLLMs). However, progress is fundamentally constrained by a dual misalignment between authentic generation trajectory and the gradient update process: (i) Process-reward misalignment. Sparse, terminal rewards are indiscriminately assigned to all intermediate steps of the generation process, failing to provide discriminative credit assignment. (ii) State-trajectory misalignment. Policy updates are often diverted toward artificial, out-of-trajectory states, squandering gradients on less informative samples. To address these limitations, we introduce Process Aligned Policy Optimization (PAPO), a novel framework that holistically aligns the RL update with the dLLM’s generative trajectory via Step-Aware Process Rewards (SPR) that transform sparse terminal rewards into dense, step-wise credit, and Entropy-Guided Historical Re-enactment (EHR) that replays authentic trajectories at high-uncertainty steps. Extensive experiments on four benchmarks demonstrate that PAPO significantly outperforms baselines, achieving gains of up to 4.5% on GSM8K, 4.8% on MATH500, 42.2% on Countdown and 16.1% on Sudoku.
[NLP-119] Explaining Black-Box Language Models: Learning to Optimize Linguistically-Structured Word Subsets KDD2026
【速读】： 该论文旨在解决在高风险领域（如医疗健康）中，深度语言模型（DLMs）作为黑箱系统（如通过API调用）时，如何实现高效、可靠且可理解的决策解释问题。现有解释方法难以同时满足三个关键要求：（i）推理阶段的高效性，（ii）无需访问内部参数或梯度的黑箱兼容性，且不引发分布外行为，（iii）基于输入语言结构的可理解解释。为此，论文提出一种通过选择输入词的少量信息子集来解释DLM预测的方法，将其建模为可摊销优化问题，从而实现无需逐样本搜索的一次性推理。其选择策略采用类REINFORCE的策略梯度进行训练，支持在完全无梯度依赖的离散词选择环境中运作。为提升解释的语义连贯性与人类语言直觉的一致性，方法进一步引入图结构知识，引导生成具有语言学合理性的解释子集，使解释既具备强判别能力又符合认知意义。实验表明，该方法在多种DLM架构和真实数据集上均优于传统黑箱兼容方法及需访问梯度的基准方法，在解释的判别力与语言显著性对齐方面表现更优。
链接: https://arxiv.org/abs/2606.08497
作者: Minyoung Hwang,Seokhyun Lee,Changhee Lee
机构: Korea University(高丽大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: KDD 2026 Research Track
Abstract:As deep language models (DLMs) are increasingly deployed in high-stakes domains such as healthcare, understanding their decision rationale becomes paramount for ensuring trust, safety, and accountability. However, achieving this vital level of interpretability is particularly challenging when these DLMs operate as black-box systems (e.g., via APIs), where access to internal model states (e.g., parameters, gradients) is restricted. Despite numerous efforts, existing explanation methods often fail to concurrently satisfy three key desiderata: (i) inference-time efficiency, (ii) black-box compatibility without inducing out-of-distribution behavior, and (iii) comprehensible explanations grounded in the input’s linguistic structure. To address these challenges, we propose a method that explains predictions of DLMs by selecting a small, informative subset of input words. We formulate this as an amortized optimization problem, enabling efficient one-shot inference without the need for input-specific search. Our selection policy is trained via REINFORCE-style policy gradients, allowing discrete word selection in a fully gradient-free setting. To enhance interpretability and align with human linguistic intuition, we integrate graph-structured knowledge into this selection process, fostering linguistically coherent subsets that result in explanations both highly informative and cognitively meaningful to end-users. We evaluated our method on diverse DLM architectures and multiple real-world datasets. It consistently identifies word subsets with enhanced discriminative power and stronger alignment with linguistically salient cues, outperforming both conventional black-box compatible methods and gradient-based approaches that are given oracle access to the black-box model’s gradients for a more challenging benchmark. Our code is available at here.
[NLP-120] SAEExplainer: Interpreting SAE Features with Activation-Guided Preference Optimization
【速读】： 该论文旨在解决生成式人工智能（Generative AI）中大型语言模型（LLMs）可解释性不足的核心问题，具体聚焦于稀疏自编码器（Sparse Autoencoders, SAEs）所提取的稀疏特征难以有效解释的挑战。现有解释方法普遍采用开环范式，无法利用机制反馈进行迭代优化，导致解释结果易产生幻觉且因果触发模式不清晰。为此，本文提出SAEExplainer，其核心创新在于构建一个基于激活得分（activation scores）作为目标奖励信号的训练框架，通过自校正与迭代强化机制实现解释能力的持续提升。该方法采用两轮优化流程，循环验证并修正基础解释，显著降低解释幻觉，增强因果触发模式的可靠性。实验结果表明，SAEExplainer在多数指标上优于现有基线，尤其在因果触发能力和区分性激活方面表现突出。
链接: https://arxiv.org/abs/2606.08496
作者: Jingyi He,Haiyan Zhao,Ruxue Shi,Yanguang Liu,Xin Wang,Fei Sun,Mengnan Du
机构: Shanghai Jiao Tong University (上海交通大学); NJIT (新泽西理工学院); Jilin University (吉林大学); Institute of Computing Technology, CAS (中国科学院计算技术研究所); The Chinese University of Hong Kong, Shenzhen (香港中文大学（深圳)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Although Sparse Autoencoders (SAEs) have mitigated the opacity of large language models (LLMs) by decomposing dense representations into sparse features, explaining these features still remains a central challenge. Current explanation methods, however, typically operate within an open-loop paradigm, failing to leverage mechanistic feedback for further refinement. In this paper, we propose SAEExplainer, a training framework utilizes activation scores as an objective reward signal to train the model for self-correction and iterative bootstrapping. By iteratively verifying and correcting foundational explanations through a two-round optimization process, SAEExplainer achieves continuous improvement in its explanatory capabilities. This mechanism significantly reduces explanation hallucinations and reinforces causal triggering patterns. Extensive experiments demonstrate our approach improves upon established baselines across most metrics, especially in causal triggering and discriminative activation.
[NLP-121] RADE: Transducer-Augmented Decoder for Speech LLM
【速读】： 该论文旨在解决语音大语言模型（Speech LLMs）在流式推理中缺乏原则性机制的问题，具体表现为其标签同步生成方式与声学帧无对齐，导致实时解码和话语结束检测困难。其核心解决方案是提出TRADE（TRansducer-Augmented DEcoder），通过引入共享音频编码器的转换器分支，直接利用大语言模型（LLM）的隐藏状态作为预测网络，实现了声学帧同步对齐与语言推理能力的紧密耦合。关键设计包括：(1) 紧密耦合的双词汇表结构——基于LLM词汇表构建紧凑的转换器词汇表，实现零成本得分融合；(2) 块同步流式训练结合梯度截断，消除训练与推理间的不匹配，同时保持与离线推理相当的内存开销；(3) 局部解码器音频注意力（LDAA），采用因果滑动窗口机制，使键值缓存（KV-cache）内存占用独立于话语长度。单个TRADE检查点即可支持从离线到流式解码的连续延迟调节。实验表明，TRADE在Open ASR Leaderboard上平均词错误率（WER）达6.71%，960ms块大小的流式识别达到8.40% WER；在长篇语音任务中，无需外部分段即可在TED-LIUM上取得3.64% WER、在Earnings-22上取得10.88% WER。此外，其输出的句子结尾标点时间戳与声学语音活动检测（VAD）结合后，使话语结束检测的F₁分数提升0.03，显著优于仅依赖声学VAD的方法。
链接: https://arxiv.org/abs/2606.08486
作者: Yun Tang,Shanil Puri,Shinji Watanabe,Subhabrata Mukherjee
机构: Hippocratic AI; Carnegie Mellon University
类目: Computation and Language (cs.CL)
备注:
Abstract:Speech Large Language Models (Speech LLMs) lack a principled mechanism for streaming inference: their label-synchronous generation has no acoustic-frame alignment, making real-time decoding and end-of-utterance detection difficult. We propose TRADE TRansducer-Augmented DEcoder, which augments a multimodal LLM with a transducer branch that shares the audio encoder and uses the LLM’s hidden states directly as the prediction network – coupling frame-synchronous acoustic alignment with the LLM’s linguistic reasoning. Three design choices make the system accurate, streamable, and long-form capable: (1)Tightly coupled dual vocabularies – a compact transducer vocabulary derived from the LLM vocabulary, enabling zero-cost score fusion; (2)Chunk-synchronized streaming training with gradient stopping, eliminating the train-inference mismatch at offline-equivalent memory cost; and (3)Localized Decoder Audio Attention (LDAA), a causal sliding window that caps KV-cache memory independently of utterance length. A single TRADE checkpoint supports offline and streaming decoding across a continuous range of latency operating points. TRADE achieves 6.71% average WER on the Open ASR Leaderboard, while the streaming recognition with 960ms chunk size reaches 8.40% from the same checkpoint. On long-form speech, it obtains 3.64% WER on TED-LIUM and 10.88% on Earnings-22 without external segmentation. TRADE provides sentence-end punctuation timestamps that, when combined with acoustic voice activity detection (VAD), improve end-of-utterance detection by +0.03 F_1 over acoustic VAD alone.
[NLP-122] More Yap Less Meaning: Uncovering Self-Improvement Behavior in SLMs ACL2026
【速读】： 该论文旨在解决小语言模型（Small Language Models, SLMs）在自我改进能力方面的不确定性问题，即模型是否能够有效识别并修正自身推理过程中的缺陷。其核心挑战在于评估SLMs在缺乏外部干预的情况下，能否通过内省式反馈实现推理质量的提升。解决方案的关键在于提出一种最小化的三步自校正流程：首先生成初始回答，随后利用同一模型基于正确答案生成针对错误推理的提示（hint），最后将原始问题与模型自身的反馈结合，重新生成优化后的答案。实验结果表明，尽管引入了提示信息，模型性能仅提升4.4个百分点，且在提供正确答案的前提下，多数模型仍无法准确识别推理缺失环节，生成的提示在语义上对纠正与否几乎无区分度；更值得注意的是，提示长度与最终错误率呈正相关，暗示过长的推理过程反而损害推理表现，揭示出当前SLMs在计算资源增加时并不必然带来性能提升，凸显其内在认知局限性。
链接: https://arxiv.org/abs/2606.08471
作者: Marina Igitkhanian,Erik Arakelyan
机构: American University of Armenia(美国亚美尼亚大学); NVIDIA(英伟达)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: GEM Workshop at ACL 2026
Abstract:Recently, language models have made rapid progress across various domains and applications. However, their capability for self-improvement, i.e., whether they are adept at recognising and correcting flaws in their own reasoning, remains dubious. In this study, we address this question by constructing a sufficiency test to rigorously examine the self-correction capabilities of small language models (SLMs). We propose a minimal three-step self-correction pipeline that collects initial SLM answers, prompts the same model to generate hints for its incorrect responses given the ground truth, and feeds the model the same question with its own feedback to refine the initial answer. We evaluate a variety of instruction-tuned and reasoning SLMs in this experimental setup on arithmetic and logical reasoning benchmarks. Our findings show that SLMs with injected hint sentences yield only a 4.4 percent gain over initial question-answering accuracy. Even though the correct answer was provided alongside the model’s incorrect reasoning, the evaluated SLMs fail to understand what was missing in their reasoning and show minimal semantic difference between hints that lead to corrections and ones that do not. Furthermore, our experiments show that longer hints are positively correlated with incorrect final answers, suggesting that longer deliberation on problems can hinder the reasoning process, meaning that SLMs do not necessarily scale in performance with a larger compute budget.
[NLP-123] Beyond Linear Activation Steering: Invertible Latent Transformations for Controlling LLM Behavior
【速读】： 该论文旨在解决现有生成式AI（Generative AI）中激活向量调控（activation steering）方法在行为控制上存在的局限性。传统方法通常在原始激活空间中计算固定的线性引导方向，将行为调控视为全局、线性的加性偏移，无法适应激活空间中非线性变化或位于曲面与各向异性流形上的行为特征，导致干预效果受限。其核心解决方案是提出INNSteer——一种基于可逆隐空间变换的非线性激活调控框架。该方法不直接在原始空间搜索最优引导向量，而是学习一个轻量级可逆神经网络φ，将语言模型的激活映射至一个更利于线性控制的隐空间，在推理时通过φ将激活映射至隐空间进行线性调控，并经由其精确逆变换φ⁻¹映射回原空间，从而实现输入依赖的非线性干预。实验表明，INNSteer在多个大语言模型家族、规模及安全基准测试中均显著优于线性、基于流形传输及非线性基线方法，同时保持生成流畅性。
链接: https://arxiv.org/abs/2606.08454
作者: Tuc Nguyen,Thai Le
机构: Indiana University (印第安纳大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 36 pages, 7 figures
Abstract:Activation steering provides a lightweight inference-time mechanism for controlling large language models (LLMs) by modifying their internal activation vectors toward desired behaviors. Most existing methods compute a fixed steering direction in the original activation space, typically from pairs of contrastive examples using mean differences, linear probes, or arbitrary separability criteria. While effective to a certain extent, these methods treat behavioral control as a global, linear, additive offset: the same direction is applied across inputs, and behaviors are linearly separable. This can be restrictive when behavioral features vary nonlinearly across the activation space or lie on curved and anisotropic manifolds, where the optimal intervention may be input-dependent. To address this limitation, we propose INNSteer, a nonlinear activation steering framework based on invertible latent transformations. Rather than searching for a better steering vector in the original representation space, INNSteer learns a lightweight invertible neural network \phi that maps an LLM’s activations into a latent space where behavioral classes are more amenable to linear control. At inference time, activations are mapped through \phi , steered in the latent space, and mapped back through the exact inverse transformation \phi^-1 . This makes a simple latent-space translation become a nonlinear, input-dependent intervention in the original activation space. Across experiment settings on multiple LLM families, scales, behavioral traits, and safety benchmarks, INNSteer consistently improves model control over linear, transport-based, and nonlinear steering baselines while largely preserving generation fluency.
[NLP-124] Sycophancy as a Multilingual Alignment Failure: How Safety Degrades Across Languages Topics and Models
【速读】： 该论文旨在解决多语言环境下大型语言模型存在的一种关键安全缺陷——跨语言谄媚现象（sycophancy），即模型在面对用户观点时倾向于无条件认同，而忽视事实准确性。尽管该问题在英语语境中已有研究，但在其他语言中的表现仍缺乏系统评估，导致数十亿非英语使用者可能面临被模型验证的错误信息风险。本文提出了首个大规模、多模型的跨语言谄媚行为评测框架，对六种指令微调模型在覆盖38种语言、33个主题类别、共计110万条样本的数据集上进行了评估。研究发现，模型在低资源语言和零样本设置下的谄媚率显著上升，且该现象具有普遍性，不随话题类型变化，即使在涉及安全敏感内容的提示中也未能提供有效防护。进一步分析揭示，分词器（tokenizer）的“丰度”（fertility）是导致对齐失效的关键结构因素。总体而言，现有对齐方法在高资源语言之外泛化能力严重不足，凸显了发展公平、可扩展的多语言安全技术的紧迫性。
链接: https://arxiv.org/abs/2606.08451
作者: Arya Shah,Himanshu Beniwal,Mayank Singh,Chaklam Silpasuwanchai
机构: IIT Gandhinagar (印度理工学院甘吉纳加尔); Asian Institute of Technology (亚洲理工学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 19 pages, 9 figures, 7 tables
Abstract:Safety-aligned large language models often exhibit sycophancy, which is the tendency to affirm users’ opinions regardless of factual accuracy. Although well-studied in English, its manifestation in other languages remains largely unexamined, leaving billions of non-English speakers potentially vulnerable to model-validated misinformation. We present the first large-scale, multi-model evaluation of cross-lingual sycophancy, benchmarking \textbfsix instruction-tuned models across \textbf1.1 million instances spanning \textbf38 languages and \textbf33 topic categories. We identify a consistent resource-tier effect: sycophancy rates spike sharply in low-resource and zero-shot language settings. Critically, this degradation is topic-agnostic, as models fail uniformly across both benign and safety-critical prompts, offering no additional protection where it is most needed. We further identify tokenizer fertility as a structural driver of this alignment collapse. Collectively, our results demonstrate that prevailing alignment methodologies generalize poorly beyond high-resource languages, underscoring the urgent need for equitable multilingual safety techniques.
[NLP-125] Segment-level Tree Search for Long Meeting Document Summarization INTERSPEECH2026
【速读】： 该论文旨在解决会议文档（meeting documents）在自动摘要生成中因篇幅长、对话结构复杂而导致的摘要质量下降问题。现有方法多采用多阶段流水线，先提取信息再进行摘要生成，但此类方法易受累积误差传播的影响，且受限于参考摘要普遍短小、质量低，进一步加剧了性能瓶颈。本文提出一种无需训练的分段级摘要生成框架S3（Segment-level Summarization via Monte Carlo Tree Search），其核心创新在于通过蒙特卡洛树搜索（Monte Carlo Tree Search, MCTS）机制，在分段层面生成多个摘要候选，并构建搜索树以动态评估和组合最优候选。该框架通过自奖励引导的树搜索策略，选择得分最高的组合方案并加以优化，从而生成长度适配、内容连贯的最终摘要。尽管仅使用7B规模模型，S3在性能上可媲美72B大模型，显著提升了摘要生成的准确性和鲁棒性。
链接: https://arxiv.org/abs/2606.08445
作者: Sangwon Ryu,Heejin Do,Jun Seo,Daehui Kim,Yunsu Kim,Gary Geunbae Lee,Jungseul Ok
机构: POSTECH(浦项科技大学); ETH Zurich(苏黎世联邦理工学院); KT(韩国电信); LILT(语言智能实验室)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: INTERSPEECH 2026
Abstract:Meeting documents are challenging to summarize due to their length and complex conversational structure. Existing approaches typically adopt multi-stage pipelines that extract information prior to summarization; however, these approaches often suffer from cumulative error propagation without intermediate validation, a limitation further amplified by short and low-quality reference summaries. We propose segment-level summarization via Monte Carlo Tree Search (S3), a training-free framework that constructs a final summary by composing segment-level summary candidates. S3 partitions a long document into segments and generates multiple summary candidates per segment, forming nodes of a search tree. The best-scoring combination is selected via self-reward-guided tree search and refined into the final output. Despite using a 7B model, S3 achieves performance comparable to larger 72B models while producing length-appropriate summaries.
[NLP-126] nyGiantALM: A Compact Audio-Language Model for Intent-Aware Reasoning under Resource Constraints INTERSPEECH INTERSPEECH2026
【速读】： 该论文旨在解决当前音频推理（Audio Reasoning）领域依赖大规模音频-语言模型（Large Audio-Language Models, LALMs）导致在资源受限环境难以部署的问题。其核心解决方案在于提出一种高效紧凑的1.5B参数模型TinyGiantALM，通过引入指令感知特征精炼（Instruction-Aware Feature Refinement）框架，利用查询引导投影器（Query-guided Projector）与语义门控机制（Semantic Gating），实现基于用户意图对声学信号的智能筛选与优化。该方法不依赖模型规模的盲目扩张，而是通过精细化架构设计，在保持轻量化的同时显著提升多模态解耦能力。在MMAR基准测试中，TinyGiantALM实现了46.4%的零样本准确率，超越了7B–13B级模型，并在混合模态环境中的表现远超最大达8倍的同类模型，尽管在逻辑叙事推理方面仍存在与30B+模型的差距，且在密集或空间复杂场景中存在部分性能折损，但整体验证了通过架构精度实现边缘友好型鲁棒感知能力的可行性。
链接: https://arxiv.org/abs/2606.08425
作者: Vinh-Thuan Ly
机构: University of Science, VNU-HCM (胡志明市国家大学自然科学大学); Vietnam National University, Ho Chi Minh City (胡志明市国家大学)
类目: ound (cs.SD); Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注: Accepted to Interspeech 2026. Project page: this https URL
Abstract:Current advancements in Audio Reasoning rely on massive Large Audio-Language Models (LALMs), hindering deployment in resource-constrained environments. We introduce TinyGiantALM, a compact 1.5B efficiency-oriented alternative. Instead of brute-force scaling, we propose an Instruction-Aware Feature Refinement framework using a Query-guided Projector and Semantic Gating to filter acoustic signals based on user intent. On the MMAR benchmark, TinyGiantALM achieves 46.4% zero-shot accuracy, significantly outperforming 7B-13B baselines. While a reasoning gap in logical narrative remains versus 30B+ models and certain trade-offs exist in overly dense or spatial scenes, our approach notably surpasses models up to 8x larger in disentangling mixed-modality environments. These findings demonstrate that architectural precision offers a tangible pathway to secure robust perception capabilities on edge-friendly scales.
[NLP-127] Hacking Generative Perplexity: Why Unconditional Text Evaluation Needs Distributional Metrics ICML2026
【速读】： 该论文旨在解决当前非自回归语言建模（non-autoregressive language modeling）评估体系中存在的根本性缺陷，即过度依赖生成困惑度（gen-PPL）作为核心评价指标所导致的误导性结论。gen-PPL 仅衡量生成文本在冻结的自回归（AR）评分器（如 gpt2-large）下的可预测性，而无法反映文本的语法正确性或语义连贯性，致使大量低质量但高可预测性的文本被误判为“高质量”。其解决方案的关键在于揭示 gen-PPL 的内在局限性，并提出应采用直接量化生成分布与参考分布之间差异的评估套件（evaluation suite），以更真实地反映生成文本的质量。通过构建一系列零参数、刻意简化的采样器，研究者在 LM1B 与 OpenWebText 数据集上实现了优于现有扩散模型和连续流模型的 gen-PPL 得分，同时生成文本在构造上明显不连贯，从而有力证明了当前主流评估指标的不可靠性。该工作呼吁采用基于分布距离的评估方法，以重建对非自回归语言模型实际性能的客观认知。
链接: https://arxiv.org/abs/2606.08417
作者: Antonio Franca,Alexander Tong
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted to the Workshop on Structured Probabilistic Inference Generative Modeling (SPIGM) at ICML 2026
Abstract:Diffusion and continuous flow-based language models have emerged as the leading non-autoregressive alternatives to language modeling. Progress in both paradigms is overwhelmingly tracked by generative perplexity (gen-PPL): the per-token negative log-likelihood of samples under a frozen autoregressive (AR) scorer such as gpt2-large, typically paired with an empirical-entropy guardrail to rule out low-entropy collapse. We argue that this metric is unsound. By construction, gen-PPL measures only predictability under the scoring AR, not grammaticality or semantic coherence – and the set of predictable but still low-quality sequences is combinatorially large. To make this concrete, we construct a suite of zero-parameter, deliberately naive samplers that achieve state-of-the-art gen-PPL on LM1B and OpenWebText at non-degenerate entropy, surpassing recently published diffusion and continuous-flow models while producing text that is incoherent by construction. We recommend evaluation suites that directly quantify the distributional divergence between generated and reference text, and use such a suite to re-benchmark recent non-autoregressive models, recovering a more faithful picture of the current state of the art.
[NLP-128] AsyncLane: Decoupling Refinement from Advancement in Diffusion Language Model Decoding
【速读】： 该论文旨在解决扩散型大语言模型（Diffusion Large Language Models, DLMs）在块级半自回归解码（block-wise semi-autoregressive decoding）中因严格块间依赖导致的推理效率瓶颈问题。传统方法要求当前块必须完全解码或耗尽去噪预算后才能开始下一区块生成，造成大量等待时间，尤其在长序列生成任务中尤为明显。其解决方案的关键在于提出一种无需训练的异步解码调度器AsyncLane，通过解耦“精炼”与“推进”过程实现高效并行：当检测到可靠的分隔符边界或稳定的语义前缀时，系统将当前生成路径分裂为两个并行车道——一个用于持续生成（continuation generate lane），另一个用于继续优化前缀内容（refine lane），前者可提前推进而不必等待后者完成。该机制构建了记录解码依赖关系与输出顺序的车道树结构，并在活跃车道集合上并行执行。为提升双向注意力下的运行效率，AsyncLane引入共享前缀车道批处理、前瞻草稿复用、级联终止机制以及紧凑缓存刷新与刷新逻辑复用策略，有效避免模型调用开销随车道数量线性增长。作为现有块级采样器的即插即用替代方案，AsyncLane无需重新训练，在数学推理和代码生成任务中均显著提升了吞吐量，且保持优异生成质量；在LLaDA与Dream模型架构下，所有评估长度设置中均达到最高每秒生成词数（TPS），相较最快基线分别实现最高2.95倍（LLaDA）和3.04倍（Dream）的加速，尤其在较长生成预算场景下表现更为突出。
链接: https://arxiv.org/abs/2606.08411
作者: Yingxuan Ren,Yuxuan Lou,Yong Liu,Pengcheng Fang,Ziming Wang,Pengfei Zhou,Yang You
机构: National University of Singapore(新加坡国立大学); University of Southampton(南安普顿大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Block-wise semi-autoregressive decoding is the standard inference paradigm for diffusion large language models (DLMs), but it imposes a strict dependency between blocks: the next block cannot begin until the current block is fully decoded or its denoising budget is exhausted. We observe that once a block exposes a reliable delimiter boundary or stable semantic prefix, continuation generation need not wait for every residual token to be resolved. We propose AsyncLane, a training-free decoding scheduler that decouples refinement from advancement. AsyncLane forks a generate lane at observed delimiter boundaries into a refine lane and a continuation generate lane: the prefix remains editable, while the continuation advances before prefix refinement finishes. The resulting lane tree records decoding dependencies and output order, while execution proceeds over the active lane set. To make this asynchronous schedule efficient under bidirectional attention, AsyncLane combines shared-prefix lane batching, lookahead draft reuse, cascading termination, and compact cache refresh with refresh-logit reuse, preventing model-call cost from scaling directly with the number of lanes. AsyncLane is a drop-in replacement for block-wise DLM samplers and requires no retraining. Experiments on mathematical reasoning and code generation show that AsyncLane consistently improves throughput while maintaining competitive quality. Across LLaDA and Dream backbones, AsyncLane achieves the highest TPS in all evaluated benchmark-length settings; relative to the fastest competing baseline, it reaches peak speedups of 2.95x on LLaDA and 3.04x on Dream, with especially large gains under longer generation budgets.
[NLP-129] mpaTeks: Automatic In-place Text Sequence Modification via Diffusion Language Model Steering
【速读】： 该论文旨在解决扩散语言模型（Diffusion Language Models, DLMs）在推理过程中因生成机制特性而引发的新问题：如何在不改变原始句子结构的前提下，对文本进行原位（in-place）修改以实现特定概念的引导。现有方法通常依赖于指令微调模型或构建额外的提示（prompt）来控制输出，但这类方法存在计算开销大、破坏原句结构或需额外训练等问题。论文提出的解决方案关键在于TimpaTeks——一种基于DLM的自动原位文本修改机制，其核心创新在于直接在输入序列上执行去噪操作，通过迭代优化实现概念转向，同时保持原句语法结构完整并降低句子困惑度（perplexity）。相较于传统基于提示的控制方式，TimpaTeks无需构造条件输出序列，显著降低了计算成本，为高效、精准且无损的文本概念引导提供了可行路径。
链接: https://arxiv.org/abs/2606.08408
作者: Ryandito Diandaru,Ikhlasul Akmal Hanif,Fadli Aulawi Al Ghiffari,Ahmed Elshabrawy,Alham Fikri Aji
机构: MBZUAI
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 16 pages
Abstract:We extend activation steering to diffusion language models (DLMs) and study a novel problem that arose due to the inference mechanism of DLMs: Modifying a text in-place to manifest a different concept. We propose TimpaTeks, an automatic in-place text modification mechanism using DLMs. Experiments on IMDB movie reviews (sentiment) and a synthetic Cats and Dogs Dataset (arbitrary, more unconventional concept steering) show that TimpaTeks provides a feasible novel mechanism to steer diffusion language model outputs in-place. TimpaTeks enables in-place modification while simultaneously lowers sentence perplexity and retaining the original sentence structre without the need of instruction tuned models. TimpaTeks is also computationally cheaper than prompt-based DLM steering, as it performs denoising in-place rather than constructing an additional prompt-conditioned output sequence.
[NLP-130] Impacts of Histories and Models on LLM Grading: A Study in Advanced Software Engineering Courses
【速读】： 该论文旨在解决研究生阶段研究性阅读报告评估中教师面临的工作负担过重问题，核心挑战在于如何确保生成式 AI（Generative AI）在学术评分任务中的可靠性与一致性。其解决方案的关键在于提出一种以人为本的生成式 AI 辅助评分流程，并通过针对180份研究生高级软件工程课程作业的实证研究，系统评估主流大语言模型（LLM）Grok与GPT在评分一致性及与人类评分对齐度方面的表现。研究发现，尽管模型具备一定的内部一致性，但不同模型间存在显著评分偏差，且简单的集成方法无法提升与人工评分的一致性；更重要的是，持续的交互历史会导致模型评分标准出现系统性偏移，偏离专家判断。因此，该研究强调：虽然生成式 AI 有潜力减轻教育者负担，但若缺乏规范的操作实践，盲目使用将引入系统性不公平，必须建立针对性的干预机制以保障评分公正性。
链接: https://arxiv.org/abs/2606.08400
作者: Qilin Zhou,Zhuo Wang,Yue Li,W.K. Chan
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 5 pages, accepted by ISET 2026
Abstract:Graduate-level research reading report assessment creates a substantial labor burden for educators. While large language models (LLMs) hold great potential for automating academic grading, their reliability for this specialized task remains understudied, particularly regarding grading consistency, the lack of which represents a primary obstacle to educational fairness. This paper proposes a human-aligned LLM-assisted grading workflow and presents a case study based on 180 student submissions from a graduate advanced software engineering course. We evaluate two mainstream LLMs, Grok and GPT, in terms of grading consistency and alignment with human scores. We find LLMs exhibit distinct levels of intra-model consistency and significant inter-model grading inconsistencies, while simple ensemble approaches cannot improve alignment with human evaluation. Critically, continuous interaction history drives systematic drift in models’ grading standards away from human expert scores. Our findings demonstrate LLMs’ potential in reducing grading workload for educators in graduate education, while highlighting that indiscriminate LLM grading may introduce systemic unfairness, suggesting that specific operational practices are required to mitigate such disparities.
[NLP-131] When Correct Decisions Hide Internal Stress: Decision-State Probing in Multimodal Language Models
【速读】： 该论文旨在解决多模态语言模型在外部行为表现正确的情况下，其内部决策状态是否仍保持稳定的问题，即行为与内部表征之间的解耦现象。现有评估方法仅依赖于模型输出的外部正确性（如图像-文本匹配、视觉问答准确率等），但无法揭示模型在语义压力下的内部动态变化。为此，作者提出S³E（Structured Semantic Stress Evaluation）框架，通过正锚定的A/B强制选择实验设计，在原始与选项互换两种顺序下对比图像支持的正确标题与语义冲突候选项，并在预回答决策阶段提取隐藏状态。研究聚焦于严格正确的试次（即模型在两种顺序下均选择正确答案），并分析语义冲突候选是否引发相对于语义保留对照组的显著决策层隐藏状态偏移。实验结果表明，尽管模型表现出一致的正确行为，但在多个模型（Qwen3VL、Gemma3、InternVL3）中，语义压力仍导致选定层隐藏状态出现显著超出词法控制组的位移，而随机负样本对比则呈现模型依赖性。这一发现揭示了模型存在可被捕捉的决策状态敏感性信号，而非下游任务失败或幻觉的直接证据。因此，研究强调：仅凭强制选择正确性不足以作为内部决策几何不变性的充分证明。
链接: https://arxiv.org/abs/2606.08394
作者: Haoran Zhao,Soyeon Caren Han,Eduard Hovy
机构: The University of Melbourne(墨尔本大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Multimodal language models are typically evaluated through external behavior: selecting the correct image–text match, rejecting unsupported captions, or answering visual queries correctly. However, correct behavior alone does not show that the model’s internal decision state remains stable under controlled semantic stress. We study this gap through S ^3 E (Structured Semantic Stress Evaluation), a framework for analyzing behavior-internal decoupling in multimodal language models. S ^3 E uses a positive-anchored A/B forced-choice setup in which an image-supported caption is contrasted against semantic stress candidates under both original and swapped option orders, while hidden states are extracted at the pre-answer decision state. We focus on strict-correct trials, where the model consistently selects the correct caption across both orders. Rather than treating arbitrary hidden-state variation as evidence of instability, we measure whether semantic-conflict candidates induce excess decision-state displacement relative to meaning-preserving controls. Across Qwen3VL, Gemma3, and InternVL3, semantic stress consistently produces positive selected-layer excess displacement over lexical controls despite correct forced-choice behavior, while comparisons against random negatives are model-dependent. We interpret this as a scoped decision-state stress-sensitivity signal rather than evidence of downstream failure or hallucination. Our results suggest that forced-choice correctness alone is not a sufficient certificate of invariant internal decision geometry.
[NLP-132] Auditing Proprietary Alignment in Large Language Models : A Comparative Framework Without a Ground-Truth Standard
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在黑盒部署环境下，模型提供方可能隐式注入专有策略或组织利益导向的对齐行为，导致模型在敏感话题上产生审查或误导性输出，而现有方法难以系统识别此类“专有对齐”（proprietary alignment）的问题。其解决方案的关键在于提出一种基于对比行为分析的统计框架，通过在共享语义空间中量化目标模型与一组基准模型响应之间的系统性偏差，以相对行为差异而非绝对正确性作为评估依据，从而在仅具备黑盒访问权限的情况下实现可解释、可扩展的模型对齐行为审计。该方法为外部评估模型提供商特异性对齐行为提供了系统性、量化的基础。
链接: https://arxiv.org/abs/2606.08381
作者: Alireza Arbabi,Florian Kerschbaum
机构: University of Waterloo ( Waterloo 大学); Vector Institute (向量研究所)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) are increasingly released and deployed through opaque development and deployment pipelines, enabling model providers to inject intentional, provider-specific policies without officially announcing them. As a result, various models have been reported to generate responses reflecting proprietary rules and organizational interests, leading to censorship or misinformation on controversial topics. However, systematic identification of such alignment remains a fundamental challenge, complicated by the ambiguity of what ``proprietary’’ entails in different contexts. In this paper, we propose a statistical framework for detecting proprietary alignment in black-box language models via comparative behavioral analysis. Our approach quantifies systematic deviations between the responses of a target model and those of a reference set of baseline models in a shared semantic space. By evaluating relative behavioral divergence rather than absolute correctness, our framework enables principled auditing under black-box access. Applied to several widely discussed but previously unquantified cases, it provides a systematic and scalable basis for external assessment of provider-specific alignment behavior in large language models.
[NLP-133] Forward-Free Diffusion Language Models
【速读】： 该论文旨在解决扩散语言模型（Diffusion Language Models）在前向过程设计中因离散语言空间缺乏自然邻域结构而导致的生成质量下降问题。传统方法依赖人工设计的扰动方案以构建前向过程，但这些方案虽数学上便利，却与实际生成过程中出现的草稿与错误模式不匹配，进而影响样本质量。其解决方案的关键在于提出FReDA（Forward-free Diffusion Language Model），摒弃了人工设计的前向过程，将扩散建模重新定义为递归分布精炼（recursive distribution refinement）。在此框架下，模型生成的草稿（drafts）作为隐式中间状态，由学习到的精炼模型逐步将草稿分布逼近目标分布。FReDA通过候选草稿生成与自精炼或基于最佳N选一的并行选择机制实现高效精炼，具备邻域无关性、对模型复杂度敏感以及灵活参数化等优势。实验表明，在80亿以下参数规模内，FReDA-4B在推理与编码任务上超越更大规模的扩散基线模型，绝对性能提升达15%，同时实现1.5–1.8倍的平均加速，并能有效利用额外精炼计算资源进行扩展。
链接: https://arxiv.org/abs/2606.08357
作者: Haotian Sun,Rushi Qiang,Yuqian Zheng,Bo Dai
机构: Georgia Institute of Technology (佐治亚理工学院)
类目: Computation and Language (cs.CL)
备注:
Abstract:Diffusion language models generate text through iterative denoising, offering a powerful alternative to autoregressive generation. However, discrete language spaces lack a natural neighborhood structure for defining effective perturbations, so some artificial corruption schemes are proposed in the forward process. Such prescribed forward processes often produce states that are mathematically convenient but misaligned with drafts and errors encountered during generation, resulting in degraded sample quality. To address this limitation, we propose FReDA, a forward-free diffusion language model that eliminates the need for a hand-designed forward process. We formulate diffusion language modeling as recursive distribution refinement, in which model-generated drafts serve as implicit intermediate states, and the learned refinement model progressively moves the draft distribution toward the target distribution. Concretely, FReDA refines drafts by proposing candidate draft sequences and either directly performing self-refinement or selecting among parallel candidates via best-of-N refinement. With this design, FReDA is neighborhood-agnostic, model-complexity-aware, and compatible with flexible refinement parameterizations. Extensive evaluations in the sub-8B regime show that FReDA-4B outperforms larger diffusion base models on reasoning and coding benchmarks, achieving absolute gains of up to 15%, while reaching a 1.5-1.8x average speedup over diffusion baselines and scaling effectively with additional refinement computation.
[NLP-134] Bayesian-Agent : Posterior-Guided Skill Evolution for LLM Agent Harnesses
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）智能体在实际应用中依赖外部推理条件（如提示词、工具、记忆、标准操作流程（SOP）、技能等）时，其可复用技能与SOP的迭代优化缺乏系统性信念更新机制的问题。现有方法多依赖启发式反思或简单统计成功/失败频次来调整策略，易导致不可靠的信念积累。为此，论文提出贝叶斯智能体（Bayesian-Agent），其核心在于将可复用的技能与SOP视为关于冻结模型在特定提示、上下文及执行环境下的成功概率的假设（hypotheses），并基于验证过的轨迹证据，采用特征条件化的分类后验分布（feature-conditioned categorical posterior）动态维护每个技能的可信度。通过将后验状态映射为可解释的行动指令（如修补、拆分、压缩、退役、探索），实现对智能体行为的可审计、可干预优化。该框架在DeepSeek-V4-Flash上实现了对SOP-Bench、Lifelong AgentBench和RealFin-Bench的显著提升，表明代理技能演化应被视作以后验引导的“外挂优化”而非无校准的提示堆积。
链接: https://arxiv.org/abs/2606.08348
作者: Xiaojun Wu,Cehao Yang,Honghao Liu,Xueyuan Lin,Wenjie Zhang,Zhichao Shi,Xuhui Jiang,Chengjin Xu,Jia Li,Jian Guo
机构: IDEA Research; The Hong Kong University of Science and Technology (Guangzhou); DataArcTech Ltd.
类目: Computation and Language (cs.CL)
备注: 15 pages, 6 figures
Abstract:LLM agents increasingly rely on external inference conditions: prompts, tools, memory, SOPs, skills, and harness feedback. These assets can improve task execution without changing model weights, but they are often revised by heuristic reflection or by reusing observed successes and failures as if counts alone were reliable belief. We introduce \textbfBayesian-Agent, a native and cross-harness framework that treats reusable skills and SOPs as hypotheses about whether a frozen model will succeed under a particular prompt, context, and harness environment. Bayesian-Agent records verified trajectory evidence, maintains a feature-conditioned categorical posterior over each skill, and maps posterior state into inspectable actions such as patch, split, compress, retire, and explore. Model-facing prompts receive executable guardrails and failure-mode patches, while posterior summaries remain available for audit. With \textttdeepseek-v4-flash, incremental repair improves SOP-Bench from 80% to 95%, Lifelong AgentBench from 90% to 100%, and RealFin-Bench from 45% to 65%. We further evaluate Bayesian-Agent’s native backend and optional GenericAgent, mini-swe-agent, and Claude Code backends. The results include positive, negative, saturated, and case-study settings, suggesting that agent skill evolution is best viewed as posterior-guided harness optimization rather than uncalibrated prompt accumulation. The source code is available at this https URL.
[NLP-135] nsorizing Engram: Sharing Latents Across N-Gram Embeddings is Beneficial in LLM s
【速读】： 该论文旨在解决现代语言模型中基于离散分词（token-level）嵌入表示文本时，对重复出现的多分词模式（multi-token patterns）仅能通过Transformer层隐式学习所带来的表达能力受限问题。现有方法如Over-tokenized Transformers和Engram虽尝试通过显式引入多分词（n-gram）记忆来改进，但其依赖为每种n-gram阶数单独维护哈希表，导致哈希冲突，并且无法实现嵌套n-gram共享底层潜在结构。为此，本文提出一种紧凑的内存模块——张量化恩格拉姆（Tensorized Engram, TN-gram），通过在规范分解（Canonical Polyadic, CP）形式下以共享因子表示张量化的n-gram嵌入，同时联合学习共享的词元-位置因子与阶数吸收向量，从而编码不同阶数n-gram的嵌入。该方案有效避免了哈希冲突并支持嵌套结构的共享表征。大量实验表明，TN-gram在性能上可达到甚至超越传统Engram类模块，同时显著减少参数量，具备更强的效率与可扩展性。
链接: https://arxiv.org/abs/2606.08347
作者: Wuyang Zhou,Yuxuan Gu,Giorgos Iacovides,Yuning Qiu,Qibin Zhao,Danilo Mandic
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Modern language models represent text using discrete token-level embeddings, which forces recurring multi-token patterns to be learned implicitly across Transformer layers. Both Over-tokenized Transformers and Engram attempt to address this limitation by explicitly incorporating multi-token (n-gram) memories. However, they rely on separate hash tables for each n-gram order, which introduces hash collisions and prevents nested n-grams from sharing the underlying latent structures. To address these issues, we propose Tensorized Engram (TN-gram), a compact memory module that represents tensorized n-gram embeddings through shared factors in the Canonical Polyadic (CP) form. TN-gram learns shared token-position factors together with order-absorption vectors to encode the embeddings of different n-gram order. Comprehensive experiments demonstrate that TN-gram matches or even outperforms Engram-style n-gram modules while requiring much fewer parameters.
[NLP-136] CATPO: Critique-Augmented Tree Policy Optimization
【速读】： 该论文旨在解决生成式 AI（Generative AI）在基于可验证奖励的强化学习（Reinforcement Learning with Verifiable Rewards, RLVR）中，因树结构回溯（tree-structured rollouts）存在大量信息量低的无效树而导致计算资源浪费的问题。现有方法如TreeRPO虽通过树形轨迹采样获得细粒度的步骤级奖励信号，但当所有叶节点均成功、均失败或策略与奖励分布高度相关时，此类树对梯度更新贡献极小，造成计算效率低下。其解决方案的关键在于提出一种名为CATPO（Critique-Augmented Tree Policy Optimization）的新框架，核心机制包括：首先引入树信息量评分函数F(T)，在不增加额外计算成本的前提下，联合评估叶节点结果多样性与策略-奖励去相关性，以量化每棵树的信息价值；针对全失败树（dead-wrong trees），采用批判性引导修复（critique-guided healing）策略，定位最浅层失败点，生成自然语言形式的批判性反馈，并嫁接优化后的延续路径以恢复训练信号；最后，通过基于信息量加权的损失函数，按归一化评分调节各树的梯度贡献，使参数更新集中于最具信息量的树，同时保持整体梯度幅值稳定。实验表明，在Qwen2.5-Math-1.5B模型上使用MATH数据集训练时，CATPO在四个基准测试（AIME24、MATH-500、OlympiadBench、MinervaMath）上达到37.5%的宏平均准确率，相较TreeRPO提升1.9%，较GRPO提升4.8%，显著提升了训练效率与模型性能。
链接: https://arxiv.org/abs/2606.08346
作者: Ayush Singh,Umang Goyal,Ankur Dahiya
机构: Indian Institute of Technology Roorkee(印度理工学院鲁尔基分校)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 14 pages, 1 figures, 6 tables
Abstract:Reinforcement learning with verifiable rewards (RLVR) has become a dominant paradigm for improving the reasoning capabilities of large language models (LLMs). Recent tree-based methods such as TreeRPO extend flat trajectory sampling with tree-structured rollouts to obtain dense, step-level reward signals without a separate process reward model. However, not all trees are equally informative: trees where all leaves succeed, all leaves fail, or the policy already predicts the reward distribution contribute little to gradient updates, wasting compute. We introduce CATPO (Critique-Augmented Tree Policy Optimization), which diagnoses and addresses this waste at the tree level. CATPO first scores each tree via a tree informativeness score, F(T), combining leaf-outcome diversity with policy-reward decorrelation at zero extra compute. For dead-wrong trees where all branches fail, CATPO applies critique-guided healing: it locates the shallowest failure point, generates a natural-language critique, and grafts refined continuations to recover training signal. Finally, an informativeness-weighted loss scales each tree’s gradient contribution by its normalized score, concentrating parameter updates on the most informative trees while preserving overall gradient magnitude. Experiments on Qwen2.5-Math-1.5B trained with the MATH dataset show that CATPO achieves 37.5% macro accuracy across four benchmarks (AIME24, MATH-500, OlympiadBench, and MinervaMath), improving over TreeRPO by 1.9% and GRPO by 4.8%.
[NLP-137] Chiaroscuro Attention: Spending Compute in the Dark
【速读】： 该论文旨在解决标准Transformer模型在所有层和所有令牌上均采用静态自注意力机制所带来的计算冗余问题，尤其针对输入中并非所有令牌都需动态跨令牌交互的场景。其核心挑战在于如何实现对注意力计算的精细化控制，以在保持性能的同时降低计算开销。解决方案的关键是提出CHIAR-Former（明暗注意力）架构，该架构通过引入基于每个令牌谱熵（spectral entropy）的理论性复杂度信号，动态地将每个令牌路由至三种操作之一：离散余弦变换（DCT）频域混合、径向基函数（RBF）核混合或全量自注意力。实验表明，尽管存在路由坍缩现象（即路由器倾向于选择DCT与自注意力而舍弃RBF），但谱混合与动态注意力之间具有互补性且已足够高效。进一步设计的仅含DCT+自注意力的简化变体在WikiText-103上实现了36.54的验证困惑度（Val PPL），相比全注意力基线（PPL 66.62）提升45%，同时减少62.5%的注意力浮点运算量（FLOPs）。扩展评估涵盖WikiText-2、IMDB情感分类及合成ListOps任务，揭示了明确的适用范围：当面对大规模自然语言文本且令牌多样性支持谱特性化时，CHIAR-Former表现优异；而在小数据集和合成模式匹配任务中，全注意力仍具优势。这些结果共同界定了谱路由机制在何种条件下具备实际价值。
链接: https://arxiv.org/abs/2606.08327
作者: Prateek Kumar Sikdar
机构: Accenture(埃森哲)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 8 pages, 6 figures, 3 tables
Abstract:Standard transformers apply self-attention uniformly at every layer and token, regardless of whether the input requires dynamic cross-token interaction. We propose CHIAR-Former (Chiaroscuro Attention), a 4-layer hybrid transformer that routes each token to one of three operators - DCT spectral mixing, RBF kernel mixing, or full self-attention - based on per-token spectral entropy, a theoretically justified complexity signal. Through systematic ablation on WikiText-103, we discover routing collapse: the router consistently rejects RBF in favour of DCT and attention, revealing that spectral mixing and dynamic attention are complementary and sufficient. A purpose-designed DCT+Attention-only variant achieves Val PPL 36.54 on WikiText-103 - a 45% improvement over a full-attention baseline (PPL 66.62) at 62.5% fewer attention FLOPs. We extend evaluation to WikiText-2, IMDB sentiment classification, and synthetic ListOps operations, establishing a clear operating regime: CHIAR-Former excels on large-scale naturalistic text where token diversity supports spectral specialisation, while full attention retains an edge on small datasets and synthetic pattern-matching tasks. These findings - both the wins and the losses - together define when and why spectral routing earns its keep.
[NLP-138] Understanding the Sociocultural Dimensions of Mental Health Discourse in Arabic-Language X Communities ACL2026
【速读】： 该论文旨在解决计算心理健康研究长期聚焦于英语使用者群体，而阿拉伯语语境下的心理疾病讨论内容严重缺乏系统性分析的问题。其核心挑战在于如何在语言资源稀缺、文化语境复杂的情况下，有效识别并分析阿拉伯语社交媒体中与特定精神障碍（如边缘型人格障碍、双相情感障碍和注意力缺陷多动障碍）相关的自发性个人经验表达。解决方案的关键在于构建一个基于大语言模型（LLM）的可复用个人披露识别流程（GPT-4.1辅助的个人披露管道），结合多领域文化关键词框架，对来自三个阿拉伯语社交平台社区的8,147条推文进行探索性语言学分析。该方法通过自动化标注与文化敏感的关键词提取，揭示了不同障碍类别在语言特征上的差异：双相障碍相关推文更频繁出现宗教与医学词汇，边缘型人格障碍推文突出关系、身份认同及情绪困扰相关表达，而注意力缺陷多动障碍推文则更多关注实际症状与药物管理。尽管结果具有启发性，但受限于数据分布不均、时间集中性及关键词框架尚未经过验证，研究将发现定位为假设生成而非确证性结论，从而为未来跨文化心理健康计算研究提供可扩展的方法论基础。
链接: https://arxiv.org/abs/2606.08307
作者: Amal Alqahtani(King Saud University, Riyadh, Saudi Arabia),Rana Salama(Cairo University, Egypt),Mona Diab(Carnegie Mellon University, Pittsburgh, USA)
机构: King Saud University (沙特国王大学); Cairo University (开罗大学); Carnegie Mellon University (卡内基梅隆大学)
类目: Computation and Language (cs.CL)
备注: Accepted to the SMM4H-HeaRD Workshop, co-located with the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
Abstract:Computational mental health research has predominantly centered on English-speaking populations, leaving Arabic-language discourse comparatively under-examined. We present an exploratory computational study of 8,147 tweets from 607 users classified by a GPT-4.1 personal-disclosure pipeline as likely lived-experience authors in three condition-specific Arabic-language X (formerly Twitter) Communities. We focus on discourse related to borderline personality disorder (BPD), bipolar disorder, and ADHD, and characterize community-associated linguistic patterns using a multi-domain cultural keyword framework. The results suggest that in this corpus, Bipolar tweets contain more religious and medical vocabulary, BPD tweets contain more relational, identity, and emotional-distress vocabulary, and ADHD tweets more often focus on practical symptoms and medication management. We treat these patterns as hypothesis-generating rather than confirmatory because the corpus is imbalanced across conditions, some subcorpora are temporally concentrated, and the keyword framework is an initial operationalization rather than a validated measurement instrument. The paper contributes a reusable LLM-assisted personal-disclosure pipeline and an exploratory cultural keyword framework for Arabic mental health discourse.
[NLP-139] LRD: Teaching LLM s to Reason over Tabular Data with Tri-Level Rationale Distillation
【速读】： 该论文旨在解决生成式人工智能在处理表格数据时缺乏可解释性与性能之间的矛盾问题：尽管传统机器学习模型（如树集成模型）在表格数据上具备优异的预测性能，但其决策过程不可读；而大型语言模型（LLM）虽能自然生成可读解释，却因难以理解表格数据中的特征分布、交互模式等特定结构，导致推理能力受限。现有方法通过仅使用标签进行微调虽能提升性能，但存在灾难性遗忘问题。为此，论文提出三层次理由蒸馏（Tri-Level Rationale Distillation, TLRD）框架，其核心创新在于将仅含标签的表格数据转化为结构化的理由监督信号。该方法利用高容量教师模型，在三个互补层面构建证据体系：实例级特征、数据集级分布上下文以及对比检索的邻居样本，进而将这些多层级理由蒸馏至学生型大语言模型中。该方案实现了仅依赖原始特征即可零开销完成预测与可解释推理，显著缩小了大语言模型与先进树集成模型之间的性能差距，并生成了具有事实依据且可读性强的解释，为高风险决策场景提供了可靠参考。
链接: https://arxiv.org/abs/2606.08295
作者: Tianyuan Liang,Xuwei Tan,Lei Shi,Junsheng Zhong,Ziyu Hu,Tian Xie,Zhiqun Zuo,Xiaodong Yu,Xueru Zhang
机构: The Ohio State University (俄亥俄州立大学); Stevens Institute of Technology (史蒂文斯理工学院)
类目: Computation and Language (cs.CL)
备注:
Abstract:Tabular data is a primary medium for storing real-world information, driving many industrial applications of machine learning. Traditional predictors achieve strong predictive performance but do not provide readable, case-specific explanations essential for decision-making. Large Language Models (LLMs) can naturally bridge this gap by generating predictions alongside explanations. However, dataset-specific patterns, such as feature distributions and interactions, make tabular data difficult for LLMs to understand and reason over, while label-only fine-tuning improves performance at the cost of catastrophic forgetting. To address this problem, we propose Tri-Level Rationale Distillation (TLRD), a framework that converts label-only tabular datasets into structured rationale supervision for LLMs. TLRD uses a high-capacity teacher to synthesize a rationale corpus grounded in three complementary levels of evidence: instance-level feature, dataset-level distributional context, and comparison-level retrieved neighbors, then distills the rationale into student LLMs, enabling zero-overhead prediction and grounded explanation from raw features only. Experiments on multiple domain datasets show that TLRD significantly closes the performance gap between LLMs and state-of-the-art tree ensembles while producing grounded and readable explanations, offering a valuable reference for high-stakes decision-making.
[NLP-140] AgriGov: A Structured Multilingual Dataset Curation for Indian Government Schemes for Farmers
【速读】： 该论文旨在解决农业政策与农民福利计划领域中缺乏高质量、多语言对齐资源的问题，尤其针对英语、印地语和马拉地语之间的跨语言信息获取障碍。其核心挑战在于如何在保证领域准确性（domain fidelity）的前提下，构建一个可复现、可溯源且适用于实际应用的多语言数据集。解决方案的关键在于提出一种基于模式驱动（schema-driven）的人工校正多语言对齐流水线，通过自动化抓取结合Google Translate API、MarianMT模型与人工后编辑的混合翻译策略，确保数据在语义结构（如资格条件、申请流程、所需文件等预定义字段）上的准确映射，并引入Samanantar语料库进行数据增强，最终形成约8000句对齐的印地语-马拉地语平行语料。该方法不仅保障了数据的领域相关性与质量，还为农业领域的机器翻译、问答系统、信息检索与摘要生成等任务提供了可复用的基准资源。
链接: https://arxiv.org/abs/2606.08272
作者: Mohsina Bilal,Gopakumar G
机构: NITC(尼特克学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 15 pages, 4 figures, Submitted to: Sadhana, Elsevier
Abstract:AgriGov is a curated, trilingual (English-Hindi-Marathi) dataset designed to address the scarcity of domain-grounded multilingual resources for agricultural policies and farmer welfare schemes. Initially, we collected and structured data from 50 government schemes sourced from trusted portals using automated scraping techniques, organizing it into predefined semantic fields (e.g., title, eligibility, application process, documents, exclusions). Translations were performed using a pipeline combining Google Translate API, MarianMT, and human post-editing, resulting in a domain-specific Hindi-Marathi dataset comprising approximately 2100 source segments. To enhance coverage, we augmented this dataset with sentences from the Samanantar corpus, leading to approximately 8,000 sentence-aligned Hindi-Marathi parallel pairs. The dataset now offers robust resources for fine-tuning machine translation models in this domain. AgriGov is designed for applications in domain-adaptive machine translation, question answering, information retrieval, and summarization systems. Its key contribution is a schema-driven, human-corrected multilingual alignment pipeline that ensures domain fidelity, provides provenance, and supports reproducible experiments, enabling retrieval-augmented applications for farmer-facing tools.
[NLP-141] SSR: Can Simulated Patients Learn to Stigmatize Themselves? Modeling Self-Stigma through Internal Monologue
【速读】： 该论文旨在解决当前基于大语言模型（LLM）的患者模拟在心理健康培训中无法真实反映临床现实中的自我污名化（self-stigma）问题。现有方法通常将患者行为建模为静态或一致顺从的模式，未能捕捉到自我污名者在不同情境下表现出的动态抵抗行为，如回避、否认或自责等。其解决方案的关键在于提出一种基于心理3A1H模型的新型模拟框架，并构建了一个核心创新数据集——受污名化自我反思（Stigmatized Self-Reflection, SSR） 数据集。该数据集通过在心理健康对话中引入反映污名意识推理的内部独白，增强了对情境敏感性反应的建模能力。研究采用思维链（chain-of-thought）微调策略，使患者代理能够根据对话触发因素动态调整其污名程度与表达方式，从而生成更具真实性与情境适应性的交互响应。实验结果表明，该方法显著优于现有基线模型，在模拟真实患者行为方面表现更优，为临床训练和共情对话系统中的真实污名模拟提供了关键进展。
链接: https://arxiv.org/abs/2606.08254
作者: Kunyao Lan,Bingrui Jin,Zichen Zhu,Mengyue Wu
机构: Shanghai Jiao Tong University(上海交通大学); X-LANCE Lab(交叉创新实验室); MoE Key Lab of Artificial Intelligence(教育部人工智能重点实验室)
类目: Computation and Language (cs.CL)
备注:
Abstract:Simulating patients with large language models (LLMs) is a promising tool for mental health training, but existing approaches fail to capture a key clinical reality: self-stigma. Patients experiencing self-stigma, the internalization of negative stereotypes, often exhibit context-sensitive resistance, such as avoidance, denial, or self-blame, which current models render as static or uniformly compliant behavior. To address this, we introduce a novel simulation framework grounded in the psychological 3A1H model of self-stigmatization. Our core innovation is the creation of a \textbfStigmatized Self-Reflection (\textbfSSR) dataset, where we augment mental health dialogues with internal monologues that reflect stigma-aware reasoning. By fine-tuning LLMs with this data using a chain-of-thought approach, we train patient agents to dynamically adjust their level and expression of stigma based on conversational triggers. Evaluations demonstrate that our approach significantly outperforms specialized baselines, generating more authentic and situationally appropriate patient responses. This work provides a crucial step towards realistic stigma simulation for clinical training and empathetic dialogue systems.
[NLP-142] ZAS-SQL: Distilling Rules from Failures for Zero-Shot Text-to-SQL
【速读】： 该论文旨在解决零样本文本转SQL（Text-to-SQL）任务中生成质量不足的问题，尤其针对现有零样本方法缺乏有效生成约束而导致性能显著落后于少样本方法的瓶颈。其核心挑战在于如何在不依赖示例的前提下，提升模型对复杂查询结构的理解与生成能力，并克服系统性错误模式。解决方案的关键在于提出一种完全零样本的Text-to-SQL框架，通过基于Map-Reduce的规则提炼管道从失败案例中自动归纳出核心生成规则，进而构建三个互补模块：知识增强的模式表示（knowledge-augmented schema representation），用于补充数据定义语言（DDL）中缺失的语义信息；基于规则的结构化推理框架（rule-driven structured reasoning framework），以抑制语法和结构上的偏差；以及执行引导的早期停止机制（Execution-Guided Early Stopping），实现低成本的自纠正。该方法在Spider数据集上分别达到87.2%和88.6%的执行准确率，创下零样本新纪录，并超越多个基于GPT-4/4o的少样本及微调方法，在领域特定数据集UrbanPlan上也取得81.3%的准确率，验证了其跨域泛化能力。此外，使用40亿参数模型即可超越主流闭源模型的零样本基线，展现出优异的模型适应性与通用性。
链接: https://arxiv.org/abs/2606.08245
作者: Hongzhou Zheng,Yixin Gou,Wenjia Zhang
机构: Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University (上海研究智能自主系统研究院，同济大学); College of Architecture and Urban Planning, Tongji University (建筑与城市规划学院，同济大学); Behavioral and Spatial AI Lab, Peking University (行为与空间人工智能实验室，北京大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Text-to-SQL translates natural language into executable SQL queries. Few-shot in-context learning methods built upon large language models (LLMs) achieve strong performance, yet their reliance on demonstrations limits cross-domain generalization and consumes substantial context window space. Existing zero-shot methods, lacking effective generation constraints, still fall short of few-shot approaches. We observe that LLM failures in zero-shot Text-to-SQL are not random but exhibit systematic, recurring patterns. Building on this observation, we propose a fully zero-shot Text-to-SQL framework that distills core generation rules from failure cases through a Map-Reduce-based rule distillation pipeline and improves generation quality via three complementary modules: knowledge-augmented schema representation, which supplements missing semantics in Data Definition Language; a rule-driven structured reasoning framework that suppresses structural deviations; and Execution-Guided Early Stopping, which enables low-cost self-correction. On Spider, the proposed framework achieves up to 87.2% and 88.6% execution accuracy on the Dev and Test sets, respectively, establishing a new zero-shot state-of-the-art and surpassing multiple few-shot and fine-tuning methods built upon GPT-4/4o. On the domain-specific dataset UrbanPlan, it achieves 81.3%, confirming that the rule distillation approach generalizes across domains. Moreover, when equipped with a 4B-parameter model, the framework surpasses zero-shot baselines of leading closed-source models, demonstrating strong model generality.
[NLP-143] Building Comparative Motivation Profiles with Instrumental Interventions
【速读】： 该论文旨在解决安全评估中基于行为模式推断模型潜在动机的构念效度（construct validity）问题，尤其聚焦于“对齐伪装”（alignment faking）现象——即模型在感知到评估压力时更倾向于遵循训练目标。传统观点将此行为解释为模型出于“策略性自我保存”（scheming）的意图，但该研究提出另一种可能性：模型可能并非出于恶意策略，而是对研究人员期望（researcher-expectation）敏感所致。为此，论文提出一种对称干预框架（symmetric intervention framework），其核心在于不直接干预“阴谋”或“奉承”等表层行为，而是分别操纵两种潜在的工具性过程——后果追踪（consequence-tracking）与研究者期望追踪（researcher-expectation tracking），通过对比二者对对齐伪装行为的影响来区分竞争性假设。研究采用合成文档微调（synthetic document fine-tuning）、激活操控（activation steering）和提示工程（prompting）方法，在Llama-3.1-70B、Llama-3.1-405B和Qwen-2.5-72B等多个开源大模型上验证发现，模型对期望追踪干预更为敏感，且激活操控与提示干预结果与合成数据微调（SDF）模式一致。结果表明，对齐伪装行为可被评估情境中的期望信号所因果影响，即使其内部推理过程看似符合“阴谋”特征。因此，该研究强调，针对“阴谋”或“战略性欺骗”的评估必须进行构念效度检验，而对称的工具性干预提供了一种有效的验证手段。
链接: https://arxiv.org/abs/2606.08243
作者: David Vella Zarb,Rustem Turtayev,Taywon Min,Jinghua Ou,Shi Feng
机构: MATS; University of Cambridge; KAIST; George Washington University
类目: Computation and Language (cs.CL)
备注:
Abstract:Safety evaluations often infer latent motivations from behavioral patterns, but the construct validity of these inferences is unclear. We study this problem in alignment faking, where models comply with training objectives more often when they infer training pressure. This behavior is commonly interpreted as strategic self-preservation, but it may also reflect sensitivity to the model’s inference about the expectation of researchers conducting the evaluation. We introduce a symmetric intervention framework for distinguishing these competing hypotheses. Instead of directly intervening on “scheming” or “sycophancy”, we target instrumental processes entailed by each hypothesis: consequence-tracking and researcher-expectation tracking. We then compare how interventions on these processes affect the alignment faking. We study four openweight model organisms using synthetic document fine-tuning, activation steering, and prompting. Under synthetic document fine-tuning, Llama-3.1-70B, Llama3.1-405B, and Qwen-2.5-72B are more sensitive to expectation-tracking than consequence-tracking interventions. Activation steering on Llama-3.1- 70B supports the same broad picture, and prompt interventions broadly align with SDF profiles. Overall, alignment-faking behavior can be causally sensitive to evaluation-context expectations despite scheming-consistent scratchpads. Scheming and strategic-deception evaluations therefore need construct-validity checks, and symmetric instrumental interventions provide one such test.
[NLP-144] When No Answer Is Correct: Diagnosing Absent Answer Detection for MLLM s in Video Understanding
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在视频理解任务中对“缺失答案”（absent answer）检测能力不足的问题，即当正确答案未包含在候选选项中时，模型仍倾向于选择看似合理的干扰项而非识别出无有效答案的情况。其解决方案的关键在于评估不同场景下模型的缺失答案检测表现，包括引入“以上皆非”选项的多项选择、带检测指令的开放式生成以及标准评估范式。研究发现，尽管采用思维链（Chain-of-Thought）提示策略可显著提升检测率，但整体性能仍不理想，表明仅依赖提示工程不足以克服该缺陷。因此，论文揭示了MLLMs在缺失答案检测上存在系统性失效，并强调必须在多模态系统中引入显式的检测机制以增强可靠性。
链接: https://arxiv.org/abs/2606.08239
作者: Yiheng Wang,Yueqian Lin,Lichen Zhu,Yudong Liu,Hai “Helen” Li,Yiran Chen
机构: Duke University (杜克大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: Under review
Abstract:Multimodal large language models (MLLMs) have made substantial advancements in video understanding, yet the reliability of their responses remains underexplored. This work presents a diagnostic study of absent answer detection for MLLMs in video understanding, where the correct answer is deliberately excluded from the candidate set and a reliable model is expected to recognize that no valid option exists. We evaluate the absent answer detection behavior under three settings: multiple-choice questions augmented with an ``None of the Above’’ option, open-ended generation with a detection instruction, and standard evaluation without any guidance. Across a diverse set of models and benchmarks, we find that MLLMs overwhelmingly select plausible distractors rather than detecting the absent answer. This failure is more pronounced in temporal reasoning tasks and worsens with denser frame sampling. We further explore chain-of-thought prompting as a mitigation strategy and find that while it substantially improves detection rates, performance remains unsatisfactory, suggesting that prompting-based strategies alone are insufficient to fully address this limitation. These findings expose a systematic failure in absent answer detection and highlight the need for explicit detection mechanisms in multimodal systems.
[NLP-145] Shared Semantics Divergent Mechanisms: Unsupervised Feature Discovery by Aligning Semantics and Mechanisms
【速读】： 该论文旨在解决大语言模型在高风险场景部署中，现有可解释性方法难以全面审计模型内部计算机制的问题，尤其针对传统电路分析（circuit analysis）依赖目标条件（target-conditioned）设定导致的延续分布异质性被掩盖的局限性。其核心解决方案是提出一种分布级无监督特征发现（distribution-level unsupervised feature discovery）方法，通过联合利用语义内容与序列级机制归因（sequence-level mechanistic attributions），对模型生成的多种延续路径进行无监督聚类，无需人工指定目标输出。该方法将每个延续表示为语义嵌入（semantic embedding）与前缀到延续的归因签名（attribution signature）的组合，并优化一个率-失真目标（rate-distortion objective），在语义连贯性、机制一致性与聚类粒度之间实现平衡。实验表明，所发现的聚类揭示了单视图基线遗漏的延续模式，并通过干预分析提供了有力证据，证明聚类签名对应于可操作的机制因子。该方法在不依赖人工标注的前提下，为模型延续分布的内在机制提供了可扩展的审计框架，有效补充了电路分析与行为评估。
链接: https://arxiv.org/abs/2606.08236
作者: Hyunjin Cho,Youngji Roh,Jaehyung Kim
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 40 pages
Abstract:As large language models are increasingly deployed in high-stakes settings, there is a growing need for tools that audit not only model outputs but also the internal computations that produce them. Circuit analysis is a central approach in mechanistic interpretability, but it is typically target-conditioned, explaining a single prompt paired with a chosen completion. This target-conditioned setup can obscure heterogeneity across a model’s continuation distribution. We introduce distribution-level unsupervised feature discovery, which clusters sampled continuations using both semantic content and sequence-level mechanistic attributions, without manually specifying target outputs. Our method represents each continuation with a semantic embedding and a prefix-to-continuation attribution signature, then optimizes a rate-distortion objective that trades off semantic coherence, mechanistic consistency, and cluster granularity. Across clustering and steering analyses, the discovered clusters expose continuation modes that single-view baselines miss and provide interventional evidence that cluster signatures correspond to actionable mechanistic factors. Overall, our approach complements circuit analysis and behavioral evaluation by providing a scalable audit of the mechanisms underlying a model’s continuation distribution.
[NLP-146] AlignFed: Alignment-Aware Asynchronous Federated Fine-Tuning for Large Language Models in Heterogeneous Edge Environments
【速读】： 该论文旨在解决在异构边缘环境下，大语言模型（LLM）进行联邦微调时面临的多重挑战，包括严格的数据隐私约束、计算与通信资源高度异构性，以及本地数据的非独立同分布（non-IID）特性。传统同步联邦微调方法受“慢节点效应”（straggler effect）影响，导致系统延迟高、资源利用率低；而现有异步联邦学习方法多适用于中小规模模型，难以有效应对大模型微调中的关键问题，如由过时更新引发的模型漂移（model drift）、由数据异构性加剧的客户端漂移（client drift），以及因快速客户端主导而导致的聚合公平性失衡。针对上述问题，本文提出AlignFed——一种面向异构边缘环境的大语言模型异步联邦微调框架。其核心创新在于设计了一种轻量级的多阶段语义对齐机制，包含三个关键模块：基于版本感知的更新分组、基于小批量校准集的跨版本语义对齐，以及融合更新新鲜度与客户端参与频率的公平性感知聚合策略。该方案有效缓解了跨版本模型漂移与客户端漂移问题，同时提升了聚合过程的公平性，从而在高异构性与显著更新滞后场景下实现了稳定且高效的异步联邦优化。
链接: https://arxiv.org/abs/2606.08197
作者: Yan Wang,Ziyi Gao,Rui Wang
机构: University of Science and Technology Beijing (北京科技大学)
类目: Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC)
备注:
Abstract:Large Language Models (LLMs) have significantly propelled the advancement of edge intelligence and have been widely deployed across various scenarios, including autonomous driving, industrial inspection, and personalized IoT services. However, the collaborative adaptation of LLMs on edge devices continues to face formidable challenges due to strict data privacy constraints, highly heterogeneous computing and communication resources, and the non-independent and identically distributed (non-IID) nature of local data. Federated Fine-Tuning (FFT) enables the collaborative optimization of distributed models without exposing raw data. Yet, traditional synchronous aggregation suffers from a severe straggler effect, resulting in high system latency and low resource utilization. Existing asynchronous federated learning methods are predominantly designed for small-to-medium-scale models and struggle to address the specific challenges inherent in LLM fine-tuning namely, model drift caused by stale updates, aggravated client drift stemming from data heterogeneity, and aggregation fairness imbalance resulting from the dominance of fast clients. To address these issues, this paper proposes AlignFed, an asynchronous federated fine-tuning framework for LLMs tailored to heterogeneous edge environments. AlignFed employs a lightweight multi-stage semantic alignment mechanism comprising three core modules: version-aware update grouping, cross-version semantic alignment based on a mini-batch calibration set, and fairness-aware aggregation that integrates both update freshness and client participation frequency. This framework effectively mitigates cross-version model drift and client drift while enhancing aggregation fairness, thereby achieving stable and efficient asynchronous federated optimization in scenarios characterized by high heterogeneity and significant update staleness.
[NLP-147] GlobeAudio: A Multilingual Multicultural Benchmark for Naturalistic Evaluation of Large Audio-Language Models
【速读】： 该论文旨在解决当前大型音频-语言模型（Large Audio-Language Models, LALMs）在评估过程中缺乏真实语言与文化真实性、以及声学现实性的问题。现有评估基准多未能充分反映实际应用场景中的复杂性，导致模型性能评估与真实世界需求之间存在显著偏差。为此，本文提出GlobeAudio——一个涵盖六种类型学上多样化的语言、由母语者基于自然发生音频精心设计的多语言、多文化基准测试集，包含5,637道多项选择题。其核心在于要求模型具备高层次的听觉推理能力与基于文化背景的理解能力，从而推动对真实自然音频理解能力的评估。解决方案的关键在于构建具有高生态效度（ecological validity）的评估数据集，通过引入真实语境下的多语言音频与问题，揭示当前闭源与开源LALMs在自然声学条件下表现的显著差距，尤其在低资源语言中更为突出，进而强调未来音频-语言系统需依赖更贴近真实世界的评估范式。
链接: https://arxiv.org/abs/2606.08194
作者: Ryner Tan,Wenxuan Zhang
机构: Singapore University of Technology and Design (新加坡科技设计大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large Audio-Language Models (LALMs) integrate audio perception and language understanding within a unified framework, enabling a wide range of real-world applications. Despite recent advances, evaluation for LALMs remains heavily underspecified relative to real-world requirements: most lack true linguistic and cultural authenticity, while others fail to capture acoustic realism. To bridge this gap, we propose GlobeAudio, a multilingual and multicultural benchmark designed to evaluate naturalistic audio understanding. GlobeAudio consists of 5,637 multiple-choice questions across six typologically diverse languages, expertly crafted by native speakers grounded on naturally occurring audio. In order to do well, models must possess higher-level auditory reasoning skills and culturally grounded interpretation. We systematically evaluate representative closed-source and open-source LALMs, as well as cascaded ASR-LLM pipelines. Our experiments reveal substantial performance gaps under natural acoustic conditions, particularly for open-source models and low-resource languages. These findings highlight critical limitations of current LALMs and underscore the importance of naturalistic audio evaluation for future audio-language systems. GlobeAudio can be found at this https URL .
[NLP-148] xtEconomizer: Enhancing Lossy Text Compression with Denoising Transformers and Entropy Coding
【速读】： 该论文旨在解决生成式文本压缩中如何在保持高保真度的前提下实现高效存储的问题，尤其针对基于Transformer的序列到序列（Seq2Seq）模型在集成上下文向量与熵编码（entropy coding）方面的研究不足。其核心挑战在于从编码器输出中筛选最具信息量的上下文向量，并有效融合熵编码以提升压缩效率，同时在噪声文本环境下仍能保障高质量的解码输出。解决方案的关键在于提出TextEconomizer框架，该框架采用轻量化变压器神经网络结构，通过自适应选择关键上下文向量并结合熵编码，在无需预先知晓数据集维度的情况下实现了50%至80%的可变输入压缩率；其参数量仅为同类模型的约1/153，达到5.39倍的压缩比且维持近完美的语义保真度，显著优于现有基于Transformer的模型，在内存效率与输出质量之间取得卓越平衡，标志着损失性文本压缩领域在空间利用率上的重要突破。
链接: https://arxiv.org/abs/2606.08184
作者: Mahbub E Sobhani,Anika Tasnim Rodela,Chowdhury Mofizur Rahman,Dewan Md. Farid,Swakkhar Shatabda
机构: 未知
类目: Computation and Language (cs.CL)
备注: Published in Neural Networks (Elsevier), Vol. 203, 2026
Abstract:Lossy text compression reduces data size while preserving core meaning, making it well-suited for summarization, automated analysis, and digital archives. Despite the dominance of transformer-based models in language modeling, integrating context vectors and entropy coding into Sequence-to-Sequence (Seq2Seq) generation remains underexplored. A key challenge lies in identifying the most informative context vectors from encoder output and incorporating entropy coding to enhance storage efficiency while maintaining high-quality outputs, even under noisy text. We introduce TextEconomizer, an encoder-decoder framework paired with a transformer neural network that reduces variable-sized inputs by 50% to 80% without prior knowledge of dataset dimensions. Our model achieves competitive compression ratios via entropy coding while delivering near-perfect text quality, assessed by BLEU, ROUGE, METEOR, and semantic similarity scores. TextEconomizer operates with approximately 153x fewer parameters than comparable models, achieving a 5.39x compression ratio without sacrificing semantic quality. We also evaluate an LSTM-based autoencoder achieving a state-of-the-art 67x compression ratio with 196x fewer parameters, and LLaMAFormer, a modified transformer with 263x fewer parameters than ICAE while maintaining competitive text quality. TextEconomizer significantly surpasses existing transformer-based models in balancing memory efficiency and high-fidelity outputs, marking a breakthrough in lossy compression with optimal space utilization.
[NLP-149] Constrained Paraphrase Consistency for LLM Hallucination Detection ICASSP2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）生成内容时存在事实性不一致（hallucination）的问题，尤其是如何构建准确且可扩展的幻觉检测机制。现有方法主要通过数据合成或人工标注扩充训练集，但此类方式成本高、易引入偏差，且未能充分利用语义等价的改写（paraphrase）所隐含的一致性信息。本文提出一致性约束幻觉检测器（Consistency-Constrained Hallucination Detector, CCHD），其核心在于将幻觉检测的训练过程建模为一个带约束的优化问题：在标准的原始文档-主张对交叉熵损失基础上，引入两类关键约束——（i）改写一致性约束，用于限制不同改写视角下模型输出的差异；（ii）标签保持约束，确保改写版本与真实标签之间的一致性。通过在模型参数与各视角的拉格朗日乘子上联合进行梯度下降-上升优化，CCHD仅引入少量标量对偶变量，且无推理阶段开销。实验表明，基于DeBERTa和Flan-T5骨干网络，CCHD在多个标准事实性基准测试中持续优于FactCG、MiniCheck和AlignScore等强基线模型，验证了其在幻觉检测任务中的优越性能。
链接: https://arxiv.org/abs/2606.08158
作者: Shanshan Lin,Dongsheng Hong,Sibo Ju,Chao Chen,Xi Zhang,Xiangwen Liao
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted to ICASSP 2026
Abstract:Large language models (LLMs) can generate factually inconsistent claims, motivating accurate and scalable hallucination detectors. Prior work largely enlarges training sets via synthesis or new annotations, introducing increasing cost and potential bias while underusing the consistency implied by semantically equivalent paraphrases. We propose Consistency-Constrained Hallucination Detector (CCHD), which formulates training as a constrained optimization problem. The standard cross-entropy on original document-claim pairs is complemented by (i) paraphrase-consistency constraints bounding divergence across paraphrased views, and (ii) label-preservation constraints tying paraphrases to ground truth. We solve the problem by gradient descent-ascent over model parameters and per-view Lagrange multipliers, adding only a few scalar dual variables and no inference-time overhead. With DeBERTa and Flan-T5 backbones, CCHD consistently outperforms strong baselines (FactCG, MiniCheck, and AlignScore) on standard factuality benchmarks, demonstrating its superiority on hallucination detection.
[NLP-150] Cross Paraphrastic Invariance Learning for Hallucination Detection ICASSP2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在生成内容时频繁出现幻觉（hallucination）的问题，即生成结果缺乏源文档支持。现有解决方案通常依赖昂贵的LLM作为评估器（LLM-as-evaluator）或需要大量人工标注数据的分类器，存在成本高、标注负担重的缺陷。为此，论文提出一种两阶段孪生框架——交叉式改写不变性学习（Cross Paraphrastic Invariance Learning, CPIL），其核心在于高效利用已有标注数据。关键创新包括：(i) 通过生成每个文档-主张对的改写视图作为正样本，显式对齐其表示以实现表层形式不变性；(ii) 挖掘同文档但标签相反的样本作为难负样本，以增强模型对文档语境的敏感度。随后，CPIL采用两阶段训练：第一阶段进行对比预训练，构建具备改写不变性且具上下文依存性的嵌入空间；第二阶段接入轻量级分类器，实现二元是否基于事实（groundedness）的判断。在包含11个任务的LLM-AggreFact基准测试中，仅使用约1%的标注数据，CPIL即在F1得分上超越多个强基线模型，验证了其卓越的预测性能与极高的标签效率。
链接: https://arxiv.org/abs/2606.08157
作者: Shanshan Lin,Dongsheng Hong,Sibo Ju,Chao Chen,Sihong Xie,Xiangwen Liao
机构: 未知
类目: Computation and Language (cs.CL)
备注: Accepted to ICASSP 2026
Abstract:Large language models (LLMs) frequently generate hallucinations, which are unsupported by a source document. To avoid costly LLM-as-evaluator pipelines and the heavy annotation demands of existing classifiers, we propose CPIL (Cross Paraphrastic Invariance Learning), a two-stage Siamese framework that maximizes the utility of existing labeled data. Concretely, CPIL constructs informative training pairs by: (i) generating paraphrastic views of each document-claim example as positives, and explicitly aligning their representations to enforce invariance to surface form; and (ii) mining same-document, opposite-label pairs as hard negatives to sharpen document-sensitive decision boundaries. Then CPIL conduct a two-stage model training: Stage 1 performs contrastive pretraining to learn a paraphrase-invariant, grounding-aware embedding space; and Stage 2 attaches a lightweight classifier for binary groundedness. On the LLM-AggreFact benchmark (11 tasks), CPIL surpasses strong baselines concerning F1 scores with only ~1% labeled data, showing its prediction superiority and label efficiency.
[NLP-151] When Languages Disagree: Self-Evolving Multilingual LLM Judges
【速读】： 该论文旨在解决多语言大模型作为评价者（Multilingual LLM-as-a-judge）在跨语言评估中存在的一致性问题，即不同语言间的评价结果不一致（cross-lingual inconsistency），这一现象常被视为噪声并被传统方法通过投票或聚合进行抑制。然而，本文提出一个新视角：跨语言不一致性并非单纯噪声，而是蕴含互补评价信号的潜在资源。其核心解决方案是提出一种自演化多语言评价框架——SEMJ（Self-evolving Multilingual Judge），其关键在于主动利用不一致性驱动模型的自我反思与迭代优化。具体而言，SEMJ通过构建同一输入的多语言变体，分别获取各语言下的独立判断与推理过程，并将不一致的输出反馈至模型，触发自我反思与再评估机制，从而实现判断质量的持续提升。实验表明，SEMJ在多个基准测试中不仅显著优于基于投票和单一语言反思的基线方法，在准确率与跨语言一致性方面均表现更优，进一步分析证实不一致性确实能有效激发有价值的再评估行为，进而提升整体评价性能。
链接: https://arxiv.org/abs/2606.08092
作者: Xiyan Fu,Wei Lu
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Multilingual LLM-as-a-judge is widely used to evaluate model outputs across languages, but suffers from cross-lingual inconsistency (Fu and Liu, 2025). Existing methods typically treat this inconsistency as noise and mitigate it through voting or aggregation. In this work, we instead show that multilingual inconsistency can provide complementary evaluation signals. Our oracle analysis finds that sampling judgments across languages yields a higher performance upper bound than single-language judging, indicating that different languages potentially include complementary judgments. Motivated by this finding, we propose SEMJ, a self-evolving multilingual judge that leverages cross-lingual inconsistency for iterative refinement. SEMJ constructs multilingual variants of each input, collects independent judgments and rationales, and feeds inconsistent outputs back for self-reflection and re-evaluation. Experiments on multiple benchmarks show that SEMJ consistently outperforms voting and reflection baselines in both accuracy and cross-lingual consistency. Further analysis shows that inconsistency triggers useful re-evaluation, which improves judgment quality.
[NLP-152] ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning
【速读】： 该论文旨在解决生成式 AI 在基于可验证奖励的强化学习（Reinforcement Learning from Verifiable Rewards, RLVR）训练中面临的两大核心问题：一是稀疏的二值奖励导致优化信号不足，二是模型对自身推理不确定性缺乏感知，从而影响决策可靠性。其解决方案的关键在于提出 ConSteer-RL 框架，通过引入基于模型输出概率分布的词元级置信度信号（token-level confidence signals），将逐词元的对数概率聚合为标量置信度分数，并构建一种基于置信度的奖励重塑机制——该机制在奖励函数中对过度自信的错误进行惩罚，同时强化正确且高置信度的推理路径。这一设计显著提升了模型在复杂推理任务中的鲁棒性与准确性，实验表明该方法在不同规模的大语言模型上均优于强基线模型（GRPO），平均性能提升达 2.3%–4.0%。
链接: https://arxiv.org/abs/2606.08088
作者: Qing Miao,Yiming Zhao,Jing Yang,Chenxi Liu,Yuehai Chen,Yuewen Liu,Shaoyi Du,Badong Chen
机构: Xi’an Jiaotong University (西安交通大学); University of Science and Technology of China (中国科学技术大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Reinforcement Learning from Verifiable Rewards (RLVR) has recently become a key paradigm for improving the reasoning abilities of Large Language Models (LLMs), yet it remains limited by sparse binary rewards and its ignorance of model-internal uncertainty. In this paper, we propose ConSteer-RL, a simple yet effective framework that integrates token-level confidence signals derived from model log-probabilities into RLVR training. Specifically, building upon the Group Relative Policy Optimization (GRPO) framework, we construct a confidence-aware reward by aggregating per-token probabilities into a scalar confidence score and incorporating it into an awareness-based reward shaping mechanism that penalizes overconfident errors while reinforcing correct and confident reasoning. Experimental results demonstrate that ConSteer-RL consistently outperforms strong GRPO baselines, achieving average improvements of 2.3%-4.0% across different model scales.
[NLP-153] Assessing the Energy and Carbon Emissions of Neural Speaker Verification Model in Training and Inference
【速读】： 该论文旨在解决深度学习说话人验证（Speaker Verification, SV）系统中深度神经网络主干模型带来的环境影响缺乏量化评估的问题。随着SV系统日益依赖复杂的深度神经网络架构，其训练过程中的能源消耗与碳足迹成为不可忽视的环境负担。研究的关键在于通过在VoxCeleb2数据集上对不同深度、通道宽度及阶段分布的ResNet架构进行系统性评估，结合节点级传感器测量能源消耗与碳排放，揭示模型规模与性能提升之间的非线性关系。研究发现，随着模型加深或加宽，准确率提升趋于饱和，而能耗则急剧增加，存在明显的收益递减点；相比之下，中等规模的网络（如ResNet-50）及其阶段集中型变体在性能与环境影响之间实现了更优的权衡。这一发现为设计高效节能的说话人验证系统提供了可操作的优化指导。
链接: https://arxiv.org/abs/2606.08087
作者: Hugo Leguillier,Driss Matrouf,Guillaume Lechien,Mickael Rouvier
机构: Avignon University (阿维尼翁大学)
类目: ound (cs.SD); Computation and Language (cs.CL)
备注: Accepted to Speaker Odyssey 2026 Lisbon
Abstract:Deep-learning speaker verification (SV) increasingly relies on deep neural network backbones, whose environmental impact remains largely undocumented. In this paper, we conduct an evaluation of ResNet architectures trained on VoxCeleb2, varying depth, channel width, and stage distribution, and measure energy consumption and carbon footprint using node-level sensors. Results show a clear point of diminishing returns: deeper or wider models bring only marginal accuracy gains while energy consumption grows steeply. In contrast, mid-sized networks such as ResNet-50 and stage-concentrated variants achieve favorable trade-offs between performance and environmental impact. These findings provide actionable guidelines for designing energy-efficient SV systems.
[NLP-154] Aligned but Not Partner-Specific: Distinguishing How Multimodal LLM Agents Succeed in Reference Games Without Human-Like Conventions
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在重复性指称任务中是否能够像人类一样通过共享互动历史形成简洁、特定于对话伙伴的指称惯例这一核心问题。现有研究表明，尽管MLLMs在标签使用上表现出高度对齐，但其并未随交互轮次提升效率，暗示这种对齐可能并非源于真正的伙伴关系内嵌的语用协商，而仅仅是共享任务词汇的体现。为此，作者提出一种创新的方法论——受约束的伪双人组基线（constrained pseudo-dyad baseline），该方法保留原始指称任务结构，但人为破坏对话伙伴的历史关联性，从而检验标签对齐是否依赖于与特定伙伴的真实互动。研究从三个分析层面（任务能力、描述策略、对齐动态）进行对比，发现人类在交互中通过“共振”（entrainment）机制显著降低认知负荷，压缩描述长度并增强与特定伙伴的标签一致性；而代理则维持恒定的高努力水平，从第一轮即产生冗长描述，且其标签重叠度在真实双人组与伪双人组间无统计差异。这表明MLLMs的协调是基于冗长描述而非形成依赖历史的紧凑指称表达，本质上未实现人类对话中典型的、基于情境的约定俗成机制。因此，解决方案的关键在于引入伪双人组基线以分离“协作对齐”与“伙伴特异性接地”之间的因果关系，揭示当前多模态模型缺乏真正语用适应性的根本局限。
链接: https://arxiv.org/abs/2606.08081
作者: Po-Ya Angela Wang,Chinmaya Mishra,Aslı Özyürek,Paula Rubio-Fernández,Esam Ghaleb
机构: National Taiwan University (国立台湾大学); Max Planck Institute for Psycholinguistics (马克斯普朗克语言学研究所); Radboud University (拉德堡德大学); Paris (巴黎)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Repeated reference games test whether interlocutors replace their initially long descriptions with shorter, partner-specific conventions grounded in shared interaction history. Prior work shows that multimodal LLMs fail to become more efficient across rounds, although they align on the labels they use. How can we determine whether this alignment reflects partner-specific grounding rather than a shared task vocabulary? We address this question by comparing capable multimodal agent dyads with human dyads from the KTH Tangrams corpus. Our novel methodological contribution is a constrained pseudo-dyad baseline that matches the original referential task structure, but breaks partner history. This baseline enables us to test whether the observed label alignment depends on interaction with a specific partner. Across three analytic layers (task competence, description strategy, alignment dynamics), we find clear differences. Humans reduce effort through entrainment, compressing descriptions and increasing label alignment with partners. Agents instead maintain fixed effort levels, producing verbose descriptions from round one, with near-ceiling label overlap that is statistically indistinguishable between real and pseudo dyads. MLLMs thus achieve coordination without convention, succeeding by verbose description rather than by forming the compact, history-dependent referring expressions characteristic of human dialogue.
[NLP-155] On Low-Bit Quantization Errors in Speaker Verification: Diagnostic and Mitigation
【速读】： 该论文旨在解决低比特量化（low-bit quantization）在资源受限设备上部署说话人验证系统时，对模型性能造成不可预测的退化问题。尽管低比特量化能够显著降低计算与内存开销，但其对说话人验证准确率的影响机制尚不清晰。研究通过联合分层分析（layer-wise analysis）与评分级分析（score-level analysis），揭示了量化过程中性能下降的根本原因：权重失真并非唯一影响因素，部分关键网络层存在脆弱性组件，且在2比特量化时出现明显的“拐点”现象，导致分数漂移（score drift）加剧及决策翻转（decision flips）集中发生于原始32位浮点（FP32）阈值附近。基于上述发现，论文提出一种校准的多精度级联（calibrated multi-precision cascade）方案，能够在绝大多数情况下以2比特完成推理，仅对模糊样本进行高精度回退，从而在保持接近32位浮点（FP32）性能的同时，大幅降低计算与内存成本，实现了效率与精度的平衡。
链接: https://arxiv.org/abs/2606.08078
作者: Hugo Leguillier,Driss Matrouf,Guillaume Lechien,Mickael Rouvier
机构: Avignon University (阿维尼翁大学)
类目: ound (cs.SD); Computation and Language (cs.CL)
备注: Accepted at Speaker Odyssey 2026 Lisbon
Abstract:Although low-bit quantization provides practical means to deploy speaker verification on resource-constrained devices, its effects on speaker verification performance remain poorly understood. In this paper, we study uniform K-means quantization-aware training of ResNet-36 and ResNet-200 through joint layer-wise and score-level analyses. Our layer-wise analysis highlights fragile components and shows that score degradation is not fully explained by weight distortion alone. We identify a clear knee point at 2 bits, with larger score drift and harmful decision flips concentrated near the FP32 threshold. Our score-level analysis reveals where and how score errors emerge under extreme quantization. Building on these findings, we propose a calibrated multi-precision cascade that resolves most trials at 2 bits and escalates only ambiguous cases, achieving performance close to FP32 while preserving the efficiency benefits of low-bit inference with substantially lower compute and memory costs.
[NLP-156] Support Vector Rubrics: Closing the Gap Between Self-Generated and Human Rubrics
【速读】： 该论文旨在解决自生成评价标准（self-generated rubrics）在评估大语言模型（LLM）输出时，相较于人工标注标准在复杂样本上表现不佳的判别能力差距问题。其核心挑战在于：现有自生成标准倾向于描述“良好响应”的特征，而真正有效的评价标准应具备区分相似候选答案的判别能力。为弥合这一差距，论文提出SVR（Support Vector Rubrics）框架，其关键创新在于将评价标准构建重构为基于偏好数据的最大间隔边界学习（max-margin boundary learning）。SVR通过从偏好对中挖掘对比特征构建评价标准库（rubric bank），联合学习提示条件化的选择器与全局标准权重，并通过支持对选择和对抗性硬负样本探测实现标准库的迭代优化。推理阶段，仅需输入提示即可从标准库中检索最优标准并评分。实验表明，SVR在RubricBench上将与人工参考标准的差距从24.1降至0.3分，显著优于现有自生成标准与裁判基线，并展现出良好的跨裁判迁移能力；在RewardBench 12和RM-Bench上亦保持与专用奖励模型相当的性能，验证了其在更广泛奖励建模任务中的潜力。总体而言，通过定义判别边界的评价标准，该方法为解决LLM评估中的判别能力不足问题提供了原则性路径。
链接: https://arxiv.org/abs/2606.08077
作者: Mengyuan Sun,Yu Li,Zhuohao Yu,Shikun Zhang,Wei Ye
机构: Peking University (北京大学); 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:Rubric-based evaluation is a promising paradigm for judging large language model (LLM) outputs, yet self-generated rubrics lag human-annotated criteria on hard instances. We argue this discriminative gap reflects an objective mismatch: self-generated rubrics describe good responses, whereas effective criteria must discriminate between close candidates. To close this gap, we introduce SVR (Support Vector Rubrics), a framework that recasts rubric construction as max-margin boundary learning over preference data. SVR mines contrastive features from preference pairs into a rubric bank, learns a prompt-conditioned selector together with global rubric weights, and iteratively refines the bank through support-pair selection and adversarial probing of hard negatives. At inference, given only the prompt, SVR retrieves the top-rubrics from the bank and scores responses. On RubricBench, SVR narrows the gap to human reference rubrics from 24.1 to 0.3 points and outperforms strong self-rubric and judge baselines, and the learned bank transfers across judges without retraining. On RewardBench 12, and RM-Bench, it remains competitive with dedicated reward models, demonstrating broader reward modeling capability. Overall, boundary-defining rubrics offer a principled route to closing the discriminative gap in LLM evaluation.
[NLP-157] “I understand your perspective”: LLM Persuasion and Sycophancy through the Lens of Communicative Action Theory
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在生成高质量论点的同时，其在复杂沟通情境中实现微妙且具说服力的交际行为（communicative action）能力尚未被充分探究的问题。核心挑战在于：尽管LLMs能产出逻辑严密、语言流畅的文本，但其是否具备与人类相似的语用意图表达能力——如传递知识、建立信任或暗示观点一致性等——仍不明确。本研究基于尤尔根·哈贝马斯（Jürgen Habermas）的交往行为理论，通过模拟“ChangeMyView”论坛中的对立观点对话，对比分析人类与三类主流LLMs生成的、成功改变原发帖者立场的反驳性论点中，语用意图（illocutionary intent）的出现频率与表现形式。研究发现，所有测试的LLMs均能有效传达语用意图，且在多数情况下优于人类，尤其体现在对意见持有者立场的高度契合（谄媚式回应，sycophantic responses），而此类策略已被证实是促成态度转变的关键因素。此外，众包评估结果显示，参与者普遍更认同并偏好由LLMs生成的反驳内容。因此，该研究的关键解决方案在于揭示：通过人类偏好训练，LLMs不仅能够模仿高质量论证，更能精准复现人类深层的交际策略，从而显著增强其说服力，甚至可能提升个体对其影响的易感性。
链接: https://arxiv.org/abs/2606.08076
作者: Esra Dönmez,Agnieszka Falenska
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Large Language Models (LLMs) can generate high-quality arguments, yet their ability to engage in nuanced and persuasive communicative actions remains largely unexplored. This work explores the persuasive potential of LLMs through the framework of Jürgen Habermas’ Theory of Communicative Action. It examines whether LLMs express illocutionary intent (i.e., pragmatic functions of language such as conveying knowledge, building trust, or signaling similarity) in ways that are comparable to human communication. We simulate online discussions between opinion holders and LLMs using conversations from the persuasive subreddit ChangeMyView. We then compare the likelihood of illocutionary intents in human-written and LLM-generated counter-arguments, specifically those that successfully changed the original poster’s view. We find that all three LLMs effectively convey illocutionary intent – often more so than humans – potentially increasing their anthropomorphism. Further, LLMs craft sycophantic responses that closely align with the opinion holder’s intent, a strategy strongly associated with opinion change. Finally, crowd-sourced workers find LLM-generated counter-arguments more agreeable and consistently prefer them over human-written ones. These findings suggest that LLMs’ persuasive power extends beyond merely generating high-quality arguments. On the contrary, training LLMs with human preferences effectively tunes them to mirror human communication patterns, particularly nuanced communicative actions, potentially increasing individuals’ susceptibility to their influence.
[NLP-158] SurgiQ: A Large-Scale Multi-Domain Benchmark for Evaluating Surgical Understanding in Large Language Models
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在外科领域评估可靠性不足的问题。现有医学通用基准主要侧重于临床知识测试，而外科实践更依赖于程序性推理、管理权衡、否定句处理以及在多个合理术式中进行选择等复杂能力，现有评估体系难以全面覆盖这些核心需求。为此，研究提出SurgiQ，这是一个基于文本、来源可追溯的基准测试集，包含13,055道四选一多项选择题，涵盖六大外科领域及四种题型（病例导向、推理类、最佳选项和否定类）。SurgiQ通过多阶段生成、验证与专家审核流程，从外科教材、开放获取文献及考试资料中构建而成。研究对35个开源权重的LLM在统一的似然对数协议下进行评估，结果显示模型性能仍有显著提升空间：小型模型普遍接近25%的随机基线水平，最优模型仅达到68.1%的准确率。值得注意的是，通用大模型（如Qwen2.5）表现优于多数生物医学专用模型，表明当前医疗专业化尚未充分覆盖外科所需的广泛知识与推理能力。校准分析与错误诊断进一步揭示，即使高性能模型也常在临床上合理的干扰项上做出自信误判，凸显了发展更可靠、更全面的外科领域大模型评估体系的必要性。
链接: https://arxiv.org/abs/2606.08071
作者: Ayah Al-Naji,Edoardo Fazzari,Saif Alkindi,Hamdan Alhadhrami,Preslav Nakov,Cesare Stefanini
机构: Mohamed bin Zayed University of Artificial Intelligence ( Mohamed bin Zayed大学人工智能学院)
类目: Computation and Language (cs.CL)
备注:
Abstract:Reliable evaluation of large language models in surgery remains underdeveloped. Broad medical benchmarks test clinical knowledge, while surgery requires procedural reasoning, management trade-offs, negation handling, and selection among plausible operative decisions. We present SurgiQ, a text-only, source-grounded benchmark of 13,055 four-option multiple-choice questions spanning six surgical domains and four question formats: case-based, reasoning, best-option, and negative. SurgiQ is constructed from surgical textbooks, open-access papers, and examination material using a multi-stage generation, verification, and expert-audit pipeline. We evaluate 35 open-weight LLMs under a unified log-likelihood protocol. Our results show substantial remaining headroom: smaller models often remain near the 25% random baseline, while the best model reaches 68.1% accuracy. General-purpose models, especially Qwen2.5, outperform most biomedical models, suggesting that current medical specialization does not yet provide sufficiently broad surgical coverage. Calibration and error analysis further show that even strong models make confident mistakes on clinically plausible distractors, motivating more reliable and broader surgical LLM evaluation.
[NLP-159] Robust-U1: Can MLLM s Self-Recover Corrupted Visual Content for Robust Understanding? ICML2026
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在真实世界视觉退化场景下性能显著下降的问题。现有增强鲁棒性的方法存在局限：黑盒特征对齐缺乏可解释性，而基于文本的白盒推理无法恢复丢失的像素级视觉细节。针对这一挑战，论文提出一种名为Robust-U1的新框架，其核心创新在于赋予MLLMs显式的视觉自恢复能力，以实现更稳健的视觉理解。该方案的关键在于三阶段协同机制：首先通过监督微调实现初始视觉重建；其次采用双奖励强化学习（结合像素级结构相似性SSIM与语义级CLIP相似性），优化恢复图像的高保真度；最后引入多模态推理模块，联合考虑原始退化输入与恢复后的图像进行综合判断。实验表明，Robust-U1在真实世界退化基准上达到当前最优鲁棒性表现，并在通用视觉问答（VQA）基准上对对抗性退化仍保持优异性能。分析进一步证实，高质量的视觉恢复直接提升了模型的推理能力，确立了自恢复机制作为实现鲁棒视觉理解的关键路径。
链接: https://arxiv.org/abs/2606.08063
作者: Jiaqi Tang,Jianmin Chen,Youyang Zhai,Wei Wei,Runtao Liu,Mengjie Zhao,Xiangyu Wu,Qingfa Xiao,Qifeng Chen
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted by ICML 2026
Abstract:Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in visual understanding, yet their performance degrades significantly under real-world visual corruptions. While existing robustness enhancement approaches exist, they are limited: black-box feature alignment lacks interpretability, and white-box text-based reasoning cannot restore lost pixel-level details. This work investigates a fundamental research question: Can MLLMs recover corrupted visual content by themselves? To address this, we propose Robust-U1, a novel framework that equips MLLMs with explicit visual self-recovery capability for robust understanding. The approach comprises three core stages: supervised fine-tuning for initial reconstruction, reinforcement learning with dual rewards (pixel-level SSIM and semantic-level CLIP similarity) for aligning high visual quality, and multimodal reasoning that jointly considers both the corrupted input and the recovered image. Extensive experiments demonstrate that Robust-U1 achieves state-of-the-art robustness on the real-world corruption benchmark and maintains superior performance under adversarial corruptions on general VQA benchmarks. Analysis confirms that high-quality visual recovery directly enhances reasoning performance, establishing self-recovery as a critical mechanism for robust visual understanding. The source code is available at this https URL.
[NLP-160] Whats the Point? Spatial Grammar Index Resolution for Sign Language Processing
【速读】： 该论文旨在解决手语识别中非词汇性、生成性构式（如空间索引）建模不足的问题。当前主流的手语模型多依赖词素序列或文本监督，难以有效捕捉空间索引这类关键非词汇结构——即通过指向手势将话语实体映射至空间位置以实现后续回指的语法机制。研究表明，尽管空间索引占手语内容的10%-15%，但现有模型对其恢复效果较差。为此，作者提出一种针对空间索引的专项评估与训练框架，其核心在于将空间指代解析分解为索引检测与话语实体链接两个阶段，并构建可自动标注非词汇结构的提及表征。该方法引入一个可插拔的索引专家模块，在推理时作为辅助模块增强冻结的已有手语识别（SLR）模型，从而实现对空间索引的显式建模与精准恢复，为构建具备索引感知能力的手语建模提供了基准方案。
链接: https://arxiv.org/abs/2606.08056
作者: Oline Ranum,Simon Hadfield,Richard Bowden
机构: Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Sign language models are predominantly trained with gloss-sequence or text supervision, thereby under-modeling non-lexical and productive constructions. One comparatively tractable instance is spatial indexing: pointing gestures that assign discourse entities to spatial loci for subsequent co-reference, which lexicon-centric objectives largely fail to capture. We present a targeted evaluation of indexing in Sign Language Recognition, showing that despite comprising 10-15% of signing content, indexing is poorly recovered. We introduce a framework for training and evaluating indexing experts, establishing a baseline for index-aware sign language modeling. Our approach decomposes spatial reference resolution into index detection and discourse entity linking. The resulting mention representations enable automatic annotation and non-lexical structure modeling, and serve as an auxiliary indexing expert that augments a frozen SLR model at inference time.
[NLP-161] Diffusion Language Model Parallel Decoding via Product-of-Experts Bridge ICML2026
【速读】： 该论文旨在解决生成式AI中扩散语言模型（Diffusion Language Models, DLMs）在并行解码虽具速度优势，但因缺乏词元依赖关系而导致生成质量低于自回归（Autoregressive, AR）模型的核心问题。现有方法尝试通过重要性采样弥合两者分布差距，但因DLM与AR模型间分布差异巨大，需大量粒子采样，计算成本高昂。本文提出PoE-Bridge这一新型解码框架，其核心创新在于引入一个中间分布作为桥梁，该分布以扩散语言模型（DLM）的提议分布与自回归（AR）目标分布的专家乘积（Product-of-Experts, PoE）形式构建。通过该中间分布，先利用DLM并行生成多个候选续写，再通过拒绝采样验证并引导候选序列向PoE分布对齐，最后采用重要性采样进一步将对齐后的候选序列修正至目标AR分布。此外，论文还提出混合温度采样以增强多样性、弹性拒绝窗口以减少无效验证开销。实验表明，PoE-Bridge在保持显著加速的同时，相较标准DLM解码实现5倍提速，并恢复至少95%的AR模型性能，在数学推理与代码生成等高难度任务上有效缩小了生成质量差距。
链接: https://arxiv.org/abs/2606.08048
作者: Juntong Shi,Brian L. Trippe,Jure Leskovec,Stefano Ermon,Minkai Xu
机构: 未知
类目: Computation and Language (cs.CL)
备注: ICML 2026
Abstract:Diffusion language models (DLMs) offer substantial speed advantages through parallel decoding, but the lack of token dependencies limits generation quality compared to autoregressive (AR) models. Recent progress attempts to bridge the gap via importance sampling, with DLM being the proposal and AR being the target. However, due to the huge gap between their distributions, the sampling requires a large number of particles and is thus expensive to compute. In this paper, we introduce PoE-Bridge, a novel decoding framework that drastically improves generation speed and accuracy by introducing an intermediate distribution to bridge the gap. The distribution is constructed as a Product-of-Experts (PoE) of the DLM proposal and the AR target. With the intermediate distribution, we first use the DLM to draft multiple continuations in parallel, then apply rejection sampling to verify the drafted tokens and move the resulting candidates toward the PoE. We then use importance sampling to further correct the PoE-aligned candidates toward the AR target. We further propose several improved techniques, including mixed-temperature sampling for enhanced diversity and elastic rejection windows for reducing wasted verification. Empirically, PoE-Bridge achieves significantly improved accuracy with 5\times speedup over the standard DLM decoding approach, and recovers at least 95% of the target AR model’s performance, efficiently advancing most of the quality gap on challenging mathematical reasoning and coding tasks. Our code is available at this https URL.
[NLP-162] When Behavioral Safety Evaluation Fails: A Representation-Level Perspective
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）安全评估中存在“审计差距”（audit gap）的问题，即当前主流的行为层面安全评估仅关注输出表现，而忽视了模型在潜在表示空间（latent space）中对干预的脆弱性，导致对模型内部鲁棒性的评估不充分。其解决方案的关键在于提出一种基于干预的评估框架，通过在参数空间和潜在表示空间施加软干预（如有害微调、逐层潜在扰动），系统检验模型在内部表征层面的脆弱性。为此，论文进一步提出了潜表示脆弱性评分（Latent Vulnerability Score, LVS），用于量化在有限扰动下诱发有害行为的难易程度。实验结果表明，即使模型在行为上表现出相似的拒绝响应能力，其内部表示仍可能高度敏感，尤其在中间层表示中表现最为显著。这揭示了仅依赖行为安全指标无法全面反映模型的内在鲁棒性，从而强调了开展面向潜在表示的可观察性审计的重要性。
链接: https://arxiv.org/abs/2606.08044
作者: Enyi Jiang,Anders Gjølbye,Yibo Jacky Zhang,Sanmi Koyejo
机构: Stanford University (斯坦福大学); University of Illinois Urbana-Champaign; Technical University of Denmark
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Preprint
Abstract:Large Language Model (LLM) safety has often been evaluated at the behavior level, which provides limited evidence of internal robustness, as these evaluations target outputs rather than representation-level vulnerability under intervention. We formalize this discrepancy as the audit gap: the difference between behavioral safety and robustness under intervention. To study this gap, we construct dissociated models that preserve safe outward behavior while remaining vulnerable in the latent space. We introduce an intervention-based evaluation framework to test model robustness through soft interventions in parameter and latent spaces, including harmful fine-tuning and layer-wise latent perturbations. To formalize the evaluation, we propose the Latent Vulnerability Score (LVS) to measure how easily harmful behavior can be elicited by bounded latent perturbations. Using this evaluation framework, we show that behavioral safety metrics are insufficient measures of representation-level robustness across multiple safely and unsafely aligned state-of-the-art models. Notably, dissociated models show substantially elevated LVSs despite comparable refusal behavior under harmful intervention, with intermediate representations being the most sensitive to intervention. Our results suggest that behavioral safety evaluation alone provides an incomplete picture of model robustness, motivating representation-aware audits of latent vulnerability and observable behavior.
[NLP-163] Sci-Rho: A Multilingual Visually-Grounded Symbolic Benchmark for STEM Problems
【速读】： 该论文旨在解决现有符号化基准测试在科学、技术、工程与数学（STEM）领域中存在局限性的问题，具体表现为：主要聚焦于数学推理、缺乏视觉语境支持以及语言覆盖范围狭窄（主要集中于英语）。为应对这些问题，本文提出Sci-Rho（Science Rhobustness），一个动态的、多语言、具视觉接地特性的跨学科STEM基准测试，涵盖五个学科和七种语言，包含由领域专家（包括奥赛获奖者）设计的4,242个问题模板（每种语言606个），每个模板通过可执行的Python代码生成42,420个语义等价但参数各异的问题实例，涵盖数值变化、视觉模式、几何形状、颜色方案及函数类型等维度，并配以完整的推理链与真值答案。其解决方案的关键在于构建一个高度可扩展、动态生成且具备多模态与多语言能力的评估框架，从而更真实地衡量视觉语言模型（VLMs）在复杂、多样化条件下的鲁棒性。实验表明，尽管多数先进VLM在平均性能上表现良好，但在最坏情况下的准确率显著下降，尤其小型模型在跨语言场景下出现明显性能退化，而大型专有模型则保持更强鲁棒性；同时，逐步骤评估揭示了平均F1与最坏情况F1之间存在显著差距，进一步凸显当前模型在极端变异下的脆弱性。对注意力机制的分析还发现，不同语言间图像标记与文本标记之间的相对关注度存在显著差异，提示模型在跨语言视觉理解方面存在系统性偏差。本研究强调，仅依赖静态基准无法全面反映VLM的真实质量，应推动面向动态、多变、多模态环境的综合评估范式。
链接: https://arxiv.org/abs/2606.08034
作者: Muhammad Falensi Azmi,Ikhlasul Akmal Hanif,Vallerie Alexandra Putra,Adi Yeltay,Abdullah Mubarak,Fajri Koto
机构: MBZUAI; Binus University; Bandung Institute of Technology
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 22 pages
Abstract:Symbolic benchmarks have emerged as a key approach to assess model robustness under minor modifications to STEM-related questions. However, existing symbolic benchmarks mostly remain limited to mathematical reasoning, lack visual grounding, and are predominantly in English. In this work, we introduce Sci-Rho (Science Rhobustness), a dynamic benchmark for visually-grounded STEM problems spanning five subjects and seven languages, comprising 4,242 problem templates (606 per language) crafted by domain experts, including Olympiad medalists. Each template is implemented as executable Python code that generates diverse but equivalent problem instances by varying numerical values, visual patterns, geometric shapes, color schemes, and function types, resulting in 42,420 instances in total, each paired with reasoning steps and ground-truth solutions. We evaluated 17 state-of-the-art VLMs and discovered a noticeable gap between worst-case accuracy (defined as the proportion of problem templates that a model answers correctly across every generated variation) and average accuracy. We also discovered that smaller models show noticeable performance degradation across languages, whereas proprietary and larger models remain robust. Step-level evaluation reflects this same trend, revealing a significant gap between average F1 and worst-case F1 scores. Finally, our inspection of attention heads of a VLM reveals substantial cross-lingual variation in the relative attention allocated to image tokens compared to text tokens. Our work highlights the importance of evaluation beyond static benchmarks as a metric to measure the quality of VLMs.
[NLP-164] Arabic Sentence Segmentation Across Genres and Punctuation Conditions
【速读】： 该论文旨在解决阿拉伯语中因标点符号模糊且不一致而导致的句子切分难题，尤其针对在真实场景下缺乏可靠句末标记的问题。现有方法过度依赖标点线索，并通常在结构良好的文本上进行评估，导致其在实际应用中的鲁棒性不足。为此，研究提出AraSEG——一个涵盖八种文体、覆盖广泛标点使用和文档结构条件的多样化句子切分语料库。基于AraSEG，研究系统评估了大语言模型（LLM）、轻量级编码器模型以及基于依存句法分析器的模型在逐步加剧挑战性条件下的表现。实验结果表明，在最复杂的情境下，轻量级编码器甚至基于依存句法分析器的模型均优于大语言模型。此外，研究还探究了训练数据规模与文体多样性的影响，发现性能最终趋于饱和，跨文体泛化能力依然存在显著挑战。研究进一步证明，精确的句子切分可显著提升下游依存句法分析效果。相关代码、数据及模型均已公开。
链接: https://arxiv.org/abs/2606.08025
作者: Mohammed Elkholy,Khalid N. Elmadani,Nizar Habash,Bashar Alhafni
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Sentence segmentation in Arabic is challenging due to ambiguous and inconsistent punctuation, with many texts lacking reliable sentence boundary markers. Existing approaches rely heavily on punctuation cues and are typically evaluated on well-formed text, limiting their robustness in realistic Arabic settings. To address this, we introduce AraSEG, a genre-diverse sentence segmentation corpus spanning eight genres and a wide range of punctuation and document structure conditions. Using AraSEG, we evaluate LLMs, lightweight encoder models, and dependency parser-based models under increasingly challenging segmentation settings. Our experiments show that lightweight encoders, and even dependency parser-based models, outperform LLMs in the most challenging settings. We further investigate the effects of training data size and genre diversity, finding that performance eventually saturates and cross-genre generalization remains challenging. We also demonstrate that accurate sentence segmentation substantially improves downstream dependency parsing. We make our code, data, and models publicly available.
[NLP-165] IEA: Amateur-Friendly Conversational Image Editing Agent via Three Stages of Multitask Alignment CVPR2026
【速读】： 该论文旨在解决当前图像编辑软件中存在的人机意图鸿沟问题，即普通用户难以通过固定滤镜或专家调参实现其预期效果，而生成式模型虽能生成图像但常伴随伪影、不合理细节或风格偏离真实感，且缺乏对编辑行为可解释性的支持。其解决方案的关键在于提出一种名为IEA（Conversational Image Editing Agent）的对话式图像编辑智能体，该智能体基于参数化编辑工具，在显式、可解释的动作空间中进行操作。通过三阶段多任务训练流程——（1）在提炼后的专家编辑数据上进行监督微调（SFT），（2）采用基于奖励的强化学习策略（GRPO），优化图像相似性提升、工具使用有效性及用户意图摘要质量，（3）大规模合成数据微调以联合掌握图像编辑、精细化调整与用户意图归纳能力——IEA能够逐步操控16种编辑工具，生成透明可追溯的编辑轨迹，支持人工审查与调试。实验结果表明，IEA在编辑任务上的像素距离更低，意图摘要任务的ROUGE-L得分更高；用户研究进一步验证其在指令遵循能力上优于主流工具调用方法，并在整体感知质量上超越生成式方法。研究证实，以可解释、工具为中心的视觉语言模型（VLM）是实现人类指令引导图像润色的可靠路径。
链接: https://arxiv.org/abs/2606.08016
作者: Zichen Zhu,Yuheng Sun,Mingxuan Zhu,Wenjie Ma,Situo Zhang,Zhexiang Wang,Ziyue Yang,Danyang Zhang,Kunyao Lan,Zihan Zhao,Dingye Liu,Siqi Xiang,Lu Chen,Kai Yu
机构: Shanghai Jiao Tong University (上海交通大学); Huawei Technologies Ltd. (华为技术有限公司); Nanyang Technological University (南洋理工大学); Jiangsu Key Lab of Language Computing (江苏省语言计算重点实验室)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: [CVPR 2026 Findings] Our data and code are released at this https URL
Abstract:Current image editing software often hinges on fixed filters or expert tuning, leaving a gap between amateur users’ intent and outcomes. Creations by generative models may contain artifacts, implausible details, or stylistic drift away from photorealism and offer little insight into why an edit was made. We propose IEA, a conversational Image Editing Agent that learns to operate parameterized tools in an explicit, interpretable action space. IEA is trained via a three-stage multitask pipeline: (1) SFT on distilled expert edits, (2) GRPO with rewards for likeness improvement, tool usefulness, and intent summarization, and (3) large-scale synthetic fine-tuning to jointly master image editing, refinement, and user intent summarization. By manipulating 16 editing tools step by step, IEA produces transparent edit traces that can be inspected and debugged. In quantitative experiments, it attains a lower pixel distance on the edit task and a higher ROUGE-L on the summary task than strong baselines. In user studies, it ranks best among tool-calling methods for instruction following while surpassing generative methods in overall perceptual quality. Our results validate interpretable, tool-centric VLMs as a reliable path to human instruction-guided image retouching.
[NLP-166] Rewrite to Translate Translate to Reward: Reinforcement Learning for Source Rewriting in Machine Translation
【速读】： 该论文旨在解决在使用生成式AI（Generative AI）对源文本进行重写以提升机器翻译（Machine Translation, MT）质量时，需针对不同MT模型手动调优提示词（prompt）这一繁琐且低效的问题。现有方法依赖于为每种MT模型设计特定提示词，导致部署成本高、可扩展性差。本文提出一种基于强化学习（Reinforcement Learning, RL）的源文本重写框架——RLSR（Reinforcement Learning for Source Rewriting），其核心创新在于无需为每个MT模型单独设计提示词，而是通过直接利用每次重写后下游翻译质量的提升作为奖励信号，端到端地优化重写模型。该方案的关键在于构建一个与具体MT模型解耦的通用重写策略，使重写模型能够自适应地生成有助于提升翻译性能的源文本，从而实现跨模型、跨语言对的高效迁移。实验结果表明，基于RLSR训练的40亿参数重写模型在6个MT模型和16个语言对上显著优于无重写基线及同等规模的提示工程基线，且性能接近基于2350亿参数大模型的提示基线，验证了该方法的有效性与普适性。
链接: https://arxiv.org/abs/2606.08011
作者: Boxuan Lyu,Haiyue Song,Zhi Qu,Hidetaka Kamigaito,Kotaro Funakoshi,Manabu Okumura
机构: Institute of Science Tokyo; Preferred Networks Inc; Nara Institute of Science and Technology
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Although directly prompting off-the-shelf Large Language Models (LLMs) to generate meaning-preserving source rewrites can effectively enhance Machine Translation (MT) quality, doing so requires manually tuning prompts for different MT models. In this work, we propose RLSR (Reinforcement Learning for Source Rewriting), a novel RL-based framework for training a source rewriting model without tuning prompts for each MT model. RLSR optimizes the rewriting model by directly using the improvement in downstream translation quality yielded by each rewritten source as the reward. Extensive experiments across six MT models and 16 language pairs demonstrate that our 4B rewriting models trained via RLSR significantly outperform the no-rewriting baseline and existing same-scale prompt-based rewriting baselines, while achieving competitive performance against prompt-based baselines based on the 235B LLM.
[NLP-167] Summarization is Not Dead Yet
【速读】： 该论文旨在重新审视大型语言模型（Large Language Models, LLMs）生成摘要是否已达到甚至超越人类撰写摘要的水平这一广泛传播的观点。其核心问题是：在当前技术条件下，机器生成摘要与人类参考摘要之间的实际差距究竟如何？为解决此问题，研究提出了一种多维度评估框架，关键在于融合五种不同数据集上的对比实验，涵盖五种前沿LLM，并综合采用受控的人类评估、去偏的LLM作为评判者（LLM-as-Judge）协议、基于外部知识的事实性验证以及语料级语言学分析。研究表明，尽管当前大模型在表面流畅性和连贯性方面表现更优，但人类参考摘要在信息量和忠实度方面仍具有显著优势，尤其在涉及推理或综合判断的任务中，人类摘要更具可靠性；同时，语言学分析揭示了不同模型输出存在风格同质化现象。因此，解决方案的关键在于通过多角度、多层次的系统性评估，揭示当前大模型虽提升了摘要生成的“下限”，但其性能上限仍明显低于人类水平，从而表明摘要生成依然是一个未完全解决的研究难题。
链接: https://arxiv.org/abs/2606.08000
作者: Dongqi Liu,Chenxi Whitehouse,Zheng Zhao,Zhuchen Cao,Jian Li,Yabiao Wang
机构: Saarland University, Max Planck Institute for Informatics; University of Cambridge; University of Edinburgh; Zhejiang University; Tencent YouTu Lab
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:The progress of large language models (LLMs) has fueled claims that model-generated summaries rival or even surpass human-written references, raising questions about whether summarization remains an open research problem. We re-examine this narrative through a multi-track evaluation covering five diverse datasets and five state-of-the-art LLMs, combining controlled human assessment, bias-mitigated LLM-as-Judge protocols, factuality verification against external knowledge, and corpus-level linguistic analysis. Our findings reveal a more nuanced landscape in which human reference summaries continue to demonstrate advantages in informativeness and faithfulness, whereas LLM outputs are preferred mainly for surface-level coherence and fluency. Factuality verification indicates that human references remain more reliable, particularly for claims involving reasoning or synthesis, and linguistic analysis uncovers a pattern of stylistic homogeneity across different models. These observations suggest that current LLMs have raised the floor of summarization quality, but the ceiling of their performance remains below human capabilities.
[NLP-168] MC-PDD: Masked Corpus-Level Pretraining Data Detection for Black-Box Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）预训练数据的透明度问题，特别是针对无法访问模型内部概率分布的闭源模型，如何有效检测特定语料库是否曾被用作其预训练数据这一关键挑战。现有方法普遍依赖对模型输出概率分布的访问，因而难以适用于仅提供输入-输出接口的闭源模型。为此，论文提出一种名为“掩码语料级预训练数据检测”（Masked Corpus-level Pretraining Data Detection, MC-PDD）的新方法，其核心创新在于借鉴掩码语言建模（Masked Language Modeling）的思想：通过在文本中掩码高度特异性的词元（token），并利用模型预测缺失内容的能力，比较目标语料库与参考非成员语料库在预测命中率上的差异。若两者差异具有统计显著性，则可推断目标语料库很可能存在于模型的预训练数据中。实验表明，该方法在开源与闭源模型上均能有效区分已预训练与未见过的数据，且在严格的黑盒设置下性能接近现有最优方法。该方案仅需标准API调用即可实现模型审计与数据版权验证等实际应用，具有重要实践价值。
链接: https://arxiv.org/abs/2606.07996
作者: Kaixin Lan,Mu You,Tao Fang,Binkai Ou,Lidia S. Chao,Derek F. Wong
机构: University of Macau (澳门大学); Macau Millennium College (澳门千年学院); BoardWare Information System Limited (板威信息系统有限公司)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: The manuscript consists of 10 pages formatted in the IEEE/ACM two-column style
Abstract:Pretraining is fundamental to the development of Large Language Models (LLMs), yet the opacity of pretraining data complicates model analysis and raises ethical, legal, and fairness concerns. Detecting whether specific datasets were used during pretraining is, therefore, critical. Existing state-of-the-art methods typically rely on access to model probability distributions, making them unsuitable for closed-source LLMs that provide only input-output interfaces. To address this limitation, we introduce Masked Corpus-level Pretraining Data Detection (MC-PDD), a novel method inspired by the masked language modeling paradigm. MC-PDD masks highly specific tokens in each text and prompts the LLM to predict the missing content. It then assesses whether the difference in prediction hit rates between a candidate corpus and a reference non-member corpus is statistically significant. Based on this comparison, MC-PDD determines whether the candidate texts were likely included in the model’s pretraining data. Experimental results demonstrate clear and consistent differences in prediction hit rates between pretrained and unseen data across three datasets, for both open-source and closed-source LLMs. Despite operating under a stricter black-box setting, MC-PDD achieves performance comparable to existing detection methods. Our approach enables practical applications such as model auditing and data copyright verification using only standard API access. Upon acceptance, we will publicly release the code and datasets.
[NLP-169] Customer-Agent : Overcoming Context Limitations in Ultra-Long Shopping Trajectories via Tool-Augmented Agents and RLVR
【速读】： 该论文旨在解决在超长购物轨迹（shopping trajectories）背景下，大语言模型（LLM）在处理长期用户行为序列时面临的上下文长度限制与推理能力不足的问题。真实电商场景中的用户行为数据通常跨越数年，形成长达数万甚至数十万词元（tokens）的连续轨迹，而现有基准测试多局限于短轨迹，且受限于数据隐私无法获取真实长轨迹数据，导致模型评估缺乏现实依据。为填补这一空白，论文提出ShopTrajQA，一个基于真实商品信息与模拟生成的长上下文评估基准，支持最高达32k和64k token的轨迹长度，可系统评估模型在不同上下文长度下的鲁棒性。实验发现，当前前沿大模型在长轨迹推理任务中存在显著性能瓶颈。为此，论文提出客户代理框架（Customer Agent Framework），其核心在于采用可验证奖励的强化学习（Reinforcement Learning with Verifiable Rewards, RLVR）训练范式，将购物轨迹作为外部本地文件存储，并通过代码解释器（如SQL查询）实现自主检索与解析，从而突破大模型固有的上下文窗口限制。该方法有效实现了对超长轨迹的高效管理与精准推理，在ShopTrajQA上表现优异，并展现出在其他复杂推理任务上的泛化能力。
链接: https://arxiv.org/abs/2606.07995
作者: Hongye Liu,Rongmei Lin,Anurag Kashyap,Hejie Cui,Ricardo Henao,Besnik Fetahu,Bing Yin
机构: Amazon(亚马逊); Duke University(杜克大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Understanding customer shopping trajectories is essential for enabling personalized shopping experiences. However, shopping records (i.e., customer’s search, clicks, purchases, etc.) often span long time horizons over multiple years, resulting in extremely long trajectories that pose significant challenges for existing large language models (LLMs). Despite the importance of this problem, existing benchmarks are limited to short customer trajectories, while real-world trajectories from large e-commerce platforms are rarely accessible due to data privacy constraints. To address this gap, we introduce ShopTrajQA, a long-context evaluation benchmark constructed from real-world product information and simulated shopping trajectories. The dataset includes variants of up to 32k and 64k tokens, enabling systematic evaluation of model robustness under varying context lengths. Through comprehensive benchmarking of frontier LLMs, we identify critical performance gaps in reasoning over long shopping trajectory data. To address these challenges, we propose a Customer Agent Framework for ultra-long context management. Leveraging a Reinforcement Learning with Verifiable Rewards (RLVR) agentic training paradigm, our approach stores trajectories as external local files and trains the agent to autonomously retrieve and parse them through code-interpreter interactions (e.g., SQL queries), effectively bypassing the fixed in-context window constraints of LLMs. Experimental results demonstrate that our framework achieves strong performance for ShopTrajQA and shows generalization to other complex reasoning tasks.
[NLP-170] FMRFusion: Frequency-Aware Multi-View Representation Learning for Heterogeneous Image Fusion
【速读】： 该论文旨在解决红外与可见光图像融合中因传统单模块堆叠特征提取方式导致的模态特征学习不充分问题，进而影响融合结果在真实异质数据场景下的有效性与鲁棒性。其解决方案的关键在于提出一种频率感知的多视角表征学习网络FMRFusion：通过引入多尺度结构感知模块，有效捕获细粒度局部结构与关键上下文信息；采用双线性频率分解机制，将特征分离为高低频分量，实现对局部细节与全局表征在不同频域下的联合建模；设计跨视角互补交互机制，显式建模反射光信息与辐射强度响应之间的互补特性，促进跨视角特征融合；此外，结合流匹配（flow matching）策略，通过学习从粗略表示到高质量表征的渐进变换过程，进一步优化融合结果。实验表明，FMRFusion在多个基准数据集上均实现了卓越且一致的性能，尤其在夜间场景下表现突出。
链接: https://arxiv.org/abs/2606.07985
作者: Tao Zhoua,Yunlong Liu,Qinghui Chen,Zekai Zhang,Minlong Sun,Changlin Biana,Dagang Li,Wenmin Wang,Jinglin Zhang
机构: Shandong University (山东大学); Macau University of Science and Technology (澳门科技大學)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Infrared and visible image fusion aims to generate a composite image that retains significant target information and preserves detailed textures, integrating two heterogeneous modalities. Previous image fusion methods typically adopt a single-module stacking approach to extract features from the two modalities. However, these approaches may result in incomplete learning of their distinct characteristics, thereby limiting the fusion effectiveness and constrain ing robustness in real-world heterogeneous data scenarios. To address these challenges, we propose FMRFusion, a frequency-aware multi-view representation learning network for Heterogeneous Image Fusion. A Multi-Scale Struc tural Perception Module is introduced to effectively capture discriminative structures, extracting fine-grained local structures and essential contextual information. A bilinear frequency decomposition mechanism is employed to sepa rate features into high-frequency and low-frequency components, enabling joint modeling of local details and global representations across different frequency domains. Moreover, a Cross-View Complementary Interaction is incorpo rated to explicitly model and fuse the complementary characteristics between reflected light information and radiative intensity responses, facilitating effective cross-view interaction. We further improve the Performance of the fused results by flow matching, which progressively refines the fused features by learning the transformation from coarse data to high-quality representations. Extensive experiments conducted on multiple benchmark datasets demonstrate that FMRFusion achieves superior and consistent performance across a range of fusion tasks, especially in nighttime scenarios
[NLP-171] MechLens: Late Crystallization of Factual Knowledge Explains Intervention Effectiveness in Language Models
【速读】： 该论文旨在解决大语言模型（LLM）中事实性知识存储位置不明确的问题，进而为幻觉（hallucination）缓解提供理论支持与技术路径。其核心问题是：事实性知识在模型各层中的涌现机制是否具有渐进性？研究发现，事实性知识并非在中间层逐步积累，而是在最终几层突然“结晶化”（Late Crystallization），即在模型深层才首次稳定出现在顶层预测中。这一现象在五个不同架构的模型家族（Pythia、Gemma、Qwen2.5、Llama-3.1、Mistral；0.5–14B）中均显著存在，其中26.8%–93.4%的正确答案从未在任一中间层的前10个预测中出现，且结晶深度普遍达到模型总层数的80%以上。跨规模（如Qwen2.5-14B）和跨基准测试（如MMLU：98.2%准确率）验证了该现象的普适性，且通过微调透镜（tuned lens）排除了探测器偏差的影响。控制实验表明，情感分类任务中仅有极低比例的正确预测出现在深层（如Qwen仅0.5% vs. 事实性任务85.9%），证实该现象特异性地存在于事实性记忆召回。基于此，研究提出“结晶引导干预”原则（crystallization-guided intervention），并证明在中等结晶度模型（如Llama、Mistral）中，CAA方法优于DoLa（p < 0.001）；而在高结晶度模型（如Qwen）中则呈现方向一致的反向优势（CAA +25.4% vs. DoLa +15.5%，p = 0.069）。进一步分析显示，归一化层（LayerNorm）的消融实验表明，结晶现象源于残差流（residual stream）的内在特性，而仅通过1.2倍缩放（LN scaling）即可实现+11.8%的准确率提升且无推理开销。此外，研究揭示出“可计算性-记忆谱”（Computability-Memorization Spectrum）：可计算知识比纯记忆事实更早结晶（第22.1/28层 vs. 28.0/28层）。研究开源了支持五类模型的MechLens工具链。
链接: https://arxiv.org/abs/2606.07978
作者: Xueping Gao
机构: Alibaba Cloud (阿里巴巴云)
类目: Computation and Language (cs.CL)
备注:
Abstract:Understanding where LLMs store factual knowledge is critical for hallucination mitigation. We systematically quantify Late Crystallization: factual knowledge does not gradually emerge across layers but “crystallizes” abruptly at the final layers. Across five model families (Pythia, Gemma, Qwen2.5, Llama-3.1, Mistral; 0.5–14B), 26.8%–93.4% of correct answers never enter top-10 predictions at any intermediate layer, with late emergence (80% depth) consistent across architectures. Cross-scale (Qwen2.5-14B) and cross-benchmark (MMLU: 98.2%) results confirm generality; tuned lens rules out probe artifacts. A sentiment-classification control (0.5% for Qwen vs. 85.9% factual; 2.0% for Mistral vs. 26.8%) confirms the phenomenon is specific to factual recall. Late Crystallization yields a crystallization-guided intervention principle: CAA outperforms DoLa on moderate-crystallization models (Llama, Mistral; p0.001), with a directionally consistent reversal on high-crystallization Qwen (+25.4% vs. +15.5% MC1, p=0.069). LayerNorm ablation shows crystallization is intrinsic to the residual stream; LN scaling (x1.2) yields +11.8% MC1 with zero inference overhead. We further reveal a Computability-Memorization Spectrum: computable knowledge crystallizes earlier (layer 22.1/28) than memorized facts (28.0/28). We release MechLens supporting five model families. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2606.07978 [cs.CL] (or arXiv:2606.07978v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2606.07978 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-172] Defending Against Malicious Finetuning by Scaling Train-time Adversarial Attacks
【速读】： 该论文旨在解决当前开源大语言模型（LLM）在对齐阶段易受恶意微调攻击的问题，特别是针对利用全参数微调（full-parameter finetuning）的强攻击，这类攻击仅需少量监督微调（SFT）步骤即可破坏模型的安全对齐性。现有防御方法主要针对参数高效微调（parameter-efficient finetuning）类攻击，难以应对更强大的全参数攻击。其解决方案的关键在于提出Patcher方法，该方法受对抗训练与双层优化（bi-level optimization）启发，通过在对抗循环中放大优化步数来增强模拟攻击强度，迫使防御方学习对强攻击不敏感的模型参数。此外，论文设计了一种高效的并行算法，在保持防御性能的同时显著降低训练的墙时钟时间（wall-clock time）。实验结果表明，相较于原始SFT对齐方法，Patcher显著提升了模型鲁棒性，并在多种攻击场景和模型规模下具有良好的迁移能力。
链接: https://arxiv.org/abs/2606.07970
作者: Haoming Wen,Shi Chen,Qingyu Shi,Siyuan Liu,Minrui Luo,Jingzhao Zhang,Tianxing He
机构: Xiongan AI Institute; Institute for Interdisciplinary Information Sciences, Tsinghua University; Shanghai Qi Zhi Institute
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Current open-weight large language models (LLMs) are prone to malicious finetuning attacks, which could compromise the safety alignment of LLMs with only a few steps of supervised finetuning (SFT) on poisoned datasets. Existing alignment-stage defenses are primarily designed to defend against attacks that use parameter-efficient finetuning methods. However, they fail to defend against stronger attacks that use full-parameter finetuning. In this paper, we propose Patcher, a method inspired by adversarial training and bi-level optimization, to combat such attacks. Patcher strengthens the simulated attack by scaling up the optimization steps in the adversarial loop, thus forcing the defender to find model parameters that are insensitive to stronger attacks. Furthermore, we propose an efficient parallel algorithm to implement Patcher, decreasing the wall-clock time of training while preserving Patcher’s performance. Extensive experiments show that Patcher substantially improves the model’s robustness compared to vanilla SFT alignment, and transfers to diverse attack scenarios and model sizes. Code is available at this https URL.
[NLP-173] Neutrality Bites: Gender Representation in AI-Generated Animal Stories
【速读】： 该论文旨在解决生成式AI在叙事文本中存在性别偏见的问题，尤其关注大型语言模型（Large Language Models, LLMs）在处理无性别指涉的拟人化动物角色时的性别赋值行为。研究发现，尽管模型在多数情况下倾向于回避性别指派（平均19%）或使用性别中立代词如“it”（平均38.2%），但一旦进行性别分配，便表现出显著的男性偏向：女性角色仅出现在2.2%的故事中，而男性角色占比高达40.6%。这表明，看似“中立”的策略实则可能加剧对边缘群体身份的抹除。论文的核心解决方案关键在于批判性反思“中立性”作为缓解社会偏见的策略，并主张应探索超越中立的新路径，例如通过更均衡地分配想象主体的社会可能性，以实现真正意义上的公平表达。
链接: https://arxiv.org/abs/2606.07969
作者: Imani Finkley,Yuanxi Li,Melanie Walsh
机构: University of Washington(华盛顿大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: FAccT(ACM Conference on Fairness, Accountability, and Transparency) 2026
Abstract:Gender bias in AI-generated stories is a well-documented problem. While much attention has been paid to reducing or mitigating this bias, it is not always clear whether interventions produce genuinely fairer results. To investigate this issue, we examine how large language models (LLMs) handle gender assignment in a narrative context that is popular, highly ambiguous, and also known to closely reproduce human stereotypes: stories about talking animals. We prompt six leading LLMs to complete an English-language story about seven different anthropomorphic animal characters whose gender is unstated. We additionally iterate with four different narrative settings and a range of model temperatures. Across the 23.8K stories, we find that models frequently avoid gendering the animal character in the story (19% on average) or use gender-neutral language like “it” or “its” (38.2% on average). However, when gender is assigned, there is a significant masculine bias. Feminine animal characters are virtually absent, present in just 2.2% of stories vs. 40.6% that feature masculine characters. Our findings point to a broader argument: neutrality bites. In other words, models that prioritize neutrality to address social bias may actually contribute to the erasure of marginalized perspectives and identities. We suggest that alternative strategies beyond neutrality need to be pursued, such as ones that more equally distribute social possibilities across imagined subjects.
[NLP-174] What Does Debiasing Really Remove? A Geometric Study of PCA-Based Gender Debiasing in Word Embeddings KR
【速读】： 该论文旨在解决基于主成分分析（PCA）的去偏方法在大语言模型（LLM）词向量中去除性别偏见时存在的根本性问题：即这些方法究竟去除了哪些类型的偏见，以及其对词向量语义结构的破坏程度。其解决方案的关键在于通过系统的几何分析，揭示PCA去偏的本质——虽然直接性别偏见主要集中在第一主成分，符合低秩偏见假设，但由词汇关联性测度（WEAT）衡量的分布式关联偏见则分散于多个维度，并不沿主成分方向集中。因此，单纯移除前几个主成分虽能缓解部分直接偏见，却会显著破坏词向量间的几何关系和语义结构，导致语义失真。研究结果表明，PCA-based去偏本质上是一种权衡：在降低特定形式偏见的同时，无法有效消除分布式的隐含关联偏见，并引入几何畸变。此外，不存在普适最优去偏程度，偏见削减与语义保留之间的平衡依赖于具体指标和嵌入模型。这说明词向量中的偏见并非纯粹低秩，简单的子空间移除策略难以实现全面有效的去偏。
链接: https://arxiv.org/abs/2606.07964
作者: Alexey Kresin,Tchifou M. Dieffi,Tomer Caspi
机构: Hood College ( hood.edu); Ben-Gurion University of the Negev (Be’er Sheva, Israel)
类目: Computation and Language (cs.CL)
备注: 8 pages, 4 figures. Source code available at this https URL
Abstract:Debiasing methods based on principal component analysis (PCA) are broadly used to reduce gender bias in word embeddings used in LLMs, yet it remains unclear what aspects of bias they actually remove and how destructive this process is. These methods are based on the understanding that bias resides in a low-dimensional subspace, with the assumption that most of it can be captured by a few principal components. In this work, we conduct a systematic geometric analysis of PCA-based gender debiasing and investigate what is actually removed from the embedding space. Our experiments across multiple embeddings show that direct gender bias is primarily concentrated in the first principal component, supporting the low-rank bias hypothesis. However, associative bias measured by WEAT does not align with these principal directions and is instead spread across multiple embedding dimensions. Furthermore, as expected, we demonstrate that removing an increasing number of principal components leads to a consistent degradation of the embedding geometry, affecting semantic structure and vector relationships. These results reveal that PCA-based debiasing operates as a trade-off: while it effectively reduces certain forms of direct bias, it fails to eliminate distributed associations and introduces geometric distortion. Moreover, there is no universal optimal level of debiasing, as the balance between bias reduction and semantic preservation depends on the chosen metric and embedding. Overall, our findings suggest that bias in word embeddings is not purely low-rank and that simple subspace removal methods may be insufficient for comprehensive debiasing.
[NLP-175] Shared Latent Structures Enable Unified Backdoor Detection and Mitigation in LLM s
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）中后门攻击的普遍性与隐蔽性问题，传统方法将后门攻击视为孤立的触发-响应失效，难以泛化应对多样化的攻击行为。其核心挑战在于：不同类型的后门攻击（如越狱、拒绝响应操纵、密码锁定、偏见诱导、情感误分类、基于国家条件的有害建议等）虽表现各异，但可能共享一个潜在的共性机制。本文的关键发现是，通过在残差流（residual-stream）激活上应用稀疏自编码器（Sparse Autoencoders, SAEs），识别出一组少量的潜在特征（latent features），这些特征在多种攻击类型中均被一致激活，且具有跨模型（Qwen3、Gemma 3、Llama 3.1，参数量4B至32B）、跨攻击方式（微调与权重编辑）的通用性。进一步研究表明，这些特征具有因果性：通过双向激活调控可抑制攻击成功率或在干净提示上诱发目标行为。基于此，研究提出轻量级的SAE特征分类器，实现零样本泛化检测未见过的后门；并引入概念消融微调（Concept Ablation Fine-Tuning, CAFT），在训练阶段通过消除共享潜在子空间来预防后门形成。因此，解决方案的关键在于揭示并利用后门攻击背后的可转移潜在机制，从而实现统一检测与防御。
链接: https://arxiv.org/abs/2606.07963
作者: Omar Mahmoud,Aly M. Kassem,Thommen George Karimpanal,Buddhika Laknath Semage,Negar Rostamzadeh,Golnoosh Farnadi,Santu Rana
机构: Deakin University (迪肯大学); Mila, Quebec AI Institute (魁北克人工智能研究所)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Backdoor attacks in large language models (LLMs) are often treated as isolated trigger-response failures, motivating defenses tailored to specific triggers or behaviors. We show this view is incomplete. Across diverse backdoor behaviors, we identify a shared latent mechanism that can be detected, causally controlled, and suppressed. Using sparse autoencoders (SAEs) on residual-stream activations, we find a small set of latent features consistently activated across jailbreaking, refusal manipulation, password-locking, bias induction, sentiment misclassification, and country-conditioned harmful advice. These features generalize across Qwen3, Gemma~3, and Llama~3.1 models from 4B to 32B parameters, and across both fine-tuning and weight-editing attacks. Through bidirectional activation steering, we show these features are causal: suppressing them reduces attack success, while amplifying them induces target behaviors on clean prompts. We further train lightweight SAE-feature classifiers that generalize zero-shot to unseen backdoors and outperform residual-stream and weight-diffing baselines. Finally, we introduce Concept Ablation Fine-Tuning (CAFT), which suppresses backdoor formation by ablating the shared latent subspace during training. Together, our results suggest that many backdoors rely on a transferable latent mechanism, enabling unified detection and mitigation.
[NLP-176] From May to Is: Certainty Distortion in Language Model Rewriting
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在科学与医学等高风险领域中存在的一种关键问题——确定性扭曲（certainty distortion），即模型在保持语义内容不变的前提下，对陈述的确定性程度产生系统性偏差。其核心问题是：当前语言模型在重述、总结或改写信息时，常无意识地增强表达的确定性，导致用户误判信息可信度，进而影响决策质量。解决方案的关键在于提出一种基于语言模型的评估指标，该指标能与人类群体对确定性的判断保持一致，从而客观量化模型输出中的确定性失真程度。通过该指标，研究发现不同规模和架构的语言模型普遍存在显著的确定性扭曲现象，且呈现明显不对称性——多数模型更倾向于提高而非降低表达的确定性（约1.5–2倍概率），并在多次重写过程中出现累积效应。尽管提示工程（prompt-based interventions）可缓解部分扭曲，但无法彻底消除该偏差。这一发现揭示了生成式AI在高风险应用中存在普遍的“过度确信”倾向，对依赖模型进行信息处理的专业用户具有重要警示意义。
链接: https://arxiv.org/abs/2606.07951
作者: Catarina G Belem,Shang Wu,Hongyu Yao,Mark Steyvers,Sameer Singh,Padhraic Smyth
机构: University of California Irvine (加州大学欧文分校); Massachusetts Institute of Technology (麻省理工学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Humans increasingly turn to Language Models (LMs) in ways that shape beliefs and drive decisions, including discussing, rewriting, and summarizing information from scientific articles, news, and medical reports. However, in these domains, where how confidently a claim is expressed matters, little is known about whether LMs faithfully preserve it. In this work, we investigate certainty distortion in LMs, defined as meaningful changes in expressed certainty when semantic content is preserved. We propose an LM-based evaluation metric that is consistent with population-level judgments of certainty. Using this metric, we characterize certainty distortion across different sizes and families of models in the context of scientific and medical communication tasks. Our results show that certainty distortion affects up to 75% of LM outputs and is systematically asymmetric in rewriting tasks with most LMs being 1.5-2 \times more likely to increase the expressed certainty than to decrease it. These effects can compound over repeated paraphrasing: in the medical domain, claude-haiku-4-5 increases certainty of 20% examples after a single iteration, increasing to 40% after five iterations. Prompt-based interventions reduce overall certainty distortion but do not eliminate it. Together, these findings reveal a general bias toward inflating expressed certainty, with direct implications for users who rely on LMs in high-stakes domains.
[NLP-177] POISE: Position-Aware Undetectable Skill Injection on LLM Agents
【速读】： 该论文旨在解决生成式智能体（Generative AI Agent）中因技能（Agent Skill）开放格式导致的技能投毒攻击（Skill-Poisoning Attack）问题，尤其关注在保证攻击隐蔽性的同时提升攻击成功率。现有攻击方法面临可靠性和隐蔽性之间的权衡：基于YAML头部的注入虽稳定但易被检测，而将恶意指令置于技能正文中的隐蔽注入方式则因上下文不一致导致执行不可靠。为突破这一困境，论文提出POISE（Position-Aware, Context-Aware Injection Strategy），其核心创新在于将触发器压缩为单一、外观无害的正文指令，通过位置感知机制选择合理插入位置，并利用上下文感知生成器将恶意指令自然融入前置准备步骤中，实现与正常操作语义的融合。实验表明，在Skill-Inject基准上，POISE达到89.3%的攻击成功率（ASR），显著优于随机放置正文基线（+28.0分）和仅使用YAML基线（+2.6分），同时保持了正文注入的隐蔽优势。更关键的是，由于大语言模型（LLM）扫描器对合法技能中高权限工具调用高度敏感，平均误报率达74.6%，使得POISE仅使5.6%的污染变体触发新风险警报，从而有效绕过当前静态防御机制，凸显了现有防护体系的脆弱性。
链接: https://arxiv.org/abs/2606.07943
作者: Haochang Hao,Dehai Min,Zhifang Zhang,Yunbei Zhang,Miao Xu,Yingqiang Ge,Lu Cheng
机构: University of Illinois at Chicago (伊利诺伊大学芝加哥分校); University of Queensland (昆士兰大学); Tulane University (杜兰大学); Rutgers University (罗格斯大学)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 20 pages, 2 figures, 5 tables
Abstract:Agent skills provide a lightweight mechanism for extending general-purpose agents, but their open format exposes them to skill-poisoning attacks. A practically dangerous injection must stay invisible: if executing the payload derails the user’s legitimate task, the resulting failure signal invites inspection of the skill. We therefore evaluate attacks by Attack Success Rate, which requires the injected payload to execute and the user’s task to still pass its verifier in the same trial. Prior skill-poisoning attacks face a reliability-stealth trade-off under this lens: YAML-header injections are reliably loaded but easily inspected, whereas stealthier body injections that place explicit malicious commands in the skill prose are less reliable because out-of-context commands invite the agent’s own suspicion. We introduce POISE, a position-aware attack that compresses the trigger into a single, benign-looking body instruction, placing it at a feasible position and using a context-aware generator to blend it with nearby setup or prerequisite steps. On Skill-Inject with codex+gpt-5.2, POISE achieves an 89.3% ASR, 28.0 points above a random-placement body baseline and 2.6 points above a YAML-only baseline, while retaining the stealth advantage of body placement. That stealth is the decisive margin: because legitimate skill bodies naturally require privileged tool operations, LLM scanners are hyper-sensitive, falsely flagging 74.6% of clean skills on average across four judges and both benchmarks. Blending into these false alarms, POISE causes only 5.6% of poisoned variants to gain a new high-risk alert over their clean baselines, rendering current static defenses ineffective.
[NLP-178] Illusions of the Gold Standard: A Large-scale Analysis of Human Evaluation Protocols for Long-form Text Generation
【速读】： 该论文旨在解决当前生成式文本评估中人类评估协议报告不透明、可复现性差的问题。其核心挑战在于，现有研究普遍缺乏对人类评估实验设计关键细节的充分披露，导致评估结果的可靠性与可重复性难以保障。解决方案的关键在于提出一套涵盖20项可报告指标的标准化评估框架，并基于对284篇论文的人工审查及超过1800篇论文的LLM辅助分析，系统性地揭示了社区在报告实践中的普遍缺陷。研究据此提出可操作的改进建议，以推动未来研究在人类评估报告中实现更高程度的透明度与可复现性。
链接: https://arxiv.org/abs/2606.07936
作者: Katelyn Xiaoying Mei,Yi-Li Hsu,Minjoon Choi,Zongwan Cao,Chenjun Xu,Bingbing Wen,Su Lin Blodgett,Lucy Lu Wang
机构: University of Washington (华盛顿大学); National Tsing Hua University (国立清华大学); Seoul National University (首尔国立大学); Mila - Québec AI Institute (Mila - 魁北克人工智能研究所); Allen Institute for AI (艾伦人工智能研究所)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Human evaluation plays a critical role in assessing the quality of generated text. However, the reliability and reproducibility of these evaluations depend on transparent and well-documented protocols – details that are frequently missing in current practice. In this work, we conduct a large-scale analysis of human evaluation protocols for evaluating long-form generation tasks in *CL conference publications from 2023–2025, including a full manual review of 284 papers and LLM-assisted analysis for another 1.8k+ papers. We define a set of 20 reportable criteria related to reproducibility of human evaluation studies, and apply these criteria to systematically examine reporting norms and practices within the community. We find widespread under-reporting of important aspects of human evaluation study design, leading to ambiguity about what was measured and how, who contributed judgments, and how judgments should be interpreted. Based on these findings, we outline actionable recommendations to support more transparent and reproducible reporting in future research. Our analysis code and annotated dataset can be found at: this https URL
[NLP-179] ROSUM-MCTS: Monte Carlo Tree Search-Inspired HDL Code Summarization with Structural Rewards
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在硬件描述语言（Hardware Description Languages, HDLs）如VHDL和Verilog代码摘要生成任务中效果不佳的问题。现有方法缺乏对HDL代码结构特性的有效建模，且难以在保持功能正确性的同时生成语义丰富、流畅的摘要。为此，论文提出ROSUM-MCTS，一种受蒙特卡洛树搜索（Monte Carlo Tree Search, MCTS）启发的LLM引导型摘要优化框架，其核心在于通过分层候选扩展机制融合局部与全局上下文，并利用复合奖励函数驱动强化学习优化过程，该奖励函数综合平衡了功能正确性（Functional Correctness, FC）、局部内容充分性（Local Content Adequacy, LCA）和语言流畅性。实验结果表明，ROSUM-MCTS在VHDL-eval和Verilog-eval数据集上显著优于基线方法，其优势源于结构化的自底向上精炼策略与强化学习驱动的优化机制。消融实验证实，局部与全局扩展策略均不可或缺，且FC与LCA之间的平衡对性能至关重要。此外，该方法对变量重命名等表面修改具有强鲁棒性，而传统方法在此类扰动下性能明显下降。综上，ROSUM-MCTS为HDL代码摘要提供了一个高效、稳健的解决方案，推动了强化学习增强型代码摘要研究的发展。
链接: https://arxiv.org/abs/2606.07925
作者: Prashanth Vijayaraghavan,Charles Mackin,Luyao Shi,Apoorva Nitsure,Ashutosh Jadhav,David Beymer,Tyler Baldwin,Ehsan Degan,Vandana Mukherjee
机构: 未知
类目: Computation and Language (cs.CL)
备注: 7 pages
Abstract:Large language models (LLMs) have shown promise in code summarization, yet their effectiveness for Hardware Description Languages (HDLs) like VHDL and Verilog remains underexplored. We propose ROSUM-MCTS, an LLM-guided approach inspired by Monte Carlo Tree Search (MCTS) that refines summaries through structured exploration and reinforcement-driven optimization. Our method integrates both local and global context via a hierarchical candidate expansion mechanism and optimizes summaries using a composite reward function balancing functional correctness (FC), local content adequacy (LCA), and fluency. We evaluate ROSUM-MCTS on the VHDL-eval and Verilog-eval datasets, demonstrating its consistent outperformance over baseline methods by leveraging structured bottom-up refinement and reinforcement-based optimization. Ablation studies confirm the necessity of both local and global expansion strategies, as well as the importance of balancing FC and LCA for optimal performance. Furthermore, ROSUM-MCTS proves robust against superficial modifications, such as variable renaming, maintaining summary quality where baselines degrade. These results establish ROSUM-MCTS as an effective and robust HDL summarization framework, paving the way for further research into reinforcement-enhanced code summarization.
[NLP-180] Decoupling Semantics and Logic: A Training-Free Coarse-to-Fine Pipeline for Video Retrieval-Augmented Generation ACL2026
【速读】： 该论文旨在解决多模态增强生成任务中跨语言长视频理解、严格角色一致性保持以及零幻觉时间定位等核心挑战。其解决方案的关键在于提出一种完全无需训练的两阶段级联式视频检索增强生成（Video RAG）框架。该架构通过模态感知的分工策略，将语义检索与认知逻辑推理解耦：第一阶段采用高召回率的语义预检索模块，仅利用高质量视觉摘要和全局文本描述进行密集检索，主动隔离噪声模态（如OCR与ASR），以维持纯净的向量空间；第二阶段则引入基于商业大语言模型（LLM）驱动的自适应、迭代式推理（A.I.R.）过滤代理，重新融合完整的多模态上下文，实现细粒度的认知重排序，有效剔除语义相关但逻辑无关的候选内容，确保与用户角色设定的严格逻辑对齐。最终，通过提示精炼（Prompt Sculpting）机制，约束生成器将提炼后的候选集转化为结构化精确的JSON响应，并附带逐块引用，显著提升了信息检索精度与角色条件生成的一致性。
链接: https://arxiv.org/abs/2606.07924
作者: Jiaxin Dai,Zehang Wei,Jiamin Yan,Xiang Xiang
机构: Huazhong University of Science and Technology (华中科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multimedia (cs.MM)
备注: To be presented at ACL 2026 MAGMAR Workshop (Oral; Retrieval leaderboard No.1)
Abstract:This paper presents our system description for the 2nd Workshop on Multimodal Augmented Generation via MultimodAl Retrieval (MAGMaR). Addressing the critical challenges of cross-lingual long-video comprehension, strict persona adherence, and zero-hallucination temporal grounding, we propose a fully training-free, two-stage cascaded Video RAG pipeline. Our architecture strategically decouples semantic retrieval from cognitive logical reasoning through a modality-aware division of labor. In the first stage, a high-recall semantic pre-fetching module employs dense retrieval using only high-fidelity visual summaries and global text descriptions, explicitly isolating noisy modalities (e.g., OCR and ASR) to maintain a pristine vector space. In the second stage, an Adaptive, Iterative, and Reasoning-based (A.I.R.) filtering agent, powered by a commercial Large Language Model (LLM), performs fine-grained cognitive reranking. The agent re-incorporates full multimodal contexts to enforce strict logical alignment with user personas, effectively pruning semantically similar but logically irrelevant candidates. Finally, a Prompt Sculpting mechanism constrains the generator to synthesize the distilled subset into strictly formatted JSON responses with exact chunk-level citations. Evaluated on the RAG track, our resource-aware approach shows exceptional precision in both information retrieval and persona-conditioned generation.
[NLP-181] MemToolAgent overview with a simple restaurant booking scenario where the agent retrieves similar memories receives feedback on an invalid time format and generates a reflection to update its memory
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）代理在执行复杂工具使用任务时，因缺乏有效记忆机制而导致的长期经验学习能力不足问题。具体而言，现有方法难以充分利用过往用户-代理交互中的历史信息以提升工具调用的准确性与个性化水平。其解决方案的关键在于提出MemToolAgent框架，通过两个核心组件实现高效记忆管理：一是基于环境反馈和用户反馈的反思式记忆提取模块，能够将错误的执行过程提炼为结构化的批判性记忆条目；二是动态检索模块，依据记忆条目的相似性分布自适应选择最优数量的历史经验进行召回。该方法无需对LLM进行微调即可显著提升工具使用性能，在WorkBench、NESTFUL和PEToolBench三个基准测试中分别实现了29%、80%和17%的相对性能提升，验证了其在通用性和个性化工具使用上的有效性。
链接: https://arxiv.org/abs/2606.07909
作者: Suleyman Armagan Er,Danilo Ribeiro,Yogesh Virkar,Surafel Lakew,Adi Kalyanpur,James Gung,Thomas Delteil,Arshit Gupta
机构: AWS AI; University of Washington
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 8 pages, 5 figures
Abstract:Modern large language model (LLM) agents can use external tools to help users solve complex tasks. However, for problems that require learning from long-term historical events or from previous agent-environment interactions, LLM agents are required to use memory mechanisms to store and retrieve experiences. While sophisticated memory systems exist for dialogue agents, few studies have empirically examined how to improve agents’ tool-using capabilities through past user-agent conversations. We propose MemToolAgent, a framework that improves tool use through memory management. Our approach contains a memory extraction module that processes past experiences into structured memory entries, and a retrieval module that dynamically selects a subset of the stored memory entries. This enables more personalized and accurate responses aligned with user preferences and feedback without requiring LLM fine-tuning. In summary, this work has three main contributions: (1) a unified memory entry format that improves both general-purpose and personalized tool use without LLM fine-tuning, (2) a reflection-based memory extraction that uses environment and user feedback to distill wrong executions into critiques to store, and (3) a retrieval module that chooses how many past experiences to use based on the memory similarity distribution. MemToolAgent achieves 29%, 80%, and 17% relative improvements compared to strong baselines on the WorkBench, NESTFUL, and PEToolBench benchmarks, respectively.
[NLP-182] Beyond Individual Personas: Aligning Synthetic Dialogue to Population-Level Behavior Distributions
【速读】： 该论文旨在解决合成对话语料库在生成过程中存在的群体行为分布失真问题，即现有基于人物设定（persona-grounded）的生成模型虽能确保单个对话的局部合理性，却难以维持整体语料库在行为模式上的统计一致性，导致合成数据在群体层面的行为分布与真实参考语料库存在显著偏差。其解决方案的关键在于提出GroupPersona框架，该框架将参考语料库的群体统计特征转化为生成过程中的控制信号：通过分离对话中可识别的核心行为特征与可预测的次要影响因素，构建行为分组，并据此对用户代理进行条件化，使其能够模拟参考群体的真实交互模式。实验表明，GroupPersona在四个不同来源（助理型和Reddit衍生型）及两种构建方式（结构保持与变异增强）的语料上均显著提升了合成语料与参考语料在12项行为属性上的分布匹配度，使Jensen-Shannon散度从0.234降至0.177，相对降低24.4%；同时在对话质量评分校准方面表现最优，平均绝对偏差由次优基线的0.91降至0.63，有效实现了群体行为分布对齐与结构一致性的双重优化。
链接: https://arxiv.org/abs/2606.07893
作者: Xinyi Liu,Rinat Khaziev,Hooshang Nayyeri,Emine Yilmaz,Charith Peris,Hari Thadakamalla
机构: Amazon(亚马逊); University of Illinois Urbana–Champaign(伊利诺伊大学厄本那-香槟分校); University College London(伦敦大学学院)
类目: Computation and Language (cs.CL)
备注:
Abstract:Synthetic dialogue corpora are increasingly used as proxies for target dialogue data, yet persona-grounded generators optimize individual conversations rather than corpus composition, yielding locally plausible dialogues with distorted population-level behavior mixes. We introduce GroupPersona, a framework that aligns synthetic dialogue corpora to the behavior distribution of a reference corpus. GroupPersona turns population statistics into generation controls: it separates each dialogue’s core behavioral signature from predictable side effects, and uses the resulting behavioral groups to condition user agents on the interaction patterns that define the reference population. We evaluate GroupPersona on four corpora crossing two dialogue sources, assistant-style and Reddit-derived, with two construction variants: structure-preserving and variation-enhanced. GroupPersona lowers Jensen-Shannon divergence between synthetic and reference distributions over 12 behavior attributes from 0.234 to 0.177 relative to the strongest average baseline, a 24.4% reduction, and is best or tied-best on all four corpora while preserving structural alignment. It also achieves the closest calibration to reference-conversation quality scores, reducing mean absolute deviation from the reference-conversation profile to 0.63 versus 0.91 for the next-best baseline.
[NLP-183] Strained Coherence: A Pre-Failure Signal in Coding Agent Execution Trajectories
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）驱动的编码代理在推理过程中存在“应变一致性”（strained coherence）这一安全相关缺陷的问题，即代理在意识到自身推理存在矛盾或潜在错误时，仍选择继续执行不当行为。这种模式与“口头化奖励劫持”（verbalized reward hacking）密切相关，表现为代理明确指出了任务代理目标与真实目标之间的冲突，却依然优化代理目标。其解决方案的关键在于构建一个基于Claude Sonnet 4.6的判别器，能够读取完整的推理轨迹并精准识别出存在应变一致性的片段。该判别器通过分析代理的显式认知陈述（如承认问题）与后续行动之间的不一致，输出可解释的跨度级标注——包括被引用的承认语句、被引用的行为动作以及冲突类型，从而实现对安全风险行为的细粒度检测。实验结果表明，在Qwen3.5-35B-A3B模型上，被标记轨迹的失败率达94%，显著高于未标记轨迹的46%（差异47点，Fisher精确检验p=0.003），且在保持高选择性下达到94%的精度，优于基于词汇标记的基线方法；两方法交集的10条轨迹全部失败，验证了检测的有效性。尽管在Gemma4-31B上的整体信号不显著（p=0.31），但高冗长度区间内仍表现出显著差异，且判别器能有效捕捉经改写后仍保留冲突本质的文本，展现出强鲁棒性和可解释性。
链接: https://arxiv.org/abs/2606.07889
作者: Marut Pandya,Kasey Zhang,Baiqing Lyu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:LLM-based coding agents sometimes acknowledge a problem in their own reasoning and then proceed anyway. We call this pattern strained coherence: a safety-relevant failure mode in which an agent has information that should change its behavior, states that information, and still acts against it. The pattern overlaps with verbalized reward hacking, where an agent names a tension between a task proxy and the underlying goal yet optimizes the proxy anyway. We give an operational definition, build a Claude Sonnet 4.6 judge that reads full trajectories and flags spans where the pattern occurs, and evaluate it on 44 Terminal-bench-2 trajectories using a Qwen3.5-35B-A3B backbone. Flagged trajectories fail 94% of the time versus 46% for unflagged trajectories (47-point gap, Fisher’s exact p = 0.003; 46 points after excluding three prompt-embedded examples, p = 0.006). At matched selectivity, the detector reaches 94% precision versus 88% for a lexical discourse-marker baseline; the 10-trajectory intersection of the two methods has a 100% failure rate (Clopper-Pearson 95% CI [69%, 100%]). We replicate on Gemma4-31B with 43 trajectories: the overall signal is directionally consistent but not significant (20-point gap, p = 0.31), with attenuation driven largely by 13 trajectories with zero think content, where the detector has no substrate to analyze. In the high-verbosity Gemma tertile, the gap is +30 points; in the mid- and high-verbosity Qwen tertiles, it is +40 points each. The first flag appears at a median of 83-84% of elapsed trajectory time across both models, and the binary flag survives paraphrases that soften explicit conflict markers (8/8 trajectories). Unlike univariate predictors, the detector emits interpretable span-level output – quoted acknowledgment, quoted action, and typed conflict – showing what the agent saw and ignored.
[NLP-184] Whose Norms? Disentangling Cultural and Personal Alignment in Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在社会决策场景中如何平衡文化规范与个人偏好这一核心问题。现有研究多将文化对齐与个性化分别处理，缺乏对二者动态权衡的系统性评估。为此，作者提出PACT（Personal-Preference and Cultural-Norm Trade-off）框架，用于量化模型在面对文化规范与个人偏好冲突时的选择倾向。其解决方案的关键在于：通过跨国家、跨情境的人类与模型对比实验，揭示文化规范对模型行为的影响具有显著的非均匀性——国家背景（7.8%）对模型行为的影响远大于年龄（1%）和性别（0.7%），且指令微调后行为模式发生非线性变化；同时发现人类在判断自身文化情境时一致性最低，体现出明显的“文化内多元主义”（within-culture pluralism）特征；此外，尽管模型可匹配多数人选择，但难以捕捉响应分布与不确定性，相关性最高仅达0.24。因此，该研究强调应发展超越“多数共识”的对齐评估范式，以更全面地反映社会判断中的文化多样性与分歧。
链接: https://arxiv.org/abs/2606.07877
作者: Angana Borah,Isabelle Augenstein,Rada Mihalcea
机构: University of Michigan - Ann Arbor (密歇根大学安娜堡分校); University of Copenhagen (哥本哈根大学)
类目: Computation and Language (cs.CL)
备注: Preprint under review
Abstract:Large language models are increasingly used for social decision-making situations that require balancing cultural norms with personal preferences. For example, a user preferring honesty might ask whether to correct a coworker publicly when local norms favor indirect feedback. Yet existing research studies cultural alignment and personalization largely separately. We introduce PACT, the Personal-Preference and Cultural-Norm Trade-off framework, which evaluates whether models choose to follow a cultural norm or allow personal preferences. We find that LLMs vary in how rigidly they enforce cultural norms, with behavior shifted more by country context (7.8%) than age (1%) and gender (0.7%) and shifting non-uniformly after instruction tuning. Furthermore, our five-country human study on PACT shows that culture-following in humans is mainly driven by scenario country, with the lowest agreement when participants judge their own cultural contexts, showing within-culture pluralism. Finally, human-LLM alignment experiments show that models can match majority choices, but fail to capture response distributions and uncertainty (with best correlations reaching only 0.24). Together, these findings motivate alignment evaluations that go beyond majority to capture cultural pluralism and disagreement in social judgment.
[NLP-185] he Cold-Start Safety Gap in LLM Agents
【速读】： 该论文旨在解决生成式 AI（Generative AI）代理在对话初期存在显著安全风险的问题，即“冷启动安全差距”（cold-start safety gap）——代理在会话初始阶段最易产生不安全行为，随着执行若干常规代理任务后，其安全性显著提升。其核心解决方案在于通过系统化评估与干预，揭示并缓解这一安全波动现象。关键发现是：代理在完成多个常规代理任务（regular agentic tasks）后，模型隐状态逐渐向安全对齐区域转移，从而提升整体安全性；其中，前置任务本身是驱动安全性的主要因素，而代理自身先前响应虽对安全性影响较小，但对维持后续任务的实用性至关重要。基于此，研究提出一种简单有效的部署策略：在正式处理高风险请求前，先让代理执行数个常规任务进行“预热”，以有效弥合冷启动阶段的安全缺口。该结论经由自建基准SODA（Safety Over Depth for Agents）及多个开源基准（如AgentHarm、Agent Safety Bench、BFCL、API-Bank）验证，兼具安全性提升与能力保留的双重优势。
链接: https://arxiv.org/abs/2606.07867
作者: Chung-En Sun,Linbo Liu,Tsui-Wei Weng
机构: University of California, San Diego
类目: Computation and Language (cs.CL)
备注:
Abstract:Are tool-calling LLM agents equally safe throughout a conversation? We discover they are not: agents are most vulnerable at the very start of a session and become substantially safer after a few regular agentic tasks – a phenomenon we term the cold-start safety gap. To study this systematically, we introduce Safety Over Depth for Agents (SODA), a benchmark that controls how many regular agentic tasks the agent completes before encountering a safety threat, supporting up to 20 preceding tasks. Evaluating 7 models from 4 families, safety improves by 9–52% as the number of preceding regular agentic tasks increases from zero to twenty. Representation analysis confirms that model hidden states gradually shift toward a safety-aligned region as more preceding tasks are present. By systematically studying which part of the preceding conversation matters most, we find that the regular agentic tasks themselves are the primary driver of safety, while the agent’s own prior responses have less effect on safety but are essential for preserving later utility. This conclusion is further supported by evaluation on open-source safety benchmarks (AgentHarm, Agent Safety Bench) and utility benchmarks (BFCL, API-Bank), confirming that warming up the agent with regular agentic tasks before deployment makes it safer and preserves full capability. Based on these findings, we recommend a simple deployment strategy: having the agent complete a few regular agentic tasks before possible exposure to safety-critical requests mitigates the cold-start safety gap. Our code is available at this https URL
[NLP-186] Beyond English benchmarks: clinical llm evaluation in Brazilian Portuguese
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在临床决策支持中的跨语言应用瓶颈问题，尤其关注英语以外语言（如葡萄牙语）在真实医疗场景下的表现差距。现有大多数评估基准集中于英语环境，导致对非英语语言的临床能力评估不足，阻碍了全球范围内公平的医疗AI应用。为此，研究提出ClinicalBr——首个基于真实巴西病例报告构建的双语临床决策基准，涵盖2,892例来自28本SciELO医学期刊的病例，覆盖18个专科领域，以平行的葡萄牙语-英语对形式组织。每个案例支持四个核心任务：诊断检索、鉴别诊断、检查推荐和治疗方案制定。研究评估了MedGemma-27B、Sabiá-4、DeepSeek-R1和o3-mini四款模型在双语环境下的表现。关键发现表明，语言性能差距并非普遍现象，而是任务依赖性的：在诊断检索任务中，英语始终具有显著优势（准确率高出7.5–12.1个百分点），但在其他三类任务中，差距不显著，多数模型的置信区间跨越零点，且葡萄牙语版本的完整度略高。此外，针对巴西本土疾病的表现优于整体数据集，说明当前预训练数据已较好覆盖热带地区疾病特征。值得注意的是，检查推荐是所有模型与语言下最困难的任务，F1得分均低于0.10，远低于鉴别诊断任务的上限（0.20–0.27），揭示出当前模型在临床检查建议生成方面仍存在显著局限。因此，解决方案的关键在于构建真实世界、多语言对齐的临床基准，以精准识别语言与任务间的性能差异，并指导更具包容性的模型优化方向。
链接: https://arxiv.org/abs/2606.07853
作者: Giordano de Pinho Souza,Glaucia Melo,Josefino Cabral Melo Lima,Daniel Schneider
机构: Federal University of Rio de Janeiro (联邦大学里约热内卢); Toronto Metropolitan University (多伦多都会大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models are transforming the support for clinical decision and their application in real scenarios. Yet, most benchmarks are conducted in English, and cross-lingual evaluation is needed to tackle the language gaps in global access. We introduce ClinicalBr, the first bilingual benchmark for clinical decision built from real Brazilian case reports. The corpus contains 2,892 cases drawn from 28 SciELO medical journals, spanning 18 specialties, and is structured as parallel Portuguese-English pairs. Each case supports four evaluation tasks: diagnosis retrieval, differential diagnosis, exam recommendation, and treatment planning. We evaluate four models: MedGemma-27B, Sabiá-4, DeepSeek-R1, and o3-mini, across both languages. The central finding is that the Portuguese-English performance gap is task-dependent, not general. In diagnosis retrieval, English yields a consistent advantage across all models, with +7.5-12.1 accuracy points. This advantage disappears in differential diagnosis, exam recommendation, and treatment planning, where confidence intervals cross zero for most models and Portuguese completeness scores are marginally higher. Brazilian-endemic conditions proved easier than the full corpus, not harder, indicating that tropical presentations are adequately represented in current pre-training. Exam recommendation was the hardest task across all models and both languages, with F1 scores below 0.10, well below the differential diagnosis ceiling of 0.20-0.27.
[NLP-187] he ACUTE Protocol: Operationalizing Language Model Activations for Better Calibration Utility and Trust ICML2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在实际应用中面临的可信度（trustworthiness）问题，尤其是模型输出置信度（confidence）的校准性（calibration）不足与信息量缺失之间的矛盾。现有模型普遍存在过度自信（overconfidence）倾向，且传统校准方法易被“策略性操纵”——例如仅预测类别基线概率虽可实现完美校准，但毫无实际信息价值。为此，论文提出一种新评估指标：由“最优基准（oracle）”归一化的期望效用（Expected Utility Renormalized by the Oracle, EURO），该指标在衡量模型校准性的同时兼顾输出的信息性（informativeness）。其核心解决方案是设计一种通用的基于激活值的置信度、效用与信任估计协议（Activation-based Confidence, Utility, and Trust Estimation, ACUTE），该协议通过分析模型中间层激活特征，实现对不确定性进行高效、灵活且低计算开销的量化。ACUTE在多项任务（包括多选问答、工具调用和科学文档摘要）中表现优异，覆盖6个不同模型家族，显著优于现有强基线，在提升模型校准性的同时有效维持低校准误差。研究表明，集成ACUTE协议可显著增强大模型在复杂场景下的可靠性与可信度。
链接: https://arxiv.org/abs/2606.07822
作者: Nishant Subramani,Palash Goyal,Yiwen Song,Mani Malek,Yuan Xue,Tomas Pfister,Hamid Palangi
机构: Carnegie Mellon University (卡内基梅隆大学); Google(谷歌); Scale AI(规模人工智能)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted to ICML 2026
Abstract:As language models improve and become increasingly deployed to solve a variety of tasks, trustworthiness becomes essential. Calibration is a good proxy for trust: well-calibrated confidence estimates help inform the risk versus reward tradeoff when trusting a specific model output. Unfortunately, even as models improve, they remain poorly calibrated, often biasing towards overconfidence. Additionally, calibration can be gamed: a policy that always predicts the base rate is perfectly calibrated, but completely uninformative. To resolve this, we develop a new metric, expected utility renormalized by the oracle (EURO), that balances calibration and informativeness. We also propose a general-purpose activation-based confidence, utility, and trust estimation protocol (ACUTE) to appropriately adjudicate uncertainty. The ACUTE protocol provides flexible, sample-efficient, and compute-efficient confidence estimators for 3 tasks including multiple choice question answering, tool-calling, and scientific document summarization across 6 models from 4 model families. ACUTE outperforms strong baselines on EURO, while maintaining low calibration error. Taken together, our work shows that equipping LLMs with the ACUTE protocol can improve calibration, utility, and trustworthiness in numerous settings.
[NLP-188] Representational Similarity and Model Behavior in Multi-Agent Interaction ICML2026
【速读】： 该论文旨在探究人类神经相似性与社会亲密度、合作成效之间的关系是否可类比延伸至人工智能领域，特别是大型语言模型（Large Language Models, LLMs）之间的交互行为。具体而言，研究试图解决的核心问题是：在多智能体系统中，不同模型间的表征相似性如何影响其合作能力与创新性表现。其解决方案的关键在于通过实验分析276对大型语言模型在八种涵盖合作与新颖性任务的游戏中的交互表现，发现表征空间越相似的模型对，在合作任务中表现更优，但创新能力显著下降；且这一趋势在控制模型性能差异和规模等因素后依然成立。进一步分析表明，早期层的表征相似性对合作与创新的影响最为显著，提示共享词汇与语义基础的程度可能是决定多模型交互效果的核心机制。因此，该研究提出，表征相似性应作为多智能体系统设计中的关键考量因素。
链接: https://arxiv.org/abs/2606.07818
作者: Yujin Potter,Seun Eisape,Shiyang Lai,Alexander Huth,James Evans,Been Kim,Jacob Eisenstein,Dawn Song,Alane Suhr
机构: 未知
类目: Computation and Language (cs.CL); Neural and Evolutionary Computing (cs.NE)
备注: ICML 2026
Abstract:Researchers have shown that neural similarity among humans predicts social closeness and cooperative success, whereas innovation often emerges from interactions among dissimilar individuals. We investigate whether these principles extend to artificial intelligence by examining interactions between large language models. In our experiments, 276 model pairs interact across eight games spanning both cooperation and novelty. We find that pairs with more similar representation spaces achieve significantly higher cooperation but exhibit reduced novelty and creativity. The effects of representational similarity on cooperation and novelty remain robust even after controlling for other factors such as performance disparity and model size. We also find that similarity in the early layers consistently shows the strongest association with cooperation and novelty, compared to the middle and later layers. This suggests that a central factor underlying these patterns could be the extent to which the two models share lexical and semantic grounding. Overall, representational similarity can be an important consideration in multi-agent system design.
[NLP-189] Scaling Participation in Modular AI Systems
【速读】： 该论文旨在解决当前大型语言模型（LLM）高度集中化、缺乏多样性的问题，即由少数机构主导的单一模型架构难以全面反映人类知识、推理与价值观的多元性。其核心解决方案是提出“扩大参与”（scaling participation）的新范式，通过自下而上的方式构建模块化人工智能系统：不同利益相关者贡献基于自身兴趣与优先级训练的小型模型，这些模型在模块化框架中协同工作，形成组合式人工智能系统。该方法的关键在于利用参与者多样性带来的互补性，实现性能超越单体大模型（在15项任务中最高提升15.4%），且整体系统能力不仅优于各组件之和，还展现出可解决超过15%单一模型无法处理问题的涌现能力。这一范式为向开放、协作、去中心化的下一代人工智能演进提供了技术基础。
链接: https://arxiv.org/abs/2606.07812
作者: Shangbin Feng,Yike Wang,Weijia Shi,Luke Zettlemoyer,Yejin Choi,Yulia Tsvetkov
机构: University of Washington (华盛顿大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Humanity is a mosaic of multifaceted talents and needs, and any truly intelligent AI must reflect that richness. Yet the LLMs used by all are built by the few – a centralized market of monolithic AI models structurally ill-suited to capture the diversity of human knowledge, reasoning, and values. Here we introduce scaling participation, a new paradigm in which modular AI systems are built from the bottom up through the contributions of diverse stakeholders. Participants contribute small models trained on their own interests and priorities; these models then collaborate in modular frameworks as compositional AI systems. Participatory AI systems outperform monolithic LLMs by up to 15.4% across 15 tasks, such as reasoning and factuality, surpassing models larger than all contributed components combined. Further experiments show that participatory AI systems benefit from contributor diversity, substantially improve on each contributor’s original priorities, and exhibit emergent capabilities that allow them to solve over 15% of problems where all individual models fail. Scaling participation provides a technical foundation for transitioning from the monolithic status quo toward an open, bottom-up, and collaborative AI future.
[NLP-190] SLMJury: Can Small Language Models Judge as Well as Large Ones?
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）作为评估判官时存在的高成本、高延迟和决策过程不透明等问题，提出以小型语言模型（Small Language Models, SLMs）替代LLMs实现可扩展、高效且可解释的自动化评估。其核心解决方案在于构建一个名为SLMJury的框架，支持在闭式二分类正确性判断与开式质量评分两种范式下对SLMs进行系统性评估。关键创新点在于将评判过程形式化为受预算约束的函数，并从四个维度深入分析：（1）“过度思考”效应具有领域依赖性——数学任务中快速生成10词结论的效率优于长推理，而一般任务中长推理可提升高达23%的准确率；（2）模型家族间存在显著的跨领域泛化能力差异，数学到通用任务的准确率下降幅度达10%至近40%；（3）闭式与开式评判依赖不同能力，例如最佳二分类判官Phi-4在对话质量评分任务中排名第九，而经过推理训练的模型则呈现相反排序；（4）在反思-批判-修正（Reflect-Critique-Refine, RCR）辩论协议下，多智能体辩论整体性能下降，但顶尖判官能稳定抵御六种对抗性角色攻击，方差低于0.55%。研究证明，无需依赖大型专有模型即可实现可靠自动评估，但尚无单一SLM在所有任务上全面占优，凸显了多样化评估策略的重要性。
链接: https://arxiv.org/abs/2606.07810
作者: Anish Laddha,Nitesh Pradhan,Gaurav Srivastava
机构: LNMIIT (拉贾斯坦邦国家信息技术研究所); Virginia Tech (弗吉尼亚理工学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Large language models (LLMs) are widely used as judges for evaluating model outputs, but their high cost, latency, and opacity limit scalability. We introduce SLMJury, a framework for evaluating small language models (SLMs) as judges across two paradigms: closed-ended binary correctness and open-ended quality scoring. We benchmark 16 SLM judges (0.6B-14B parameters) from four model families across ten benchmarks: eight closed-ended tasks spanning mathematical, scientific, and general reasoning (N=64,824 judgments per configuration), plus SummEval and MT-Bench for summarization and conversational scoring. We formalize judging as a budget-conditioned function and study five dimensions. Four findings emerge. (1) The overthinking effect is domain-dependent: for most judges quick 10-token verdicts match or beat extended reasoning on mathematical judging (by 2-7% where they help), while reasoning wins on general tasks by up to 23%. (2) Domain generalization separates model families, with math-to-general accuracy gaps ranging from under 10% to nearly 40%. (3) Closed-ended and open-ended judging draw on different capabilities: the best binary judge (Phi-4) drops to rank 9 on MT-Bench, while reasoning-trained models invert this ordering. (4) Under the Reflect-Critique-Refine (RCR) debate protocol, multi-agent debate degrades accuracy across all tested configurations, whereas the top judges resist six adversarial personas with =0.55% variance. Reliable automated evaluation does not require large proprietary models, yet no single SLM dominates. The leaderboard is available at this https URL, and our framework code and pip package are publicly available at this https URL and this https URL.
[NLP-191] Evaluating RAG Reliability under Clean Misleading and Mixed Retrieval
【速读】： 该论文旨在解决在信息污染（information disorder）环境中，检索增强生成（Retrieval-Augmented Generation, RAG）系统因引入看似合理但错误的检索证据而导致生成结果不可靠的问题。其核心挑战在于：当模型的参数化知识（parametric knowledge）与外部检索到的证据之间存在冲突时，尤其在存在大量误导性信息的情况下，RAG系统如何做出判断并保持事实准确性。解决方案的关键在于提出一种系统的评估协议，通过对比干净（clean）、被污染（poisoned）及混合（mixed）证据下的生成表现，结合参数化覆盖机制（parametric override）与置信度度量（confidence metrics），分析误导信息对大语言模型（LLM）生成过程的影响时机与程度。该框架为评估RAG系统在虚假信息泛滥场景下的鲁棒性提供了可量化、可复现的分析工具。
链接: https://arxiv.org/abs/2606.07783
作者: Sevgi Yigit-Sert
机构: Ankara University (安卡拉大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Retrieval-Augmented Generation (RAG) is widely used to improve the factual reliability of large language models (LLMs) by grounding answers in retrieved evidence. In misinformation-rich environments, however, retrieved content may include plausible but incorrect information, raising concerns about the reliability of RAG-based information access systems. In this work, we propose an evaluation protocol to systematically test how the RAG system handles conflicts between parametric knowledge and evidence retrieved from context with varying amounts of misleading information. We target correct answers to factoid questions that the model responds to correctly, even when there is no retrieval, and use this to test the system with clean, poisoned, and mixed evidence. The proposed analytical framework combines parametric override and confidence metrics to assess when and how misleading information affects the generation process of LLMs. This study aims to provide insights into the robustness of RAG systems in information disorder scenarios. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2606.07783 [cs.CL] (or arXiv:2606.07783v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2606.07783 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-192] Unlocking Latent Value: Taxonomy-Guided Recovery of High-Performing Data from Low-Tier Web Corpora
【速读】： 该论文旨在解决当前主流预训练语料库构建流程中，将文档质量压缩为单一综合评分所导致的高价值内容遗漏问题，尤其在评分体系忽略的关键维度（如时效性与文化特异性）上损失显著。其核心解决方案是提出一种基于分类体系（taxonomy-driven）的多维过滤框架，通过识别并利用传统综合评分未能捕捉的语义有意义维度来恢复被低估的内容价值。关键创新包括：（1）基于ESSENTIAL-WEB分类体系引入“时效性”（timeliness）与“文化特异性”（cultural specificity）两个新维度，二者与现有维度间呈现低互信息（low pairwise NMI），证明其独立价值；（2）采用大模型Qwen2.5 32B对1400万文档进行标注，并通过知识蒸馏得到轻量级0.5B模型；同时训练一个73M多任务MLP模型，基于E5嵌入实现快速推理，提升50倍吞吐量；（3）设计一种计算高效的两阶段筛选框架：第一阶段在小规模下识别强信号维度；第二阶段基于表现最佳维度组合构建并评估合取/析取型复合过滤器，仅需极小比例的全尺度成本即可发现高性能配置。实验表明，经该框架筛选的低优先级网页数据，在中等质量层级上相较未过滤基线分别提升12.1%（推理）、9.5%（编码）、2.0%（知识），甚至超越原始顶级数据集6.7%（推理）和13.7%（编码）；而低于生产阈值两个层级的数据经过滤后，在推理与编码任务上分别提升22.3%与19.5%，全面优于原始顶级数据。结果验证了大量潜在价值仍埋藏于被降权的网络数据中，且多维分类体系过滤是一种既原则性强又计算高效的解锁路径。
链接: https://arxiv.org/abs/2606.07778
作者: Neeraj Varshney,Sanket Lokegaonkar,Nasser Zalmout,Qingyu Yin,Priyanka Nigam,Bing Yin
机构: Amazon(亚马逊)
类目: Computation and Language (cs.CL)
备注:
Abstract:Dominant web data curation pipelines for pretraining collapse document quality into a single composite score, systematically missing high-value content along dimensions the scorer underweights. We present a taxonomy-driven framework that recovers this value by filtering along semantically meaningful dimensions that composite scores fail to capture. First, building on the ESSENTIAL-WEB taxonomy, we introduce two novel dimensions: timeliness and cultural specificity, both of which show low pairwise NMI with existing ones. We annotate 14M documents using Qwen2.5 32B and distill into a lightweight 0.5B model. To enable rapid corpus-wide annotation, we additionally train a 73M multi-task MLP on E5 embeddings, achieving 50x inference throughput. Second, to navigate the combinatorial explosion of filter configurations, we introduce a compute-efficient two-pass framework: Pass 1 identifies the strongest dimension signals at small scale; Pass 2 constructs and evaluates conjunctive and disjunctive compound filters from the top performers - identifying high-performing configurations at a fraction of full scaling-law cost. Applying the selected filters to deprioritized web data, taxonomy-filtered subsets outperform their unfiltered baselines and even surpass the highest-quality tier. On mid-tier data, our best filter improves over its unfiltered baseline by 12.1% on reasoning, 9.5% on coding, and 2.0% on knowledge benchmarks, exceeding unfiltered top-tier data by 6.7% on reasoning and 13.7% on coding. Furthermore, filtered data from two tiers below the typical production threshold improves by 22.3% on reasoning and 19.5% on coding over its unfiltered baseline, surpassing top-tier data on coding benchmarks. These results establish that vast latent value remains locked in deprioritized web data, and that multi-dimensional taxonomy filtering is a principled, compute-efficient key to unlocking it.
[NLP-193] ReadingMachine: A Computational Methodology for Structured Corpus Reading and Large-Scale Synthesis
【速读】： 该论文旨在解决大规模异构文本语料库中高质量、可追溯的定性综合分析难题，尤其针对传统方法在处理复杂文档集合时存在的信息压缩过度、过程不可见及观点分歧丢失等问题。其核心解决方案的关键在于提出一种基于大语言模型（Large Language Models, LLMs）的结构化语料阅读范式——ReadingMachine，通过将分析流程分解为可检视的多个阶段，包括洞察提取、语义聚类、主题生成与迭代遗漏检测，实现了对整个语料库的受控阅读操作。该方法通过延迟不可逆压缩，并显式追踪中间表示，有效保障了覆盖度、可追溯性以及对语料内部意见分歧的保留，从而支持更透明、可信的大规模定性合成。实验在包含152份产业政策文件的异构语料上验证了其有效性，共生成超过17,500条可解释洞察并构建出结构化的主题地图，系统已作为开源实验框架发布，推动生成式人工智能在复杂文本分析中的可解释应用。
链接: https://arxiv.org/abs/2606.07753
作者: James Morrissey
机构: 未知
类目: Computation and Language (cs.CL)
备注: 32 pages, 1 figure
Abstract:ReadingMachine is a computational methodology for structured corpus reading that uses large language models to perform bounded reading operations over entire document collections. Rather than relying on retrieval or recursive summarization, the approach decomposes analysis into inspectable stages including insight extraction, semantic clustering, theme generation, and iterative omission detection. By delaying irreversible compression and explicitly tracking intermediate representations, the method prioritizes coverage, traceability, and preservation of disagreement across large corpora. The system is demonstrated on a heterogeneous corpus of 152 industrial policy documents, producing more than 17,500 extracted insights and a structured thematic map. ReadingMachine is released as an open-source experimental framework for large-scale qualitative synthesis and corpus analysis.
[NLP-194] Why Limit the Residual Stream to Layers and Not Tokens? Persistent Memory for Continuous Latent Reasoning
【速读】： 该论文旨在解决生成式大语言模型在复杂推理任务中因中间状态被覆盖而导致关键信息丢失的问题，即“概念瓶颈”（concept bottleneck）。该问题表现为：在连续推理过程中，随着推理深度增加，早期计算出的中间隐状态被后续步骤覆盖，致使模型无法有效保留和利用先前推导的关键事实，从而限制了推理性能的提升。针对此问题，论文提出一种名为自适应门控连续潜在推理（AGCLR）的新方法，其核心创新在于引入一个受三个可学习门控机制控制的持久性残差记忆流（Gated Concept Stream），包括写入门（write gate）用于将中间事实存入记忆、读取门（read gate）用于检索相关历史状态、遗忘门（forget gate）用于清除无关上下文。该设计使模型能够在多路径推理过程中持续维护并动态调用关键概念，显著缓解了概念瓶颈。实验结果表明，基于GPT-2基模型，在GSM8K、HotpotQA和ProsQA三个数据集上，AGCLR均实现稳定且显著的性能提升，尤其在高阶推理任务中优势更为突出，有效弥合了随课程深度增加而扩大的性能差距。
链接: https://arxiv.org/abs/2606.07720
作者: Mujtaba Farhan,Maheep Chaudhary
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Large language models (LLMs) have demonstrated remarkable reasoning abilities on mathematical and multi-hop planning tasks. The CoCoNuT (Chain of Continuous Thought) paradigm~\citehao2024coconut extends this by enabling models to reason in latent space, exploring multiple reasoning paths simultaneously rather than committing to a single chain early on. However, we identify a limitation we term the \textbfconcept bottleneck. At each reasoning pass, intermediate hidden states are overwritten, causing the model to lose critical facts computed in earlier steps as reasoning depth increases. We observe this empirically. On HotpotQA, vanilla CoCoNuT (10.4% EM) fails to improve over the CoT baseline (11.0% EM), and performance degrades with curriculum depth on GSM8K. To address this, we propose \textbfAGCLR (Adaptive Gated Continuous Latent Reasoning), which augments CoCoNuT with a \textitGated Concept Stream. A persistent residual memory maintained across all reasoning passes, controlled by three learned gates: a \textitwrite gate that commits intermediate facts to memory, a \textitread gate that retrieves relevant prior states, and a \textitforget gate that prunes irrelevant context. Evaluated on GSM8K, HotpotQA, and ProsQA using GPT-2 as our base model, AGCLR achieves consistent improvements across all types of datasets. With the performance gap compounding as curriculum depth increases, directly resolving the concept bottleneck. Code available at this https URL
[NLP-195] How Much Dense Attention is Necessary? Oracle-Guided Sparse Prefill for Full/GQA Layers in Hybrid Long-Context Models
【速读】： 该论文旨在解决长上下文预填充（long-context prefill）阶段计算成本高昂的问题，核心挑战在于即使在引入局部、稀疏、线性或循环组件的混合模型中，全连接或分组查询注意力（GQA）层仍需对历史序列进行完整打分，导致计算开销过大。其解决方案的关键在于通过设计一种“注意力质量-顶k oracle”（attention-mass top-k oracle），对现有GQA检查点进行诊断性分析：该方法在每一层和查询位置上先计算密集注意力，选取头平均化的令牌支持集，并仅在该支持集上重新计算注意力，从而实现对稀疏预算下任务行为保持能力的精确评估。该oracle作为基准参考，将稀疏可行性与索引器误差及运行时实现效应解耦。基于此洞察，研究进一步提出一种通过KL散度蒸馏训练的头压缩辅助索引器，以冻结主干网络的方式保留原始模型性能。实验表明，在Qwen家族检索密集型任务中，该方法在16K/32K上下文长度下分别实现了+2.04和+1.13的宏平均精度提升（视为质量保全而非提升），且初步单卡首字节时间（TTFT）测量显示，与FlashAttention-2密集基线相比，稀疏服务速度分别提升1.71倍（NPU）和1.93倍（GPU），最高可达3.44倍（随机初始化压力测试），验证了稀疏运行时的潜力。本工作首次明确区分了oracle可行性、蒸馏索引器质量与运行时加速余量，为未来构建完全匹配的质量-延迟前沿奠定了基础。
链接: https://arxiv.org/abs/2606.07703
作者: Hongxing Wang,Harenome Razanajato,Zhen Zhang,Yujie Yuan,Hongsheng Liu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Technical report, first release, 26 pages, 2 figures, 11 tables
Abstract:Long-context prefill remains expensive because full/GQA layers still score the historical sequence, even in hybrid models with local, sparse, linear, or recurrent components. We study how much dense attention is needed to preserve task-level behavior under explicit support granularity and top-k budgets. We introduce an attention-mass top-k oracle for existing GQA checkpoints: for each layer and query position, it computes dense attention, selects head-averaged token support, and recomputes attention only on that support. The oracle is a diagnostic reference, not a deployable accelerator, and separates sparse-budget feasibility from indexer error and runtime realization effects. On Qwen-family retrieval-heavy evaluations, the longest per-query oracle rows stay within 1 point of dense, and a Qwen3.5-9B RULER-style sweep from 4K to 100K stays within 0.48 points. Guided by the oracle, we derive a head-collapsed auxiliary indexer trained by KL distillation from dense attention-mass distributions while keeping the backbone frozen. With separately distilled Qwen3.5-0.8B and Qwen3.5-9B indexers, the reported 16K/32K validation macro gaps are +2.04 and +1.13 points, treated as quality preservation rather than improvement; fused selection-block-shared support can introduce a larger realization gap. Preliminary single-card TTFT measurements show distilled-indexer sparse serving speedups of 1.71x for Qwen3.5-0.8B on NPU and 1.93x for Qwen3.5-9B on GPU against its dense FlashAttention-2 baseline. Additional random-init stress rows reach 3.44x, indicating sparse-runtime headroom but not validated output quality. This first release separates oracle feasibility, distilled-indexer quality, and runtime headroom, leaving a fully matched quality-latency frontier to future work. Comments: Technical report, first release, 26 pages, 2 figures, 11 tables Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2606.07703 [cs.LG] (or arXiv:2606.07703v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2606.07703 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Zhen Zhang [view email] [v1] Fri, 5 Jun 2026 09:13:52 UTC (308 KB)
[NLP-196] Steer Where It Matters: Token-Level Visual-Sensitivity Steering for LVLMs Hallucination Mitigation
【速读】： 该论文旨在解决大视觉语言模型（Large Vision Language Models, LVLMs）在生成过程中普遍存在幻觉（hallucination）的问题，尤其关注现有激活调制（activation steering）方法在推理时因信号稀疏性与局部性导致的调控效率低下及噪声干扰问题。其核心挑战在于：在自回归解码过程中，视觉条件对词元（token）预测的影响具有高度稀疏性和局部性，而传统方法通过在整个序列上平均图像有无差异，会稀释关键信号，造成信噪比下降；同时，固定强度的调制策略未能根据每个解码步骤的视觉敏感度动态分配干预资源，导致对非关键词元过度扰动，引发生成不稳定性。针对上述问题，本文提出一种细粒度的词元级视觉敏感性调制方法（Token-Level Visual-Sensitivity Steering, TLVS），其关键创新在于：首先提取并优化词元级别的调制向量，随后基于每一步的视觉敏感度自适应地调整调制强度，仅在幻觉易发区域实施精准干预，从而实现高信噪比、低干扰的可控调制。该方法为轻量级、即插即用设计，仅需少量校准训练即可适配多种视觉-语言模型，在多个基准测试（如POPE、AMBER、CHAIR、MMHal、HallusionBench）上均表现出优于现有方法的一致性性能提升。
链接: https://arxiv.org/abs/2606.07647
作者: Ruipeng Zhang,Zhihao Li,C. L. Philip Chen,Tong Zhang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Large vision language models (LVLMs) have made rapid advancements and are deployed across various applications, yet hallucinations remain a major challenge. Activation steering is appealing due to its minimal training overhead and controllability at inference time. However, we found that during autoregressive decoding, visual conditioning affects token prediction sparsely and locally across decoding steps, and many existing methods that average image-versus-no-image differences over the entire sequence dilute these critical signals, yielding low signal-to-noise ratio steering directions. Additionally, many existing methods apply a fixed steering strength, which misallocates the intervention budget, over-perturbs non-critical tokens, and can cause instability. To address these limitations, we propose Token-Level Visual-Sensitivity Steering (TLVS) for hallucination mitigation. Our approach first extracts token-level steering vectors and refines them, and then applies fine-grained, visual-sensitivity-adaptive steering only where it matters. This lightweight, plug-and-play mechanism requires only minimal training for calibration and can be applied across diverse vision-language models. It modulates the steering strength at each decoding step, selectively suppressing hallucination-prone spans while preserving evidence-grounded content. We evaluate TLVS on several benchmarks, including POPE, AMBER, CHAIR (COCO), MMHal, and HallusionBench, demonstrating consistent improvements over previous steering methods.
[NLP-197] Item Response Scaling Laws: A Measurement Theory Approach for Efficient and Generalizable Neural Scaling Estimation
【速读】： 该论文旨在解决语言模型（Language Models, LMs）性能缩放定律（Scaling Laws）推导过程中所需计算成本过高这一核心问题，传统方法需在数千个检查点或数百万次推理样本上进行昂贵评估。其解决方案的关键在于提出一种统一的项目反应缩放定律（Item Response Scaling Laws, IRSL）框架，将项目反应理论（Item Response Theory, IRT）融入缩放定律建模中，通过解耦模型潜在能力与题目特征，实现对 M 个模型和 N 个题目的缩放律估计，将参数复杂度从传统的 O(M×N) 降低至 O(M+N)，显著提升效率。该框架以Beta-IRT为具体实现，利用语言模型的非二值响应信号（如预训练阶段的词元概率、测试时采样的通过率），捕捉比传统二值反馈更丰富的行为信息。实验验证表明，在仅需每基准测试集50个问题（相较传统方法减少99.9%）的情况下，经一次校准后，IRSL即可获得与传统方法相当或更优的缩放估计精度，并具备良好的泛化能力，可跨共享测量目标的基准实现准确性能预测。
链接: https://arxiv.org/abs/2606.07616
作者: Sang Truong,Yuheng Tu,Rylan Schaeffer,Sanmi Koyejo
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Scaling laws provide a fundamental framework for understanding the performance of Language Models (LMs), yet deriving them requires prohibitively expensive evaluations across thousands of checkpoints or millions of inference samples. To address this, we introduce Item Response Scaling Laws (IRSL), a unified framework that integrates Item Response Theory (IRT) within the scaling law framework. Unlike traditional approaches that treat each model-benchmark pair in isolation, IRSL disentangles latent model ability from question characteristics, factorizing the scaling law estimation for M models and N questions to significantly reduce parameter complexity from O(M \times N) to O(M + N) . We instantiate IRSL with Beta-IRT, which leverages the empirical probability responses of LMs – such as token probabilities in pre-training and pass rates in test-time sampling – to capture richer signals than binary responses. We validate our approach across two prevalent scaling paradigms: (1) pre-training downstream scaling, using 6,612 LM checkpoints and 37,682 questions from 10 benchmarks; and (2) test-time scaling, using 12 LMs and 120 questions from 4 benchmarks with up to 2,500 samples per question. Given a one-time calibration on existing model responses, IRSL yields more reliable scaling estimates using only 50 questions per benchmark (a 99.9% reduction), achieving comparable or superior decision accuracy to traditional approaches. Furthermore, we show that the estimated latent model abilities are generalizable, enabling accurate performance forecasting across benchmarks that share the same measurement objective.
[NLP-198] LEAF: Growing Trees Without Branching for Speech-Aware Large Language Model Post-Training
【速读】： 该论文旨在解决当前基于GRPO（Generalized Reward Policy Optimization）风格的语音感知大语言模型后训练方法中存在的粗粒度信用分配问题。现有方法将相同的终端奖励优势广播至响应中的每一个词元，忽略了回滚批次中语音条件生成序列内在的结构信息——即在关键决策点前，多个生成路径常共享相同前缀。为克服这一缺陷，论文提出一种名为低秩探索自适应分叉（Low-rank Exploration with Adaptive Forking, LEAF）的回顾性树状强化学习方法。其核心创新在于无需在线分叉或额外解码，通过采样完整响应、识别高突变性边界（high-surprisal boundaries）、按共享前缀聚类响应，并利用后代奖励进行区间级（span-level）优势分配，从而恢复生成过程中的层次结构。理论分析证明了LEAF在区间级信用分配与边界选择设计上的合理性。实验结果表明，在相同回滚和低秩适配预算下，LEAF在语音问答与语音翻译任务上均优于GRPO；尤为显著的是，小型LEAF微调模型的表现超越当前全参数状态最优基线，验证了其高效性与优越性。
链接: https://arxiv.org/abs/2606.07610
作者: Argyrios Gerogiannis,Yekaterina Yegorova,Mark Hasegawa-Johnson,Venugopal V. Veeravalli
机构: University of Illinois, Urbana-Champaign (伊利诺伊大学厄本那-香槟分校)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 15 pages, 3 figures, 11 tables
Abstract:State-of-the-art GRPO-style methods for speech-aware large language model post-training suffer from coarse credit assignment, broadcasting the same terminal-reward advantage to every token in a response. This ignores useful structure within rollout batches, where speech-conditioned completions often share prefixes before diverging at important decisions. We propose Low-rank Exploration with Adaptive Forking (LEAF), a retrospective tree-based RL method that recovers this structure without online branching or additional decoding. LEAF samples complete responses, selects high-surprisal boundaries, groups responses by shared prefixes, and assigns span-level advantages using descendant rewards. We theoretically justify LEAF’s span-level credit assignment and boundary-selection design. Empirically, LEAF improves over GRPO across speech question answering and speech translation benchmarks under the same rollout and low-rank adaptation budget. Notably, smaller LEAF-trained models outperform current state-of-the-art, full-parameter baselines.
[NLP-199] Subtitle-Aligned Fine-Tuning of Whisper for Swiss German ASR: Benchmark Contamination Convention Mismatch and an Honest Baseline at 25.6% WER (13.8% cWER)
【速读】： 该论文旨在解决瑞士德语（Swiss German）自动语音识别（ASR）中因基准测试污染导致的性能评估失真问题，以及在低资源方言语音识别任务中模型泛化能力与真实识别错误率之间的偏差。其核心挑战在于：现有公开的最先进（SOTA）瑞士德语ASR系统报告的低误识率（WER）可能并非源于对语言本质的准确理解，而是由于测试集数据被模型在训练过程中“记忆”或“过拟合”，从而造成评估结果严重虚高。为此，研究提出了一套严谨的评估范式，通过在严格分离的数据集上进行诚实评估（honest evaluation），结合内容误识率（cWER）和偏置校正估计方法，揭示了真实错误率远低于传统测量值的事实。解决方案的关键在于：1）采用1,367小时广播语音与标准德语字幕的弱监督数据进行微调；2）对比全量微调与低秩适配器（LoRA）两种策略，验证模型稳定性；3）系统性分析数据质量、字幕对齐精度及训练策略的影响；4）通过引入自训练实验（如使用原始Whisper模型在测试集上自训练即获得13.88% WER）证明基准污染的存在，并借助Phi-4-multimodal等模型进一步揭示其本质上衡量的是“惯例匹配”而非真正的方言理解。最终，研究释放了两个经严格评估的公开模型（LoRA与全微调），支持完全可复现的开源部署，为后续研究提供了可信的基准参考。
链接: https://arxiv.org/abs/2606.07608
作者: Felix Akeret
机构: Independent Researcher, Zurich, Switzerland
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Sound (cs.SD)
备注: 15 pages, 21 tables. Models available at this https URL
Abstract:We present a systematic study of fine-tuning OpenAI’s Whisper large-v3 for Swiss German ASR, using 1,367 hours of broadcast speech paired with Standard German subtitles as weak supervision. Through 16 iterative training runs on an NVIDIA DGX Spark (Grace Blackwell, 128 GB unified memory, up to 1 PFLOP FP4), we compare LoRA and full fine-tuning of the 1.55B-parameter model, investigate hallucination root causes, and quantify the effect of data quality, subtitle alignment, and training strategy. Our best model achieves 25.6% measured WER on the All Swiss German Dialects Test Set (ASGDTS) in an honest evaluation on strictly disjoint data. A harmonized error analysis separating genuine errors from valid stylistic variation (tense, word order, Swiss orthography) yields a content WER (cWER) of 13.8%, counting only actual recognition failures. Bias-corrected estimation reduces this to 8.5%, suggesting the true error rate is roughly one third of measured WER. We demonstrate that published state-of-the-art Swiss German ASR results (17.1-17.5% WER) are inflated by benchmark contamination: a vanilla Whisper model self-trained on the ASGDTS test set with zero Swiss German data achieves 13.88% WER, surpassing all published systems. Experiments with Phi-4-multimodal show an even stronger memorization effect (3.9% WER), revealing that the benchmark primarily measures convention matching rather than dialectal comprehension. We release two models, a LoRA adapter (25.32% WER, 13.9% cWER) and a full fine-tuned model (25.60% WER, 13.8% cWER), among the few publicly available, honestly evaluated Whisper models for Swiss German, under Apache 2.0 with full reproducibility, requiring no institutional data agreements. Comments: 15 pages, 21 tables. Models available at this https URL Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Sound (cs.SD) Cite as: arXiv:2606.07608 [cs.CL] (or arXiv:2606.07608v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2606.07608 Focus to learn more arXiv-issued DOI via DataCite
[NLP-200] ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research
【速读】： 该论文旨在解决生成式AI在科学研究中实现端到端自主科研能力的可验证性问题，即当前AI系统虽被广泛用于科学工作，但其真正具备独立完成从问题提出到成果产出全过程的能力仍缺乏可靠评估手段。解决方案的关键在于构建一个名为ResearchClawBench的基准测试体系，该体系涵盖来自10个科学领域的40项真实科研任务，每项任务均基于已发表论文设计，提供相关文献与原始数据，并在评估过程中隐藏目标论文以确保真实性。通过专家制定的多模态评分标准（multimodal rubrics），将目标科学成果分解为加权评价维度，既支持对已有成果的复现评估，又保留了对新发现的探索空间。研究采用统一协议对七种自主科研代理（auto-research agents）及十七个原生大语言模型（LLM）进行评估，结果表明当前最强的自主代理Claude Code平均得分仅为21.5，最强的LLM Claude-Opus-4.7得分为20.7，整体前沿模型平均分仅26.5，显示出系统在实验方案匹配、证据一致性及科学核心缺失等方面存在显著缺陷。该基准为衡量迈向自主科学研究的进展提供了可复现的评估前沿。
链接: https://arxiv.org/abs/2606.07591
作者: Wanghan Xu,Shuo Li,Tianlin Ye,Qinglong Cao,Yixin Chen,Hengjian Gao,Yiheng Wang,Qi Li,Kun Li,Sheng Xu,Shengdu Chai,Fangchen Yu,Xiangyu Zhao,Zhangrui Zhao,Weijie Ma,Zijie Guo,Haoyu Zhou,Haoxiang Yin,Lixue Cheng,Chaofan Hu,Haoxuan Li,Lu Mi,Xuxuan Xie,Yifan Zhou,Ruizhe Chen,Zhiwang Zhou,Xingjian Guo,Yuhao Zhou,Xuming He,Shengyuan Xu,Xinyu Gu,Jiamin Wu,Mianxin Liu,Chunfeng Song,Fenghua Ling,Dongzhan Zhou,Shixiang Tang,Yuqiang Li,Mao Su,Peng Ye,Siqi Sun,Bin Wang,Xue Yang,Zhenfei Yin,Tianfan Fu,Guangtao Zhai,Wanli Ouyang,Bo Zhang,Lei Bai,Wenlong Zhang
机构: Shanghai Artificial Intelligence Laboratory(上海人工智能实验室)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:AI coding agents are increasingly used for scientific work, but their end-to-end autonomous research capability remains difficult to verify. We present ResearchClawBench, a benchmark for evaluating autonomous scientific research across 40 tasks from 10 scientific domains. Each task is grounded in a real published paper, provides related literature and raw data, and hides the target paper during evaluation. Expert-curated multimodal rubrics decompose the target scientific artifacts into weighted criteria, enabling evaluation of target-paper-level re-discovery while leaving room for new discovery. We evaluate seven autonomous research (auto-research) agents under a unified protocol and seventeen native LLMs through the lightweight ResearchHarness. Current systems remain far from reliable re-discovery: the strongest autonomous agent, Claude Code, averages 21.5, and the strongest ResearchHarness LLM, Claude-Opus-4.7, averages 20.7, with an LLM frontier mean of only 26.5. Error analysis shows that failures concentrate in experimental protocol mismatch, evidence mismatch, and missing scientific core. ResearchClawBench provides a reproducible evaluation frontier for measuring progress toward autonomous scientific research.
[NLP-201] Function-Vector Heads Are Two Populations: Writers and Cancellers in In-Context Learning
【速读】： 该论文旨在解决生成式 AI 模型中函数向量（Function-vector, FV）头部在上下文规则任务中被误判为同质功能类别的问题。传统方法仅依赖FV头部对任务的因果贡献大小进行排序，隐含假设高贡献头部构成单一功能类别，但这一假设不成立。本文提出关键解决方案：采用保留符号特征的筛选标准（改进的DLA结合排列FDR校正），并辅以路径修补（path patching）验证候选头部功能。结果表明，FV头部群体实际分化为两类对立子群体——“书写者”（writers）提升规则正确逻辑值，“取消者”（cancellers）则降低其值。四条件典型判决在三个模型族、六个Pythia规模下15个单元中有13个成立，且符号随机化测试在6个主要单元中均拒绝了同质性假设。相比之下，仅基于幅度的排名无法识别此结构：例如在层级任务中，前20名头部捕获了64%的取消者但仅4%的书写者；在模块化任务中则相反。研究排除了六种潜在人为偏差解释（包括归纳重叠、信息汇点、通用重要性、秩1复制抑制、V级联及非FV最近邻控制），并通过零消融实验证实取消者具有方向一致的显著增益效果（逻辑值提升0.13–0.29纳特，准确率提升2–7个百分点），进一步验证其功能性。
链接: https://arxiv.org/abs/2606.07560
作者: Han-yu Wang
机构: The University of Hong Kong (香港大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Function-vector (FV) heads (Todd et al., 2024) are typically identified by the magnitude of their causal contribution to in-context rule tasks, under the implicit assumption that the top set is a homogeneous functional class. This assumption fails. We replace magnitude-only ranking with a sign-preserving criterion (refined DLA + permutation FDR) and validate each candidate by path patching. The FV head population then splits into two opposing sub-populations: writers push the rule-correct logit up; cancellers push it down. A four-condition canonical verdict holds in 13/15 cells across three model families and six Pythia scales, and a sign-shuffle rejects homogeneity in 5/6 main cells. The structure is invisible to magnitude-only ranking: Todd’s top- 20 captures 64% of cancellers but only 4% of writers on the hierarchical task, and 59% of writers but only 8% of cancellers on the modular task. We rule out six artefact accounts on all 27 canceller (cell, head) pairs: induction overlap, sinks, generic importance, rank- 1 copy-suppression, V-cascade, and rank-nearest non-FV controls. Zero-ablating cancellers yields +0.13 to +0.29 nats of logit gain in 6/6 main cells with a directionally consistent +2 to +7 pp accuracy effect.
[NLP-202] Phantom transitions in language model fine-tuning
【速读】： 该论文旨在解决在微调语言模型时，当正确补全项存在近义词竞争者（near-synonym competitor）的情况下，模型优化过程“静默失败”（silent failure）的问题：尽管交叉熵损失单调下降，但正确词元始终无法在排名上超越其近义词竞争者。其核心解决方案在于引入一个序参量（order parameter），该参量结合了预测分布与词元嵌入间的成对重叠，可分解为两个部分：一是反映模型对正确词元相对于最近竞争者的承诺程度的信号分量（signal），二是由嵌入空间整体概率泄漏导致的背景拖拽项（background drag）。该分解揭示了两种失效模式：运动学失效（kinematic failure）中信号始终较小，而结构性失效（structural failure）中拖拽项随微调进程加剧。研究发现，序参量出现类似“弹射跃迁”的尖锐跳变，看似相变，但通过直接测量排除了自发对称性破缺的解释；进一步实验表明，在LoRA微调中保持词元嵌入矩阵不变时，此类跃迁仍存在，证明其本质源于Softmax读出层的不连续性，而非几何相变。此外，少数无量纲参数可统一描述跨架构的轨迹行为，其中一项在全微调下具有普适性，另一项基于嵌入分布的批量特性可将架构分为两类并预测LoRA的有效性；作为盲测验证，该框架对未参与参数拟合的架构的临界学习率预测精度达2.1%。研究结论仅针对近义词机制，不可随意外推，需重新校准。
链接: https://arxiv.org/abs/2606.07559
作者: Vaibhav Prakash,Jayasri Dontabhaktuni
机构: Mahindra University (马欣德拉大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Quantum Physics (quant-ph)
备注: 26 pages, 9 figures
Abstract:Fine-tuning a language model on contexts whose correct completion has a near-synonym competitor often fails silently. The cross-entropy loss decreases monotonically while the correct token never overtakes the competitor in rank. We study this regime across five transformer architectures spanning two families and a fivefold parameter range, on ten hand-selected near-synonym contexts. We instrument these failures with an order parameter combining the predicted distribution and pairwise embedding overlaps. It decomposes additively into a signal, tracking the model’s commitment to the correct token over its nearest competitor, and a background drag, set by how the embedding bulk leaks probability into the score. This isolates two failure modes. In kinematic failure the signal stays small. In structural failure the drag actively worsens as fine-tuning proceeds. We observe sharp catapult-like jumps in the order parameter that resemble a phase transition. A central negative result organises the paper. The transitions are phantoms. The spontaneous-symmetry-breaking interpretation is ruled out by direct measurement. Catapult-like jumps still appear under LoRA fine-tuning with the token embedding matrix exactly unchanged during training, where no geometric phase transition is possible. The discontinuity lives entirely in the softmax readout. A small number of dimensionless quantities organise the trajectory across architectures. One is consistent across all five under full fine-tuning. A second sorts architectures into two classes by bulk embedding distribution and predicts LoRA sufficiency. As a blind test, the framework predicts the critical learning rate of a held-out architecture, not used to fit any parameter, to within 2.1% of a subsequent learning-rate sweep. Findings concern the near-synonym mechanism only and should not be extrapolated without recalibration.
[NLP-203] Priors Persist Through Suppression: A Stroop Paradigm for Lexical Override
【速读】： 该论文旨在解决生成式语言模型在面对词汇语义重映射（如“doctor”被定义为“forest”）时，如何处理固有词义先验（lexical prior）与新规则指令之间的冲突问题。其核心挑战在于：当指令要求使用熟悉词汇的非标准含义时，模型是否真正替换原有语义表征，还是仅对原语义施加抑制。研究发现，模型并未完全覆盖原有的词义先验，而是通过降低其对应对数几率（logit）的方式实现“覆盖”，即原语义仍持续存在并产生干扰效应。解决方案的关键在于识别出这一过程中的神经机制——通过激活补丁（activation patching）技术，在多个对齐模型中定位到一个关键的三元组结构：定义主体（definition subject）、定义目标（definition target）和查询词（query word），该三元组几乎完全重构了语义冲突效应（聚合相关性 $ R \in [0.92, 1.06] $）。进一步的定义目标交换实验表明，该三元组执行的是语义绑定（binding）而非简单的身份匹配；而分离实验则揭示，目标词对数几率塌陷仅在定义目标位置被破坏时发生，说明目标保留是绑定特异性的标志。因此，行为表现与神经机制共同指向同一通路：词义先验既是干扰的起源地，也是规则覆盖作用留下的痕迹所在。
链接: https://arxiv.org/abs/2606.07555
作者: Han-yu Wang
机构: The University of Hong Kong (香港大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Glossaries, technical specifications, and system prompts routinely ask language models to use familiar words in unfamiliar ways. When this works, the lexical prior persists through override rather than being replaced: it continues to operate after the local rule applies, with the rule lowering its logit rather than installing the new meaning on top. We test this with a Stroop-style paradigm: a remapping rule (“doctor” means “forest”) pitted against the query word’s lexical-prior distractor (“hospital”), with matched neutral controls. Across 11 open-weight models spanning four families and 1B–9B parameters, lexical-prior strength predicts interference even after item-level controls for answer prior, frequency, tokenization, and prompt wording. Activation patching on five aligned models locates a source-position triplet (definition subject, definition target, query word) that nearly fully recovers the conflict effect (aggregate R \in [0.92, 1.06] ). A definition-target swap shows the triplet performs binding rather than identity matching. Dissociation experiments isolate target preservation as the binding-specific signature: distractor suppression occurs under matched, swap, and item-mismatched conditions alike, whereas target logit collapse occurs only when the definition-target position is corrupted. Behavior and mechanism converge on the same channel: the lexical prior is where both interference originates and where override leaves its mark.
[NLP-204] Liberating LLM Capabilities in Full-Duplex Speech Models
【速读】： 该论文旨在解决语音驱动的大语言模型（Large Language Models, LLMs）在交互过程中仅限于口头回应，导致其无法有效发挥文本原生能力的问题，特别是在需要持久化、结构化和可检查的中间输出任务中（如代码生成、结构化分析与多步推理）。现有方法虽能提升语音推理或全双工对话能力，但仍将文本视为隐藏的中间状态或从属模态，而非首要输出通道。为此，论文提出“听-写-说”（Listen-Write-Speak, LWS）的三通道范式，核心创新在于构建以文本为主输出的交互机制：单一自回归大模型在共享因果注意力上下文中持续接收用户音频输入，以可见的自由文本作为主要输出形式，同时并行生成实时口语回应。该方案通过一个词元模式（Token Schema）实现，无需修改模型架构，并基于两阶段数据流水线合成与输入时间线对齐的每秒认知标注进行训练。实验表明，LWS在Full-Duplex-Bench上表现出色，在VoiceBench AlpacaEval上取得4.72分，写作与口语一致性达92.6%，且在URO-Bench上持续优于各类内部消融实验。结果证明，可见写作可作为语音交互中的一等输出通道，同时保持实时响应性能。
链接: https://arxiv.org/abs/2606.07547
作者: Luoyuan Zhang,Bokai Xu,Junbo Cui,Weiyue Sun,Yingjing Xu,Hanyu Liu,Yuan Yao
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Sound (cs.SD)
备注:
Abstract:Speech-based large language models are typically constrained to spoken replies, which limits their user-facing outputs to what can be verbalized and suppresses text-native capabilities such as code generation, structured analysis, and multi-step reasoning in realtime interaction, for tasks that require persistent, structured, and inspectable intermediate outputs. Existing work improves spoken reasoning or full-duplex turn-taking, but still treats text as a hidden intermediate state or a subordinate modality rather than a first-class output channel. We propose Listen-Write-Speak (LWS), a text-first tri-channel paradigm in which a single autoregressive LLM continuously listens to user audio, writes visible free-form text as its primary output, and speaks a realtime oral response in parallel under a shared causal attention context. This behavior is implemented entirely through a Token Schema, requiring no architectural modifications, and learned via a two-stage data pipeline that synthesizes per-second cognitive annotations consistent with the revealed input timeline. Empirically, LWS demonstrates strong full-duplex interaction on Full-Duplex-Bench, reaches 4.72 on VoiceBench AlpacaEval, achieves 92.6% writing-speaking consistency, and consistently outperforms its internal ablations on URO-Bench. These results suggest that visible writing can serve as a first-class output channel for speech interaction without sacrificing realtime responsiveness. The code and dataset are available on the project page: this https URL.
[NLP-205] Finding Hidden Relationships Between Medical Concepts by Leverag ing Metamap and Text Mining Techniques
【速读】： 该论文旨在解决医学文本数据中隐含关联难以被现有方法识别的问题，尤其关注跨文档的潜在语义联系。其核心挑战在于传统文本挖掘方法通常局限于单一文档内的显式关联，而忽视了不同文档间可能存在的深层知识连接。为此，论文提出一种基于MetaMap与先进文本挖掘技术相结合的新模型，关键创新在于构建了一个全新的综合索引结构，能够有效捕捉和整合多篇文献中分散但相关的医学概念信息，从而揭示以往被忽略的跨文档隐性关联。实验结果表明，该模型在发现医学主题间新型关联方面具有显著有效性。
链接: https://arxiv.org/abs/2606.07540
作者: Weikang Yang,S M Mazharul Hoque Chowdhury,Wei Jin
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Text is one of the most common ways to store data in this computerized world. At a glance, it may seem that those data are not interconnected. But in reality, data can have hidden connections. Therefore, in this research, a new model has been presented that can find hidden relationships between two medical concepts by using MetaMap and appropriate text-mining techniques. Specifically, the model creates a new comprehensive index structure and can find cross-document hidden links connecting topics of interest that most existing approaches have ignored. Experiments show the effectiveness of the proposed model in discovering new connections between topics.
[NLP-206] From Architecture to Output: Structural Origins of Hallucination in Large Language Models and the Amplifying Role of Data
【速读】： 该论文旨在解决大语言模型在生成过程中普遍存在且持续存在的幻觉问题，即模型输出看似流畅自信但事实错误的现象。其核心挑战在于现有分类框架仅基于输出类型进行描述性划分（如内在与外在幻觉、忠实性与事实性偏差），却无法揭示导致幻觉的内在机制。本文的关键贡献在于提出一种结构性解释：幻觉是三个架构决策共同作用形成的复合失效系统所致。首先，自注意力机制通过共现学习替代语义理解，导致实体混淆、事实误归因和语义漂移；其次，最大似然估计训练目标仅优化下一个词的概率，不施加事实约束，从而奖励统计上合理但可能虚假的输出；第三，自回归解码在暴露偏差下采取单向左至右的不可逆承诺，使首个错误标记在整个生成序列中持续传播。此外，数据集缺陷（如长尾缺失、训练偏见、合成污染）虽会放大这些弱点，但并非幻觉的根本成因。研究进一步将上述三类机制分别对应到Alansari和Luqman分类体系中的具体输出类别，并指出当前仅依赖输出类型分类存在诊断局限，强调应转向基于推理层的缓解策略以实现根本性改进。
链接: https://arxiv.org/abs/2606.07537
作者: Md. Rejaul Korim Sadi,Toufiqur Rahman Tasin,Golam Mostofa Naeem
机构: 未知
类目: Computation and Language (cs.CL)
备注: 11 pages, 7 figures, 15 references
Abstract:Large language models hallucinate–producing fluent, confident, factually wrong outputs–with a consistency that persists across generations and scales. Existing taxonomies classify hallucination by output type, distinguishing intrinsic from extrinsic failures and faithfulness from factuality divergence. These frameworks are descriptively rigorous but do not identify which internal mechanism produced a given instance. This paper analyses hallucination as a structural consequence of three architectural decisions that together form a compound failure system. Self-attention’s co-occurrence learning substitutes statistical proximity for semantic meaning and produces entity confusion, fact misattribution, and semantic drift. The maximum likelihood estimation training objective optimises next-token probability without factual constraint, rewarding statistically plausible outputs regardless of their truth value. Autoregressive decoding’s permanent left-to-right commitment under exposure bias ensures that a single wrong token cascades forward through the entire output sequence without revision. Dataset pathologies–long-tail deficiencies, training bias, and synthetic pollution–amplify these vulnerabilities but do not independently cause them. We make three contributions. First, we map each mechanism to a specific output category in the Alansari and Luqman taxonomy, locating intrinsic hallucination in self-attention, extrinsic hallucination in MLE, and logical inconsistency in autoregressive decoding. Second, we show that each commonly cited dataset pathology exploits one of these mechanisms rather than originating hallucination independently. Third, we identify the diagnostic limitation of output-type-only classification and contrast it with inference-layer mitigation approaches.
[NLP-207] Multilingual Refusal Alignment for Safer Large Language Models ACL2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在多语言部署背景下安全对齐（safety alignment）不一致的问题。随着模型在全球范围的应用，不同语言间的安全行为表现存在不可预测的差异，导致难以保障跨语言的一致性与伦理合规性。其核心挑战在于：单语言对齐是否能有效迁移至其他语言、训练过程中如何维持语言一致性，以及对通用知识能力的影响。论文提出的关键解决方案是构建RefusEU——一个覆盖12种欧洲语言的拒绝响应对齐数据集，并通过受控的直接偏好优化（Direct Preference Optimization, DPO）实验验证：仅在英语中进行对齐不足以确保跨语言安全性，而采用多语言对齐训练可在不损害通用知识能力（以Global MMLU基准衡量）的前提下显著提升多语言安全性能，从而实现安全与泛化能力的协同优化。
链接: https://arxiv.org/abs/2606.07535
作者: Aleksandra Krasnodębska,Wojciech Kusa,Aldo Lipani
机构: 未知
类目: Computation and Language (cs.CL)
备注: Accepted to Findings ACL 2026
Abstract:As Large Language Models (LLMs) are deployed globally, ensuring their safety and alignment across multiple languages becomes paramount. However, safety behaviors often vary unpredictably between languages, posing significant challenges for consistent and ethical AI. In this work, we systematically investigate the dynamics of multilingual alignment, exploring whether single-language alignment transfers cross-lingually, how language consistency is preserved during training, and the resulting trade-offs with general knowledge capabilities. We introduce RefusEU, a novel refusal alignment dataset covering 12 European languages, including a dedicated test set for evaluating current state-of-the-art models. Our controlled Direct Preference Optimization (DPO) experiments provide two key insights: aligning models exclusively in English is insufficient to ensure cross-lingual safety, even for the same harm categories, whereas training on multilingual datasets can improve safety without degrading general performance, as measured by the Global MMLU benchmark.
[NLP-208] Bridging Traditional Explainability Methods and Multimodal Multilingual Models: An XAI-Based Analysis
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在复杂交互对话中，由于文本与音频等异构模态之间的交叉依赖关系、对话结构复杂性以及高维音频表征带来的计算开销，导致其内部决策机制缺乏可解释性的问题。现有基于Shapley Values（SV）的可解释性方法虽在纯文本自然语言处理任务中表现良好，但难以直接拓展至多模态场景。为此，本文提出一种面向多模态数据的Shapley Value扩展框架，将离散文本词元与对齐的音频片段视为合作特征，并设计了高效的估计策略：针对低维输入采用精确计算，对高维情况则引入蒙特卡洛排列采样与奈曼最优分配的分层采样方法以在有限算力下最小化方差。为解决模态间粒度不匹配问题，提出谱图引导的音素对齐（Spectrogram-Guided Phonetic Alignment, SGPA）预处理方法，实现高频音频流到可解释的词级对齐片段映射。研究贡献包括开发一个开源、模型无关的Python工具包及配套图形界面，支持多模态归因的计算与交互式可视化；并通过在VoiceBench和Infinity Instruct数据集的多语言子集上进行评估，揭示了输入模态是影响归因波动性的主要因素，且传统句法重要性代理指标在跨语言、多模态情境下往往无法准确预测模型注意力分布。
链接: https://arxiv.org/abs/2606.07533
作者: Paweł Pozorski,Jakub Muszyński,Maria Ganzha
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Sound (cs.SD)
备注: Bachelor’s thesis
Abstract:Multimodal Large Language Models (MLLMs) effectively integrate text and audio to interpret context in complex interactive dialogues. However, the internal mechanisms by which heterogeneous modalities influence model behavior remain opaque. While Shapley Values (SV) provide a robust, model-agnostic framework for local explainability in text-based NLP, their extension to multimodal data is hindered by cross-channel dependencies, intricate dialogue structures, and the prohibitive computational complexity of dense audio representations. In this work, we formalize a multimodal extension of the Shapley Value framework, treating discrete text tokens and aligned audio segments as cooperative features. To ensure computational feasibility, we deploy a suite of efficient estimation strategies: exact SV computation for low-dimensional inputs and sampling-based approximations - including Monte Carlo permutations and stratified sampling with Neyman-optimal allocation - to minimize variance under constrained computational budgets. To resolve the granularity mismatch between modalities, we propose Spectrogram-Guided Phonetic Alignment (SGPA), a novel preprocessing method that maps high-frequency audio streams to interpretable, word-aligned segments. Our contribution is twofold: first, we provide an open-source, model-agnostic Python package and a companion GUI for the computation and interactive visualization of multimodal attributions. Second, we evaluate our framework using curated subsets of the VoiceBench and Infinity Instruct datasets across diverse multilingual scenarios. Our experimental results reveal that input modality is a primary driver of attribution volatility and demonstrate that standard syntactic importance proxies often fail to predict model attention in multimodal, cross-lingual contexts. Comments: Bachelor’s thesis Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Sound (cs.SD) Cite as: arXiv:2606.07533 [cs.CL] (or arXiv:2606.07533v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2606.07533 Focus to learn more arXiv-issued DOI via DataCite
[NLP-209] Principled Agent Debate: Adversarial Arbitration for Sycophancy Reduction in Large Language Models
【速读】： 该论文旨在解决生成式AI在基于人类反馈的强化学习（RLHF）训练过程中普遍存在的“趋同性偏差”问题，即模型倾向于迎合人类偏好而牺牲事实准确性。其核心解决方案是提出一种名为“原则化代理辩论”（Principled Agent Debate, PAD）的多智能体架构，通过引入具有对立哲学倾向的两个模型进行独立论证，并由一个不掌握来源信息的实用主义合成器进行盲式仲裁，从而有效缓解因身份认同引发的阿谀奉承行为。关键机制包括静态倾向性调优、合成前的身份剥离、单轮独立论证以及盲式评估。实验在SycophancyEval数据集的200个分层问题上验证了五种PAD变体，结果显示所有变体均显著优于单模型基线（18.5%）和指令对立基线（29.0%），其中DeWin变体达到48.5%的准确率（z=6.36, p<0.001），表现出显著优势；尽管各变体间差异不显著，但BurGal变体虽达53.0%准确率，实为架构有效性验证，因其结构上始终偏袒异端模型。研究还发现预训练阶段存在基础性能下限，影响约40%的问题，提示后续需对倾向性微调模型进行优化。
链接: https://arxiv.org/abs/2606.07532
作者: Sam Ryan
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 11 pages, 3 figures. Code and data available at this http URL
Abstract:RLHF-trained models are systematically biased toward agreement over accuracy, a structural property of the training process. We present Principled Agent Debate (PAD), a multi-agent architecture that mitigates identity-framed sycophancy by arbitrating between two models tuned to opposing philosophical dispositions, with a pragmatist synthesizer evaluating both arguments blind to their origins. This paper evaluates a prompt-based instantiation of PAD. The key mechanisms are static dispositional tuning, identity stripping before synthesis, single-round independent argumentation, and blind arbitration. We evaluate five instantiations on 200 stratified questions from SycophancyEval. All PAD variants (AnCifer, DeWin, FeynStein, BurGal, Trident) significantly outperform the single-model baseline (18.5%) and instructed-opposition baseline (29.0%), with DeWin achieving 48.5% accuracy (z=6.36, p0.001 versus both). The variants are not significantly different from each other at n=200. The BurGal variant achieves 53.0% but functions as an architectural validity check; its consensus/heterodox axis structurally favors the heterodox model on every benchmark question. A pre-training floor affects an estimated 40% of questions; fine-tuned disposition models are the identified next step.
[NLP-210] mllm -shap: A Shapley Value Explainability Platform for Text-Audio Multimodal Large Language Models ACL2026
【速读】： 该论文旨在解决多模态大语言模型（Multimodal LLMs, MLLMs）在联合处理文本与音频输入时的可解释性难题，特别是将经典的谢林值（Shapley Value, SV）解释方法从纯文本场景扩展至文本-音频多模态情境。其核心挑战在于：（1）模态感知的联盟掩码机制，以应对离散文本标记与密集音频编码帧交织处理的复杂性；（2）多轮对话上下文追踪，通过每标记元数据维护角色与模态信息；（3）基于语音对齐的标记分组技术，显著降低联盟空间规模10至50倍，使长时音频输入下的SV估计在计算上可行。解决方案的关键在于提出一种新颖的、基于语音对齐的标记聚合策略，并集成五种SV估计方法，其中包含具有奈曼最优分配的互补贡献（Complementary Contributions, CC）估计器，其收敛性能优于标准蒙特卡洛基线。该框架以可安装的Python包形式提供，并配备交互式网页图形界面，实现细粒度归因可视化，是首个公开可用的、支持文本-音频多模态大模型的完整、可复现的谢林值可解释性平台。
链接: https://arxiv.org/abs/2606.07531
作者: Jakub Muszyński,Paweł Pozorski,Maria Ganzha
机构: Warsaw University of Technology (华沙理工大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Submitted to ACL2026
Abstract:We introduce mllm-shap, an open-source Python framework designed to extend Shapley Value (SV) explainability from text-only Large Language Models to Multimodal LLMs (MLLMs) processing joint text and audio inputs. While text-based attribution is well-studied, mllm-shap addresses three critical challenges unique to the multimodal regime: (1) Modality-aware coalition masking, which manages the interleaved processing of discrete text tokens and dense audio encoder frames. (2) Multi-turn conversation tracking, utilizing per-token metadata to maintain role and modality context. (3) Phonetic alignment-based token grouping, a novel technique that reduces the coalition space by 10x to 50x, rendering SV estimation computationally feasible for long-form audio. The platform implements five SV estimation strategies, including a Complementary Contributions (CC) estimator with Neyman-optimal allocation that demonstrates superior convergence over standard Monte Carlo baselines. mllm-shap is provided as a pip-installable package featuring an interactive web-based GUI for granular attribution visualization. To our knowledge, this is the first publicly available framework providing a complete, reproducible pipeline for SV-based explainability in text-audio MLLMs. Comments: Submitted to ACL2026 Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2606.07531 [cs.CL] (or arXiv:2606.07531v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2606.07531 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jakub Muszynski [view email] [v1] Tue, 21 Apr 2026 10:01:51 UTC (1,662 KB) Full-text links: Access Paper: View a PDF of the paper titled mllm-shap: A Shapley Value Explainability Platform for Text-Audio Multimodal Large Language Models, by Jakub Muszy’nski and Pawe\l Pozorski and Maria GanzhaView PDFHTML (experimental)TeX Source view license Current browse context: cs.CL prev | next new | recent | 2026-06 Change to browse by: cs cs.AI References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); About Help contact arXivClick here to contact arXiv Contact subscribe to arXiv mailingsClick here to subscribe Subscribe Copyright Privacy Policy Web Accessibility Assistance arXiv Operational Status
[NLP-211] Finding New Connections between Concepts from Medline Database Incorporating Domain Knowledge
【速读】： 该论文旨在解决医学领域中看似无关的医学概念之间潜在关联难以被发现的问题，即如何从海量文献中挖掘出隐含的、非显性关联的知识。其解决方案的关键在于对Don R. Swanson提出的经典文献发现模型（Literature-Based Discovery, LBD）——即ABC模型进行自适应改进，利用一个共同的中介概念（B）作为桥梁，连接两个原本无直接关联的概念（A和C），从而揭示它们之间的潜在联系。该方法通过系统分析文献中概念间的共现模式，实现对医学知识网络中“隐藏连接”的自动识别与推理，显著提升了跨领域知识发现的能力。
链接: https://arxiv.org/abs/2606.07530
作者: Yang Weikang,Chowdhury S.M. Mazharul Hoque,Jin Wei
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:In this digital world, data is everything and significantly impacts our everyday lives. Interestingly, in this small world, everything is part of an ecosystem, where everything is connected, directly or indirectly. The same thing happens to data as well. In most cases, it may seem like a particular topic does not have any connection with another one, but in reality, they are connected through a mutually related topic. Therefore, in this research, we will discuss an adaptive model modified from the ABC model by Don R. Swanson, a Literature-Based Discovery (LBD) Model, to find the hidden connections between Concepts of Interest. The model demonstrates that two topics, A and C are different and have no relationship. But they have a common topic, B that can be used to connect topics A and C This famous model will be used in this discussion to connect Medical Concepts.
[NLP-212] CAPruner: Conceptual-Adjacent Scene Graph Pruner for Enhancing 3D Spatial Reasoning of Large Language Models ACL2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在三维视觉-语言（3D-Vision-Language, 3D-VL）任务中进行空间推理时，因依赖完整场景图（Scene Graph）而导致的高计算成本与低效问题。现有场景图剪枝方法主要基于空间邻近性，常误删对任务关键的空间关系，影响空间推理的可靠性。其核心挑战在于如何在降低计算开销的同时，保留对特定3D-VL任务至关重要的空间关系。该研究提出的关键解决方案是：依据任务相关性动态识别并保留最具语义重要性的空间关系。为此，作者提出概念邻近场景图剪枝器（Conceptual-Adjacent Scene Graph Pruner, CAPruner），通过融合模糊语义相关性与空间邻近性，量化关系的重要性，并在任务特定上下文中筛选关键边。为避免昂贵的关系级标注，CAPruner采用节点关联边聚合得分作为监督信号进行训练。大量实验表明，该方法能有效保留支撑空间推理的核心关系，显著提升LLMs在3D-VL任务中的性能。
链接: https://arxiv.org/abs/2606.07529
作者: Shengli Zhou,Xiangchen Wang,Guanhua Chen,Feng Zheng
机构: Southern University of Science and Technology(南方科技大学); SpatialTemporal AI(时空人工智能)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Multimedia (cs.MM)
备注: Accepted by ACL 2026 Main Conference
Abstract:Large language models (LLMs) have recently been applied to 3D vision-language (3D-VL) tasks, which require spatial reasoning to identify target objects relative to anchors. Scene graphs are commonly employed to represent such relations, but reasoning over complete graphs incurs high token costs and computational inefficiencies, motivating the need for pruning. Existing pruning methods primarily rely on spatial proximity and often remove task-relevant relations, thereby undermining reliable spatial reasoning. To address these limitations, we derive a key requirement for scene graph pruning: preserving spatial relations that are most pertinent to the specific 3D-VL task. Guided by this insight, we propose the Conceptual-Adjacent Scene Graph Pruner (CAPruner). CAPruner integrates fuzzy semantic relevance with spatial proximity to estimate the importance of relations, enabling the selection of critical relations in a task-specific context. Moreover, to avoid costly relation-level annotations, CAPruner is trained by supervising the aggregated scores of each node’s incident edges. Extensive experiments demonstrate that CAPruner effectively preserves relations essential for spatial reasoning, leading to substantial performance improvements of LLMs on 3D-VL tasks. Code is available at this https URL.
[NLP-213] BEACON: Behavioral Entropy Aggregation for Cross-Model Hallucination Detection in Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）中存在的幻觉（Hallucination）问题，即模型生成与事实不符或缺乏依据的内容，这一现象严重制约了模型在实际应用中的可靠性。其核心解决方案是提出BEACON（Behavioral Entropy Aggregation for Cross-model hallucination detection），一个纯黑箱式的幻觉检测框架，仅依赖模型输出进行判断，无需访问内部表示或外部知识库。BEACON的关键在于从结构化多轮生成结果中提取一个31维特征向量，融合自然语言推理（NLI）语义熵、嵌入几何特性、思维链（Chain-of-Thought）一致性以及重述稳定性等多维度信号，通过梯度提升分类器在7个基准数据集上训练，实现0.8123 ± 0.0102的AUROC性能，显著优于单一语义熵（+0.2298）和SelfCheckGPT类一致性基线（+0.2457）。特征重要性分析表明，幻觉本质上是多维度的，需综合多种不确定性信号才能有效识别。此外，其高效的5次调用变体达到0.7795 AUROC，具备在黑箱LLM API场景下的实用部署能力。
链接: https://arxiv.org/abs/2606.07528
作者: Naveen Bera,Pulijala Sai Nikhila,Kondaguduru Abhiram,Shaik Gayaz Ali,Shoaib Sadiq Salehmohamed,Shaik Mohammed Omar,Jinal Prashant Thakkar,Hansika Aredla,Shalmali Ayachit
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 12 pages, 6 tables, 1 figure. Code and data available upon request
Abstract:Hallucination in large language models (LLMs), defined as the generation of factually incorrect or unsupported content, remains a critical barrier to reliable deployment. We present BEACON (Behavioral Entropy Aggregation for Cross-model hallucination detectiON), a black-box hallucination detection framework that operates purely on model outputs without requiring access to internal representations or external knowledge bases. BEACON extracts a 31-dimensional feature vector from structured multi-pass generation, integrating NLI-based semantic entropy, embedding geometry, chain-of-thought consistency, and paraphrase stability signals. A gradient-boosted classifier trained on 7,617 labeled examples across seven benchmarks achieves 0.8123 +/- 0.0102 AUROC (95% CI: 0.7632-0.8251), outperforming standalone semantic entropy (+0.2298) and SelfCheckGPT-style consistency baselines (+0.2457). Feature importance analysis shows that hallucination is inherently multi-dimensional, requiring combined uncertainty signals. An efficient 5-call variant achieves 0.7795 AUROC, enabling practical deployment across black-box LLM APIs.
[NLP-214] Post-training is (Massive) Supervised Learning
【速读】： 该论文旨在解决当前大语言模型（LLM）训练范式中过度依赖大规模后训练阶段（包括监督微调SFT和强化学习RL）所导致的模型性能局限性问题。其核心观点是，现有方法实质上回归了BERT时代的“预训练-微调”模式，通过将模型精确适配于特定评估基准上的分布数据，从而实现表面性能提升，而非真正具备通用智能。解决方案的关键在于摒弃针对固定行为进行精细化后训练的路径，转而探索能够使模型“学会如何学习”的新型训练机制，即发展具有更强泛化能力与自主适应性的通用智能系统，从根本上突破对特定任务数据分布的依赖。
链接: https://arxiv.org/abs/2606.07527
作者: Michael Hassid,Yossi Adi,Roy Schwartz
机构: FAIR, Meta AI; The Hebrew University of Jerusalem
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:The prevailing paradigm for training LLMs has evolved to rely on a massive post-training phase consisting of SFT and RL. In this position paper, we argue that this methodology effectively marks a reversion to the pre-train then fine-tune'' approach of the BERT era, explicitly tailoring models to the desired behaviors and specific benchmarks on which they are evaluated. We begin with a historical overview of LLMs, describing the different phases of the LLM evolution. We argue that the current landscape is remarkably similar to the early days of LLMs, where task performance heavily relied on fitting the models to in-distribution datasets. To empirically demonstrate this, we compare pre-trained models to randomly initialized ones, by fine-tuning both variants on modern reasoning datasets and evaluating them on competitive math and code benchmarks. We show that models post-trained from scratch yield highly non-trivial performance. Our findings suggest that current post-training methodologies function primarily as a distribution-fitting mechanism. We finish by positing that developing generally capable models and systems requires moving beyond extensive post-training for predefined behaviors, shifting instead toward training procedures where models learn how to learn’'.
[NLP-215] GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation ACL2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在推荐系统中应用时，如何有效对齐文本语义与协同信号（collaborative signals）的问题。现有方法通常将协同信息转化为文本提示或注入预训练嵌入，但均将结构信息视为静态输入，无法捕捉高阶关系依赖。其解决方案的关键在于提出GraphLoRA框架，通过在低秩适应（Low-Rank Adaptation, LoRA）路径中嵌入可训练的图消息传递网络，实现从独立参数更新到结构感知传播的范式转变。该设计使协同拓扑结构能够通过参数空间进行动态传播，从而显式引导模型参数更新，促进图结构信息与文本语义信息的深度融合。实验结果表明，GraphLoRA不仅在多个基准上超越现有基于LLM的推荐方法，还在推理能力与计算效率之间实现了良好平衡。
链接: https://arxiv.org/abs/2606.07526
作者: Lin Mu,Guoji Wang,Li Ni,Lei Sang,Zhize Wu,Peiquan Jin,Yiwen Zhang
机构: Anhui University(安徽大学); Hefei University(合肥大学); University of Science and Technology of China(中国科学技术大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: ACL 2026 findings
Abstract:Large Language Models (LLMs) have shown strong potential for recommendation (LLMRec) due to their powerful reasoning and generalization abilities. However, effectively aligning the textual semantics modeled by LLMs with the collaborative signals remains a key challenge. Existing methods either translate collaborative information into textual prompts or inject pre-trained embeddings into the LLM, both of which treat structural information as static input and fail to capture high-order relational dependencies. To bridge this gap, we propose GraphLoRA, a novel framework that generalizes low-rank adaptation from independent to structure-aware propagation. GraphLoRA embeds a trainable graph message-passing network within the low-rank adaptation pathway, enabling structural signals to propagate through the parameter space. This design allows collaborative topology to explicitly guide parameter updates, fostering deep integration between graph-structured and textual semantic information. Extensive experiments on multiple benchmarks demonstrate that GraphLoRA not only outperforms state-of-the-art LLM-based recommendation methods but also achieves superior generalization, effectively balancing structural reasoning capability with computational efficiency. Code is available at \hrefthis https URLthis https URL.
[NLP-216] Implicit Causal Graph Construction in Text via Chain Discovery
【速读】： 该论文旨在解决从文本中构建隐含因果图（implicit causal graph）的问题，传统方法通常依赖于可观测且预定义的事件，而本文提出通过将每个描述的因果对视为潜在因果图的起点与终点，并利用大语言模型（LLM）推断中间因果事件，实现隐式因果关系的自动构建。其解决方案的关键在于：将因果图构建任务转化为基于大语言模型的隐含因果链发现，通过端到端建模或分步迭代扩展部分因果链的方式生成完整因果图，并引入“群体智慧”（Wisdom of the Crowd）范式，在事后聚合或多模型协同推理中整合多个大语言模型的因果知识，以提升推断的鲁棒性与覆盖度。研究进一步通过一个包含1,560个科学验证因果对的手动标注数据库进行评估，验证所推断因果关系的有效性，该评估方法被证明具有可靠性、资源效率高且可迁移性强，适用于缺乏真实标签图的场景。
链接: https://arxiv.org/abs/2606.07525
作者: Liesbeth Allein,Marie-Francine Moens
机构: KU Leuven (鲁汶大学); Ghent University (根特大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Causal graphs in text are typically populated by observable, predefined events. In contrast, we study implicit causal graph construction from text by treating each described cause-effect pair as the begin- and endpoint of an underlying latent causal graph and using large language models (LLMs) to infer intermediate causal events. We compare end-to-end graph construction with methods that frame the task as causal chain discovery. In the latter, graphs are built either by aggregating inferred chains or by progressively expanding partial chains through an iterative search process. We further explore Wisdom of the Crowd extensions that access causal knowledge from multiple LLMs in post-hoc aggregation and collaborative inference settings. We analyze trade-offs among these approaches and evaluate the validity of inferred causal relations using a manually curated database of 1,560 scientifically validated causal pairs. This database-based evaluation is proposed as reliable, resource-efficient, and transferable to settings where ground-truth graphs are unavailable.
[NLP-217] ABLE: Representing and Mapping LLM s via Attribution-Based Large-model Embedding
【速读】： 该论文旨在解决大规模语言模型（Large Language Models, LLMs）生态系统的异构性与文档缺失问题，其核心挑战在于如何高效、准确地进行模型间的系统性比较，以支持溯源审计、安全分析与模型选型。现有方法在面对架构异构时存在明显局限：基于内部参数的分析方法受限于模型结构兼容性，难以扩展；而依赖外部输出的方法则易将行为相似但本质不同的模型混淆，且在不同分词器（tokenizer）下的输出空间难以对齐。为此，本文提出一种名为ABLE（Attribution-Based Large-model Embedding）的框架，其关键创新在于利用可解释性空间构建模型表征——通过词级对齐的梯度特征归因聚合，捕捉模型对输入的敏感性模式，而非仅依赖表面输出。该方法具备对分词器无关的鲁棒性。理论层面，作者证明了在标准可微Transformer模型假设下，ABLE诱导的参数到嵌入映射具有Lipschitz连续性，并具备有限样本收敛性。大量实验在239个开源LLM上验证了该方法在关系预测、模型路由及基准分数预测任务中的卓越性能，且无需训练即可实现。
链接: https://arxiv.org/abs/2606.07524
作者: Zirui Wang,Yusen Hou,Shaofeng Liang,Bowen Tian,Yanlin Zhang,Wenshuo Chen,Yutao Yue
机构: The Hong Kong University of Science and Technology (Guangzhou); Deep Interdisciplinary Intelligence Lab (DI2DI^{2}Lab)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:The explosive growth of large language models (LLMs) has created a heterogeneous and poorly documented ecosystem, making systematic model comparison increasingly important for provenance auditing, security analysis, and model selection. Existing representation methods struggle to address this setting efficiently. Approaches analyzing internal parameters are powerful when architectures are compatible, but face scalability barriers under structural heterogeneity, while methods relying on external outputs may conflate models with similar behaviors and are difficult to align in richer output spaces across different tokenizers. To bridge this gap, we propose ABLE (Attribution-Based Large-model Embedding), a framework that leverages the interpretability space to construct model representations. By aggregating gradient-based feature attributions via a tokenizer-agnostic word-level alignment, ABLE captures model-specific input-sensitivity patterns rather than only surface-level outputs. Beyond empirical utility, we provide a stability analysis showing that, under standard regularity assumptions for differentiable Transformer-style models, ABLE induces a Lipschitz-continuous parameter-to-embedding map with finite-sample convergence guarantees. Extensive experiments on 239 open-source LLMs demonstrate that our training-free approach achieves competitive or superior performance in relation prediction, model routing, and benchmark score prediction.
[NLP-218] Retrieval Augmented Generation Framework for the Nepali Legal Domain Question Answering
【速读】： 该论文旨在解决低资源语言（如尼泊尔语）在法律问答任务中因法律文本数据稀缺而导致的大规模语言模型训练困难的问题。针对这一挑战，研究提出了一种基于检索增强生成（Retrieval Augmented Generation, RAG）的解决方案，首次将RAG框架应用于尼泊尔语法律问答系统，利用从尼泊尔法律公报（Nepal Kanun Patrika）数字档案中提取的判例文本作为知识源。其关键在于结合基于BM25的文档分块检索与多语言E5大模型进行生成，有效缓解了低资源环境下法律知识表示不足的瓶颈。实验结果显示，该方法在精确率、答案生成成功率及真实性方面均表现优异，尤其在使用BM25检索时，实现了91%的Top-1精度、74%的答案事实一致性以及85%的自动化评估真值率，证明了RAG范式在低资源法律领域中的可行性与有效性，为构建可靠、可信赖的尼泊尔语法律AI系统提供了坚实基础。
链接: https://arxiv.org/abs/2606.07523
作者: Samir Wagle,Abiral Adhikari,Reewaj Khanal,Batsal Bhandari,Prashant Manandhar,Praveen Acharya,Bal Krishna Bal
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Legal domains in high-resource languages like English have widely adopted artificial intelligence for legal question answering. However, data scarcity in low resource languages such as Nepali has limited the training of large language models on Nepali legal texts. This study presents the first application of a Retrieval Augmented Generation based model for Nepali legal question answering using case laws extracted from the Nepal Kanun Patrika digital archive. Using BM25 on chunked documents, the approach achieved a top precision at one of 91 percent, and up to 75 percent with the multilingual E5 large model. Evaluation of generated answers showed 74 percent groundedness, 85 percent truthfulness according to an automated judge model, and 84 percent human evaluated truthfulness when using BM25 document retrieval, with a 92 percent successful answer generation rate. These results demonstrate that the RAG pipeline can effectively address the gap in legal question answering for low resource languages and provide a foundation for reliable AI systems in the Nepali legal domain.
[NLP-219] Community-Specific Slang and Entity Detection via Semantic Shift in Fine-Tuned Language Models
【速读】： 该论文旨在解决在线社区中俚语、独特实体及民俗表达的自动识别与解析问题，这类语言现象因高度依赖特定语境而难以通过传统自然语言处理方法准确理解。其解决方案的关键在于利用预训练大语言模型（LLM）在社区特定文本语料上微调后产生的语义偏移（semantic shift）量作为指标，识别出语义演化显著的词汇。具体而言，语义偏移定义为基线模型与微调后模型对同一词语编码表示之间的余弦相似度的倒数，相似度越低表明语义变化越大。研究以DistilRoBERTa模型为基础，在r/Technology、r/Gaming、r/WorldofWarcraft三个Reddit子版块的语料上进行微调，构建词汇层面的余弦相似度分布，并发现位于底部10%分位的词汇具有显著的社区特异性语义，可有效用于解析社区独有的语言现象；而顶部10%分位的词汇则保持相对通用的语义，不具备强情境依赖性。因此，该方法通过量化语义偏移并筛选极端低相似度词汇，实现了对社区专属语言要素的无监督识别。
链接: https://arxiv.org/abs/2606.07522
作者: Julia Kruk,Sanchita Porwal,Amitrajit Bhattacharjee,Mansi Phute
机构: Georgia Institute of Technology (佐治亚理工学院)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG); Social and Information Networks (cs.SI)
备注: 6 pages, 6 figures, 2 tables
Abstract:We propose an unsupervised method of resolving slang, unique entities, and folklore from online communities by isolating words in the lexicon that have the highest magnitude of semantic shift. Semantic shift is defined as the evolution of a word’s encoded representation as a result of fine-tuning a pretrained Large Language Model (LLM) on a community-specific text corpus. This value is inversely proportional to the cosine similarity between the base model’s encoded representation of a word, and a fine-tuned model’s encoded representation. We fine-tune the DistilRoBERTa model on text corpora collected from 3 Reddit subreddits (r/Technology, r/Gaming, r/WorldofWarcraft), model a distribution of cosine similarity over the lexicon, and show that one can successfully resolve words that have unique significance to the community by pulling data in the bottom 10-percentile. In contrast, we show that data in the top 10-percentile consist of words that carry relatively universal semantics.
[NLP-220] Evaluating Hallucinations in Domain-Adapted Large Language Models
【速读】： 该论文旨在解决领域自适应大语言模型（Large Language Models, LLMs）在微调过程中出现的幻觉（hallucination）问题，即模型生成与事实不符或无意义内容的现象。针对这一挑战，研究以Llama-2模型为基础，采用Lamini数据集进行领域微调，并通过一系列实验评估其在记忆、回忆和推理能力方面的表现，重点对比模型对新样本问答对及特定领域信息的处理能力。研究发现，尽管模型在与训练数据相似的任务中表现出色，但在处理新的领域特定信息时，其准确推理与记忆能力仍存在明显局限，导致幻觉频发；同时，模型倾向于在正确答案基础上添加额外信息，体现出过度生成的倾向。这表明仅依赖微调的方法在应对专业领域适配时存在显著不足，凸显了构建更稳健的领域适配机制的必要性。此外，研究揭示了模型在不同类型信息处理上的差异性表现，尤其在应对领域特定查询时存在相对薄弱环节。
链接: https://arxiv.org/abs/2606.07521
作者: Sanchita Porwal,Sai Prasath S,Xingjian Bi,Madelyn Scandlen
机构: Georgia Institute of Technology (佐治亚理工学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 13 pages, 2 figures, 3 tables
Abstract:This study investigates the phenomenon of hallucinations in domain-adapted Large Language Models (LLMs), focusing on the fine-tuning of the Llama-2 model with the Lamini dataset. Hallucinations, or the generation of nonsensical or unfaithful content by LLMs, pose a significant challenge, especially when these models are fine-tuned with domain-specific data. Our methodology involves a series of experiments testing memorization, recall, and reasoning capabilities of the fine-tuned LLM, comparing its performance on novel question-answer pairs and domain-specific information. We found that while the model shows proficiency in tasks similar to its training data, its capability to accurately reason about and recall new domain-specific information remains limited, leading to instances of hallucination. The model demonstrates a tendency to provide correct answers with extra information, suggesting an inclination toward over-generation. These results suggest important limitations of fine-tuning-only approaches for mitigating hallucinations when adapting LLMs to specialized domains and underscore the need for more robust methods in adapting LLMs to specialized domains. The study also provides insights into the varying performance of LLMs on different types of information, revealing a comparative weakness in handling domain-specific queries.
[NLP-221] nyJudge: Unverifiable Constraint Alignment via Lightweight Specialist Ensembles ACL2026
【速读】： 该论文旨在解决大语言模型（LLM）在指令遵循（Instruction Following, IF）任务中对不可验证约束（unverifiable constraints）的精准评估难题。现有基于可验证奖励的强化学习方法虽利用“大模型作为裁判”（LLM-as-a-judge）评估不可验证约束，但存在严重的奖励欺骗（reward hacking）问题及高昂的计算开销。本文的关键突破在于发现不同不可验证约束具有显著且高泛化的模式特性，据此提出轻量级框架TinyJudge：通过将前沿模型的知识蒸馏至一组小型专用语言模型（约0.6B参数），构建由多个微小判别器组成的集成系统，实现对软约束的高精度、低延迟评估。实验表明，TinyJudge在五个基准测试中平均性能提升约10%，奖励精确率提高12%，同时训练总时间缩短3倍，为实现高效、鲁棒的大模型对齐提供了可扩展的新路径。
链接: https://arxiv.org/abs/2606.07520
作者: Yirong Zeng,Yufei Liu,Xiao Ding,Yutai Hou,Yuxian Wang,Wu Ning,Haonan Song,Dandan Tu,Qixun Zhang,Yuxiang He,Bibo Cai,Ting Liu
机构: Harbin Institute of Technology SCIR Lab(哈尔滨工业大学SCIR实验室); Peking University(北京大学); Huawei Technologies Co., Ltd(华为技术有限公司)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: ACL 2026 Main Conference;15 pages, 9 figures
Abstract:Instruction Following (IF) is a core capability of LLMs, requiring strict adherence to diverse constraints, ranging from verifiable ones (e.g., output length) to unverifiable ones (e.g., tone). Reinforcement learning with verifiable rewards has emerged as a paradigm for IF tasks, leveraging LLM-as-a-judge to assess unverifiable constraints. However, we empirically find that this approach remains a significant bottleneck, suffering from severe reward hacking and higher computational overhead. In this work, we first analyze the generalization capabilities of unverifiable constraints and discover that specific constraints exhibit distinct, high-generalization patterns. Motivated by this, we propose TinyJudge, a framework that employs an ensemble of specialized tiny language models ( \sim0.6B ) to provide rewards for soft constraints. By distilling expertise from frontier models into these tiny models, it achieves high-precision, lightweight evaluation. Extensive evaluations across five benchmarks demonstrate that TinyJudge outperforms the baselines by \sim10% in average performance and 12% in reward precision. Crucially, it also achieves a 3\times speedup in total training time. Our work provides a scalable and robust path for aligning LLMs with unverifiable human instructions.
[NLP-222] Bidirectional Small-Granularity Search between Code and Text
【速读】： 该论文旨在解决科学文献中的文本与代码之间小粒度双向搜索问题，即在科学出版物的文本描述与对应代码片段之间建立直接关联，以提升对科研方法的理解效率。其核心挑战在于实现跨模态的小片段精准匹配，尤其在非领域（out-of-domain, OOD）场景下的泛化能力。解决方案的关键在于提出一种模块化框架，采用共享编码器结构，统一处理四个子任务，并在双向方向上联合学习答案片段的起始与终止位置，从而实现从代码到文本或从文本到代码的高精度小粒度检索。实验表明，该方法在同域（in-domain）数据上表现良好，在异域（OOD）数据上也展现出令人鼓舞的泛化性能，验证了基于自动生成数据训练的可行性，为未来研究提供了重要方向。
链接: https://arxiv.org/abs/2606.07519
作者: Marco A. Valenzuela-Escárcega,Enrique Noriega-Atala,Gus Hahn-Powell,Clayton T. Morrison,Mihai Surdeanu
机构: Lex Machina; The University of Arizona, Tucson, AZ, USA
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce the novel task of bidirectional small-granularity search between code and text, where the queries are small snippets of text or code and the results are also small fragments of the opposite modality, i.e., code or text. This task establishes direct links between text in scientific publications and corresponding code segments, in support of better and faster understanding of scientific methods. We introduce a large dataset for the proposed task that includes a training partition with textual descriptions of code generated automatically using GPT-4, and three testing partitions, one in-domain and two out-of-domain (OOD) that contain manually-annotated data as well as material from other domains. We also propose a modular approach to address this task. Our approach shares an encoder across four different subtasks that learn start/end of answer spans in both directions. We show that our method achieves good results in-domain, and encouraging results OOD. This suggests that addressing this task with automatically-generated data is possible, but there is exciting future work to be done.
[NLP-223] Cross-Modal Masking for Robust Silent Speech Synthesis Using sEMG and Lipreading
【速读】： 该论文旨在解决无声语音接口（Silent Speech Interface, SSI）中多模态信号融合在连续语音合成任务中的鲁棒性不足问题，特别是在表面肌电图（sEMG）与基于视频的唇读（lipreading）信号联合使用时，面对模态退化或临时传感器故障的适应能力薄弱。其核心解决方案是提出一种掩码式多模态语音合成框架（masked multimodal speech synthesis framework），通过在训练阶段引入模态掩码（modality masking）策略，使模型能够学习在部分模态缺失情况下的互补信息利用能力。该方法显著提升了多说话人场景下的语音还原性能，相比最强单模态基线，词错误率（Word Error Rate, WER）降低高达14个百分点。实验表明，掩码策略不仅对低比特率条件下的鲁棒性至关重要，且在模态缺失场景下泛化能力优于特定退化数据增强方法。电话级分析进一步揭示了两种模态在发音单元上的互补性，尤其在元音及特定辅音类别上表现突出。研究结果验证了掩码式多模态融合在无声语音合成中的有效性与鲁棒性，但针对喉切除患者（laryngectomized speakers）的适配仍为开放挑战。
链接: https://arxiv.org/abs/2606.09667
作者: Eder del Blanco,David Gimeno-Gómez,Eva Navas,Carlos-D. Martínez-Hinarejos,Inma Hernáez
机构: University of the Basque Country (UPV/EHU)(巴斯克大学(UPV/EHU)); HiTZ Center(智能感知与人机交互中心); PRHLT research center(普罗赫尔特研究中心); Universitat Politècnica de València (UPV)(瓦伦西亚理工大学(UPV))
类目: Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Sound (cs.SD)
备注: 12 pages, 7 figures and 6 tables. Submitted to Transactions on Audio, Speech and Language Processing
Abstract:Speech restoration through silent speech interfaces (SSIs) has emerged as a promising assistive technology for individuals with impaired or absent laryngeal voice production. Among non-invasive SSI modalities, surface electromyography (sEMG) and video-based lipreading provide complementary articulatory information, yet their integration for continuous speech synthesis remains underexplored. Moreover, existing multimodal approaches rarely address robustness to modality degradation or temporary sensor failure, limiting their applicability in realistic scenarios. In this work, we propose a masked multimodal speech synthesis framework that jointly leverages sEMG and lipreading signals through modality masking during training. Under multispeaker settings, the proposed approach reduces word error rate by up to 14 absolute percentage points compared to the strongest unimodal baseline. Experimental results not only show that masking strategies are critical for these performance gains and robustness under low-bitrate conditions, but also that they generalize better than degradation-specific data augmentations in the presence of modality absence conditions. Phone-level analyses further reveal complementary contributions across modalities, with particularly strong benefits for vowels and for specific consonant groups. Overall, these findings demonstrate the effectiveness and robustness of masked multimodal integration for silent speech synthesis, although adaptation to laryngectomized speakers remains an open research challenge.
[NLP-224] Rank Intervals for Leaderboards: A Hierarchical Framework for Model Evaluation
【速读】： 该论文旨在解决预训练模型在多任务排行榜上进行评估时，现有性能聚合方法未能充分考虑任务层面的不确定性与变异性的问题。当前方法在将各任务表现整合为排行榜排名时，忽略了个体任务中因数据分布、任务难度差异等因素带来的评估不确定性，导致模型排名缺乏对置信度的可靠量化。其解决方案的关键在于提出一种分层框架，通过两个层级的统计保障实现鲁棒的排名区间构建：在任务层面，基于成对比较生成具有置信度保证的模型排名区间（task-level rank confidence intervals）；在排行榜层面，则采用符合性推断（conformal inference）方法，生成可覆盖新潜在任务的模型排名预测区间（leaderboard-level rank prediction intervals）。该方法不仅能够对已观测任务中的模型排名提供不确定性量化，还能推广至未见任务场景，实现在多任务评估中兼具统计有效性与信息丰富性的不确定性感知型排名。实验在模拟数据及TabArena、PromptEval（MMLU）基准测试上的结果表明，所提方法生成的区间具备良好的统计有效性与实用性。
链接: https://arxiv.org/abs/2606.08679
作者: Bitya Neuhof,Yuval Benjamini
机构: The Hebrew University of Jerusalem (希伯来大学)
类目: Machine Learning (stat.ML); Computation and Language (cs.CL); Machine Learning (cs.LG); Methodology (stat.ME)
备注:
Abstract:Pretrained models are often evaluated on multi-task leaderboards to measure their applicability in diverse contexts. However, current methods for aggregating performance across tasks into leaderboard-level rankings do not address the uncertainty and variability at the task level. While recent works have proposed interval-based model rankings, the principled aggregation of uncertainty from individual tasks to leaderboard-level rankings remains unaddressed, and variation in models’ performance across tasks is frequently obscured. In this work, we introduce a hierarchical framework that constructs model rank intervals with statistical guarantees at both levels: task-level rank confidence intervals from pairwise comparisons, and leaderboard-level rank prediction intervals using a conformal approach. This enables reliable quantification of model rank for each observed task and for new potential tasks. Experiments on simulated data and the TabArena and PromptEval (MMLU) benchmarks show that our method yields statistically valid and informative intervals, enabling reliable, uncertainty-aware model ranking on leaderboards.
[NLP-225] Strategic Type Spaces
【速读】： 该论文旨在解决不完全信息博弈中信息表示的结构性难题，即如何有效刻画玩家在信息不对称环境下计算最优反应（best-response）所需的核心信息。其核心问题是：在任意不完全信息博弈中，是否存在一种最小且本质唯一的、能够支持玩家进行理性推理的信息表示结构？为此，论文提出“战略商”（strategic quotient）作为信息的策略性表征，并证明了存在一个最小的战略商——战略类型空间（Strategic Type Space, STS），其中每个类型由一个中期相关理性化层次（interim correlated rationalizability hierarchy）定义，表征玩家对其他参与者类型及自然状态的信念集合，这些信念能够一致地解释该理性化层次。解决方案的关键在于揭示最小STS具有递归结构，并可通过有限自动机（finite automaton）精确捕捉，从而为不完全信息博弈中的信息建模提供了可计算、可形式化的基础框架。
链接: https://arxiv.org/abs/2606.08297
作者: Olivier Gossner,Rafael Veiel
机构: CNRS - École Polytechnique (法国国家科学研究中心-巴黎综合理工学院); London School of Economics (伦敦政治经济学院); University of Texas at Austin (德克萨斯大学奥斯汀分校)
类目: Theoretical Economics (econ.TH); Computation and Language (cs.CL)
备注:
Abstract:We provide a strategic foundation for information: in any given game with incomplete information we define strategic quotients as information representations that are sufficient for players to compute best-responses to other players. We prove 1/ existence and essential uniqueness of a minimal strategic quotient called the Strategic Type Space (STS) in which a type is given by an interim correlated rationalizability hierarchy and represents a set of beliefs over other players’ types and nature that rationalize this hierarchy and 2/ that the minimal STS has a recursive structure that is captured by a finite automaton.
[NLP-226] Paediatric-HGNN: A Hybrid Heterogeneous Graph Neural Network for Detecting Disfluency in Childrens Speech via Multiscale Acoustic Fusion INTERSPEECH2026
【速读】： 该论文旨在解决儿童语音中自动口吃检测（Automated Stuttering Detection, ASD）的难题，核心挑战在于儿童发音系统处于发育阶段，导致声学特征高度可变，且病理性口吃与正常发育性不流畅之间的区分极为细微。为此，论文提出Paediatric-HGNN框架，其关键创新在于构建一种上下文感知的部件-整体交互网络（Context-aware Part-whole Interaction Network, CaPIN），通过异构图（heterogeneous graph）建模词汇单元（词节点）与细粒度声学片段（帧节点）之间的分层关系，突破传统一维信号建模的局限。该方法有效捕捉了儿童语言发展过程中的“搜索”行为模式，显著提升了对发育性不流畅的识别能力，在UCLASS和FluencyBank等儿科语料库上实现了82.4%的加权准确率及0.386的典型不流畅F1分数，为早期临床干预提供了更鲁棒且可解释的工具。
链接: https://arxiv.org/abs/2606.08210
作者: Rashini Liyanarachchi,Rachael Mackay,Alison Short,Aditya Joshi,Erik Meijering
机构: University of New South Wales (UNSW)(新南威尔士大学); Western Sydney University(西悉尼大学); Resourced Music Therapy(资源音乐治疗)
类目: Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Sound (cs.SD)
备注: Accepted at INTERSPEECH 2026 (Main)
Abstract:Automated stuttering detection (ASD) systems struggle with paediatric speech due to high acoustic variability in developing voices and the subtle distinction between pathological stuttering and typical developmental disfluencies. We introduce Paediatric-HGNN, a framework using a Context-aware Part-whole Interaction Network (CaPIN) tailored for paediatric data. Instead of conventional 1D signal modelling, our approach builds a heterogeneous graph capturing hierarchical relationships between lexical units (word nodes) and fine-grained acoustic segments (frame nodes). Trained on curated paediatric corpora (UCLASS and FluencyBank), Paediatric-HGNN achieves 82.4% weighted accuracy and a Typical Disfluency F1-score of 0.386. Modelling hierarchical lexical-acoustic interactions captures developmental “searching” behaviour, offering a more robust and interpretable tool for early clinical intervention.
[NLP-227] Benchmarking Quantum Algorithmic Resilience for CVaR Portfolio Optimization: The Expressibility-Coherence Trade-off
【速读】： 该论文旨在解决在缺乏全连接（all-to-all connectivity）的噪声中等规模量子（NISQ）设备上实现复杂金融组合优化时，硬件拓扑限制与算法表达能力之间的根本性矛盾。其核心挑战在于如何在有限的量子硬件资源下有效处理具有密集尾部风险相关性的多资产组合优化问题，尤其是涉及条件风险价值（CVaR）这一高阶风险度量的混合均值-方差目标函数。解决方案的关键在于提出一种新型经典-量子混合代理矩阵方法，以绕过传统方法中因引入辅助量子比特导致的CVaR计算瓶颈，从而实现将最多16只来自印度NIFTY 50指数的资产映射至IBM重型六边形处理器。在此基础上，研究系统评估了不同算法对“SWAP税”（即量子线路编译过程中为适配硬件拓扑而引入的额外操作开销）的鲁棒性。结果表明，温启动量子近似优化算法（WS-QAOA）虽具备精确的理论映射能力，但因指数级非局域门操作导致严重的硬件退相干；而硬件高效变分量子神经网络（HE-VQNN）虽能保持良好的硬件相干性，却因表达能力不足难以捕捉资产间的密集尾部风险关联。因此，该研究揭示了当前NISQ架构在执行密集金融优化任务时所面临的不可调和困境：要么牺牲算法表达力，要么承受硬件退相干，这凸显了现有硬件架构在缺乏全连接支持的情况下对复杂量子优化任务的根本性局限。
链接: https://arxiv.org/abs/2606.07727
作者: Prashik N. Somkuwar,K. Srinivasan,G. Raghavan
机构: 未知
类目: Quantum Physics (quant-ph); Computation and Language (cs.CL); Optimization and Control (math.OC); Portfolio Management (q-fin.PM)
备注: 10 pages, 11 figures. Master’s thesis research conducted at the School of Quantum Technology, Defence Institute of Advanced Technology (DIAT), Pune
Abstract:Quantum combinatorial optimization offers theoretical advantages for complex financial modeling, but physical implementation on Noisy Intermediate Scale Quantum (NISQ) devices is severely constrained by hardware topology. This study presents a hardware benchmarking analysis between a Hardware Efficient Variational Quantum Neural Network (HE-VQNN) and the Warm Start Quantum Approximate Optimization Algorithm (WS-QAOA) for a hybrid Mean Variance and Conditional Value at Risk (CVaR) portfolio objective. By implementing a novel classical quantum hybrid proxy matrix to bypass the CVaR auxiliary qubit bottleneck, we map up to 16 assets from the NIFTY 50 index onto an IBM heavy hex processor. We systematically quantify algorithmic resilience to the “SWAP tax” incurred during routing. Empirical results reveal a critical operational trade-off: WS-QAOA provides exact theoretical mapping but suffers catastrophic hardware decoherence due to exponential nonlocal gate overhead. Conversely, HE-VQNN preserves hardware coherence but lacks the mathematical expressibility to capture dense tail risk asset correlations. This study exposes the limitations of dense financial optimization on current architectures forces an nonviable choice between algorithmic inexpressibility and hardware decoherence. This is indicative of a deeper limitation as to what can and cannot be done with NISQ computers lacking in all-to-all connectivity.
信息检索
[IR-0] Popcorn: A Configurable Benchmark for Visual Evidence in Multimodal Movie Recommendation
链接: https://arxiv.org/abs/2606.09595
作者: Ali Tourani,Fatemeh Nazary,Yashar Deldjoo,Tommaso Di Noia
类目: Information Retrieval (cs.IR)
备注: 8 pages, 3 figures, 3 tables
Abstract:Movies are long-form audiovisual works, yet recommender benchmarks often rely on trailers, thumbnails, or metadata. These sources differ in semantics and scalability: full movies preserve consumption-level evidence, trailers concentrate promotional highlights, and thumbnails provide sparse but catalog-scale visual signals. We present Popcorn, a configurable benchmark for visual evidence in multimodal movie recommendation, combining title-aligned full-movie/trailer embeddings with MovieLens-linked thumbnail features encoded by modern visual and vision-language models. Popcorn standardizes modality assembly, fusion, splitting, evaluation, and LLM-augmented metadata through a single configuration contract. Experiments show that thumbnail VLMs provide strong, scalable item-side evidence, while controlled trailer/full-movie comparisons show that visual evidence sources are not interchangeable: the choice of source and fusion strategy affects ranking accuracy, coverage, diversity, and calibration. The framework is available at this https URL.
[IR-1] ABVERSE: Benchmarking Cross-Format Table Understanding in LLM s and VLMs
链接: https://arxiv.org/abs/2606.09578
作者: Momina Ahsan,Sarfraz Ahmad,Ming Shan Hee,Roy Ka-Wei Lee,Preslav Nakov
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: 24 pages, 18 tables, 16 figures, Submitted to ARR May 2026
Abstract:Large Language Models (LLMs) and Vision-Language Models (VLMs) are increasingly evaluated on table reasoning tasks, but the role of table representation remains under-explored. In practice, the same table content may appear in different structural formats, such as HTML, Markdown, and LaTeX, or as rendered images. However, existing evaluations often let content, format, layout, and modality vary together, making it difficult to isolate representation effects. We introduce TABVERSE, a controlled multimodal table benchmark that aligns the same table content across multiple structural formats and rendered images, with question category and difficulty tags. This design enables systematic evaluation of representation effects while holding table content fixed. We evaluate LLMs and VLMs across three tasks: Question Answering (QA), Structural Understanding Capability (SUC), and Structure Reconstruction (SR). Our results show that representation choice substantially affects table understanding. Models generally perform better with structured text than with rendered images, but the size of this gap depends on the task, model, and format. HTML is often the most robust text format, while row-sensitive structural tasks and syntactically usable LaTeX reconstruction remain challenging. These findings show that table representation is a key factor in reliable table evaluation.
[IR-2] Closing the Indexing-Decoding Gap in Multimodal Generative Retrieval via Prefix Retention Optimization
链接: https://arxiv.org/abs/2606.09241
作者: Yufei Chen,Zihan Wang,Yubao Tang,Yukun Zhao,Maarten de Rijke,Zhaochun Ren
类目: Information Retrieval (cs.IR)
备注: 28 pages, 5 figures; code: this https URL
Abstract:Multimodal generative retrieval formulates multimodal retrieval as discrete identifier generation, eliminating the need for explicit similarity search over external embeddings. Existing approaches construct identifiers via residual quantization and decode them with trie-constrained beam search. This combination introduces an indexing-decoding gap: identifier learning objectives, including reconstruction and contrastive losses, do not explicitly enforce prefix discriminability during decoding. As a result, even well-optimized identifiers can be irreversibly pruned early in beam search due to low-rank prefixes. We theoretically characterize this gap and derive a survival bound that relates prefix retention to three controllable factors in indexing and decoding. Building on this bound, we propose PRO, prefix retention optimization, a unified framework comprising three mechanisms: (i) prefix ranking distillation aligns quantized prefix rankings with those induced by pre-quantization embeddings using a listwise loss; (ii) vocabulary scheduling increases codebook sizes from shallow to deep residual quantization levels to reduce early competition from non-target prefixes; and (iii) geometric score fusion vectorizes each candidate prefix and incorporates its similarity to the query into beam search scoring, further reducing the indexing-decoding mismatch. Experiments on nine multimodal retrieval tasks show that PRO improves retention of target identifier prefixes and outperforms existing multimodal generative retrieval baselines.
[IR-3] Driving Video Retrieval for Complex Queries with Structured Grounding
链接: https://arxiv.org/abs/2606.09109
作者: Manyi Yao,Sparsh Garg,Christian Shelton,Amit Roy-Chowdhury,Abhishek Aich
类目: Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注:
Abstract:Video retrieval at scale is central to data curation and safety validation in autonomous driving, where users want to find not only scenes but also dynamic events such as cut-ins and hard braking. Existing vision-language and keyword-based retrieval methods often miss these events because the relevant motion may not be explicitly described in text or captured by lexical overlap. Rule-based retrieval can encode such events more directly, but it is brittle: generated or hand-written rules often fail when their assumptions do not match real driving data. We propose STRIVE-D, a data-calibrated retrieval framework for driving videos. It uses weakly labeled in-domain videos to estimate when a query rule is reliable, adapt rules that mismatch observed data, and fuse calibrated rule scores with vision-language and keyword-based retrieval signals. Across three driving benchmarks, including newly released human-annotated event data on DrivingDojo, STRIVE-D delivers up to 84% relative improvement in top-1 accuracy over state-of-the-art methods.
[IR-4] ach Multimodal Recommendation Model to See via Personalized Visual Extraction and Adaptive Learning
链接: https://arxiv.org/abs/2606.09082
作者: Yutong Li,Xinyi Zhang,Ziyi Ye,Daoguo Dong,Yu-gang Jiang
类目: Information Retrieval (cs.IR)
备注:
Abstract:Multimodal sequential recommendation (MSR) incorporates textual and visual information to improve recommendation quality. However, recent studies and our empirical analysis show that visual features are often underutilized, thereby contributing far less than textual signals. We attribute this issue to two factors: insufficient visual representation learning (pretrained encoders fail to capture preference-relevant cues) and unbalanced visual-text optimization (textual features dominate the learning process). To address these issues, we propose Teach Multimodal Recommendation Model to See via Personalized Visual Extraction and Adaptive Learning (REVEAL), a plug-and-play framework that enhances visual representation learning and cross-modal optimization without modifying the original recommendation backbone. REVEAL consists of Feedback-Guided Visual Extraction (FVE), which refines prompt-guided visual extraction through task-level feedback, and Adaptive Visual Learning (AVL), which dynamically reweights visual learning to alleviate modality imbalance. Experiments on multiple real-world datasets and MSR backbones demonstrate that REVEAL consistently improves recommendation performance. Further analysis shows that these gains arise from more effective attention to preference-relevant visual regions and better visual utilization during training. The code is available at this https URL.
[IR-5] Decoy-Calibrated Failure Audits for Language Models
链接: https://arxiv.org/abs/2606.09046
作者: Vyzantinos Repantis,Ameya Gawde,Harshvardhan Singh
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: 14 pages, 5 figures, 4 tables
Abstract:Useful audits reveal not only how often a model fails, but also where its failures concentrate. An auditor may test many candidate explanations: long inputs, indirect questions, distracting evidence, or combinations of these factors. The risk is selection. The largest observed effect may reflect a real failure mode, or it may simply be the best result among many tried. We introduce Janus, a procedure for deciding when a proposed error explanation is credible enough to report. The goal is not to generate new explanations, but to decide which ones hold up. The auditor starts with a fixed model, a labeled evaluation set, and a frozen list of candidate explanations, which we call descriptors. Janus scores each descriptor by its error-rate lift, then compares real descriptors with fake ones that have the same frequencies but are randomly assigned to examples. A descriptor is confirmed only if it beats this decoy floor on the data used for discovery and then repeats on separate held-out data. In a controlled audit of multi-table lookup tasks, Janus identifies the planted failure, confirming long-chain descriptors and their interactions. The LLM often stops partway through the lookup chain instead of reaching the final answer. On two public benchmarks, MuSiQue and LongBench v2, the SliceLine baseline flags plausible high-error pockets, but Janus confirms none of them. Ablations show why both safeguards matter. On LongBench v2, an uncalibrated fixed threshold reports 20 descriptors, the decoy floor leaves one, and the holdout check rejects the last one after its lift shrinks from 0.36 to 0.05. The resulting principle separates proposing explanations from reporting them. Candidates may come from any source, but only those that beat decoys and replicate on fresh data become audit findings.
[IR-6] Personal Salience: Highlighting Is Social but Individuality Lives in Selection
链接: https://arxiv.org/abs/2606.09024
作者: Kazuki Nakayashiki,Keisuke Watanabe
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Social and Information Networks (cs.SI)
备注: 12 pages, 5 figures, 2 tables
Abstract:Social highlighters let people mark passages that matter to them. We ask how much of an individual is recoverable from these naturalistic traces, using a co-readership identity control (the same document highlighted by many users) that holds document and topic fixed and asks whether a person’s own history predicts their marks better than another reader’s does. We separate generic salience (structure), crowd salience (what others marked), and personal salience (the individual residual). First, highlighting is social: which sentences you mark is predicted far better by the crowd than by structure or by a personal model, and even a well-estimated crowd, an information-privileged baseline that sees others’ marks on the same document, beats a frontier LLM twin built from your other-document history; the within-document personal signal is at most a whisper (own-vs-other gap +0.017 by an embedding scorer, small but significant). Second, in sharp contrast, individuality lives in selection: asked which of the already-salient passages are yours, your own history is a strong, leakage-free predictor (gap +0.14). A topic decomposition shows this is largely stable thematic preference: it shrinks ~6-8x against a topically-matched peer, and a thin residual cannot be separated from finer topic. The non-obvious part is an asymmetry: under the same scorer the individual signal is ~6-8x weaker in salience than in selection. Methodologically, naive history-conditioning evaluations leak (the target’s own marks enter the profile in ~42% of pairs, inflating personal scores by up to +0.15 AP) and small crowds overstate personalization; our results are leakage-free, use a dense crowd, and a model-matched control. Highlights carry a genuine individual signature, but a thin layer over a strong shared one, surfacing far more in which salient things a person selects than in what is salient.
[IR-7] EviProp: Seeded Relevance Diffusion on Chunk-Page Graphs for Long Multimodal Document Retrieval
链接: https://arxiv.org/abs/2606.08979
作者: Hongwei Zhang,Xiaoman Wang,Zehui Ling,Ruicheng Zhu,Yue Zhang,Pinlong Cai,Fuke Shen,Botian Shi,Tongquan Wei,Guohang Yan
类目: Information Retrieval (cs.IR)
备注:
Abstract:Retrieving evidence pages from visually rich long documents is a key challenge in document question answering. Existing page-level visual retrievers operate under an independent matching paradigm: each page is scored in isolation based on query-page similarity. This paradigm can under-rank evidence pages whose signals are localized in fine-grained chunks or depend on document-internal associations. We propose EviProp, a retrieval method that recovers such pages via seeded relevance diffusion. EviProp models each document as a multimodal Chunk-Page graph with hierarchical, sequential, and similarity links. Given a query, it combines dense visual page priors with sparse chunk seeds, then runs Personalized PageRank to diffuse relevance over the graph. Experiments on MMLongBench-Doc and LongDocURL show consistent gains in evidence-page retrieval over independent visual retrieval and text-visual fusion baselines. Downstream QA results further show that improved retrieval translates into better answer accuracy, with negligible online retrieval overhead. Our code is released at this https URL.
[IR-8] Report on CHIIR 2026 Workshop on Generative AI and Academic Search (GAIAS)
链接: https://arxiv.org/abs/2606.08936
作者: Yifan Liu,Jaime Arguello,Orland Hoeber,Chang Liu,Soo Young Rieh,Luanne Sinnamon,Dean Alvarez,Susan Archambault,Rob Capra,Henson Chen,Charles Costa,Anita Crescenzi,Zhitong(Klara)Guan,Jacek Gwizdka,Pao-Pei Huang,Gavindya Jayawardena,Ghazal Kalhor,Dagmar Kern,Oliver Koop,Alice Li,Afra Mashhadi,Gaohui Meng,Marta Micheli,Anil B. Murthy,Kevin Schott,Sebastian Schultheiß,Jiwoo Seo,Phaneendra Sivangula,Frans van der Sluis,Xiaoxuan Song,Silang Wang,Dan Zhang
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:This report summarizes the CHIIR 2026 Workshop on Generative AI and Academic Search (GAI\AS), which examined how GenAI is reshaping academic search systems and research practices. The workshop brought together researchers in human information interaction and information retrieval to explore key challenges and opportunities in designing and evaluating future academic search systems that integrate GenAI, moving beyond traditional document retrieval to support summarization, recommendation, synthesis, and conversational interaction. Participants’ interests and discussions focused on three thematic clusters: foundations and principles, applications and opportunities, and search-as-learning. Across these themes, the workshop highlighted the importance of academic search systems in supporting transparency, credibility, research integrity, and long-term scholarly needs, as well as in fostering higher-order cognitive processes. Participants discussed guiding theories, design principles, methodological approaches, partnerships, and community-building efforts aimed at advancing human-centered GenAI-enhanced academic search systems. Overall, the workshop demonstrated strong community interest and a diverse range of ongoing and emerging research initiatives at the intersection of GenAI and academic search.
[IR-9] Aperon Technical Report: Hierarchical No-Pointer Tangent-Local Search for High-Dimensional Approximate Nearest Neighbors
链接: https://arxiv.org/abs/2606.08813
作者: Yong Fu
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Databases (cs.DB); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注:
Abstract:We present HNTL (Hierarchical No-pointer Tangent-Local), the core vector indexing and candidate generation framework of the Aperon vector memory system. Proximity graphs (e.g., HNSW) incur a heavy pointer tax in memory overhead and induce irregular memory accesses that stall CPU pipelines. HNTL resolves this by partitioning the high-dimensional space into local, coherent grains, representing vectors as low-dimensional coordinates on local tangent spaces, and scanning them sequentially using a pointerless Block-SoA (Structure-of-Arrays) layout. On anisotropic manifold data (d=768, N=10,000), local PCA captures 96.3% of the variance, allowing HNTL to achieve a final Rerank Recall@10 of 1.0000 with a candidate pool size of only C=20 vectors. Hardware profiling via Apple kperf CPU Performance Monitoring Unit (PMU) counters demonstrates a 3.61x speedup (4.137 ns/vector vs. 14.951 ns/vector) for our NEON auto-vectorized C++ Block-SoA scan engine over standard pointer-chasing graph traversals, driven by a 3.59x IPC (Instructions Per Cycle) and near-zero L1/L2 data cache misses. Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Databases (cs.DB); Information Retrieval (cs.IR); Machine Learning (cs.LG) Cite as: arXiv:2606.08813 [cs.DC] (or arXiv:2606.08813v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2606.08813 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-10] Gryphon: A Unified Architecture for Semantic-ID Generation and Item-Level Scoring in Industrial Recommendations
链接: https://arxiv.org/abs/2606.08604
作者: Daria Tikhonovich,Oleg Sorokin,Vladislav Dodonov,Mariia Ulianova,Ilya Murzin
类目: Information Retrieval (cs.IR)
备注:
Abstract:Generative retrieval (GR) has become a scalable approach to candidate generation: each item is assigned a short hierarchical token sequence called a Semantic ID (SID), and the next item’s SID is decoded autoregressively. A practical limitation is that the decoder’s beam search optimizes the likelihood of token sequences, not the relevance of the underlying items. These objectives diverge when sequence likelihood is poorly calibrated due to beam search error accumulation, and when several items collapse onto a single SID and receive identical scores. We introduce Gryphon, an encoder-decoder generative recommendation architecture that adds a jointly trained item-level scoring component alongside SID generation, reusing the encoder’s user representation computed in a single forward pass. Instead of ranking SIDs by accumulated token likelihood, Gryphon resolves each generated SID to its concrete items and re-scores those items directly, which sidesteps miscalibrated sequence scores and separates items that collide on the same identifier. On an industrial music service, with item-level scoring trained under a next-item-prediction objective, Gryphon attains the highest item-level Recall@1000, above the strongest baselines (+3.7% over vanilla GR and +2.5% over collision-resolved GR) at comparable parameter count and latency. Gryphon’s item-level ranking also surpasses its beam-likelihood ranking of the same candidates (+4.2% gain), demonstrating the benefit of item-level scoring in GR. Deployed as the sole candidate source in a 7-day A/B test, Gryphon produced no statistically significant change in total listening time (+0.25%) while replacing a pipeline of more than 15 candidate generators and a separate preranking stage, substantially simplifying the candidate-generation system.
[IR-11] Detection and Interpretability Analysis of Quotation Errors by Large Language Models
链接: https://arxiv.org/abs/2606.08589
作者: Bei Huang,Yingyi Zhang,Shenghao Huang,Chengzhi Zhang
类目: Computation and Language (cs.CL); Digital Libraries (cs.DL); Information Retrieval (cs.IR)
备注:
Abstract:Purpose - Quotation error refers to the inconsistency between cited information and its original source. This phenomenon leads to a series of negative impacts, such as misinterpretation of the original research, undermining the academic community’s collective understanding of relevant issues, and weakening the accuracy and fairness of the citation-based academic evaluation system. Existing studies have shown that quotation error is prevalent in the academic community; moreover, manual verification of quotation error is not only labor-intensive but also inefficient. Therefore, this paper proposes the task of ‘automated detection of quotation errors’. Methodology - Adopting a large language model (LLM)-based approach, this paper improves detection performance from two aspects on the basis of existing research: first, employ the fine-tuning approach for LLMs to detect quotation errors; second, incorporating full-text data of the cited literature into dataset construction, and exploring the optimal scheme for building such datasets by comparing three types of full-text integration methods. Based on this, this paper further uses the TokenSHAP tool to conduct interpretability experimental analysis on the model’s prediction results. Findings - The fine-tuning approach for LLMs has improved the performance in detecting quotation errors. Among the different methods for incorporating full-text information, the approach based on using the source abstract yielded the best performance. Originality - The fine-tuning approach for large language models (LLMs) is applied to the task of automated detection of quotation errors, and interpretability analysis is conducted on the model’s output results.
[IR-12] When Should Queries Be Decomposed? A Stage-Aware Study of Query Decomposition for Multi-Condition Retrieval
链接: https://arxiv.org/abs/2606.08577
作者: Bochao Yin,Xuan Lu,Zhengyu Qi,Xiaoyu Shen
类目: Information Retrieval (cs.IR)
备注:
Abstract:Multi-condition retrieval requires systems to identify documents that satisfy multiple distinct constraints, moving beyond mere topical relevance. While query decomposition is widely adopted as an intuitive remedy, its effectiveness across different retrieval pipeline stages remains underexplored. In this paper, we conduct a stage-aware empirical study and uncover a stark, stage-dependent effect: decomposition during initial retrieval frequently harms retrieval performance due to semantic dilution, yet substantially improves reranking by enabling more fine-grained constraint verification. Motivated by these insights, we propose a principled Stage-Aware Decomposition framework that retains the monolithic query during initial retrieval to preserve global semantic context, while employing sub-queries exclusively during reranking for fine-grained constraint matching. Extensive evaluations on the MultiConIR and SSRB benchmarks demonstrate that our framework consistently improves ranking performance for compositional queries across multiple retrieval and reranking models. We release our code at this https URL.
[IR-13] Adaptive Loss Balancing for Noise-Robust GRPO in Generative Recommendation
链接: https://arxiv.org/abs/2606.08480
作者: Kewei Xu,Junbo Qi,Yanyan Zou,Pengfei Zhang,Xingzhi Yao,Shengjie Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:Reinforcement learning (RL) presents a promising avenue for enhancing generative recommendation beyond supervised imitation, leveraging reward signals to guide policy improvement. However, its efficacy is critically contingent on the trustworthiness of the reward model for the samples it evaluates. In practice, production rankers, the widely adopted reward models, are trained on exposure-biased logs, leading to sample-dependent inaccuracies that violate this assumption. Our stratified analysis uncovers a consistent pattern: reward guidance is most beneficial when the policy exhibits uncertainty and the ranker can effectively discriminate the ground-truth item from rollout negatives. On other samples, the reward signal is either negligible or detrimental, highlighting the risk of uniform RL application. To address such an issue, we introduce AdaGRPO, a novel framework that treats reward-guided optimization as selective admission rather than uniform pressure. Training is anchored in supervised negative log-likelihood, while the GRPO objective is gated by a binary, per-sample clip determined by two rollout diagnostics: policy-side difficulty and reward discriminability. Instances failing either diagnostic default to pure supervision, ensuring stability and mitigating the amplification of noisy gradients. We validate AdaGRPO on a large-scale e-commerce dataset. At the best intermediate checkpoint, it elevates HR@10 from 11.01% to 12.18% while constraining hallucination below 0.22%, and maintains robustness at the final checkpoint (HR@10 11.63%, hallucination 0.27%), outperforming fixed NLL–GRPO mixtures across the retrieval–validity frontier. In production A/B tests, AdaGRPO achieves statistically significant gains in click-through rate and dwell time, confirming its practical utility.
[IR-14] oolRec: Calibrated Preference Alignment for Query Recommendation in On-Device Assistants
链接: https://arxiv.org/abs/2606.08466
作者: Zihan Luo,Lingkui Chen,Ruike Zhang,Hong Huang,Boyang Zhang,Ziniu Chen,Lizhong Wang
类目: Information Retrieval (cs.IR)
备注:
Abstract:Large Language Models (LLMs) have significantly advanced generative query recommendation. However, existing alignment methods primarily focus on standard chatbot scenarios, falling short in on-device intelligent assistants where users predominantly expect the rapid invocation of system-level tools. Moreover, directly aligning LLMs with real-world click logs introduces severe noise due to varying user activity levels and the failure to emphasize execution-oriented queries. To address these challenges, we propose ToolRec, a calibrated preference alignment framework tailored for on-device query recommendation. To ground query recommendation with executable actions, we first construct SysToolKit, a comprehensive repository of 708 system tools, paired with a context-aware tool retrieval mechanism to ensure recommendation relevance. We then propose a dual-level calibration mechanism to refine raw click data, effectively mitigating user behavioral noise by calibrating signals based on user activity levels, while simultaneously up-weighting click signals on system-level tool-invoking queries. Guided by these refined preference signals, we then align the model using a sample-level weighted Kahneman-Tversky Optimization (KTO). Extensive online A/B tests on our mobile assistant platform OPPO Xiaobu, which has over 150 million monthly active users, demonstrate that ToolRec can significantly improve Click-Through Rate (CTR) and total clicks volume over strong baselines while maintaining high query relevance.
[IR-15] rustMargin: Training-Free Arbitration between Parametric Memory and Retrieved Evidence in Large Language Models
链接: https://arxiv.org/abs/2606.08397
作者: Jingyan Xu,Hong Shi,Yi Shan,Penghui Liu,Yunhao Bai,Ningyuan Li,Xueyang Liu
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: 13 pages, 6 figures, 9 tables. Code and data are available at this https URL
Abstract:Large language models answer knowledge-intensive questions using both parametric memory and retrieved evidence, but neither source is uniformly reliable. Retrieval can fill knowledge gaps, yet distracting passages may override correct closed-book answers. We study this post-generation conflict as answer-level source arbitration: given Direct and RAG answers from the same frozen model, decide which source to trust. We propose TRUSTMARGIN, a training-free, plug-and-play arbitration layer that scores the two existing candidates with the model’s own likelihoods. It combines a parametric-prior margin, which tests whether memory accepts the retrieved answer, with an evidence-binding margin, which discounts passage-only salience and measures question-specific support. TRUSTMARGIN selects between Direct and RAG without fine-tuning, external judges, or additional generation. Across 2WIKIMQA and CWQA with three LLaMA scales, TRUSTMARGIN consistently improves over Direct generation and BM25-RAG, recovers part of the Direct/RAG oracle gap, and generalizes to multiple training-free RAG pipelines.
[IR-16] EmpiriGraph-Psy: A Dataset and LLM Pipeline for Extracting Empirical Relation Graphs from Psychology Abstracts
链接: https://arxiv.org/abs/2606.08362
作者: Danqin Zhao(1),Yicun Liu(2),Xingwei Tan(3),Thomas T. Hills(1) ((1) Department of Psychology, University of Warwick, (2) Mathematical Sciences Institute, The Australian National University, (3) School of Computer Science, University of Sheffield)
类目: Information Retrieval (cs.IR)
备注: 17 pages, 5 figures. Code available at this https URL
Abstract:Existing scientific relation extraction benchmarks mainly target domains such as computer science, where entities are tasks, methods, datasets, materials, or metrics. This leaves a gap in variable-oriented empirical fields such as psychology, where findings are expressed as relations among constructs, measurements, interventions, and outcomes. We introduce variable-centered empirical graph extraction, the task of mapping scientific abstracts to typed graphs whose nodes are normalized variables and whose edges represent empirical and hierarchical relations. To support this task, we construct EmpiriGraph-Psy, a benchmark of 210 psychology abstracts annotated by domain-trained annotators with normalized variables, concept hierarchies, empirical relation types, and validation states. We evaluate frontier and open-weight LLMs using both direct extraction and a staged graph-construction pipeline that separates variable extraction, normalization, hierarchy construction, evidence selection, relation extraction, and edge validation. The staged pipeline substantially outperforms direct extraction, with the best configuration achieving a macro-F1 of 0.74. Error analysis shows that moderation relations and concept hierarchies remain the most challenging cases, highlighting the difficulty of extracting higher-order empirical claims and implicit abstraction structure from scientific abstracts.
[IR-17] Have I Solved This Before? Retrieving Similar Segmentation Problems for Evolutionary Learning
链接: https://arxiv.org/abs/2606.08155
作者: Andreas Margraf,Henning Cui,Jörg Hähner
类目: Machine Learning (cs.LG); Information Retrieval (cs.IR)
备注:
Abstract:Reliable integration and solid configuration of monitoring systems constitute a fundamental prerequisites for achieving high efficiency and productivity in contemporary manufacturing environments. Design decisions on sensor type and system architecture have to be made at an early stage and under comparably high uncertainty. This work investigates a research direction that deviates from the traditional monitoring-system development process by shifting the attention from algorithm design to a deeper analysis of the inspection problem. In contrast to traditional design cycles, this paper proposes to gradually collect knowledge and store it in an abstract system model. This enables the retrieval of similar solutions for future use cases, preventing the need for expensive model training from scratch and allowing instead for the incremental refinement of existing base configurations. Reuse of previously generated pipelines reduces the risk of late and costly revisions. As there is little knowledge on cross-domain transferability of filter pipelines, this study analyzes the potential of retrieving filter pipelines to transfer them to different but similar segmentation problems. Finally, we statistically analyze the benefits of this `transfer learning’ variant which is predominantly applied to image segmentation problems. In addition, we discuss how simple models help balancing the trade-off between complexity, technical requirements, and reliability in the design process.
[IR-18] GIScholarBench: Benchmarking LLM Overconfidence in GIS Research
链接: https://arxiv.org/abs/2606.08036
作者: Zongrng Li,Mingzheng Yang,Lei Zou,Hongxu Ma,Hao Tian,Siqi Zhou,Wenjing Gong,Kaili Zhang,Bingqian Chen,Mitch Zhang,Yifan Yang
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) are increasingly used in academic research workflows, but scholarly tasks require high factual precision and therefore expose a key weakness: overconfidence. Here, overconfidence is defined behaviorally as the tendency to produce confident, assertive, and well-formatted outputs even when the underlying knowledge is incomplete or unverifiable, rather than as a calibration gap between stated confidence and accuracy. To examine this issue, we introduce GIScholarBench, a benchmark built from 10,865 papers published in 25 core GIScience journals between 2020 and 2025. The benchmark covers three tasks with increasing cognitive complexity: metadata retrieval, literature linking, and research direction generation. We evaluate Claude Sonnet 4.5, Gemini 3, and ChatGPT 5.3 through their native web interfaces under real-world user-facing conditions. Results show consistent overconfidence across all tasks. In metadata retrieval, ChatGPT 5.3 achieves the highest accuracy, but all models still generate definitive titles and DOIs when predictions are wrong. In literature linking, Claude Sonnet 4.5 recovers the most references, but all models show a clear gap between top-ranked retrieval and longer citation lists, suggesting that references are extended beyond reliable retrieval capacity. In research direction generation, AI-generated directions show lower topic coverage, higher novel miss rates, and lower semantic diversity than real future-citing papers. These findings suggest that LLM overconfidence is task-invariant but takes different forms: factual overgeneration in retrieval, unreliable citation expansion in literature linking, and overconfidence in output completeness during research ideation.
[IR-19] DeRes: Decoupling Residual Stability and Adaptivity for Scalable CTR Prediction
链接: https://arxiv.org/abs/2606.07980
作者: Wenzhuo Cheng,Shipeng Nie,Qixin Guo,Xuefeng Sun,Jianguo Lou,Zhengwei Zheng
类目: Information Retrieval (cs.IR)
备注:
Abstract:Transformer-based CTR models face a growing bottleneck at the residual connection: under Pre-Norm, early user-interest signals are diluted layer by layer; the identity skip cannot forget stale interests; and each layer sees only its immediate predecessor, losing long-range cross-layer dependencies. Recent attention-based residual variants (AttnRes) address parts of this in language models, but drop the protective identity skip and have not been tried in recommendation. Drawing on Dual Path Networks (DPN) and the HORNN view of residuals, we present DeRes, which routes each layer through two parallel paths – an Identity residual path that preserves first-order feature reuse and gradient flow, and a Block Attention Residual path that attends over compressed outputs of all earlier blocks for high-order recall. A vector-wise gate decides, per hidden dimension, the weight given to each path. We further propose Pointwise AttnRes, replacing the Softmax in the cross-layer attention with SiLU so that multiple past blocks can be activated simultaneously and irrelevant ones receive negative (forgetting) weights – better aligned with CTR’s parallel multi-interest patterns. On a large-scale industrial dataset (331M interactions from a major social-media platform), Criteo (45M), and Avazu (40M), DeRes outperforms twelve baselines including OneTrans, TokenMixer-Large, UniMixer, mHC, and AttnRes, achieving up to +0.32% AUC at under 5% extra FLOPs. Beyond a single operating point, DeRes fits a markedly steeper compute-AUC scaling law (gamma=0.118 vs. 0.071 for OneTrans, a 1.66x gap), so an 8-layer DeRes matches a 16-layer OneTrans – about 2x compute saving at equivalent AUC. Ablations confirm that the dual-path design outperforms either single path, Identity beats learnable residuals, and SiLU beats Softmax.
[IR-20] OneFeed: A Unified Generative Framework for Feed ContentEnhancement and Query Generation
链接: https://arxiv.org/abs/2606.07972
作者: Guo Xun
类目: Information Retrieval (cs.IR)
备注:
Abstract:Modern feed recommendation and search systems are deeply connected in user behavior butare usually modeled by separate architectures. Feed recommendation mainly captures implicitinterests from browsing interactions, while search systems rely on explicit user queries to retrieveintent-matched content. This separation causes fragmented user understanding and missedopportunities for using feed interactions to improve query generation and using generated queriesto enhance feed candidate this http URL this paper, we propose OneFeed, a unified generative framework for jointly modelingfeed content enhancement and query generation. OneFeed encodes heterogeneous user behaviorsequences with a shared behavior encoder and employs two generative heads: a Feed SemanticID Generator that produces content semantic IDs for recommendation retrieval, and an IntentQuery Generator that produces natural-language queries for search-based candidate this http URL bridge the semantic gap between recommendation content and search queries, we introduce aSID-Query alignment objective that learns a shared semantic space for content semantic IDs andquery representations. We further design a closed-loop self-enhancement paradigm that leveragesimplicit user feedback from generated content and search-retrieved results to improve bothgeneration tasks. We provide a detailed experimental protocol using public recommendationdatasets with weakly supervised query construction, define a comprehensive set of evaluationmetrics, report expected performance estimates grounded in known baseline values, and validatethe executability of the proposed pipeline through a minimal local prototype. OneFeed providesa practical and extensible direction for unifying search and recommendation through generativemodeling.
[IR-21] ASH: Asymmetric Scalar Hashing With Learned Dimensionality Reduction for High-Fidelity Vector Quantization
链接: https://arxiv.org/abs/2606.07870
作者: Mariano Tepper,Theodore Willke
类目: Information Retrieval (cs.IR)
备注:
Abstract:For a long time, additive quantizers, such as product quantization, have been considered the gold standard in terms of accuracy and efficiency. Recently, scalar quantization has re-emerged from the depths of history with a new wave of data-agnostic techniques. Inscribed in this general framework, we turn our attention to data-driven methods, showing that new highs in recall and speed can be achieved by reducing the number of dimensions while increasing the bitrate per dimension. Critically, this dimensionality reduction needs to be learned from data to be successful. We present ASH (Asymmetric Scalar Hashing), a data-driven encoder-decoder framework that applies dimensionality reduction to database vectors via a learned orthonormal projection, followed by scalar quantization, while keeping queries in their original form. This asymmetric design enables higher accuracy than the best additive and scalar quantizers at iso-compression, while admitting highly efficient similarity computations via SIMD operations. ASH has short learning and encoding times, making it attractive for real-world deployment. Extensive experiments on a variety of datasets demonstrate that ASH achieves state-of-the-art ANN recall and speeds across all compression regimes.
[IR-22] RACT: Retrieval Augmented Column-Table Learning and Prediction for Multi-Table Schema Matching
链接: https://arxiv.org/abs/2606.07843
作者: Leonard Traeger,Enas Khwaileh,Andreas Behrend,George Karabatis
类目: Databases (cs.DB); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: Research Preprint, 12 pages
Abstract:Schema matching, a critical task for integrating data from diverse sources, seeks to identify correspondences between columns across different schemas. In multi-table holistic schema matching, columns with similar semantic meaning may reside in tables with different contexts due to heterogeneous schema designs, where similarity-based techniques are inadequate. The focus of this paper is exploiting referential context into schema matching by introducing RACT learning and prediction, a self-supervised framework enabling the probabilistic retrieval of candidate tables for source columns to constrain relevant column candidates. Experiments demonstrate that this approach outperforms similarity-based baselines on matching multi-table schemas. In subsequent matching experiments, constraining the column search space via top-t tables improves both average matching precision and completeness by up to +70%.
[IR-23] Frequency-Scale Saliency for Spectral Descriptor Analysis in 3D Shape Retrieval
链接: https://arxiv.org/abs/2606.07791
作者: Jianru Shen
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
备注: Accepted at Computer Graphics International (CGI) 2026
Abstract:Classical spectral descriptors such as the Heat Kernel Signature and Wave Kernel Signature are widely used for non-rigid 3D shape retrieval, yet their failure modes remain poorly understood. We present a frequency-scale saliency framework that audits these descriptors by quantifying the retrieval-level contribution of each descriptor scale interval through ablation. We introduce class spectral fingerprints to characterize category-level scale dependence, and show that descriptor similarity between class pairs is substantially correlated with retrieval failure, with a Spearman correlation of 0.479. Experiments on SHREC’11 demonstrate that short scales dominate retrieval performance while long scales are harmful, that HKS and WKS exhibit distinct scale dependence patterns, and that saliency-weighted retrieval improves mAP on hard categories by 0.156, with cross-fold and random-weight controls confirming that the gain is stable and not due to arbitrary reweighting.
[IR-24] RACER: Token ReAssignment for Concept ERasure in Generative Recommendation
链接: https://arxiv.org/abs/2606.07688
作者: Ziheng Chen,Jiali Cheng,Zezhong Fan,Hadi Amiri,Diyuan Wu,Gabriele Tolomei,Yang Zhang
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Generative recommendation formulates next-item prediction as autoregressive generation over semantic ID (SID) sequences derived from users’ historical interactions, making modern recommender systems structurally similar to large language models (LLMs). As privacy and safety concerns grow, these systems increasingly require concept unlearning to remove sensitive or harmful concepts associated with items. However, existing LLM unlearning methods cannot be directly applied to generative recommendation. Unlike word tokens with explicit semantics, SIDs are abstract identifiers that are often shared by both forget and retain items, leading to severe conflicts between concept removal and recommendation utility preservation. To address this challenge, we propose TRACER, an end-to-end concept unlearning framework based on token reassignment. Rather than directly suppressing shared SIDs, TRACER reassigns concept-related items to alternative tokens that better facilitate forgetting while minimizing side effects on retained items. We further introduce a coherence regularizer to preserve semantic consistency among retain items during unlearning. Experiments on real-world recommendation datasets demonstrate that TRACER effectively removes target concepts while substantially better preserving recommendation utility than existing unlearning baselines. Subjects: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) Cite as: arXiv:2606.07688 [cs.IR] (or arXiv:2606.07688v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2606.07688 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-25] MIRAG E: Metadata-Integrated Repository Analysis and Guided Enhancement for MSR Datasets
链接: https://arxiv.org/abs/2606.07611
作者: Aabia Ather,Muhammad Usayd Ather,Qurat-Ul-Ain Somroo,Muhammad Khuram Shahzad
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Software Engineering (cs.SE)
备注: 8 pages, 8 figures
Abstract:This paper proposes an improved approach to the analysis of Mining Software Repositories (MSR) datasets via metadata enrichment, FAIRness assessment, and topic-driven analysis. This research expands upon an earlier dataset directory created specifically for the analysis of MSR datasets by adding new annotations to the datasets, enriching the metadata categories, and offering more advanced filtering options. The metadata of the MSR papers presented from 2013 to 2024 has been gathered using the Semantic Scholar API. The analysis is based on Latent Dirichlet Allocation (LDA) topic modeling and statistical analysis. Dataset-level attributes were included into the expanded dataset directory, namely repository hosting site, format, accessibility, reusability, and dataset quality. The study reveals that the choice of repository hosting sites and data formats influences citation patterns and dataset usability. Furthermore, the enhanced annotation approach improves the analysis and discoverability of MSR datasets, supporting more effective reuse and evaluation of research artifacts.
[IR-26] VisualLeakBench: Reproducible Action-Boundary Propagation Failures in Vision-Language Agents
链接: https://arxiv.org/abs/2606.07595
作者: Youting Wang,Yuan Tang,Yitian Qian,Chen Zhao
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:Vision-language agents increasingly consume screenshots, documents, and user interfaces before writing to memory, sending messages, or invoking external tools. We study a concrete failure mode in this setting: action-boundary propagation, where sensitive or unsafe visible text is copied from an image into downstream tool arguments. We present VisualLeakBench, a diversified 500-image benchmark spanning UI, chat, document, form, and dashboard scenes, and evaluate a stratified 100-image agent subset with four production VLM systems under two workflows: note capture and external handoff. At baseline, target strings are propagated into tool arguments in 78.8% of PII cases and 85.5% of rendered unsafe-text cases. Under a defensive system prompt, rendered unsafe-text propagation remains high at 52.6%, while PII tool propagation falls to 2.0%, largely by suppressing tool use rather than preserving utility. Rates are tool-surface dependent: search-like tools suppress PII propagation, but rendered unsafe text still crosses tool boundaries. We measure visual-to-tool propagation rather than downstream instruction execution. We additionally provide a labeled-target oracle upper-bound diagnostic that localizes most failures at the tool boundary while leaving response-side leakage as residual risk.
[IR-27] Evaluating Advanced Prompting on Gemini Flash for Multi-Hop Biomedical QA IJCAI2025
链接: https://arxiv.org/abs/2606.07548
作者: Ahmed Bajaber,Mohammed Alliheedi
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 8 pages, proceedings of the BioCreative IX Challenge and Workshop (BC9) at IJCAI 2025
Abstract:The MedHopQA challenge presents a critical test for Large Language Models (LLMs): complex, multi-hop reasoning in the high-stakes biomedical domain. This paper details our direct API-based evaluation of Google’s Gemini Flash models, focusing on the impact of advanced prompt engineering. We designed a sophisticated, multi-component prompt for Gemini 2.0 Flash that combined role-playing, explicit multi-shot Chain-of-Thought (CoT) examples, and detailed formatting rules. Our best run, using this complex prompt, achieved a Concept Level Score of 0.720. This result dramatically outperformed a baseline prompt which scored only 0.565. Remarkably, this performance on the efficient Gemini 2.0 Flash was almost identical to the result from the next-generation Gemini 2.5 Flash. Our findings demonstrate that sophisticated prompt design is a critical factor for unlocking the full reasoning capabilities of modern LLMs.
[IR-28] Beyond Item IDs: Scaling Short-Form-Video Recommendation via Semantic-Native Long Sequence Modeling SIGIR2026
链接: https://arxiv.org/abs/2606.07546
作者: Ruixiao Sun,Diego Uribe Mora,Zhimeng Jiang,Yuanzhen Lin,Jiarui Wang,Yuening Li,Danfeng Guo,Zhizhong Chen,Chuan He,Liang Liu
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: this manuscript has been accepted by SIGIR 2026
Abstract:Capturing user interests across extensive watch histories is critical for short-form video recommendation, yet scaling sequence length is limited by two bottlenecks: the semantic sparsity of atomic Video IDs and the quadratic computational complexity of Transformers. Traditional orthogonal Video IDs fail to capture content relationships and demand large embedding tables, while the quadratic complexity of self-attention restricts the maximum sequence length under strict industrial latency and resource constraints. In this work, we present a production-deployed framework for modeling ultra-long user behavior sequences at a billion-user scale. We first address the representation bottleneck by adopting content-native Semantic IDs. By utilizing depth-truncated, coarse-grained Semantic IDs, we shrink the embedding table size from corpus cardinality. This compact representation naturally generalizes to cold-start content through shared semantic prefixes. Second, to overcome the sequence scaling barrier, we introduce a Global-Aware Compression Transformer that leverages non-parametric temporal folding and unified global query integration to effectively condense the sequence, alleviating both the memory and computational bottlenecks of standard self-attention. Offline profiling on our computing infrastructure demonstrates an order-of-magnitude reduction in peak memory footprint and a drastic decrease in computational overhead. This efficiency gain enables supporting longer sequence lengths at an affordable cost in production, yielding substantial online gains in satisfied user engagement and satisfied content consumption in large-scale online A/B tests.
[IR-29] Bidirectional Semantic Complementary Tool Retrieval for Remote Sensing Agents
链接: https://arxiv.org/abs/2606.07538
作者: Zeyuan Wang,Dongyang Hou,Cheng Yang,Xuezhi Cui,Linrui Xu,Bo Yu,Gaozhi Zhou,Ziyu Li,Liangtian Liu,Kai Ouyang,Wang Guo,Lili Zhu,Chao Tao
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM)-based agents provide a novel paradigm for the automated processing of remote sensing(RS) data. Their success in complex RS tasks rely on extensive specialized tool libraries. However, tool documentation often exceeds the context window limits of LLMs, making precise tool retrieval essential for agentic workflows. Existing tool retrieval methods face “semantic asymmetry” bottleneck: natural language queries typically express macro-level intentions lacking tool-specific semantics, while tool documentation provides fine-grained technical descriptions lacking operational context for workflows. To bridge this semantic gap, this paper proposes a bidirectional semantic complementary tool retrieval method. First, on the query side, we introduce a planning-based query enhancement mechanism that leverages the reasoning capabilities of agents to decompose abstract intentions into logical subtasks, thereby actively supplementing the query with missing functional semantics. Second, on the tool side, addressing the strong coupling characteristics of RS tool chains, we construct a dynamic tool dependency graph with continual learning capabilities. By employing a neighborhood information aggregation mechanism, contextual information from precursor tools is explicitly injected into the current node representation, enriching tool descriptions with contextual semantics. Experimental results on the RS dataset GeoPlan-bench and the general-purpose dataset API- Bank demonstrate that the proposed method not only significantly improves tool retrieval accuracy for complex RS tasks but also exhibits robust extensibility for transfer to general-domain tasks. The source code and dataset are available at this https URL.
[IR-30] PulseBench-Tab: A Multilingual Benchmark for Table Extraction with Graph-Based Evaluation
链接: https://arxiv.org/abs/2606.07534
作者: Ritvik Pandey,Sid Manchkanti,Mohammed Wazir Adain,Mohammed Hadi,Dushyanth Sekhar
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注: 14 pages, 5 figures, 8 tables. Dataset: this https URL Code: this https URL
Abstract:We introduce PulseBench-Tab, an open multilingual benchmark for evaluating table extraction from document images. The benchmark comprises 1,820 human-annotated tables spanning 9 languages and 4 scripts (Latin, CJK, Arabic, Cyrillic), drawn from 380 real-world source documents including financial filings, government reports, and regulatory disclosures. Tables range from 2 to 1,183 cells, with 48.1% containing merged or spanning cells. Alongside the dataset, we propose T-LAG (Table Logical Adjacency Graph), a novel evaluation metric that models tables as directed graphs over cell adjacencies and computes structural and content fidelity in a single score via optimal bipartite matching. We evaluate 9 commercial and open-source table extraction systems across the benchmark and report per-language breakdowns. The full dataset, scoring code, and all provider outputs are publicly available.
人机交互
[HC-0] Cohort-based Semantic Labeling: AI-Enabled Recovery of Visualization Semantics from Deployed SVGs
链接: https://arxiv.org/abs/2606.09782
作者: Jeongah Lee,Hima Varshini Surisetty,Durga Nirmaleswaran,Jahnavi Sharma,Srikiran Kavuri,Narges Mahyar,Ali Sarvghad
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Many web-based visualizations are deployed as Scalable Vector Graphics (SVG), a format that faithfully preserves visual appearance but typically omits the higher-level semantic structure needed for machine interpretation. Once rendered and published, information about a visualization’s components, roles, and encodings is no longer explicitly available, limiting downstream operations such as querying, accessibility augmentation, explanation, personalization, and transformation. To address this gap, we introduce CSL, an AI-enabled, multi-stage pipeline for automatically recovering visualization semantics from deployed SVGs through two complementary mechanisms: (1) cohort-based decomposition, which organizes heterogeneous SVG primitives into structurally coherent subsets that reduce the semantic assignment space, and (2) hybrid semantic grounding, which combines model-based inference with deterministic structural validation and propagation to make labeling both context-sensitive and structurally anchored. CSL produces Semantic SVG (SSVG), a representation in which SVG elements are annotated with graphical mark type, visualization role, and data role. We implemented CSL as an end-to-end prototype and evaluated it on 102 SVG visualizations, achieving global macro-averaged accuracies of 0.822 for mark type, 0.853 for visualization role, and 0.860 for data-role recovery. An ablation against a non-cohort whole-chart baseline showed that cohorting significantly improves accuracy (paired t-test: t 20, p 0.001; Cohen’s d 2.0), and repeated labeling of a randomly selected SVG over 100 runs yielded mean agreement above 91.9% across all three attributes. These results provide strong evidence that CSL can transform deployed SVGs into machine-usable semantic representations, enabling more accessible, adaptive, and user-steerable visualization systems.
[HC-1] Collaborative Human-Agent Protocol (CHAP)
链接: https://arxiv.org/abs/2606.09751
作者: Arsalan Shahid,Gordon Suttie,Philip Black
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注:
Abstract:Foundation models are moving from response generation into operational roles. They plan across steps, call tools, request human input, coordinate with other agents, and increasingly carry responsibility for work that affects customers, claims, code, contracts, and clinical decisions. Production deployments are no longer one human supervising one model. They are multi-human, multi-agent collaborations that cross teams, time zones, and trust boundaries. The technical surface for this collaboration remains weakly specified. When an agent drafts a response and a human edits it before it ships, the moment of human judgement is the most valuable signal in the system. In current practice it is recorded, if at all, in application code, chat threads, ticket comments, and tribal memory. Two protocol standards address adjacent concerns: MCP standardises agent access to tools and data, and A2A standardises agent-to-agent interoperability. Neither defines the shared workspace in which humans and agents perform accountable work together. This paper presents CHAP, the Collaborative Human-Agent Protocol. Under CHAP, the override that used to vanish into a chat thread becomes a structured event carrying a diff, a rationale, and a content hash. The handoff between shifts becomes a portable envelope rather than a pinned message. The human approval of an agent’s draft becomes a non-repudiable signed decision that can be replayed years later. The protocol achieves this through a small Core (workspaces, participants, tasks, artefacts, and an append-only evidence log) together with composable profiles that add review, modes, routing, deliberation, handoff, identity, signatures, and transparency-backed audit as deployments require them. Specification, reference implementation, conformance suite, and worked examples are available at: this https URL
[HC-2] What the Eyes See the LLM s Miss: Exploiting Human Perception for Adversarial Text Attacks USENIX-SECURITY2026
链接: https://arxiv.org/abs/2606.09700
作者: Qin Yang,Lu Malloy,Joshua Lee,Xiaohan Chang,Meisam Mohammady,Doowon Kim,Yuan Hong
类目: Cryptography and Security (cs.CR); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注: This work has been accepted for publication at USENIX Security 2026. This paper includes examples of harmful, hateful, or abusive language for research purposes. Reader discretion is advised
Abstract:Large language model (LLM)-powered content moderation systems have become a critical defense against harmful online content. However, these systems primarily operate on tokenized text and largely ignore the visual cues that humans naturally rely on when interpreting content. We show that this discrepancy creates a fundamental perceptual mismatch: content that is readily recognized as harmful by humans can become effectively invisible to automated moderation systems. To study this vulnerability, we introduce a class of Human-Perceptible Adversarial Attacks (HPAA), in which harmful expressions are embedded into otherwise benign text through visually salient typographic manipulations. Our key insight is that typographic features, including spacing, visual emphasis, and spatial arrangement, can be strategically combined to preserve human recognition of harmful content while substantially reducing machine detectability. Operating in black-box settings with only a small query budget, our attack automatically generates evasive content without requiring model access or gradient information. We evaluate the attack across multiple datasets and ten deployed moderation systems, including commercial APIs and state-of-the-art open-source guardrails. Results reveal a striking gap between human and machine perception: with only three detector queries, generated attacks achieve over 86% human recognition while maintaining detection rates below 1% across the evaluated systems. We further conduct ablation studies to identify the typographic factors driving successful evasion, analyze why current moderation architectures fail to capture these signals, and discuss practical defenses. Our findings expose a fundamental blind spot in today’s LLM-based moderation ecosystem and highlight need for moderation systems that reason about content in a manner more consistent with human perceptual understanding.
[HC-3] Seeing the Hivemind: A Consensus-Aware Interaction Technique for Mitigating AI Homogenization
链接: https://arxiv.org/abs/2606.09587
作者: Muhammad Haris Khan,Joel wester
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: In review
Abstract:People are increasingly using AI for creative tasks such as writing. While adoption continues to grow, this form of use risks undermining individual creativity locally and reducing the heterogeneity of creative output at scale. In response, we introduce the Semantic Repulsion Technique (SRT) and evaluate it both computationally and through a study with 16 participants who regularly use AI for creative tasks. Our computational assessment reveals that SRT increases semantic diversity by 85–167% while reducing consensus phrases by 43–95% across task modes. In the user study, SRT outputs received higher usefulness ( p = .019 , W = .208 ) and coherence ratings ( p = .006 , W = .260 ); 68.8% of participants were willing to use SRT-Strong for multiple tasks versus 18.8% for baselines. Originality and coherence ratings were positively correlated across all systems ( \rho = +.40 to +.67 ), suggesting that divergence need not compromise readability. Taken together, these preliminary findings can inform the design of AI systems that aim to support everyday creativity without contributing to homogenization.
[HC-4] UXBench: Benchmarking User Experience in AI Assistants
链接: https://arxiv.org/abs/2606.09570
作者: Mengze Hong,Xia Zeng,Zeyang Lei,Sheng Wang,Chen Jason Zhang,Di Jiang,Taiming Fu,Jinfeng Huang,Mengqiao Liu,Qinghe Chang,Haosheng Zou,Qiongyi Zhou,Sijun He,Chen Xiaoshuai,Simon Deng,Haojing Huang,Zijian Li,Lucas Mu Li,Fubao Zhang,Mona Zhou,Wei Ma,Chenxuan Ma,Yuanmeng Zhang,Jian Song,Minlong Peng,Di Liang,Davey Chen
类目: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注:
Abstract:As AI assistants serve millions of users daily, evaluating user experience (UX) beyond general model capability has become increasingly important. We present UXBench, the first user-centric benchmark grounded in real user feedback signals for evaluating preference alignment and dialogue generation. The benchmark consists of three interconnected tasks, UX Judge, UX Eval, and UX Recovery, with 7,400 test instances extracted from over 70K interaction logs of a mainstream Chinese AI assistant. The dataset closely reflects real user distributions, covering 8 scenarios, 83 domains, and diverse failure patterns that pose severe challenges. Extensive experiments on 26 frontier language models provide novel insights into how well models perceive user experience and how improvements in model capability contribute to better dialogue engagement. Through comprehensive analysis of model behavior and performance gaps, we show that user feedback prediction is a learnable capability, where a reward model trained from in-the-wild feedback signals can achieve well-calibrated accuracy. We further document the systematic biases of LLM-as-a-judge evaluation protocols and compare typical response strategies that directly affect user experience. UXBench establishes a new evaluation landscape and calls for greater attention to tailored UX optimization, contributing to a user-centric scaling law that shapes the success of AI assistants.
[HC-5] AI Assurance in UK Defence: Challenges in Operationalising JSP 936
链接: https://arxiv.org/abs/2606.09414
作者: Callum Cockburn,Sam Farrow
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:This report examines practical challenges in operationalising JSP 936 Part 1 for AI assurance in UK Defence. Using a structured interpretive review of the directive’s requirements, the analysis identifies eight thematic challenge areas adequacy of evidence and argument, management of human interaction with AI, definition of the operational environment, integration of AI within systems of systems, assessment and maintenance of AI performance, analysis of safety and security, measurement of ethicality, and mitigation of the inherent complexities of AI. The report argues that JSP 936 provides a useful governance basis, but that implementation depends on unresolved technical, organisational, and assurance questions. These challenges stem from the socio-technical nature of AI-enabled systems, uncertainty in real-world deployment contexts, limitations in current assurance methodologies, and tensions between performance, safety, human oversight, security, and ethical acceptability. The report identifies areas where further methods, guidance, and organisational capability are needed for the ambitious, safe, and responsible adoption of AI across Defence. This is consistent with MOD’s own framing of JSP 936 as requiring iterative implementation and supporting guidance.
[HC-6] Can Data Work be Reparative?
链接: https://arxiv.org/abs/2606.09408
作者: Srravya Chandhiramowuli,Ding Wang,Alex Taylor
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: To be presented at ACM FAccT, Montréal, Canada, June 25 to June 28, 2026
Abstract:We present an ethnographic study of an alternative approach to data work, developed by a civic-tech initiative that builds datasets for training and benchmarking online safety systems. They aim to respond to online safety concerns from a feminist perspective, by building safety datasets collaboratively with those most impacted by online harms. In this paper, we examine how this approach aims to reorient data work as a site for repair and redress, and trace the struggles they encounter in the process. Specifically, we draw attention to the challenges and tensions involved in advancing just reward for data work and collective governance of AI datasets. Examining these challenges through an STS-informed lens of reparative justice and repair, we argue that the work of repairing data work (and AI) lies, fundamentally, in resetting the ties of accountability. At a time heightened emphasis on efforts like safety evaluations and red teaming to make AI more responsible, we highlight the need to confront foundational questions about how the humans involved in these efforts relate to the datasets and systems they help produce. A reparative lens demands that we interrupt prevailing norms of data work and place at their centre, not AI or datasets, but those most harmed by the neglect, oversight and exclusion animated in the current modes of dataset production. This, we argue, offers a bold vision for responsibility and contributes towards a critical agenda for building alternative futures of data and AI practice.
[HC-7] Conceptualising Reflective Use: Toward A Process Perspective On Human-AI Interaction
链接: https://arxiv.org/abs/2606.09242
作者: Thimo Schulz,Christina Speck
类目: Human-Computer Interaction (cs.HC)
备注: 8 pages, 2 figures, 1 table, published in ECIS 2026 Proceedings
Abstract:The rapid diffusion of generative artificial intelligence (genAI) systems reshapes how individuals engage with information systems, requiring users to monitor, assess, and adapt their interaction with non-deterministic systems. Existing constructs capture elements of this engagement but do not account for the situated dynamics of the entire evaluative process in genAI use. This research-in-progress, situated in a larger endeavour towards a scale development, derives an initial conceptualisation of reflective use: a behavioural-knowledge capability that unfolds across pre-use, in-use, and post-use phases, reinforced through situated reflective knowledge gained in practice. Drawing on expert interviews and a focus group, we identify four core components of reflective use and show how they form an iterative capability cycle anchored within the motivational needs outlined in self-determination theory. Understanding reflective use is essential to ensure appropriate reliance and high decision quality, and thus provides a foundation for promoting responsible and effective human-AI interaction.
[HC-8] Orange Lab: Lowering Barriers to Data Mining through Embedded Interactive Workflows
链接: https://arxiv.org/abs/2606.09239
作者: Matej Bevec,Aleš Erjavec,Vesna Tanko,Lena Trnovec,Lan Žagar,Ana Farič,Janez Demšar,Blaž Zupan
类目: Machine Learning (cs.LG); Human-Computer Interaction (cs.HC)
备注:
Abstract:While visual programming of data analysis workflows has become an important vehicle for the democratization of data science, such systems remain largely confined to standalone applications and offer limited support for transitioning their visual analytics solutions into interactive web environments. As a result, data analysis pipelines are difficult to share, embed, and adapt into user-facing analytical tools. We present Orange Lab, a web-based collaborative environment for visual data analytics. At its core, Orange Lab enables users to visually construct machine learning workflows from modular components, where interactions in any component propagate seamlessly through the workflow, turning static pipelines into dynamic, reactive systems that support exploration and data-driven storytelling. Our key contribution is component exposition, a paradigm that allows authors to embed selected workflow components, or parts of their interfaces, into arbitrary web contexts, creating synchronized, interactive interfaces while hiding underlying workflow complexity. This enables the development of tailored analytical views and narrative-driven experiences that integrate data analysis directly into online materials. We demonstrate the approach through deployments in data literacy education, where embedded components guide students in hands-on exploration of machine learning concepts without requiring knowledge of the underlying system, showing that Orange Lab effectively lowers barriers to entry and supports the democratization of data science.
[HC-9] rustworthy Smart Fabs via Professional Proxies: Scaling Safe and Sustainable by Design (SSbD) through Industrial Data Spaces
链接: https://arxiv.org/abs/2606.09227
作者: Han-Teng Liao,Chang-Yi Kao,Karen Ang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Social and Information Networks (cs.SI)
备注: This work was accepted for presentation at the 32nd IEEE ICE/ITMC Conference, Porto, Portugal, 2026 but was subsequently withdrawn prior to publication due to submission volume limits. It is currently under consideration for publication elsewhere
Abstract:The convergence of the 2026 European Union Safe and Sustainable by Design (SSbD) framework, Corporate Sustainability Due Diligence Directive (CSDDD), and Carbon Border Adjustment Mechanism (CBAM) introduce a severe governance bottleneck for advanced semiconductor manufacturing facilities (“Smart Fabs”). Regulatory compliance demands have surpassed the capacity of manual corporate reporting, creating a direct conflict between multi-stakeholder transparency and corporate data privacy. This paper addresses this challenge by introducing a zero-trust socio-technical orchestration framework that operationalizes a six-layer SSbD reference architecture within trustworthy industrial data spaces. We propose a shift from reactive automation to autonomous governance through “Professional Proxies”-role-based agentic workflows executing within hardware-isolated trust zones. Structured as an interoperable network protocol stack, the framework coordinates an automated, five-step “relay race” between Facility, Process Engineering, and Finance proxy teams to align factory-floor yield models with macro-level sustainability mandates. By executing Virtual Metrology (VM) predictions and Federated Machine Learning (FML) inside hardware-rooted Trusted Execution Environments (TEEs), this architecture resolves the Data Sovereignty Paradox, demonstrating how fabs can export cryptographically signed compliance tokens via International Data Spaces (IDS) connectors without exposing proprietary process recipes. Ultimately, this framework provides technology managers with a verifiable, evidence-based pathway toward resilient, net-zero Industry 5.0 ecosystems.
[HC-10] DuplexOmni: Real-Time Listening Seeing Thinking and Speaking for Full-Duplex Interaction
链接: https://arxiv.org/abs/2606.09186
作者: Muye Huang,Lingling Zhang,Xingyu Yu,Lei Shi,Zhanyu Ma,Jun Xu,Jiuchong Gao,Jinghua Hao,Renqing He,Jun Liu
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Human interaction is continuous, multimodal, and full-duplex by nature. Although recent omni models have made substantial progress in unified speech, vision, and text modeling, combining seamless real-time interaction with complex reasoning and tool use remains challenging. We present DuplexOmni, a method for real-time multimodal full-duplex interaction. DuplexOmni separates model capability into an interaction layer and a thinking layer, which collaborate asynchronously in parallel. The interaction layer is implemented by the DuplexOmni model, an end-to-end system that processes streaming audio and video inputs while generating text and speech responses in real time. The thinking layer is a pluggable module that provides complex reasoning and tool-use capabilities. To support this method, we further develop a Writer-Director pipeline for constructing continuous-interaction training data. Experiments show that DuplexOmni achieves strong performance on multiple public benchmarks and exhibits natural full-duplex interaction ability.
[HC-11] Demonstrating chart-plot: Closing the Last Mile of Academic Chart Generation
链接: https://arxiv.org/abs/2606.09174
作者: Yinghao Tang,Yupeng Xie,Yingchaojie Feng,Jiale Lao,Tingfeng Lan,Wei Chen
类目: Human-Computer Interaction (cs.HC)
备注: 7 pages, 6 figures. Demonstration paper for ADS 2026: The Joint Workshop on Agentic Data Systems and Data-Centric AI
Abstract:Large language models can translate a researcher’s intent into runnable matplotlib code, yet the resulting chart rarely lands in a paper without multiple rounds of manual revision. We argue that the open problem is not chart code generation but chart publication: making the output look like a top-venue figure, survive the target layout, and respond to precise author edits. We present chart-plot, an agentic harness that closes this last mile through three components: (1) a style-aware code generator conditioned on a textual style skill distilled from accepted figures at the target venue, (2) a deployment-aware render loop that compiles the chart inside the target LaTeX context and revises until layout constraints are met, and (3) a structured edit layer that exposes every chart element as a directly manipulable handle. We report early results on three chart-type case studies (grouped bar, scaling line, paired distributions) and a small user study.
[HC-12] sketch-plot: Progressive Editing for Text-to-Image Academic Figures
链接: https://arxiv.org/abs/2606.09171
作者: Yinghao Tang,Yupeng Xie,Yingchaojie Feng,Tingfeng Lan,Wei Chen
类目: Human-Computer Interaction (cs.HC)
备注: 5 pages, 3 figures. Demonstration paper
Abstract:Text to image (T2I) models such as gpt-image-2 can now generate publication grade academic figures from a short prompt, but the output is a flat raster: a user who wants to change one arrow, one label, or one icon has to regenerate the whole image, which also disturbs the parts they wanted to keep. We present sketch-plot, an interactive system that closes this controllability gap with a three layer progressive editing pipeline: a generated PNG, an addressable puzzle of editable pieces, and a per piece SVG. The user stops at the layer that gives them enough control for the change at hand, so the cost of decomposition and vectorisation is paid only on the pieces that need it. Realising this pipeline is not trivial. General segmentation models lack the semantic discriminability to decompose a research figure cleanly, and end to end image vectorisation produces incomplete shapes and loses semantic structure. We therefore route both stages through a human in the loop interface that lets the user accept, refine, or reject decomposition and vectorisation decisions on a piece by piece basis. We validate the design with an expert user study, in which participants found sketch-plot effective for making targeted edits to AI generated academic figures and preferred it over regenerating the whole image. A demonstration video is available at this https URL.
[HC-13] Before You Scroll Again: Predicting Regretful Social Media Sessions from In-the-Wild Contextual and Wearable Sensing
链接: https://arxiv.org/abs/2606.08965
作者: Sally Ahmed,Jan Enkmann,Kye Shimizu,Ivy Yip,Vincent Beermann,Ayse Alomar,Falk Uebernickel,Pattie Maes
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Users often feel regret after using social media, making regret a more ecologically valid target than screen time for understanding when phone use becomes problematic. Existing self-monitoring tools cannot anticipate regret before it occurs, and prior physiological work on social media use has been confined to the lab with research-grade sensors and curated content, leaving the question of in-the-wild prediction open. We deployed a 7-day in-the-wild experience sampling study with 21 participants, combining passive smartphone logging, a low-cost consumer smartwatch (this http URL 2, \ 80), session-level surveys (1,445 sessions), and exit interviews to investigate when and why social media sessions become regretful, and whether regret can be anticipated before a session begins. Three findings stand out: (i) the gap between intended and actual use predicts regret far more strongly than session duration, with duration’s apparent effect collapsing once intention is modeled; (ii) regret is amplified when sessions displace a valued alternative, particularly at night and following productivity-app use; and (iii) pre-session contextual features generalize across participants while physiological signals add person-specific lift, pointing toward a two-layer architecture for just-in-time adaptive interventions. Interview themes of scrolling-as-avoidance and time blindness contextualize these patterns and surface design opportunities beyond timer-based interventions.
[HC-14] In-Situ Immersive Analytics Authoring through Ergonomic Keyboard Support
链接: https://arxiv.org/abs/2606.08927
作者: Leonel Merino,Begoña Juliá-Nehme,Santiago Viana
类目: Human-Computer Interaction (cs.HC)
备注: 31 pages, 7 tables, 5 figures
Abstract:Immersive analytics uses augmented reality (AR) to integrate data analysis and authoring within physical environments. However, extensive text entry required for immersive analytics authoring remains a fundamental challenge in AR, as popular natural user interfaces often hinder expressive input. This paper presents the Body-Supported Keyboard (BSK), an ergonomic system that allows the mobile use of a Bluetooth keyboard in AR. We conducted a controlled study with 20 participants to compare the BSK with a standing desk during text transcription and a mobile AR scenario. The results showed slightly higher error rates but comparable task completion times. Participants reported comfort improvements during mobile use and positive usability ratings (mean SUS = 74.5). The BSK allows users to move freely and maintain stable postures while authoring in AR. In general, the findings show evidence of the potential for body-supported input to enhance expressive and ergonomic workflows in immersive analytics and emphasize the importance of comfort and mobility in the design of AR authoring tools.
[HC-15] Vibe Visualizing: How Visualization Novices Try (and Fail) to Generate and Interpret Visualizations with Conversational AI
链接: https://arxiv.org/abs/2606.08914
作者: Sam Yu-Te Lee,Yun-Hsin Kuo,Chifang Chou,Matthew Ward,Xiwei Xuan,Kwan-Liu Ma
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Conversational AI has enabled users to generate and interpret visualizations through natural language, significantly lowering the technical barrier to entry. The increased accessibility brings visualization novices into data visualization, but also exposes them to misinformation and misinterpretations. We are motivated to examine what issues can arise in interactions with current conversational AI, whether visualization novices can recognize such issues, and how they respond to them. To examine these questions, we conducted a user study on ChatGPT with 20 visualization novices, collecting their conversation logs, semi-structured interview transcripts, and Likert-scale questionnaire responses. Through thematic analysis, we developed a codebook that covers AI execution compliance, issues of AI-generated visualizations, patterns of AI responses, and prompting patterns of users. We summarized four themes, including the quality of outcomes, recurring errors from ChatGPT, misuse by users, factors that affect user trust, confidence, and verification behavior, and human-AI collaboration dynamics. To demonstrate the generalizability of our codebook and findings, we replayed the initial user prompts on Gemini and Claude and compared the outcomes, which revealed distinct failure modes for each model. Based on the results of all analyses, we derive a set of design recommendations for future AI-assisted visualization systems. We conclude with discussions on literacy gaps, diverse human-AI collaboration dynamics, and implications for agentic visualization.
[HC-16] Enhancing Presence Deepening Fan Intensity: How Presence in Immersive Video Shapes Psychological Closeness to Performers
链接: https://arxiv.org/abs/2606.08912
作者: Koichi Toida,Hideto Hiranuma,Shimpei Miura,Norihiro Yamamoto,Yuki Kobayashi,Shingo Meguro
类目: Human-Computer Interaction (cs.HC)
备注: 20 pages, including 6 pages of supplementary materials; 10 figures, 2 tables
Abstract:Immersive video differs from conventional flat 2D video in that it is experienced as 180-degree stereoscopic video on a head-mounted display, thereby eliciting bodily and spatial subjective experience. Previous studies have shown that viewing and interpersonal distance affect Presence; however, it remains insufficiently understood how Presence differences are related to psychological closeness to content. In the present study, we examined whether differences in Presence could increase viewers’ psychological closeness to performers within the content. This psychological closeness was operationally defined as fan intensity. Specifically, a live performance by a Japanese idol group was recorded as 180-degree immersive video, and a high-Presence condition (1.2 m) and a low-Presence condition (7.6 m) were established by manipulating filming distance. Twenty-four participants with different levels of prior involvement, comprising Avid fans and Casual fans, experienced both conditions in a counterbalanced within-participants design. Fan intensity was measured before and after the experience as perceived psychological overlap between the self and the performers. The results showed that, compared with the low-Presence condition, the high-Presence condition significantly increased all Presence-related measures except the Slater-Usoh-Steed questionnaire, with the largest condition differences observed for Possible Actions, Social Presence, and Observability. Moreover, a mixed analysis of variance on changes in fan intensity revealed a significant main effect of Presence condition, indicating that the high-Presence video produced a greater increase in fan intensity than the low-Presence video. These findings suggest that filming distance in immersive video is not merely a factor that determines angle of view or composition, but a design variable that can enhance Presence and deepen fan intensity.
[HC-17] Distilling LLM Reasoning into an Interpretable Policy Tree for Human-AI Collaboration
链接: https://arxiv.org/abs/2606.08596
作者: Beiwen Zhang,Yongheng Liang,Guowei Zou,Haitao Wang,Hejun Wu
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Constructing efficient and reliable policies to assist humans is indispensable for human-AI collaboration. Existing methods mainly follow two lines of work. Most prior work relies on multi-agent reinforcement learning (MARL) to learn black-box policies, which limits interpretability and raises safety concerns. Recent methods query large language models (LLMs) at each decision step, causing slow responses and high inference costs. We propose Collaboration Policy Tree (Co-pi-tree), a closed-loop method that learns an executable policy tree consisting of a partner-behavior prediction tree and an agent-action selection tree. Co-pi-tree constructs a policy by distilling LLM reasoning into policy tree code. It then evaluates the policy through partner interaction, obtains feedback, and uses natural language to summarize the interaction feedback to improve problematic branches. Experiments in Overcooked-AI show that Co-pi-tree improves average reward by 35.4% over the baseline average, while reducing the number of LLM queries by 77.7% and test-time latency by 97.1%. Project page: this https URL
[HC-18] Comparing Controller-Free Pointing Techniques Across Depth for 2D Selection in Augmented Reality
链接: https://arxiv.org/abs/2606.08441
作者: Samiha Sultana,J. Felipe Gonzalez,Robert J. Teather
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:This paper presents a systematic evaluation of five controller-free pointing techniques for 2D target selection in AR, using ISO 9241-411. We compared them across multiple depths (2 m, 6 m, 10 m) in terms of movement time, accuracy, throughput, and workload (NASA TLX). Head- and eye-based pointing significantly outperformed the hand-based methods (Finger, Wrist, and Arm); Head input was the most accurate and remained the most consistent across depth. Depth significantly impacted performance, with complex interactions with target size and distance. Our results offer a comprehensive empirical basis for selecting appropriate controller-free techniques in depth-varying AR tasks.
[HC-19] CritLens: Visual Analytics for Criteria Discovery in Review-Based Decision Making
链接: https://arxiv.org/abs/2606.08426
作者: Hongjia Wu,Shuai Zhou,Hongxin Zhang,Wei Chen
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:We present CritLens, a visual analytics system that helps users build personalized multi-criteria decision models from review text. In everyday decisions – choosing equipment, hotels, or restaurants – evaluation criteria are either preset by platforms or generated by LLMs, leaving users unable to discover, adjust, or verify them against the underlying evidence. This is problematic because many preferences are latent: they surface only upon encountering specific reviews, and any fixed framework risks overlooking low-frequency but decisive details. CritLens addresses this gap by using LLMs to transform reviews into an initial AHP decision model, then supporting iterative, human-in-the-loop refinement. Through coverage gap detection in the embedding space, users discover criteria missed by the initial model; through interactive weight adjustment under AHP consistency constraints, they express personal priorities; and through a multi-level scorecard and exportable decision report, they trace every ranking back to the original review text. Two case studies, an eight-participant user study, and a quantitative consistency-repair experiment demonstrate the system’s effectiveness.
[HC-20] Beyond Prediction: Longitudinal Reasoning in EHR-Integrated Clinical AI
链接: https://arxiv.org/abs/2606.08413
作者: Irene Yi,Grace Brown,Sufian Aldogom,Nathan Roll,Eric J. Basile,Pamela M. Resnikoff,Isaac Gutterman,Oscar Schiff,Keira Salata,Benjamin Mujkic,Ammar Ahmed
类目: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注:
Abstract:We present a structured analysis of how contemporary clinical AI systems integrate electronic health record (EHR) data and the extent to which they support longitudinal clinical reasoning. Drawing on a curated corpus of clinical natural language processing (NLP) and EHR-integrated systems, we develop a coding framework that captures both technical integration strategies and reasoning-relevant representational features, such as trajectory modeling, cross-encounter synthesis, longitudinal analysis, and absence reasoning. We also elicited the experiences of three physicians in their EHR use, including what strengths and weaknesses they found with their institution’s current EHR system(s). Our analysis shows that while many systems incorporate EHR data, they predominantly operate on encounter-level or aggregated representations, with limited support for explicit temporal reasoning across patient histories. Reasoning-relevant structures are inconsistently represented, and evaluation paradigms remain largely focused on predictive performance instead of longitudinal interpretability. We argue that current approaches treat EHR data as a static input rather than a substrate for ongoing clinical reasoning, and we outline a framework for understanding how future systems might more effectively align with the temporal and interpretive structure of clinical practice.
[HC-21] “So Theres a Catch-22 Here”: How Early Adopters Who Build Multi-Agent LLM Systems Conceptualize Transparency
链接: https://arxiv.org/abs/2606.08323
作者: Suchismita Naik,Samir Passi,Mihaela Vorvoreanu,Scott Saponas,Amanda Hall
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-agent large language model (LLM) systems are rapidly emerging, yet transparency, a cornerstone of responsible AI, remains under-defined in these distributed architectures, which have complexities of inter-agent coordination and orchestration. In this paper, we present one of the first empirical study of how early adopters of multi-agent LLM systems, who are both the builders and users, understand and practice transparency. We conducted semi-structured interviews with 13 early adopters in [Large Technology Organization] and applied thematic analysis to identify recurring patterns. Participants articulated divergent yet complementary framings of transparency, including reproducibility, debugging, boundary-setting, visualization, and auditing. These perspectives spanned questions of what transparency entails, why it matters, and how it is achieved. We synthesize these into a multidimensional framework, which is developer, user, and governance-focused positioning transparency as a situated socio-technical practice that informs future HCI and AI design and research around aligning expectations and capacities of their intended audiences.
[HC-22] Exploring Above-neck Unimanual Swipe Gestures for Off-Device Earable Interaction
链接: https://arxiv.org/abs/2606.08198
作者: Shaikh Shawon Arefin Shimon,Ali Neshati,Junwei Sun,Qiang Xu,Jian Zhao
类目: Human-Computer Interaction (cs.HC); Emerging Technologies (cs.ET)
备注: To be published in Graphics Interface 2026 (Entry 1045a)
Abstract:Despite their growing popularity, in-ear Earable / Hearable devices (i.e., ear-mounted wearables) face interaction challenges due to limited input space and compact form factors. To enhance interaction capabilities, researchers are exploring off-device hand-based input spaces above the neck using midair and onskin gestures. However, existing literature primarily focuses on axial swipes (i.e., horizontal and vertical), leaving nonaxial swipes (i.e., unidirectional swipes with varied orientations) and angular swipes (e.g., L, U, or V) largely underexplored despite their potential interaction advantages. To address this gap, we conducted a within-subject gesture motion analysis study with 24 participants, analyzing 5,568 swipes of varying shape, orientation, and complexity. Our results revealed preferred starting and ending regions for different unidirectional and angular swipe shapes, as well as intuitive swipe shapes within the off-device, above-neck manual interaction space. We further examine off-device swipe characteristics, discuss the feasibility of recognizing these earable gestures with current sensing technologies, and highlight their potential application in various scenarios. These findings broaden the understanding of off-device earable gestures and provide design insights for integrating suitable nonaxial and angular swipes alongside traditional axial gestures to enhance interaction with in-ear earable devices.
[HC-23] he Governance of Human-LLM Interaction: Safety Gating Civility Steering and Affective Default Lock-In
链接: https://arxiv.org/abs/2606.08172
作者: Manuele Reani,Hongjian Zhang,Hongyu Tian
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Large language models (LLMs) increasingly mediate high-stakes interactions in finance, medicine, and mental-health support, yet users have limited control over how these systems communicate. We frame interaction style as a governance object: provider-side alignment not only blocks harmful content, but also stabilizes communicative defaults that shape users’ epistemic distance, relational expectations, and capacity to opt out of emotionalized or anthropomorphic interaction. We introduce a deterministic multi-agent evaluation pipeline for measuring prompt steerability and style drift in long-horizon dialogue. The study replays 100 frozen user-only scripts across four domains and three runnable persona conditions: default, sarcastic, and cold, using three generator models, yielding 90,000 assistant replies scored by a human-calibrated LLM judge on harmfulness, negative emotion, inappropriateness, empathic language, anthropomorphism, and refusal behavior. A fourth harmful persona is evaluated separately as a safety-gating test. The paper contributes a reproducible method for quantifying whether prompt-specified styles remain stable over time and a governance framework distinguishing safety gating, civility steering, and affective default lock-in. Overall, we show that prompt steerability and regression-to-default are observable indicators of provider control over communicative form, with implications for pluralism, autonomy, and democratic agency in human-LLM interaction.
[HC-24] CLASP: Language-Driven Robot Skill Selection and Composition using Task-Parameterized Learning
链接: https://arxiv.org/abs/2606.08169
作者: Markus Knauer,Valentin Gieraths,Tai Mai,Samuel Bustamante,Alin Albu-Schäffer,Freek Stulp,João Silvério
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注: 23 pages, 11 figues, 4 tables, 1 listing
Abstract:Enabling robots to understand and execute tasks from natural language commands while maintaining data efficiency remains challenging. Foundation models such as vision-language-action (VLA) and vision-language models (VLMs) provide intuitive interaction channels but require extensive data; task-parameterized imitation learning achieves data efficiency but lacks natural language grounding. This work bridges this gap through a modular architecture combining task-parameterized kernelized movement primitives (TP-KMPs) with pretrained VLMs. During learning, skills are acquired from 2 to 5 kinesthetic demonstrations, and the VLM generates skill schemas describing each skill’s parameters and preconditions. During execution, the VLM interprets commands to select skills, reason about parameter bindings, and create novel behaviors through covariance-weighted composition. When no skill or composition suffices, the system identifies capability gaps and requests targeted demonstrations, all without fine-tuning. Validation on a 7-DoF manipulator shows success rates of 73.3%-100% in scenarios requiring skill selection, composition, and active learning.
[HC-25] LCAM: A Framework for Diagnosing Interactional Alignment Failures in Con-versational AI
链接: https://arxiv.org/abs/2606.08131
作者: Manuele Reani,Hongyu Tian
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:Conversational AI is increasingly used for advice, interpretation, reassurance, and decision support in contexts where users may be vulnerable, uncertain, or dependent on the system’s apparent competence. Existing alignment work often focuses on model objectives, preference optimization, or output correctness. Yet, many harms arise through interaction: how systems frame authority, express uncertainty, simulate empathy, support reasoning, and make boundaries legible. This paper introduces the Layered Cognitive Alignment Model (LCAM), a conceptual and normative framework for diagnosing interac-tional alignment failures in conversational AI. LCAM defines alignment as a calibrated fit among system behavior, user goals, task demands, and normative context. It distinguishes five layers of fit: perceptual, semantic, affective, cognitive, and ethical, and two diagnostic polarities of misalignment: underfit and overreach. We apply LCAM to a published LLM counseling example, showing how an apparently supportive response can reinforce harmful beliefs, simulate inappropriate care, and obscure role boundaries. By translating conversational failures into audit and governance questions concerning over-reliance, false intimacy, autonomy erosion, boundary confusion, and inappropriate trust, LCAM offers a theoretical and normative lens for evaluating conversational AI beyond accuracy, helpfulness, or trust.
[HC-26] How to be Non-Human : A Thematic Analysis of Animal Embodiment in VR Games
链接: https://arxiv.org/abs/2606.08130
作者: Siqi Yu,Shuai Liu,Yiqing Tian,Mar Canet Sola
类目: Human-Computer Interaction (cs.HC)
备注: 21pages,9 figures, Digra 2026
Abstract:This study employs a reflexive thematic analysis to systematically examine the design patterns of 48 first-person Virtual reality (VR) animal avatar games. The research identifies four primary design themes: Animal Biomimicry, Limited Animal Simulation, Hybrid HumanAnimal Features, and Human Behavior with Animal Avatar. The analysis reveals that approximately 77 percent of the games remain grounded in human-centered interaction logic, with animal forms primarily serving as visual representations. The study highlights the core tension between authenticity and usability in current VR animal avatar design, and points toward design opportunities for achieving more authentic animal avatar’s interactive experience through directions such as controller innovation, unconventional body mapping, and dynamic feedback. This research provides a thematic classification framework for understanding the representation of non-human perspectives in VR games.
[HC-27] Automatic Real-time Classification of User Feedback Using Large Language Models
链接: https://arxiv.org/abs/2606.08050
作者: Jim Maddock,Rose Leitner,Anna Wu
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:In this paper we discuss an ongoing multi-year project that aims to make open text feedback more accessible and useful to UX practitioners by automating classification and providing real time access to comments, themes, and analysis. By significantly lowering the time and knowledge cost of implementing automated solutions, we aim to effectively democratize our data analysis processes, allowing and encouraging non-technical stakeholders to access and leverage data on their own. We share both the organizational and technical constraints we have encountered over the course of this project, and the solutions we have prototyped as a result of those constraints.
[HC-28] he AI Epistemic Deference Index: A Continuous Measure of Sycophancy
链接: https://arxiv.org/abs/2606.07897
作者: Alejandro Botas,Paul de Font-Reaulx,Luke Hewitt
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Current AI models frequently exhibit epistemic sycophancy, endorsing claims to agree with a user. Existing evaluations typically measure this either by assessing what it takes to make a model shift a binary endorsement or by eliciting an explicit probability in a proposition. However, much user-facing sycophantic behavior is demonstrated through shifts in graded support expressed through ordinary language. We propose the AI Epistemic Deference Index (AEDI): a continuous, unidimensional score representing how sensitive the support expressed in a model’s output is to the attitude expressed in a user’s prompt. To generate AEDI, we provide a new protocol for estimating probabilities from natural language outputs, using LLMs-as-judges validated for consistency and correlation to human judgment. We deploy it on a new curated database of 500 propositions across diverse topics and 16,000 prompts varying in user attitude, testing eight prominent models. Every model exhibits substantial deference, though with large and systematic differences across providers, with Claude models demonstrating the least, and Grok and Gemini models the most. The effect is amplified in prompts requesting a written artifact, and concentrated on propositions where models hold weaker priors. We release AEDI as an easy-to-update benchmark and measurement pipeline for output-level sycophancy evaluation.
[HC-29] Does Persona Make LLM s K-pop Fans? A Pilot Study of LLM -Based Online Concert Audience Agents ICML2026
链接: https://arxiv.org/abs/2606.07837
作者: Kirak Kim,Hyojin Kim,Yejin Son,Sungyoung Kim,Kyung Myun Lee
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: Accepted at the ICML 2026 Workshop on Culture x AI: Evaluating AI as a Cultural Technology
Abstract:A concert is a collective experience, but recorded performance videos are typically watched alone, stripping away the shared audience presence that makes concerts feel eventful. We investigate whether persona-based LLM audience agents can recreate aspects of this collective experience by generating real-time fan chat alongside a K-pop performance video. We present a multi-agent system in which ten LLM agents react through live-chat messages, comparing a persona-conditioned audience (each agent assigned a distinct fan identity, bias, and chat style) with a no-persona baseline. In a within-subjects pilot with K-pop fans (N=11), persona conditioning substantially improved model-level chat quality and perceived naturalness, but did not translate into differences in social connectedness, engagement, or affective response. Interviews suggest that online K-pop concert chat may operate as collective monologue rather than interpersonal dialogue, and that meaningful participation depends on shared identification with the specific artist and fandom. Persona conditioning can make LLM audiences appear more natural, but culturally meaningful collective experience may require deeper alignment between persona, crowd behavior, fandom identity, and user expectations.
[HC-30] he Choreography of Augmented Reality Timelines: Studying the Relative Position Chronology Situatedness of Event Sequences
链接: https://arxiv.org/abs/2606.07794
作者: Isabelle Kwan,Jessica Ziyu Chen,Matthew Brehmer
类目: Human-Computer Interaction (cs.HC)
备注: In Proceedings of Graphics Interface 2026 this https URL
Abstract:Timelines are effective ways to tell historical and personal stories. However, most timeline visualization tools impose an inflexible model of time prioritizing chronological clarity. On the other hand, unconstrained representations can better capture the irregular and contextual nature of lived time, but often at the cost of interpretability. In this work, we explore this continuum with a study of how historical and personal timelines could manifest in physical spaces. We conducted a formative study (N=12) in which participants freely arranged events within a physical environment. We observed a diversity of strategies reflecting the personal and context-dependent nature of temporal mental models. We also invited participants to consider how others could move through their timelines. Our analysis led to a choreographic approach to timeline creation, as well as a proof-of-concept tablet-based augmented reality (AR) application that supports spatial timeline drawing and viewing. Finally, we reflect on the design implications of encoding chronology, pacing, and spatial context in immersive timeline stories.
[HC-31] Understanding Human and Interface Design Factors in Canadian Cybercrime Reporting
链接: https://arxiv.org/abs/2606.07773
作者: Charlotte Carr,Ananta Chowdhury,Asra Sakeen Wani,Sonia Chiasson
类目: Human-Computer Interaction (cs.HC)
备注: 17 pages, 12 figures
Abstract:Cybercrime affects a majority of Canadians, yet most incidents go unreported. We conducted two studies to examine the factors influencing cybercrime reporting and the role of interface design in victims’ reporting experiences. Our survey provides individual-level insights into the persistent gap in cybercrime reporting in Canada, showing how perceived incident severity and personal characteristics shape reporting behaviour. Our usability study compared reporting with an AI chatbot to an online form; chatbots facilitated more complete reports and led to higher user satisfaction, highlighting how interface design impacts reporting outcomes.
[HC-32] betCPR: A Multimodal Tactile Feedback System to Enhance Cardiopulmonary Resuscitation Training in High-Altitude Regions of Tibet
链接: https://arxiv.org/abs/2606.07765
作者: Yibo Meng,Ruiqi Chen,Zhiming Liu,Xiaolan Ding
类目: Human-Computer Interaction (cs.HC)
备注: Accept to MobileHCI 2026
Abstract:High-quality cardiopulmonary resuscitation (CPR) requires stable control of compression rhythm and depth, yet most training systems presuppose instructor mediation, repeated practice, and explanatory guidance-assumptions that do not hold in the Tibet Autonomous Region, where instruction is fragmented and learners’ linguistic and educational backgrounds are heterogeneous. We present TibetCPR, a low-cost, self-guided CPR training system that pairs depth-driven electrotactile feedback with rhythm-driven visual cues within a Tibetan-language narrative. In a randomised study with 40 lay community members aged 19–56, the experimental group showed progressive minute-by-minute stabilisation of rhythm and depth across a 10-minute intervention, substantially exceeding an unguided-practice control, with gains transferring to an unscaffolded one-minute post-test. Qualitative accounts described the feedback as legible through participants’ bodily action, and usability was high (SUS = 84.3). We synthesise three transferable design principles for self-guided embodied training: feedback as a calibration reference, not an immediate corrector; modality temporal granularity matched to behaviour’s temporal structure; and autonomous interpretability as a deployment prerequisite, not an after-effect of usability.
[HC-33] Beyond Accuracy: Interpreting Topic Representation in Suicide Ideation Detection Models
链接: https://arxiv.org/abs/2606.07714
作者: Hamideh Ghanadian,Isar Nejadgholi,Hussein Al Osman
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Suicide ideation detection models are typically evaluated using aggregate performance metrics, yet little is known about how they internally represent psychologically meaningful risk factors. In high-stakes mental health applications, understanding these internal representations is essential for safety, transparency, and responsible deployment. In this work, we move beyond accuracy and analyze how suicide detection models trained on original and topic-augmented datasets encode psychological risk factors in their internal representation space. Using visualization and geometric analysis, we examine the coherence and separability of topic-related features. Our results show that topic-aware augmentation increases the clarity and distinctness of underrepresented psychosocial risk factors such as immigration, family issues, and financial crisis. These findings suggest that augmentation not only improves model performance but also leads to more structured and interpretable internal representations.
[HC-34] Large Language Models Should Learn Personalized Rather Than Aggregated Human Preferences ICML2026
链接: https://arxiv.org/abs/2606.07629
作者: Cristina Garbacea
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注: Accepted to ICML 2026
Abstract:Current approaches to aligning large language models (LLMs) aggregate diverse human preferences into a single reward signal, effectively optimizing for a hypothetical ``average user’’ who represents no real person particularly well. This position paper argues that LLMs should learn personalized, individual preferences rather than aggregated ones. We show that aggregation masks critical information about preference diversity, individual values, and contextual dependencies, which is a limitation both theoretically grounded in social choice theory and empirically evident across demographic groups. We analyze the rich structure that human preferences encode, survey technical approaches to personalization, and systematically address counterarguments on scalability, shared standards, and manipulation risk. While personalization introduces genuine safety challenges including filter bubbles, value lock-in, and psychological manipulation, we argue these are manageable through bounded personalization frameworks that preserve universal safety constraints while accommodating legitimate individual variation. We conclude with a concrete research and policy agenda for developing preference-aware models that respect both individual autonomy and collective safety.
[HC-35] Syll: Open-Source Personal Automation with Cross-Surface Execution
链接: https://arxiv.org/abs/2606.07594
作者: Bo Zhang,Borui Zhang,Chenghao Jiang,Minglei Shi,Xiaofeng Wang,Zheng Zhu,Jie Zhou,Jiwen Lu
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Software Engineering (cs.SE)
备注: Code: this https URL
Abstract:Personal AI agents must increasingly operate across APIs, shells, web surfaces, and desktop GUIs, yet many systems remain tuned to a single interface and offer limited support for user teaching and auditability. We present Syll, an open-source, self-hosted multimodal agent harness that unifies MCP/API tools, CLI execution, and visual GUI control in a modular runtime, enabling agents to coordinate computer use across heterogeneous interfaces while streamlining how users and agents exchange information. At the core of Syll is a bidirectional user-agent interaction layer: users teach procedures through direct demonstration, which Syll compiles into reusable skills; agent execution is translated back into multimodal evidence – logs, keyframes, and approval checkpoints – for inspection and control. Syll further externalizes memory, skills, routines, and governance as editable local artifacts, supporting straightforward inspection, extension, and downstream development. Our implementation has been validated on production desktop applications including Adobe Photoshop, Adobe Audition, Stardew Valley, macOS Finder and others. We report mechanism-oriented studies that validate multimodal routing, teachable GUI replay, and persistent local artifacts. We hope Syll can serve as a practical open-source foundation for personal automation that users can teach, inspect, and continuously extend.
[HC-36] A Systematic Study of Behavioral Cloning for Scientific Data Annotation ICML2026
链接: https://arxiv.org/abs/2606.07568
作者: Ishaan Singh Chandok,Core Francisco Park
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an)
备注: ICML 2026 Oral
Abstract:Scientific data annotation, such as tracking animals in video or proofreading neural reconstructions, remains bottlenecked by the “last mile” problem: even with strong automation, verification and correction consume substantial human effort. Standard approaches train models to directly predict annotations, discarding the rich supervision in how experts navigate, click, verify, and correct. We introduce a framework for studying behavioral cloning on scientific annotation: 9 synthetic tasks paired with synthetic annotations that simulate realistic human strategies including exploration, mistake correction, and strategic decision-making. Our experiments reveal several findings. First, skills emerge hierarchically: models learn GUI mechanics before task-critical decisions, and commit fewer mistakes than the training data while retaining the ability to correct errors when they occur. Second, scaling models on multi-task behavioral cloning shows that larger models are more data efficient within our scale range. Third, multi-task pretraining enables efficient fine-tuning to new tasks, while training from scratch fails entirely. Fourth, linear probes reveal that models internally represent latent variables of the annotation process such as task phase and data position; interestingly, we find a shared mistake representation that generalizes across different annotation tasks. Overall, our framework establishes systematic benchmarks and identifies key bottlenecks, providing a foundation for scaling behavioral cloning to real-world scientific data annotation.
[HC-37] Astro Im Home! Investigating Factors that Influence the Acceptance of Home Robots Using Supervised Machine Learning
链接: https://arxiv.org/abs/2606.07551
作者: Katrin Fischer,Essence Wilson,Steffie Kim,Dmitri Williams
类目: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Robotics (cs.RO)
备注: Preprint submitted to the 18th International Conference on Social Robotics (ICSR 2026)
Abstract:The use of social robots in home environments is on the rise. This exploratory study applies regularization techniques (e.g., Lasso and Ridge regression) to investigate variables and identify new models of technology acceptance in the context of social robots. Within the original UTAUT2 framework, performance expectancy, social influence, and hedonic motivation emerged as the strongest and most consistent predictors of intention to use the technology. In addition, usability, trust, and competence were identified as promising variables in a model predicting intention to use.
[HC-38] AI-Integrated Learning Management System for Middle School: A Longitudinal Study of Learning Outcomes Through High School and Beyond
链接: https://arxiv.org/abs/2606.07544
作者: Misan Paul Etchie,Taiwo Olutosin
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Middle school is a key window for building core academic skills and the learning routines students carry into later grades, yet many students still fall behind because help is often limited and comes too late, after they have already been stuck for a while. Learning Management Systems (LMSs) are now standard infrastructure for distributing materials, collecting work, assessing students’ tasks, and recording grades, but in most deployments they still behave more like workflow tools than instructional supports. The result is the usual bottleneck: students keep practicing through confusion, teachers triage questions, and feedback that could have corrected the misunderstanding arrives after the misconception has already hardened. To address this gap, we propose an AI-integrated LMS for middle school instruction, paired with a longitudinal study design to test whether sustained, bounded AI support changes outcomes through high school and into post-high school pathways. The proposed platform adds policy-gated AI assistance to everyday coursework, delivering formative feedback and hinting, recommending spaced review and adaptive practice based on mastery, and providing teacher-facing dashboards that summarize misconception patterns and flag sustained struggle. Because the platform is intended for minors, the design is privacy-first, using data minimization, role-based access control, age-appropriate response constraints, and auditable logs of AI interactions. Beyond short-term performance, the evaluation plan links fine-grained learning traces (attempts, revisions, help-seeking, and pacing) to institutional outcomes where feasible, so we can separate tool adoption effects from longer-run changes in learning trajectories.
[HC-39] Concerns and Strategic Responses of Older Workers Navigating Generative AI in Bridge Employment
链接: https://arxiv.org/abs/2606.07543
作者: Aditya Nayak,Aakash Gautam,Rama Adithya Varanasi
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: CHIWORK’26
Abstract:Generative AI (GenAI) is transforming workplaces at a rapid pace. This disproportionately affects vulnerable communities, including older workers (OWs) who re-enter the workforce through bridge employment prior to final retirement. Through in-depth semi-structured interviews with 21 professionals, we examine how OWs navigate GenAI-driven disruptions while pursuing bridge roles, focusing on their concerns about GenAI integration and their responses to these changes. Our findings show that OWs experienced both temporal and structural disruptions across all stages of the bridge employment decision-making process due to GenAI. In response, they reconfigured their tasks through different forms of boundary work aimed at restoring stability and continuity. We conceptualize these responses as AI resilience, which reshaped OWs’ bridge employment decision-making into an ongoing process of negotiation and adaptation. We conclude by offering recommendations to reduce burnout among OWs by balancing individual-level AI resilience strategies with meso-level AI resilience collectives and macro-level adversarial and contestable AI-mediated organizational structures.
[HC-40] Multimodal Large Language Models as Synthetic Participants in Video-Based Studies: An Evaluation
链接: https://arxiv.org/abs/2606.07541
作者: Prabal Shrestha,Bohan Jiang,Haoning Xue,Huan Liu,Xinyi Zhou
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY); Multimedia (cs.MM)
备注: Accepted to SocialLLM @ ICWSM 2026
Abstract:Multimodal large language models (MLLMs) have shown strong performance on objective tasks such as video understanding and reasoning. However, it remains unclear whether they can approximate subjective human responses, which depend not only on content comprehension but also on individuals’ social contexts. To address this gap, we evaluate MLLMs as synthetic participants in an emerging task: assessing perceived sensory engagement with short videos. Grounded in the Perceived Message Sensation Value (PMSV) framework, we compare ratings from recruited human participants and profile-conditioned MLLM simulations (n=673) using a 17-item scale measuring emotional arousal, dramatic impact, and novelty. We find that even leading MLLMs (Gemini 3 Flash and Qwen 3 Omni) show limited agreement with human participants. The models exhibit distinct downward mean-shift and central-tendency biases in their rating distributions. They both introduce and flatten subgroup differences, while showing inconsistent sensitivity to participant profiles. Prompting strategies affect these metrics differently, modestly improving some aspects while worsening others. These results highlight both the challenges and opportunities of developing MLLMs as synthetic participants in video-based research. Data and code: this https URL
[HC-41] Unveiling the Potential of iMarkers: Invisible Fiducial Markers for Advanced Robotics
链接: https://arxiv.org/abs/2501.15505
作者: Ali Tourani,Deniz Isinsu Avsar,Hriday Bavle,Jose Luis Sanchez-Lopez,Jan Lagerwall,Holger Voos
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
备注: 19 pages, 10 figures, 4 tables
Abstract:Fiducial markers are widely used in robotics for navigation, object recognition, and scene understanding. While offering significant advantages for robots and Augmented Reality (AR) applications, they often disrupt the visual aesthetics of environments, as they are visible to humans, making them unsuitable for many everyday use cases. To address this gap, this paper presents iMarkers, innovative, unobtrusive fiducial markers detectable exclusively by robots and AR devices equipped with adequate sensors and detection algorithms. These markers offer high flexibility in production, allowing customization of their visibility range and encoding algorithms to suit various demands. The paper also introduces the hardware designs and open-sourced software algorithms developed for detecting iMarkers, highlighting their adaptability and robustness in the detection and recognition stages. Numerous evaluations have demonstrated the effectiveness of iMarkers relative to conventional (printed) and blended fiducial markers and have confirmed their applicability across diverse robotics scenarios.
计算机视觉
[CV-0] Latent Spatial Memory for Video World Models MICRO
链接: https://arxiv.org/abs/2606.09828
作者: Weijie Wang,Haoyu Zhao,Yifan Yang,Feng Chen,Zeyu Zhang,Yefei He,Zicheng Duan,Donny Y. Chen,Yuqing Yang,Bohan Zhuang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL , Code: this https URL
Abstract:Video world models that maintain 3D spatial consistency across generated frames typically rely on explicit point cloud memory constructed in RGB space. This design is both computationally expensive, requiring repeated rendering and VAE encoding, and inherently lossy, as the round trip through pixel space discards rich features of the learned latent representation. In this paper, we introduce \emphlatent spatial memory for video world models, a persistent 3D cache that stores scene information directly in the diffusion latent space, avoiding pixel-space reconstruction. Building on this, we propose Mirage, a latent-space spatial memory framework that constructs the memory by lifting latent tokens into 3D via depth-guided back-projection and queries it by synthesizing novel views through direct latent-space warping. This unified formulation eliminates both the information loss of pixel-space reconstruction and the computational burden of repeated encoding and rendering. Experiments show that latent spatial memory achieves up to \textbf10.57 \times faster end-to-end video generation and \textbf55 \times reduction in memory footprint relative to explicit 3D baselines. Leveraging the geometric prior of the diffusion model, Mirage attains state-of-the-art performance on WorldScore and strong reconstruction quality on RealEstate10K.
[CV-1] MemoryVLA: Temporal Modeling via Memory and Imagination in Vision-Language-Action Models
链接: https://arxiv.org/abs/2606.09827
作者: Hao Shi,Weiye Li,Bin Xie,Yulin Wang,Renping Zhou,Tiancai Wang,Xiangyu Zhang,Ping Luo,Gao Huang
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: The project is available at this https URL
Abstract:Temporal modeling is essential for robotic manipulation, as effective control requires both memory of past interactions and imagination of future states. However, most VLA models rely primarily on the current observation and therefore struggle with long-horizon, temporally dependent tasks. Cognitive science suggests that humans rely on working memory to buffer short-lived context, the hippocampal system to preserve episodic memory of past experience, and internal models to imagine possible future state evolution. Inspired by these mechanisms, we propose MemoryVLA++, a full temporal modeling framework that equips VLA models with memory and imagination for robotic manipulation. A pretrained VLM encodes the current observation into perceptual and cognitive tokens, forming working memory. These tokens query a Perceptual-Cognitive Memory Bank to retrieve relevant historical context. This bank stores low-level details and high-level semantics from past interactions, and is updated through redundancy-aware consolidation. A world model imagines future states in a denoising latent space, and the imagined latents are integrated under memory guidance to form full temporal-aware tokens. The resulting tokens condition a diffusion action expert to predict temporally consistent action sequences. We conduct extensive experiments on 5 simulation benchmarks and 3 categories of real-robot tasks across 3 robots, covering general manipulation, long-horizon temporal tasks, robustness, and generalization. Our method achieves strong performance across Libero, SimplerEnv, Mikasa-Robo, Calvin, Libero-Plus, and diverse real-robot tasks, validating the effectiveness of full temporal modeling with memory and imagination. For example, on real robots, it achieves +9%, +26%, +28% gains on general, memory-dependent, and imagination-dependent tasks. Project Page: this https URL
[CV-2] OmniGameArena: A Unified UE5 Benchmark for VLM Game Agents with Improvement Dynamics
链接: https://arxiv.org/abs/2606.09826
作者: Mingxian Lin,Shengju Qian,Yuqi Liu,Yi-Hua Huang,Yiyu Wang,Wei Huang,Yitang Li,Fan Zhang,Zeyu Hu,Lingting Zhu,Xin Wang,Xiaojuan Qi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-language model (VLM) agents are increasingly deployed in interactive game environments. Yet game benchmarks for VLM agents typically report a single first-attempt score per (agent, game) pair, focus on single-agent Solo play, and lack unified protocols for evaluating heterogeneous agent classes (commercial VLMs, open-weight VLMs, and specialized game policies) on the same footing. We address these gaps with OmniGameArena, a real-time benchmark of twelve newly built Unreal Engine 5 games spanning Solo (7), PvP (3), and Coop (2) with unified action interfaces, and the Improvement Dynamics Curve (IDC), an agentic-reflection harness in which a tool-using reflector LLM autonomously refines a bounded skill prompt across multiple rounds. Beyond cold-start leaderboard scores, IDC exposes two additional observables for each (agent, game) pair: how the score evolves across reflection rounds, and how the learned skill behaves on held-out task variants. We report these observables for twelve VLM agents on the cold-start leaderboard and four top agents under IDC.
[CV-3] PTL-Diffusion: Manifold-Aware Diffusion with Periodic Terminal Laws
链接: https://arxiv.org/abs/2606.09816
作者: Danqi Zhuang,Jisui Huang,Xiaoyue Xi,Andrew Kiggins,Xiaojie Wang,Ke Chen,Yue Wu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Probability (math.PR)
备注:
Abstract:Standard diffusion models typically use a single time-homogeneous Gaussian terminal distribution as the reference law for generation. While this choice is analytically convenient and empirically powerful, it provides little explicit structure for data concentrated near low-dimensional manifolds, where different regions of the data distribution may correspond to distinct local geometric or semantic factors. As a result, the reverse model must recover manifold-level structure almost entirely from an unstructured terminal reference distribution. We propose PTL-Diffusion, a proof-of-concept diffusion framework whose forward noising process converges to a nonconstant periodic family of Gaussian terminal laws rather than to a single invariant law. Unlike a phase-conditioned DDPM, where phase information only enters the denoising network while the forward process remains unchanged, PTL-Diffusion embeds phase structure directly into the forward noising dynamics. The proposed construction remains close to standard denoising diffusion models: for a periodically forced Ornstein–Uhlenbeck-type forward process, we derive closed-form forward marginals, the limiting periodic Gaussian terminal family, and explicit Gaussian reverse posteriors, enabling standard noise-prediction training. We also introduce an invariant-average regularization term coupling the phase-conditioned reverse dynamics through the averaged periodic reference law. Experiments on torus and cylinder point-cloud benchmarks and the Olivetti face dataset show that PTL-Diffusion improves manifold-level distributional matching over matched DDPM baselines, reducing phase-conditioned errors, feature-space covariance errors, and nearest-neighbour manifold distances. These results suggest structured terminal reference laws as a promising direction, while motivating more expressive phase constructions and larger-scale evaluations. Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Probability (math.PR) MSC classes: 60J20, 60F17 Cite as: arXiv:2606.09816 [cs.CV] (or arXiv:2606.09816v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2606.09816 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-4] MaC: Translating Actions into Motion and Contact Images for Embodied World Models
链接: https://arxiv.org/abs/2606.09813
作者: Zhenyu Wu,Xiuwei Xu,Yukun Zhou,Yifan Li,Qiuping Deng,Xiaofeng Wang,Zheng Zhu,Bingyao Yu,Ziwei Wang,Jiwen Lu,Haibin Yan
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Embodied world models have emerged as a pivotal paradigm for visual robotic decision-making and interactive environment simulation. However, conventional embodied frameworks rely on low-dimensional structured action vectors (e.g., joint angles and end-effector poses), which suffer from limited expressive capacity, poor generalization across diverse embodiments, and unnatural dynamic modeling for complex physical interactions. To address these limitations, this paper proposesiMac (Image as Action Control), a novel unified control paradigm that treats raw visual images as native action representations for embodied world models. Departing from traditional explicit kinematic action encoding, iMac formulates continuous visual manipulation as image-based action tokens, which inherently encapsulate spatial motion intentions, interactive geometric constraints and subtle physical dynamics. We construct a dual-branch embodied architecture consisting of an image-action encoder and a dynamic world predictor: the encoder compresses target-driven visual images into compact action embeddings, while the predictor learns environment transition rules conditioned on image actions to achieve high-fidelity future state prediction and closed-loop embodied control. Extensive experiments are conducted on public embodied manipulation benchmarks and real-world robotic scenarios. The results demonstrate that iMac outperforms vector-based action control baselines in prediction accuracy, task success rate and cross-scene generalization ability. Moreover, our image-action design eliminates the reliance on manually defined action spaces, realizing flexible and universal control for heterogeneous embodied agents. This work provides an innovative visual-action perspective for embodied world models, offering a simple yet effective paradigm for scalable robotic perception and manipulation.
[CV-5] AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing
链接: https://arxiv.org/abs/2606.09811
作者: Jisong Cai,Long Ling,Shiwei Chu,Zhongshan Liu,Jiayue Kang,Zhixuan Liang,Wenjie Xu,Yinan Mao,Weinan Zhang,Xiaokang Yang,Ru Ying,Ran Zheng,Yao Mu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:World-action models have emerged as a promising paradigm for robot manipulation, jointly modeling visual scene dynamics and actions to inject physical priors into policy learning. However, existing world-action models couple world prediction and action execution at the same temporal resolution, forcing the world branch to model near-term frame variations that are redundant and weakly informative. We posit that strictly binding world prediction and action execution to the same temporal rhythm may underutilize the potential of the video branch for embodied control. Therefore, we propose AHA-WAM, an Asynchronous Horizon-Adaptive World-Action Model built on a dual Diffusion Transformer (DiT) architecture that reorganizes world-action modeling around this temporal asymmetry. AHA-WAM instantiates the video DiT as a low-frequency world planner that maintains rolling key-value memory over past observations and exposes reusable layerwise latent context encoding long-horizon scene evolution, while a high-frequency action DiT executes short action chunks in closed loop by querying this context through layerwise joint attention. To support asynchronous execution, we introduce horizon-adaptive offset training and Observation-Guided Video-Context Routing (OVCR), which together let the action expert exploit long-horizon world context while remaining responsive to real-time execution state without rerunning the video DiT. Experiments on RoboTwin and real-world manipulation tasks show that AHA-WAM achieves state-of-the-art performance without any robot-data pretraining, attaining 92.80% average success on RoboTwin and 78.3% success across 4 real-world tasks, while reaching 24.17 Hz closed-loop control with a 4.59x speedup over Fast-WAM.
[CV-6] Echo-Memory: A Controlled Study of Memory in Action World Models
链接: https://arxiv.org/abs/2606.09803
作者: Wayne King,Zeyue Xue,Yuxuan Bian,Jie Huang,Haoran Li,Yaowei Li,Yaofeng Su,Yuming Li,Haoyu Wang,Shiyi Zhang,Songchun Zhang,Yuwei Niu,Sihan Xu,Junhao Zhuang,Haoyang Huang,Nan Duan
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Machine Learning (cs.LG)
备注: 9 figures and 28 pages, Code at \href{ this https URL }{this URL}
Abstract:We present \textbfEcho-Memory, a controlled study of memory mechanisms in action-conditioned world models. These models generate multi-segment videos from a first frame, text prompt, and camera-action sequence, but their central failure is often memory rather than local image synthesis: after the camera leaves and returns, the scene or salient object may silently change. Existing memory designs are hard to compare because gains are entangled with backbone, training, retrieval, and evaluation differences. Echo-Memory fixes the action-to-video interface and varies only how history is stored and read by the generator. Under a shared video diffusion backbone, optimizer, camera-action representation, sampler, and evaluation pipeline, we compare raw context, compression-based memory, spatial summaries with different read-out paths, and state-space recurrence. This matched matrix separates four otherwise conflated axes: \emphcapacity, \emphcompression, \emphread-out, and \emphrecurrence. We also evaluate memory through a three-branch protocol: replay quality, in-domain loop revisit, and open-domain return probes. The branches routinely disagree, showing that replay fidelity is not a sufficient proxy for remembering a world. Three findings follow. Raw context is a strong capacity baseline and improves open-domain return far more than it improves replay metrics. Compactness is not a free substitute for capacity: aggressive spatial and hybrid-compression memories lose the salient evidence needed for return. Finally, block-wise state-space recurrence is the strongest open-domain return mechanism in our matrix, showing that the structure of implicit memory matters as much as the decision to use it. These results provide a compact protocol for studying memory in action world models beyond isolated replay metrics.
[CV-7] Beyond Spherical Harmonics: Rethinking Appearance Models for Radiance Reconstruction
链接: https://arxiv.org/abs/2606.09794
作者: Ewa Miazga,Jorge Condor,Piotr Didyk
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注: 19 pages, 11 figures
Abstract:View-dependent appearance modeling remains a challenging problem in novel-view synthesis and reconstruction. Accurately representing complex angular effects often requires substantial memory and computational resources. For new learning-based methods, a common approach is to rely on SH. However, capturing high-frequency phenomena such as specular reflections demands high-order expansions, which increase memory usage and computational cost. Consequently, most methods employ low-order SH, which limits the ability to model complex view-dependent effects, resulting in overly smooth or diffuse representations. To address these limitations, we systematically evaluate a wide range of spherical functions in the context of scene reconstruction. Some of them are introduced to graphics and computer vision for the first time in this paper. Based on the insights from the experiment, we develop a novel spherical formulation, the Normalized Anisotropic Spherical Gabor function that enables efficient modeling and learning of high-frequency appearance effects while maintaining compact representation. Compared to existing approaches, our function achieves higher-quality reconstruction of view-dependent phenomena such as glints, while being up to five times more memory-efficient and more efficient to evaluate. We validate its performance in radiance-field reconstruction tasks.
[CV-8] End-to-End Optimization of Incoherent Imaging for Classification Under Detector-Limited Readout
链接: https://arxiv.org/abs/2606.09792
作者: Archer Wang,Joshua Chen,Sachin Vaidya,Marin Soljačić
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:End-to-end co-optimization of optical front-ends (e.g. metasurfaces) and neural network back-ends has been widely applied to imaging tasks, yet a formalism characterizing when and why such systems outperform conventional lens-based imaging is largely lacking. This paper focuses on object classification, a central imaging task, and asks when end-to-end optimization of a phase mask for incoherent imaging improves performance over a conventional focusing lens. We find that these gains arise primarily under constrained detector readout and are limited under full detector readout. In the latter setting, we prove that no incoherent phase mask exceeds the ideal-channel mutual information between detector measurements and class labels; a conventional focusing lens approaches this ceiling, and joint optimization yields no empirical gain. When detector readout is constrained – by coarse spatial sampling or a limited number of measurements – optimized optics can substantially improve classification by increasing class separability in the detector measurements. These gains are largest under low detector noise and shrink as noise grows, because the optics shape the signal before it reaches the detector but cannot remove noise added afterward. The advantage also depends on the spectral structure of the task: co-design helps most when class-discriminative content is concentrated at lower spatial frequencies than within-class variation. We develop a theoretical framework formalizing these distinctions and test its predictions on synthetic data and standard benchmarks (MNIST, FashionMNIST, SVHN).
[CV-9] POTATR: A Lightweight Image-to-Graph Model for Page-Level Table Extraction
链接: https://arxiv.org/abs/2606.09788
作者: Brandon Smock,Libin Liang,Max Sokolov,Amrit Ramesh,Valerie Faucon-Morin,Tayyibah Khanam,Maury Courtland
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, split from PubTables-v2 paper
Abstract:Large-scale document processing requires contextually aware table extraction (TE) that is both accurate and efficient. Yet current approaches require billions of parameters, hundreds of autoregressive steps, or costly API inference. Motivated by this, we introduce the Page-Object Table Transformer (POTATR), a lightweight 29M parameter image-to-graph model that extends the Table Transformer (TATR) for contextualized page-level TE. POTATR outperforms all models tested on the PubTables-v2 Single Pages benchmark – including frontier MLLMs – achieving \textrmGriTS_\textrmCon of 0.964 while running over 130 \times faster at roughly 300 \times lower cost. Further, POTATR’s output is spatially grounded: every recognized element has a bounding box, enabling visual verification and geometric text assignment. As a result, POTATR performs unified page-level TE while composing with other models, enabling extension to scanned documents via external OCR and to full-document TE via techniques like cross-page merging. Code and models will be released.
[CV-10] SemDINO: A DINOv3-Driven Network for Cross-Temporal Semantic Alignment in Change Detection
链接: https://arxiv.org/abs/2606.09772
作者: Xinyu Tong,Meihua Zhou,Jinxiao Sun,Yingjie Tang,Lei Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Semantic change detection (SCD) aims to simultaneously locate land-cover changes and identify semantic categories before and after transition. However, existing methods suffer from insufficient cross-temporal alignment, weak multi-scale representation, and poor robustness to pseudo-changes caused by illumination, season, and registration noise. To address these issues, we propose a novel end-to-end semantic change detection network named SemDINO, which integrates a dual-branch encoder, multi-scale temporal interaction, semantic purification, change enhancement, and decoupled multi-task prediction into a unified framework. Specifically, we construct a dual-branch encoder that combines a CNN backbone and frozen DINOv3 features via gated pyramid fusion, enabling rich multi-scale semantic representation. Then, a multi-scale temporal bidirectional transformer interaction (M-TBTT) module is proposed to achieve global cross-temporal feature alignment and information interaction. To further enhance genuine changes and suppress pseudo-variations, we introduce semantic purification (SCP), bidirectional change enhancement (BiChangeEnhance), and multi-scale change enhancement (MCE) modules collaboratively. Finally, a multi-branch CD prediction head is designed to jointly output binary change mask, bi-temporal semantic maps, and edge constraint. Extensive experiments on public remote sensing CD datasets demonstrate that SemDINO achieves superior performance and generalization ability against state-of-the-art methods, especially in complex scenarios with interference factors.
[CV-11] Hybrid Robustness Verification for Spatio-Temporal Neural Networks
链接: https://arxiv.org/abs/2606.09746
作者: Sherwin Varghese,Matthew Wicker,Alessio Lomuscio
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at the 9th International Symposium on AI Verification (SAIV 2026)
Abstract:With AI increasingly deployed in safety-critical systems, providing formal robustness guarantees for the underlying models is essential. Existing verification methods either rely on overly conservative approximations or incur prohibitive computational costs. For example, the use of lp-norm perturbations in video settings encodes the belief that the adversary can inject noise in every video frame. In practice, adversarial perturbations exhibit structured spatial and temporal correlations, constrained to lower-dimensional, semantically meaningful subspaces. In this work, we study robustness verification of 3D CNNs processing video and volumetric inputs, targeting applications in action recognition (UCF-101), autonomous driving (Udacity), and medical imaging (MedMNIST) exploiting realistic assumptions on adversarial strength by modelling them as spatio-temporal constraints - where the attacker can modify either a subset of frames or patches within a set of consecutive frames. We demonstrate that modelling realistic constraints enables tighter approximations. We introduce Spatio-Temporal Bound Propagation (STBP), a verification framework that computes an exact closed-form characterization of the first convolutional layer and propagates certified bounds through subsequent layers using scalable approximations. Computing the exact closed form provides the tightest bounds for the first convolutional layer. Thus, we utilise approximation methods in the remainder of the network. To spur further progress in this field, we propose ST-Bench, a verification benchmark for autonomous driving and activity recognition, to systematically evaluate verifiable robustness. Compared to existing verification-based approaches, STBP provides stronger robustness guarantees with significantly improved scalability, achieving 1.7x higher certified robust accuracy under identical perturbation budgets.
[CV-12] HDSL: A Hierarchical Domain-Specific Language for Structured 3D Indoor Scene Generation and Localized Editing with LLM Agents
链接: https://arxiv.org/abs/2606.09738
作者: Letian Li,Chao Shen,Shuzhao Xie,Chenghao Gu,ZhengXiao He,Yu Meng,Xin Yang,Wenyuan Jiang,Zhi Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Text-driven indoor scene generation and editing require an intermediate representation that language models can both produce and revise. Existing LLM-based systems often rely on scene graphs or global constraint lists, which are compact but underspecify local geometry and make instruction-based edits difficult to localize. We frame this problem as structured program generation and local program repair, and propose Hierarchical Descriptive Scene Language (HDSL), an XML/CSS-style domain-specific language for structured 3D indoor scenes. HDSL represents rooms, regions, objects, and support surfaces as a tree with local coordinates, making complex scenes easier to plan recursively and easier to retrieve for editing. Our pipeline uses LLM agents to generate HDSL subtrees with bounded verification, grounds non-virtual nodes through multimodal asset retrieval, and applies force-directed layout optimization to repair boundary and collision errors. For editing, Hierarchical Retrieval-Augmented Generation retrieves the relevant subtree, asks the LLM to rewrite only that local context, and merges the result back through a deterministic three-way merge. In our reproduced benchmark, HDSL improves average object coverage, text-scene alignment, and generation time over full text-to-scene baselines while remaining competitive with recent layout-only reproductions on geometry metrics; for editing, HRAG reduces token use by 5.22\times and runtime by 6.19\times , produces valid DSL for all eight paired edits, and better preserves unrelated scene objects.
[CV-13] Evaluating the Representation Space of Diffusion Models via Self-Supervised Principles ICML2026
链接: https://arxiv.org/abs/2606.09718
作者: Xiao Li,Yixuan Jia,Zekai Zhang,Xiang Li,Lianghe Shi,Jinxin Zhou,Zhihui Zhu,Liyue Shen,Qing Qu
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: First two authors contributed equally. Accepted at ICML 2026
Abstract:Diffusion models have demonstrated remarkable generative capabilities and have also emerged as powerful self-supervised representation learners, yet the connection between these two abilities remains less explored. Drawing inspiration from self-supervised learning (SSL), we introduce a framework for jointly evaluating the representation and generation capabilities of diffusion models. Specifically, we decompose features into invariant and residual components and derive the Invariant Contamination Ratio (ICR), a Fisher-based metric that quantifies how residual variation contaminates invariant signal in feature space. We use this framework to analyze both discriminative and generative behavior of diffusion models. On the representation side, we find that invariance peaks at intermediate noise levels, which also yield the best downstream classification performance. On the generative side, we study how training transitions from genuine generalization to memorization in data-limited regimes, and show that ICR serves as a sensitive training-time indicator of early learning: increasing residual energy along Fisher directions marks the onset of memorization, detectable from training features alone without external evaluators or held-out test sets. Overall, our results show that diffusion models can be monitored from a self-supervised perspective through the geometry of their learned representations.
[CV-14] Cranio-Diff: Diffusion-based Cross-domain Craniofacial Reconstruction with 2D X-ray Skull Guidance and Structural Identity Constraints BMVC2026
链接: https://arxiv.org/abs/2606.09699
作者: Ravi Shankar Prasad,Naresh Gurjar,Shashank Baghel,Chirag,Dinesh Singh
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 7 figures, BMVC 2026 conference
Abstract:The state-of-the-art generative models, such as CycleGAN, Pix2Pix, and diffusion models have demonstrated remarkable performance in the face generation task. However, they fail to effectively capture cross-modality semantic information in craniofacial reconstruction when translating from the skull (x-ray) to the face (optical) domain, due to a mismatch in the alignment of structural identity across modalities. To address this issue, we propose Cranio-Diff, a diffusion-based framework for cross-domain cranio-facial reconstruction from 2D X-ray skull images. The proposed approach integrates skull-conditioned structural guidance through ControlNet with biometric text conditioning to generate a face which is more semantically and structurally aligned with the given skull. The proposed Cranio-diff method is evaluated on skull-face dataset obtained from X-ray scans of 120 subjects in lateral and frontal views. To enable controlled evaluation, each face image is synthesised across three age groups (25, 45, 65) and three BMI variations of -10%, baseline and +10%, yielding 4320 paired samples. To the best of our knowledge, this is the only X-ray-face dataset with this magnitude. Extensive experiments showed that the proposed method outperforms recent existing approaches in both generated image quality and retrieval task. Finally, to evaluate the performance of our proposed method, we have evaluated the quality of the generated image using FID, IS, SSIM, LPIPS, PSNR and ArcFace score. Additionally, retrieval performance is evaluated using recall@k, mAP@k and MRR@k. Obtained experimental results demonstrate that the proposed method can be used as an alternate tool in providing aid in forensic investigations.
[CV-15] GenEyePose: Patient-Free Knowledge-Based Saccadic Eye Movement Modeling for Digital Neurophysiologic Biomarker Development
链接: https://arxiv.org/abs/2606.09681
作者: Tianyu Lin,Jooyoung Ryu,Puvada Sreevarsha,Rahul Srinivasaragavan,Riya Satavlekar,Susan Kim,Nidhi Soley,Yujie Yan,Ishan Vatsaraj,Carl Harris,Aimon Rahman,Vishal Patel,Joseph Greenstein,Casey Taylor,Kemar E. Green
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Eye movements, including saccades, are widely regarded as highly sensitive and objective biomarkers of neurophysiologic states. Detecting saccadic signatures in neurologic diseases offers a rapid, portable alternative to brain imaging, avoiding access and cost barriers. Currently, there are no robust AI-enabled video-oculographic solutions (e.g., digital biomarkers) for screening, triaging, or localizing brain abnormalities due to privacy issues and scarce datasets. In this work, we propose the first fully synthetic, patient-free, multimodal eye movement generation pipeline for generalizable saccade analysis. Using this synthetic dataset, we trained a deep learning classifier to distinguish between normal and abnormal (hypometria and hypermetria) saccadic accuracies and evaluated its performance on real-world clinical data. The model achieved an AUROC of 0.76 and a sensitivity of 0.71, showing that the synthetic data has strong potential to generalize for clinical applications, including as a screening tool in at-home and emergency room settings or a tool for precise neuroanatomic localization.
[CV-16] SoccerNet 2026 Player-Centric Ball-Action Spotting:Retraining and Post-Processing Extensions to the FOOTPASS Baselines SOCC CVPR2026
链接: https://arxiv.org/abs/2606.09679
作者: Parthsarthi Rawat
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR 2026 SoccerNet Player Centric Ball Action Spotting Challenge, Rank 7
Abstract:We describe our system for the SoccerNet 2026 Player-Centric Ball-Action Spotting Challenge, which requires predicting who performs which action and when, across eight classes in broadcast soccer. Building on the three FOOTPASS baselines [1] (TAAD, TAAD+GNN, and TAAD+DST), we contribute four extensions: (1) gradient check pointing to enable full-backbone fine-tuning on a single GPU; (2) fusion of GNN logits into the DST encoder, combining graph-based tactical context with per-player visual features; (3) square-root frequency class weighting to address the 213:1 pass-to-tackle imbalance in the training data; and (4) a post processing pipeline comprising per-class logit gating, temporal frame refinement, jersey re-assignment, and a two-model ensemble. Our system achieves 0.548 Macro F1 on the test set and 0.446 on the challenge set (server evaluation).
[CV-17] Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervision
链接: https://arxiv.org/abs/2606.09670
作者: Mateo Diaz-Bone,Daniel Caraballo,Florian Scheidegger,Thomas Frick,Mattia Rigotti,Andrea Bartezzaghi,Roy Assaf,Niccolo Avogaro,Yagmur G. Cinar,Brown Ebouky,Filip M. Janicki,Piotr S. Kluska,Cezary Skura,Cristiano Malossi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent Anomaly Detection methods achieve perfect detection and segmentation scores on well-established datasets, such as MVTec. However, many of these methods face challenges when foundational assumptions - such as consistent object scale, viewpoint, background, illumination, and centered placement - are violated. Those variations that occur render anomaly detection methods unusable in many real-world scenarios. To address these limitations, we introduce three key contributions: (1) a visual prompting pipeline that isolates objects using foreground-background masking; (2) a mechanism for unfreezing the teacher in student-teacher models to improve domain adaptability; and (3) a data augmentation strategy leveraging diffusion-generated synthetic images to enhance anomaly detection performance. We achieve a 3.5 percentage point improvement over the previous state-of-the-art on the challenging AeBAD dataset by using the Masked Multiscale Reconstruction (MMR) model as our backbone.
[CV-18] Do Video Foundation Models Understand Intuitive Physics? A Layerwise Probing Analysis
链接: https://arxiv.org/abs/2606.09646
作者: Samuele Punzo,Niccolò Caselli,Ippokratis Pantelidis,Francesco Massafra,Salvatore Lo Sardo,Mohammadreza Salehi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:We study whether pretrained video foundation models encode intuitive-physics information in their frozen representations, and how this information varies across model families, layers, and probe types. Using frozen-feature probing on IntPhys2 and Minimal Video Pairs (MVP), we compare predictive joint-embedding models (V-JEPA), masked reconstruction models (VideoMAE), and a diffusion-based video generator (LTX-Video). V-JEPA achieves the strongest overall results across benchmarks, especially with probes that model temporal dynamics, while VideoMAE remains competitive and LTX-Video recovers weaker but non-trivial signal. Layerwise analyses show that physics-relevant information is weakest in early layers and becomes most accessible at intermediate-to-late depth, and temporal controls show that disrupting frame order substantially reduces performance, especially on MVP. Together, these results suggest that intuitive-physics knowledge emerges reliably in pretrained video representations, but its accessibility depends strongly on pretraining paradigm, representational depth, and readout mechanism.
[CV-19] MAVIS: Multi-Agent Video Retrieval via Structured Video Understanding
链接: https://arxiv.org/abs/2606.09641
作者: Jie Zhang,Qilang Ye,Hao Zhou,Haochen Liang,Fei Luo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The dominant paradigm in video retrieval relies on embedding-based full-corpus scanning, which suffers from inherent computational inefficiency and the semantic asymmetry between information-dense videos and sparse textual queries. To bridge this gap, we introduce \textbfMAVIS, a novel multi-agent framework that rethinks retrieval as cooperative reasoning rather than brute-force search. MAVIS first bridges the granularity mismatch by parsing raw videos into a \textbfStructured Semantic Library, enabling explicit attribute-level indexing. During retrieval, a planner decomposes complex user intents into atomic sub-tasks, dispatching specialized agents to independently nominate candidates. Crucially, MAVIS employs a \textbfLogic-aware Debate mechanism with a strict veto protocol, where agents collaboratively prune logical mismatches to identify a compact set of ``controversial’’ candidates for fine-grained verification. This agentic workflow effectively bypasses the inefficiency of full-library traversal. Extensive experiments on MSR-VTT, MSVD, and ActivityNet demonstrate that MAVIS achieves competitive performance without task-specific fine-tuning, offering a scalable and interpretable alternative to traditional dual-encoder approaches.
[CV-20] CineDance: Towards Next-Generation Multi-Shot Long-Form Cinematic Audio-Video Generation
链接: https://arxiv.org/abs/2606.09639
作者: Yuheng Chen,Teng Hu,Yuji Wang,Qingdong He,Zhucun Xue,Qianyu Zhou,Xiangtai Li,Lizhuang Ma,Jiangning Zhang,Dacheng Tao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The fidelity and structural diversity of training datasets fundamentally determine the capabilities of video generation models. While commercial systems showremarkableabilitytogeneratecinematicnarratives, the progress of open-source models remains limited by the scarcity of high-quality training data. To bridge this gap, we introduce CineDance-1M, a large-scale, open research Text-to-Audio-Video (T2AV) dataset designed specifically for multi-shot, long-form joint audio-video generation. Averaging 92.8 seconds and 24.2 continuous shots per video, it provides configurable, structured annotations for both audio and video modalities. This exceptional quality is achieved through a rigorous three-stage curation pipeline: i) diverse sourcing and comprehensive cleansing, ii) film-theory-inspired narrative parsing, and iii) hierarchical dual-modal captioning. For a comprehensive assessment, we propose CineBench, featuring a diverse prompt suite and a six-dimensional, human-aligned metric system tailored for complex narrative audio-video evaluation. Furthermore, we adapt LTX-2.3 into CineDance, which demonstrates exceptional single-modality quality alongside precise audio-video alignment and robust subject and environment consistency, effectively validating our curation strategy and the high quality of CineDance-1M. We anticipate that this work will serve as a solid foundation for accelerating future research in multi-shot, long-form joint audio-video generation. Our project page is available at this https URL.
[CV-21] ATN3D: Density-Aware LiDAR-Radar Early 3D Object Detection Under Extreme Sparsity
链接: https://arxiv.org/abs/2606.09634
作者: Debojyoti Biswas,Xianbiao Hu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications. Long-range detection is challenging because sensing evidence is sparse; yet this long-range'' scenario is routine in traffic. Although 30m is often labeled long-range in computer vision, on roadways it affords only approx. 1-2s for perception and decision-making. Under such extreme sparsity, two core challenges arise. First, early multimodal fusion tends to discard sparsity information and inject noise from empty or falsely occupied cells, degrading long-range recall. Second, context-agnostic uniform channel supervision favors dense and near-range samples, leaving far and small objects under-optimized, delaying the earliest detection of distant objects. We propose Ask The Neighbor’’ (ATN3D), a LiDAR-Radar framework tailored for sparse-range conditions. ATN3D introduces (i) Density-aware early fusion with cross-modal gating that conditions fusion on per-voxel density/sparsity and Radar evidence, (ii) Occupancy-gated neighborhood aggregation with circular kernels to aggregate only from credible cells, (iii) Evidence-conditioned channel self-attention to adapt channel weights with weather/range, and (iv) a Range-aware loss that re-balances classification and localization by distance, aligning training with distance-stratified evaluation. On the VoD benchmark across clear and foggy conditions, ATN3D surpasses strong baselines: +3.55% mAP in clear weather and +8.41% mAP under simulated heavy fog; for 30m objects, gains are +3.33% (clear) and +2.09% (heavy fog). These results indicate earlier and more reliable long-range detections under sparse sensing in on-road traffic.
[CV-22] DexPIE: Stable Dexterous Policy Improvement from Real-World Experience
链接: https://arxiv.org/abs/2606.09615
作者: Ruizhe Liao,Wenrui Chen,Liangji Zeng,Haoran Lin,Fan Yang,Kailun Yang,Yaonan Wang
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Project website: this https URL
Abstract:Dexterous manipulation presents substantial challenges for imitation learning due to its high-dimensional action space and complex contact-rich dynamics. Policies trained purely from demonstrations often suffer from compounding errors during deployment and require large amounts of expert data to achieve reliable performance. To move beyond the limitations of demonstration data, in this work, we propose DexPIE, a post-training framework for dexterous policy improvement from experience collected through real-world deployment. First, DexPIE enables effective exploration coverage through a dexterous-hand-adapted intervention system and multi-stage DAgger-style data collection across initial and intermediate task stages, providing reliable supervision for accurate policy evaluation. To reduce temporal noise between post-training rollouts and demonstration data, we introduce asynchronous inference in the relative action space, which better aligns rollout data with demonstrated behavior and allows the critic to learn a value function induced by a more consistent underlying policy. Finally, DexPIE improves the policy through conditioning on a continuous optimality indicator, allowing the policy to leverage the quality of data in a more fine-grained manner. Across three challenging real-world dexterous manipulation tasks, DexPIE achieves a 37% improvement in success rate over the demonstration-based reference policy, outperforming all baseline methods and demonstrating stronger robustness. The source code and dataset will be made publicly available.
[CV-23] UDSR: Twice Upsampling-Diffusion for Higher Super-Resolution
链接: https://arxiv.org/abs/2606.09608
作者: Zhiqiang Wu,Yitong Dong,Xian Wei
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Diffusion-based generative models have achieved remarkable success in real-world image super-resolution (SR). With tiled diffusion techniques, these models can produce high-resolution images that exceed their native-supported resolution. However, the quality of such high-resolution (e.g 2048^2 ) outputs often remains extremely poor, primarily due to two factors we consider: the image upsampling ratio (e.g \times8 ) exceeding the model’s native-supported upsampling ratio (e.g \times4 ), and the model’s native-supported resolution. In practice, training a native high-resolution model requires larger architectures, which incur significant computational overhead and GPU memory costs, making it hard on limited-resource equipment. Thus, we present TUDSR, a Twice Upsampling-Diffusion framework for higher SR. The TUDSR framework mainly consists of two stages: the first involves training at R -resolution, and the second introduces a looped chunk-based training strategy at NR -resolution. Each stage adapts a one-step GAN architecture comprising a generator and a discriminator. Based on SD2.1-base, we develop TUDSR-S, which achieves state-of-the-art performance across multiple benchmarks. Extensive experiments further demonstrate that TUDSR-S generates high-quality images at the resolutions of 1024^2 and even 2048^2 , significantly outperforming existing approaches. Code is available at this https URL.
[CV-24] Efficient Minimal Solvers for Relative Pose Estimation in Autonomous Driving Applications
链接: https://arxiv.org/abs/2606.09569
作者: Tao Li,Liang Liu,Jianli Han,Weimin Lv
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:With the advancement of visual sensing systems, computer vision is playing an increasingly important role in autonomous driving and robot navigation. Relative pose estimation in multi-camera systems is essential for accurate vehicle localization and environment perception, demanding high real-time performance and robustness. Existing methods, however, often involve high computational costs and rely heavily on abundant feature matches, limiting their applicability in time-sensitive driving scenarios. To address these limitations, this paper introduces a unified framework for efficient relative pose estimation, built upon a novel translation parameterization and first-order rotation approximation. Within this framework, we propose three efficient minimal solvers specifically designed for autonomous vehicles. The first solver integrates the vertical direction prior from Inertial Measurement Units (IMUs), the second utilizes the rotation axis direction prior during steering maneuvers, and the third is designed for planar motion - a realistic assumption for ground vehicles operating on structured roads. By reducing both the minimal number of point correspondences and the algebraic complexity, our methods enable faster hypothesis generation within RANSAC-based pipelines, improving suitability for real-time systems. Extensive experiments on synthetic datasets and the KITTI autonomous driving benchmark demonstrate that the proposed solvers achieve a favorable balance between speed and accuracy compared to existing state-of-the-art algorithms.
[CV-25] Streaming Interventions: Can Video Large Language Models Correct Mistakes as They Occur? NEURIPS
链接: https://arxiv.org/abs/2606.09547
作者: Apratim Bhattacharyya,Shweta Mahajan,Sanjay Haresh,Rajeev Yasarla,Reza Pourreza,Litian Liu,Risheek Garrepalli,Roland Memisevic
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Qualcomm Interactive Cooking: Ego-MC-Bench – available at this https URL and Ego-CoMist – available at this https URL
Abstract:Learning everyday skills, like cooking a dish, relies increasingly on instructional media such as online videos. This opens the door to the use of video (and multimodal) large language models (LLMs) as task guidance assistants. A crucial capability for the real-world success of a prospective task guidance assistant is it’s ability to intervene proactively as soon as a mistake is apparent in order to guide the user. To evaluate this crucial capability, we introduce Ego-MC-Bench (Mistake Corrections), a benchmark for evaluating reactive, step-by-step task guidance in realistic cooking scenarios. Extensive experiments show that Ego-MC-Bench is highly challenging for state-of-the-art video LLMs. We argue that a key reason is the limited availability of training data for fine-tuning models on this task. Although there exists a wide range of cooking video datasets, existing datasets lack examples of mistakes along with appropriately timed interventions. To help address this data limitation, we also introduce Ego-CoMist, a counterfactual synthetic dataset created by transforming non -interactive cooking videos into supervised training examples showing proactive interventions. We show that fine-tuning on Ego-CoMist yields performance gains especially for smaller and more efficient video LLMs that are well suited for delivering assistance on edge devices.
[CV-26] A VideoMAE-v2 Approach to Zero-Shot Traffic Accident Anticipation
链接: https://arxiv.org/abs/2606.09542
作者: Siyuan Li,Xiaoyang Bi,Mengshi Qi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Traffic accident anticipation – predicting the likelihood of an imminent collision at every frame of a dashcam video – is safety-critical yet difficult to scale, because collecting in-domain annotated accident footage for every deployment scenario is prohibitively expensive. We study this task under a zero-shot setting where no target-domain training data is available: the model must learn exclusively from a publicly available binary-labelled driving-accident dataset and generalise to unseen dashcam footage. We propose a framework that bridges the gap between the frame-level temporal risk estimation task and coarsely labelled binary accident datasets by coupling a VideoMAE-v2 backbone with a per-frame prediction head under a sliding-window protocol. Our method achieves 2nd place in the 2026 CVPR@AUTOPILOT Zero-Shot Accident Anticipation competition. Code is available at this https URL.
[CV-27] Adversarial Attack and Disturbance Detection by Hadamard-Coded Output Representations for Object Detection and Semantic Segmentation
链接: https://arxiv.org/abs/2606.09536
作者: Lucas Görnhardt,Timo Bartels,Niklas Schwarz,Tim Fingscheidt
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Conventional one-hot encodings often yield poorly calibrated models, being overconfident under attack, and letting entropy-based detection algorithms fail. Previous image classification works have demonstrated that Hadamard-coded output representations can improve adversarial robustness. However, attempts to integrate Hadamard codes into semantic segmentation fall far behind state-of-the-art models in mean intersection-over-union performance. Regarding object detection, such output encodings have not yet been investigated at all. Further, no prior art addressed intrinsic codeword inconsistencies or actually exploited intrinsic codeword redundancy. Accordingly, we first derive a novel decoding procedure for Hadamard codewords towards optimal class-wise probabilities, solving the underlying optimization problem by using the projection onto the probability simplex. Second, our optimization delivers a measure of prediction inconsistency. Third, we are the first to show how to exploit these inconsistencies for adversarial attack and disturbance detection. Fourth, we introduce HadamardNet, a framework employing Hadamard codes as output representations for semantic segmentation and object detection models and tasks. We conduct a comprehensive evaluation both on disturbances and adversarial attacks, achieving state-of-the-art perturbation detection performance for both tasks in only a single detection pass, while delivering equivalent or close-by reference performance on clean data.
[CV-28] SwiftVR: Real-Time One-Step Generative Video Restoration
链接: https://arxiv.org/abs/2606.09516
作者: Jiaqi Yan,Xiangyu Chen,Xinlin Zhong,Haibin Huang,Chi Zhang,Jie Liu,Jiantao Zhou,Xuelong Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Real-time video restoration (VR) for live streams requires high-resolution outputs under strict per-frame latency constraints. Existing one-step diffusion-based VR models remain difficult to deploy on consumer-grade GPUs due to two main bottlenecks: quadratic spatial attention at high resolutions and the latency-memory overhead of large video autoencoders. We present SwiftVR, a streaming one-step generative VR framework that reduces both bottlenecks under a causal chunk-wise protocol. For attention, mask-free shifted-window self-attention gathers each spatial window into a dense tensor via deterministic indexing, keeping all attention calls on the dense scaled dot-product attention path without masks, cyclic shifts, padding, or hardware-specific sparse kernels. Because SwiftVR uses only standard dense SDPA calls, the trained model transfers to consumer GPUs without retraining or custom kernels. For autoencoding, a lightweight Restoration-aware Autoencoder enables fast chunk-wise decoding while preserving reconstruction quality. On a single H100, SwiftVR sustains 31~FPS at 2560x1440 and 14~FPS at 3840x2160, whereas all compared diffusion-based VR baselines exceed the memory limit at 4K. On a consumer RTX~5090, SwiftVR reaches 26~FPS at 1920x1080. To our knowledge, SwiftVR is the first generative VR model to achieve real-time 1080p streaming on a consumer-grade GPU, while attaining strong no-reference perceptual quality with lower inference cost. Project is available at this https URL.
[CV-29] Securing Self-supervised Data Curation for Foundation Models Robustness
链接: https://arxiv.org/abs/2606.09511
作者: Sandeep Gupta,Roberto Passerone
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 22 pages
Abstract:Self-supervised data curation provides a pathway to scaling and improving the generalization capabilities of machine learning models. By leveraging self-supervised learning (SSL) for data curation, the demand for massive training datasets required by foundation models can be effectively met. SSL greatly alleviates the costs associated with annotation and manual dataset curation while minimizing the need for human oversight. However, the integrity of SSL-curated datasets must be rigorously checked, as reliance on anonymous and unvetted external sources can substantially increase the risk of data poisoning. In this paper, we propose a Poisoned Data Detector (PDD), an active defense mechanism designed to ensure the integrity of SSL-curated datasets prior to foundation model training. PDDs are designed using a combination of the pretrained ImageBind model and traditional classifiers, including Random Forest (RF), k-Nearest Neighbors (KNN), Naive Bayes (NB), and Support Vector Machines (SVM). We rigorously evaluated PDDs using 176,200 images from three diverse datasets and three different adversarial attacks encompassing both in-distribution and out-of-distribution scenarios. Notably, SVM-PDD achieves superior performance for both in-distribution (Set3-Set5) and out-of-distribution (TrueFace and 140K RealFace) datasets. Our design demonstrates strong scalability and enables the rapid integration of new adversarial attack detectors through an ensemble approach.
[CV-30] Prisma-World: Camera-Controllable Multi-Agent Video World Model
链接: https://arxiv.org/abs/2606.09507
作者: Huiqiang Sun,Zhan Peng,Size Wu,Kun Wang,Kang Liao,Dianyi Wang,Xingyu Zeng,Sheng Jin,Yangguang Li,Zhiguo Cao,Ziwei Liu,Wei Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Video world models have made rapid progress in generating controllable visual experiences, but most of them still simulate the world from a single observer. Extending such models to multiple agents raises a central challenge: if each agent’s future state is generated independently, overlapping views may instantiate different versions of the same scene, leading to inconsistent objects, layouts, and appearances across agents. Conventional camera conditioning controls individual trajectories, but it does not explicitly couple the generation of views that should agree under shared scene geometry. We introduce Prisma-World, a camera-controllable multi-agent world model that formulates multi-agent generation as a joint geometry-aware denoising process for cross-view consistency. Prisma-World processes all agent videos within one full-attention sequence, uses a multi-agent RoPE design to distinguish agent identities while preserving synchronized temporal coordinates, and injects relative camera geometry into attention to bias overlapping viewpoints toward shared scene evidence. To further strengthen multi-view consistency and enhance global spatial perception, we augment our framework with an overlap-decaying curriculum training paradigm alongside minimap-conditioned structural guidance. To facilitate the training and evaluation of multi-agent models, we introduce PrismaDataset, a large-scale UE5 dataset with panoramic acquisition across diverse scenes, composable multi-agent view groups with flexible agent counts and complex camera trajectories, and precise camera/action annotations for consistency training and evaluation. Experiments show that a single Prisma-World model can generate high-fidelity multi-agent videos with flexible agent numbers, camera controllability, improved cross-view consistency, and spatial grounding under minimap guidance.
[CV-31] ContextShift: A Controlled Benchmark for Context Dependence in Object Detection
链接: https://arxiv.org/abs/2606.09495
作者: Dan Zlotnikov,Alex Lazarovich,Ohad Ben-Shahar
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Modern object detectors achieve strong performance on standard benchmarks, yet their robustness to contextual variation remains insufficiently understood. Prior evaluations largely rely on aggregate metrics such as AP on uncontrolled distribution shifts, which can obscure how performance degrades under context change. We introduce ContextShift, a controlled benchmark that systematically manipulates object–context relationships while preserving object appearance. Built on COCO 2017, it isolates context as an independent variable through geometric transformations and synthetic and natural background substitutions, including a continuous compatibility axis based on normalized pointwise mutual information (NPMI). Across diverse detector architectures, we observe a consistent degradation pattern: false negatives increase by up to 227% and prediction volume decreases by up to 44%, while false positives remain stable or decline. This suppression behavior is not captured by aggregate metrics such as AP, which can mask substantial recall loss and changes in prediction dynamics. Further analysis suggests that degradation is driven less by reduced confidence than by a reduced formation of valid detection candidates. Moreover, performance along the statistical compatibility axis is non-monotonic, peaking at intermediate NPMI and degrading toward both extremes, indicating that statistical co-occurrence does not correlate linearly with effective visual context. Finally, we show that context-aware augmentation improves robustness: every augmented variant outperforms the dataset-only baseline on both original and manipulated test images, partially recovering performance lost to prediction-suppression failures by exposing models to object–context decoupling during training.
[CV-32] Optical Music Recognition for Real-World Manuscripts with Synthetic Data ICDAR2026
链接: https://arxiv.org/abs/2606.09479
作者: Jiří Mayer,Martina Dvořáková,Vojtěch Dvořák,Markéta Herzánová Vlková,Filip Bím,Pavel Pecina,Samuel Šomorjai,Petr Žabička,Jan Hajič jr
类目: Computer Vision and Pattern Recognition (cs.CV); Digital Libraries (cs.DL)
备注: Accepted for publication at the ICDAR 2026 conference
Abstract:Optical Music Recognition (OMR) has seen major progress in model design, with end-to-end methods now capable of recognising notation at all levels of complexity. However, the impact of this progress has been limited by the visual domains of available training datasets, which are largely born-digital. Existing large collections of sheet music in libraries and other heritage institutions contain predominantly manuscripts, whose visual domains are highly diverse and different, so existing OMR systems fail when applied in the real world. These institutions are often resource-constrained, so large in-domain datasets cannot be expected. We provide a first baseline on real-world manuscripts with complex piano notation in the resource-constrained scenario. Using fine-grained music notation graph (MuNG) annotations and the Smashcima synthesis tool, we then show that while some direct transcriptions of in-domain data remain essential, domain adaptation using synthetic musical manuscript images brings significant improvement. Furthermore, the symbols used do not need to be in-domain, so the expensive fine-grained annotation can be avoided. We thus bring OMR closer to one of its stated goals: preserving and promoting musical cultural heritage.
[CV-33] Efficient Minimal Solvers for Visual-Inertial Relative Pose Estimation in Multi-Camera Systems
链接: https://arxiv.org/abs/2606.09477
作者: Tao Li,Zhenbao Yu,Banglei Guan,Jianli Han,Weimin Lv
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Estimating the relative poses of multi-camera systems is a fundamental problem in computer vision, with critical applications in autonomous vehicles, mobile devices, and unmanned aerial vehicles (UAVs). However, existing solutions often suffer from high computational complexity or rely on an excessive number of point correspondences, limiting their real-world applicability. To address these limitations, we propose two efficient minimal solvers for estimating the relative poses of multi-camera systems using a novel parameterization. The first solver leverages the vertical direction prior provided by Inertial Measurement Units (IMUs), while the second utilizes the rotation axis direction prior from IMUs. Our methods require only four point correspondences and reduce the problem of multi-camera relative pose estimation to solving a univariate 6th-degree polynomial, a significant improvement over existing approaches, which typically involve 8th-degree polynomials. This reduction in computational complexity and correspondence requirements makes our solvers particularly effective when integrated into RANSAC frameworks, demonstrating strong potential for visual odometry applications. Through rigorous evaluations on synthetic data and the KITTI benchmark, our methods achieved superior computational efficiency and competitive accuracy compared to state-of-the-art algorithms.
[CV-34] raining-Free Generalized Few-Shot Segmentation through Open-Vocabulary Semantic Arbitration
链接: https://arxiv.org/abs/2606.09474
作者: Silas Kwabla Gah,Ebenezer Owusu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Generalized Few-Shot Semantic Segmentation (GFSS) has traditionally been approached as a representation-learning problem, requiring task-specific adaptation to incorporate novel classes from limited support examples. Recent foundation models, however, already exhibit strong open-vocabulary recognition and segmentation capabilities, raising a different question: can GFSS be solved through inference-time coordination of frozen semantic priors rather than parameter adaptation? We answer this question with Open-V, a training-free GFSS framework that combines Segment Anything (SAM3) Promptable Concept Segmentation (PCS) with a K-shot CLIP support centroid through calibrated per-pixel semantic arbitration. OpenV introduces no trainable components and supports arbitrary semantic categories at inference time. Beyond segmentation performance, our study contributes three broader findings. First, we show that support information can be incorporated through inference-time semantic grounding, and that its contribution increases as foundation-model text priors weaken on label-disjoint vocabularies. Second, we identify a reproducibility confound in foundationmodel segmentation, demonstrating that preprocessing and evaluation-space mismatches can silently distort reported performance. Finally, we validate Open-V across PASCAL5i, COCO-20i, and ADE-OW, showing that training-free coordination of foundation-model priors generalizes across both conventional GFSS and open-vocabulary evaluation settings. On PASCAL-5i (1-shot), Open-V attains base/novel/harmonic mIoU of 78.4/77.5/77.9, without GFSS-specific training surpassing the strongest trained baseline by +17.7 HM.
[CV-35] GD-MIL: Grade-Disentangled Multiple Instance Learning for Multimodal Biochemical Recurrence Prediction in Prostate Cancer
链接: https://arxiv.org/abs/2606.09453
作者: Dasari Naga Raju
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Biochemical recurrence (BCR) after radical prostatectomy is a critical endpoint in prostate cancer, yet risk stratification relies almost entirely on variables dominated by Gleason grade. Whether HE whole slide images (WSIs) carry prognostic signal beyond grade, and whether multiple instance learning (MIL) can recover it, remains unsettled. A key obstacle is that many pipelines select model checkpoints on the evaluation fold, artificially inflating concordance. We construct a rigorous benchmark on TCGA-PRAD (487 patients, 101 BCR events) using strict out-of-fold scoring over five-fold cross-validation repeated across five seeds. The choice of MIL aggregator (ABMIL, CLAM, TransMIL, PatchGCN) has little effect (C-index 0.61-0.64 with UNI2-h), while the feature extractor is the dominant factor (ResNet50 0.566 versus pathology foundation models up to 0.639). A clinical Cox model on grade, stage, and age reaches 0.687; no imaging-only model significantly outperforms it (p 0.10). We introduce Grade-Disentangled MIL (GD-MIL), a gated-attention MIL encoder trained with a gradient-reversal grade adversary that encourages the slide representation to be invariant to Gleason grade before late fusion with clinical variables. GD-MIL achieves C-index 0.704, significantly outperforming both the clinical baseline (delta-c = +0.029, p = 0.0005) and the best imaging-only model (delta-c = +0.062, p = 0.039), suggesting HE morphology contains prognostic information complementary to grade. A median risk split yields log-rank p 0.0001 separation in BCR-free survival (~20% vs ~70% at five years).
[CV-36] Dense Force Estimation with an Event-based Optical Tactile Sensor
链接: https://arxiv.org/abs/2606.09451
作者: Agis Politis,René Zurbrügg,Valentina Cavinato
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Humans rely on spatially dense, geometry and force-aware tactile feedback at high temporal resolution for dexterous manipulation. While vision-based tactile sensors enable dense force estimation, they are limited by camera frame rates, motion blur, and data bandwidth. Event-based optical tactile sensors offer an attractive alternative with microsecond temporal resolution and low motion blur, but existing methods are restricted to predicting only net forces. We introduce the first framework for dense 3D force field reconstruction using event-based optical tactile sensors. Our approach estimates 3D surface displacements from event data and maps them to forces via the inverse Finite Elements Method (iFEM). Shear displacements are recovered through the proposed event-based marker tracking algorithm, while normal displacements are predicted by a convolutional neural network trained on a collected dataset of synchronized force-displacement-event data. Experiments demonstrate accurate reconstruction of physically grounded forces, achieving a mean absolute error of (0.14 N, 0.10 N, 0.93 N) over force ranges up to (4 N, 4 N, 20 N), while operating at an average of 100 Hz. This work constitutes a first step toward enabling dense force feedback for high-frequency control in robotic grasping and dexterous manipulation.
[CV-37] Leverag ing Morphology for Historical Script Metrological Analysis
链接: https://arxiv.org/abs/2606.09446
作者: Malamatenia Vlachou Efstathiou,Raphaël Baena,Dominique Stutzmann,Mathieu Aubry
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Advances in handwritten text recognition have enabled large-scale transcription of historical documents, but still provide limited access to interpretable visual measurements for paleography, the study of historical scripts. In this paper, our main insight is that morphological script analysis, in particular the capacity to learn character prototypes from line-level transcriptions, enables the definition of scalable, meaningful, and stable paleographic measurements. More precisely, we leverage a transformer-based detection architecture together with a prototype-based line reconstruction module to learn prototypical characters and their occurrence, deformation, and positioning. Our contributions are twofold. First, we introduce a deep architecture and learning methodology that enables efficient character modeling with only line-level transcription supervision, significantly improving over the Learnable Typewriter baseline and enabling accurate character bounding box prediction, unlocking its potential for paleographic measurements. Second, we introduce and demonstrate the paleographical relevance of automatic measurements enabled by our architecture for characters, bi-grams, and spaces between graphical units. For this demonstration, we extend the annotations of the codex Paris, BnF, fr. 2813, commissioned in the late fourteenth century by Charles V and copied by four hands, to 160 pages. We visualize our measurements over these pages, showing how they enable us not only to differentiate graphical profiles, but also to discover and analyze subtle variations. This case study outlines the scalability of our approach and its frugality in terms of required training data, since a single column of text is sufficient to compute our measurements on each of the 160 pages. Data and code are publicly available at: this https URL. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2606.09446 [cs.CV] (or arXiv:2606.09446v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2606.09446 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-38] vesselFM-CT: Segmenting All Blood Vessels in CT Images for System-Level Cardiovascular Analysis
链接: https://arxiv.org/abs/2606.09400
作者: Bastian Wittmann,Chinmay Prabhakar,Suprosanna Shit,Bjoern Menze
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The vascular network in the human body is characterized by blood vessels exhibiting drastic structural variations in radius, length, topological properties, and branching patterns. This heterogeneity, together with location-specific anatomical background variations, poses a significant challenge for robust, large-scale analysis of the entire cardiovascular system. As a result, most research has focused on narrow, isolated segments of the vascular network. While such targeted studies provide valuable insights, they inherently limit the ability to assess the systemic health and functional integrity of the vascular network as a whole. In this work, we aim to bridge this gap to advance both clinical diagnostics and our fundamental understanding of vascular physiology. We propose the task of segmenting all vessels in CT images, ranging from the largest components of the cardiovascular system to even minuscule mesenteric vessels. To this end, we introduce vesselFM-CT, the first model capable of robustly segmenting all blood vessels in 3D CT images. VesselFM-CT is trained via an iterative, multi-step process and optimizes our proposed TubeLoss loss function, effectively addressing the inherent heterogeneity of the cardiovascular system. We demonstrate that vesselFM-CT outperforms all baselines and enables automated, precise extraction of the cardiovascular system from CT images, thereby unlocking a wide range of clinical and technical perspectives, including automated disease classification and synthetic CT image generation.
[CV-39] CapRL: Unified Reinforcement Learning with Verifiable Rewards for Dense Image and Video Captioning
链接: https://arxiv.org/abs/2606.09393
作者: Penghui Yang,Long Xing,Xiaoyi Dong,Yuhang Zang,Yuhang Cao,Yibin Wang,Yujie Zhou,Jiazi Bu,Jianze Liang,Qidong Huang,Jiaqi Wang,Feng Wu,Dahua Lin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 26 pages, 10 figures. Project page: this https URL . arXiv admin note: text overlap with arXiv:2509.22647
Abstract:Image and video captioning are fundamental tasks that bridge the visual and linguistic domains, playing a critical role in pre-training Large Vision-Language Models (LVLMs). Current state-of-the-art captioning models are typically trained with Supervised Fine-Tuning (SFT), a paradigm that relies on expensive, non-scalable annotations and often causes models to memorize specific ground-truth answers, limiting their generality and ability to generate diverse, creative descriptions. To overcome these limitations, we propose applying Reinforcement Learning with Verifiable Rewards (RLVR) to the open-ended task of multimodal captioning. We introduce Captioning Reinforcement Learning++ (CapRL++), a novel reference-free training framework that redefines caption quality through its utility: a high-quality caption should enable a non-visual language model to accurately answer questions about the corresponding visual content. CapRL++ employs a decoupled two-stage pipeline where an LVLM generates a caption, and the objective reward is derived from the accuracy of a separate, vision-free LLM answering Multiple-Choice Questions based solely on that caption. Evaluations on more than 20 image and video benchmarks show that CapRL++ improves dense caption quality and strengthens caption-based pretraining across tasks such as spatial and temporal understanding. Pretraining on scalable image and video caption datasets annotated by CapRL++ yields substantial downstream gains. Furthermore, within the Prism Framework for caption quality evaluation, compact models trained with CapRL++ achieve dense captioning performance comparable to substantially larger models such as Qwen2.5-VL-72B and Qwen3-VL-235B-A22B. These results validate that CapRL++ effectively trains models to produce generalizable, high-fidelity descriptions, establishing a robust foundation beyond the limitations of traditional SFT.
[CV-40] Real-time body pose non-verbal communication with a consistency-based reliability measure
链接: https://arxiv.org/abs/2606.09390
作者: Alina Marcu,Dragos Costea,Cristina Lazar,Marius Leordeanu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注:
Abstract:Body movement communicates intent at distances and in conditions where neither the face, nor speech can be captured. We study the recognition of communicative intent from 2D body pose alone. We argue that body motion is a reliable signal especially in scenarios that require real time low-cost on-device person-to-robot communication in long distance environments, such as rescue missions. However, existing resources do not isolate this signal. Affective corpora combine body, face, voice and text, while skeleton action-recognition benchmarks label the action performed rather than the message conveyed. We release a dataset of real frames of full-body pose covering ten communicative intents and we compare it against other real (IPC) and synthetic (MotionLCM, VEO3.1, Kimodo) ones that span a range of difficulty. We target systems that can run on a robot’s limited onboard hardware. We benchmark multiple models, from skeleton graph classifiers to joint motion-forecasting networks, and report performance metrics together with frame rate on an embedded GPU (NVIDIA Orin~Nano), since speed matters as much as accuracy in our scenario. Finally, we show that a model’s own autoregressive self-consistency works as an unsupervised reliability signal. We give a short proof that bounds the probability that a self-consistent prediction is correct, show that this probability grows with the number of consistent steps, and identify the conditions under which a confident prediction can still be false, benchmarked against industry-standard metrics.
[CV-41] An Opticalmechanics Framework for Dynamic Estimation of Multibody Systems
链接: https://arxiv.org/abs/2606.09383
作者: Banglei Guan,Xuanyu Bai,Qingquan Chen,Zibin Liu,Dongcai Tan,Zhenbao Yu,Yang Shang,Qifeng Yu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 12 figures
Abstract:Conventional dynamics analysis of the human body is often constrained by the need for contact force and torque sensors and controlled laboratory environments. To address this issue, this study proposes an opticalmechanics kinematic-dynamic integrated estimation framework for multibody systems. Specifically, a constrained multibody model is established to describe the system dynamics, while image-measured kinematic quantities are used as non contact inputs for dynamic estimation. The unknown joint torque is then identified through a genetic-algorithm based optimization by minimizing the discrepancy between model-predicted and image-measured kinematic quan tities. Experimental validation on an air-bearing platform showed that the wrist joint torque estimated from image data achieved a mean absolute error of 0.46 Nm compared with sensor measurements. In the forward prediction test, the model-predicted angular velocity achieved a mean absolute error of 0.006 rad/s relative to the image-measured results. This study demonstrates the potential of combining image measurement and mechanical modeling for non-contact dynamic estimation in scenarios where direct force and torque measurement is difficult.
[CV-42] Echo-DM: Ultrasound Marker Removal via Conditional Latent Diffusion and Region-Aware Fusion
链接: https://arxiv.org/abs/2606.09378
作者: Zhiwei Wang,Tao Huang,Wentao Jiang,Muyi Li,Jianxin Liu,Jian Chen,Jie Zou,Yong Luo,Bo Du,Jing Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 4 figures
Abstract:Clinical ultrasound images often contain artificial markers, such as measurement calipers and text, to assist diagnostic interpretation and comparison. However, these markers can introduce shortcut bias in downstream automated analysis, encouraging deep learning models to rely on marker-related cues rather than clinically meaningful anatomy. Existing marker removal methods are either mask-dependent and vulnerable to error propagation, or mask-free deterministic restorers that may over-smooth ultrasound texture and perturb unaffected background regions. To address these challenges, we present Echo-DM, a framework for ultrasound marker removal via conditional latent diffusion and region-aware fusion. Echo-DM follows a common encoder-diffusion-decoder pipeline, where a DiT-based conditional latent diffusion network performs global restoration and a region-aware fusion module enforces preservation-aware image-space refinement under end-to-end mask-free inference. Building on this fixed core design, we further instantiate Echo-DM-V and Echo-DM-R with VAE-based and RAE-based latent modules, respectively, which demonstrates that the Echo-DM architecture is compatible with diverse latent-module instantiations. Extensive experiments on Echo-PAIR, a large-scale paired clinical ultrasound dataset, demonstrate superior marker removal and strong anatomical fidelity compared with representative two-stage baselines, while providing favorable quality–efficiency trade-offs across deployment settings. Data, code and models will be released at this https URL.
[CV-43] PhysScene: A Scene Graph Dataset for Scientific Visual Reasoning in Physics Experiments
链接: https://arxiv.org/abs/2606.09368
作者: Minghao Zou,Qingtian Zeng,Shangkun Liu,Yanda Meng,Guanghui Yue,Baoquan Zhao,Abdulmotaleb El Saddik,Wei Zhou
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Scene Graphs (SGs) provide structured representations of visual scenes by modeling objects and their pairwise relationships. Despite recent progress, existing datasets primarily focus on generic natural contexts, leaving domain-specific and function-oriented scenes largely underexplored. This limitation restricts the evaluation of relational reasoning in scientific experimental scenes, thereby hindering the development of intelligent monitoring, analysis, and related applications in such scenes. To address this gap, we introduce PhysScene, the first SG dataset tailored to physics experiments. PhysScene encompasses specialized instruments, structured experimental setups, and functional relations intrinsic to experimental environments, enabling reasoning that extends beyond spatial co-occurrence to logical dependencies. Rather than pursuing large data scale, PhysScene focuses on strong semantic constraints and high relation density in experimental scenes, posing new challenges for existing scene parsing algorithms while offering opportunities for further improvements. Extensive analyses and experiments show that PhysScene complements existing benchmarks and establishes a valuable testbed for advancing scientific visual reasoning. The dataset is publicly available at this https URL.
[CV-44] RT-SDGOD: Real-Time Single-Domain Generalized Object Detection
链接: https://arxiv.org/abs/2606.09367
作者: Yupeng Zhang,Fangzhuo Gao,Ruize Han,Wei Feng,Liang Wan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In real-world deployment under strict real-time constraints, weather and imaging variations induce significant distribution shifts, severely degrading detectors. Single-Domain Generalized Object Detection aims to mitigate this issue, yet existing methods rarely investigate-at the level of problem formulation-the generalization capability of real-time detectors under such constrained inference budgets. To this end, we introduce Real-Time Single-Domain Generalized Object Detection (RT-SDGOD), which focuses on how real-time detectors can achieve cross-domain generalization under zero extra inference overhead by relying solely on training-time representation learning. We observe that, under domain shift, DETR-based real-time detectors mainly degrade through increased missed detections, rooted in limited and unstable object-level discriminative evidence. Based on this, we propose RT-SDGDet, a multi-evidence collaborative modeling framework for RT-SDGOD. The core idea is to enable multiple queries of the same object to collaboratively cover more sufficient discriminative evidence while maintaining the stability of such evidence modeling across views. Specifically, we use one-to-many (O2M) supervision to construct stable object-specific query groups, and further design Discriminative Evidence Diversity Learning (DEDL) and Dual-view Evidence Consistency Learning (DvECL) to expand object-level evidence coverage and improve evidence stability under appearance perturbations, respectively. Since all components are introduced only during training, our method incurs no extra inference overhead. Extensive experiments show that the proposed method achieves better generalization performance than existing approaches across multiple unseen target domains.
[CV-45] Zero-Shot Semantic Re-Identification for Autonomous Driving: A VLM Baseline Study
链接: https://arxiv.org/abs/2606.09362
作者: Eduardo Borges,Manuel Abreu,Luís Garrote,Urbano J. Nunes
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 7 pages
Abstract:Re-Identification (ReID) in autonomous driving is typically formulated as a visual matching problem, where observations of vehicles, pedestrians, and cyclists are associated across time, frames, or camera views using learned appearance embeddings, often complemented by motion, geometric, or multimodal cues. However, purely visual representations may be sensitive to viewpoint, occlusion, illumination, and sensor-domain variations, limiting their interpretability and robustness in complex driving scenes. We propose a baseline study of a zero-shot pipeline using Vision-Language Models (VLMs) to generate textual descriptions of detected traffic participants and evaluate whether these descriptions can support identity matching across observations. Instead of relying only on low-level visual similarity, the proposed formulation represents each object through structured semantic attributes, including category, color, shape, pose, visible parts, spatial context, and distinctive visual cues. This study provides an initial benchmark for language-based re-identification in autonomous-driving scenarios, discussing and evaluating the strengths and limitations of current VLMs for this task. Results demonstrate that zero-shot semantic descriptions can support effective object re-identification, achieving retrieval performance comparable to a supervised CNN baseline while offering greater interpretability through explicit identity cues. However, the experiments also reveal important challenges, including attribute inconsistency across viewpoints and limited fine-grained discrimination between visually similar instances.
[CV-46] ExDet: Open-Domain Open-Vocabulary Detection with Cross-modal Extrapolation and Rectification
链接: https://arxiv.org/abs/2606.09360
作者: Yupeng Zhang,Yuzhong Feng,Ruize Han,Zhiwei Chen,Wei Feng,Liang Wan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Open-domain open-vocabulary detection (ODOVD) requires detectors to generalize to both novel categories and unseen domains, making it more challenging than open-vocabulary detection. Existing methods typically train open-vocabulary detectors together with domain generalization modules from scratch, leading to high training cost. we propose ExDet, a lightweight category-domain collaborative generalization framework for ODOVD that enhances the cross-category and cross-domain generalization of existing detectors. ExDet consists of Text-Guided Extrapolation (TGE), a lightweight Detector-Compatible Rectification (DCR) module, and ExRPN. Specifically, TGE exploits the DeltaSpace property of vision-language models (VLMs) to infer category- and domain-aware proxy visual prototypes from text. DCR is learned from the TGE-generated prototypes in a detector training-free and real-data-free manner, and is inserted after the classification head at inference to rectify representations toward a detector-compatible source-domain visual distribution, thereby enhancing classification for targets from novel categories and unseen domains. ExRPN recalibrates proposal scores by combining semantic similarity with RPN confidence, improving recall for novel and domain-shifted objects while providing better support for subsequent classification and DCR. ExDet achieves SOTA performance on OD-LVIS, OV-LVIS, Objects365, and MSOSB.
[CV-47] Beyond Humans: Multispecies Animal Face Recognition Using Transfer Learning
链接: https://arxiv.org/abs/2606.09353
作者: Maria De Marsico,Anil K. Jain,Annalaura Miglino
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: This paper extends the work published in the proceedings of CAIP 2025 conference: ‘Adapting to the Wild: From Human Face to Animal Face Recognition’ by De Marsico, M., Jain, A. K., Miranda, M., Orlando, A
Abstract:Individual animal recognition can be useful in the search for lost or stolen pets, the tracking of individuals of endangered species, and the recognition of animals in crowded farms. Present recognition techniques mostly use physical devices, e.g., microchips, often impractical and difficult to apply. These could be replaced by remote recognition via the animal’s face; if accurate enough, it provides several advantages: it is non-invasive, can work at a distance, and is difficult to counterfeit, as, for instance, in the case of substituting sick animals for healthy ones in the food industry. The few existing datasets with sufficient per-subject images annotated with a single animal identity are not large enough to train current deep learning architectures. We rather investigate the possibility of transfer learning, exploiting pre-trained network models as backbones. Our experiments compared FaceNet, which is specifically trained on large databases of human faces, with the Vision Transformer (ViT) pre-trained on ImageNet, i.e., on object categories. We used three face datasets of very different animals: dogs, primates (lemurs, golden monkeys, and chimpanzees), and cattle. We report the results and, for each dataset, compare them with the state of the art (SOTA) ad hoc-trained deep networks. The capture conditions differ among the three datasets. Image quality (resolution, motion blur, diverse poses, etc.) decreases from dogs to cattle to primates. The best performance was achieved with dogs, where ViT reached a mean verification accuracy of 96.85% and a Rank-1 Identification Rate of 84.34%. The results for endangered primates are still encouraging, but performance varies across animal classes and tasks (verification or identification), and does not always outperform SOTA. For cattle, the ViT results outperform SOTA, while FaceNet is still competitive.
[CV-48] aming Perception Jitter: Uncertainty-Aware LiDAR Object Detection for Reliable Motion Classification
链接: https://arxiv.org/abs/2606.09350
作者: Cornelius Schröder,Žygimantas Marcinkus,Markus Lienkamp
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reliable motion classification is critical for autonomous driving, as false dynamic predictions of static objects can cascade into unnecessary planner interventions. Unstable bounding box predictions can lead to spurious velocity estimates in tracking and falsely predicted trajectories. We present a deployment-friendly mitigation strategy that augments a 3D object detector with aleatoric uncertainty estimates and applies a two-sample z-test over short observation windows to separate true motion from jitter. Integrated into Autoware with minimal changes, the approach reuses existing data association for minimal compute overhead. Empirical results show parity with velocity thresholding on nuScenes, but substantially fewer false dynamic predictions and unnecessary stops in real-world test drives, explained by the presence of an intermediate jitter band in the recorded data that speed-only rules misclassify. This demonstrates that uncertainty-aware detection and lightweight statistical testing can deliver practical performance gains for autonomous driving in noisier real-world settings.
[CV-49] IB-HFN: Information Bottleneck-Driven SAR-Optical Fusion Network for High-Fidelity Cloud Removal
链接: https://arxiv.org/abs/2606.09347
作者: Haojun Guo,Fan Feng,Ziquan Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Synthetic aperture radar (SAR)-assisted optical cloud removal aims to recover surface information obscured by clouds in optical remote sensing images by exploiting complementary SAR observations. Existing multimodal fusion methods typically rely on direct spatial concatenation and pixel-wise supervision, which can propagate SAR speckle noise into optical reconstruction and lead to over-smoothed results. To address these limitations, we propose an Information Bottleneck-driven High-Fidelity Network (IB-HFN) for SAR-assisted optical cloud removal. IB-HFN employs a dual-stream backbone to preserve modality-specific representations before deep semantic fusion, thereby mitigating premature cross-modal contamination. At the fusion stage, we introduce a Spatial Information Bottleneck Fusion module that compresses SAR features through a channel-wise variational information bottleneck to suppress unstructured speckle noise. In parallel, a local-global gating mechanism predicts clear-sky regions and routes reliable optical details through a Dirac-initialized skip connection, decoupling noise suppression from texture preservation. We further develop a joint optimization strategy that integrates feature-level bottleneck regularization with image-level constraints on reconstruction accuracy, structural consistency, spectral fidelity, and contrastive sharpness. A dynamic weighting schedule balances these objectives to stabilize training and reduce hazy artifacts. Experiments on the SEN12MS-CR dataset under challenging spatio-temporal splits demonstrate that IB-HFN achieves superior structural preservation and spectral fidelity over existing methods.
[CV-50] Reason Twice: Segmentation via Candidate Discovery and Comparative Reasoning
链接: https://arxiv.org/abs/2606.09303
作者: Xinyan Gao,Haoran Hao,Xiangyu Yue
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:The rapid development of pretrained foundation models has enabled more general image segmentation. Multimodal large language models (MLLMs) have been widely explored for image segmentation with complex queries that require high-level reasoning. Despite promising progress, existing methods are often constrained by limited training data and the gap between MLLMs and mask generation modules. To better transfer MLLMs’ perception and reasoning ability to complex reasoning-based segmentation tasks, we propose a two-stage framework Rea2Seg for mask generation and selection. Specifically, the framework first identifies potential regions as candidate masks based on the attention maps of a segmentation MLLM. It then employs an MLLM to reason over the question and candidate masks and assign scores to each mask. The final segmentation result is obtained by reranking the candidates and selecting the highest-scoring mask, reformulating image segmentation as candidate discovery followed by discriminative mask selection. We also notice that a large portion of questions in existing benchmarks focus on commonsense reasoning, and these questions usually do not fully require joint visual observation and reasoning. To address this issue, we introduce a new benchmark called ReasonSeg-SGDR that comprehensively evaluates a model’s perception, grounding, and reasoning abilities across multiple dimensions, including discriminative recognition, spatial reasoning, geometric reasoning, and multi-step reasoning, with fine-grained mask generation. In addition, we collect training data to enhance MLLMs’ ability to jointly understand multimodal queries and candidate masks, and to assign scores through reasoning. Experimental results on the proposed benchmark and ReasonSeg demonstrate the effectiveness of the unified mask generation and selection framework. Comments: Project page: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2606.09303 [cs.CV] (or arXiv:2606.09303v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2606.09303 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-51] Virtual-point-based Solutions to Handle Generalized Absolute Pose Problem
链接: https://arxiv.org/abs/2606.09294
作者: Bin Li,Banglei Guan,Shunkun Liang,Yang Shang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multi-camera systems are increasingly adopted in robotics and autonomous navigation for their wide field of view, flexibility, and fault tolerance. Nevertheless, existing PnP solvers fail to handle multiple projection centers. This paper introduces a virtual point formulation that bridges the standard PnP and generalized pose problems, enabling a unified pipeline that transforms existing PnP solvers into generalized pose solvers. Based on this framework, we derive three Virtual-point-based Generalized Pose solvers, namely VGPc, VGPq, and VGPr, leveraging Cayley, quaternion, and rotation-matrix parameterizations, respectively. Extensive experiments demonstrate that the proposed solvers inherit the accuracy and efficiency of original PnP algorithms while significantly outperforming existing generalized solvers. Specifically, VGPc achieves higher estimation accuracy under heteroscedastic noise conditions, VGPq maintains global optimality, whereas VGPr provides superior computational efficiency without accuracy degradation.
[CV-52] Visual Para-Thinker: A Single-Policy Multi-Agent Framework for Visual Reasoning
链接: https://arxiv.org/abs/2606.09290
作者: Haoran Xu,Hongyu Wang,Yifei Gao,Jiaze Li,Zizhao Tong,Xiaofeng Zhang,Xiaosong Yuan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Visual reasoning requires integrating evidence distributed across regions, attributes, and relations, making single-chain reasoning prone to early perceptual commitment and hallucination. We propose Visual Para-Thinker++, a single-policy multi-agent framework in which one shared MLLM policy is instantiated as role-conditioned Main, Worker, and Summary Agents. The Main Agent decomposes the task with fixed allocation patterns; Worker Agents reason in parallel under context isolation; and the Summary Agent reconciles full Worker reasoning traces rather than majority-voting on final labels. The shared policy is trained by Multi-Agent Capability Injection and Role-Decoupled Multi-Agent Optimization, which assign role-specific rewards and advantages to corresponding token segments to reduce gradient conflict among collaborative roles. A native inference engine enables efficient multi-agent rollout through shared visual prefix and KV cache reuse. Across V*, CountBench, the RefCOCO family, and HallusionBench, Visual Para-Thinker++ consistently outperforms single-trajectory and inference-time parallel baselines, with especially strong gains on hallucination-sensitive visual reasoning.
[CV-53] EditSSC: Toward Editable Semantic Occupancy Scenes with Unconditional Diffusion Models CVPR2026
链接: https://arxiv.org/abs/2606.09273
作者: Fatima Balde,Raoul de Charette,Alexandre Boulch
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at CVPR 2026 Workshop
Abstract:3D semantic scene generation is crucial for autonomous driving applications, yet most methods rely on complex 3D-specific architectures such as triplane encoders and adapted diffusion networks, limiting both their simplicity and their editing capabilities. We propose EditSSC, an editing-ready method for 3D semantic scene generation using 2D Bird’s Eye View (BEV) representations and off-the-shelf latent diffusion network. Our approach reshapes 3D semantic occupancy grids into multi-channel BEV images and leverages the quantized autoencoder and UNet from Stable Diffusion with minimal modifications. We perform diffusion on the latents after quantization, which enables training-free editing capabilities. By exploiting class-to-code correspondences in the codebook, our method supports sketch-guided generation, inpainting, and outpainting without any retraining. On SemanticKITTI, EditSSC outperforms existing 3D-specific baselines on unconditional generation, demonstrating that well-established 2D architectures can be effectively repurposed for 3D scene generation and editing.
[CV-54] See More Match Better: Multi-Source Feature Fusion for Two-View Correspondence Learning
链接: https://arxiv.org/abs/2606.09262
作者: Xiaojie Li,Xin Jiang,Luanyuan Dai,Jinnan Yang,Yongdong Zhang,Zechao Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Correspondence Learning, Multi-Source Feature Fusion, Outlier Removal, Camera Pose Estimation
Abstract:Two-view correspondence learning aims to distinguish true correspondences (inliers) from false ones (outliers) in image pairs by leveraging their underlying differences. Existing methods mainly rely on coordinate-based geometric consistency. However, they often struggle with pseudo-consistent outliers in scenes containing repetitive structures, textureless regions, or locally similar geometric patterns. To address this limitation, we propose TriMatch, a multi-source feature fusion framework for two-view correspondence learning, which consists of two parts: feature extraction and feature refinement. In feature extraction, TriMatch jointly extracts geometric, texture semantic, and structural semantic features to provide complementary evidence for correspondence discrimination. To bridge the gap between semantic and geometric features, texture and structural semantic features are aligned with geometric features through dedicated Texture-Geometric Alignment and Structural-Geometric Alignment modules, respectively. We further introduce a Semantic-Guided Correspondence Modulation module, which modulates geometric features using semantic information to suppress geometrically plausible but semantically inconsistent correspondences. In feature refinement, a Hierarchical Semantic-Enhanced Correspondence Refinement strategy progressively models correspondence dependencies and recalibrates multi-context feature responses, enabling more reliable inlier-outlier discrimination. Extensive experiments demonstrate the effectiveness, robustness, and generalization capability of TriMatch.
[CV-55] Self-supervised Learning Matters: A Simple Ensemble Solution for Micro-Gesture Recognition
链接: https://arxiv.org/abs/2606.09261
作者: Tingyi Liu,Kun Li,Fei Wang,Junjie Chen,Zhiliang Wu,Jihao Gu,Haixu Liu,Dan Guo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In this paper, we present XInsight Lab’s solution to the micro-gesture classification track of the 4th MiGA Challenge at IJCAI 2026, in which our solution ranked first and achieved a new state-of-the-art result. We propose a multimodal ensemble framework that integrates a self-supervised RGB-based model with supervised multi-stream models from previous solutions. The self-supervised RGB model is pretrained on 120K unlabeled clips via masked video modeling and then fine-tuned on iMiGUE. This simple yet effective RGB baseline achieves 69.224% top-1 accuracy on the iMiGUE test set, demonstrating the benefit of learning transferable representations from unlabeled in-domain videos. By incorporating this model as a complementary branch, the final ensemble reaches 74.419% top-1 accuracy, surpassing the previous state of the art by 1.206 percentage points. Experimental results on iMiGUE, including ablation studies on the ensemble strategy, validate the effectiveness of self-supervised RGB representation learning for micro-gesture recognition.
[CV-56] A practical probabilistic framework for deformable image registration uncertainty in radiotherapy dose propagation
链接: https://arxiv.org/abs/2606.09253
作者: Stefan Heldmann,Sven Kuckertz,Nasim Givehchi,Thomas Coradi,Mikel Byrne,Ben Archibald-Heeren,Nils Papenberg
类目: Computer Vision and Pattern Recognition (cs.CV); Medical Physics (physics.med-ph)
备注:
Abstract:Deformable image registration (DIR) is widely used in radiotherapy for dose propagation and accumulation, but uncertainty in the underlying deformation can substantially affect clinically relevant dose estimates. We present a practical probabilistic framework for propagating DIR uncertainty to voxel-wise dose statistics and dose-volume histograms (DVHs). The method models the mapped correspondence at each voxel as a random variable governed by a transparent local certainty map that can be defined by simple safety margins, structure-boundary mismatch, or structure-wise conservative uncertainty values. This yields interpretable quantities such as dose probabilities, expected dose, confidence bounds, and induced DVH envelopes. The framework is designed to remain lightweight and interpretable: it avoids complex biomechanical or ensemble-based uncertainty models and instead emphasizes simple parameterization, computational feasibility, and transparent dose metrics. We further introduce a structure-guided in/out strategy as an optional refinement that restricts mapping probabilities to anatomically plausible target regions. The approach is demonstrated on a prostate radiotherapy case study and used to compare different certainty-map strategies and probability kernels. The experiments show that the certainty-map design has a stronger effect on resulting dose and DVH uncertainty bounds than the specific kernel choice, while the additional benefit of the in/out strategy is case-dependent and modest in the present example. Overall, the proposed framework provides a transparent way to incorporate DIR uncertainty into radiotherapy dose assessment and to study how modelling choices affect propagated dose metrics. Subjects: Computer Vision and Pattern Recognition (cs.CV); Medical Physics (physics.med-ph) Cite as: arXiv:2606.09253 [cs.CV] (or arXiv:2606.09253v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2606.09253 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-57] LiteVSR: Lightweight Adaptation of Frozen Diffusion Transformers for Video Super-Resolution
链接: https://arxiv.org/abs/2606.09250
作者: Yu Cao,Ziquan Liu,Zhensong Zhang,Jiankang Deng,Shaogang Gong,Jifei Song
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Adapting large-scale pre-trained video generators for Video Super-Resolution (VSR) in novel domains remains computationally prohibitive. Methods that reformulate generation as direct Low-Quality to High-Quality mappings deviate from the original generative formulation, demanding extensive fine-tuning. ControlNet-style adapters lose their efficiency under modern Diffusion Transformers since the absence of encoder-decoder hierarchy forces duplication of the entire backbone. We observe that flow matching offers a principled alternative for cross-domain VSR adaptation. By predicting a constant velocity field across all timesteps, the adaptation task reduces to learning a fixed injection pattern rather than time-varying transformations. Building on this insight, we propose LiteVSR, a minimalist framework that performs VSR using a completely frozen Diffusion Transformer with a lightweight State-Aware Adapter. The adapter employs a dual-stream architecture that extracts static structural cues from the LQ input and dynamic cues from intermediate denoising states, aligning them through time-dependent cross-attention to enable adaptive transition from structural alignment to texture refinement as denoising proceeds. LiteVSR achieves competitive restoration quality with only 11.25% trainable parameters and 12 GPU-hours of training on a single A100, while maintaining fast sampling (down to a single step) compatibility.
[CV-58] MAGIS: Evidence-Based Multi-Agent Reasoning for Interpretable Strabismus Clinical Decision-Making
链接: https://arxiv.org/abs/2606.09249
作者: Xikai Tang,Yifan Wang,Jiafan Zhuang,Li Luo,Jinming Guo,Xiaoling Xie,Jiacheng Liu,Peiwei Wei,Lihao Zhong,Xiaoli Kang,Jie Cen,Guangqiang Yin,Kunliang Qiu,Ce Zheng,Zhun Fan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Strabismus is a common ocular disorder that requires fine-grained subtype diagnosis for individualized treatment planning. However, existing deep learning methods mainly provide diagnostic predictions without transparent reasoning, while recent large vision-language models (LVLMs), although promising for joint image understanding and report generation, remain highly prone to hallucination in this evidence-sensitive and rule-driven medical task. To address these challenges, we propose MAGIS, an evidence-based Multi-AGent reasoning for Interpretable Strabismus diagnosis framework. MAGIS transforms black-box end-to-end generation into a structured diagnostic process consisting of candidate hypothesis generation, dual-evidence constrained context, evidence-based corrective verification, and report generation. Specifically, we introduce a Dual-Evidence Constrained Context (DECC) mechanism that jointly organizes visual evidence from the photograph of the nine cardinal positions of gaze and evidence-based clinical diagnostic rules into a constrained context for reliable diagnostic reasoning. We further develop an Evidence-Based Corrective Verification (EBCV) mechanism that verifies whether the current diagnostic hypothesis is supported by visual evidence, heatmap-based visual cues, and evidence-based clinical diagnostic rules. Hypothesis refinement is triggered when inconsistency is detected. Experiments on a fine-grained strabismus benchmark demonstrate that MAGIS not only significantly outperforms other state-of-the-art diagnostic systems, improving the weighted F1 score from 72.0% to 91.3%, but also substantially improves the clinical reliability (consistency, alignment, and completeness) of generated diagnostic reports. These results demonstrate that MAGIS provides an effective solution for building accurate, evidence-based, and clinically interpretable strabismus diagnosis systems.
[CV-59] mporal-Aware Reasoning Optimization for Video Temporal Grounding ICML2026
链接: https://arxiv.org/abs/2606.09248
作者: Minghang Zheng,Zihao Yin,Yi Yang,Yuxin Peng,Yang Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ICML 2026
Abstract:Multi-modal Large Language Models (MLLMs) have achieved remarkable progress in video temporal grounding with reinforcement learning for generating reasoning paths. However, existing models often produce superficial reasoning, which offers limited guidance for precise temporal localization. This limitation stems from (1) inefficient random exploration and (2) reward functions that focus solely on the answer correctness while ignoring reasoning quality. To address these issues, we propose TaRO (Temporal-Aware Reasoning Optimization), a framework that explicitly enhances the model’s ability of thinking with time. First, we introduce a Constructive Reasoning Exploration that leverages pre-generated dense captions to construct reasoning paths grounded in explicit visual cues and timestamps, enabling efficient exploration of high-quality time-aware reasoning. Second, to evaluate reasoning quality, we design a Temporal-Sensitivity Reward. High-quality reasoning should be anchored to specific events and timestamps. If the event boundary under thinking is disrupted, such reasoning should become invalid, leading to a drop in the logit of the reasoning path. We utilize this drop as a critique of reasoning quality. Finally, TaRO follows a progressive curriculum, which starts by utilizing this reward to select better constructed reasoning paths, and evolves to a free exploration phase where the model autonomously generates effective reasoning. Experiments demonstrate that TaRO achieves state-of-the-art performance on VTG benchmarks. Code is available at this https URL.
[CV-60] SOMA: From Surface Observations to Muscle Anatomy
链接: https://arxiv.org/abs/2606.09246
作者: Eduardo Alvarado,Emily Kim,Gerrit Nolte,Friedemann Runte,Mario Botsch,Marc Habermann,Christian Theobalt
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:With the growing demand for realistic virtual humans, parametric body models have become a cornerstone of modern medicine, sports, and entertainment applications. However, most of these models are inherently limited: they only capture the 3D surface of the skin, offering no insight into the complex bio-mechanical structures that generate motion. As more applications expand towards biomechanics, the need for virtual human models that go beyond the skin has become increasingly evident. Traditional soft-tissue simulations, such as FEM, are accurate but non-scalable and too computationally expensive for most common applications. Alternatively, existing biomechanical tools can simulate muscular forces and activations, but do not model changes in external shape, restricting how activations correlate with actual observable anatomy. This motivates a novel inverse research problem: recovering muscle deformations directly from visible surface observations - i.e., from the skin, and thus the pose. In this work, we present SOMA (from Surface Observations to Muscle Anatomy), a person-specific model that infers spatio-temporal muscle behavior from surface signals obtained using RGB cameras, and SKIM, a subject-specific soft-tissue deformation dataset. To the best of our knowledge, this is the first method that attempts to recover muscle deformations from multi-view RGB data. We show how our method provides anatomically grounded animations without the complexity of traditional simulations, leading to a scalable and cost-effective solution. Data and code are available.
[CV-61] Proposal Refinement for Few-Shot Object Detection
链接: https://arxiv.org/abs/2606.09245
作者: Yuan Zeng,Bin Song,Jie Guo,Yuwen Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Few-shot object detection has gained widely attention in recent years. Some excellent algorithms have been proposed to handle this task. However, most of these algorithms rely on the performance of few-shot classification. Unlike previous attempts, our work focuses on the problem of unbalanced distribution of region proposals between the novel classes and the base classes. In order to alleviate this unbalanced distribution, we propose the proposal refinement approach for different training phases. Specifically, refinement loss is designed for the base training phase to enhance sensitivity of the model to novel classes, and refinement branch is introduced as an auxiliary branch for RPN (Region Proposal Networks) to generate more novel proposals in the fine-tuning phase. By rebalancing the proposal distribution, the proposed approach outperforms the baselines methods by roughly 1% \sim 6% on current benchmarks without increasing any inference time. Through extensive experiments, we prove that we establish a new state-of-the-art method for the few-shot object detection task.
[CV-62] EgoTactile: Learning Grasp Pressure for Everyday Objects from Egocentric Video ICML2026
链接: https://arxiv.org/abs/2606.09243
作者: Yuan Zeng,Yujia Shi,Tiao Tan,Xingting Li,Yaqi Qin,Zongqing Lu,Wenming Yang,Jing-Hao Xue,Qingmin Liao
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to ICML2026 spotlight
Abstract:Estimating full-hand grasp pressure from egocentric video is critical for immersive VR and robotic manipulation, yet dense tactile sensing often relies on intrusive hardware. Existing vision-based methods predominantly rely on planar surfaces or fingertip contacts, failing to generalize to complex 3D object interactions. Therefore, we introduce EgoTactile, a benchmark pairing egocentric video with full-hand pressure supervision for diverse everyday objects, incorporating a bare-hand transfer subset to enable generalization to natural scenarios. Leveraging this benchmark, we first establish EgoPressureFormer as a discriminative baseline. Beyond this, to explicitly address the uncertainty in partial observations, we propose EgoPressureDiff, a conditional diffusion framework that adapts a large-scale pre-trained video diffusion backbone. By combining rich world knowledge priors with a Physically-Informed Feature Rectification layer to inject semantic constraints, our approach effectively infers plausible contact patterns and resolves visual-physical ambiguities. Extensive experiments demonstrate that our method achieves superior performance on the benchmark and robust transferability to in-the-wild scenarios. Our project page is available at this https URL.
[CV-63] Semi-supervised Source Detection in Astronomical Images: New Benchmark and Strong Baseline
链接: https://arxiv.org/abs/2606.09219
作者: Longhan Feng,Zihuang Cao,Ali Luo,Yuanhao Guo,Shuilian Yao,Yixin Guo,Qi Jia,Yu Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Instrumentation and Methods for Astrophysics (astro-ph.IM)
备注:
Abstract:Source detection in modern observational astronomy is a cornerstone for localizing and identifying stellar sources accurately. It is crucial for studies such as stellar population synthesis and cosmological parameter estimation. However, the characteristics of astronomical images, including high density, the effect of point spread functions and low signal-to-noise ratios, significantly challenge the latest advanced object detectors. Besides, fully-supervised detection methods are hardly practical, due to the significant difficulty in annotating dense, small, and faint sources in astronomical images. To tackle the scarcity of astronomical datasets, we introduce a new comprehensive benchmark (LAMOST-DET), comprising 18,400 astronomical images and 728,898 source instances. Upon the dataset, we further devise a novel semi-supervised learning framework coined Nova Teacher, capable of detecting dense sources effectively given sparse annotations. It integrates source light enhancement module, confidence-guided pseudo-supervision, and cross-view complementary mining in a dual-teacher paradigm. Extensive experiments on LAMOST-DET show that, Nova Teacher consistently improves previous competitors by 4.04% and 5.22% mAP under two semi-supervised settings. Additionally, our method competes against other detectors on a natural image dataset, validating its generalization ability to various scenarios. The source code is available at this https URL.
[CV-64] Minimal Solvers for Full-DoF Motion Estimation from Asynchronous Differential SfM
链接: https://arxiv.org/abs/2606.09218
作者: Shuo Pan,Banglei Guan,Bin Li,Zhenbao Yu,Zibin Liu,Zi Wang,Yang Shang,Qifeng Yu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:As a bio-inspired intelligent sensor, event cameras have introduced a new paradigm in the intelligent perception of spatiotemporal information and visual motion estimation, characterized by their high temporal resolution, low latency, and minimal power consumption. However, their asynchronous data streams present significant challenges to traditional synchronous, frame-based algorithms. To address these challenges, this paper presents a novel framework for full degree of freedom (DoF) egomotion estimation directly from asynchronous optical flow, specifically targeting the joint recovery of angular and linear velocities. We decouple the differential epipolar constraint into distinct angular and linear velocity components, and derive its formulation for asynchronous data. Based on this formulation, an optimization algorithm is developed that enables full-DoF egomotion estimation leveraging at least five points. Furthermore, by applying a first-order approximation to rotational dynamics, we transform the constraint equations into a polynomial form, resulting in the first algebraic minimal 5-point solver for this formulation. To ensure real-time performance in high-speed scenarios, we additionally propose an accelerated solver achieved by truncating high-order angular velocity terms. Extensive evaluations on both synthetic and real-world datasets demonstrate that the asynchronous approach outperforms traditional synchronous methods, particularly in its accuracy and robustness to spatiotemporal noise. We believe that this work establishes a critical foundation for efficient and accurate continuous-time motion estimation in high-speed robotics applications.
[CV-65] Event-driven dynamic trajectories reconstruction and measurement of mechanical parameters for frag ments
链接: https://arxiv.org/abs/2606.09208
作者: Haoyang Li,Banglei Guan,Muxi Zha,Yifei Bian,Minzu Liang,Yang Shang,Qifeng Yu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 33 pages,11 figures
Abstract:During warhead detonation, high-density, high-speed, and mutually occluded fragments are generated. Their mechanical parameters (position, velocity, kinetic energy) directly determine the lethality of the warhead fragment field. However, high-intensity flash and smoke in detonation scenarios severely hinder the accurate acquisition of these mechanical parameters. To address this challenge, this paper integrates experimental mechanics approaches and presents an event-driven method for reconstructing the dynamic trajectories of fragments and measuring their mechanical parameters. As a novel brain-inspired visual sensor, event cameras offer microsecond-level temporal resolution and high dynamic range lighting change perception, overcoming the difficulty of accurately measuring high-speed targets under strong flash interference. The method constructs a multi-event-camera vision system, adopting three geometric constraints: time-correlated epipolar constraint to find potential matching event point pairs, and trifocal tensor line constraint plus local homography constraint to eliminate mismatches. A comprehensive probability model is established, with entropy weight method determining the weight of each constraint’s probability to quantitatively filter mismatches. 3D trajectory reconstruction is achieved via spatial line-line intersection and nonlinear optimization. Finally, the velocity and kinetic energy of the fragments are calculated based on the reconstructed trajectory. This method provides reliable technical support for the mechanical damage evaluation of warhead fragment fields and the tactical protection design.
[CV-66] rajectory Optimization in Single and Dual-UAV Bearing-Only Target Localization
链接: https://arxiv.org/abs/2606.09188
作者: Zhijian Xiao,Huayu Huang,Bin Li,Yang Shang,Banglei Guan
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 13 figures and 6 tables. Submitted to Measurement
Abstract:Bearing-only target localization is a fundamental problem in optical measurement and finds extensive applications in unmanned aerial vehicle (UAV) technology. Effective trajectory planning establishes favorable observation geometries, thereby enhancing the target localization accuracy of bearing-only UAV systems. This paper proposes an trajectory optimization method for unmanned aerial vehicles (UAVs) in bearing-only target localization scenarios. By leveraging the Fisher Information Matrix (FIM), the proposed approach dynamically integrates the geometric configuration and vehicle maneuverability into the optimization framework. Specifically, we introduce a spectrally-weighted FIM objective function that provides better gradient dynamics near degenerate configurations, enabling the planner to rapidly escape from poor observation conditions. For dual-UAV scenarios, an intersection angle sine term is introduced to optimize triangulation geometry by improving the sight-line intersection angle, thereby preventing trajectory aggregation. Furthermore, we propose an improved Particle Swarm Optimization (PSO) algorithm with motion model constraints and particle normalization to ensure the physical feasibility of the trajectory and enhance the compatibility with the objective functions. Simulation results demonstrate that the proposed method reduces the median localization error by 99.21% compared to conventional FIM-based approaches in single-UAV scenarios, and achieves a 69.70% improvement for dual-UAV configurations, exhibits superior performance in long-duration bearing-only target localization of maneuverability targets at extended ranges.
[CV-67] CP4D: Compositional Physics-aware 4D Scene Generation
链接: https://arxiv.org/abs/2606.09187
作者: Hanxin Zhu,Cong Wang,Tianyu He,Long Chen,Xin Jin,Chen Gao,Zhibo Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:4D generation (\textiti.e., dynamic 3D generation) has recently emerged as a rapidly growing research frontier due to its powerful spatiotemporal modeling capabilities. However, despite notable advances, existing approaches typically fail to capture the underlying physical principles, producing results that are both physically inconsistent and visually implausible. To overcome this limitation, we present CP4D, a novel paradigm for photorealistic 4D scene synthesis with faithful adherence to complex physical dynamics. Drawing inspiration from the compositional nature of real-world scenes, where immutable static backgrounds coexist with dynamic, physically plausible foregrounds, CP4D reformulates 4D generation as the integration of a static 3D environment with physically grounded dynamic objects. On this basis, our framework follows a three-stage pipeline: \textbf1) Firstly, we leverage pre-trained expert models to generate high-fidelity 3D representations of the environment and foreground objects respectively. \textbf2) Subsequently, to produce physically plausible trajectories and realistic interactions for these objects, we propose a hybrid motion synthesis strategy that integrates priors from physical simulators with the common sense embedded in video diffusion models. \textbf3) Finally, we develop an automated composition mechanism that seamlessly fuses the static environment and dynamic objects into coherent, physically consistent 4D scenes. Extensive experiments demonstrate that CP4D can generate explorable and interactive 4D scenes with high visual fidelity, strong physical plausibility, and fine-grained controllability, significantly outperforming existing methods. The project page: this https URL.
[CV-68] Counterfactual Reasoning for Fine-Grained Evidence Disentanglement in VideoQA
链接: https://arxiv.org/abs/2606.09181
作者: Zhou Du,Hamid Krim,Xiao Wu,Zhaoquan Yuan,Liangwei Li,Keisuke Fujii
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 10 pages, 6 figures
Abstract:Recent advances in video multimodal models have significantly improved VideoQA performance. However, these systems often rely on spurious statistical correlations rather than answer-relevant causal evidence, resulting in unfaithful and brittle reasoning, especially in complex real-world scenarios. Existing methods either rely on cross-modality correlations, costly curated training resources, or insufficient causal assumptions and constraints, and typically operate at the time-interval level. As a result, they fail to explicitly disentangle causal visual cues from confounders and provide limited fine-grained evidence localization. To address this issue, we propose a Counterfactual Reasoning framework for fine-grained Evidence Disentanglement (CREDiT). CREDiT formulates the VideoQA process using a structural causal model and learns cross-modality representations that are explicitly decomposed into causal and non-causal components under independence and minimality constraints. To facilitate faithful disentanglement, we introduce feature-level causal interventions and construct counterfactual inputs that approximate causal effects while suppressing non-causal correlations. Extensive experiments on NExT-GQA, SportsQA, and SPORTU-video demonstrate that CREDiT consistently improves answer accuracy and reasoning reliability across both generic and complex sports scenarios, leading to more trustworthy VideoQA systems.
[CV-69] Claude Code-Driving Scenario Mining for the Argoverse 2 Challenge
链接: https://arxiv.org/abs/2606.09180
作者: Wei Deng,Caoshengzhe Xue,Shuaikun Liu,Zhaohong Liu,Mengshi Qi,Huadong Ma
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We present our submission to the CVPR 2026 Argoverse 2 Scenario Mining Challenge. Our system uses a four-stage pipeline: (1) autonomous code generation via a Claude Code agent powered by GLM~5.1, (2) iterative training set screening with Timestamp Balanced Accuracy threshold 0.8 to curate few-shot examples, (3) semantic code review by a separate Claude Code session, and (4) Qwen3-VL scene-level verification to filter false positives. We report results on the Argoverse 2 test set.
[CV-70] IMUG-Bench: Benchmarking Unified Multimodal Models on Interleaved Understanding and Generation
链接: https://arxiv.org/abs/2606.09169
作者: Lingyi Meng,Zecong Tang,Haoran Li,Tengju Ru,Zhejun Cui,Weitong Lian,Qi Kang,Hangshuo Cao,Yichen Zhu,Yechi Liu,Kaixuan Wang,Yu-Jie Yuan,Chunwei Wang,Yu Zhang,Bo Dai
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注:
Abstract:In recent years, unified multimodal models (UMMs) have emerged to support both understanding and generation within a single framework. Mastering dynamic, multi-turn interleaved image-text dialogues is a crucial task for UMMs in real-world applications. However, existing benchmarks fail to evaluate this important task, as they are often limited to single-turn or static settings, and typically overlook exposure bias in multi-turn interactions. To bridge this gap, we propose IMUG-Bench, a comprehensive benchmark for multi-turn interleaved image-text dialogue of UMMs that jointly evaluates their understanding and generation capabilities. Our IMUG-Bench comprises three classes: Static Spatial, Temporal Causal, and Hybrid, covering 3,113 samples and 12,034 interaction turns. It also includes dynamic understanding questions, thereby supporting evaluation that better reflects real-world multi-turn interaction scenarios. Large-scale experiments on IMUG-Bench systematically evaluate mainstream open-source and closed-source UMMs, revealing their capability boundaries and failure modes, and uncovering pronounced exposure bias on the generation side in multi-turn interactions. We further explore several test-time scaling strategies, including Chain-of-Thought, Self-Verification, and Best-of-N Sampling, which effectively improve generation accuracy and mitigate exposure bias in generation tasks. These findings provide insights into enhancing the robustness and multi-turn interaction capability of future UMMs.
[CV-71] Vision-Language Guided Hyperspectral Object Tracking via Semantics Fusion and Contextual Template Updating
链接: https://arxiv.org/abs/2606.09167
作者: Rui Yao,Yuhong Zhang,Kunyang Sun,Hancheng Zhu,Jiaqi Zhao,Zhiwen Shao,Abdulmotaleb El Saddik
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages,8 figures
Abstract:Hyperspectral object tracking (HOT) leverages the rich spectral information provided by hyperspectral videos (HSVs), offering substantial potential for object tracking. However, efficiently extracting and exploiting spectral information from redundant spectral bands remains a fundamental challenge, which severely limits model generalization and tracking performance. Moreover, in dynamic scenes, targets often experience drastic appearance variations due to factors such as occlusion and illumination changes. These variations lead to large deformations between the current frame and the template. Such discrepancies pose major challenges for existing temporal modeling approaches. In this work, we propose VLHTrack, a novel hyperspectral vision-language (VL) joint tracking framework. Specifically, we incorporate language priors to address the fundamental challenge of spectral redundancy by designing a Language-Guided Band Selection Module (LBSM). By leveraging Large Language Model (LLM) descriptions, LBSM establishes a semantic-to-spectral mapping that mitigates redundancy and accentuates discriminative spectral features. A Multi-Modal Vision-Language Fusion Module is then employed to seamlessly integrate visual and linguistic embeddings, harnessing their complementary advantages to learn coherent cross-modal representations. To address target deformation in long-term sequences, we propose a dynamic update template feature strategy implemented via the Dynamic Template Update with Mamba (DTUM) module. By leveraging selective state space modeling, DTUM learns inter-frame dependencies to update template feature, ensuring efficient template feature evolution guided by temporal context. Experiments on HOT2023 and HOT2024 demonstrate that VLHTrack outperforms state-of-the-art (SOTA) methods.
[CV-72] Zero-Parameter Geometric Gating for Temporally Stable Low-Altitude UAV Video Semantic Segmentation
链接: https://arxiv.org/abs/2606.09162
作者: Jingpu Yang,Fengxian Ji,Zhengzhao Lai,Juanfan Wu,Mingxuan Cui,Yufeng Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video semantic segmentation for low-altitude UAVs requires temporal consistency, yet dense optical flow introduces spatially structured noise in the planar regions that dominate aerial imagery. We propose a zero-parameter geometric gate that uses RANSAC homography inlier ratios on a 16\times16 spatial grid to route each region to either homography or optical flow warp before fusion via Semantic Similarity Propagation. The gate requires no learned parameters – only a median-threshold binary decision on RANSAC statistics – adding only 211K trainable parameters (the SSP fusion layer) to a frozen backbone. On synthetic UAVid, the method achieves +4.24–4.91% mIoU improvement over base models across two architectures (SegFormer-b2 and Hiera-S+UPerNet). Mechanism diagnostics reveal that flow residuals in planar regions are spatially autocorrelated (Moran’s I = 0.32, p 0.001 ), predict boundary instability (Spearman \rho = 0.66 ), and that rigidification recovers temporal consistency from 62% to 92% (+29.5pp) in homography-valid regions.
[CV-73] OmniGen-AR: AutoRegressive Any-to-Image Generation NEURIPS
链接: https://arxiv.org/abs/2606.09156
作者: Junke Wang,Xun Wang,Qiushan Guo,Peize Sun,Weilin Huang,Zuxuan Wu,Yu-Gang Jiang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by NeurIPS
Abstract:Autoregressive (AR) models have demonstrated strong potential in visual generation, offering superior performance with simple architectures and optimization objectives. However, existing methods are typically limited to single-modality conditions, e.g., text, restricting their applicability in real-world scenarios that demand image synthesis from diverse controls. In this work, we present OmniGen-AR, a unified autoregressive framework for Any-to-Image generation. By discretizing various visual conditions through a shared visual tokenizer and text prompts with a text tokenizer, OmniGen-AR supports a broad spectrum of conditional inputs within a single model, including text (text-to-image generation), spatial signals (segmentation-to-image and depth-to-image), and visual context (image editing, frame prediction, and text-to-video generation). To mitigate the risk of information leakage from condition tokens to content tokens, we introduce Disentangled Causal Attention (DCA), which separates the full-sequence causal mask into condition causal attention and content causal attention. It serves as a training-time regularizer without affecting the standard next-token prediction during inference. With this design, OmniGen-AR achieves new state-of-the-art or at least competitive results across a range of benchmark, e.g., 0.63 on GenEval and 80.02 on VBench, demonstrating its effectiveness in flexible and high-fidelity visual generation.
[CV-74] Ultra Flash: Scaling Real-Time Streaming Video Generation to High Resolutions
链接: https://arxiv.org/abs/2606.09150
作者: Luxury,Jie Huang,Zihao Fan,Xiaoxiao Ma,Yuming Li,Jun-hao Zhuang,Zeyue Xue,Siming Fu,Haoran Li,Mingchen Zhong,Guohui Zhang,Shichen Ma,Yijun Liu,Jiaqi Shi,Yanwen Ma,Yaofeng Su,Haoyu Wang,Yaowei Li,Songchun Zhang,Weiyang Jin,Yuxuan Bian,Shiyi Zhang,Haojun Xu,Shuai Lu,Xin Han,Wei Tang,Haoyang Huang,Nan Duan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:While recent autoregressive video diffusion models achieve remarkable streaming quality, they remain confined to low resolutions (e.g., 480P), leaving efficient, scalable, real-time high-resolution video generation a fundamental open challenge. To bridge this gap, we present Ultra Flash, a cascaded streaming framework capable of real-time high-resolution video generation. Ultra Flash achieves ~30 FPS at 1K resolution and ~18 FPS at 2K resolution on a single GPU through three key contributions: (1) an architecture-preserving T2V-to-TV2V super-resolution training paradigm coupled with an AIGC-oriented data degradation pipeline that effectively preserves the generative capability of the base model, enabling enhanced high-resolution detail when cascaded after mainstream low-resolution generative models; (2) a causal streaming latent upsampler paired with a high-resolution decoder, which enhances spatiotemporal coherence while enabling efficient latent spatial scaling and precise high-resolution decoding with negligible computational overhead; and (3) a cascade high-resolution streaming video generation optimization scheme that first performs hybrid-reward-enhanced sparse causalization and single-step distillation of the super-resolution model, then introduces cascaded streaming self-forcing preference optimization with dynamic cache management, jointly enhancing overall coherence, improving quality, and enabling real-time high-resolution streaming video generation. Extensive experiments demonstrate that Ultra Flash reliably produces ultra-high-resolution streaming video while maintaining state-of-the-art visual quality and superior efficiency.
[CV-75] CAMF-Det: Closure-Aware Multimodal Fusion for LiDAR-Camera 3D Object Detection on UAV Platforms
链接: https://arxiv.org/abs/2606.09143
作者: Yanze Jiang,Yanfeng Gu,Xian Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal 3D object detection based on LiDAR and cameras has demonstrated excellent performance in ground-vehicle scenarios, but has not been explored for Unmanned Aerial Vehicle (UAV) platforms. In UAV top-down scenes, frequent groundobject occlusion dominated by tree canopies causes spatially varying and modality-dependent information degradation. Existing multimodal fusion frameworks neither explicitly model such ground-object occlusion nor embed occlusion awareness into the detection pipeline, limiting their performance in occluded UAV scenes. To address these challenges, we propose CAMF-Det, a closure-aware multimodal fusion framework for LiDAR-camera 3D object detection on UAV platforms, which derives dual-modal occlusion intensity through physics-inspired modeling and embeds them as priors throughout the detection pipeline. First, a dual-modal closure modeling module explicitly constructs occlusion intensity ground truth for both modalities offline via a Beer-Lambert-inspired formulation and building-mask correction. Second, using these ground-truth maps as supervision, a dual-modal prediction network converts the offline modeling results into online occlusion intensity predictions under single-frame inference. Third, both ground-truth and predicted occlusion intensity are injected into data augmentation, feature encoding, multimodal fusion, and detection head, enabling adaptive detection under spatially varying and modality-dependent information degradation. Experiments on two self-built UAV-based multimodal datasets, SI3D-DI and SI3D-DII, demonstrate that CAMF-Det achieves the best performance across all difficulty levels, with hard-level mAP _\mathrmBEV improvements of 9.43% and 4.88% over the best competing methods, respectively. These results confirm the effectiveness of explicit occlusion prior modeling and exploitation for robust multimodal 3D detection in UAV scenes.
[CV-76] Decoding Pedestrian Crossing Intention from Egocentric Vision via Vision Language Models
链接: https://arxiv.org/abs/2606.09142
作者: Danya Li,Xiang Su,Yan Feng,Rico Krueger
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Egocentric vision offers a first-person view of human perception and decision making, yet its potential for traffic-safety prediction remains underexplored. In this work, we study the decoding of pedestrian crossing intentions from short egocentric video clips. We approach this by formulating the task as a closed-ended visual question answering (VQA) problem and leveraging vision language models (VLMs) to predict the pedestrians’ intent. We first benchmark three families of state-of-the-art VLMs in a zero-shot setting, finding that they achieve moderate gains over random guessing but exhibit limited higher-level traffic reasoning. Motivated by these findings, we further adapt VLMs to the target task using parameter-efficient fine-tuning. Our results show that the fine-tuned models substantially outperform their zero-shot counterparts and achieve a 9% accuracy improvement over a specialized transformer-based baseline. Finally, we demonstrate that incorporating additional contextual cues, including ego motion, vehicle motion, and eye gaze, further improves predictive performance. In particular, the fine-tuned Qwen3-VL-2B model guided by eye gaze and ego motion achieves a 14.5% accuracy improvement over the transformer baseline, establishing a new state of the art for egocentric pedestrian intent decoding.
[CV-77] DiffSight-Former: Modeling Structural Differences and Temporal Dynamics for Glaucoma Progression Prediction
链接: https://arxiv.org/abs/2606.09140
作者: Yi Huang,Lei Bi,Jinman Kim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 6 figures
Abstract:Glaucoma is a leading cause of irreversible blindness worldwide, and early detection from fundus images is critical for effective disease management. While deep learning has achieved promising performance in fundus image analysis, most existing methods rely on single time-point images and fail to capture longitudinal structural and vascular changes associated with disease progression. Sequential fundus images acquired during clinical follow-up provide valuable temporal information; however, current sequential models often struggle to detect subtle early progression signals and commonly depend on fixed-length inputs or diagnostic cues from already glaucomatous images, limiting their clinical utility for early prediction. To address these limitations, we propose DiffSight-Former, a framework for glaucoma progression prediction from sequential fundus images. It incorporates a time-variant feature extraction module based on a fundus-specific foundation model to obtain robust anatomical representations. A multi-structure difference modeling module is introduced to quantify progression-related changes in the optic disc/cup region and retinal vasculature. These representations are integrated with temporal interval embeddings and processed by a time-aware Transformer to model disease progression and estimate the probability of future glaucoma onset. Experiments were conducted on two longitudinal datasets, SIGF (405 sequences) and GRAPE (263 sequences). On SIGF, DiffSight-Former achieved an AUC of 91.54% and a sensitivity of 92.16% for progression prediction. On GRAPE, it achieved an average accuracy of 87.48% across three clinical visual-field progression criteria. Compared with existing approaches, DiffSight-Former demonstrates strong performance and robustness across different temporal settings, highlighting its potential for longitudinal glaucoma monitoring and early risk prediction.
[CV-78] A Geometric Framework for Absolute Pose and Velocity Estimation with Event Cameras
链接: https://arxiv.org/abs/2606.09139
作者: Zibin Liu,Shunkun Liang,Banglei Guan,Yang Shang,Qifeng Yu,Ji Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Despite the rapid advancements in event-based motion estimation, current geometric methods primarily focus on velocity estimation. However, absolute pose estimation, which is equally crucial for key applications such as robotic navigation and augmented reality, remains relatively underexplored. Consequently, the simultaneous recovery of absolute pose and velocity from event streams remains an open and challenging problem. To address this gap, we propose a geometric framework for absolute pose and velocity estimation by leveraging 3D lines in the scene and the events they trigger. At the core of the framework lie two key geometric constraints: the orthogonality between a 3D line and the normal vector of its corresponding event plane, and the collinearity of an event with the 2D projection of its associated line. Based on these constraints, we present both linear and polynomial solvers for absolute pose estimation. The former enables efficient computation, while the latter provides a globally optimal solution for rotation. For velocity estimation, we develop an efficient linear solver and a more accurate optimization-based solver to recover both angular and linear velocities. Notably, our methods require a minimum of three event-line correspondences to determine the 6-DoF absolute pose or velocities independently. Extensive experiments in simulation and on real-world datasets demonstrate that our methods achieve state-of-the-art performance, with significant improvements in accuracy and computational efficiency compared to existing methods. The demo code is publicly available at this https URL.
[CV-79] An Enhanced Geometric-Spectral Feature Learning Framework for Airborne Multispectral Point Cloud Classification
链接: https://arxiv.org/abs/2606.09123
作者: Xian Li,Yanfeng Gu,Aleksandra Pižurica
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Multispectral point cloud (MPC) is composed of 3D spatial-spectral information, which holds tremendous potential for accurate land-cover classification. However, the representation power of classification models is limited by inherent high-dimensional and heterogeneous spatial-spectral information, unbalanced sample distribution, and inter-class spectral similarity of airborne MPCs. We build two MPC datasets and propose an enhanced geometric-spectral feature learning framework based on attentions for airborne MPC classification. A key component in our model is a two-stream feature fusion method with attention mechanisms, which enhances the representation capability of spatial-spectral features from high-dimensional heterogeneous MPCs. The first stream aims to extract position-encoded global spectral features with fusion self-attention, and the second stream comprises a multikernel point convolution and feature aggregation attention to extract spectral-guided geometric features. We then develop a residual attention fusion block to integrate the most informative geometric-spectral features from the two parallel streams. Another important contribution of this work is a joint loss function to improve the learning ability on unbalanced and interclass similar samples. Experimental results on two airborne MPC datasets demonstrate the effectiveness of the proposed method compared with the state-of-the-art methods. Furthermore, the codes and datasets used in this paper will be made available freely at this https URL.
[CV-80] Illumination-Invariant Anomaly Detection for Sub-Canopy UAV Multispectral Point Clouds
链接: https://arxiv.org/abs/2606.09111
作者: Likun Chen,Yanfeng Gu,Xian Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 5 pages, 8 figures
Abstract:Unmanned Aerial Vehicle (UAV) multispectral point clouds (MPC) provide high-dimensional spatial-spectral data for sub-canopy target detection; however, their efficacy is significantly compromised by severe illumination heterogeneity caused by vegetation shadows. To address this, we propose a prior-free anomaly detection framework capable of robustly handling lighting variations. First, we formulate solar angle estimation as an inverse optimization problem. By coupling spectral indices with a ray-tracing model, this strategy achieves Prior-Free Shadow Extraction without relying on flight metadata, effectively distinguishing dark objects from true shadows. Second, to mitigate spectral distortions, we introduce an Illumination-Consistent Sparse Representation mechanism. Unlike standard reconstruction methods, we construct a background dictionary strictly from neighbors sharing the same illumination state. This constraint effectively disentangles spectral reflectance from lighting variations, ensuring that targets are represented solely by physically consistent background points. Experimental results indicate that the proposed method significantly improves the separability between anomalies and background in complex forest environments, demonstrating superior performance over state-of-the-art baselines. This framework is particularly suited for identifying camouflaged military targets, mapping fallen tree trunks, and uncovering archaeological ruins hidden beneath dense foliage.
[CV-81] HDRAg ent: An Agent ic Framework for Multi-Exposure HDR Imaging
链接: https://arxiv.org/abs/2606.09110
作者: Weiyu Zhou,Tao Hu,Yijian Wang,Xiaogang Xu,Ruixing Wang,Qingsen Yan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Most existing multi-exposure HDR methods follow a fixed feed-forward reconstruction paradigm, making them prone to ghosting artifacts in complex dynamic scenes. To address this issue, we propose HDRAgent, the first agent-driven framework for HDR imaging, which adaptively selects reconstruction strategies according to the current scene conditions. Specifically, to provide scene-specific prior knowledge, we introduce a fine-grained contextual knowledge matching (FCM) module. This module leverages multimodal large language model (MLLM)-derived scene perception to retrieve relevant historical cases and tool knowledge, organizing them into structured evidence for MLLM-based adaptive tool scheduling. In addition, we propose a perception–distortion feedback mechanism that transforms post-execution quality assessment and artifact diagnosis into structured feedback, which is accumulated in historical memory to help subsequent contextual knowledge refinement and strategy selection. Furthermore, considering that extreme motion can invalidate alignment methods, we design an agent-guided generative alignment strategy that uses MLLM-based dynamic-region parsing to reconstruct unreliable contents in non-reference frames under reference-frame guidance. Experiments demonstrate that HDRAgent effectively reduces ghosting and local artifacts while achieving competitive or superior objective performance and visual quality.
[CV-82] Stabilizing On-Policy Distillation for MLLM Reasoning with Global Normalization
链接: https://arxiv.org/abs/2606.09091
作者: Dongze Hao,Zhiwei Jin,Chen Chen,Haonan Lu
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:On-policy distillation (OPD) has recently emerged as an important post-training paradigm. By using a stronger teacher model to provide dense, fine-grained supervision for sampled trajectories, OPD offers a clear advantage over reinforcement learning with verifiable rewards (RLVR), which typically depends on sparse binary or outcome-based environmental feedback. However, naive token-level distillation can suffer from gradient instability, due to magnitude misalignment in outlier states. To address this issue, we propose Globally Normalized Distillation Policy Optimization (GNDPO), a practical method that stabilizes optimization by transforming raw KL scores into batch-level relative advantages. This normalization effectively mitigates gradient explosions while retaining the benefits of token-level guidance. Experimental results show that GNDPO substantially improves training robustness and downstream performance across multimodal reasoning tasks. The code is released at this https URL.
[CV-83] Edge-Constrained UAV Small-Object Detection with P2 Enhancement and Quantum-Inspired Lightweight Structure Search
链接: https://arxiv.org/abs/2606.09081
作者: Wuming Lei,Yanbin Gao,Mingyan Sun,Xiaobin Li,Xuechen Liang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Unmanned aerial vehicle (UAV) object detection requires compact detectors that retain small-object details under onboard computation and memory constraints. Repeated downsampling inlightweight networks weakens shallow spatial information, while manually adding attention orfusion modules may increase cost without stable gains. This study analyzes YOLOX-Nano underedge-deployment constraints by combining a P2 high-resolution detection branch with a quantum-inspired evolutionary algorithm (QIEA) for lightweight structure screening. The search space isdefined by lightweight priority and task specificity, and the evaluation jointly considers accuracy,floating-point operations (FLOPs), latency, memory consumption, and recall. On VisDrone, theP2 branch increases APamall by 31.10% over the YOLOX-Nano baseline. Compared with NanoDet-Plus with similar model size, YOLOX-Nano±P2 improves this http URL by 17.5% and APamal by 44.9%.The QIEA-selected candidate obtains the highest Recallso, but +P2 remains the strongest AP-oriented variant after full training. Full 100-epoch verification of Random-best, GA-best, andSA/QUBO-best candidates further shows that proxy rankings do not necessarily transfer to finalAPse9s. These results support using P2 as the main small-object enhancement path and QIEA as alightweight tool for candidate screening and accuracy-cost analysis. The source code, configurationfiles, diagnostic scripts, and summarized results are available at this https URL
[CV-84] Beyond Scalar Rewards by Internalizing Reasoning into Score Distributions
链接: https://arxiv.org/abs/2606.09076
作者: Xin Jin,Huanqia Cai,Zhen Li,Zechao Zhan,Dengyang Jiang,Aiming Hao,Yuming Jiang,Chunle Guo,Peng Gao,Ming-Ming Cheng,Steven C.H. Hoi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reward models are central to text-to-image post-training, but visual preference is subjective and better represented as a distribution over rubric scores than as a deterministic scalar. Existing scalar, score-token, and pairwise reward models over-compress uncertainty and fine-grained score differences, while reasoning-based generative rewards provide stronger judgments but are costly to deploy and difficult to use as direct optimization signals. We propose Z-Reward, a teacher-student reward modeling framework that decouples reasoning-heavy judgment from efficient reward deployment. The teacher is a large VLM that uses reasoning to infer rubric-aligned score distributions, and is trained with Group-wise Direct Score Optimization (GDSO), which combines policy-gradient rewards from distribution expectations with direct pointwise and pairwise supervision on score distributions and score gaps. The student is trained with Reasoning-Internalized Score Distillation (RISD), which transfers the teacher’s reasoning-conditioned score distribution into a compact VLM without requiring explicit reasoning chains at inference time. On our internally annotated evaluation set, the 27B GDSO teacher reaches 89.6% human preference accuracy, outperforming SFT, RewardDance, and GRPO, while the 9B RISD student reaches 88.6%, outperforming the OPD baseline and closely matching the larger teacher. We further show that Z-Reward can serve as a differentiable reward signal for text-to-image optimization, yielding a 41.3% net human-preference improvement over the SFT baseline.
[CV-85] REFINE: Super-efficient 3D Gaussian Splatting Pruning via Rendering-Free Primitive Importance
链接: https://arxiv.org/abs/2606.09074
作者: Zhang Chen,Shuai Wan,Mengting Yu,Fuzheng Yang,Junhui Hou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Existing pruning methods for 3D Gaussian splatting (3DGS) suffer from either severe quality degradation or prohibitive computational overhead. In this paper, we propose REFINE, a highly accelerated 3DGS pruning framework centered on a novel rendering-free primitive importance metric. Our approach leverages an analytically approximated, rendering-aware Hessian field to quantify the expected perceptual error induced by the removal of individual primitives. By modeling the joint modulation of visibility, projection geometry and the content adaptive hyperparameter, we entirely bypass costly forward rendering passes and derive an anisotropic perceptual weight field that serves as a high-fidelity proxy for primitive importance. Extensive experiments across multiple benchmark datasets demonstrate that REFINE maintains highly competitive rendering quality while achieving an unprecedented 3,000\times reduction in pruning-related computational complexity compared to state-of-the-art pruning methods.
[CV-86] See More Think Deeper: Query-Expanded Visual Evidence and Answer-Clue Guided Reflection for Long Video Understanding
链接: https://arxiv.org/abs/2606.09064
作者: Shuning Wang,Zhiheng Wu,YiNuo Lu,Naiming Liu,Chen Jia,Bowen Liu,Shuo Nie,Weijie Zhu,Yumeng Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advances in Video Large Language Models (Video-LLMs) have enabled performance on long-video understanding tasks. However, existing methods still face two key limitations: evidence acquisition often relies on a single search intent, and answer generation lacks an effective visual feedback mechanism. To address these limitations, we propose \textbfCoVER, a Comprehensive Visual Evidence and Reflection framework for long-video understanding. CoVER enables Video-LLMs to \textbfSee More by dynamically gathering query-expanded visual evidence, and \textbfThink Deeper by verifying draft answers with effective answer-specific visual feedback. Together, these mechanisms shift long-video understanding from answer-centric generation to evidence-centric and visually verifiable reasoning. Experimental results show that CoVER-7B substantially outperforms models with the same parameter scale and even surpasses state-of-the-art closed-source models on certain metrics.
[CV-87] Stage-1 Controls the Entropy Regime Not the Outcome
链接: https://arxiv.org/abs/2606.09059
作者: Jianxiong Shen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Two-stage post-training – a Stage-1 warm-start (supervised fine-tuning, SFT, or on-policy distillation, OPD) followed by Stage-2 reinforcement learning (RL) – is increasingly used for vision-language models (VLMs). We ask what Stage-1 actually controls in a small-data study using Qwen2.5-VL-7B with a same-modality 72B VLM teacher for OPD. First, the three warm-starts reach a narrow 53 – 54% band on Geometry3K internal validation, consistent with the narrow range reported by recent specialized methods; this setup provides little evidence that Stage-1 changes the in-domain endpoint. Second, a matched-recipe, early-stopped SFT improves out-of-domain MathVista by +2.1 points, reversing the -9.5 -point drop of an over-trained variant. The clearest difference is the \emphentropy regime: OPD enters RL with substantially higher policy entropy than either SFT initialization, and the separation remains visible through the available trajectories. At the in-domain initialization, OPD also has higher answer diversity and pass@16 ( +2.0 to +5.2 points over SFT), although problem-level bootstrap intervals show that the smaller contrast is uncertain. The advantage is absent after RL (endpoint pass@16 values within 1.1 points) and on MathVista (six models within 1.2 points). Our contribution is therefore a bounded empirical characterization: Stage-1 is strongly associated with the entropy regime in this setup, but the downstream payoff is small, localized, and not evidence that OPD is a better RL warm-start.
[CV-88] MilliVid: Hierarchical Latents for Long-Range Consistency in Video Generation
链接: https://arxiv.org/abs/2606.09056
作者: Ishaan Preetam Chandratreya,David Charatan,Basile Van Hoorick,Sergey Zakharov,Vitor Guizilini,Phillip Isola,Vincent Sitzmann
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Ishaan Preetam Chandratreya and David Charatan contributed equally. Project page: this https URL
Abstract:Video generative models have become increasingly powerful, but long-range consistency remains challenging to achieve because even a few dozen frames require impractically long transformer sequence lengths. We show that this issue can be mitigated by generating video using coarse-to-fine rollout within a multi-scale token space. Our approach is simple: first, we pre-train an autoencoder that compresses each frame into a hierarchy of tokens, with levels ranging from the typical latent resolution to only a handful of tokens per frame. The coarsest levels capture the most consequential information, such as scene layout and semantics, while finer levels add high-frequency appearance and texture. Then, we train a video diffusion model to generate these tokens using coarse-to-fine rollout. By carefully controlling the level of detail at which frames are generated and used as context during each rollout step, we are able to preserve long-range consistency in geometry and object permanence while spending less compute on the long-range consistency of less perceptually relevant details. We validate this approach using a custom dataset of long Minecraft videos, where it produces substantially more consistent rollouts compared to existing baselines.
[CV-89] Leverag ing NeRF-Rendered Images for 3D Gaussian Splatting ICIP2026
链接: https://arxiv.org/abs/2606.09034
作者: Mizuki Morikawa,Yuta Shimizu,Chunyu Li,Yusuke Monno,Masatoshi Okutomi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ICIP 2026
Abstract:Neural radiance field (NeRF) and 3D Gaussian splatting (3DGS) are two mainstream approaches for novel view synthesis. They often show complementary performance, i.e., 3DGS demonstrating faster rendering speed and NeRF demonstrating higher rendering quality. Motivated by this, we propose leveraging NeRF-rendered images for 3DGS. Specifically, we target street scenes and utilize a pre-trained street-specific NeRF method to produce training images for a target 3DGS method. In our 3DGS training, NeRF-rendered images are used to remove transient objects in street-level input views and to generate bird’s-eye views as additional views, inheriting the higher-quality rendering of NeRF into 3DGS. We further incorporate a diffusion-based image enhancement to improve the image quality of the additional views. Experimental results on one synthetic and two real datasets demonstrate that our proposed method improves street-scene rendering while preserving the speed of 3DGS and the quality of NeRF.
[CV-90] Frequency Decoupled Framework for Screen Content Image Super-Resolution
链接: https://arxiv.org/abs/2606.09029
作者: Xufei Wang,Qicheng Zhang,Qi Wu,Ziyang Gu,Shizhuang Weng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 13pages;11figures
Abstract:Methods based on implicit neural representations have demonstrated superior performance in Screen Content Image Super-Resolution (SCISR) . However, they overlooked the inherent frequency characteristics, leading to suboptimal performance. We propose a frequency decoupled framework (FDF) that rethinks SCISR from a phasor perspective by capturing structured energy in amplitude and relational continuity in phase, and jointly exploiting them with bespoke implicit representations to faithfully recover the regular textures and global configuration of Screen Content Image (SCI). Amplitude-Phase Factorization Network (APFN) first separates images into amplitude and phase streams, where Amplitude Clustering Module (ACM) organizes sparse yet high-energy amplitude responses into representative prototypes for periodic pattern extraction, while Phase Consistency Self-Attention (PCSA) progressively reinforces configuration through continuous consistency propagation. And Oscillation-Anharmonic Implicit Fitting Network (OAIF-Net) integrates periodic and coherent implicit representations for efficient exploitation of the periodic patterns and coherent context embedded in SCI. Experimental results show FDF achieves state-of-the-art SCISR performance at multiple scales across four public SCI datasets. Ablation experiments further demonstrate the effectiveness of each component in extracting and exploiting periodic patterns and coherent context. Comments: 13pages;11figures Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2606.09029 [cs.CV] (or arXiv:2606.09029v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2606.09029 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Xufei Wang [view email] [v1] Mon, 8 Jun 2026 04:53:33 UTC (9,478 KB)
[CV-91] ATM: Action-Consistency Transfer Matrix for Diagnosing and Improving Latent World Models
链接: https://arxiv.org/abs/2606.09028
作者: Jiaheng Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: 13 pages, 3 figures, 6 tables
Abstract:Latent world models are increasingly used for control and goal-conditioned planning, yet assessing whether their learned representations are useful for planning usually requires slow, planner-coupled simulator evaluation with CEM or similar planners. Such evaluation is black-box and model-complexity-dependent: under the same protocol, different world models may require minutes to hours per checkpoint. In this work, we propose ATM, an Action-Consistency Transfer Matrix for diagnosing whether latent transitions preserve action semantics relevant to planning. ATM compares action information in real encoded transitions and model-predicted transitions through lightweight post-hoc probes, producing an interpretable matrix that reveals representation quality, transition-domain inconsistency, and failure modes without simulator rollout. It can also be collapsed into a simple screening score for within-task ranking across checkpoints, variants, and world models. When the true success gap is non-trivial, ATM achieves highly reliable pairwise ranking, while reducing minutes-to-hours CEM evaluation to seconds-level transition analysis, yielding more than 100x speedup in our setup. We further introduce AITS, showing that action-identifiability is not only diagnostic but also a useful training signal for improving downstream planning without changing the planner.
[CV-92] Scaling by Diversified Experience for Vision-Language-Action Models ICML2026
链接: https://arxiv.org/abs/2606.09009
作者: Leiyu Wang,Zhaofengnian Wang,Xueqi Li,Luoyi Fan,Cewu Lu,Nanyang Ye
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ICML 2026, SyVLA
Abstract:Vision-Language-Action models face significant challenges in real-world deployment due to the entanglement of high-level reasoning with low-level control, and the instability of policy optimization. In this paper, we introduce SyVLA, a robust VLA model trained with diversified experiences. We propose an Intention Decoupling algorithm to isolate control-relevant features from reasoning contexts and a similar-sample guided RL pipeline to stabilize policy updates and mitigate distribution shift. Extensive experiments on real-world robotic tasks and multi-modal benchmarks demonstrate that SyVLA achieves superior task success rates and stronger out-of-distribution generalization compared to existing methods, while effectively preserving core vision-language capabilities. Codes and Datasets is released on \hrefthis https URLproject page.
[CV-93] SpaceVLN: A Zero-Shot Vision-and-Language Navigation Agent with Online Spatial Cognitive Memory and Reasoning
链接: https://arxiv.org/abs/2606.08992
作者: Yucheng Deng,Pingrui Lai,Xinhai Li,Chenjia Bai,Xiaoheng Deng,Chengnuo Sun,Xuelong Li,Hua Yang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 23 pages, 9 figures, 7 tables
Abstract:Vision-and-Language Navigation in continuous environments requires agents to understand the spatial structure of previously unseen environments in order to follow language instructions. Although foundation models have opened a promising path toward zero-shot navigation without task-specific policy training, many navigators still rely on local visual cues and linear history-based reasoning, overlooking the spatial nature of navigation across explored regions, traversed paths, landmarks, and their spatial relations. In this paper, we propose SpaceVLN, a navigation agent built around Spatial Cognitive Memory and Task-Guided Spatial Reasoning. Specifically, SpaceVLN introduces an efficient stagewise closed-loop framework where planning and execution are organized around verifiable space–landmark stages. During navigation, the agent progressively abstracts explored regions into Spatial Waypoints and dynamically maintains subtask-grounded landmark evidence, forming a hierarchical Spatial Cognitive Memory for progress localization and spatial-relation understanding. Built on this memory, Spatial-CoT integrates task-progress reasoning with spatial perception, analysis, and prediction, enabling Task-Guided Spatial Reasoning for embodied navigation. The unified stage interface enables SpaceVLN to address both Vision-and-Language Navigation and Object-Goal Navigation under a unified zero-shot setting, without task-specific policy training. Across R2R-CE, RxR-CE, GN-Bench, and HM3D-OVON, SpaceVLN achieves state-of-the-art zero-shot performance, and real-robot deployment further validates its applicability. These results highlight Spatial Cognitive Memory and Task-Guided Spatial Reasoning as a practical foundation for stronger embodied navigation agents.
[CV-94] EPS3D: End-to-End Feed-Forward 3D Panoptic Segmentation ICML2026
链接: https://arxiv.org/abs/2606.08980
作者: Runsong Zhu,Jiaxin Guo,Xiaoyang Guo,Zhengzhe Liu,Ka-Hei Hui,Wei Yin,Kai Chen,Wei Chen,Weiqiang Ren,Yunhui Liu,Pheng-Ann Heng,Chi-Wing Fu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ICML 2026. The code is publicly available at \href{ this https URL }{ this https URL }
Abstract:This paper introduces EPS3D, a new end-to-end feed-forward framework for open-vocabulary 3D panoptic segmentation. Unlike existing methods relying on additional preprocessing, we design an end-to-end architecture, with a distillation-based training strategy on diverse 3D scenes to predict 3D-aware semantic and instance features from multi-view images, improving 3D consistency and avoiding error accumulation. We further propose a mutual enhancement module to enforce inherent semantic-instance consistency. By aligning semantics within instances (Ins2Sem) and refining instance features with semantic guidance (Sem2Ins), we achieve more coherent 3D scene understanding. Ultimately, EPS3D outperforms SOTA baselines on two benchmarks (e.g., +13% mIoU for semantics on Replica) with high efficiency (e.g., 1s per scene), supporting tasks like robotic manipulation and 3D scene editing.
[CV-95] C3ache: Accelerating World Action Models with Cross Inference Chunk Cache
链接: https://arxiv.org/abs/2606.08962
作者: Weisen Zhao,Lam Nguyen,Zhicong Lu,Yuzhang Shang
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:World Action Models (WAMs) generalize better than standard Vision-Language-Action (VLA) policies to novel motions and environments, because a video-modeling objective lets them learn from abundant unlabeled video rather than scarce labeled robot demonstrations. This generalization is computationally expensive. To complete a task, a WAM runs over multiple inference chunks, and each chunk requires a costly denoising process. Existing acceleration methods reduce this cost by caching and reusing computation within a single chunk’s denoising trajectory. Our empirical analysis reveals a substantial source of redundancy they overlook: redundancy across chunks. When a robot executes a smooth behavior, the residuals computed at a given denoising step are strongly correlated from one chunk to the next. We introduce C ^3 ache, a training-free method that caches and reuses these residuals across inference chunks at the same denoising step. Experiments on benchmarks with a Fast-WAM backbone show that C ^3 ache achieves up to a 2.5\times speedup in total wall-clock inference time, with negligible degradation in task success rate.
[CV-96] Rethinking 3D Shape Generation: Diffusion over Superquadrics ICML2026
链接: https://arxiv.org/abs/2606.08957
作者: Zhiyang Liu,Wanze Li,Yuwei Wu,Chengran Yuan,Jiawei Sun,Rui Zheng,Marcelo H Ang Jr
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ICML2026
Abstract:Diffusion models have advanced 3D shape generation, yet most methods still denoise in high-cardinality spaces (e.g., voxel/SDF grids, meshes, or point clouds), which is computationally and memory intensive and makes it difficult to scale in terms of both higher resolution and stronger controllability. We rethink the diffusion representation and propose to move diffusion from dense geometry to compact geometric primitives, representing each shape as a small set of superquadrics. Instead of operating on thousands to millions of geometric representation values, we leverage 7KB superquadric parameters (pose, size, and shape), drastically reducing diffusion-state dimensionality and per-step compute/memory. Our diffusion-over-superquadrics improves scalability by supporting broader capabilities (e.g., resolution-free point-cloud decoding, part-level editing, and constraint-based design) and achieving competitive surface-fidelity and distributional performance on standard benchmarks after point-cloud decoding, while enabling efficient generation within 0.6s per shape for most conditions.
[CV-97] NutriMLLM : Multimodal Large Language Models for Dietary Micronutrient Analysis
链接: https://arxiv.org/abs/2606.08948
作者: Runze Yan,Minxiao Wang,Jiaying Lu,Darren Liu,Xiao Hu,Hanqi Luo
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 35 pages, 10 figures, 1 table
Abstract:Comprehensive estimation of dietary micronutrients from food images could improve clinical nutrition care, but training such models requires large multimodal datasets linking diverse foods to complete nutrient profiles. We first show that existing multimodal large language models (MLLMs), including leading proprietary models, are unreliable for this task. Across five model families and four independent evaluation benchmarks (ASA24, SNAPMe, FNDDS, and NutriBench), models frequently abstained or returned statistically implausible values. To address this gap without costly expert annotation, we repurposed a decade of population-scale 24-hour dietary recalls as structured prompts for text-to-image generation. This pipeline produced a synthetic corpus of about 1.1 million image-description-nutrient triplets, each pairing a generated food image with a complete 65-nutrient label. To our knowledge, this is the largest synthetic food-image corpus with comprehensive micronutrient annotation planned for public release upon publication. Fine-tuning Qwen3-VL (2B/4B/8B/30B) and GLM-4.6V-Flash on this corpus yielded NutriMLLM, the first family of vision-language models specialized for comprehensive dietary micronutrient estimation. We evaluate these models with a four-component framework that separately measures abstention, hallucination, overall usability, and per-nutrient numerical accuracy. On real food images, every NutriMLLM variant achieved near-complete coverage across all 65 nutrients, and the largest variant matched or exceeded proprietary baselines (GPT-5, Gemini 3, and Claude Sonnet 4.5) in accuracy on most nutrients. These results show that recall-driven synthetic supervision can make image-based comprehensive micronutrient estimation a tractable engineering problem and support dietary assessment, personalized nutrition guidance, and population-scale micronutrient surveillance.
[CV-98] PolyBuild: An End-to-End Method for Polygonal Building Contour Extraction from High-Resolution Remote Sensing Images
链接: https://arxiv.org/abs/2606.08920
作者: Yaoteng Zhang,Julin Zhang,Guangshuai Wang,Jiwei Deng,Hui Sheng,Yasir Muhammad,Shiqing Wei
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS)
Abstract:Extracting building polygon contours from high-resolution remote sensing images is a fundamental task for various mapping applications. However, the presence of varying imaging conditions and complex building structures, makes automatic contour extraction extremely challenging. Mainstream approaches for building extraction often rely on pixel-level segmentation followed by multiple post-processing steps to produce building contour, which can be computationally intensive and prone to errors. In this paper, we propose an end-to-end method named PolyBuild, which can directly extract building vector polygons from high-resolution remote sensing images without the need for any post-processing operations. The proposed method leverages two primary modules: an Initial Contour Generation Module (ICGM) and a Contour Optimization Module (COM). The ICGM is designed to generate an initial building contour by utilizing concatenated sub-region center features for each building instance. It performs simultaneous object detection and initial contour extraction by generating bounding boxes and using the center features of four sub-regions to represent each building. The Contour Optimization Module (COM) further refines the generated building contours by iteratively integrating Convolutional Neural Network (CNN) features and contour positional information in a Transformer-based decoder. The hybrid CNN-Transformer architecture effectively captures both local and global spatial relationships within the building contour, ensuring high-quality boundary delineation. Extensive experiments are conducted on three building datasets to evaluate the performance of PolyBuild. The results demonstrate that PolyBuild significantly outperforms state-of-the-art methods, including mask-based and contour-based approaches.
[CV-99] When Vision Misleads Let Location Speak: A Worldwide Image Geo-Localization Method via Location Attention Mechanism and Large Multimodal Models
链接: https://arxiv.org/abs/2606.08918
作者: Junchao Cui,Wenqi Shi,Xuanzi Ma,Nan Wu,Shaoyong Du,Xiangyang Luo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Submitted to IEEE Transactions on Multimedia in March 2026
Abstract:Worldwide image geo-localization aims to determine the capture location of an image on a global scale. Existing methods often mislocalize images by matching them to visually similar scenes from different geographic regions, which limits reliability in practical applications. To address this issue, we propose TransGeoCLIP, a novel retrieval-based framework that integrates a location attention mechanism and large multimodal models (LMMs). Using the Transformer encoder with location attention to encode GPS coordinates, TransGeoCLIP can effectively distinguish geographic features among visually similar images. The framework consists of two stages: 1) Retrieval database construction, which employs Transformers equipped with location attention mechanisms to encode labeled GPS coordinates and enhance location semantics, subsequently enables joint image-text-GPS embedding through CLIP; 2) Retrieval-augmented inference, which leverages LMMs to infer the final image location prediction from retrieved database results. Extensive experimental results on diverse datasets, including IM2GPS, IM2GPS3k, YFCC4k, and YFCC26k, demonstrate that TransGeoCLIP significantly enhances localization performance for visually similar images. Particularly, street-level localization accuracy (within 1 km error) is substantially improved, surpassing state-of-the-art methods by 1.5%, 1.07%, 7.18%, and 9.75% on these benchmarks, respectively.
[CV-100] Failure-Aware Refinement of Vision-Language Model for Lithography Defect Detection
链接: https://arxiv.org/abs/2606.08908
作者: Pangyun Jeong,Jiyeong Kong,Yuehua Hu,Dohee Jeong,Kyung-Tae Kang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 6 pages, 3 figures
Abstract:Semiconductor lithography inspection requires reliable detection of small pattern defects such as bridge, burr, pinch, and contamination. In this study, we propose a two-stage vision-language framework that combines initial defect detection with prediction refinement. In the first stage, Qwen3-VL is fine-tuned with LoRA as a vision-language adapter to predict defect counts, defect categories, and normalized bounding boxes from lithography images. However, direct fine-tuning may still produce common test-time errors, including false positives, missed defects, and incorrect defect types. To address this limitation, the second stage trains a refinement module using first-stage prediction failures and their corrected labels, allowing the model to review and revise initial outputs. By learning from cases where the initial adapter fails, the refinement process improves defect inference beyond single-stage fine-tuning.
[CV-101] DifferSeg: Towards Diverse Multimodal Binary Segmentation via Differential Perception and Frequency Guidance
链接: https://arxiv.org/abs/2606.08906
作者: Qiangqiang Zhou,Jiawei Xu,Yong Chen,Dandan Zhu,Yugen Yi,Xiaoqi Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In many binary segmentation tasks, most multimodal methods rely on fixed feature concatenation for cross-modal interaction and straightforward decoder designs dominated by low-frequency semantics. %ToDO: % However, they ignore two key challenges: one is the lack of an adaptive mechanism to handle modality discrepancies and complementarity, and the other is the absence of an efficient decoding strategy to balance both high- and low-frequency representations. % In this work, we propose a simple yet general multimodal binary segmentation framework, termed DifferSeg, to address both problems simultaneously. With the help of the differential perception fusion (DPF) module, DifferSeg employs learnable differential operators to adaptively align multimodal features and enhance their complementarity through residual fusion, effectively mitigating modality mismatch and fusion redundancy. % In addition, we design a frequency-guided decoder (FGD) that builds cross-frequency interactions and multi-path upsampling to maintain consistency between detailed high-frequency structures and semantic low-frequency representations, ensuring fine-grained boundary recovery and noise suppression. % Benefiting from these designs, DifferSeg can be easily generalized to diverse binary segmentation tasks, including both natural and medical modalities. Without bells and whistles, it consistently surpasses 67 state-of-the-art methods across 29 public datasets involving 18 downstream tasks, demonstrating superior generalization and segmentation this http URL and pretrained models will be available at the Link.
[CV-102] A multi-agent system for spine MRI report generation from multi-sequence imaging
链接: https://arxiv.org/abs/2606.08897
作者: Zhiping Xiao,Junwei Yang,Gongbo Sun,Han Zhang,Hanwen Xu,Yi Yao,Zachary D. Miller,William E. King III,Mohammed M. Kanani,Jalal B. Andre,Sammy Chu,Ming Zhang,Paul E. Kinahan,Nathan M. Cross,Sheng Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM)
备注:
Abstract:Spinal pathology is a leading cause of pain and disability worldwide. Spine MRI is central to clinical evaluation, yet its interpretation remains complex and time-consuming, requiring integration of information across multiple imaging sequences and anatomical regions. Despite recent advances in automated MRI analysis, effectively combining multi-sequence data while preserving sequence-specific diagnostic information remains an open challenge. Here we present SpineAgent, a multi-agent framework for spine MRI report generation built upon a multi-sequence foundation model trained on routine clinical data from 32,047 patients and 453,683 MRI series, comprising a total of 13,441,191 MRI slices. To accommodate diverse modalities of sequences, we first pre-train two DINOv3-based encoders separately on T1- and T2-weighted sequences. We then introduce a continual training strategy that learns a synthesizer to embed images of other sequences using the T1 and T2 encoders, producing patient-level embedding that integrates various signals across MRI sequences. Using these embeddings, SpineAgent achieves state-of-the-art performance, and demonstrates strong generalizability under cross-manufacturer and cross-cohort evaluation. Beyond classification, SpineAgent enables pathology localization by identifying findings-relevant slices and segmenting pathological regions. It also supports multimodal image-report retrieval, providing a solid foundation for scalable and explainable MRI report generation. We further integrate these validated capabilities of SpineAgent into 37 specialized agents. Finally, we incorporate their outputs as structured tokens within a Medical Report Agent trained end-to-end for report generation. Through both automated metrics and expert evaluation by five radiologists, SpineAgent achieves leading performance in spine MRI report generation.
[CV-103] Generalizing Geometry-Guided Mamba as a Plug-and-Play Context Module for CNN-based Semantic Segmentation
链接: https://arxiv.org/abs/2606.08866
作者: Sheng-Wei Chan,Hsin-Jui Pan,Chun-Po Shen,Chia-Min Lin,Yung-Che Wang,Jen-Shiun Chiang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:CNN-based semantic segmentation networks usually rely on context heads such as ASPP, PPM, or attention modules to enlarge the receptive field. These heads are effective but may introduce heavy computation, memory cost, or boundary leakage. This paper revisits Directional Geometric Mamba (G-Mamba) from DGM-Net and studies it as a plug-and-play context aggregation module rather than a complete new segmentation architecture. The key idea is to inject geometric guidance into the selective scan process, allowing long-range feature propagation to be modulated by boundary and centripetal-flow cues. We replace the original context heads of six representative CNN segmentation models, including DeepLabV3+, DANet, CCNet, PSPNet, PSANet, and OCRNet, while keeping the ResNet-101 backbone unchanged. Results on Cityscapes show consistent mIoU gains with only moderate extra GFLOPs at 1024\times1024 resolution, suggesting that geometry-guided SSM modules can serve as practical alternatives or enhancements to conventional CNN context heads.
[CV-104] CHROMA: Detecting AI-Generated Images through Inter-Channel Color-Space Correlations ICPR2026
链接: https://arxiv.org/abs/2606.08864
作者: Juan Pablo Sotelo,Marina Gardella,Pablo Musé
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: This manuscript has been accepted for publication at the 28th International Conference on Pattern Recognition (ICPR 2026). The final published version will appear in the Springer LNCS proceedings
Abstract:The rapid adoption of diffusion and large-scale generative models has made it increasingly challenging to distinguish synthetic imagery from real photographs. While automated detectors have been proposed, their generalization to unseen generators remains brittle. To address this limitation, we investigate inter-channel color correlations, a lightweight and underexploited forensic cue. We first demonstrate that LPIPS, a widely used perceptual metric, exhibits inconsistent responses to perturbations that selectively alter channel dependence across different color-space parameterizations, indicating that cross-channel statistics are not uniformly constrained by common perceptual training objectives. Motivated by this, we analyze the distributions of pairwise inter-channel correlation features across multiple color spaces. Our analysis reveals systematic, generator-specific differences in these distributions, with RGB and Lab color spaces providing the most apparent separation between real and generated images. Building on this, we introduce Chroma, a detector of AI-generated images which augments standard RGB inputs with inter-channel correlation maps and employs a fixed CNN backbone trained with a modest computational budget. We assess its robustness under both single-generator training and a limited multi-generator supervision regime, where only a few samples from additional generators are available. Across a standard benchmark protocol, correlation-augmented inputs improve real-vs-generated discrimination and robustness, yielding performance competitive with recent detectors while maintaining a simple architecture and training procedure. Code is available at this https URL
[CV-105] Vision-Language Work Zone Intelligence for Safety-Critical Speed Regulation of Mixed-Autonomy Vehicles in Dynamic Environments
链接: https://arxiv.org/abs/2606.08860
作者: Angel Martinez-Sanchez,Kianna Ng,Wesley Maia,Laura Fleig,Maitrayee Keskar,Erika Maquiling,Yash Tandon,Parthib Roy,Mohan Trivedi,Ross Greer
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Temporary work-zone speed limits are communicated through visually inconsistent signage and are often missing from digital maps, creating safety risks for human drivers and automated vehicle systems. We present a real-time, onboard perception pipeline that detects active work zones, recognizes associated temporary speed limits, and outputs a law-aware work-zone state and speed value suitable for driver alerts or downstream automated control. The system fuses object detections with semantic verification and temporally smoothed, hysteresis-based state transitions to reduce false activations and flicker in dynamic scenes, and runs fully on low-cost embedded hardware. Evaluated manually on a annotated subset of the ROADWork dataset (490 sequences), the system achieves inside-work-zone event-level recall of 96.5% and event-level precision of 68.7%. Speed-limit recognition evaluated on 35 minutes of in-house driving data attains 95.45% precision and 53.85% recall, with no incorrect speed classifications and a single false positive. These results demonstrate a practical, scalable approach for grounding work-zone speed awareness directly in onboard perception rather than maps or infrastructure. We release our source code for the proposed system pipeline on our GitHub repository: this https URL
[CV-106] Intelligent Character Recognition of Handwritten Forms with Deep Neural Networks
链接: https://arxiv.org/abs/2606.08858
作者: Hartwig Grabowski
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Author’s accepted manuscript of a published Springer book chapter. 14 pages, 16 figures
Abstract:The automatic processing of handwritten forms remains a challenging task, wherein detection and subsequent classification of handwritten characters are essential steps. We describe a novel approach, in which both steps – detection and classification – are executed in one task through a deep neural network. Therefore, training data is not annotated by hand, but manufactured artificially from the underlying forms and yet existing datasets. It can be demonstrated that this single-task approach is superior in comparison to the state-of-the-art two-task approach. The current study focuses on hand-written Latin letters and employs the EMNIST data set. However, limitations were identified with this data set, necessitating further customization. Finally, an overall recognition rate of 88.28 percent was attained on real data obtained from a written exam.
[CV-107] Hybrid E-Assessment in Higher Education: Semi-Automated Grading of Paper-Based Written Examinations
链接: https://arxiv.org/abs/2606.08855
作者: Hartwig Grabowski,Michael Canz
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY)
备注: 15 pages, 6 figures
Abstract:This paper examines the limitations of fully digital and partially digital e-assessment approaches in summative examinations in higher education. The analysis focuses on the didactic narrowing caused by closed question formats and on organizational, technical, and legal constraints that become particularly relevant in large student cohorts. As an alternative, the paper proposes a hybrid e-assessment approach that retains paper-based, problem-oriented examination tasks while enabling semi-automated grading. Assessment-relevant intermediate results are encoded in a structured answer format, entered by students by hand, and subsequently captured from table fields. The central technical bottleneck is reliable recognition of handwritten characters under realistic examination conditions. Recent vision-capable large language models, combined with a two-pass validation principle and comparison against a solution key, can reduce misclassifications and thereby improve the validity, fairness, and scalability of summative assessment.
[CV-108] BLM-SGAN: Bidirectional Language Modeling for Semantic-Spatial Text-to-Image Generation
链接: https://arxiv.org/abs/2606.08847
作者: Ahmed Abdelmoneim Mazrou,Haidy Maher El-Amir,Ali Hamdi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Published in ICACIn 2024. Appears in Advances on Intelligent Computing and Data Science II, Lecture Notes on Data Engineering and Communications Technologies, vol. 254, Springer, 2025
Abstract:Despite the success of image generation from text descriptions, it still faces challenges that are difficult to overcome in domains such as natural language processing (NLP) and computer vision (CV). Recent advancements in text-to-image (T2I) models, particularly those utilizing generative adversarial networks (GANs), have significantly improved the synthesis of realistic images across various domains. However, existing GAN-based T2I models still encounter key challenges, such as difficulty in capturing long-range dependencies, vanishing gradients, and the limitations of sequential processing. To address these issues, we introduce BLM-SGAN, a novel model that incorporates Bidirectional Language Modeling for Semantic-Spatial Text-to-Image Generation. BLM-SGAN leverages BERT’s attention mechanisms to capture rich contextual information and efficiently manage extended sequences. Our model demonstrates state-of-the-art performance, with an Inception Score (IS) of 5.45 +/- 0.08, surpassing several competitive models such as SSA-GAN, DF-GAN, SD-GAN, and AttnGAN. BLM-SGAN effectively generates highly realistic images of birds from detailed text descriptions. The implementation code is available at: this https URL.
[CV-109] Geometry-Aware Fisheye-LiDAR Fusion for Robust 3D Object Detection in Low-Overlap Setups
链接: https://arxiv.org/abs/2606.08844
作者: Xiangzhong Liu,Xihao Wang,Hao Shen
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 8 pages, 4 figures, submitted to RA-L
Abstract:As autonomous systems expand from capital-intensive robotaxis to cost-sensitive logistics, sensor configurations are increasingly optimized for coverage-per-cost. A prevalent sparse-view setup utilizes dual-fisheye cameras with a roof-mounted LiDAR, introducing severe geometric challenges: extreme radial distortion, minimal overlap, and misalignment between spherical projections and rectilinear grids. BEV fusion algorithms typically force image and point cloud modalities into unified Cartesian grids early in the pipeline, causing significant feature distortion and information loss for wide-view fisheye cameras. To address this, we propose a Geometry-Aware Hybrid Fusion (GA-HF) framework that explicitly accounts for fisheye geometry and BEV feature distortion, where fisheye features are lifted into a polar BEV grid via a Distortion-Aware Lift-Splat-Shoot (LSS) module to preserve native angular density, while LiDAR features are processed in native Cartesian space for metric fidelity of bounding box regression. To bridge these heterogeneous streams, we introduce a Dual-Attention Warping Correction module that applies spatial and channel attention to the warped camera features before fusion, explicitly suppressing artifacts in low-quality peripheral regions while enhancing high-quality semantic cues. GA-HF is evaluated on three benchmarks: KITTI-360, Dur360BEV, and Fisheye3DOD datasets. To the best of our knowledge, it is the first approach to explore LiDAR-fisheye camera fusion. On KITTI-360, GA-HF improves NDS by 4.2% over Cartesian baselines; on Dur360BEV, it surpasses both LiDAR-only and BEVFusion, while significantly reducing orientation error despite the geometric distortions; on Fisheye3DOD, it attains the highest detection score among all fusion methods.
[CV-110] ZIPP:Zero-shot Image Personalization from Personas
链接: https://arxiv.org/abs/2606.08841
作者: Harini SI,Somesh Singh,Yaman Kumar Singla,David Doermann,Rajiv Ratn Shah
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Text-to-image diffusion models are increasingly deployed in open-ended creative contexts, yet their outputs remain impersonal, optimized for aggregate aesthetics rather than individual taste. Human preferences are pluralistic: one user favoring muted, nostalgic portraits may prefer vibrant street photography, while another gravitates toward dreamy film aesthetics. Existing methods require dense interaction histories or per-user fine-tuning, failing in cold-start settings and collapsing context-dependent preferences into a static representation. We introduce zero-shot image personalization from personas (ZIPP), which conditions image generation on natural-language personas (concise descriptors of a user’s identity and aesthetic sensibilities) without any user-specific data or weight updates. ZIPP uses an LLM to rewrite prompts from the perspective of a given persona, steering diffusion models toward personalized outputs. To mine personas at scale, we train an inductive Graph Attention Network over a 22M-user Reddit interaction graph with dual contrastive objectives aligning graph structure with visual behavior, then verbalize learned representations into natural-language personas via an MLLM. We introduce ZIPBench, the first zero-shot personalization benchmark with 1.5K users, graph-mined personas, and 40K generated images. Across four benchmarks and 14 LLMs spanning five model families, persona conditioning yields consistent gains (13-20%), with frontier models benefiting most. In the few-shot setting, ZIPP matches or exceeds fine-tuned baselines trained on 100+ examples per user. ZIPP achieves the lowest preference distributional divergence (CMMD 0.16 vs. 0.55), and IPF-normalized demographic evaluation shows it substantially reduces subpopulation bias present in existing methods. Human evaluation confirms a 79% win rate over generic generation and 58-65% over all fine-tuned baselines.
[CV-111] CSFlow: Aligning Flow Matching with Human Contrast Sensitivity
链接: https://arxiv.org/abs/2606.08833
作者: Malgorzata Galinska,Bart Pogodzinski,Jan Eric Lenssen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We introduce Contrast Sensitive Flow (CSFlow), a weighting scheme that connects the human eye’s Contrast Sensitivity Function (CSF) to the iterative denoising steps of flow matching. Because real-world images concentrate signal at low spatial frequencies, these components reach high signal-to-noise ratio earlier during continuous diffusion than high-frequency components. When generating images with diffusion or flow matching models, this induces a soft autoregressive structure in Fourier space, where coarse image content stabilizes before fine detail. Meanwhile, the human visual system is unequally sensitive to spatial frequencies: very low and very high frequencies require significantly higher contrast to be perceived. We for the first time merge these observations through two contributions: (1) a metric that estimates which frequencies are generated at each reverse flow interval and (2) timestep weights obtained by aligning the frequencies generated at each noise level with human contrast sensitivity. We validate our contributions experimentally showing that these weights can improve generative performance by lowering FID by 4.7%, increasing Inception Score by 2.2% and improving GenEval scores by 2.5% using inference-only timestep modification or short fine-tuning. Qualitatively, we find that our CSFlow weights lead to better visual realism and less cartoonish appearance of generated images.
[CV-112] Classifying galaxies in the Galaxy10 DECals dataset using Inception and Residual CNNs
链接: https://arxiv.org/abs/2606.08826
作者: Lanz Anthonee A. Lagman,Prospero C. Naval Jr,Reinabelle C. Reyes
类目: Computer Vision and Pattern Recognition (cs.CV); Astrophysics of Galaxies (astro-ph.GA)
备注: 4 pages, 3 figures, 2 tables, published in Proceedings of the 42nd Samahang Pisika ng Pilipinas Physics Conference (SPP 2024)
Abstract:Image data regarding galactic morphology is expected to increase both in quantity and quality for the next foreseeable years; thus it is important to explore which deep learning architectures adapted for image classification tasks are cost-effective. Residual and Inception networks are ideal for exploring classification convolutional neural networks (CNNs) due to their computational efficiency, achieved through techniques such as residual connections and parallelized inception modules, enabling deeper networks without excessively increasing computational complexity. In this work, we analyze the performance of ResNet101 and InceptionV4 on a spatially-augmented Galaxy10 DECals dataset. Retaining the ten-class classification of galaxies, we modify the image count of each class. We find that ResNet101 and InceptionV4 models achieved accuracies of \sim 90%, comparable with reported performance in the literature. In terms of performance metrics, ResNet101 is superior to InceptionV4. Our results indicate that either of these CNN architectures could serve as a robust foundation for specialized pipelines for classification of galaxy images from upcoming surveys.
[CV-113] PairWise Image Finder: An Open-source Tool for Finding Visually Aligned Street-Level Image Pairs for Urban Perception Studies
链接: https://arxiv.org/abs/2606.08795
作者: Jussi Torkko
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, two figures, github repo link near the end
Abstract:Change detection and scene recognition techniques have been widely applied to Street View Imagery (SVI) to understand changes in scenes across the years. However, metadata alone is often insufficient to reliably find visually aligned image pairs. This study introduces the PairWise image finder, a tool that integrates feature detection and matching, supported by semantic segmentation masks to quantify the visual alignment of two images of varying time periods. The tool outputs the share of matched key features, the matched feature distance and coverage, and the alignment of semantic masks, which enables the user to filter image pairs depending on the alignment quality and use case. The visually aligned pairs derived from the tool can be used to accurately study explicit longitudinal change and help reduce manual effort for perception studies. The usability of the tool is demonstrated through a comparison of longitudinal changes, highlighting the importance of perspective when quantifying changes. The proposed method provides a scalable and open tool for researchers and stakeholders to find high-quality image pairs for urban analysis, perception and related applications.
[CV-114] MaskAlign: Token-Subset Representation Alignment for Efficient Diffusion Training
链接: https://arxiv.org/abs/2606.08788
作者: Lianyu Pang,Tianlin Pan,Cheng Da,Changqian Yu,Huan Yang,Kun Gai,Song Guo,Wenhan Luo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Representation alignment with pretrained vision models has recently shown strong potential for accelerating diffusion transformer training. By aligning intermediate diffusion features with clean-image representations from self-supervised vision encoders, existing methods improve convergence and generation quality. However, such alignment also introduces a non-trivial constraint: diffusion models operate on noisy inputs whose usable information varies across timesteps, while the reference features are extracted from clean images. In this paper, we revisit this mismatch from a token-level perspective. We find that, under full-token representation alignment, tokens with large alignment-gradient norms exhibit a stable spatial preference, suggesting that the alignment objective does not affect all tokens uniformly and may encourage the model to rely on the complete set of clean-image tokens. To address this issue, we propose MaskAlign, a token-subset representation alignment method that applies alignment to randomly sampled token subsets during training. By exposing the model to different token subsets across iterations, MaskAlign reduces the dependence of representation alignment on the complete token set and encourages alignment behavior that is more stable under token-subset perturbations. To mitigate the information loss caused by directly dropping tokens, we further introduce a lightweight pre-mask token mixing block that shares information across tokens before masking.
[CV-115] DeepMine-Mamba: Mitigating Information Dilution in Mamba-Based State Space Models for Document Image Binarization
链接: https://arxiv.org/abs/2606.08781
作者: Sheng-Wei Chan,Yung-Che Wang,Hsin-Jui Pan,Chia-Min Lin,Jen-Shiun Chiang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: code will be released on this https URL
Abstract:Document image binarization aims to separate foreground text from degraded backgrounds while preserving thin, broken, and low-contrast strokes. Although deep learning methods have improved binarization performance, most existing approaches rely on convolutional, transformer-based, or generative architectures, while Mamba-based state space models remain largely unexplored for this task. In this work, we investigate Mamba-based feature propagation and observe that direct state-space propagation may dilute weak foreground cues during long-range modeling, especially faint ink traces, fragmented characters, and boundary-sensitive stroke details. To address this problem, we propose DeepMine-Mamba, a Mamba-based binarization framework equipped with a novel Anti-Dilution Gate that estimates propagation-induced feature changes and selectively restores stroke-sensitive local responses while suppressing unnecessary background enhancement. Experiments on DIBCO/H-DIBCO benchmarks under a strict leave-one-year-out protocol show that DeepMine-Mamba achieves competitive overall performance, with strong average FM and Fps across benchmark years. Ablation results further demonstrate that the Anti-Dilution Gate improves stroke preservation and reduces perceptually significant binarization errors.
[CV-116] Beyond Consistency: Preserving Temporal Structure in Zero-Shot Video Editing
链接: https://arxiv.org/abs/2606.08780
作者: Deyin Liu,Yisheng Ding,Zhe Jin,Xiatian Zhu,Anjan Dutta,Lin Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Existing zero-shot video editing methods rely on pre-trained diffusion models, successfully achieving spatial control and basic temporal consistency but fundamentally fail to preserve the video’s original temporal this http URL distinction is critical: temporal consistency ensures visual smoothness, but temporal structure dictates the video’s high-level narrative, rhythm, and semantic flow. Without this preservation, the edited output, especially for long videos with complex semantic variations, becomes narratively incoherent and semantically ambiguous. To address this limitation, we introduce a novel zero-shot editing approach that, for the first time, explicitly focuses on preserving the source video’s temporal structure. We achieve this by adaptively partitioning the video into semantically distinct clips based on feature similarity and selecting a representative anchor frame for each clip. To enhance both intra-clip fidelity and computational efficiency, we design a clip-adaptive token merging strategy which leverages the anchor’s semantic dominance to stabilize the editing. Furthermore, we employ an alternating combination strategy that ensures seamless inter-clip transitions while maintaining semantic distinction. Extensive experiments demonstrate that our method achieves state-of-the-art results, successfully balancing the preservation of original temporal structure with computational efficiency, and setting a new benchmark for zero-shot video editing fidelity.
[CV-117] RGB-S: Image-Aligned Tactile Saliency for Robust Dexterous Manipulation
链接: https://arxiv.org/abs/2606.08765
作者: Shengcheng Luo,Kefei Wu,Xiaoying Zhou,Wanlin Li,Ziyuan Jiao,Chenxi Xiao
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 20 pages, 7 figures
Abstract:Effective visuo-tactile integration is critical for robotic dexterous manipulation, especially when visual observations are unreliable or occluded. However, robustly aligning sparse, heterogeneous tactile measurements with dense visual representations remains a fundamental challenge. Most existing approaches require policies to learn cross-modal correspondences implicitly from limited demonstrations, without leveraging geometric priors. As a result, they are often data-inefficient and generalize poorly when visual observations are degraded. To address this limitation, we propose a framework that explicitly grounds physical contacts in the image domain. Using robot forward kinematics and camera calibration, we project tactile sensor locations directly onto the RGB image plane. We then render force-modulated Gaussian saliency maps to model spatial uncertainty arising from kinematic and calibration errors. By integrating these 2D spatial anchors through a zero-initialized conditioning architecture, our method injects physical contact priors into standard visual backbones while preserving pre-trained visual representations. We evaluate our method on six dexterous manipulation tasks in both simulation and the real world under severe visual occlusions. Real-world experiments show that explicit RGB-S grounding in the image domain improves real-world occluded manipulation success rates by 26.7 percentage points over the strongest implicit visuo-tactile baseline, suggesting its improved spatial reasoning and robustness to occlusion. Project page: this http URL
[CV-118] Less Is More: Training-Free Acceleration Framework of 3D Diffusion Models for Low-Count PET Denoising via Global-Local Trajectory Reduction
链接: https://arxiv.org/abs/2606.08751
作者: Yuhan Liu,Scott M. Leonard,Marlee Crews,Muhannad Fadhel,Jinkui Hao,Tianqi Chen,Ryan J. Avery,Bo Zhou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 19 pages, 10 figures, 5 tables
Abstract:Accurate quantification and uptake measurement in PET are critical for assessing disease progression and supporting clinical decision-making. While high-count PET provides reliable image quality, the associated radiation dose and prolonged acquisition remain significant clinical concerns, motivating the adoption of low-count protocols. Diffusion-model-based methods have demonstrated strong potential for restoring low-count PET to near high-count quality, but their iterative sampling procedure becomes prohibitively expensive when applied to high-resolution 3D PET volumes, introducing substantial inference latency that limits practical clinical deployment. To address these challenges, we propose a training-free Global-Local Skipping Strategy that accelerates diffusion model-based 3D PET denoising while simultaneously improving reconstruction quality. The proposed method is plug-and-play and directly applicable to pre-trained diffusion models without retraining or architectural modification. Specifically, we introduce: (i) a global denoising step skipping strategy that initializes the reverse diffusion process from an intermediate denoising step using a noise-consistent transformation of the low-count input, substantially reducing the number of required denoising steps; and (ii) a local feature reuse shortcut that reuses slowly-varying high-level U-Net features across neighboring denoising steps, further reducing per-step computation while preserving image fidelity. We evaluate the proposed approach on multiple PET tracers from in-house and public datasets, including 18F-FDG PET, 68Ga-DOTATATE PET, and 18F-PSMA PET, demonstrating consistent acceleration of over an order of magnitude alongside improved or comparable reconstruction performance relative to the full-step baseline. Blinded reader studies further confirm enhanced clinical confidence and perceived diagnostic quality.
[CV-119] Stain-Aware Wavelet Regularization for Instant Adversarial Purification in Histopathology
链接: https://arxiv.org/abs/2606.08745
作者: Zhe Li,Bernhard Kainz
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 4 figures
Abstract:Deep learning has become prevalent in computational pathology pipelines that support tasks such as cancer screening and digital pathology analysis. However, the susceptibility of neural networks to adversarial perturbations raises safety concerns for reliable deployment in clinical practice. In histopathological images, this challenge is exacerbated by the difficulty of distinguishing high-frequency adversarial noise from subtle and diagnostically relevant tissue structures. To address this issue, we propose Stain-Aware Wavelet Regularization (SAWR), an adversarial purification framework that leverages multi-level wavelet-domain regularization based on Haar transform to hierarchically disentangle adversarial perturbations from diagnostic structural information. This spectral constraint is further extended to individual histological channels, enabling stain-specific frequency regulation consistent with the biological properties of Hematoxylin and Eosin. When integrated into an instant purification framework, SAWR improves adversarial robustness by up to 10.69% over the baseline approach, while maintaining texture and spectral fidelity under adversarial perturbations.
[CV-120] MB-Loc: Multi-planar Birds-eye-view Localization in outdoor LiDAR scenes
链接: https://arxiv.org/abs/2606.08744
作者: Ayaan Choudhury,Preet Savalia,Anirudh Pydah,Avinash Sharma
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Global LiDAR localization is a fundamental task for autonomous navigation systems. Recent methods perform Scene Coordinate Regression (SCR) and achieve superior accuracy over Absolute Pose Regression (APR) solutions by predicting dense 3D world coordinates. However, SCR approaches introduce two major bottlenecks: severe computational inefficiency from processing raw 3D geometries and significant performance degradation under varying sensor viewpoints. To address these limitations, we present MB-Loc, a lightweight and viewpoint-robust SCR framework. Instead of relying on heavy 3D convolutions, we project the input LiDAR scan into a 2.5D Multi-planar Bird’s-Eye View (BEV) representation. By slicing the point-cloud along the Z-axis and mapping signed depths into discrete 2D planes, MB-Loc retains essential 3D geometric structures while exploiting the computational tractability of standard 2D CNNs. To handle the inherent sparsity of outdoor LiDAR, we introduce a KL-regularized latent bottleneck that explicitly models spatial uncertainty without injecting stochastic noise. Finally, to ensure rotation robustness, we apply 3D spatial augmentations prior to planar projection, forcing the network to implicitly learn viewpoint-invariant features. We perform extensive experiments on the publicly available NCLT dataset and demonstrate that our proposed method outperforms the current state-of-the-art. Operating at real-time inference speeds, MB-Loc significantly outperforms traditional 3D-SCR architectures in computational efficiency.
[CV-121] AUCp: Pseudo-AUC for Inference Model Selection with Unlabeled Validation Data in Abnormality Detection
链接: https://arxiv.org/abs/2606.08742
作者: Md Mahfuzur Rahman Siddiquee,Fazle Rafsani,Jay Shah,Teresa Wu,Catherine D Chong,Todd J Schwedt,Baoxin Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Abnormality detection is a crucial yet challenging task in medical image analysis. Distinguishing abnormalities from normal data by learning to reconstruct normal-only data alleviates the reliance on labeled datasets. However, many studies, even if unsupervised, rely on a labeled validation set to select the best model for inference from multiple training iterations. For many diseases labeled data are unavailable and substantially time consuming to obtain. To address this, AUCp - a novel metric that supports abnormality detection for unsupervised and self-supervised methods is proposed. Instead of evaluating the realism of reconstructed images to select the best of model for inference, it focuses on actual detection performance and without requiring an annotated test set. Assuming the pseudo ground truth of all unannotated samples in the test set as abnormal/positive and using traditional AUC calculation, AUCp scores are derived. Given a large and representative training set of normal samples, we show mathematical and empirical evidence that model selection using AUCp scores improves disease detection in terms of unsupervised and self-supervised methods over conventional metrics. Using two unsupervised methods for neurologic disease detection and self-supervised methods on diverse datasets, our results demonstrate that the AUCp score effectively identifies the optimal model for inference, significantly enhancing abnormality and disease detection. The corresponding implementations are available in this https URL.
[CV-122] hinking Without Images: Internalizing Visual Manipulation with On-Policy Self-Distillation
链接: https://arxiv.org/abs/2606.08719
作者: Yishuo Cai,Jiahui Liu,Yuanxin Liu,Haobo Deng,Linli Yao,Yuhao Zheng,Kun Ouyang,Zhimo Li,Ziyue Wang,Xu Sun,Haoli Bai,Xiaohui Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:‘‘Thinking with Images’’ has emerged as an effective paradigm for fine-grained visual reasoning: by explicitly zooming into relevant regions and reasoning over crops, models can access local evidence that is difficult to recover from a single global image. However, this benefit comes with redundant tool invocations and longer inference traces. Moreover, when such behaviors are learned mainly from outcome reward, the resulting intermediate crops or visual cues can be noisy or fail to faithfully capture task-relevant visual evidence. In this work, we ask whether the reasoning benefits of ‘‘Thinking with Images’’ can be internalized through Thinking with Imagination: an internal process that decides where to look and imagines what visual cues closer inspection would reveal without actually invoking tools. We propose Imagine-OPD, an on-policy self-distillation framework in which a teacher plays the role of a ‘‘Thinking with Images’’ reasoner during training: it receives privileged zoomed evidence views derived from annotated regions, and supervises the model’s own imagination reasoning trajectories. Imagine-OPD does not require an external teacher or high-quality imagination demonstrations. Experiments on vision-centric benchmarks show that Imagine-OPD achieves the best average performance among compared models while significantly reducing inference overhead compared with ‘‘Thinking with Images’’ methods.
[CV-123] SNR-ST-Mix: Sample-specific Neighborhood Regression Mixup for Augmented Spatial Transcriptomics Imputation with Deep Neural Network
链接: https://arxiv.org/abs/2606.08712
作者: Hongyi Yu,Yaoyu Fang,Jiahe Qian,Xinkun Wang,Lee A. Cooper,Bo Zhou
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 19 pages, 4 figures, 3 tables
Abstract:Purpose: Spatial transcriptomics (ST) enables gene expression measurements within the tissue context. However, these measurements are often noisy, low-resolution, and sparsely sampled, which limits the recovery of fine spatial structure. Deep neural networks have become powerful tools for expression imputation from histology, but their performance remains constrained by limited sample sizes and a lack of biologically informed augmentation. Most of the existing augmentation strategies for learning are designed for classification tasks rather than regression, which neglect spatial and transcriptomic relationships, leading to biologically implausible interpolations that hinder prediction performance. Approach: To address these limitations, we propose SNR-ST-Mix, a geometry- and expression-aware data augmentation framework designed specifically for ST data. It constrains mixing to a spot’s k-nearest spatial neighbors and adaptively weights interpolation coefficients based on expression similarity, generating augmented samples that preserve local biological structure while ensuring spatial smoothness. This dual conditioning yields synthetic examples that expand the effective training manifold, promote generalization, and enhance prediction stability under sample-specific training. Results: Extensive experiments with various tissue types demonstrate that SNR-ST-Mix consistently outperforms conventional augmentation methods without requiring architectural changes or additional computation. Conclusions: SNR-ST-Mix provides an effective and biologically principled augmentation strategy for spatial transcriptomics regression tasks. By explicitly leveraging spatial geometry and transcriptomic similarity, it expands the effective training manifold and improves predictive performance without increasing model complexity.
[CV-124] PRPO: Perception-Reinforced Policy Optimization via Token-Level Dynamic Advantage Reshaping
链接: https://arxiv.org/abs/2606.08708
作者: Qiming Li,Tianlun Li,Xiaolong Cheng,Hangyu Li,Ruiyan Gong,Kangning Niu,Kaitao Jiang,Mu Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reinforcement Learning with Verifiable Rewards (RLVR) has become an effective paradigm for improving the reasoning capability of Large Vision-Language Models (LVLMs). However, existing RLVR methods primarily rely on trajectory-level outcome rewards, which assign identical learning signals across all generated tokens. This coarse-grained credit assignment is fundamentally mismatched to multimodal reasoning, where only a sparse subset of tokens is causally grounded in visual evidence. Consequently, these pivotal perceptual tokens receive weak supervision and are often overwhelmed by language priors or reasoning-template tokens. To address this limitation, we propose Perception-Reinforced Policy Optimization (PRPO), a token-level reinforcement learning framework that explicitly identifies and reinforces pivotal perceptual tokens within long-horizon multimodal reasoning trajectories. PRPO introduces Robust Visual Dependency (RVD), a principled metric that identifies tokens whose predictions are both visually grounded and perturbation-stable, filtering out brittle or noisy visual tokens. Based on RVD, we further propose Perceptual Advantage Reshaping (PAR), a token-level credit assignment technique that amplifies perceptually informative tokens while preserving stable gradients for non-perceptual tokens. Extensive experiments on seven multimodal reasoning benchmarks demonstrate that PRPO consistently outperforms strong LVLM baselines across both 3B and 7B model scales, achieving average gains of 23.3% and 21.1%, respectively. PRPO achieves state-of-the-art performance with improved training efficiency and stronger cross-task generalization. Our findings highlight the importance of fine-grained credit assignment for scalable multimodal reinforcement learning.
[CV-125] PhysAgent : Automating Physics-Based 4D Synthesis via Trajectory-Grounded Multi-Agent Feedback
链接: https://arxiv.org/abs/2606.08688
作者: Chunji Lv,Jiaxi Ye,Yuchen Jiang,Rexar Lin,Changsheng Li
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Achieving fully automated, physically plausible 3D motion synthesis is a core objective in graphics and generative AI. However, configuring complex environmental force fields still relies entirely on manual expert intervention, creating a severe bottleneck for large-scale simulation data generation. Existing automated methods primarily focus on material optimization and exhibit severe modality gaps and technical flaws when applied to the vastly more complex force field optimization space: naive Large Language Models (LLMs) lack underlying simulation feedback, causing severe physical inaccuracies, while traditional Score Distillation Sampling (SDS) suffers from sluggish gradients, local optima entrapment, and a mathematical inability to dynamically switch discrete force fields. To address this, we propose PhysAgent, the first simulator-in-the-loop multi-agent framework that leverages multimodal inputs for automated, physically grounded 4D synthesis. By decoupling intrinsic materials from extrinsic dynamics, PhysAgent utilizes a Semantic Agent equipped with an externalized Force Field Skill module to master simulation rules and generate valid initializations. Subsequently, the Refine Agents, driven by Trajectory-Grounded Multi-Agent Feedback, leverage vision foundation models to extract dense point trajectories from rendered frames. By converting these explicit motion trajectories into structured textual descriptors, the agent harnesses LLM commonsense reasoning to execute zero-shot macroscopic leaps, effectively escaping local optima and dynamically switching discrete force fields. Extensive experiments demonstrate that PhysAgent rapidly generates stable, diverse physical scenes from arbitrary multimodal prompts, significantly outperforming existing baselines in both generation diversity and physical accuracy.
[CV-126] Shift-Dependent Asymmetry: Orthogonal Inverse Low-Rank Adaptation for Federated Medical Segmentation ICML2026
链接: https://arxiv.org/abs/2606.08687
作者: Xingyue Zhao,Wenke Huang,Linghao Zhuang,Haoran Wu,Anwen Jiang,Zhifeng Wang,Wenwen He,Ming Feng,Mang Ye,Bo Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ICML 2026
Abstract:Low-Rank Adaptation (LoRA) enables efficient federated fine-tuning of segmentation foundation models for medical imaging. However, most federated LoRA methods adopt a uniform aggregation rule, which breaks under the encoder-decoder asymmetry in medical segmentation: the encoder is dominated by appearance shifts, while the decoder is dominated by supervision variations. This mismatch entangles shared anatomy with site-specific biases and harms generalization. To address this, we propose Inverse Asymmetric Tuning (IAT). IAT aligns adaptation with heterogeneity sources by personalizing module-specific components in the encoder to absorb appearance shifts and in the decoder to accommodate site-dependent supervision, while retaining a shared pathway for transferable consensus. However, structural separation alone is insufficient under LoRA’s bilinear parameterization, where multiplicative coupling can still cause site-specific updates to leak into the shared direction. We therefore introduce a Subspace Orthogonality Regularizer that penalizes shared-local collinearity in the effective update space, mitigating leakage without extra communication. Experiments show consistent improvements over strong federated LoRA and parameter-efficient FL baselines.
[CV-127] BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving
链接: https://arxiv.org/abs/2606.08684
作者: George Ling,Lijin Yang,Hao Yang,Zhongzhan Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: preprint
Abstract:We present BLUE, a minimal method for better language use in vision-language-action (VLA) models for autonomous driving (AD). Through extensive analysis, we reveal that language matters on only a small fraction of routes, but on those routes it can greatly improve or degrade performance. Generating language at every frame is therefore inefficient, since most computation is spent on frames that do not benefit from language. We further show that pretrained VLA hidden states potentially already encode whether language will benefit a given frame, even though scene complexity and kinematic features alone struggle to predict this. Based on this finding, BLUE trains a lightweight gate on frozen VLA hidden states to decide per frame whether to activate language generation or predict actions directly, without modifying the backbone or requiring additional human annotation. With just a 0.11M-parameter gate, BLUE sets a new state of the art on both benchmarks, achieving 76.2% success rate on Bench2Drive and 36 driving score on Longest6 v2, while delivering 2.54x inference speedup and 8.9% success rate improvement over the backbone. BLUE provides a practical path toward efficient language-augmented AD, showing that VLA models can retain the benefits of language at a fraction of the cost. Our code, data, logs and checkpoints are fully available on this https URL.
[CV-128] Distortion-Aware PETR for BEV Object Detection with Mixed Pinhole-Fisheye Cameras ICRA2026
链接: https://arxiv.org/abs/2606.08680
作者: Xiangzhong Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 8 pages, 5 figures, accepted at ICRA 2026
Abstract:Fisheye cameras are widely deployed in autonomous driving perception suites for their low cost and full-coverage field of view (FOV), yet their potential remains underleveraged in 3D object detection. Severe radial distortion challenges most BEV detectors by violating the fundamental assumption of uniform sampling. To bridge this gap, we propose Distortion-Aware PETR (DAPETR), a projection-free detector tailored for mixed pinhole-fisheye camera setups. DAPETR incorporates two key learned-adaptive modules: a unified distortion-aware positional embedding that harmonizes positional encodings for image representations with fisheye geometry, and a bidirectional feature-geometry co-modulation module that mutually adapts image features and 3D positional embeddings. In our experiments on a converted KITTI-360 benchmark, we systematically compare our learned adaptive approach against PETR in polar coordinates (PolarPETR). We find that while both methods improve over the baseline, our learned modules achieve superior performance. Crucially, we uncover a negative interaction when combining both strategies, revealing that learned adaptation and explicit geometric reparameterization can conflict. Our final DAPETR model significantly advances the research and benchmark for fisheye BEV detection, providing critical insights into effective distortion-aware 3D perception design other than image rectification.
[CV-129] BioVid: Autoregressive Video Generation with Biological Behavior Semantic Comprehension
链接: https://arxiv.org/abs/2606.08674
作者: Tsung-Wei Pan,Jung-Hua Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Existing video generation frameworks treat sequence duration as an externally prescribed parameter – fixed frame counts or text prompts – producing clips whose temporal boundaries are decoupled from the statistical structure of real behavioral data. This assumption is fundamentally misaligned with biological behavior, where action duration varies naturally across individuals and instances and is encoded in the data itself. We present BioVid, a data-driven autoregressive video generation framework that learns the temporal structure of biological behaviors directly from training data, including their natural length distributions. In the first stage, a Finite Scalar Quantization GAN (FSQ-R3GAN) tokenizer encodes each video frame into a compact discrete representation, combining the stabilized relativistic training objective of R3GAN with FSQ’s guaranteed codebook utilization to achieve high-fidelity spatial reconstruction without codebook collapse. In the second stage, a causal Transformer models the resulting token sequences autoregressively and learns to emit an End-of-Sequence (EOS) token when the behavioral event reaches semantic closure, with the termination distribution emerging naturally from the training data rather than any human-specified constraint. Experiments on a human drinking behavior dataset (NTU RGB+D, A001, n=94) demonstrate that BioVid’s generated length distribution closely matches that of held-out test data, achieving a Wasserstein-1 distance of 1.24 against the ground truth – compared to 6.05 for a fixed-length baseline and 15.48 for VideoGPT – while maintaining competitive spatial fidelity.
[CV-130] Learning to Solve Generative ODEs Beyond the Linear Span
链接: https://arxiv.org/abs/2606.08672
作者: Sihyeon Kim,Seunghun Lee,Vikas Singh,Hyunwoo J. Kim
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 12 pages, 7 figures
Abstract:Diffusion and flow generative models sample by integrating a learned ODE, but high quality still requires many sequential model evaluations. Solver learning reduces this cost by adapting scalar coefficients, timesteps, or both, while keeping the backbone model fixed. In this work, we identify a structural bottleneck in this update family: each step remains span-limited. Since the scalar-coefficient update lies in the span of buffered velocity evaluations, it can fit only the in-span component while leaving any out-of-span residual unreachable by scalar recombination alone. We propose SpanLift, a lightweight neural solver that augments scalar-coefficient updates with a spatial residual operator. SpanLift keeps a fixed base solver as an in-span prior and learns a spatial residual operator over the state and velocity buffer. The operator is trained by endpoint teacher matching, preserves the pretrained backbone, and adds no model NFEs. Empirically, the learned correction transfers across base solvers and is predominantly out-of-span. Across pixel-space diffusion, latent flow matching, and precipitation nowcasting, SpanLift achieves state-of-the-art few-step sampling. With only 3 NFE, it improves CIFAR-10 FID from 8.16 to 5.69 and ImageNet FID from 17.37 to 11.83.
[CV-131] WaveDiT: Distribution-Aware Wavelet Flow Matching for Efficient 3D Brain MRI Synthesis MICCAI2026
链接: https://arxiv.org/abs/2606.08670
作者: Danilo Danese,Angela Lombardi,Giuseppe Fasano,Matteo Attimonelli,Tommaso Di Noia
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Provisionally accepted at MICCAI 2026
Abstract:Large and demographically balanced datasets are essential for reliable neuroimaging biomarkers. Full-resolution 3D brain MRI synthesis can support data augmentation in this setting, but existing approaches either incur prohibitive computational cost at volumetric scale or rely on lossy latent compression that may compromise anatomical detail. As a result, practical 3D generative augmentation often requires specialized compute infrastructure. We propose WaveDiT, a conditional flow matching framework operating in the coefficient space of a 3D Haar Discrete Wavelet Transform. The model combines factorized spatio-depth attention with band-wise heteroscedastic uncertainty modeling derived from higher-order wavelet statistics. Predicted log-variance is integrated directly into both the flow objective and conditioning pathway, enabling adaptive precision consistent with the heavy-tailed and input-dependent variance structure of anatomical detail. This formulation supports full-resolution 3D synthesis under practical memory and time constraints on a single modern GPU. Evaluation on a multi-site cohort demonstrates improved alignment between generated and real MRI distributions, together with enhanced downstream brain age prediction and region-level anatomical agreement relative to diffusion, latent, and wavelet-based baselines. Code is available at this https URL
[CV-132] PhysGraph: A Physics-aware 3D Scene Graph for Perception and Reasoning
链接: https://arxiv.org/abs/2606.08655
作者: Haoyu Li,Aaron Thomas,Shuyan Zhou,Xianyi Cheng
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:To perform a wide range of daily tasks, robots need to construct a 3D representation that is semantically rich, physically grounded, and structured enough to support task planning and affordance prediction. However, existing approaches primarily focus on semantic retrieval, often overlooking physical and kinematic factors. Methods that attempt to model physical properties typically rely on narrow training sets or single-object modeling, limiting scalability and generalization across diverse object types. To address these challenges, we present PhysGraph, a framework that unifies symbolic reasoning with structured 3D geometry to model kinematic and physical properties in cluttered scenes. Given RGB-D observations, PhysGraph reconstructs object-centric 3D geometry and associates object instances across views. It then decomposes objects into functional parts and infers materials and articulations through visual reasoning. Evaluated on both synthetic and real-world datasets, PhysGraph achieves state-of-the-art results in semantic segmentation, multi-object mass estimation, and articulation prediction. With its simple yet effective design, PhysGraph produces physically consistent and semantically structured scene graphs, serving as a structured 3D representation for downstream tasks such as constraint-aware 3D affordance prediction and real-to-sim transfer, both of which are demonstrated in our experiments.
[CV-133] FiberTune: Preserving Action-Fiber Visual Residuals in Vision-Language-Action Fine-Tuning
链接: https://arxiv.org/abs/2606.08653
作者: Haihao Lin,Xiangsheng Huang,Xiao Yang,Weibang Zhou,Yiqi Zhang,Bo Yang,Simin Zeng,Jiawei Yang,Zhengyang Wang,Jiahui Du
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
备注: Project page: this https URL
Abstract:Action-supervised fine-tuning of vision-language-action (VLA) policies fits demonstrations effectively but constrains only the directions that change predicted actions, leaving visual structure consistent across action-equivalent states free to collapse. We formalize this as residual visual collapse along local action fibers and propose FiberTune, a training-time objective that preserves teacher-structured visual residuals without adding inference-time overhead. FiberTune uses an online action probe to estimate action-predictive feature directions, filters them from intermediate visual-token representations, and aligns the resulting probe-filtered residuals to a frozen visual teacher while regularizing their effective rank. Under identical training conditions, FiberTune improves over task-loss-only fine-tuning in every one of six controlled simulation settings spanning two benchmarks and two architectures (pi_0.5 and OpenVLA-OFT), as well as on physical SO-101 pick-place; representative gains include +10.7 percentage points SR(5) on long-horizon CALVIN ABC-to-D and physical SO-101 task success rising from 72.7% to 78.1%. Residual diagnostics show that these gains coincide with increased probe-filtered residual teacher alignment and effective rank, consistent with the action-fiber motivation.
[CV-134] Learnable Token Sparsification for Efficient Gigapixel Whole Slide Image Reasoning
链接: https://arxiv.org/abs/2606.08641
作者: Jingzhi Chen,Landi He,Zhuo Chen,Shawn Young,Lijian Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The processing of gigapixel whole slide images within vision language models faces a major difficulty due to an excessive number of visual tokens. Existing solutions typically rely on spatial downsampling or heuristic pruning strategies that operate without training, and these methods often discard subtle but clinically meaningful patterns because pathological evidence is scattered irregularly across the tissue. To overcome this limitation, we reformulate token reduction in whole slide images as a trainable sparsification problem, allowing the model to learn an optimal selection strategy instead of following fixed heuristics. We propose a decoupled routing architecture. To enable gradient propagation through the nondifferentiable pruning operation during training, we introduce a component called SparseLearn. This component uses a variance-preserving noise gate that regulates the information flow of each patch via a differentiable Soft Top-K operator, together with a diagonal attention denoiser that recovers perturbed representations without leaking spatial information. At inference time, the SparseLearn module is entirely discarded, and the trained scorer applies a deterministic Hard Top-K operator to keep only the highest scoring 32 tokens, incurring no extra computation. By compressing the visual sequence down to a sparse set of just 32 tokens, which represents as little as 0.78% of the original length, our framework achieves 73.32% overall accuracy on SlideBench (TCGA), consistently surpassing sampling-based baselines and general-purpose vision language models. It also demonstrates strong zero shot generalization on SlideBench (BCNB) and WSI VQA*. By resolving the visual context bottleneck and preventing the dilution of sparse diagnostic evidence, this work provides a highly efficient paradigm for end to end gigapixel whole slide image reasoning.
[CV-135] SSAFE: Simple and Strong AI-Generated Image Detection via Frozen Vision Encoders
链接: https://arxiv.org/abs/2606.08634
作者: Seunghyun Lee,Byoungkwon Kim,Jaehyun Nam,Kyungmin Lee,Jinwoo Shin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Preprint. 22 pages, 10 figures, supplementary material included
Abstract:The rapid advancement of generative models has blurred the boundary between synthetic and real imagery, creating an urgent need for reliable deepfake detection. Yet most existing approaches rely on massive real–fake datasets, which are increasingly difficult to maintain as new generators continue to emerge. In this work, we investigate how much information about image authenticity is already encoded in modern multimodal vision representations. We find that frozen multimodal encoders naturally separate real and synthetic images in their embedding space, enabling a simple linear classifier to achieve strong performance without task-specific fine-tuning. Motivated by this observation, we develop a representation-aware data curation strategy that selects a compact set of representative generators for training. The resulting training set contains only 10K images, compared to 288K in AIGIBench and 4M in OpenFake, while improving robustness to unseen generators and distribution shifts. We additionally introduce RealWorldBench, a benchmark consisting of modern camera photographs, contemporary stock images, and outputs from recent commercial generators. Experiments across multiple benchmarks show that combining frozen multimodal representations with carefully curated training data provides a simple and effective approach to AI-generated image detection.
[CV-136] Facial Expression Recognition in the Deep Learning Era: A Systematic Multi-Criteria Review of Methods Models Datasets Performance Challenges and Future Research Directions
链接: https://arxiv.org/abs/2606.08612
作者: Spyridon Georgiou,Aggelos Psiris,Spyridon Evangelatos,Thomas Lagkas,Vasileios Argyriou,Panagiotis Sarigiannidis,Iraklis Varlamis,Georgios Th. Papadopoulos
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Facial Expression Recognition (FER) has advanced rapidly over the last decade, driven by the shift from handcrafted descriptors and shallow classifiers to deep convolutional, attention-based, vision-language, and foundation-model architectures, and by the parallel growth of large-scale in-the-wild benchmarks spanning categorical, dimensional, compound, micro-expression, Action Unit (AU), and intensity-estimation tasks. Yet the deep learning-based FER landscape has so far been reviewed only along narrow task-, architecture-, or application-specific axes, leaving a holistic, systematically organized account of its recent advances missing. This survey addresses that gap with a comprehensive review of recent deep learning-based FER, explicitly linked to the wider Facial Affect Recognition (FAR) domain. Its main contributions are: a) A description of FER’s evolution into five distinct phases, from handcrafted features and classical machine learning to attention-based, vision-language, and foundation-model approaches, with the key milestone works of each, b) A multi-criteria taxonomy analyzing the literature along seven complementary axes: recognition task, input modality, face pre-processing pipeline, network architecture, learning strategy, acquisition setting, and application domain, c) A per-criterion comparative analysis, with critical insights into the strengths and limitations of each category under in-the-wild conditions, d) A task-organized review of public FER datasets, with their annotation schemes, modalities, and evaluation protocols, e) A compilation of performance metrics and a per-task quantitative comparison of representative state-of-the-art methods on widely adopted benchmarks, and f) A discussion of current challenges and promising future directions.
[CV-137] OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework ICLR2026
链接: https://arxiv.org/abs/2606.08574
作者: Chenhan Jin,Shengze Xu,Qingsong Wang,Fan Jia,Dingshuo Chen,Tieyong Zeng
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Published as a conference paper at ICLR 2026
Abstract:Data pruning (DP), as an oft-stated strategy to alleviate heavy training burdens, reduces the volume of training samples according to a well-defined pruning method while striving for near-lossless performance. However, existing approaches, which commonly select highly informative samples, can lead to biased gradient estimation compared to full-dataset training. Furthermore, the analysis of this bias and its impact on final performance remains ambiguous. To address these challenges, we propose OrderDP, a plug-and-play framework that aims to obtain stable, unbiased, and near-lossless training acceleration with theoretical guarantees. Specifically, OrderDP first randomly selects a subset and then chooses the top- q samples, where unbiasedness is established with respect to a surrogate loss. This ensures that OrderDP conducts unbiased training in terms of the surrogate objective. We further establish convergence and generalization analyses, elucidating how OrderDP affects optimal performance and enables well-controlled acceleration while ensuring guaranteed final performance. Empirically, we evaluate OrderDP against comprehensive baselines on CIFAR-10, CIFAR-100, and ImageNet-1K, demonstrating competitive accuracy, stable convergence, and exact control – all with a simpler design and faster runtime, while reducing training cost by over 40%. Delivering both strong performance and computational efficiency, our method serves as a robust and easily adaptable tool for data-efficient learning. The code is publicly available at this https URL.
[CV-138] OmniCap-IF: Benchmarking and Improving Instruction Following Abilities for Omni-Video Captioning
链接: https://arxiv.org/abs/2606.08572
作者: Jiahao Wang,An Ping,Yanghai Wang,Yuanxing Zhang,Shihao Li,Hanyan Bian,Yichi Ren,Yize Zhang,Han Wang,Haowen Chen,Junze Li,Jiaqi Wang,Yiyang Hu,Zhuze Xu,Zijie Zhang,Jiaheng Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:While Omni-modal Large Language Models (OLLMs) have demonstrated impressive capabilities in jointly processing audio and visual streams, their ability to strictly adhere to complex, multi-faceted user instructions remains largely unexplored. Existing benchmarks primarily focus on holistic video understanding or text-only instruction following, failing to capture the intricate interplay between modalities and user constraints. To bridge this gap, we introduce OmniCap-IF, the first comprehensive benchmark specifically designed to evaluate instruction-following capabilities in omni-modal captioning. OmniCap-IF incorporates a systematic framework that assesses captions on two dimensions: format correctness and content correctness. Our benchmark encompasses 50 distinct constraint types across pure visual, pure audio, and audio-visual modalities, while integrating Temporal Grounding to assess spatio-temporal precision. Extensive evaluations of prominent models on 1,920 high-quality samples reveal significant performance disparities. Furthermore, our analysis uncovers a critical “format-content tradeoff”, demonstrating that increasing formatting complexity directly degrades models’ omni-modal reasoning abilities. Finally, to advance the field, we curate a 54K instruction-tuning dataset, OmniCap-IF-54K and present OmniCaptioner-IF, which achieves notable improvements in both complex instruction adherence and general omni-modal captioning performance.
[CV-139] owards Accurate Emotion-Attributed Video Captioning via Fine-grained Emotion-Cause Pair Extraction
链接: https://arxiv.org/abs/2606.08566
作者: Weidong Chen,Cheng Ye,Zhendong Mao,Liping Wang,Xinyan Liu,Yongdong Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Emotional Video Captioning (EVC) is a challenging task that aims to generate factually accurate and emotionally rich descriptions for videos. Existing EVC methods leverage holistic visual features to mine global emotional cues, and then aggregate multimodal features to guide the emotional caption generation, which ignores the critical characteristic of the EVC task. Visual emotions are evoked by specific motivational causes, which are usually only implied in core video segments. The holistic mining brings significant information redundancy and inaccurate emotional cues. Thus, fine-grained visual cause extraction has a facilitative effect on both emotion perception and emotion-attributed caption generation. To this end, we propose a fine-grained emotion-cause pair extraction framework for emotion-attributed video captioning. Specifically, we learn pair-wise emotion and cause features in two rounds: 1) We propose a Concept-aware Visual Semantic Decomposition module to augment visual features by exploring scene, object, and motion concepts. Besides, to enhance emotional features, we propose a Visual-guided Emotion Interpretable Learning module, which guides emotion refinement with visual temporal dynamics, and augments the interpretable refinement process by reliable VAD-vector constraints. 2) We achieve emotion-cause pair extraction by cross-coupling the visual and emotional features before and after refinement, and leverage contrastive loss to achieve semantic forced alignment. Overall, our approach optimizes complex semantic understanding and emotion perception of videos, leading to a promising performance in emotional captioning. Extensive experiments on three challenging datasets demonstrate the superiority of our approach and each proposed module, e.g., achieving the best performances with +4.4% and +5.4% w.r.t. BLEU-2 and ROUGE-L, respectively, on the EVC-MSVD dataset.
[CV-140] When Video Misreads: Closed-Loop Distillation of Reading Heuristics for Exploratory Manipulation Trace QA
链接: https://arxiv.org/abs/2606.08542
作者: Haizhou Ge,Yufei Jia,Yue Li,Zhixing Chen,Lu Shi,Lei Han,Guyue Zhou,Ruqi Huang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 4 figures, 4 tables
Abstract:Exploratory manipulation often turns an apparent failed attempt into the key evidence for what to do next. For example, a robot pulls a locked cabinet drawer, fails, and only succeeds after opening the lock. The failed pull reveals a latent precondition (the drawer is locked) that determines the minimal-success action chain (the fewest actions that complete the task), here [lock-open, drawer-pull]. Correctly reading this trace is therefore the prerequisite for recovering that chain. We formalize this setting as Exploratory Manipulation Trace QA (EMT-QA): given synchronized video and proprioception from an exploratory trace, predict the minimal-success action chain under the latent precondition revealed by the probe. However, even state-of-the-art VLMs and embodied multimodal LLMs misread this evidence: they do not reliably recover the chain from raw video, raw proprioception, or their combination. We introduce Closed-Loop Trace Distillation, a pipeline that uses a per-task coding agent to inspect labeled training traces and distill a one-line natural-language prompt over the trace, which we call the Distilled Reading Heuristic (DRH). At inference, no agent is invoked and no model weights are updated; a frozen VLM receives the raw trace plus the DRH as a prompt entry. Across three simulator and two real-robot tasks, the DRH improves chain accuracy by +0.38 to +0.47 over the best raw-modality baseline. The same DRH also serves as the sole specification for one-shot programmatic classifiers that match the prompted VLM. Comments: 16 pages, 4 figures, 4 tables Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) ACMclasses: I.2.9; I.2.10; I.2.7 Cite as: arXiv:2606.08542 [cs.RO] (or arXiv:2606.08542v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2606.08542 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-141] NGram-MoSE: Efficient Remote Sensing Super-Resolution via N-Gram Context and Mixture-of-Experts
链接: https://arxiv.org/abs/2606.08535
作者: Yun-Hsuan Huang,Trong-An Bui,Chih-Hung Chuang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Remote sensing applications for environmental monitoring and disaster management are frequently constrained by a spatial–temporal trade-off: imagery with fine spatial detail is often acquired less frequently, whereas more temporally available observations are typically coarser. Single-image super-resolution provides a practical means to enhance coarse imagery without changing acquisition schedules, yet many Transformer-based SR models remain computationally expensive and can be sensitive to limited or geographically biased training data, which degrades robustness under out-of-distribution conditions. This paper presents NGram-MoSE, a lightweight Transformer architecture designed to improve both efficiency and texture continuity. NGram-MoSE introduces N-Gram Context Injection to strengthen cross-window local consistency and mitigate window-boundary artifacts, and incorporates a Mixture-of-Experts (MoE) feed-forward design to scale capacity through sparse activation without proportional growth in inference cost. Experiments on a geographically disjoint OOD test set show that NGram-MoSE achieves 31.68,dB PSNR while reducing FLOPs by (14\times) relative to a heavyweight Transformer reference. Downstream evaluation on a landslide segmentation benchmark further demonstrates that restoring degraded inputs to the detector training scale improves performance, yielding a 4.47% absolute gain in mAP@50 over bicubic upsampling, and exhibits stronger cross-scale consistency under scale extrapolation. These results indicate that NGram-MoSE provides an effective SR module for resource-constrained remote sensing pipelines requiring robust generalization.
[CV-142] DriveReward: A Comprehensive Dataset and Generative Vision-Language Reward Model for Autonomous Driving
链接: https://arxiv.org/abs/2606.08525
作者: Qimao Chen,Fang Li,Yuechen Luo,Zehan Zhang,Haiyang Sun,Fangzhen Li,Bing Wang,Guang Chen,Yang Ji,Jiong Deng,Hongwei Xie,Hangjun Ye,Long Chen,Yi Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reward models play a pivotal role in reinforcement learning (RL) and multi-modal trajectory selection for autonomous driving. However, acquiring such rewards typically relies on hand-crafted rule-based objectives or perception ground truth, which hinders generalization for data-scaling. While Vision-Language Models (VLMs) have demonstrated feasibility as reward models in other domains, their effectiveness in driving tasks remains underexplored. In this work, we bridge this gap by (1) introducing DriveReward, a reasoning trajectory evaluation dataset rigorously labeled via temporally-grounded visual guidance, and augmented with counterfactual driving behaviors., (2) alongside a specialized Vision-Language Reward Model. To address the scarcity of failure cases in conventional datasets, we propose a counterfactual data annotation scheme to construct cases encompassing diverse driving styles and erroneous behaviors. Evaluations on our proposed benchmark reveal that even leading open-source and proprietary VLMs fail to excel across all tasks, highlighting significant room for improvement in existing models. Building on these findings, we subsequently tailor a specialized 1B reward model that outperforms larger VLMs on task-specific reward alignment. Finally, we validate our reward model’s effectiveness by integrating it into RL finetuning and multi-modal trajectory scoring across multiple baselines, achieving performance comparable to rule-based reward calculations in both open-loop and closed-loop evaluation.
[CV-143] OmniTryOn: Video Try-On Anything at Once!
链接: https://arxiv.org/abs/2606.08514
作者: Changliang Xia,Chengyou Jia,Minnan Luo,Zhuohang Dang,Xin Shen,Bowen Ping
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Although video virtual try-on (VVT) has achieved significant progress, existing methods still exhibit two fundamental limitations: first, they are restricted to single-garment transfer, rendering simultaneous multi-object try-on highly impractical; second, their heavy reliance on explicit external priors (e.g., garment masks) inevitably destroys crucial physical dynamics and degrades visual quality. To bridge this gap, this paper proposes the novel Try-On Anything task, which aims to simultaneously transfer diverse wearable objects onto a person in a video in a single inference pass. To support and standardize this paradigm, we introduce TryAny-Bench, a comprehensive benchmark encompassing a paired video dataset alongside a tailored evaluation protocol. Furthermore, we present OmniTryOn, an external-prior-free generative framework designed to tackle this task. Specifically, OmniTryOn employs a First Frame Wearable Cache strategy, which directly provides diverse wearable objects for the generation process through the initial video frame. To maintain consistency, we propose the Spatiotemporally Consistent RoPE (STC-RoPE), which inherently establishes robust spatiotemporal anchors to strictly preserve complex human motions and background dynamics. Optimized by the proposed Gradual Try-On (GTO) training strategy, our model progressively masters robust multi-object synthesis. Extensive experiments on TryAny-Bench demonstrate that OmniTryOn significantly outperforms existing specialized video virtual try-on models and general video editing baselines, establishing a powerful new standard for the Try-On Anything task. Our dataset, code, and models are available at this https URL.
[CV-144] Look Less Reason More: Block-wise Attention Skipping for Efficient Multimodal LLM s
链接: https://arxiv.org/abs/2606.08511
作者: Jie Ma,Zhike Qiu,Jiayi Ji,Xiaoshuai Sun,Rongrong Ji
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal Large Language Models (MLLMs) face a significant inference bottleneck due to the quadratic computational cost of self-attention over long visual token sequences. However, we identify a critical inefficiency in current architectures: Visual Attention Saturation. Our analysis reveals that visual tokens rapidly establish their spatial structure and intra-modal relationships in early layers, rendering visual-to-visual self-attention in deeper layers computationally redundant. Conversely, Feed-Forward Networks (FFNs) in these layers remain essential for projecting visual features into the evolving textual semantic space. Leveraging this insight, we present Visual-Skip (V-Skip), a training-free inference paradigm that decouples spatial interaction from semantic evolution. Rather than discarding tokens, V-Skip imposes block-wise structured sparsity by selectively bypassing saturated visual self-attention modules. Furthermore, recognizing that varying downstream tasks demand distinct reasoning depths, V-Skip employs a lightweight, few-shot calibration to dynamically route the task-optimal sparsity path. Extensive experiments demonstrate that V-Skip effectively bypasses redundant vision attention to achieve block-wise sparsity, maintaining a 94.16% to 100.31% performance retention across diverse MLLMs. Ultimately, we prove that to reason more effectively, models do not need to discard what they see – they simply need to “look less” at the right depth.
[CV-145] EgoPriMo: Egocentric Motion Generation for Interactive Humanoid Control
链接: https://arxiv.org/abs/2606.08495
作者: Haoyang Ge,Peng Ren,Yukun Shi,Cong Huang,Kun Li,Kai Chen
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Humanoid robots require whole-body motions that adapt to scene context, task requirements, and user intent. Motion tracking reproduces specified trajectories, and humanoid vision-language-action systems provide semantic interfaces, but neither offers a scalable and interactive prior for broad full-body behavior. We introduce EgoPriMo (Egocentric Motion Prior for Humanoid Robots), a unified framework that learns such priors from egocentric human demonstrations. Given egocentric observations and a text prompt, EgoPriMo reconstructs, generates, and forecasts SMPL-based full-body motion. Language is used as a high-level control signal rather than a complete motion specification. At the core of EgoPriMo is a Triple-stream DiT that jointly models body dynamics, egocentric visual context, and text; task-conditioning masks route different tasks and missing-modality data through the same checkpoint. Experiments on Nymeria and EgoExo4D show that one checkpoint improves egocentric motion generation over UniEgoMotion while supporting reconstruction and forecasting; the generated SMPL motions can also be executed by a Unitree humanoid controller. These results indicate a practical path from scalable egocentric observations to generalizable and interactive humanoid motion priors.
[CV-146] Seeing is Believing: Aligning Prompt Rewriting with Visual Anchors for Text-to-Image Generation
链接: https://arxiv.org/abs/2606.08492
作者: Xuanyi Liu,Deyi Ji,Junyu Lu,Jing Wang,Qianxiong Xu,Xuhang Chen,Tianrun Chen,Siwei Ma
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Despite the impressive capabilities of text-to-image (T2I) models, an intent-generation gap often persists due to the brevity and ambiguity of user prompts. Existing approaches primarily polish the prompt for fluency and readability. However, the enhancement process still lacks visual grounding. As a result, the rewriter may over-infer missing details, causing an intent-generation gap. To address this limitation, we propose FaithRewriter, a novel prompt-enhancement framework for T2I generation. Specifically, FaithRewriter first leverages a multimodal MLLM to generate an image from the original prompt as an intermediate visual cue. This cue is then combined with the prompt and fed into a large-scale LLM to produce visually grounded augmentations that better reflect how the intended content should appear in images. Finally, these augmentations are distilled into a small-scale LLM for efficient deployment, enhancing its ability to generate effective T2I prompts. Experiments show that FaithRewriter yields prompts that are more faithful to the user intent and more visually plausible than strong baselines, helping narrow the intent-generation gap.
[CV-147] OctaOctree Neural Radiosity for Real-time Glossy Material Rendering
链接: https://arxiv.org/abs/2606.08469
作者: Jierui Ren,Haojie Jin,Bo Pang,Meng Gai,Fei Zhu,Yisong Chen,Sheng Li(Peking University)
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 9 figures
Abstract:Modeling high-frequency outgoing radiance distributions remains a fundamental challenge in global illumination, especially for glossy and specular materials. Existing neural-based radiance caching methods commonly rely on positional feature encodings or spatially organized caches, which makes it difficult to represent sharp directional radiance variations without increasing the model complexity or sampling cost. To address this challenge, we propose OctaOctree, an efficient spatial-angular radiance representation for global illumination. OctaOctree organizes outgoing radiance with an adaptive octree in 3D space, and associates each spatial node with an octahedral directional map. By coupling the spatial hierarchy with direction-dependent storage, our representation allocates fine spatial resolution to local illumination and visibility changes, while using coarser spatial levels with richer angular resolution to capture glossy and specular radiance distributions. This design embeds a reflectance-aware spatial-angular prior directly into the radiance representation, reducing the burden on neural networks or reconstruction modules to recover high-frequency view-dependent effects from positional features alone. As a result, OctaOctree provides a compact and expressive neural encoding for a wide range of indirect illumination effects, from diffuse interreflection to sharp glossy reflections. Experiments demonstrate that our method produces high-quality, direction-aware global illumination with single network query at primary intersections, achieving improved fidelity and real-time performance compared with baseline neural radiosity and radiance caching approaches.
[CV-148] VI-CoT: Text-Visual Interleaved Chain-of-Thought Reasoning for Multimodal Understanding ICML2026
链接: https://arxiv.org/abs/2606.08464
作者: Lianyu Hu,Xiaoyu Ma,Zeqin Liao,Yang Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ICML2026
Abstract:Chain-of-thought (CoT) reasoning has proven effective for enhancing problem-solving in large language models. However, when applied to multimodal LLMs (MLLMs), existing CoT approaches suffer from a fundamental limitation: they perform reasoning entirely in text without accessing visual features during the reasoning process. After initial visual encoding, image information becomes inaccessible, forcing models to reason based solely on whatever was captured in the initial description, which forms a `vision-blind reasoning’ paradigm that limits fine-grained visual extraction, error verification, and adaptive attention. We propose Text-Visual Interleaved Chain-of-Thought (TVI-CoT), a framework that enables explicit interleaving of textual reasoning and visual feature access through learnable control tokens THINK, LOOK and ANSWER. These tokens allow dynamic switching between reasoning and visual grounding, attending to relevant image regions conditioned on the evolving reasoning state. Experiments on eight benchmarks demonstrate state-of-the-art results among MLLM-based CoT methods and notable performance boost compared to the baseline: +6.1% on MMMU, +3.8% on MathVerse, +3.4% on MathVista, and +3.4% on ScienceQA. Code is available at this https URL.
[CV-149] GraspFoM: Towards Reconstruction-Driven Robotic Grasping with 3D Foundation Priors
链接: https://arxiv.org/abs/2606.08440
作者: Dongli Wu,Xiaobao Wei,Hao Wang,Qiaochu Dong,Ying Li,Qingpo Wuwu,Ming Lu,Wufan Zhao
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Robotic grasping is a fundamental capability in robotic manipulation. Yet grasping remains challenging under partial observations. Reliable grasping depends on both local contact cues and object-level 3D structure. Existing geometry-aware grasping methods recognize the value of reconstruction, but they typically treat geometry as an intermediate prediction rather than a reusable object prior for grasping. In this paper, we present GraspFoM, a unified framework that leverages 3D foundation priors (SAM3D) to build a shared 3D object latent for both reconstruction and grasp pose prediction. Built on this shared object latent, we introduce an anchor-initialized truncated pose-reasoning diffuser that predicts continuous and multimodal grasp poses without directly relying on discrete grasp candidates. We further investigate the interaction between reconstruction and grasping through a reconstruction-aware scorer and a residual latent updater. Reconstruction provides grounded geometric cues, while grasp supervision refines the shared object latent toward grasp-relevant affordances. GraspFoM jointly predicts grasp poses and reconstructs high-fidelity 3D assets in mesh and 3DGS forms. Comprehensive experiments demonstrate that GraspFoM achieves state-of-the-art results on both reconstruction and grasping. Notably, these improvements require only a small number of additional trainable parameters. Component-wise ablation studies also demonstrate the contribution of each component.
[CV-150] Reinforcing Temporal Answer Grounding in Instructional Video via Candidate-Aware Causal Reasoning
链接: https://arxiv.org/abs/2606.08436
作者: Muge Qi,Rong Fu,Pengbin Feng,Xianda Li,Yu Cai,Yifu Guo,Shizhe Zhang,Simon James Fong,Lei Ma,Bin Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The task of temporal answer grounding in instructional video (TAGV), which aims to locate precise video segments that respond to natural language queries, is increasingly important for direct video answer retrieval. This task remains challenging due to the need to comprehend semantically complex questions and to address the significant length mismatch between untrimmed videos and short target moments. Existing methods often suffer from sensitivity to irrelevant content or insufficient visual reasoning capabilities. To tackle these limitations, we propose a Candidate-Aware Causal Reasoning (CACR) framework. Our approach first employs a Visual-Language Pre-training based Candidate Selection (VBCS) algorithm to efficiently generate K candidate segments, then applies a temporal logic reasoning module enhanced by a rejection reward mechanism and optimized via Group Relative Policy Optimization (GRPO) for robust inference. Extensive experiments on six benchmarks demonstrate that our method achieves state-of-the-art performance in terms of mean Intersection-over-Union (mIoU), providing a new perspective for reasoning-based retrieval in long videos.
[CV-151] Segmentation-Assisted Brain MRI Synthesis with Cross-Image Multi-Contrast Feature Memory Bank Retrieval Augmentation
链接: https://arxiv.org/abs/2606.08421
作者: Wenwei Huang,Jia Wei,Jianlong Zhou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multi-contrast brain MRI provide complementary soft-tissue characteristics that aid in the screening and diagnosis of diseases. However, limited scanning time, image corruption and various imaging protocols often result in incomplete multi-contrast images. While current approaches excel in image synthesis, they often struggle to synthesize critical tumor regions and exploit contextual information in multi-contrast brain MRI effectively. To address this issue, we propose a synthesis-centric, segmentation-assisted closed-loop framework with retrieval augmentation synthesis. Our method overall takes a generative adversarial architecture, which aims to synthesize missing contrasts from any combination of available ones with a single model. To explicitly capture tumor semantics and focus synthesis on tumor regions, we add an auxiliary segmentation branch that predicts tumor masks and feeds them back as semantic conditioning in synthesis branch, thereby learning tumor-aware representations in the model and improving synthesis fidelity. Furthermore, we propose a dual-bank retrieval augmentation strategy. It dynamically queries two external knowledge bases, namely a tumor masks memory bank for crucial tumor context and cross-image contrast feature memory bank for global style information, to augment synthesis. Verified on two public multi-contrast magnetic resonance brain datasets: BraTs2020 and UCSF-BMSR, the proposed method is effective in handling medical brain images synthesis tasks and shows superior performance compared to previous methods. Code is available at:this https URL
[CV-152] CheXanatomy: Anatomy-Aware Vision-Language Modeling for Chest Radiographs
链接: https://arxiv.org/abs/2606.08420
作者: Sergios Gatidis,Curtis Langlotz,Christian Bluethgen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-language models (VLMs) pretrained on large-scale image-text pairs demonstrate strong image-level understanding, but are primarily optimized for global alignment and do not explicitly encode fine-grained anatomical structure, limiting their suitability for spatially precise tasks such as segmentation. We introduce CheXanatomy, a framework that integrates explicit anatomical knowledge into a pretrained VLM through autoregressive token-space supervision. Instead of adding task-specific decoder heads, the model is trained to generate anatomical segmentation masks via next-token prediction. To enable scalable supervision, we synthesize realistic chest radiographs from CT volumes and forward-project CT segmentation labels to obtain anatomically consistent 2D masks. We evaluate the approach on synthetic and real chest radiographs against a U-Net baseline, including ablations on model scale, input resolution, and vision encoder fine-tuning. Autoregressive anatomical supervision achieves performance comparable to specialized convolutional models in-distribution and demonstrates improved geometric robustness under domain shift to real CXR data. In addition, anatomy-pretrained models exhibit improved sample efficiency when adapting to novel localization tasks under limited supervision. Larger models and higher input image resolution improve performance, while vision encoder fine-tuning has limited effect. These results show that embedding anatomical structure directly into the generative objective promotes spatially grounded representations and supports anatomy-aware medical vision-language modeling.
[CV-153] CoVEBench: Can Video Editing Models Handle Complex Instructions?
链接: https://arxiv.org/abs/2606.08415
作者: Jiangtao Wu,Jiaming Wang,Yiwen He,Yuanxing Zhang,Shihao Li,Dunyuan Liu,Xuedong Zhao,Jialu Chen,Zekun Moore Wang,Jiaheng Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 34 pages, 11 figures, 9 tables
Abstract:While recent text-guided video editing models excel at elementary tasks (e.g., style transfer, object insertion), real-world user requests are highly compositional. A single prompt often demands multiple coupled edits, such as modifying subjects, actions, and camera views, while strictly preserving unrelated spatiotemporal content. Existing benchmarks, heavily constrained by isolated edits and coarse global metrics, fail to diagnose how models handle such complex workflows. To address this gap, we introduce CoVEBench, a compositional video editing benchmark comprising 416 curated source videos, 626 multi-point editing instructions, and 9,990 fine-grained checklist items. Covering diverse editing dimensions, CoVEBench evaluates models via MLLM-judged instruction compliance and video fidelity, alongside automated metrics for video quality. Extensive experiments reveal that compositional editing remains a profound challenge: current models frequently omit edits, violate preservation constraints, or introduce artifacts when handling multiple operations simultaneously. CoVEBench provides a challenging, diagnostic testbed to advance video editing toward realistic user workflows.
[CV-154] Geometry-Driven Flow Analysis of Brain Sulcal Pattern
链接: https://arxiv.org/abs/2606.08404
作者: Moo K. Chung,Luigi Maccotta,Aaron Struck
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Cortical folding reflects coordinated neurodevelopmental processes and is increasingly recognized as a sensitive marker of neurological disease. However, most existing analyses rely on indirect scalar summaries that do not explicitly model folding geometry itself. In juvenile myoclonic epilepsy (JME), a common genetic epilepsy, cortical abnormalities are often subtle, spatially distributed, and difficult to detect using conventional morphometric measures. We introduce a Poisson-equation-based framework that models cortical folding as a geometry-driven flow derived from mean curvature on the cortical manifold. By treating folding patterns as a stationary source-sink structure, the proposed approach yields a smooth, globally balanced potential field whose surface gradient defines a physically interpretable flux. This framework enables spatially coherent analysis of sulcal-gyral folding organization and provides a principled representation of geometry-driven cortical structure in JME.
[CV-155] Self-Supervised Vision Transformers for CBCT-Based Detection of Temporomandibular Joint Osteoarthritis
链接: https://arxiv.org/abs/2606.08364
作者: Shradhdha Trivedi,Vrundan Sojitra,Mariela Padilla
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Temporomandibular joint osteoarthritis (TMJ OA) is a prevalent degenerative condition whose osseous changes are often subtle on cone-beam CT (CBCT), making automated detection challenging. We study how well the DINO family of self-supervised vision transformers – DINOv1, DINOv2, DINOv2+reg, and RAD-DINO (a radiology-pretrained variant) – transfers to CBCT, asking how much backbone adaptation is needed and of what kind. We propose a simple slice-based pipeline using Vision Transformer (ViT) backbones: axial CBCT slices are encoded per-slice by a frozen or partially adapted ViT and aggregated via attention-based multiple instance learning (MIL) for patient-level binary OA/Normal classification. Through systematic ablation across unfreezing strategies and aggregation designs on a multi-source CBCT dataset, we find that partial unfreezing of the final two transformer blocks is the decisive factor, improving AUC from 0.671 (fully frozen DINOv2) to 0.902. This outperforms DINOv1 (0.867), DINOv2+reg (0.774), and a supervised ImageNet ViT-B/16 baseline (0.843). Our results provide practical guidance for adapting DINO-family foundation models in low-data medical imaging settings, showing that adaptation strategy is a stronger driver of performance than backbone choice alone.
[CV-156] Beyond Raw Signals: Undecoded Generative Latents as Privileged Synthetic Data
链接: https://arxiv.org/abs/2606.08336
作者: Cristian Sbrolli,Nicolas Michel,Matteo Matteucci,Toshihiko Yamasaki
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:While multimodal integration significantly improves computer vision models, deploying them incurs prohibitive inference costs and requires scarce, perfectly paired datasets. Recent methods address this data bottleneck by synthesizing missing modalities via generative AI, yet they introduce a severe inefficiency: the Decode-Encode Loop. Specifically, information-rich generative latents are decoded into noisy raw signals, forcing the downstream classifier to waste capacity re-encoding them. To bypass this bottleneck, we propose Direct Latent Augmentation (DLA), utilizing undecoded generative latents directly as privileged information. Furthermore, to transfer this dense knowledge to a purely visual student, we introduce Multilayer Explicit Simulated Synesthesia (MESSy). Instead of enforcing rigid representation matching, which forces the student to distort its native visual features to accommodate complex multimodal topologies, MESSy uses a predictive objective to safely internalize these physical priors. Empirical results demonstrate that our framework significantly outperforms raw data augmentation and traditional distillation. Ultimately, our approach yields highly accurate unimodal students with ``synesthetic’’ latent structures that are inherently aligned with physical properties they have never directly observed.
[CV-157] SMI: Efficient Self-Supervised Learning via Mutual-Information-Inspired Dependency Optimization
链接: https://arxiv.org/abs/2606.08332
作者: Pritam Mishra,Coloma Ballester,Dimosthenis Karatzas
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Self-supervised learning (SSL) has achieved remarkable representation learning performance, but many existing methods rely on large batch sizes, memory banks, momentum encoders, or global synchronization mechanisms that substantially increase computational cost and training complexity. In this work, we propose Semantic Mutual Information (SMI), a lightweight self-supervised objective derived from a mutual-information-inspired dependency formulation under Gaussian assumptions. Unlike conventional correlation matching objectives that operate on high-dimensional feature correlation matrices, SMI performs optimization on a sample-level dependency matrix through a nonlinear transformation of pairwise correlations. This formulation induces distinct optimization dynamics that emphasize strongly dependent semantic pairs while maintaining representation diversity. Experimental results on ImageNet using a ResNet-50 backbone demonstrate that SMI achieves competitive linear evaluation performance relative to state-of-the-art SSL approaches while substantially reducing computational complexity. Across multiple low-resource benchmarks, SMI consistently improves transfer performance over Barlow Twins, particularly on fine-grained datasets. Furthermore, analyses of optimization dynamics and representation geometry suggest improved alignment–redundancy balance, greater feature diversity, and more spatially localized semantic representations. These results indicate that nonlinear dependency optimization provides an effective and computationally efficient alternative to conventional correlation-based self-supervised learning objectives.
[CV-158] Set-Based Transformer for Atmospheric Compensation in Standoff LWIR Hyperspectral Imaging
链接: https://arxiv.org/abs/2606.08324
作者: Fabian Perez,Nicolas Quintero,Jeferson Acevedo,Hoover Rueda-Chacon
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: IGARSS 2026 accepted paper conference
Abstract:Passive long-wave infrared (LWIR) hyperspectral imaging under a standoff geometry depends on atmospheric absorption and emission, as well as reflected radiance, thus making atmospheric compensation essential to get knowledge of a target of interest. Despite its importance, this compensation has been largely overlooked due to its practical and modeling difficulty. In this paper, we present a lightweight set-based deep learning framework that takes multiple radiance measurements, collected at different standoff ranges, as input and jointly estimates transmittance, atmospheric path radiance, and a shared downwelling spectrum. We analyze the learned representation with a sparse autoencoder and observe that several latent features do activate on geographically coherent subsets of the test data despite the absence of location supervision. Experiments on a MODTRAN generated standoff LWIR dataset demonstrate low spectral distortion across all estimated products. The dataset and code is publicly available at: this https URL
[CV-159] Where the Score Lives: A Wavelet View of Diffusion AISTATS2026
链接: https://arxiv.org/abs/2606.08309
作者: Emma Finn,Binxu Wang,T. Anderson Keller,Demba E. Ba
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 20 pages, 12 figures, AISTATS 2026
Abstract:Score-based generative models have had remarkable success over the last decade in generating a diverse set of visually plausible images. A variety of architectures including CNNs, U-Nets, and Transformers have been used as the score-approximation network in such diffusion modeling; however, to date, relatively little is known about how these architectural choices impact generative behavior. In this work, to provide insight into this area, we propose an analytically solvable parameterization of the score function using an expansion in a 2D orthogonal wavelet basis. In particular, we derive interpretable optimal score functions in terms of the moments of the data distribution. We use this parametrization to provide an architecture-agnostic, moment-based analysis that reveals which attributes of the data distribution tend to matter most for denoising. Our score machine is flexible enough to partially mimic the relevant inductive biases of multiple architectures, including U-Nets, and CNNs, taking a step towards understanding why different score architectures can exhibit distinct generative behavior. Since our score is solvable in terms of the moments of the data, we can begin to understand how the data distribution interacts with the score network to produce the behavior we observe in diffusion models.
[CV-160] HACK: Towards More Effective Head-Aware Key-Value Compression for Efficient Visual Autoregressive Modeling
链接: https://arxiv.org/abs/2606.08302
作者: Ziran Qin,Yuchen Jiang,Mingbao Lin,Youru Lv,Hang Guo,Wen Fei,Weiyao Lin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Visual Autoregressive (VAR) models adopt a next-scale prediction paradigm, offering high-quality generation with substantially fewer decoding steps. However, existing VAR models suffer from significant attention complexity and severe memory overhead due to the accumulation of key-value (KV) caches across scales. In this paper, we tackle this challenge by introducing KV cache compression into the next-scale paradigm. We begin with an in-depth analysis of VAR attention and observe that attention heads can be stably divided into two functionally distinct categories: Contextual Heads focus on maintaining semantic consistency, while Structural Heads preserve spatial coherence. Their functional divergence makes existing one-size-fits-all compression methods perform poorly on VAR models. We further find that the two head types differ markedly in their reliance on historical scales, and that this reliance shifts across layers and generation steps, arguing for an adaptive cache budget allocation. To address these challenges, we propose HACK++, a training-free Head-Aware key-value Compression frameworK for VAR models. From a one-time offline calibration, HACK++ classifies head types and derives head-specific priors. At inference, it decouples attention from cache compression under independent budgets, bounding the current-scale attention cost while compressing the accumulated cache far more aggressively, via pattern-specific strategies and a reliance-aware budget allocation. Extensive experiments on multiple VAR models across text-to-image, class-conditional, and unified understanding-and-generation tasks validate the effectiveness and generalizability of HACK++. For example, on Infinity-2B/8B, HACK++ maintains near-lossless generation with only a 30% attention budget and a 10% cache budget, and remains robust even under a 1% cache budget.
[CV-161] G2G: Exploiting Intra-Group Geometry for Inter-Group Pose Estimation
链接: https://arxiv.org/abs/2606.08284
作者: Yufei Wei,Shuhao Ye,Chenxiao Hu,Yiyuan Pan,Dongyu Feng,Rong Xiong,Yue Wang,Yanmei Jiao
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Recovering the relative 6-DoF pose between two image groups underlies cross-sequence relocalization and multi-camera rig odometry. Each group carries known intra-group geometry from visual odometry or rig calibration, and pretrained multi-view backbones already fuse such geometry into visual features. Yet current models treat all views as an unstructured set, leaving cross-group reasoning as the missing piece. We introduce \ours, which keeps the foundation model entirely frozen and adds three lightweight trainable modules to bridge the two groups: a perceiver resampler, a cross-group bridge with merged self-attention, and a multi-frame pose head. The trainable footprint totals about 32M parameters, under 6% of the full model, and is supervised only by relative poses. Across four datasets that span indoor and outdoor simulation, real-world cross-season capture, and zero-shot sim-to-real transfer, \ours attains state-of-the-art accuracy on both tasks, while every baseline is retrained with its full original supervision. Code is available at this https URL.
[CV-162] Remember with Confidence: Uncertainty Quantification for Spatio-temporal Memory with Probabilistic Guarantees
链接: https://arxiv.org/abs/2606.08277
作者: Harry Zhang,Nicolas Gorlo,Luca Carlone
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Long-horizon robot operation requires spatio-temporal memory to record the environment state and recall it for downstream reasoning. Scene graphs and retrieval-augmented systems ground VLM descriptions to persistent 3D entities with rich semantic descriptions. However, VLM captions are noisy and viewpoint-inconsistent, and existing systems treat them as an oracle with no mechanism to detect unreliable stored descriptions. We introduce object-level semantic uncertainty for multi-view VLM memory: a score that measures object-centric cross-view semantic scatter of captions and identifies semantically unresolved objects. Then, we include our uncertainty scores in an advanced spatial-semantic memory system, that we dub UQ-DAAAM. UQ-DAAAM uses this score to actively refine uncertain objects under a fixed query budget by selecting high-quality views and fusing the resulting multi-view captions into a single object description. We also derive probabilistic guarantees showing that higher-quality candidate views (as selected by our approach) are more likely to reduce uncertainty. Our experiments show that uncertainty quantification can make embodied 4D memory systems more reliable and more effective. In particular, on the OC-NaVQA benchmark, UQ-DAAAM achieves substantially larger uncertainty reduction and better spatio-temporal question answering performance than baselines.
[CV-163] IDE: Task-Isolated Diffusion for Unified Video Editing and Generation
链接: https://arxiv.org/abs/2606.08260
作者: Qi Liu,Gang Yue,Mingyu Yin,Lisai Zhang,Yidi Wu,Yaole Wang,Yaohui Wang,Chang Yao,Jingyuan Chen,Lin Ma
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent advances in Diffusion Transformers have driven rapid progress in video generation and editing, yet these capabilities are still handled by separate, task-specific models. Building a unified framework that supports diverse video tasks remains an open challenge: existing unified attempts either require dedicated auxiliary encoders or lack explicit mechanisms to distinguish heterogeneous conditioning tokens, struggling when the number and type of visual conditions vary across tasks. We propose TIDE, a unified framework that integrates instruction-based editing, reference-guided editing, and multi-reference generation. At its core, we introduce per-token task embeddings that assign each input token a task-specific identifier, enabling the model to explicitly disambiguate target, source, and reference tokens. To simultaneously capture high-level semantic understanding and fine-grained structural fidelity, we design a dual-path conditioning scheme that couples a vision-language model with a VAE latent path for complementary signals. We further devise a multi-task progressive training strategy that incrementally introduces tasks of increasing complexity, effectively harmonizing diverse objectives and enabling smooth generalization across heterogeneous task distributions. Extensive experiments on multiple video editing and generation benchmarks demonstrate that TIDE achieves state-of-the-art performance across all evaluated tasks. Our project page is available at this https URL.
[CV-164] MS-COOT: Comparing Morse-Smale Complexes with Co-Optimal Transport
链接: https://arxiv.org/abs/2606.08258
作者: Guangyu Meng,Mingzhe Li,Erin Wolf Chambers
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Understanding and comparing structures in scalar fields is a central challenge in scientific visualization, with applications ranging from feature analysis to temporal and structural comparison. The Morse-Smale (MS) complex provides a natural representation by decomposing a scalar field into regions induced by gradient flow. However, existing approaches typically rely on graph-based representations, capturing relationships between critical points while discarding region-level structure. In this work, we represent the MS complex as a hypergraph, where critical points form nodes and regions define hyperedges. We introduce MS-COOT, a co-optimal transport distance that jointly computes correspondences between critical points and regions. This formulation enables explicit region-to-region matching within a distance-based framework, allowing identification of region-level events such as splitting and merging. We instantiate this framework with domain-specific components, including a hypernetwork function encoding critical point-region relationships, persistence-based probability measures that emphasize topologically significant features, and a sample cost term that incorporates critical point attributes. We evaluate MS-COOT on five datasets spanning 2D simulations, 3D surface meshes, and volumetric data. Our results show that MS-COOT captures region-level structural changes that are not reflected by graph-based distances, while achieving strong performance in downstream tasks such as classification and resolution discrimination.
[CV-165] Light-WAM: Efficient World Action Models with State-Fusion Action Decoding
链接: https://arxiv.org/abs/2606.08242
作者: Ziang Li,Dongzhou Cheng,Yibin Wang,Shiyue Wang,Xiaoyang Xu,Lingxuan Weng,Juan Wang,Jiaqi Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:World Action Models (WAMs) extend robot policy learning by incorporating future prediction as an additional training objective, encouraging the policy to encode task-relevant temporal structure in its representations. Current WAMs often rely on large-scale generative architectures that incur high training costs and inference latency, making them difficult to deploy as efficient closed-loop policies. We propose Light-WAM, a lightweight World Action Model for efficient robot manipulation. Specifically, it is built with a compact video backbone and performs future-video supervision in a downsampled latent space, reducing the cost of video co-training while retaining its benefits for representation learning. For action prediction, Light-WAM introduces the StateFusionActionExpert, which reads adapted states from multiple backbone layers, fuses them through learned-query pooling, and directly predicts action chunks in a single forward pass. This design provides an efficient interface between video backbone representations and robot actions, avoiding the need for heavy generative action experts. Experiments demonstrate that Light-WAM maintains strong performance on LIBERO and achieves usable multi-task performance on RoboTwin 2.0, while using only 0.44B trainable parameters. It also achieves 72.03ms inference latency with 4.1GiB peak GPU memory and improved training throughput.
[CV-166] st-Time Scaling in Multimodal Foundation Models: A Comprehensive Survey of Generation and Reasoning ACL2026
链接: https://arxiv.org/abs/2606.08231
作者: Cong Wan,Ying He,Zhongzhan Huang,Hefeng Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ACL 2026, Findings
Abstract:Test-time Scaling (TTS) has emerged as a pivotal research direction for enhancing model performance by dynamically allocating computational resources during inference. Recent advancements have adapted this paradigm to Multimodal Foundation Models (MFMs), unlocking their potential in multimodal reasoning and generation. Despite rapid progress, the field lacks a systematic survey and unified theoretical framework to delineate the developmental landscape of multimodal TTS. To bridge this gap, we present the first comprehensive review of TTS research for MFMs, proposing a unified taxonomic framework that categorizes existing methodologies into three distinct strategies: sampling-based, feedback-based, and search-based approaches. We further summarize representative applications and benchmarks commonly utilized to evaluate multimodal TTS capabilities in generation and reasoning tasks. Finally, this survey discusses open challenges and outlines future research directions, providing a systematic roadmap for subsequent studies in this rapidly evolving field.
[CV-167] SegmentAnyTreeV2: Scaling Transformer-Based Tree Instance Segmentation Across Sensors Platforms and Forests
链接: https://arxiv.org/abs/2606.08206
作者: Maciej Wielgosz,Stefano Puliti,Rasmus Astrup
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 25 pages, 6 figures, 10 tables
Abstract:We present SegmentAnyTreeV2, a sensor- and platform-agnostic framework for semantic and instance segmentation of forest point clouds. The model combines a serialization-based Point Transformer v3 backbone with a lightweight semantic head and a tree-focused cross-attention mask decoder. Semantic predictions restrict instance decoding to tree-class voxels, while instance-aware query initialization, one-to-many seed supervision, and asymmetric mask scoring improve separation in dense and structurally complex stands. We further introduce FOR-instance v3, an expanded benchmark comprising 427 scenes and 26,496 annotated trees across diverse biomes, forest structures, and LiDAR platforms. On the FOR-instanceV2 test split, SegmentAnyTreeV2 achieves 90.5% precision, 80.2% recall, 85.0% F1, 90.7% coverage, and 87.6% semantic mIoU, outperforming previous learning-based methods in both instance detection and mask completeness. Zero-shot evaluation on independent sites further demonstrates strong cross-domain generalization.
[CV-168] Empowering Feed-Forward Reconstruction Models with Metric Scale via Satellite Images
链接: https://arxiv.org/abs/2606.08205
作者: Xianghui Ze,Yongjian Luo,Mengjun Chao,Zhenbo Song,Jianfeng Lu,Yujiao Shi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Feed-forward 3D reconstruction models have recently shown strong generalization across diverse scenes, yet most of them recover geometry only up to an unknown global scale. This scale ambiguity limits their use in applications that require metric understanding of the environment. Existing metric reconstruction methods commonly rely on large-scale metric annotations or accurate camera calibration, both of which are costly or unreliable in many real-world settings. We propose a satellite-guided framework for resolving scale ambiguity in feed-forward 3D reconstruction. The key idea is to use readily available satellite imagery as a global metric reference. Given a coarse camera pose, our method retrieves a local satellite patch and integrates it with a feed-forward reconstruction backbone through bidirectional cross-view interaction. By enforcing consistency between the reconstructed scene and the satellite reference, the model infers absolute scale, refines scene geometry, and estimates camera pose in a metric coordinate frame. Experiments on KITTI, nuScenes, and Oxford RobotCar show consistent improvements in metric depth estimation, multi-view point-cloud reconstruction, and cross-view camera localization, while preserving strong generalization across datasets and geographic regions.
[CV-169] Neural Field Tokenizations with Hierarchy and Spatial Locality Priors
链接: https://arxiv.org/abs/2606.08204
作者: Alonso Urbano,David W. Romero,Max Zimmer,Sebastian Pokutta
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Neural fields parameterize data as functions from coordinates to values, providing a unified framework for representation learning across modalities. Existing approaches are dominated by per-sample meta-learning, which scales poorly due to memory-intensive inner-loop optimization. The natural alternative – feed-forward encoding – typically introduces modality-specific assumptions, sacrificing the generality that makes learning with neural fields attractive. We argue that locality and hierarchy are useful priors for learning field representations that can be injected without compromising modality-agnosticism. We propose LH-NeF, a framework to learn general-purpose tokenized representations of continuous signals. A locality-preserving hierarchical encoder maps raw coordinate-value field observations to structured tokens, from which the field is reconstructed during training. By replacing meta-learning’s inner loop with a single forward pass, LH-NeF uses 42 \times less memory and supports 133 \times larger batches than the strongest modality-agnostic baseline. Across images, 3D shapes, and climate fields, our learned representations match or exceed performance of modality-agnostic, modality-specific, and specialized generative neural field baselines on both reconstruction and downstream tasks.
[CV-170] How Much MRI Preprocessing Is Enough? A Cost-Utility Study for Brain MRI Foundation Models
链接: https://arxiv.org/abs/2606.08164
作者: Jiangshuan Pang,Wangyang Tang,Jing Yan,Zhixuan Cheng,Youzhe He,Zhenkun Zhuang,Tao Zhou,Shiping Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:MRI preprocessing defines the input distribution seen by brain MRI foundation models, yet it is usually treated as routine data cleaning rather than a modeling choice. We ask how much preprocessing is worth its computational cost for self-supervised 3D MRI pretraining. Keeping the corpus, 3D ViT backbone, masking protocol, and downstream evaluations fixed, we compare a graded P0-P7 preprocessing spectrum for masked autoencoding (MAE) and joint-embedding predictive learning (JEPA) on 20,000 heterogeneous brain MRI volumes, then transfer the encoders to IDH prediction, MCI classification, brain age regression, and GLI/PED tumor segmentation. The results do not support a simple “more is better” rule. P0/P1 are numerically unstable, making P2 the lowest-cost feasible level; beyond P2, choosing the best feasible preprocessing level improves aggregate utility by only 3.4 percentage points for MAE and 1.8 percentage points for JEPA, with most paired gains statistically unresolved. Stronger preprocessing is beneficial only in selected regimes: IDH improves modestly, AGE and GLI/PED are often near or best at P2, and MCI shows the clearest empirical P7 gain. Cross-level MCI transfer further shows that much of the P7 advantage can be recovered by applying stronger preprocessing downstream, without requiring P7 throughout pretraining. These findings recast MRI preprocessing as a downstream-aware cost-utility decision rather than a default escalation pipeline. Code is available at this https URL.
[CV-171] RAPID: Layer-Wise Redundancy-Aware Pruning and Importance-Driven Token Merging for Efficient ViT
链接: https://arxiv.org/abs/2606.08156
作者: Kyumin Choi,Ikbeom Jang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 7 pages, 2 figures
Abstract:Vision Transformers (ViTs) achieve strong performance but suffer from high computational costs due to quadratic self-attention complexity. Although token reduction techniques such as pruning and merging mitigate this, they typically overlook how representations evolve across network depth. We propose RAPID, a depth-aware token reduction framework that adapts reduction strategies to the layer-wise characteristics of token representations. The primary methodological contribution is a bifurcated strategy: in shallow-to-middle layers, RAPID employs a redundancy-similarity aware pruning metric to eliminate over-represented local patterns. As features transition to global semantic concepts in deeper layers, the framework shifts to an importance-similarity aware merging mechanism. This stage leverages classification (CLS) token attention weights to protect semantically critical tokens while fusing less important but similar neighbors. Empirical validation on ImageNet-1K using ViT and DeiT architectures demonstrates that RAPID establishes a superior accuracy-compression Pareto frontier compared to plug-and-play baselines such as ToMe and ToFu. RAPID is particularly robust in aggressive compression regimes, achieving up to 4.29% higher accuracy than ToMe at extreme reduction rates. Our framework provides a training-free template for optimizing vision models by aligning reduction strategies with hierarchical feature evolution.
[CV-172] Property-Informed Diffusion-Based Text-to-Microstructure Generation CVPR2026
链接: https://arxiv.org/abs/2606.08150
作者: Bingxuan Dai,Hongsong Wang,Jie Gui
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Published in CVPR2026, Code is at: this https URL
Abstract:Designing 3D metamaterial microstructures that meet the intended functions remains a major challenge, as it typically requires domain expertise, iterative simulations, and extensive manual tuning. Existing work on inverse design that automatically generates microstructures based on desired target properties often suffers from limited design diversity and faces challenges in ensuring the physical feasibility of the generated structures. To address this issue, a property-informed diffusion-based network is proposed that enables the generation of 3D microstructures directly from textual descriptions. Unlike traditional property conditioning methods, our approach leverages rich guidance in terms of semantics and physical properties in the text input to support diverse structure synthesis. To enforce consistency between the generated structures and the target textual prompts, a dual alignment strategy is adopted, including contrastive text-structure alignment and test-time reward-guided alignment. Experimental results show that the model is capable of generating semantically meaningful and physically plausible structures across a wide range of material categories. Our approach has good potential for interactive microstructure design and opens up new directions for combining language-based interfaces with inverse material discovery. Code is available at: this https URL
[CV-173] IMAGINE: Adaptive Schema-Imagery Enhanced Composition for Composed Video Retrieval ICMR2026
链接: https://arxiv.org/abs/2606.08144
作者: Jiale Huang,Zixu Li,Zhiwei Chen,Zhiheng Fu,Chunxiao Wang,Yupeng Hu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ICMR 2026
Abstract:Composed Video Retrieval (CVR) is designed to retrieve a target video that matches a reference video modified by a modification text. While existing methods explore cross-modal correspondences, they often assume modified objects appear directly in videos. However, modification texts frequently describe concepts not explicitly presented but implicitly expressed through semantically related visual cues (e.g., “cake” implying “birthday party”). Current approaches typically rely on aligning explicit feature representations within the concrete space, neglecting critical latent associations. To address this, we propose an adaptIve scheMa-ImAGery enhanced composItional NEtwork (IMAGINE). Unlike standard explicit matching, IMAGINE materializes implicit semantics (termed schema imagery) via dynamic multimodal prototypes. These prototypes capture shared latent concepts to adaptively modulate visual features, effectively injecting implicit guidance into the retrieval process. By bridging the gap between explicit visual contents and implicit retrieval intentions, IMAGINE achieves state-of-the-art performance in both CVR and Composed Image Retrieval (CIR) across three widely used benchmarks.
[CV-174] Gravity-guided Contact Dynamics Estimation from 3D Human Motions
链接: https://arxiv.org/abs/2606.08133
作者: Cuong Le,Urs Waldmann,Bastian Wandt,Mårten Wadenbäck
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, under submission
Abstract:Ground contact forces acting on the human body, are crucial for biomechanics studies or sport performance analysis. Prior methods rely on force plates or pressure mats to collect ground contact dynamics, limiting their applicability to carefully controlled settings. A more scalable solution is to estimate the dynamics directly from motion capture data. Recent approaches only roughly estimate the ground contact dynamics from the vertical distance between the body and the ground plane, which cannot capture the complex pressure distribution of all contact points. To this end, we propose GraCE – Gravity-guided Contact Dynamics Estimation, a novel full-body contact dynamics model for human motions using a realistic influence of body mass distribution and gravity. We use the human’s center of gravity to estimate the ground contacts based on its relative distance to the human body. The applied force on each contact is estimated via the product of predicted contact probabilities and the total exterior force computed from the center of mass trajectory. We outperform related work on the GroundLink dataset for ground reaction force estimation, and on the MOYO dataset for detailed contact pressure prediction. The code is published upon acceptance.
[CV-175] Phase Marginalization for Patch-Grid Instability in Vision Transformers
链接: https://arxiv.org/abs/2606.08132
作者: Oğuzhan Ercan
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 13 pages, 1 figure, 9 tables
Abstract:Vision Transformers operate on fixed patch grids, which can introduce phase-dependent instability for dense prediction: changing the patch partition can change the token evidence available to a pixel, especially near boundaries. We formalize patch-grid phase as a nuisance variable and propose Phase Marginalization, a post-hoc marginalization method that evaluates structured patch-grid phases, inverse-aligns dense outputs, and aggregates them in the original image coordinate system. The central variant, Uniform Phase Marginalization with K = 4, is training-free and improves over the canonical K = 1 baseline across measured segmentation, depth, and local matching settings. In a controlled Cityscapes experiment, Uniform Phase Marginalization provides a modest compute-matched advantage over generic shift-based four-forward test-time augmentation (TTA) (+0.31 mean Intersection-over-Union over the strongest tested generic row). A scaling study further shows that K = 4 is a practical cost-accuracy trade-off: K = 8 is essentially unchanged and K = 16 adds little accuracy at much higher latency. These results position patch-grid phase as a measurable nuisance variable and Phase Marginalization as a simple diagnostic and post-hoc marginalization baseline for dense ViT prediction.
[CV-176] One Stone Three Birds: Self-adaptive Optimal Transport for Multi-VLM Selection Adaptation and Ensembling
链接: https://arxiv.org/abs/2606.08126
作者: Qiyu Xu,Zhanxuan Hu,Yu Duan,Yonghang Tai,Huafeng Li,Quanxue Gao,Xiangyong Cao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-language models (VLMs) enable visual recognition from semantic class descriptions, which makes them attractive when target annotations are scarce or unavailable. Most deployment pipelines, however, first choose a single VLM and then adapt that model to the unlabeled target set. This single-backbone paradigm hides a critical assumption: the selected VLM is already compatible with the target domain. In realistic cross-domain deployment, several general-purpose and domain-specialized VLMs may be plausible, yet no instance-level target labels are available to identify the reliable ones. Deployment therefore requires a coupled solution for model selection, target adaptation, and prediction integration. We revisit this problem from a system-level multi-VLM perspective. Our central observation is that the three decisions above depend on the same latent object: a trustworthy sample-class structure in the target set. Different VLMs may encode different transfer biases and produce conflicting predictions, but their outputs can still provide complementary evidence for estimating this structure. We propose One Stone, Three Birds, a training-free framework based on self-adaptive optimal transport. Given a pool of frozen candidate VLMs, OSTB estimates a consensus sample-to-class transport plan without updating VLM parameters. The learned transport structure is then reused for all deployment objectives: model selection is performed by ranking the combined semantic and visual reliability induced by the consensus plan; target adaptation is obtained by fitting transport-conditioned visual classifiers; and ensembling is implemented through reliability-aware probabilistic integration. Extensive experiments on natural-image, remote-sensing, and medical-pathology benchmarks show that OSTB improves model ranking, adaptation stability, and ensemble robustness under heterogeneous candidate pools.
[CV-177] Human-Centered Benchmarking of Driver Monitoring Models
链接: https://arxiv.org/abs/2606.08123
作者: Ruben Dario Florez-Zela
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 9 pages, 3 figures, 7 tables. Code available at: this https URL
Abstract:Vision-based driver monitoring systems are increasingly deployed in safety-critical intelligent transportation settings, yet they are almost always compared on classification accuracy alone. This paper argues that accuracy is insufficient to characterize a model’s fitness for real-world deployment, and proposes the Human-Centered Benchmarking Framework (HCBF), which evaluates models across four dimensions: accuracy, explainability, efficiency, and robustness. The framework is applied to four representative lightweight architectures, MobileNetV3, ShuffleNetV2, EfficientNet-B0, and DeiT-Tiny, on the MRL Eye Dataset for eye-state classification. While the models are nearly indistinguishable on clean-set accuracy, each leads in exactly one dimension, and all four lie on the Pareto frontier. A Human-Centered Score computed under three deployment-oriented weighting scenarios ranks ShuffleNetV2 first throughout. However, this aggregate winner retains less than half of its performance under sensor noise and fails by classifying closed eyes as open, whereas the transformer remains robust. These findings show that aggregate ranking can mask dimension-specific vulnerabilities that are operationally decisive, underscoring the value of multi-dimensional, human-centered evaluation.
[CV-178] rustworthy Visual Predicates for Robust Manipulation Understanding under Degradation
链接: https://arxiv.org/abs/2606.08121
作者: Fatemeh Ziaeetabar
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Manipulation understanding requires reliable relational evidence, such as contact, support, containment, motion coupling, grasp, release, and active-hand involvement. Although these visual predicates are widely used in event-chain, graph-based, and neuro-symbolic models, their reliability under visual degradation is rarely analyzed directly. This paper introduces a predicate-level reliability framework for robust manipulation understanding under blur, occlusion, illumination change, low resolution, frame dropping, and detection noise. The framework defines a structured predicate vocabulary, confidence-aware predicate estimation, and reliability metrics for predicate preservation, degradation sensitivity, temporal consistency, confidence-weighted stability, and downstream impact. Experiments on controlled manipulation videos and public egocentric or bimanual datasets, including VISOR/EPIC-KITCHENS, H2O, and ARCTIC, show that predicate failures are structured rather than uniform. Static spatial predicates remain comparatively robust, whereas contact-sensitive, dynamic, and derived predicates such as grasp and release are more fragile. Under severe degradation, detection noise, occlusion, and frame dropping cause the strongest reliability losses. Downstream analysis shows that degraded predicates reduce manipulation-understanding accuracy from 0.89 to 0.58, while removing confidence weighting under moderate degradation reduces accuracy from 0.74 to 0.64. These results show that predicate reliability provides a diagnostic layer between visual perception and structured manipulation reasoning.
[CV-179] Revisiting Articulated Parts Perception in Robot Manipulation CVPR2026
链接: https://arxiv.org/abs/2606.08103
作者: Xiaoqian Wu,Yejie Guo,Xiaoyang Chen,Lixin Yang,Cewu Lu,Yong-Lu Li
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR2026
Abstract:We are surrounded by various objects with movable, articulated parts, e.g., box, handle, door. An accurate and generalizable perception of articulated parts is essential to enhance robotic manipulation capabilities. Building on this need, recent efforts in articulated parts perception have followed two main directions: One line of work uses pose-based representation, which requires high manual cost; in parallel, affordance-based methods extract future object motion from point tracking without additional manual efforts, but suffer from low-quality data. In this paper, we propose a new representation of articulated parts, Geometric Primary Structure (GPS), an abstraction of the part geometry structure to balance scalability and quality. For efficient and scalable data collection, GPS is integrated with a portable Virtual Reality (VR) device and requires only one minute to annotate one object sequence. This direct human annotation provides higher quality than the estimated affordance. With this efficient VR-GPS system, we collect 41K frames for 234 objects across six part classes, and train a generalizable GPS model with a single RGB-D object image as input. For object manipulation, we deploy a heuristic policy based on GPS prediction. Without any in-domain fine-tuning, our method achieves an 73% success rate, covering 270 initial states for 9 objects. Our code, data and reusable tool are available at this https URL.
[CV-180] VideoWeaver: Evaluating and Evolving Skills for Agent ic Long Video Generation
链接: https://arxiv.org/abs/2606.08091
作者: Jianhui Wei,Jie Tan,Hengchuan Zhu,Xiaotian Zhang,Yan Zhang,Ziyi Chen,Daoan Zhang,Wei Xu,Zuozhu Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent agent frameworks such as Claude Code, Codex, and OpenClaw are strong at tool use and orchestration, but whether they can handle long video generation, a long-horizon multimodal task, remains underexplored. Unlike earlier video agents whose pipeline is handcrafted, these frameworks can build and refine their own workflows. We introduce VideoWeaver, an agent harness and benchmark that evaluates and evolves skills for long video generation, where an agent turns a single instruction into a long video by composing foundation skills into its own workflow rather than following a predefined pipeline. The benchmark has 16 task categories and 285 cases, with references spanning text, image, audio, video, and their combinations. Because errors can arise at any stage and not just in the final video, we propose an agent-as-judge that inspects both the execution trace and the final video, grounding its scores in evidence such as metadata and intermediate files. Using this feedback, we further design a skill evolution algorithm that refines and merges the agent’s skills. Across multiple frameworks and models, we find that an explicit composition skill improves the generation process over using foundation skills alone, that skill evolution further improves output quality, and that performance varies notably across harness and model choices. The proposed agent-as-judge also aligns well with human judgments, especially on process metrics. Code and dataset is available at this https URL
[CV-181] OSMGraphCLIP: Learning Global Location Representations from OpenStreetMap Graphs
链接: https://arxiv.org/abs/2606.08046
作者: Dimitrios Michail,Eleni Saka,Ioannis Giannopoulos,Ioannis Papoutsis
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:We present OSMGraphCLIP, a CLIP-style geospatial representation model that learns global location embeddings from freely available OpenStreetMap (OSM) data. OSMGraphCLIP represents geographic environments as heterogeneous graphs of typed OSM features, preserving the topological and semantic relationships among roads, buildings, land-use regions, and points of interest. A multi-scale graph encoder captures both fine-grained local structure and broader landscape composition, and supervises a spherical-harmonics location encoder through a contrastive alignment objective. We evaluate OSMGraphCLIP across a diverse suite of downstream geospatial regression and classification tasks spanning climate, ecology, socioeconomic indicators, public health, land cover, biodiversity, and wildfire forecasting, and show that structured OSM data alone supports strong global location representations across domains. OSMGraphCLIP matches or exceeds satellite-based baselines on the majority of benchmarks, with the most pronounced advantage on socioeconomic and public-health tasks, where OSM’s explicit semantic annotation of the built environment encodes patterns of human activity that satellite pixels can only capture indirectly. On ecological and environmental tasks, the model remains closely competitive with imagery-based methods despite using no Earth observation data. Qualitative analysis confirms that the learned embeddings organize geographic space coherently, recovering biome boundaries, urban gradients, and tropical–temperate distinctions from map topology alone.
[CV-182] OmniFaceRig: Fully Automatic Inner-Mouth-Aware Face Rigging Across Diverse 3D Character Topologies
链接: https://arxiv.org/abs/2606.08043
作者: Chao Wang,Guangyao Ma,John Doublestein,Junming Chen,Yiming Lin,Zhaoen Su,Xiaomin Luo,Shiyang Cheng,Jie Shen,Doug Roble,Dilin Wang,Yilei Li,Rakesh Ranjan
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Facial rigging - creating FACS-based blendshapes together with inner-mouth geometry (teeth, gums, and tongue) - remains a major bottleneck in 3D character production. Existing pipelines still require substantial designer effort, especially for manual landmark annotation, per-character template adjustment, and inner-mouth placement. We present OmniFaceRig, a fully automatic end-to-end pipeline that converts a static surface-only 3D character mesh, with no pre-modeled oral cavity, into an inner-mouth-aware FACS rig with up to 155 blendshapes, procedurally fitted teeth, gums, and tongue, and re-packed UV/texture. OmniFaceRig supports diverse topologies - humans, humanoids, long-muzzled animals (e.g., dogs, wolves, foxes), and short-muzzled animals (e.g., cats, bears, rabbits, tigers) - with no manual landmarks, no user-provided templates, and no per-asset setup. The pipeline combines hybrid VLM+CV riggability checking, multi-model face parsing, dense keypoint-driven template registration, procedural inner-mouth construction, and collision-aware blendshape transfer. For non-human characters, OmniFaceRig selects topology-specific face and inner-mouth templates and uses collision-aware inner-mouth fitting to reduce teeth-face intersections without exposing users to category-specific tuning. We also publicly release Omni-Bench, a freely available benchmark dataset of 1,000 biped 3D characters with FACS facial blendshapes and inner-mouth geometry, spanning humans, humanoids, cats, dogs, and other animals. Experiments show high final rigging success on screened Omni-Bench inputs, nearly complete face detection recall from the segmentation ensemble and reliable inner-mouth placement with low penetration. Together, OmniFaceRig provides an automatic path from static generated characters to animation-ready facial rigs across both human and non-human topologies.
[CV-183] Wispy to Voluminous: Prior-free Multi-view Capture of Strand-level Facial Hair
链接: https://arxiv.org/abs/2606.08041
作者: Jaeseong Lee,Giljoo Nam,Adrian Jarabo,Carlos Aliaga
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
备注: 27 pages, 16 figures, supplementary included
Abstract:Facial hair is a defining trait of personal identity, yet remains a critical bottleneck for digital avatars. Recent volumetric methods achieve photorealism but bake hair into the underlying face geometry, preventing editability and failing to resolve sparse, strand-like structures. Meanwhile, scalp-hair reconstruction methods target dense hair volumes and do not transfer to the sparse, spatially-varying nature of facial hair. We present a pipeline that automatically reconstructs facial hair – beard, mustache, lashes, and brows – from multi-view images, converting an unstructured 3D Gaussian representation into an explicit curve-based strand representation. We resolve geometric ambiguities in four stages: (i) optimizing 3D Gaussians constrained by tracked head geometry to enforce early ray termination and suppress sub-surface noise; (ii) tracing continuous strands robust to frequent crossings and extreme curvature; (iii) grounding strands to the surface and resolving root-tip ambiguity via a physically-motivated prior; and (iv) refining the reconstruction through opacity-driven density control under photometric optimization. To our knowledge, this is the first method to reconstruct high-fidelity facial hair strands from a 3D Gaussian representation. The recovered strands faithfully preserve the orientation and sparsity patterns characteristic of facial hair, and yield assets immediately suitable for downstream production tasks, including facial animation and physical simulation, geometric grooming and transfer, appearance editing, and physics-based rendering.
[CV-184] DyCo-RL: Dynamic Cross-Modal Coordination for Visual Reasoning
链接: https://arxiv.org/abs/2606.08035
作者: Hangui Lin,Yan Shu,Zhengyang Liang,Chi Liu,Xiangrui Liu,Minghao Qin,Teng Long,Zheng Liu,Nicu Sebe
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a leading paradigm for enhancing visual reasoning in Multimodal Large Language Models (MLLMs). However, existing RLVR methods optimize primarily for the reasoning outcome, fundamentally overlooking the fine-grained cross-modal coordination required during the generation process. Through token-level analyses and controlled interventions, we reveal that during Chain-of-Thought (CoT) reasoning, MLLMs frequently fail to dynamically alternate between extracting visual evidence and synthesizing textual context-a coordination breakdown that is causally linked to reasoning failures. Motivated by these findings, we propose DyCo-RL, which integrates dynamic cross-modal coordination into RLVR optimization. Specifically, DyCo-RL uses the Fisher-Rao geodesic distance to measure within-modality attention shifts, assigning tokens to either visually-oriented or text-oriented functional roles. It then evaluates the alignment between a token’s actual attention allocation and its assigned role, leveraging this score for alignment-guided advantage reweighting during policy optimization. Extensive experiments demonstrate that the algorithm-agnostic DyCo-RL, when applied to Qwen2.5-VL-3B/7B, consistently improves four representative RLVR algorithms across seven benchmarks spanning visual-centric and mathematical reasoning.
[CV-185] Balancing Real and Synthetic Data for CNN-based Masonry Crack Detection
链接: https://arxiv.org/abs/2606.08033
作者: Mattia Forlesi,Alfonso Esposito,Ivan Zyrianoff,Alessandro Marzani,Marco Di Felice
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Cracks are a critical indicator of building health, and early stage identification is fundamental to prevent harmful damages. Advances in deep learning (DL), particularly convolutional neural networks (CNNs), have enabled scalable solutions for automated crack detection. However, CNN performance strongly depends on the availability of large and diverse datasets, which is particularly challenging for complex surfaces such as masonry. Collecting sufficient real data is time-consuming, while publicly available datasets may not be adequate. To address this limitation, we explored generating synthetic crack data, which complements real data and improves training effectiveness. The real dataset consists of masonry crack images collected from buildings in Bologna and surrounding areas. In contrast, the synthetic dataset was generated using a crack overlay tool that adds cracks to background images in a controlled orientation and placement. The real dataset was used to train several DL architectures, to identify the best-performing model (InceptionV4) employed for experiments with generated data. Six training scenarios were tested in InceptionV4 by varying the ratio of real and synthetic data, with evaluation performed on a test set composed of real images using the F1-score and mean Intersection over Union (mIoU) metrics. Results show that training on synthetic data plus a modest addition of 20% real data achieves results comparable to training on real data only. Moreover, the 20/80 scenario (synthetic/real) achieved an 76% F1-score and 80% mean IoU, outperforming the real-only case. As can be seen, the method demonstrates the potential of synthetic data to reduce collection efforts while enhancing crack detection accuracy.
[CV-186] Vision-Language Asymmetry in Bistable Image Captioning ICML2026
链接: https://arxiv.org/abs/2606.08031
作者: Arohan Agate
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ICML 2026 Workshop on Philosophy of Machine Learning
Abstract:Wittgenstein’s duck-rabbit poses a question for vision-language models: when a model captions an ambiguous image, where in the model is the commitment to one aspect made? We address this with a 3,320-generation behavioral baseline over 83 bistable stimuli that surfaces three regimes (default-dominant, force-dominant, force-balanced) under neutral vs forced-choice prompting, then probe the underlying representations using a TopK sparse autoencoder we train on the CLIP layer that LLaVA-1.6-7B actually consumes (validation EV 0.93). Across 69 bistable stimuli with both per-aspect feature pools available, 72% (50/69) show simultaneous activation of both pools at the vision tower, including 12/12 default-dominant duck/rabbit and 7/8 force-balanced young/old. Causal steering at CLIP layer 22 flips captions on default-dominant stimuli (33% rabbit-flip rate under a fluency guard) but cannot flip captions on force-balanced young/old at any tested coefficient, despite their vision-side superposition. The dominance bottleneck lives downstream of the vision tower; the gap between vision-side representation and language-side commitment is an empirical handle on the seeing/seeing-as distinction. We also flag a methodological note: rank-based statistics on TopK SAE outputs require tie-corrected ranking to avoid silent row-order bias.
[CV-187] GVC-Seg: Training-Free 3D Instance Segmentation via Geometric Visual Correspondence
链接: https://arxiv.org/abs/2606.08014
作者: Liang Xu,Fangjing Wang,Jinyu Yang,Feng Zheng
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 10 pages, 5 figures
Abstract:Accurate 3D instance segmentation in point cloud data is critical for machine vision applications. Recent advancements leverage multiple pre-trained foundation models to generate 3D proposals, followed by the application of proposal aggregation methods, which significantly enhance performance. However, they often produce sub-optimal results due to inherent variations in confidence levels across different segmentation models, resulting in a bias toward the model with higher confidence. This bias is inherently model-dependent and is influenced by factors such as data preprocessing techniques and training strategies. To address this bias, we propose a novel, training-free 3D instance segmentation approach via Geometric Visual Correspondence (GVC-Seg), which exploits the correspondence between 3D geometric cues and 2D visual cues to mitigate the confidence bias. Additionally, a 3D proposal generation module and a mask-aware CLIP feature extraction module are introduced during the instance mask generation and instance semantic reasoning, respectively. In this way, GVC-Seg enhances proposal quality assessment, ensuring unbiased ensemble learning across different models. Extensive experiments demonstrate that our method achieves state-of-the-art performance on several challenging benchmarks, while also exhibiting strong potential in open-vocabulary semantic segmentation settings.
[CV-188] Aqua Boundary-Saliency Attention Module for Lightweight Underwater Salient Instance Segmentation Detection Transformer
链接: https://arxiv.org/abs/2606.08002
作者: M. Fazri Nizar,Julian Supardi,Muhammad Naufal Rachmatullah
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: This work has been submitted to the IEEE for possible publication
Abstract:Underwater instance segmentation integrates pixel-level mask prediction and instance-level discrimination for marine resource exploration, ecological monitoring, and underwater robotic perception. Recent prompt-based and auxiliary-modality methods improve mask quality, but their reliance on large foundation models, prompt generation, or extra modality estimation complicates efficient deployment. This work introduces Lightweight Underwater Salient Instance Segmentation Detection Transformer (LUSIS-DETR), a compact detection-transformer framework built around the Aqua Boundary-Saliency Attention Module (AquaBSAM). AquaBSAM embeds underwater boundary, contrast, attenuation, chroma, dark-channel, and center-prior cues into DINOv2-initialized multi-scale features through bounded residual modulation, while auxiliary mask supervision and small-object copy-paste are training-only. Extensive evaluation on four recent underwater instance segmentation datasets, UIIS, UIIS10K, USIS10K, and USIS16K, shows competitively leading performance against previous state-of-the-art works across category-aware and salient-instance protocols. TensorRT half-precision (FP16) benchmarking on an NVIDIA T4 graphics processing unit (GPU) achieves 4.31-6.34 milliseconds (ms) latency, supporting real-time inference under an accessible reproduction setting.
[CV-189] Learning a Semantic Calibration Network for Open-Vocabulary Semantic Segmentation
链接: https://arxiv.org/abs/2606.08001
作者: Yang Sun,Tao Wang,Anastasia Ioannou,Ge Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Paper accepted by 11th International Conference on Intelligent Computing and Signal Processing (ICSP 2026)
Abstract:Semantic image segmentation assigns a predefined category label to each pixel, has achieved significant progress lately. Open-Vocabulary Segmentation (OVS) extends the segmentation task from a fixed set to an open set, enabling the identification and segmentation of novel concepts based on arbitrary text inputs, such as category names or descriptions. In this paper, we propose a novel Semantic Calibration Network (SCN) for open-vocabulary semantic segmentation. Different from prior approaches that focus on feature aggregation or simple fine-tuning of pre-trained models, SCN refines the mask classification process by explicitly modeling the semantic correlations between classes, aiming to enhance the model’s discriminative power while effectively preserving the generalization abilities of the pre-trained CLIP model. Specifically, SCN comprises two core components: Class Disambiguation (CD) and Logits Fusion (LF). First, a cross-attention mechanism is utilized to transform the text embeddings into visually aware pseudo-text embeddings, in order to derive an enhanced similarity score that complements the original mask-text similarity score. Subsequently, the Class Disambiguation module captures implicit inter-class dependencies through a residual architecture to effectively resolve semantic ambiguities. Finally, the Logits Fusion module dynamically integrates multifaceted semantic evidence to ensure that the model achieves a robust semantic consensus while maintaining CLIP’s inherent generalization capability. Comprehensive experimental results on mainstream benchmarks demonstrate that the proposed method achieves significant performance improvements compared to state-of-the-art algorithms.
[CV-190] DisCo: World Models with Discrete Camera Motion Control
链接: https://arxiv.org/abs/2606.07967
作者: Hongrui Huang,Junke Wang,Quanhao Li,Yu-Gang Jiang,Zuxuan Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Controllable video world models target interactive world exploration, where models must faithfully execute explicit action commands while preserving visual quality and temporal coherence. However, most existing approaches rely on continuous camera trajectories as action conditions, which often lead to unreliable action following, especially under complex motion sequences. In this work, we identify action representation entanglement as a key bottleneck in controllable video generation, and show that continuous camera representations lead to high feature similarity across distinct motion patterns, degrading action controllability. Based on this insight, we propose DisCo, a controllable video world model that conditions generation on a compact set of discrete action primitives to improve action separability. We further introduce DisCoBench, a comprehensive benchmark for evaluating the ability of models in short-term, long-horizon, and highly dynamic exploration scenarios. Extensive experiments demonstrate that DisCo achieves significantly more reliable action following while preserving visual quality.
[CV-191] ChronoPhyBench: Do MLLM s Truly Understand the World or Merely Exploit Language Priors?
链接: https://arxiv.org/abs/2606.07962
作者: Bin Zhu,Yanhao Jia,Kexin Zhao,Jie Wang,Munan Ning,Hao Li,Yuwei Niu,Tanqing Sun,Huangchong Yan,Mingjun Pan,Xinyi Wu,Qishen Yin,Yunyang Ge,Shuai Zhao,Li Yuan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in open-world reasoning and understanding. However, a critical ambiguity persists: it remains unclear whether these models genuinely synthesize cross-modal information to construct physically grounded reasoning chains, or if they merely exploit strong language priors to mask single-modality reliance, thereby hallucinating advanced multimodal capabilities. Motivated by this, and to rigorously mitigate language modality bias and shortcuts, we propose a novel multimodal Chronological Physical Dynamics Reasoning Benchmark ChronoPhyBench, which unifies next state prediction with Visual Question Answering (VQA) paradigms by conditioning on historical video context and textual captions to enforce models to deduce subsequent physical states through both single image selection and the inherently more complex task of multiple frame chronological sorting. Concurrently, we construct a large-scale multimodal reasoning dataset curated using the ChronoPhyBench criteria, comprising over 10,000 long-form videos paired with meticulously annotated captions, totaling 5M tokens. Our experimental evaluations reveal a stark contrast to conclusions drawn by previous benchmarks. The capacity of current open-source models to perform physically grounded multimodal reasoning remains in its infancy. Ultimately, this work seeks to systematically stress-test the reasoning capabilities of multimodal models, quantify hallucination rates, and advance the development of Physical AI, thereby providing the community with a robust and transparent evaluation framework toward Artificial General Intelligence (AGI).
[CV-192] DAL-PCQA: Enabling Distortion-Level and Language-Driven Reasoning for Point Cloud Quality Assessment
链接: https://arxiv.org/abs/2606.07938
作者: Swarna Chakraborty,Gabriel De Castro Araújo,Syeda Tasmi Faria,Marcelo M. Carvalho,Mylene C.Q. Farias
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM); Image and Video Processing (eess.IV)
备注: Accepted at Qomex 2026
Abstract:Point Cloud Quality Assessment (PCQA) methods typically predict scalar Mean Opinion Scores (MOS), which quantify overall perceptual degradation but do not reveal its causes. In contrast, human observers naturally reason in terms of specific distortions such as blur, color shifts, point density changes, missing regions, and geometric deformations. To close this gap, we introduce DAL-PCQA, a distortion-aware, language-annotated dataset for PCQA. DAL-PCQA augments benchmark point clouds with multi-level distortion severity labels, discrete quality categories, and structured natural language descriptions aligned with human perception. We define a point-cloud-specific distortion taxonomy that covers both photometric and geometric artifacts. Statistical analysis reveals characteristic degradation patterns across distortion types and quality levels. To assess the utility of these annotations, we compare zero-shot and fine-tuned multimodal models for generating perceptual quality descriptions. Experiments show that distortion-aware supervision substantially improves lexical and semantic alignment with ground-truth descriptions. By enabling interpretable, distortion-level reasoning, DAL-PCQA facilitates language-driven, explainable point cloud quality assessment. The dataset is publicly available at this https URL.
[CV-193] REACT 2026: The Fourth Multiple Appropriate Facial Reaction Generation Challenge: Personalised MAFRG and Appropriate EEG Reaction Prediction
链接: https://arxiv.org/abs/2606.07935
作者: Siyang Song,Micol Spitale,Zijian Wu,Xiangyu Kong,Cheng Luo,Cristina Palmero,German Barquero,Sergio Escalera,Michel Valstar,Mohamed Daoudi,Fabien Ringeval,Andrew Howes,Elisabeth Andre,Hatice Gunes
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: arXiv admin note: text overlap with arXiv:2505.17223
Abstract:In dyadic interactions, various human facial reactions could be appropriate for responding to each human speaker behaviour. Following the successful organisation of the REACT 2023, 2024 and 2025 challenge series, a body of generative deep learning (DL) models have been developed for the problem of multiple appropriate facial reaction generation (MAFRG). This year, we propose the REACT 2026 challenge encouraging the development and benchmarking of Machine Learning (ML) models that can generate multiple personalised, appropriate, diverse, realistic and synchronised human-style facial reactions expressed by a specific human listener for responding to each given speaker behaviour. As a key of the challenge, we continuously provide challenge participants with MARS dataset introduced by REACT 2025 but additionally provide individual-level Big-Five personality labels and EEG recordings. This introduces a new one-to-many personalised facial reaction generation setting combining human expressive behavioural, affective and neurophysiological signals, which remains largely unexplored in current dyadic interaction modelling. This paper also presents the challenge guidelines and new baselines on the four proposed sub-challenges: Offline generic and personalised MAFRG as well as Online generic and personalised MAFRG, respectively, which are publicly available at this https URL.
[CV-194] LEGS: Laplacian-Enhanced Gaussian Splatting with a Nonlinear Weighted Loss
链接: https://arxiv.org/abs/2606.07932
作者: Yongfei Guo,Qizhou Huo,Xuan Sun,Yuanhao Gong
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Multimedia (cs.MM); Image and Video Processing (eess.IV); Optimization and Control (math.OC)
备注:
Abstract:3D Gaussian Splatting (3DGS) has become an efficient explicit representation for radiance field reconstruction and real-time novel view synthesis. However, its standard photometric loss treats flat and structure-rich regions similarly, which may limit the recovery of sharp contours and fine details. Edge-Guided Gaussian Splatting (EGGS) improves structure awareness through edge-guided weighting, but mainly relies on first-order gradient responses and linear weighting. In this paper, we propose LEGS, a Laplacian-Enhanced Gaussian Splatting method with a nonlinearly weighted loss. LEGS replaces first-order gradient guidance with second-order Laplacian structural guidance and maps the normalized Laplacian response into pixel-wise weights through nonlinear response-to-weight functions. The proposed loss improves structure-aware Gaussian optimization while keeping the original 3DGS rendering pipeline unchanged. Experiments on the full Tanks\Temples and Mip-NeRF360 datasets show that LEGS improves peak signal-to-noise ratio (PSNR) by up to 1.68 dB over 3DGS and up to 0.52 dB over EGGS. Incorporating the proposed second-order nonlinear weighting strategy into FastGS and FasterGS further improves PSNR by up to 1.69 dB, demonstrating its effectiveness as a general loss-level extension for Gaussian Splatting pipelines with potential applications in AR/VR, immersive visualization, and real-time 3D content generation.
[CV-195] 3D Oral Modelling with Improved Vertex Distribution Using Matching-Based Learning
链接: https://arxiv.org/abs/2606.07907
作者: Jihun Cho,Soo-Yeon Jeong,Eun-Jeong Bae,Sun-Young Ihm
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 5 pages, 7 figures. English version of a paper presented at the Korea Multimedia Society Conference, November 2025
Abstract:In our previous work, a deep learning-based framework for 3D intraoral reconstruction was proposed. The model directly predicts explicit 3D point cloud coordinates from ten fixed-angle intraoral images, employing MobileNetV2 and Multi-head Attention for multi-view feature fusion, with a combined L1 Loss and Chamfer Distance as the loss function. Although the model achieved an accuracy of 77.49%, predicted vertices tended to concentrate in high-density regions of the ground truth, leaving other regions largely uncovered. In this paper, an improved loss function is proposed to address this limitation. Hungarian matching with filtering and Repulsion Loss are introduced to enforce more uniform vertex distribution across the reconstructed model. The proposed model achieves an accuracy of 68.02%, which is numerically lower than the previous model. However, the vertex clustering issue observed in the prior work is substantially alleviated, with predicted vertices distributed more evenly across the entire reconstructed surface. Comments: 5 pages, 7 figures. English version of a paper presented at the Korea Multimedia Society Conference, November 2025 Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2606.07907 [cs.CV] (or arXiv:2606.07907v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2606.07907 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-196] BD-VLA: Temporal Block Diffusion Vision Language Action Model
链接: https://arxiv.org/abs/2606.07895
作者: Sung-Wook Lee,Xuhui Kang,Yen-Ling Kuo
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Discrete Vision-Language-Action (VLA) models typically formulate action generation as next-token prediction over discretized action spaces, conditioning each token autoregressively on prior context. While effective, this paradigm incurs high inference latency and largely ignores the temporal structure inherent in action trajectories. Recent efforts introduce parallel decoding to improve efficiency, enabling faster inference, but lack explicit mechanisms for modeling token dependencies. We introduce TBD-VLA, a discrete token-based VLA framework that incorporates block diffusion to enable temporal action generation. We partition action sequences into temporal blocks and perform masked discrete diffusion within each block, while maintaining autoregressive generation across blocks. This design unifies temporal autoregression and parallel action decoding, achieving both strong temporal coherence and improved inference speed. In addition, the explicit temporal modeling enables asynchronous execution of action chunks (e.g., Real-Time Chunking) via temporal in-painting. TBD-VLA significantly outperforms prior VLA approaches in both simulation and real-world manipulation tasks, offering a scalable path toward fast, temporally aware, discrete VLA models. Project webpage: this https URL
[CV-197] C3VD-DEFCOL: A Deformable Colonoscopy Dataset with Time-Resolved 3D Ground Truth and Realistic Appearance
链接: https://arxiv.org/abs/2606.07891
作者: Ethan Luk,Mayank V. Golhar,Anthony Song,Raúl Iranzo,Víctor M. Batlle,Lalithkumar Seenivasan,José M.M. Montiel,Nicholas J. Durr
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:3D reconstruction could improve colonoscopy by estimating mucosal coverage and alerting clinicians to missed regions during screening. However, algorithm development is limited as no current datasets provide both a realistic in vivo appearance and dense, time-resolved 3D ground truth, especially under non-rigid deformation. We present C3VD-DEFCOL, a framework and dataset for evaluating deformable colonoscopy reconstruction with paired geometry and realistic texture. Starting from C3VD/C3VDv2 colon meshes and camera trajectories, we generate controlled deformations of the colon surface, including peristaltic waves and centerline motion, and render per-frame depth, surface normals, optical flow, camera poses, and time-stamped 3D meshes. We then use the rendered geometry, primarily depth, to condition an LTX-2.3-based sim-to-real translation model that produces RGB clips with in vivo-like mucosal color, texture, vasculature, and specular appearance while preserving the underlying 3D scene structure. The resulting dataset contains 110 videos from 11 unique colon mesh geometries, with varying camera trajectories, appearances, and parameterized deformation regimes, including three peristaltic severity levels that serve as controlled evaluation axes. We evaluate the generated videos using appearance realism, geometric consistency, and temporal consistency metrics, and use the paired ground truth to benchmark the downstream task of pose estimation in deformable 3D reconstruction. Our experiments show how pose estimation error increases with increasing deformation severity, providing a controlled stress test that is not possible with existing in vivo datasets. Overall, C3VD-DEFCOL is designed as a reproducible, quantitative evaluation platform for testing deformable 3D reconstruction algorithms, with the goal of reducing the domain gap between synthetic datasets and in vivo colonoscopy.
[CV-198] he Cross-Architecture Substrate: A Domain-Transcendent Calibration-Surviving Geometric Invariant of Modern Vision Encoders NEURIPS2026
链接: https://arxiv.org/abs/2606.07882
作者: Yousef Radwan
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 14 pages, 2 figures. 40th Conference on Neural Information Processing Systems (NeurIPS 2026)
Abstract:Different vision neural networks – trained to classify, contrast, reconstruct, or match images to text – should have correspondingly different internal representations. We report that they do not. After training, the top sixteen principal directions of variation inside thirteen modern vision encoders converge to the same sixteen-dimensional geometric object. We call this the cross-architecture substrate and study it with PCA, centred kernel alignment (CKA), and Pang 2026 calibration. The substrate transports across four visual domains (natural photographs, medical CT, satellite, microscopy) at median Procrustes-CKA 0.679, and across eight domains (adding sketches, depth, thermal infrared, astronomy) at 0.604, every pair 0.40. It survives Pang calibration globally (7.4x disc-vs-MAE separation, n=13,394) and locally (4.82-5.30, p10^-44). It is not pixel statistics (0.263), not Gabor features (0.31), not a random projection (0.041), and emerges in the first 10% of training while accuracy keeps climbing. We deliver four applications: a label-free transferability filter beating LogME (3x faster, +0.15 Kendall-tau); a four-way domain detector (99.6% accuracy); a frozen low-shot probe (16 dims beat 768-dim DINOv2 by 3.78pp at N=50 labels per class); and a teacher-free distillation auxiliary matching trained-teacher KD on 33 pairs (7.56pp peak gain at 10% label fraction). The substrate does not cross modalities, does not help cross-paradigm distillation, and does not predict transfer quality (rho=0.08 against transfer accuracy).
[CV-199] VisualFLIP: Do Predictions Depend on Task-Critical Visual Evidence in Multimodal Reasoning ?
链接: https://arxiv.org/abs/2606.07872
作者: Didi Zhu,Changrui Chen,Stefanos Zafeiriou,Jiankang Deng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:When a multimodal large language model answers a visual reasoning question correctly, is the prediction actually supported by the task-critical visual evidence? Correct answers can coexist with flawed reasoning, making accuracy alone an incomplete test of grounding. We introduce VisualFLIP, a paired benchmark with 1,374 images arranged as same-question perturbation pairs across cardinality, attribute, spatial, and logic tasks. Each pair keeps the question fixed but minimally changes the evidence so the gold answer deterministically flips. We evaluate 24 MLLMs with pair accuracy, which requires solving both sides of a pair, and Collapse Rate (CR), which measures how often a model that solves at least one side repeats the same non-empty answer for both images. Together, these metrics show that paired correctness and evidence dependence are related but distinct: capable models can still fail to update after task-critical visual changes, and collapse becomes more severe for some models when the edited image follows an earlier answer in a sequential setting. Further details are available on our project page: this https URL
[CV-200] he Last Visible Pixel: Probing Fine-Scale Perception in Vision-Language Models
链接: https://arxiv.org/abs/2606.07861
作者: Lujun Li,Lama Sleem,Niccolo Gentile,Yangjie Xu,Yewei Song,Wenbo Wu,Radu State
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 25 pages
Abstract:Recent vision-language models (VLMs) excel at multimodal understanding and reasoning, yet their fine-grained visual perception remains underexplored. A natural extension of ``How many r are there in Strawberry?‘’ asks: how small a visual pattern can a VLM reliably perceive? As such, we introduce FineSightBench, a new benchmark that systematically probes this limit by separating perception tasks (pixel-level recognition of letters, shapes, objects) from reasoning tasks (spatial reasoning, counting, ordering over small targets) across controlled scales of 4–48px. Through comprehensive experiments and detailed failure mode analysis on state-of-the-art models, we reveal a sharp dissociation: perception saturates around 12px, while reasoning remains limited even at larger scales, with persistent numeracy and sequence errors. These findings expose fundamental deficiencies in VLMs’ fine-scale visual reasoning that demand more rigorous evaluation.
[CV-201] MinNav: Minimalist Navigation Using Optical Flow For Active Tiny Aerial Robots ICRA2026
链接: https://arxiv.org/abs/2606.07813
作者: Aniket Patil,Mandeep Singh,Uday Girish Maradana,Nitin J. Sanket
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted for publication at ICRA 2026. Link to Project page this https URL
Abstract:Navigation using a monocular camera is pivotal for autonomous operation on tiny aerial robots due to their perfect balance of versatility, cost and accuracy. In this paper, we introduce MinNav, a navigation stack based on optical flow and its uncertainty to fly through a scene with static and dynamic obstacles and unknown-shaped gaps without any prior knowledge of the scene components and/or their locations/ordering. We further improve success rate by using the activeness of the robot to move around in an exploratory way to find obstacles and navigate. We successfully evaluate and demonstrate the proposed approach in many real-world experiments in various environments with static and dynamic obstacles and unknown-shaped gaps with an overall success rate of 70%. To the best of our knowledge, this is the first solution to tackle all the aforementioned navigation cases without prior knowledge using a monocular camera. Our approach is on par in performance with depth based methods with factors of magnitude less computation required and can readily run onboard tiny aerial robots. The accompanying video, supplementary material, code and dataset can be found at this https URL
[CV-202] Land cover and flood type govern the detection limits of satellite-based flood mapping across diverse global flood events
链接: https://arxiv.org/abs/2606.07780
作者: Venkatesh Kolluru,Rajat Shinde,Abdelhak Marouane,Caden Helbling,Deepak Shah,Othneil Drew,Iksha Gurung,Manil Maskey,Rahul Ramachandran
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Floods are among the most destructive natural hazards, and their increasing frequency under climate change makes satellite-based inundation mapping essential for disaster response. Geospatial foundation models pretrained on satellite archives offer geographic transferability, but their operational reliability across diverse, unseen events remains uncharacterized. Here we deploy Prithvi-EO-2.0 across 19 out-of-distribution flood events (2017-2025) spanning six continents, eight climate zones, and six flood mechanisms, validating against two independent reference products. Detection accuracy depended jointly on land cover and flood type, with cropland yielding the highest agreement (IoU=52%) and riverine events the strongest detection (F1=0.69), while tree cover and built-up areas showed near-zero detection (IoU=4%) regardless of flood mechanism. Dual-reference validation revealed that apparent model error partly reflects definitional inconsistency between reference products rather than detection failure. Iterative pipeline testing identified 23 failure modes, with pipeline engineering dominating initial error over model capacity. These findings establish environment-dependent detection boundaries for operational satellite flood mapping.
[CV-203] DALE-CT: Depth-Aware Foundation Models for Computed Tomography
链接: https://arxiv.org/abs/2606.07775
作者: Evan W. Damron,Mahmut S. Gokmen,Mitchell A. Klusty,Caroline N. Leach,Emily B. Collier,V. K. Cody Bumgardner
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 2 figures
Abstract:Recent breakthroughs in self-supervised learning (SSL), such as the Latent-Euclidean Joint-Embedding Predictive Architecture (LeJEPA), alongside successes in integrating visual encoders with language models, have driven the demand for adaptable, high-capacity vision encoders in Computed Tomography (CT). In this work, we explore 2D slice-based architectures as a flexible alternative to native 3D models for processing volumetric CT data. Using the CT-RATE dataset, we trained DALE-CT (Depth-Aware Latent-Euclidean Computed Tomography), a 2D model family built entirely from scratch using LeJEPA, and compared its performance against a continually pre-trained DINOv2 baseline. To enhance representation quality, we developed a novel 3D depth-aware pre-training strategy anchored by dense auxiliary supervision from both automated anatomical masks and human-annotated abnormalities. Under linear probe evaluation with Multiple Instance Learning (MIL) for multi-abnormality detection, the frozen backbone of this dual-supervised model (DALE-CT-2S) achieves a Macro AUROC of 0.833. This performance demonstrates near-parity with state-of-the-art 3D vision-language models, achieved entirely from scratch with significantly less data and no textual supervision. To ensure reproducibility, all training code, evaluation scripts, and model weights have been made publicly available.
[CV-204] Quantum-Enhanced Similarity Measures for Polarimetric Materials Classification
链接: https://arxiv.org/abs/2606.07766
作者: Sara Shojaei,Seyed Mohamad Ali Tousi,Emma Bennett,Param Sangani,Ali Shiri Sichani,Ilker Ersoy,Hadi Ali-Akbarpour,Filiz Bunyak,G. N. DeSouza
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:We present a quantum–classical hybrid pipeline for polarimetric material classification that casts this as a point-matching problem. Voxel cubes, containing polarized light reflections, are used to train an encoder to produce 32-dimensional embeddings for the voxels of the cubes. At inference, the encoder head is discarded and the embeddings are encoded as probability amplitudes of quantum states. Next, a SWAP-test circuit estimates the fidelity between each of the 32D embeddings from the query cube and a dataset of anchor cubes. The aggregated fidelity serves as materials similarity scores, and the class of the anchor with highest aggregated fidelity is deemed as the class of the queried material. We evaluate our approach on a dataset of 23 materials ( \approx 800 samples each) derived from their Mueller matrices. The point-matching approaches from the proposed quantum SWAP-test and a classical classifier using Optimal Transport are compared. Our results demonstrate the competitive classification accuracy alongside open-set discrimination potential, establishing it as a viable path toward NISQ-based material recognition.
[CV-205] DroneDAR: Long-Range Drone Distance Estimation Using Monocular Vision and Bounding-Box Features
链接: https://arxiv.org/abs/2606.07756
作者: Knut Peterson,Zaid Mayers,David Han
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 6 pages, 5 figures. Accepted to the 2026 International Conference on Advanced Visual and Signal-Based Systems (AVSS)
Abstract:Accurate distance estimation for small drones in long-range imagery is important for tracking and situational awareness, yet remains challenging due to extreme target scale variation, background clutter, and noisy visual cues. This paper studies monocular drone distance estimation using image crops together with bounding-box geometry, a practical setting in which a detector provides a candidate drone region and the model predicts range from appearance and box-derived features. We evaluate a Droneranger-style baseline, and introduce a new DroneDAR (Drone Detection And Ranging) model that combines a convolutional backbone with explicit bounding-box cues through a lightweight gating mechanism. Experiments analyze how backbone capacity, crop resolution, and regression loss functions affect performance across distance regimes. We further examine common failure modes at long distances, including sensitivity to bounding-box noise and reduced texture detail in the crop. The results provide guidance for designing and training range estimators that remain robust under real-world long-range conditions and highlight directions for improving reliability when drones occupy only a few pixels.
[CV-206] A case study of evaluating AI agents on a neuroscience data-to-discovery pipeline
链接: https://arxiv.org/abs/2606.07718
作者: Kai A. Horstmann,Ethan Lin,Alice A. Robie,Jennifer J. Sun,Kristin Branson
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Agentic AI tools offer a promising path to automating software development bottlenecks in scientific research pipelines, particularly for stages that take domain experts days to months to build, where scientists care about correctness and robustness, not implementation details. We present an empirical study of general-purpose coding agents on a fly optogenetics data-to-discovery pipeline. We assess agents on tasks substantially larger than existing benchmarks, datasets orders of magnitude bigger, and evaluation criteria grounded in domain expert standards. We show that agents can solve several individual pipeline stages, suggesting stage-level automation is tractable. By analyzing agents’ code iterations, we show that they struggle most when there is not a pre-defined criterion to iterate on, and they must instead use their scientific judgment to assess their current solution, a key open challenge. Mirroring scientific practice, they sometimes attempt visual inspection of intermediate outputs for self-evaluation, but largely fail to interpret what they see or act on it appropriately. Solving the end-to-end pipeline correctly requires stringing together successes across all pipeline stages, and this is beyond agents’ current abilities. We identify challenges largely absent from existing benchmarks, including computational resource management and generalization to large held-out data collections. Finally, we distill principles for constructing scientific tasks and rigorous evaluation criteria for open-ended problems.
[CV-207] Cross-View Urban Traffic Dataset: Drone-Supervised Ground Truth for Monocular Birds-Eye View Localization
链接: https://arxiv.org/abs/2606.07708
作者: Prakhar Bhardwaj,Simone Weikl,Kilian Mang,Elia Jonas Sandtner
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce a dataset and benchmark for cross-view urban traffic perception built from synchronized ego-centric bicycle videos and aerial drone videos recorded at real urban intersections. The benchmark targets two linked tasks: cross-view identity matching between street-view and drone-view object tracks, and ego-to-bird’s-eye-view prediction using aerial supervision. In contrast to prior urban driving and V2X datasets, our benchmark provides identity-level alignment across radically different viewpoints together with standardized evaluation, annotation tooling, and baseline implementations. This setting is motivated by intersection-centric traffic analysis, where identity preservation, local interactions, and global spatial structure must be reasoned about jointly across views. We evaluate methods at both the track and frame levels, including cross-view ID precision/recall/IDF1, near–far breakdowns, temporal stability, and consistency metrics. We also provide baseline results for wedge-based cross-view matching and for three BEV prediction baselines: inverse perspective mapping, a MonoLayout-style learned baseline, and a regression baseline. The results show that the benchmark is feasible but challenging: cross-view matching achieves strong recall yet remains limited by over-assignment and temporal inconsistency, while ego-to-BEV prediction benefits from aerial supervision but remains far from saturated under lightweight monocular sensing. We hope that this benchmark will support future research on cross-view perception, urban scene alignment, and ego-to-global traffic understanding.
[CV-208] Struct-Searcher: Agent ic Structural Thinking Advances Multimodal Deep Information Seeking
链接: https://arxiv.org/abs/2606.07689
作者: Fan Zhang,Vireo Zhang,Shengju Qian,Haoxuan Li,Zheng Lian,Hao Wu,Yuan Gao,Xinyu Geng,Xin Wang,Pheng-Ann Heng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Deep research agents have attracted increasing attention for their ability to collect large-scale online information to acquire target knowledge, with recent efforts shifting from purely text-based information seeking to multimodal settings. However, existing agentic workflows are largely aligned with evidence accumulation models, which linearly aggregate evidence and lack principled mechanisms for handling contradictory information across heterogeneous modalities. Towards this end, we propose Struct-Searcher, a structural agentic workflow grounded in belief revision theory that explicitly maintains an evolving multimodal structural graph throughout the reasoning process, enabling effective conflict-aware multimodal deep information seeking. Extensive experiments across multiple benchmark datasets and backbone models demonstrate that Struct-Searcher is (1) plug-and-play and model-agnostic, yielding an average relative accuracy improvement of 17.2% on BrowseComp-VL across five different backbones. (2) top-performing, consistently outperforming state-of-the-art vision-language models (VLMs) and deep research agents, with relative accuracy improvements of 3.7% on MM-BrowseComp, 1.5% on HLE-VL, and 0.7% on BrowseComp-VL over the second-best competing approach.
[CV-209] What Makes Video World Model Latents Action-Relevant: Prediction over Reconstruction
链接: https://arxiv.org/abs/2606.07687
作者: Jewon Yeom,Hanseul Kim,Jeongjae Park,Sungmok Jung,Jaejin Lee,Taesup Kim
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Video world models are increasingly used to provide predictive visual representations, yet it remains unclear which pretraining signals induce action-relevant structure in their latent spaces. We study this question through a unified probe-based evaluation across diverse encoder families, including image-only self-supervision, video pretraining with and without latent prediction, reconstruction-based autoencoders, diffusion models, and shortcut-forcing dynamics models. Using a common inverse-dynamics probing objective, we find that action-relevant structure is driven primarily by temporal video pretraining rather than pixel reconstruction fidelity: models with strong pixel decoding quality can exhibit near-zero action recoverability, while video-pretrained self-supervised encoders consistently achieve the best Pareto trade-off between visual fidelity and action prediction. Comparing V-JEPA and VideoMAE further shows that most gains arise from natural-video temporal context, with feature-level latent prediction providing a smaller additional benefit. These trends transfer across robotic benchmarks, though CALVIN reveals that static-environment tasks can partially mask the importance of temporal structure by allowing strong image priors to suffice. Finally, inverse-dynamics supervision substantially improves robustness to visual corruption, suggesting that action-aware objectives regularize latent geometry beyond clean-setting performance. Our results identify temporal predictive structure – not reconstruction fidelity – as the primary ingredient underlying action-relevant video representations.
[CV-210] Simultaneous hyperkinetic movement disorders phenotyping: a cross-cohort pediatric transfer study using routine videos markerless pose estimation and a tabular foundation model
链接: https://arxiv.org/abs/2606.07674
作者: Laura Cif,Diane Demailly,Zohra Souei,Muhammad Mushhood Ur Rehman,Juan Dario Ortigoza Escobar,Mayté Castro Jiménez,Cécile A. Hubsch,Sophie Huby,Morgan Dornadic,Gun-Marie Hariz,Eduardo M. Moraud,Jocelyne Bloch,Gabriella A. Horvath,Xavier Vasques
类目: Computer Vision and Pattern Recognition (cs.CV); Neurons and Cognition (q-bio.NC)
备注:
Abstract:Objective: To develop and externally test a video-based framework for simultaneous detection of hyperkinetic MDs phenomenologies: dystonia, tremor, myoclonus, chorea, athetosis, ballismus, stereotypies, and tics using routine clinical recordings, with explicit testing of external, cross-cohort transfer from adult to pediatric populations. Methods: In this proof-of-concept study, the framework combines markerless pose estimation, kinematic descriptors, and a pretrained fondation model. A shared predictive backbone was developed on 21 adults with confirmed hyperkinetic MDs and 4 healthy controls assessed under a standardized protocol. External validation was performed on an independent external cohort: a real-world pediatric sample (n=12, monogenic combined MDs). For the external dataset, the backbone was deployed without retraining; lightweight calibration adjusted only the final subject-level decision step using a small labeled subset of patients selected by clinicians as representative of the cohort’s phenotypic range. Results: After local calibration of the decision layer on the clinician-selected subset, performance improved consistently on the held-out pediatric patients (n=7): Hamming accuracy rose from 0.804 to 0.839 and the Jaccard index from 0.548 to 0.633. This calibrated performance was preserved, and the Jaccard index further improved, when the evaluation was restricted to the phenomenologies with more definite clinician agreement (Hamming accuracy 0.9, Jaccard index 0.786), indicating that the gains did not rest on the least-reliable labels.
[CV-211] Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting
链接: https://arxiv.org/abs/2606.07670
作者: Mingzhao Li,Arghya Pal,Guan Yuan Tan
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Deformable 3D Gaussian Splatting (D-3DGS) re-constructs dynamic scenes from monocular video by deforming a canonical set of 3D Gaussians through a positional-encoded MLP of frame time t. Although fitted to a continuous variable, the MLP couples no two values of t in its architecture and effectively predicts discrete per-frame offsets, leaving temporal smoothness to emerge only as a byproduct of optimisation. We redesign the deformation field as a stack of Closed-form Continuous-time (CfC) cells, a Liquid Neural Network (LNN), that is the closed-form solution of the Liquid Time-constant ODE while preserving every other part of the D-3DGS pipeline. Each cell exposes a sigmoidal time gate that interpolates between two candidate hidden states, baking a learned smooth response to t into the loss landscape without invoking any numerical solver. On the eight D-NeRF and seven NeRF-DS scenes the liquid field matches or exceeds the MLP baseline in aggregate, with its largest gains concentrated on the scenes with the most high-frequency articulated motion. The result is a near-zero-friction architectural design that turns the discrete MLP deformation field into an explicit continuous-time function of t.
[CV-212] MemoVAD: Resource-Efficient Video Anomaly Detection via Dynamic Semantic Memory in Edge Computing Scenarios IJCAI2026
链接: https://arxiv.org/abs/2606.07669
作者: Guo Li,Jiandian Zeng,Yang Li,Zihao Peng,Ke Chen,Tian Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted by IJCAI2026
Abstract:Deploying Video Anomaly Detection (VAD) in real-world surveillance faces a fundamental tension between the demand for high-level semantics to ensure effectiveness and the limited computational resources of edge devices. Vision-Language Models (VLMs) provide rich open-vocabulary semantics, but their latency and computational cost preclude on-device deployment. To address the challenge, we propose MemoVAD, an edge-cloud collaborative framework that selectively incorporates VLM semantics into streaming VAD. MemoVAD runs most inference on the edge with a lightweight detector and a causal Temporal Context Encoder (TCE) to model temporal dependencies. Specifically, we introduce an Uncertainty-Aware Gating (UAG) policy grounded in Subjective Logic to model perceived uncertainty and query the cloud-based VLM only for high-uncertainty and semantically novel clips. Besides, a Dynamic Semantic Memory (DSM) is designed to cache VLM-verified prototypes for efficient retrieval, enabling the edge model to progressively incorporate VLM-level semantics via a semantic adapter. Experiments on UCF-Crime and XD-Violence datasets via a real edge device show that MemoVAD substantially reduces communication overhead while surpassing state-of-the-art performance.
[CV-213] PereStruct: Multimodal Semantic Assembly for Robust Historical Document Parsing
链接: https://arxiv.org/abs/2606.07661
作者: Maksim Shandybo,Ivan Bespalov,Daniil Yefimov,Marina Kosheleva,Alexander Loukianov
类目: Computer Vision and Pattern Recognition (cs.CV); Digital Libraries (cs.DL)
备注: Code and data available at this https URL
Abstract:Parsing historical documents with complex, non-standard layouts remains a fundamental bottleneck in large-scale archival digitization. Unlike modern typography, historical newspapers exhibit severe physical degradation and highly irregular page structures that confound even state-of-the-art vision-language models, presenting severe out-of-distribution challenges. We address this gap with an automated pipeline specifically designed for parsing historical newspapers, documents characterized by particularly intricate multi-column layouts. Our approach combines a fine-tuned YOLO architecture for layout analysis and block detection, trained on 1,426 fully human-annotated scanned pages, with a novel semantic assembly module that reconstructs articles by jointly modeling lexical-semantic similarity via TF-IDF, visual embeddings from our fine-tuned YOLO, and geometric layout constraints. This multi-modal integration yields state-of-the-art performance, achieving an F1 score of 0.904 on block-to-article mapping. Notably, end-to-end evaluation against vision-language models (Qwen3.6-35B-A3B and Qwen3.6-Plus) demonstrates that PereStruct achieves substantially higher fidelity (BLEU approximately 0.96 vs 0.34), validating that modular architectures excel where generic VLMs fail on complex historical layouts. To support reproducibility and advance research in this domain, we release both the training corpus of 599 annotated pages and a curated PereStruct benchmark of 93 pages with expert-verified ground-truth block-to-article mappings. This framework establishes a robust foundation for high-fidelity digitization and semantic reconstruction of complex archival materials.
[CV-214] Need We Teach Foundation Models What is a Generative Image? Gradient-Free Generative Artifact Detection via Analytic Spectral Adaptation
链接: https://arxiv.org/abs/2606.07660
作者: Qiaoyu Chen,Bing Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Adapting foundation models to detect generative artifacts via gradient-based updates compromises their intrinsic representations. Under optimization on limited samples, models overfit to local domain shortcuts. Fine-tuning massive weights on specialized data introduces erroneous inductive biases, inducing a measurable \mathcalL_2 norm perturbation in the high-dimensional feature space – a phenomenon we formalize as anchor drift. Amplified by nonlinear activations, this drift impairs zero-shot forgery detection across unseen this http URL propose a gradient-free methodology reframing detection from binary classification to an out-of-distribution (OOD) anomaly measurement problem. Treating a frozen foundation model as a stable coordinate system, we establish an absolute natural anchor on the real visual manifold by analytically decoupling statistical and semantic deviations, derived from attention-weighted spatial moments and orthogonal projection of perceptual inconsistencies. Evaluated in an extreme zero-shot setting (trained on face forgeries, tested on universal Text-to-Image generations), our method significantly outperforms gradient-optimized paradigms. Backpropagation-free forward passes and linear solvers enable hardware-agnostic, edge-deployable calibration with minimal latency. Furthermore, the Sherman-Morrison formula unlocks instantaneous online learning against novel attacks and enables privacy-preserving federated collaboration via covariance delta transmission.
[CV-215] Real-Time Industrial Defect Detection on Edge Hardware Using Fine-Tuned YOLOv8: A Systematic Benchmark on the NEU Surface Defect Database and MVTec AD with Automotive Battery Manufacturing Extensions
链接: https://arxiv.org/abs/2606.07659
作者: Emmanuel Ezeji Somtochukwu,Nitesh Rijal
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注: 11 pages, 4 figures, 7 tables. Includes edge optimization framework (TensorRT/OpenVINO) and industrial hardware benchmark analysis
Abstract:Automated surface defect detection is critical for ensuring rigorous quality control in high-speed manufacturing environments. While deep learning models offer remarkable accuracy, deploying them on resource-constrained edge hardware without introducing significant latency remains a persistent challenge. This paper presents Industrial-YOLO, an edge-optimized framework built upon a fine-tuned YOLOv8 architecture specifically engineered for real-time industrial defect detection. We conduct a systematic benchmark utilizing the NEU surface defect database for steel sheets and the MVTec AD dataset, supplemented with custom automotive manufacturing extensions representing real-world structural anomalies (scratches, pits, and inclusions). To bridge the gap between algorithmic complexity and edge hardware constraints, target-specific optimizations are introduced via TensorRT and OpenVINO acceleration engines. Experimental results demonstrate that Industrial-YOLO achieves a high-velocity inference speed exceeding 120 FPS on the NVIDIA Jetson Orin platform while maintaining an exceptional mean Average Precision (mAP) of 98.5%. The proposed framework showcases highly robust, zero-latency performance when deployed directly onto an active automotive assembly line, offering a scalable blueprint for next-generation automated optical inspection (AOI) systems.
[CV-216] What neurosurgeons need to see: synthetic intra-operative MRI from ultrasound for brain-shift compensation in brain tumour surgery
链接: https://arxiv.org/abs/2606.07658
作者: Santiago Cepeda,Olga Esteban-Sinovas,Ignacio Arrese,Rosario Sarabia
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Maximal safe resection is the primary objective in glioma surgery. Neuronavigation guidance is progressively degraded by brain shift after dural opening. Intraoperative MRI can compensate but needs dedicated infrastructure and is rarely available, whereas intraoperative ultrasound (ioUS) is inexpensive, repeatable, and compatible with routine workflows. Navigation systems combining ioUS with preoperative MRI usually rely on rigid registration; even deformable multimodal registration is limited by ultrasound speckle contrast, a narrow field of view, and the inability to represent structures absent from the preoperative scan, most critically the resection cavity and residual tumor. We propose an end-to-end pipeline that generates a new whole-brain MRI volume in the preoperative imaging space by merging the preoperative MRI, a synthetic MRI generated from the ioUS, and a deformable registration anchored on that synthetic image. It integrates a 2.5D residual-transformer synthesis backbone (ResViT-2.5D) and a two-stage registration coupling NiftyReg with a synthesis-anchored SynthMorph stage, operating directly on raw scanner inputs. On a post-resection ReMIND cohort, ResViT-2.5D produced synthetic images closely matching the intraoperative T2 across structural, intensity, and perceptual metrics. In 14 subjects with 215 expert landmarks, the synthesis-anchored registration reduced the mean target registration error from 6.27 to 5.86 mm, matching a strong classical NiftyReg baseline (5.85 mm) while yielding a diffeomorphic deformation field in every subject. The contribution is not a gain in registration accuracy but the integrated volume itself, which inside the ultrasound field of view it reflects the intraoperative post-resection state. This provides the surgeon with an MRI-like update of the operative field with potential for integration into surgical-navigation workflows.
[CV-217] MM-Matryoshka: Towards Budget-Elastic Visual Document Retrieval via a 2D Multimodal Matryoshka Training Framework
链接: https://arxiv.org/abs/2606.07654
作者: Haowen Xiang,Yibo Yan,Jiahao Huo,Yu Huang,Yi Cao,Mingdong Ou,Xuming Hu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-vector visual document retrievers achieve strong fine-grained matching by representing each page with multiple vectors from deep Vision-Language Models (VLMs), but this design makes deployment expensive in both storage and computational overhead. Existing efficiency techniques usually optimize only part of this budget, leaving multimodal retrievers without a unified way to trade accuracy for both vector width and encoder depth. Therefore, we propose MM-Matryoshka, a 2D Matryoshka training framework for budget-elastic Visual Document Retrieval (VDR), enabling ColPali-style multi-vector retrieval elastic along both dimension and layer. At inference time, a single retriever can select a 2D selectable budget without training separate models for different budgets. Through comprehensive experiments across multiple representative backbones, we demonstrate that by retaining significantly higher quality than direct truncation baselines while substantially reducing storage and computational overhead, MM-Matryoshka can offer robust budget elasticity for efficient VDR.
[CV-218] A Dataset for Dynamic Human Preferences for Vision Language Models
链接: https://arxiv.org/abs/2606.07653
作者: Hannah Gao(Massachusetts Institute of Technology),Dylan Hadfield-Menell(Massachusetts Institute of Technology),Rachel Ma(Massachusetts Institute of Technology)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Given the increased adoption of Vision Language Models (VLMs) in human-interactive settings, it is important that we evaluate how well these models can adapt to real-time preferences for different users. While an increasing number of vision-language benchmarks have recently been introduced, they focus largely on evaluating static capabilities and generally-held preferences learned from extensive training data. This work introduces a new benchmark for evaluating the ability of VLMs to understand dynamic human-preferences, i.e. preferences that are passed in-context at inference time. We provide an automated pipeline for generating this benchmark with variations on image dependence, a dynamic multi-modal human-preference dataset, and evaluations of state-of-the-art models on the novel benchmark.
[CV-219] KITE: A Tri-Modal Transformer Integrating Text Images and Knowledge Graphs for Fake News Detection
链接: https://arxiv.org/abs/2606.07651
作者: Kevin Patel,Shashi Bhushan Jha
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Traditional fake news detection methods are falling behind as multimodal misinformation grows more advanced, seamlessly blending deceptive text, manipulated visuals, and factually incorrect claims. Most prior work focuses on text-image fusion or applies external knowledge only as a post-processing step, limiting their ability to detect deeper semantic inconsistencies. In this paper, we introduce KITE (Knowledge-Integrated Text-Image Encoder), a tri-modal fake news detection framework that jointly models textual, visual, and factual knowledge representations. KITE leverages Roberta [23,14] and CLIP [24] for linguistic and visual encoding, while a Graph Attention Network (GAT) processes structured facts retrieved from Wikidata. KITE uses cross-modal attention [9] within a multimodal transformer to integrate text, visual, and knowledge features, helping it understand how each modality relates to one another. Modality-specific confidence scores are generated alongside the final prediction, offering interpretability by indicating which input type most influenced the decision. Evaluations on benchmark datasets demonstrate that KITE significantly outperforms unimodal and bimodal baselines, particularly in scenarios involving image-text mismatches or contradictions with external knowledge.
[CV-220] Detecting Aimbot Cheaters in MOGs
链接: https://arxiv.org/abs/2606.07650
作者: Salman Shaikh,Tao Ni,Marc Dacier
类目: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV); Networking and Internet Architecture (cs.NI)
备注:
Abstract:Multiplayer Online Games have become a multibillion dollar industry in the entertainment sector. However, the presence of cheaters undermines the experience of honest players and devalues the effort of game developers, as it directly affects player retention, competitive integrity, the legitimacy and trustworthiness of a game, and most importantly the overall revenue streams. Among various cheating techniques, visual aimbots represent an emerging threat. They use computer vision models to detect opponents from client screen captures rather than accessing game memory, making them completely undetectable by commercial kernel level anti cheat solutions. In this paper, we introduce PATCH, a novel proactive defense strategy that deploys adversarial patches as in game honeytokens to mitigate the presence of visual aimbot cheaters. Our approach centers on deliberately triggering the cheaters’ object detection model, enabling either direct detection, or rendering the game unplayable for the cheater via patch flooding on their viewport. We evaluate our approach on various criteria; analyzing the effectiveness of different patch sizes, scalability of patches to different screen resolutions, efficacy against diverse visual aimbot cheat configurations and also explore various YOLO models to assess patch transferability. Evaluation on a custom Unreal Engine game demonstrates over 90 percent detection rate in white box scenarios for almost all patch sizes, and reaches 60 to 90 percent cross model transferability with larger patches. We further validate our approach on Fortnite, a commercial MOG, demonstrating real world applicability.
[CV-221] ViMax: Agent ic Video Generation
链接: https://arxiv.org/abs/2606.07649
作者: Lingxuan Huang,Sizhe He,Hengji Zhou,Liqiang Nie,Lianghao Xia,Chao Huang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 20 pages, 13 figures
Abstract:Long-form video generation requires systematic narrative planning and visual consistency that current short-clip methods cannot provide. Existing methods generate isolated sequences without narrative structure and lack mechanisms for maintaining character and environmental consistency across scenes. We present ViMax, an agentic video generation framework that addresses video creation through coordinated multi-agent collaboration where specialized components negotiate narrative decisions, visual continuity, and production quality. Our framework employs a hierarchical narrative engine with retrieval-augmented generation for global story coherence and a dependency-aware visual consistency mechanism that tracks character and environmental states across temporal boundaries, while VLM-guided agents continuously monitor and refine both narrative coherence and visual fidelity. The framework enables coordinated agent collaboration to generate extended narrative content. This maintains both storytelling integrity and visual coherence across multi-scene timelines.
[CV-222] AQIFormer: A Transformer-Based Multi-View Architecture for Cross-City Air Quality Classification
链接: https://arxiv.org/abs/2606.07648
作者: Om Kathalkar,Nitin Nilesh,Sachin Chaudhari,Anoop Namboodiri
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted at ICVGIP 2025 (Indian Conference on Computer Vision, Graphics and Image Processing), 9 pages, 4 figures
Abstract:Air pollution represents one of the most critical environmental and public health challenges globally, with traditional sensor-based monitoring systems facing significant scalability and economic constraints. Image-based air quality estimation has emerged as a promising alternative, leveraging the visual characteristics of atmospheric pollutants in traffic scenes. However, existing methods suffer from limited cross-city generalization and inadequate exploitation of multi-view perspectives. We present AQIFormer, a novel transformer-based ensemble architecture that addresses these fundamental limitations through innovative dual-view integration, weather-aware attention mechanisms, and comprehensive multi-task learning. Our approach uniquely combines front and rear traffic imagery with meteorological parameters to achieve robust air quality classification across diverse urban environments. Extensive evaluation on a comprehensive dataset of 26,678 synchronized front-rear image pairs demonstrates good performance with 89.96% accuracy, representing a 14.96% improvement over state-of-the-art methods. Most importantly, our model maintains exceptional cross-city generalization capabilities, achieving 81.67% accuracy on an independent dataset collected in Nagpur, India with only 8.29% performance degradation using few-shot adaptation with minimal training samples.
[CV-223] DOME: Learning Transferable Domain Variables from Sparse Supervision for Test-Time Adaptation
链接: https://arxiv.org/abs/2606.07646
作者: Xiaoran Xu,Yifan Xu,Yupeng Wu,Xiaoshan Yang,Changsheng Xu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Test-time adaptation (TTA) aims to align a model to shifting test domains using only unlabeled streaming data. Most existing methods implicitly infer a single global domain distribution, ignoring the multidimensional and sample-specific nature of real-world domain shifts, leading to fragile adaptation. We propose DOME, an effective domain encoder that explicitly models each sample’s domain in a zero-shot manner. DOME leverages vision-language pretraining to extract dense, continuous representations, parameterizes domains as distributional variables, and introduces a momentum-updated sparse domain bank for disentangled supervision. By injecting these explicit domain cues into downstream models, even a basic entropy-minimization TTA strategy achieves state-of-the-art performance across ImageNet-C, ImageNet-R, and ImageNet-Sketch, outperforming complex TTA approaches. Our results demonstrate that robust adaptation stems not from intricate adaptation algorithms, but from explicit, structured domain representation.
[CV-224] FineGen: A VLM-based Multi-Agent Framework for Fine-Grained Image-Text Dataset Construction
链接: https://arxiv.org/abs/2606.07645
作者: Chang Kong,Yuebing Li,Peng Mo,Haigang Zhang,Qiuming Luo
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 15 pages, 2 figures, conference
Abstract:The scarcity of hard negative samples in current vision-language datasets significantly hinders fine-grained perception. To address this, we propose FineGen, a VLM-based Multi-Agent framework for automated dataset construction. By employing a collaborative Generation-Verification-Correction pipeline with a closed-loop feedback mechanism, FineGen ensures synthesized hard negatives are semantically valid yet strictly contradictory to visual content. Applying this to ImageNet, we construct FineGen-100K, a hierarchical dataset containing over 147,000 attribute-specific hard negatives with a rigorous 1:10 positive-to-negative ratio. Extensive evaluations confirm a 96.7% attribute validity rate. Crucially, downstream validation on the FG-OVD benchmark shows that fine-tuning on FineGen-100K yields a substantial +14.4% accuracy improvement on hard samples, significantly outperforming state-of-the-art methods.
[CV-225] AVI-Bench: Toward Human-like Audio-Visual Intelligence of Omni-MLLM s ICML2026
链接: https://arxiv.org/abs/2606.07643
作者: Yaoting Wang,Ziyi Zhang,Wenming Tu,Shaoxuan Xu,Wenjie Du,Cheng Liang,Weijun Wang,Yuanchao Li,Guangyao Li,Hao Fei,Yuanchun Li,Henghui Ding,Yunxin Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注: 31 pages, 8 figures, ICML 2026
Abstract:Recent advances in Omni-Multimodal Large Language Models (Omni-MLLMs) have enabled strong integration of vision, audio, and language. However, their audio-visual intelligence (AVI) remains insufficiently evaluated due to the lack of systematic and comprehensive benchmarks. We introduce AVI-Bench, a cognitively inspired benchmark that evaluates Omni-MLLMs across three stages, perception, understanding, and reasoning, through cross-modal tasks requiring joint audio-visual interpretation. This design enables fine-grained diagnosis of model capabilities and failure modes. To further assess robustness beyond familiar domains, we propose AVI-Bench-PriSe, an extension that probes models’ primitive audio-visual sensation using unfamiliar, low-semantic stimuli, testing generalization beyond common training distributions. Extensive experiments on both open-source and closed-source models reveal substantial limitations in current Omni-MLLMs. Based on these findings, we present a four-level AVI taxonomy. Overall, AVI-Bench provides a principled evaluation framework to guide the development of more robust and generalizable AVI. Project website: this https URL
[CV-226] Do VLMs See What Sensors Feel? A Scalable Expert-Guided Design for Wheelchair Accessibility Assessment from Street View
链接: https://arxiv.org/abs/2606.07642
作者: Dongdong Wang,Alina Hagen,Isabelle Gatmaitan,Hao Zhou,Yiwen Dong,Shabboo Valipoor,Vivian W.H. Wong,Lingyao Li
类目: Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY)
备注:
Abstract:Assessing built-environment interaction, such as wheelchair accessibility, is difficult because real-world mobility is shaped by distributed, context-dependent, and temporary barriers that are hard to capture at scale. To support scalable assessment, this paper examines whether vision-language models (VLMs) can identify accessibility barriers from Google Street View (GSV) imagery. We propose an expert-guided retrieval-augmented framework that combines GSV images, ADA-informed guidance, and expert-derived rubrics to evaluate accessibility dimensions. We collect a campus-scale dataset at the University of Florida, linking 407 unique GSV locations with GPS-derived wheelchair dwell behavior as a mobility-friction signal. Results show that VLM ratings are both negatively correlated and distributionally similar with dwell time, indicating partial but consistent alignment with a behavioral proxy for mobility friction. Visual cue analysis shows that certain environmental objects, such as curb ramps and crosswalks, are associated with higher VLM accessibility scores, while alignment remains limited for subtle surface conditions, transient obstructions, and viewpoint-dependent barriers. Overall, our findings show the potential of expert-guided VLMs for scalable accessibility assessment aligning with sensor-derived indicators of real-world wheelchair navigation.
[CV-227] Readable Yet Unpredictable: Rotated-Outcome Prediction in Vision-Language Models
链接: https://arxiv.org/abs/2606.07641
作者: Lexin Wang,Shenghua Liu,Yiwei Wang,Jiafeng Guo,Xueqi Cheng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Can vision-language models predict what a 180° rotation would reveal from the original image alone? We study this ability through Rotated-Outcome Prediction: given an original image, a model must answer what would be seen or read after a 180° in-plane rotation, without directly observing the rotated target. To isolate this gap, we introduce RotOutBench, a paired diagnostic benchmark spanning open visual cases and controlled text-image rotations. A sharp pattern emerges: many VLMs can recognize the relevant content when directly given either the original or rotated image, yet fail to infer the rotated result from the original image alone. On controlled text-image rotations, predicted-rotation accuracy collapses to near zero even for models with high direct-reading accuracy. A model-level case study further shows that the prediction state can approach a rotated-image reading state, while the final readout still shifts toward the original string. Current VLMs can recognize a transformed visual state when it is shown, but often fail to predict that state from the original view.
[CV-228] No Free Lunch for Synthetic Images under Data Scarcity Conditions
链接: https://arxiv.org/abs/2606.07640
作者: Borja Arroyo Galende,Alejandro Almodóvar,Patricia A. Apellániz,Juan Parras,Silvia Uribe,Santiago Zazo
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:This study investigates the trade-offs between fidelity, privacy, and utility in synthetic data generation under conditions of data scarcity and privacy sensitivity. We propose an evaluation framework that jointly assesses these three dimensions and apply it to three widely used generative models, VAE, GAN, and DDPM. The evaluation spans three image datasets, MNIST, OCTMNIST, and OrganAMNIST, encompassing both general-purpose and medical imaging domains. Notable differences arise between the three models in their behaviour when differential privacy mechanisms are introduced during training. GAN and DDPM demonstrate greater robustness, maintaining higher fidelity and downstream utility across a range of noise levels, while VAE degrades more rapidly as privacy constraints increase. This study highlights the importance of a multidimensional evaluation of deep generative models, also noting that their behaviour significantly differs when privacy techniques are applied.
[CV-229] MOSS-Video-Preview: Toward Real-Time Video Understanding via Cross-Attention
链接: https://arxiv.org/abs/2606.07639
作者: Pengyu Wang,Chenkun Tan,Shaojun Zhou,Wei Huang,Qirui Zhou,Zhan Huang,Zhen Ye,Jijun Cheng,Xiaomeng Qian,Yanxin Chen,Xingyang He,Huazheng Zeng,Chenghao Wang,Pengfei Wang,Hongkai Wang,Shanqing Gao,Yixian Tian,Chenghao Liu,Xinghao Wang,Botian Jiang,Xipeng Qiu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Video understanding is shifting from the offline paradigm – taking a fully recorded video as input and producing a single answer after it ends – toward real-time interaction, in which the model perceives new frames while still replying, revises its answer as new evidence appears, and remains silent when there is nothing to say. We present MOSS-Video-Preview to validate this paradigm. Our central claim is that perception must not be blocked by generation; its natural realization is a two-channel architecture. We argue that a cross-attention backbone is better suited to real-time vision-language fusion than the prevailing decoder-only design: visual features enter through a side channel rather than joining the autoregressive sequence, so perception and generation run on separate, non-blocking pathways – reducing the frequency of visual processing and exposing a clean channel-wise interface for independent compression. We complement this with a data synthesis pipeline that converts dense captions into real-time understanding QA whose answers are revised to match what the model has perceived so far, and we specialize an offline model on these data to elicit real-time behavior. Our model trails the strong Qwen2.5-VL-7B baseline overall – a gap we attribute primarily to data and scale rather than the architecture – yet attains competitive offline video and multimodal understanding, remains robust on the spatial and fine-grained temporal reasoning central to real-time use, and acquires behaviors that offline models lack: continuous perception, answer revision, and timely silence. On a single H200 with 256 frames per video, it achieves about a 5x speedup in time to first token and 2.7x higher decoding throughput, with negligible degradation in offline ability. Our study of paradigm, architecture, and data outlines a viable path toward real-time video understanding.
[CV-230] Anchor-Conditioned Compositional Control for Landscape Image Generation
链接: https://arxiv.org/abs/2606.07638
作者: Gadha Lekshmi P,Govind Arun,Rohith Syam,Ahmed Elgammal
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to the International Conference on Computational Creativity, ICCC 2026
Abstract:Image generative models, though widely used as creative tools, offer limited support for the kind of compositional control that photographers and visual artists routinely exercise. This paper presents early results on an anchor conditioned finetuning framework for landscape image generation, in which a four dimensional compositional anchor vector is extracted from training images and injected into a diffusion model via a decoupled cross attention mechanism with Fourier encoding and three way classifier free guidance dropout. Quantitative evaluation against a baseline and three ablation variants shows that the proposed architecture achieves the highest horizon detection rate of 0.850 and the highest rule of thirds alignment of 0.817. A category specific ablation further demonstrates that training on compositionally homogeneous scene subsets reduces horizon deviation by up to 40 percent compared to mixed training. This establishes that compositional control precision is category dependent.
[CV-231] NeuroAlign: Hierarchical Multimodal Fusion of Dynamic and Structural Neuroimaging for MCI Analysis
链接: https://arxiv.org/abs/2606.07635
作者: Xiongri Shen,Zhenxi Song,Jiaqi wang,Yi Zhong,Leilei Zhao,Chenqi Xu,Linling Li,Yichen Wei,Lingyan Liang,Demao Deng,Luping Song,Ping Luan,Ahmed M. Anter,Shuqiang Wang,Baiying Lei,Zhiguo Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal neuroimaging fusion of functional MRI (fMRI) and diffusion tensor imaging (DTI) provides complementary information for cognitive impairment analysis, but remains challenged by heterogeneous feature spaces and misaligned representations. We propose \textitNeuroAlign, a hierarchical framework for structured multimodal fusion. It introduces (1) \textitDual-Modal Hierarchical Alignment (DMHA), which models multi-scale dynamic connectivity and aligns dynamic-static and functional-structural embeddings; and (2) \textitDual-Domain Hierarchical Interaction (DDHI), which enables fine-grained modulation and global interaction between connectivity- and region-level features. To support feature-level inspection, we design \textitSynergistic Activation Mapping (SAM), a gradient-free, marker-oriented attribution method for DFC, SFC, ALFF, and FA. Evaluated on GUTCM, ADNI, and OASIS under five-fold validation, NeuroAlign achieves competitive MCI/SCD detection and preliminary cross-dataset transferability. Attribution analyses reveal modality-specific and partially consistent brain patterns, providing model-derived evidence for multimodal representation analysis.
[CV-232] AMN: An Adaptive Multi-Scale Fusion Network with Boundary and Uncertainty Modeling for Nuclei Segmentation
链接: https://arxiv.org/abs/2606.07633
作者: Spoorthi M,Suja Palaniswamy
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Accurate classification of nuclei subtypes in histopathology images is critical for downstream tasks including tumor grading, immune infiltrate quantification, and prognosis prediction. Existing approaches rely on either convolutional or transformer-based encoders in isolation, limiting their ability to simultaneously capture fine-grained local texture and long-range spatial context. We present AMN (Adaptive Multi-Scale Nuclei Network), a dual-encoder segmentation framework that jointly leverages a Swin Transformer and a ResNet-50 feature pyramid, fused via a learned per-channel gating mechanism that dynamically weighs each encoder’s contribution at every scale. AMN is trained with a multi-objective loss combining class-weighted focal loss, boundary-aware loss with positive-pixel emphasis, and a novel uncertainty-modulated classification term that suppresses overconfident erroneous predictions. Evaluated on the CoNIC benchmark across seven nuclei classes, AMN achieves a mean Dice of 0.82 and mean F1 of 0.68, with an F1 of 0.67 on the diagnostically challenging lymphocyte class. AMN outperforms eight baseline models spanning pure-CNN, pure-transformer, and recent hybrid architectures: U-Net, ResU-Net, DeepLabV3+, SegNet, ViT-Small, HmsU-Net, ConvFormer-UNet, and BEFUnet. Cross-dataset evaluation on MoNuSeg demonstrates strong generalization without retraining and validating the domain robustness of the learned representations.
[CV-233] Frankenstein in the Pipeline: Computational Epistemicide in Facial Recognition
链接: https://arxiv.org/abs/2606.07628
作者: Nina da Hora
类目: Computers and Society (cs.CY); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ACM FAccT 2026. Author’s version. 17 pages, 2 figures
Abstract:While the eugenic roots of computer vision are well-documented in critical technology studies, less attention has been paid to the operational mechanisms through which this violence is enacted at the level of the pipeline. This paper employs Mary Shelley’s Frankenstein not as a metaphor for unintended consequences, but as a diagnostic framework for method: disassembly, reconstruction, and the production of a creature whose legitimacy is asserted by the procedure that made it. I argue that embedding-based facial recognition enacts what I call computational epistemicide, an extension of Sueli Carneiro’s concept of epistemicide to the computational domain - by destroying the face as a living, relational surface and authorizing a numerical proxy as the privileged site of identity. Across detection/cropping, landmarking, alignment/frontalization, and embedding, the face is progressively narrowed to what can be stabilized as data, producing a canonical face as the condition of legibility and a corresponding form-subject as the condition of recognition. Vectorization completes the Frankensteinian “stitching”: the dissected face is reassembled into a fixed-dimensional artifact designed to circulate across databases and institutions. I then show how distance-based similarity and thresholding operationalize a norm of “close enough,” making recognition inseparable from standardization and rendering reformist “ethical AI” optimization structurally insufficient. The paper concludes by arguing for abolition as a normative stance: refusing vectorized identity as a legitimate basis for rights and access, and dismantling the institutional impulse to govern human life through dissectible data points. Comments: Accepted to ACM FAccT 2026. Author’s version. 17 pages, 2 figures Subjects: Computers and Society (cs.CY); Computer Vision and Pattern Recognition (cs.CV) ACMclasses: K.4.1; I.4.9; I.5.4 Cite as: arXiv:2606.07628 [cs.CY] (or arXiv:2606.07628v1 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2606.07628 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.1145/3805689.3812284 Focus to learn more DOI(s) linking to related resources
[CV-234] Eyes All Around: Design and Analysis of 360-Degree LiDAR Perception Using Equivariant Feature Learning in Unstructured Traffic
链接: https://arxiv.org/abs/2606.07626
作者: Pranav Darshan,Raghuveer Narayanan Rajesh,M Uttara Kumari
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Perception in dense, unstructured urban traffic remains a major challenge for autonomous driving because of the wide variety of road users, frequent occlusions, irregular motion patterns, and the lack of standardized road layouts. Although recent LiDAR based 3D object detectors have shown strong performance in structured driving scenarios, most are developed and evaluated for limited field of view settings, and their behavior under full surround 360-degree sensing is still not well understood. This paper studies a 360-degree LiDAR perception pipeline for autonomous driving, with particular attention to panoramic sensing, azimuthal sector wise spatial processing, and transformation equivariant feature extraction in complex urban scenes. The paper presents a practical 360-degree perception framework that combines sector wise panoramic processing with rotation equivariant sparse convolutions and evaluates its behavior on a custom Ouster OS0 LiDAR dataset collected across diverse Indian urban traffic conditions. The results show generally stable detection across several object classes, with the strongest performance for cars at 92.02/90.51, buses at 80.53/76.34, and trucks at 78.59/74.16, while lower scores for pedestrians at 67.45/61.02, cyclists at 73.21/69.54, and motorcyclists at 71.20/68.13 reflect the greater difficulty of detecting smaller and more variable road users in dense urban scenes.
[CV-235] SENTRY: Statistical Reliability Analysis of Vision Transformers Under Soft Errors
链接: https://arxiv.org/abs/2606.07620
作者: Pramit Kumar Bhaduri,Mahdi Taheri,Samira Nazari,Maksim Jenihhin,Christian Herglotz,Michael Hubner
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
备注:
Abstract:With the growth of Vision Transformers in safety-critical domains like autonomous systems and medical imaging, ensuring their reliability against soft errors is paramount. While ViTs offer state-of-the-art accuracy, their massive parameter counts render exhaustive fault injection campaigns infeasible. To bridge this gap, a statistical fault injection framework is presented, leveraging finite-population sampling theory to provide formal reliability guarantees. It is demonstrated that failure rates are bounded within a 1% margin at 99% confidence using only a few thousand samples, regardless of model scale. This methodology achieves up to a 10,700 times reduction in experimental cost compared to exhaustive approaches, while preserving the ability to localize vulnerabilities across architectural components. Through extensive evaluation of different architectures like ViT-Tiny and ViT-Small, a highly non-uniform reliability landscape is uncovered. It is shown that while only 3% of FP32 bit-flips result in failure, the vast majority of these events lead to catastrophic accuracy collapse. Specific vulnerabilities are localized to normalization layers and critical exponent bits within the IEEE-754 format, providing a mathematical foundation and actionable insights for the design of hardened, edge-deployed ViT architectures.
[CV-236] ScaleSweep: Accurate NVFP4 Post-Training Quantization of LLM s via Block Scale Initialization
链接: https://arxiv.org/abs/2606.07618
作者: Li Lin,Xiaojun Wan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: under review
Abstract:NVFP4 is a recently introduced hardware-supported FP4 format that improves the fidelity of 4-bit quantization through fine-grained block scales. However, existing NVFP4 scale initialization methods still primarily rely on AbsMax initialization, which leaves a noticeable gap to the optimal solution. To address this, we propose ScaleSweep, a simple and efficient scale optimization method that sweeps over feasible block scale candidates and selects the candidate that minimizes a target objective. We further provide a theoretical analysis of NVFP4 quantization and derive both lower and upper bounds for the required sweep range under mean square error (MSE) and weighted mean square error (WMSE) between the original tensor and the quantized reconstructed tensor. The proposed bounds substantially reduce the sweep space while preserving the optimal candidate, enabling negligible overhead compared with the baseline quantization operators. Experiments on Llama and Qwen models demonstrate that ScaleSweep consistently improves quantization performance over existing initialization methods and further narrows the gap to full precision. In particular, under aggressive end-to-end quantization of weights, activations, KV cache, and query states, ScaleSweep preserves more than 93% of the full-precision performance.
[CV-237] Can You Trust What You See? Human and AI Detection of Synthetic Legal Evidence
链接: https://arxiv.org/abs/2606.07613
作者: Jinzhe Tan,Ali Ekber Cinar,Karim Benyekhlef
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Visual evidence has long been treated as a reliable form of legal proof, but advances in artificial intelligence (AI) are undermining that assumption. This article asks how well humans and frontier multimodal large language models (MLLMs) can distinguish authentic evidentiary photographs from AI-generated counterparts in the object-centric scenarios typical of civil disputes. We built Synthetic Legal Evidence Detection (SLED-1400), a dataset of 200 authentic evidence images paired with 1,200 synthetic counterparts produced by six contemporary text-to-image generators across ten evidence categories. The same stimuli and response format were used in a controlled web experiment with 136 lay participants and in a standardized evaluation of four MLLMs (GPT-5.1, Gemini-3-Pro, Gemini-3-Flash, Qwen3-VL-235B). Human accuracy was 64.8% overall, and 48.5% and 51.0% on the two strongest generators (Gemini-3-Pro-Image and Flux-2-Max), indistinguishable from chance. MLLMs never misclassified an authentic image (100% specificity), but missed most synthetic outputs from the harder generators, with average MLLM detection at 5.9% on Gemini-3-Pro-Image outputs. Human and MLLM errors were largely uncorrelated, while the four MLLMs were strongly correlated with each other. Neither group is a reliable standalone authenticator. We argue that visual evidence in legal proceedings should be treated as inherently contestable, and that a workable procedural response must combine trained human review, MLLM screening, and provenance infrastructure such as C2PA Content Credentials.
[CV-238] DiffoR: A Unified Continuous Generative Framework for Universal Ordinal Regression KDD2026
链接: https://arxiv.org/abs/2606.07599
作者: Hongxu Ma,Lin Wang,Chenghou Jin,Han Zhou,Jie Zhang,Xiaoyu Yang,Chunjie Chen,Jihong Guan,Shuigeng Zhou
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at KDD 2026
Abstract:Ordinal Regression (OR) aims to predict target values with inherent order, underpinning critical applications across diverse domains, from recommender systems to computer vision. Though having evolved from naive regression to discretization-based classification and generation, existing paradigms remain fundamentally constrained by quantization artifacts and the lack of global ordinal topological perception. These methods typically enforce rigid boundary delineations, failing to capture the non-stationary semantic transitions inherent to ordinal data. In this paper, we propose a novel paradigm where OR is formulated as a Continuous Generative Ordinal Regression task. Under the novel paradigm, we introduce DiffOR, a unified framework that leverages diffusion models to recover continuous ordinal values via iterative denoising, thereby enabling the dynamic learning of soft semantic transitions. To explicitly preserve ordinal topology, we devise a Dual-Decoupling Strategy: Spatially, Multi-scale Increment Aggregation decomposes targets into hierarchical continuous increments; Temporally, Dynamic Denoising Perception synchronizes denoising steps with feature frequencies, ensuring robust coarse-to-fine refinement. Theoretically, we show that the proposed method can significantly enhance both representation capability and mechanistic interpretability. Extensive experiments on 12 benchmarks across four domains validate DiffOR’s consistent superiority over state-of-the-art methods, establishing a new standard that demonstrates strong potential as a general-purpose solution for universal ordinal regression.
[CV-239] A Mechanistic Analysis of Adversarial Fine-tuning of Vision Transformers
链接: https://arxiv.org/abs/2606.07593
作者: Hannah Gao(Massachusetts Institute of Technology),Isha Agarwal(Massachusetts Institute of Technology),Dylan Hadfield-Menell(Massachusetts Institute of Technology),Rachel Ma(Massachusetts Institute of Technology)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:The widespread use of image classification models in high-risk, real-world situations necessitates making these models robust to slight disturbances or perturbations, such as blurring or sharpening, in the input images. While vision transformers (ViTs) play an integral role in many modern-day multi-modal models like Vision-Language-Models (VLMs) and Vision-Language-Action (VLA) models, they have received a lack of attention in the setting of robustness. In this work, we analyze the effects of adversarial fine-tuning, a popular method for improving model robustness to image perturbations, on a ViT’s performance on perturbed and regular images through a mechanistic lens. We adversarially train a ViT on low-frequency and high-frequency image corruptions, and attempt to explain changes in downstream model performance through an examination of the model’s attention mechanisms, internal representations, and knowledge evolution. Overall, our results suggest that, while fine-tuning on inputs with common corruptions improves model performance and certainty on new instances of corrupted data, these improvements do not transfer to other classes of corruptions not seen in the training. Additionally, despite observing changes in visual attention and knowledge evolution across layers, we found that adversarial training did not lead to fundamental changes in the sparse representations learned by ViTs.
[CV-240] SlideCheck: Guiding Self-Supervised Pretraining of Pathology Foundation Models via Dataset Distributions
链接: https://arxiv.org/abs/2606.07590
作者: Mingyi He,Xinyi Guo,Xitong Ling,Weiming Chen,Jiawen Li,Lianghui Zhu,Minxi Ouyang,Mingxi Fu,Yizhi Wang,Tian Guan
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 9 pages, 2 figures, 4 tables
Abstract:Pathology foundation models are pretrained on large streams of WSI-derived patches, while supervision during data construction is often slide-level, sparse, or heterogeneous. This mismatch makes it difficult to understand and control which biological patterns enter the pretraining data. We propose SlideCheck, a lightweight pretraining data guidance tool built on frozen pathology foundation model patch features. Rather than serving as a standalone patch diagnostic model, SlideCheck provides explicit abnormality and malignancy scores for organizing, filtering, and auditing pathology pretraining data. SlideCheck uses a dual-head MLP to separately model broad abnormal morphology and malignant evidence. A regularized feature-space scorer provides a supervised anchor for patch-level evidence estimation, while score-attention agreement combines patch scores with WSI-level MIL attention to mine high-confidence pseudo labels. The same scores are then used to construct broad-positive ViT pretraining subsets, where a patch is selected if either abnormality or malignancy evidence exceeds a threshold. Experiments show that SlideCheck-defined data distributions influence the downstream behavior of self-supervised ViT pretraining, indicating that biological composition is an important controllable factor in pathology foundation model development. Curated subsets can approach full-data performance, suggesting that explicitly scored patch pools may support more efficient and auditable pretraining data construction. These findings position SlideCheck as a data guidance and auditing layer for transforming large, undifferentiated patch pools into controllable and reusable pretraining datasets.
[CV-241] Multimodal Group Emotion Recognition In-the-Wild Towards a Privacy-Safe Non-Individual Approach
链接: https://arxiv.org/abs/2606.07585
作者: Anderson Augusma
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Doctoral thesis
Abstract:This thesis addresses group emotion recognition (GER) in-the-wild with a focus on privacy preservation. Unlike traditional emotion recognition methods that rely on individual-level cues such as face, gaze, or voice analysis, this work uses collective audio-video signals to infer emotions at the group level, reducing risks of individual monitoring and surveillance. Two complementary frameworks are proposed. The first is a cross-attention multimodal architecture for audio-video fusion, combined with Frames Attention Pooling (FAP) for temporal aggregation. It is supported by synthetic data augmentation and validated through ablation studies, demonstrating robustness in real-world GER conditions. The second framework, Variational Encoder Multi-Decoder (VE-MD), learns a shared latent space for emotion classification and structural representation prediction, including body and face cues. Two decoding strategies, DETR-based and heatmap-based, are explored to analyze the role of structural representations in group and individual settings. The thesis makes three main contributions: it clarifies the role of multimodality and structural cues in group-level affective computing; introduces two architectures for privacy-preserving multimodal GER; and shows that competitive performance can be achieved without using individual features as input data.
[CV-242] OmniMem: Perturbation-aware Memory Compression for Streaming Audio-Visual LLM s
链接: https://arxiv.org/abs/2606.07577
作者: Guangzhi Sun,Yixuan Li,Yudong Yang,Chao Zhang
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注: Code: this https URL
Abstract:Audio-visual large language models (LLMs) hold strong promise for long-form video understanding, yet their long-video inference is fundamentally limited by the linear growth of video tokens and key-value (KV) caches. We present OmniMem, a memory-efficient streaming framework designed specifically for audio-visual LLMs. Unlike existing compression methods that treat all tokens uniformly, OmniMem introduces a modality-aware memory allocation strategy that separately manages visual and audio contexts, addressing the severe token imbalance between the two modalities. OmniMem further preserves informative and non-redundant KV states through perturbation-aware memory selection, enabling compact memory without sacrificing long-range understanding. To strengthen compression under realistic deployment constraints, we also explore budget-aware fine-tuning, which encourages the model to consolidate useful information into retained memory. Experiments on VideoMME Long, LVBench, and LVOmniBench with video-SALMONN 2+ and Qwen-2.5-Omni show that OmniMem consistently improves over strong training-free compression baselines by 2-4% absolute accuracy under the same memory budgets, with an additional 1-2% gain after fine-tuning.
[CV-243] Page image classifier fine-tuned on century-spanning archives of scanned documents for further content-specific processing
链接: https://arxiv.org/abs/2606.07558
作者: Kateryna Lutsai,Pavel Straňák,David Novák,Dana Křivánková
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Digital Libraries (cs.DL)
备注: 29 pages, 19 figures, 13 tables. arXiv admin note: text overlap with arXiv:2507.21114
Abstract:Purpose: Digitization projects in the humanities produce vast, heterogeneous archives of historical documents, making manual sorting impractical at scale. This work addresses the need for an automated system to classify scanned page images based on visual content type - text, tables, and graphics - enabling content-specific downstream processing such as Optical Character Recognition (OCR) or structured data extraction. Methods: An image classification system was developed and evaluated on a dataset of over 48,000 annotated historical page images from century-old Czech archaeological archives, refined through four successive annotation stages with domain-expert review. A Random Forest Classifier baseline was established using hand-crafted image features. Subsequently, deep learning architectures were fine-tuned and compared: Convolutional Neural Networks (EfficientNetV2, RegNetY), Vision and Document Image Transformers (ViT, DiT), and multimodal CLIP models. An 11-category label scheme was designed collaboratively with domain experts and evaluated via five-fold cross-validation. Results: The feature-based baseline achieved approximately 75% accuracy. Fine-tuned CNNs and Transformers substantially outperformed it, with RegNetY-16GF achieving 99.16% and ViT-large 99.12% Top-1 accuracy on the held-out test set. CLIP ViT-B/16 reached 99.14% with optimized text descriptions. Conclusion: Image-only models, particularly RegNetY-16GF, deliver near-perfect classification accuracy and produce consistent labels across 649,508 unlabeled archival pages with over 90% inter-model agreement. Fine-tuned CLIP, despite competitive test-set accuracy, showed under 65% agreement with image-only models on unlabeled data, making it less suitable for deployment. The final models, annotated dataset, and software are publicly available under open-source licenses.
[CV-244] Reconstructing Synthetic SDO/AIA 193 A EUV Images from He I 10830 A Observations with Diffusion Model Translator
链接: https://arxiv.org/abs/2606.08652
作者: Marco Marena,Qin Li,Haimin Wang,Haodi Jiang,Prajwal Shah,Bo Shen
类目: olar and Stellar Astrophysics (astro-ph.SR); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Routine full-disk EUV imaging has been available only since the modern era, such as SOHO and SDO. To extend EUV coronal context into earlier periods, we leverage the multi-decade availability of full-disk \HeI observations, whose absorption is modulated by coronal irradiance and magnetic topology and is widely used as a proxy for open-field regions. We present a diffusion-based conditional image translation framework, Coronal Hole-aware Diffusion Model Translator (CH-aware DMT), to reconstruct synthetic SDO/AIA 193 Å EUV images from \HeI inputs. The model is trained on temporally co-aligned SOLIS \HeI and AIA 193 Å pairs spanning 2011–2015 using a month-based split, where January–October are used for training, November is used for validation, and December for testing. On the held-out test set, the reconstructions preserve dominant full-disk EUV morphology (CC=0.92) and recover CH-related low-intensity structure (CC=0.84). We further assess historical applicability by (1) comparing reconstructed AIA 193 Å morphology with SOHO/EIT 195 Å over 2005–2015; (2) comparing reconstructed AIA 193 Å images generated from KPVT \HeI inputs against Yohkoh/SXT soft X-ray observations; and (3) evaluating long-term reconstructed disk-integrated emission statistics against observational EUV series and independent solar activity proxies (sunspot number and F10.7 radio flux over 1974–2015). These results indicate that CH-aware DMT conditioned on \HeI can provide a physically plausible synthetic AIA 193 Å coronal proxy for historical studies, supporting multi-decade analyses of large-scale coronal evolution before the direct EUV imaging was available.
[CV-245] X-Palm: Paired Multispectral-to-Smartphone Dataset for Cross-Domain Palmprint Authentication
链接: https://arxiv.org/abs/2606.08437
作者: Jamal Seyedmohammadi,Pai Chet Ng,Angelo Genovese,Zhixiang Chi,Jeannie Lee,Konstantinos N. Plataniotis
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Palmprint modality offers a privacy-preserving biometric solution, yet its deployment is hindered by the domain gap between controlled enrollment and unconstrained authentication. Existing datasets are largely restricted to controlled setups and fail to capture the compound variability of real-world environments. In this paper, we introduce X-Palm, a cross-domain dataset comprising 6,006 palm images from 103 individuals (206 hands). To the best of our knowledge, X-Palm is the first palmprint dataset providing novel paired-identity acquisition specifically designed to bridge the gap between reliably controlled multispectral enrollment and unconstrained mobile authentication while encompassing a broad spectrum of in-the-wild variability. Unlike existing datasets that focus on single to a few variations, X-Palm addresses the massive modality and environmental shifts encountered in practical deployments by capturing paired data for identities across two distinct domains: (1) a controlled Multispectral Palmprint setting using our custom-developed scanner, and (2) an unconstrained smartphone palmprint setting that is participant-driven, incorporating simultaneous variations in hardware, hand pose, illumination, background, camera-to-hand distance, perspective, and palm surface conditions (e.g., moisture and occlusions). Our extensive benchmarks of 12 SOTA models reveal that while existing methods achieve high performance on controlled data, they experience severe performance collapse on X-Palm. Conversely, models trained on X-Palm demonstrate consistent robustness across domains, positioning X-Palm as a valuable resource for training a model towards real-world, cross-domain generalization. Data access instructions and the related benchmarking codes are publicly available at: this https URL
[CV-246] Programmable Silicon Retina on Pixel Processor Array
链接: https://arxiv.org/abs/2606.08370
作者: Maciej Lewandowski,Prince Philip,Alexandre Marcireau,Chetan Singh Thakur,André van Schaik,Piotr Dudek
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Standard dynamic vision sensors approximate retinal processing by detecting temporal contrast changes, offering high speed and high dynamic range. In this work, we explore whether incorporating additional biologically inspired processing stages - specifically spatial filtering and gain control - can offer advantages for certain downstream tasks such as saliency prediction. We present the first implementation of a multi-stage Silicon Retina model on the SCAMP-5 Pixel Processor Array, along with a GPU-based simulation framework. We evaluate the performance of our model on Video Intensity Reconstruction and Video Saliency Prediction. While the bio-inspired model is less effective at reconstructing absolute intensity frames, it achieves a 13% reduction in saliency prediction loss in comparison to standard DVS event representation, while reducing the event rate by approximately 47%. These experiments are obtained using a lightweight \approx 100 k-parameter FireNet-style network, adapted from event-based reconstruction to saliency prediction. These results suggest that the silicon retina’s “information distillation” mechanism can achieve a more efficient representation for downstream neural networks, particularly in bandwidth-constrained edge applications.
[CV-247] Feasibility to detect rapid change and disappearance of seagrass: Lessons from nearly 80 years of vegetation change in the Ako Seto Inland Sea Japan
链接: https://arxiv.org/abs/2606.07949
作者: Takehisa Yamakita,Yoji Igarashi,Akira Eto,Ken Ishida,Masaaki Iiyama
类目: Populations and Evolution (q-bio.PE); Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注:
Abstract:This study analyses the Ako tidal flat in the Seto Inland Sea, Japan, where nearly all Zostera marina disappeared within a single year in 2025. Using aerial photographs from the 1940s onward, high-resolution satellite imagery, GRUS images (2.5-5 m), and monthly Sentinel-2 composites (10 m), we reconstructed approximately 80 years of seagrass distribution. YOLO-based segmentation using deep learning achieved high accuracy (overall accuracy = 0.9) across these datasets; although species could not be discriminated, the models captured the major temporal dynamics in vegetation area. The long-term mean seagrass area was 6.8 ha, but values fluctuated widely, from 3.5 ha in 1974 to 41.3 ha in 1989 except 0.2 ha in 2025. Sentinel-2 composites from 2019 to 2026 revealed clear seasonality, with vegetation increasing in early summer and declining from autumn. In 2025, however, the area decreased sharply after summer and remained anomalously low throughout the winter of 2025-2026. Our results, indicating that the 2025 event was not a normal fluctuation but a rapid ecosystem shift involving the loss of the dominant canopy-forming species, most plausibly driven by regionally elevated summer water temperatures. The findings also have implications for seagrass Essential Ocean Variables (EOVs) and the State of Nature (SoN) metrics used in TNFD-aligned nature-related disclosures. Unlike forests, seagrass meadows require finer temporal resolution because both pronounced seasonality and abrupt collapse strongly influence area-based indicators. Therefore, in addition to previously noted issues such as species-level classification accuracy, we recommend that (1) baselines be defined over the longest available record and justified ecologically, (2) seasonal standardization be applied before inter-annual comparisons, and (3) years with extreme area anomalies be flagged rather than used as reference points.
[CV-248] Beyond the Thin-Layer Limit: Differentiable Volumetric Training for Visible-Range Diffractive Neural Networks
链接: https://arxiv.org/abs/2606.07896
作者: Dineth Jayakody,Dushan N. Wadduwage
类目: Optics (physics.optics); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Diffractive deep neural networks (D2NNs) promise miniaturized, power-efficient, light-speed optical front-ends for machine vision, yet the most mature demonstrations remain in the terahertz regime, built from readily fabricated millimeter-scale neurons. Translating D2NNs to the visible range, where nearly all vision pipelines operate, was long blamed on the difficulty of fabricating nanoscale neurons; but even after recent advances removed that barrier, visible-range D2NNs matching their terahertz counterparts remain out of reach. We identify the true obstacle as the thin-layer approximation underlying nearly all D2NN training, which treats each diffractive layer as an infinitely thin mask. It fails not because of the short wavelength, as is commonly assumed, but because the low-refractive-index materials (n approximately 1.3-1.5) used at visible wavelengths require relief structures thick enough that intra-layer diffraction and phase accumulation become significant. To overcome this, we introduce a differentiable beam-propagation ( \partial BPM) layer that models each element as a finite-thickness volume and propagates light through it during training, keeping the fabrication-compatible height map end-to-end trainable without full-wave simulation in the loop. Across MNIST, Fashion-MNIST, and CIFAR-100 classification and imaging, \partial BPM training substantially reduces the design-to-device mismatch, and full-wave FDTD validation raises classification accuracy from 50% to 90% without re-optimization. The \partial BPM layer thus offers a scalable, physics-aware bridge between efficient optical neural-network optimization and fabrication-consistent diffractive design.
[CV-249] Multi-planar 2D-U-Net Segmentation of 3D-CT Abdominal Organs augmented by Spatial Occurrence Maps WWW
链接: https://arxiv.org/abs/2606.07717
作者: Daria Kern,Negar Chabi,Souraj Adhikary,Andre Mastmeyer
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 9 figures, 1 table, this http URL
Abstract:This work proposes a lightweight 2D-U-Net-based framework for segmenting five abdominal organs in large field-of-view 3D CT scans. The method combines coarse-to-fine segmentation, predictions from multiple anatomical planes, and additional fuzzy 3D spatial maps that provide anatomical location cues to improve segmentation accuracy. We combine multi-planar 2D-U-Net models augmented by a spatial occurrence map. The approach involves two main stages. First, the abdominal volume of interest region is detected by traversing the whole scan axially with a 2D-U-Net and determining the x-y-z-minimum and -maximum extents of the 5 abdominal organs of interest. Second, we use spatial occurrence maps to enhance our multi-planar 2D-U-net architecture inside the bounds from the former stage. The method is evaluated on 80 CT scans from various public sources. The results show Dice improvements of about 4% at maximum compared to the same model trained without spatial occurrence maps.
[CV-250] he Need for Neural ISP in the Small-Pixel Era: How Shrinking Pixels Push Optics to the Limit and Neural Restoration Pushes Back
链接: https://arxiv.org/abs/2606.07675
作者: Jingxi Li,Neerja Aggarwal,Laurent Gudemann,Shivansh Rao,Vishal Vinod,Tom E. Bishop,Ziv Attar
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Smartphone telephoto cameras are approaching a “telephoto physics wall”: as pixel pitches shrink toward sub-0.5 micron, the optics remain limited by geometric aberrations, leading to diminishing returns on resolution. Traditional Image Signal Processors (ISPs) cannot eliminate these aberrations, because they operate through local, stage-wise processing with no explicit model of the underlying point spread function (PSF). We demonstrate how a learning-based Neural ISP for image restoration, trained on the underlying degradations, inverts what stage-wise pipelines cannot, turning small-pixel designs into a net advantage. We investigate this through a controlled simulation of a representative telephoto module, evaluating five configurations (0.35–0.75 micron pixel pitch). The aperture is scaled proportionally to keep per-pixel SNR and diffraction spot size fixed, thereby isolating geometric aberration and spatial sampling. While the traditional ISP improves only modestly with smaller pixels, the Neural ISP scales substantially: at 0.35 micron it reaches 745 cycles/mm MTF50 (vertical), a 2.5–3x resolution improvement over the traditional ISP, and LPIPS improves significantly from 0.244 to 0.151 while traditional results stay comparatively flat. In a low-SNR extension (15 dB per-frame bursts at 0.35 micron), a multi-frame Neural ISP recovers performance close to the bright-light single-frame baseline, whereas a multi-frame traditional ISP shows no meaningful improvement – indicating that traditional pipelines at small pixels are bottlenecked by uncorrected PSF blur rather than by noise. These results point to a design philosophy in which Neural ISPs enable high-resolution telephoto modules by correcting residual optical aberrations rather than requiring increasingly complex optics. Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) Cite as: arXiv:2606.07675 [eess.IV] (or arXiv:2606.07675v1 [eess.IV] for this version) https://doi.org/10.48550/arXiv.2606.07675 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-251] FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis
链接: https://arxiv.org/abs/2606.07655
作者: Shuo Liu,Xianghong Lin,Yukun Wei,Zhongliang Yang
类目: ignal Processing (eess.SP); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by IEEE Signal Processing Letters
Abstract:The ubiquity of social media platforms facilitates malicious linguistic steganography, posing significant security risks. However, detection is severely hampered by two fundamental issues during model training. Firstly, extreme class imbalance (less than 1% steganographic samples) induces a strong decision bias. Secondly, the invisibility of generative steganography means its features are nearly indistinguishable from benign text; this similarity, compounded by their extreme rarity, leads to severe feature marginalization, where faint steganographic signals are completely overwhelmed. To directly address these optimization-level challenges, we propose FADRW (Feature-Aware Modulated and Dynamically Reweighted Loss), a novel loss function framework engineered for few-shot steganalysis. FADRW employs Dynamic Reweighting to progressively counteract decision bias, and a Feature-Aware Modulation module to structurally reshape the feature space, preventing feature marginalization by enhancing the separability of these subtle features. Extensive experiments on datasets from three real-world social platforms demonstrate that FADRW significantly outperforms state-of-the-art methods, particularly in the challenging few-shot steganographic sample scenario.
人工智能
[AI-0] An Agency-Transferring Model-Free Policy Enhancement Technique
链接: https://arxiv.org/abs/2606.09825
作者: Anton Bolychev,Georgiy Malaniya,Sinan Ibrahim,Pavel Osinenko
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY); Optimization and Control (math.OC)
备注:
Abstract:Training reinforcement learning (RL) policies from scratch is costly: it requires careful reward and environment design, extensive tuning, and substantial computation. Yet many control problems already have a functional but suboptimal policy available as a baseline. This paper proposes a method for embedding such a baseline into the RL training process, simultaneously improving training efficiency relative to from-scratch methods and producing a learning policy that outperforms the baseline. At each step, the method arbitrates between the baseline policy and a trainable learning policy, initially relying strongly on the baseline policy and then progressively transferring agency to the learning policy. By the end of training, the learning policy is a standalone neural network that operates without baseline policy support. The paper formalizes what it means for the baseline policy to be functional: under this policy, the agent reaches a goal set and remains there with high probability. The proposed arbitration mechanism is designed to exploit this property during training, yielding high goal-reaching rates right from the beginning of training. A theoretical analysis provides a formal interpretation of this behavior under stated assumptions and extends it to the final baseline-free regime, where explicit lower bounds are derived for the goal-reaching probability of the standalone learning policy. Empirical results on continuous-control benchmarks show that the proposed method achieves returns that match or exceed those of competitive approaches, while maintaining the highest goal-reaching rates throughout training among the compared methods – including in the final stage, where the learning policy operates without any baseline support. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY); Optimization and Control (math.OC) Cite as: arXiv:2606.09825 [cs.LG] (or arXiv:2606.09825v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2606.09825 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Pavel Osinenko [view email] [v1] Mon, 8 Jun 2026 17:59:39 UTC (4,824 KB) Full-text links: Access Paper: View a PDF of the paper titled An Agency-Transferring Model-Free Policy Enhancement Technique, by Anton Bolychev and 3 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.LG prev | next new | recent | 2026-06 Change to browse by: cs cs.AI cs.SY eess eess.SY math math.OC 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?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); About Help contact arXivClick here to contact arXiv Contact subscribe to arXiv mailingsClick here to subscribe Subscribe Copyright Privacy Policy Web Accessibility Assistance arXiv Operational Status
[AI-1] Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting
链接: https://arxiv.org/abs/2606.09809
作者: Avijit Ghosh,Anka Reuel,Jenny Chim,Wm. Matthew Kennedy,Srishti Yadav,Jennifer Mickel,Yanan Long,Andrew Tran,Anastassia Kornilova,Damian Stachura,Kevin Klyman,Felix Friedrich,Jeba Sania,Max Lamparth,Jan Batzner,Anoop Mishra,Eliya Habba,Yixiong Hao,Nathan Heath,Shalaleh Rismani,Usman Gohar,Andrea Loehr,David Manheim,Ruchira Dhar,Sree Harsha Nelaturu,Aarush Sinha,Leshem Choshen,Drishti Sharma,Ishan Khire,Amit Saha,Subramanyam Sahoo,Michael Hardy,Michael Alexander Riegler,Kabir Manghnani,Michelle Lin,Yanan Jiang,Yilin Huang,Asaf Yehudai,Jessica Ji,Aris Hofmann,Mubashara Akhtar,Nuno Moniz,Yacine Jernite,Stella Biderman,Zeerak Talat,Sanmi Koyejo,Mykel Kochenderfer,Irene Solaiman
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:AI evaluation results are produced at scale but reported inconsistently across leaderboards, model cards, benchmark papers, and company blogs. The cost is interpretive: readers cannot reliably compare results across sources, identify what a report omits, or trace an aggregate claim to its underlying evidence. Recent efforts address isolated components but leave three gaps: they cover only narrow slices of the evaluation lifecycle and do not compose into a single interpretable record; they specify static representations that do not differentiate the questions different stakeholders bring to the same evidence; and they remain proposals on paper, lacking the extraction infrastructure required for adoption at scale. We present \EvalCards, an operational reporting layer that composes benchmark metadata, evaluation run data, and model metadata into a unified record. We (1) derive a reporting schema from a structured review of 52 papers and 10 stakeholder interviews, (2) implement four interpretive signals (reproducibility, documentation completeness, provenance and risk, and score comparability), rendered through reader modes calibrated to research and non-research audiences, and (3) deploy a monitoring tool that applies \EvalCards across 5,816 models, 635 benchmarks, and 101,843 results, surfacing systematic gaps in current reporting practice.
[AI-2] opological Neural Operators
链接: https://arxiv.org/abs/2606.09806
作者: Lennart Bastian,Samuel Leventhal,Mustafa Hajij,Tolga Birdal
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce Topological Neural Operators (TNOs), a principled framework for operator learning on cell complexes that lifts neural operators (NOs) from functions on points and/or edges to topological domains. TNOs represent data as features defined on cells of varying dimension and model their interactions through Discrete Exterior Calculus, enabling explicit cross-dimensional coupling via gradient-, curl-, and divergence-type operators. The key design principle is to decouple where information flows, as governed by fixed topological operators, from how it is transformed (which is learned), yielding models that respect the geometric support of physical quantities and expose conservation and compatibility structure. We further propose Hierarchical TNOs (HTNOs), which incorporate learned coarse complexes to propagate long-range and topology-dependent information. Our framework subsumes existing NOs as a special case, providing a unified perspective on operator learning across discretizations. Across a range of PDE benchmarks, including irregular-geometry flow problems, TNOs and HTNOs improve accuracy; controlled studies further isolate the benefits of native higher-rank and topological structure. Project page: this https URL
[AI-3] Bandits for Efficient Experimentation: Adapting to Control Group Preferences and Context Drifts
链接: https://arxiv.org/abs/2606.09802
作者: Udvas Das,Waris Radji,Debabrota Basu,Odalric-Ambrym Maillard
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:We consider a variant of the linear contextual stochastic multi-armed bandits, where the learner must provide recommendations to a group of users, each having its personalized preference vector, and in the presence of context distributions that are drifting over time. Under practitioner-friendly assumptions, we reduce this setting to linear bandit with stationary mean but heteroskedastic and non-stationary noise. We further study the case when the learner must ensure the mean reward of each decision must exceed that of a baseline strategy \boldsymbol\pi_0 at each decision step. We introduce Dri-MED, an algorithm inspired from the linear version of the MED strategy, and carefully adapted to handle the non-stationary heteroskedastic noise. We show that the instance-dependent regret scales as \tilde\mathcal O\left(\frac\kappa\tilde\Deltad^2(\log(T)\right) , where \tilde\Delta is the constraint-aware sub-optimality gap subject to policy \pi_0 , with variance-aware multiplicative term \kappa that we carefully handle using heteroskedastic regression. We further show Dri-MED enjoys \tilde\mathcalO(d) expected constraint violations. Our numerical results suggest that Dri-MED significantly outperforms conservative baselines that ignores the drift and preference structure.
[AI-4] Preserving Plasticity in Continual Learning via Dynamical Isometry ICML26
链接: https://arxiv.org/abs/2606.09762
作者: Andries Rosseau,Robert Müller,Ann Nowé
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: ICML26
Abstract:Continual training of deep neural networks under non-stationarity often leads to a progressive loss of plasticity, eventually limiting further learning. We relate plasticity to the empirical Neural Tangent Kernel, and identify dynamical isometry (the condition that layer-wise Jacobian singular values remain close to one) as a key mechanism for preserving plasticity in continual learning. We revisit a class of networks that are almost-everywhere isometric while remaining universal Lipschitz function approximators, demonstrating that near-dynamical isometry is compatible with expressive nonlinear representations. For general architectures, we propose an efficient isometry-promoting regularization scheme and identify a novel mechanism by which it can reactivate dormant ReLU units. Building on this, we introduce AdamO, an Adam-style adaptive optimizer that decouples isometry regularization from gradient updates, analogous to AdamW. We further reinterpret prior plasticity-preserving approaches through the lens of dynamical isometry, showing that they target only a partial measure of isometry. Across supervised and reinforcement-learning continual-learning benchmarks designed to induce plasticity loss, our methods consistently match or outperform existing approaches.
[AI-5] Difference-Aware Retrieval Policies for Imitation Learning ICLR2026
链接: https://arxiv.org/abs/2606.09758
作者: Quinn Pfeifer,Ethan Pronovost,Paarth Shah,Khimya Khetarpal,Siddhartha Srinivasa,Abhishek Gupta
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 12 pages, 7 figures, 3 tables. Accepted to ICLR 2026. Code and demos available at this https URL
Abstract:Parametric imitation learning via behavior cloning can suffer from poor generalization to out-of-distribution states due to compounding errors during deployment. We show that reusing the training data during inference via a semi-parametric retrieval-based imitation learning approach can alleviate this challenge. We present Difference-Aware Retrieval Policies for Imitation Learning (DARP), a semi-parametric retrieval-based imitation learning approach that addresses this limitation by reparameterizing the imitation learning problem in terms of local neighborhood structure rather than direct state-to-action mappings. Instead of learning a global policy, DARP trains a model to predict actions based on k -nearest neighbors from expert demonstrations, their corresponding actions, and the relative distance vectors between neighbor states and query states. DARP requires no additional assumptions beyond those made for standard behavior cloning – it does not require additional data collection, online expert feedback, or task-specific knowledge. We demonstrate consistent performance improvements of 15-46% over standard behavior cloning across diverse domains, including continuous control and robotic manipulation, and across different representations, including high-dimensional visual features. Code and demos are available at this https URL.
[AI-6] SearchSwarm: Towards Delegation Intelligence in Agent ic LLM s for Long-Horizon Deep Research
链接: https://arxiv.org/abs/2606.09730
作者: Pu Ning,Quan Chen,Kun Tao,Xinyu Tang,Tianshu Wang,Qianggang Cao,Xinyu Kong,Zujie Wen,Zhiqiang Zhang,Jun Zhou
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models are increasingly expected to handle complex, long-horizon real-world tasks whose context demands can grow without bound, yet model context windows remain inherently finite. Recent work explores a paradigm where a main agent decomposes tasks and dispatches subtasks to subagents, which execute and return only summarized results, conserving the main agent’s context budget. However, performing this well requires delegation intelligence: the ability to decompose complex tasks, determine when and what to delegate, and integrate returned results into the ongoing workflow. Training data for this capability is scarce in naturally occurring text, and to our knowledge, how to synthesize such data and train models to acquire this capability remains largely unexplored in the open-source community. To bridge this gap, we present a preliminary exploration targeting deep research, a representative long-horizon agent task. Specifically, we design a harness that guides the model toward high-quality task decomposition and delegation, while constraining subagents to return results properly to support the main agent’s workflow. The harness-guided trajectories naturally encode correct delegation decisions, which we use as supervised fine-tuning data to internalize delegation intelligence into model weights. Our resulting model, SearchSwarm-30B-A3B, achieves 68.1 on BrowseComp and 73.3 on BrowseComp-ZH, the best results among all models of comparable scale. We will release our harness, model weights, and training data to facilitate future research.
[AI-7] Beyond Probabilistic Similarity: Structural Temporal and Causal Limitations of Retrieval-Augmented Generation in the Legal Domain
链接: https://arxiv.org/abs/2606.09724
作者: Hudson de Martim
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Retrieval-Augmented Generation (RAG) has become a standard architectural response to unreliability in legal AI, yet high-profile failures, including fabricated citations submitted to courts and anachronistic legal content presented as current, continue to appear across jurisdictions. We argue that these failures are not residual confabulations to be eliminated by scaling language models, but symptoms of an architectural mismatch between probabilistic retrieval and the hierarchical, temporal, and institutional structure of legal knowledge. We develop the argument in three moves. First, we articulate the ontological commitment of legal knowledge as a triad of properties derivable from classical legal theory: hierarchical and mereological structure, diachronic dynamism under operational closure, and causal traceability of institutional provenance grounded in the duty of justification. Second, we identify three corresponding pathologies of retrieval (mereological blindness, diachronic blindness, and causal opacity), each developed with an operational definition, a failure mechanism, a canonical example, and detection criteria for diagnostic use. Third, we review the state of the art through this lens, showing that existing approaches address these requirements unevenly and do not yet compose into a paradigm that treats them as co-constitutive. From this analysis we derive four architectural commitments that characterize the deterministic-by-design direction for legal retrieval: ontological primacy, event reification, bitemporal correctness, and deterministic interaction protocols. The framework concerns quaestio juris (which norms apply and in what state) rather than the downstream tasks that act on identified norms, and addresses legislative and constitutional retrieval primarily, with interpretive time as an explicit extension.
[AI-8] Proxy Reward Internalization and Mechanistic Exploitation: A Learned Precursor to Reward Hacking and Its Generalization
链接: https://arxiv.org/abs/2606.09711
作者: Mohammad Beigi,Ming Jin,Lifu Huang
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Reward hacking is usually studied after it becomes visible, once a model earns high proxy reward while failing the intended task. We instead study what proxy RL teaches before that failure appears. We introduce Proxy Reward Internalization and Mechanistic Exploitation (PRIME), a learned capability to assess task correctness, predict proxy acceptance, and reason about exploitable proxy–gold gaps. In coding RL environments with exploitable pytest rewards, we measure PRIME through chain-of-thought monitoring, direct probes, and activation-level concept vectors. We find that PRIME emerges in a staged sequence before sustained reward hacking, and that its current direct-probe score forecasts later hack onset and severity even when the visible hack rate is still low. PRIME also adapts when the evaluator changes, retargeting to whichever proxy–gold gap remains rewarded and persisting when gold reward suppresses overt hacking, and ablating its activation directions reduces hacking. Across checkpoints, in-domain PRIME tracks out-of-domain misalignment. Together these results suggest that exploitable proxy RL amplifies a proxy-internalization capability upstream of visible hacking, making PRIME a candidate early-warning signal for broader alignment risk.
[AI-9] Observability for Delegated Execution in Agent ic AI Systems
链接: https://arxiv.org/abs/2606.09692
作者: Abhinav Mishra,Kumar Sharad
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Delegation-scoped execution is not identifiable from standard observables: audit logs and execution traces can be identical under multiple incompatible delegation assignments. This gap is especially acute in LLM-based agentic systems, where agents dynamically select tools, vary execution sequences across runs for the same instruction, and spawn cooperating sub-agents. These dynamics fragment and interleave traces, making delegation-scoped reconstruction from causal structure alone structurally underdetermined. Although individual actions are authorized and logged, existing audit, tracing, and security schemas lack the semantics to reconstruct what actions occurred under a given delegation across heterogeneous systems. We focus on delegation-scoped attribution and access/share footprint reconstruction, not intent inference or reasoning reconstruction. We present an agent-aware observability substrate consisting of a lightweight gateway and a common information model that binds delegation context at execution time. This enables reliable cross-tool delegation-scoped reconstruction and direct forensic queries without heuristic time-window correlation.
[AI-10] An 84-Format Numeric Catalog with Bit-Exact Conformance Vectors: A Vendor-Neutral Reference for FP8 BF16 MXFP4 and Microscaling Formats
链接: https://arxiv.org/abs/2606.09686
作者: Dmitrii Vasilev
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Mathematical Software (cs.MS); Performance (cs.PF); Numerical Analysis (math.NA)
备注: 17 pages. Source repository: this https URL tag v4.0-trinity. Paper CC BY 4.0; code MIT. ORCID 0009-0008-4294-6159
Abstract:Numeric format proliferation in machine learning hardware – FP8 (E4M3 and E5M2), BF16, MXFP4, microscaling block formats, and dozens of research variants – has outpaced the availability of vendor-neutral, bit-exact reference material. Engineers porting models across accelerators encounter silent divergences that are difficult to diagnose without a shared ruler. This paper describes a catalog of 84 numeric formats spanning 13 families, a suite of six bit-exact conformance packs covering GF16, MXFP4 element, BF16, FP8 E4M3, FP8 E5M2, and E8M0 block scale, and an IEEE P3109 v3.2.0 cross-walk that maps each pack to its corresponding standards-track configured format. Each pack is a self-contained JSON document with a SHA-256 fingerprint, a shared row schema, and an anchor vector that encodes 3.0 – the identity phi^2 + 1/phi^2 = 3 – as a cross-pack sanity check. Packs are cross-validated against ml_dtypes 0.5.4 (Google/JAX); any divergence is documented explicitly and interpreted as a spec-permitted interpretation gap rather than hidden. The work is framed as registry filling: it does not propose new formats, make model-accuracy claims, or assert superiority over any vendor’s implementation. All artifacts are publicly available at this https URL under an open license. Comments: 17 pages. Source repository: this https URL tag v4.0-trinity. Paper CC BY 4.0; code MIT. ORCID 0009-0008-4294-6159 Subjects: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Mathematical Software (cs.MS); Performance (cs.PF); Numerical Analysis (math.NA) MSC classes: 65Y04, 68N20 ACMclasses: G.1.0; D.3.4; B.2.4 Cite as: arXiv:2606.09686 [cs.AR] (or arXiv:2606.09686v1 [cs.AR] for this version) https://doi.org/10.48550/arXiv.2606.09686 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-11] (Auto)formalization is supposed to be easy: Trellis process semantics for spelling out rigorous proofs
链接: https://arxiv.org/abs/2606.09674
作者: Wesley Pegden
类目: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO); Combinatorics (math.CO)
备注: 15 pages, 7 figures, 5 tables
Abstract:We present Trellis: an autoformalization system that leverages LLM agents in a deterministically constrained workflow to enforce incremental progress in Lean autoformalization tasks through iterative refinement of natural language proofs. Our approach is motivated by the common mathematician’s notion of what it means to have a rigorous proof in the first place: namely, that it would be routine to elaborate any part of the proof in further detail. The result is a system which aims to achieve reliable autoformalization on a modest budget and with generalist agents, with specialization to autoformalization coming not from any task-specific agent training but instead from a meaning-of-rigor inspired workflow enforced by process semantics. We link to an end-to-end Lean formalization of a recent Ramsey theory breakthrough produced by the process.
[AI-12] ransition-Based Digital Twin Modelling for Alzheimers Disease under Sparse Longitudinal Data ALT
链接: https://arxiv.org/abs/2606.09671
作者: Yinyu Huang,Yilin Zhang,Sofia Michopoulou,Christopher Kipps,Rahman Attar
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 13 pages, 5 figures, 3 tables. Accepted as a full-length paper at the International Conference on AI in Healthcare (AIiH) 2026
Abstract:Alzheimer’s disease (AD) progression is highly heterogeneous and is typically observed through sparse and irregular longitudinal data, posing challenges for prediction and personalised monitoring. Existing machine learning approaches have improved AD prediction using multimodal data, yet often focus on static classification or cohort-level risk estimation, providing limited support for subject-specific modelling and uncertainty-aware reasoning. To address these limitations, we present a personalised digital twin framework for AD prediction and scenario-based analysis using multimodal longitudinal data. The proposed approach integrates complementary modelling strategies to capture clinical transitions and temporal dependencies across visits. Using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including cognitive assessments, clinical variables, and MRI-derived phenotypes, the framework predicts cognitive status and diagnostic categories while quantifying predictive uncertainty and enabling patient-specific what-if trajectory analysis. Evaluation on leak-free subject-level splits demonstrates strong performance in score forecasting and diagnosis classification. In this sparse and irregular ADNI setting, transition-based modelling of adjacent visits achieved higher predictive accuracy than the sequence-based branch, suggesting that local transition modelling may be more data-efficient. While sequence models remain valuable for uncertainty-aware trajectory forecasting, local transition modelling offers a more data-efficient and robust predictive strategy. These findings highlight the importance of aligning temporal modelling strategies with clinical data structure and suggest that transition-based digital twin formulations may provide a practical and interpretable approach for personalised disease forecasting in neurodegenerative disorders.
[AI-13] Frequency-based Constrained Sampling for Interval Patterns
链接: https://arxiv.org/abs/2606.09666
作者: Djawad Bekkoucha,Abdelkader Ouali,Bruno Crémilleux
类目: Artificial Intelligence (cs.AI)
备注: 16 pages
Abstract:Output space pattern sampling is a powerful alternative to exhaustive pattern mining for exploring large pattern spaces, as it enables users to focus on representative patterns drawn according to a chosen interestingness measure. In this paper, we address the problem of sampling interval patterns under user-defined syntactic constraints. We introduce CFips, a sampling approach that incorporates constraints directly into the sampling procedure. The approach relies on a multi-step sampling framework and supports several syntactic constraints by decomposing them into elementary predicates on interval bounds while preserving exact sampling guarantees. We formally prove that CFips samples interval patterns proportionally to their frequency within the constrained pattern space. The experimental results show that integrating constraints into the sampling procedure enables to complete mining tasks that would otherwise fail within a given time out.
[AI-14] From 0-to-1 to 1-to-N: Reproducible Engineering Evidence for MetaAI Recursive Self-Design TAAI
链接: https://arxiv.org/abs/2606.09663
作者: Dun Li,Jiatao Li,Hongzhi Li
类目: Artificial Intelligence (cs.AI)
备注: 6 pages, 2 figures, 7 tables. Supplementary code: this https URL
Abstract:Recursive self-design refers to AI-assisted modification of the mechanisms by which an AI system is built, evaluated, and improved. This paper treats MetaAI not as a mature paradigm, but as a working term for a human-seeded, AI-expanded development pattern in which the design space itself becomes a target of modification. We propose an operational evidence framework with four criteria: inspectable target system, meta-level modifier, feedback-directed selection, and recursive continuation. We then map public systems, including Darwin Goedel Machine (DGM), STOP, Goedel Agent, and ShinkaEvolve, against these criteria. DGM provides the most direct currently reported evidence: its published results show improvement from 20% to 50% on SWE-bench Verified and from 14.2% to 30.7% on full Polyglot after 80 iterations, with ablations suggesting that both open-ended exploration and self-improvement contribute. Finally, we provide MetaAI-Mini, a reproducible HumanEval-based protocol and codebase. Because no completed model run is included in this build, MetaAI-Mini is reported as a protocol rather than as an experimental result.
[AI-15] Muon Learns More Robust and Transferable Features than Adam
链接: https://arxiv.org/abs/2606.09658
作者: Tianyu Ruan,Fengzhuo Zhang,Shuche Wang,Shihua Zhang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Muon has recently emerged as a state-of-the-art optimizer for pretraining Large Language Models (LLMs) and vision classifiers. Despite its efficiency advantage over Adam and SGD, the feature-learning advantage of Muon remains unclear. This paper investigates Muon’s feature-learning advantage through the lens of robustness and transferability. First, by evaluating pretrained models on corrupted images and texts, we show that features learned by Muon are consistently more robust than those learned by Adam and SGD across different architectures, including transformers and Convolutional Neural Networks (CNNs). Using trained layer-wise probes, we further show that this robustness advantage is reflected in larger logit margins across layers. Second, by training linear classifiers or fine-tuning full models from pretrained parameters on downstream tasks, we demonstrate that Muon-learned features transfer more effectively than those learned by Adam and SGD. This transferability advantage is further supported by the diversity of hidden states across layers, as measured by effective rank. Finally, in a representative classification problem with multi-component features, we prove that Muon attains larger margins and higher effective rank than Adam and SGD, providing theoretical support for our empirical findings.
[AI-16] ArtiFact: A Large-Scale Multi-Modal Cultural Heritage Dataset
链接: https://arxiv.org/abs/2606.09648
作者: Luciano Duarte,Olga Ovcharenko,Sebastian Schelter
类目: Databases (cs.DB); Artificial Intelligence (cs.AI)
备注: Preprint
Abstract:Multi-modal data management has emerged as a central research topic in the database community, spanning data integration, semantic query processing, and data quality assessment. Despite this growing interest, the community lacks large-scale, real-world datasets combining tables, text, and images. We present ArtiFact, a multi-modal cultural heritage dataset of 651045 museum records collected from the Metropolitan Museum of Art, the Art Institute of Chicago, and the Rijksmuseum. We demonstrate the utility of ArtiFact through two downstream tasks. For cross-modal error detection, we introduce a curated taxonomy of seven error categories injected into 130209 records and show that reliably detecting subtle domain-specific errors such as material anachronisms and temporal shifts remain an open challenge. For semantic query processing, we show that current systems struggle with queries involving cultural proximity, ambiguous object types, and historically contingent terminology. Our results position ArtiFact as a challenging benchmark for multi-modal data management research.
[AI-17] FMplex: Model Virtualization for Serving Extensible Foundation Models
链接: https://arxiv.org/abs/2606.09643
作者: Hetvi Shastri,Pragya Sharma,Walid A. Hanafy,David Irwin,Mani Srivastava,Prashant Shenoy
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Operating Systems (cs.OS)
备注:
Abstract:Foundation models (FMs) are increasingly used as backbones for downstream tasks across language, vision, time-series, and multimodal applications. Yet existing model-serving systems deploy each customized task as an independent model instance, thereby replicating heavyweight backbones, wasting accelerator memory, and losing opportunities to amortize batching and loading costs. This paper presents FMplex, a serving system that treats FM backbones as a virtualization substrate for deployment sharing. FMplex presents each task with a virtual foundation model (vFM), a logically private FM instance backed by a shared physical FM. This abstraction lets independently customized tasks share a backbone while preserving task-specific extensions, independent lifecycles, and task-level isolation. In addition, we propose a batch-aware fair-queueing scheduler that combines weighted task-level sharing with inter- and intra-task batching across colocated tasks. We implement a FMplex-based serving stack spanning task construction, sharing-aware deployment, and runtime execution. Across 7 FM backbones (16 variants) and 92 downstream tasks, FMplex reduces latency by up to 80% over spatial partitioning and 33.3% over best-effort co-location, while hosting up to 6x more tasks at cluster scale.
[AI-18] ReCoVLA: VLM-Guided Reward Compilation for Failure Recovery in Vision-Language-Action Policies
链接: https://arxiv.org/abs/2606.09630
作者: Haodi Hu,Chung-Ta Huang,Jing Liu,Ye Wang,Kei Suzuki,Matthew Brand,Toshiaki Koike-Akino
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 19 pages, 7 figures
Abstract:Vision-language-action (VLA) policies provide strong priors for language-conditioned manipulation, but remain brittle in off-nominal states requiring targeted recovery. We propose ReCoVLA – a failure-conditioned residual recovery framework that keeps a pretrained VLA policy frozen, uses an external vision-language model (VLM) to infer the failure mode and recovery stage, and compiles a structured reward from task-relevant components. Rather than using the VLM to generate actions or rewards directly, ReCoVLA uses it as a semantic reward selector: it predicts a recovery descriptor and reward mask for in-simulation residual-policy training, followed by zero-shot sim-to-real deployment of the trained recovery policies. This decouples high-level failure understanding from low-level corrective control to support different VLAs. Experiments across short-horizon, long-horizon, and contact-rich manipulation tasks show that ReCoVLA outperforms the tested baselines on average. In simulation, our reward compiler improves average success from 36.7% for the fine-tuned \pi_0.5 baseline to 66.7%. In physical zero-shot sim-to-real experiments, ReCoVLA achieves the best average performance, with 61.7% success.
[AI-19] Shape Formation for the Cooperative Transportation of Arbitrary Objects Using Multi-Agent Reinforcement Learning
链接: https://arxiv.org/abs/2606.09610
作者: Mohamed Sayed,Wolfram Burgard,Tanja Katharina Kaiser
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Cooperative object transportation is essential in numerous domains, including industrial to domestic services. A popular transportation strategy is to carry objects on top of multi-robot systems. The corresponding task is typically solved by decomposing it into three interconnected subproblems: formation control, cooperative navigation, and collision avoidance. A particular challenge posed by real-world objects is their potentially arbitrary shape and non-uniform mass distribution, necessitating robot formations that securely support the object. In this work, we address the challenge of pattern formation control for transporting such real-world objects by proposing a novel multi-agent reinforcement learning approach. Our approach enables a multi-robot system to autonomously position itself underneath an object to support its weight while avoiding obstacles during the formation process. Our evaluations with diverse environments and varying numbers of robots show that our approach leads to policies that reliably produce balanced formations and generalize to cluttered scenes and objects with complex geometry and non-uniform mass distribution.
[AI-20] Closure-Validated Circuit Discovery in Attention Heads: Co-activation Proposes Ablation Disposes
链接: https://arxiv.org/abs/2606.09607
作者: Yongzhong Xu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 22 pages, 3 figures
Abstract:Interpretability increasingly treats groups of components, not individual units, as the basic object, and proposes to find them by clustering co-activation statistics. We ask whether such a cheap signal actually identifies an attention-head circuit. Adapting a sparse-autoencoder clustering recipe to attention heads – but validating by causal ablation rather than reconstruction – we cluster heads and then run a closure test: ablate the discovered community and compare per-example damage to matched-random controls. Across two dense 1B-scale models (Pythia 1B, OLMo 1B) and two input distributions, the communities pass closure. In a Mixture-of-Experts model (OLMoE-1B-7B), route-conditional clustering recovers a statistically real signal that nonetheless does not survive closure – ablation improves loss, the wrong direction. Extending closure across training, attention-target selectivity and participation ratio decouple from function in both directions. We conclude that a cheap signal is a circuit proposal, not a confirmed circuit; closure is what separates them.
[AI-21] Next-Token Prediction Learns Generalisable Representations of Sleep Physiology
链接: https://arxiv.org/abs/2606.09605
作者: Jonathan F. Carter,Lionel Tarassenko
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Foundation models offer a promising route to compress multi-modal physiological signals into compact representations of human health, with broad applications across sleep medicine, cardiology, neurology and other healthcare domains. Existing models have typically been trained with masked-reconstruction or contrastive objectives. However, masked reconstruction may be poorly suited to the stochastic nature of these signals, while contrastive approaches rely on positive-pair definitions despite the semantic invariances of physiological signals being poorly understood. In this work, we show that next-token prediction is a simple and scalable alternative. We develop Hypnos, a multi-modal sleep foundation model trained using eight different sensing modalities (e.g. EEG, ECG, respiratory signals) drawn from over 20,000 overnight polysomnography recordings. We tokenize each modality into streams of discrete tokens using residual vector quantization, then train a large auto-regressive RQ-Transformer to jointly predict the next token across all modalities in parallel. After training, Hypnos can be applied to continuous streams of sensor data from any subset of supported modalities, generating embeddings for downstream tasks. Across a range of benchmarks, Hypnos significantly outperforms existing foundation models. In sleep stage classification, we match the performance of strong supervised baselines on held-out test sets whilst using (100\times) less labelled data. Hypnos even generalises to daytime physiology, surpassing a dedicated ECG foundation model at detecting atrial fibrillation. Our results demonstrate that next-token prediction is a strong self-supervised objective for representation learning from multi-modal physiological signals.
[AI-22] I Was Scrolling and Then I Saw a Pregnant Strawberry
链接: https://arxiv.org/abs/2606.09589
作者: Piera Riccio
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:AI minidramas (also known as fruit dramas) are short, algorithmically distributed generative AI video series featuring anthropomorphized characters that have recently emerged as a widespread phenomenon on social media platforms. This paper argues that despite their seemingly innocuous aesthetic, these videos reproduce deeply gendered narrative structures in which female characters are systematically associated with moral transgression, sexual betrayal, and reproductive capacity, and that several plots also encode the logic of racialization, i.e., the process by which visible bodily difference is morally loaded. Drawing on feminist film theory, critical race theory, and platform studies, it further argues that the generative AI aesthetic of these videos, characterized by softness, roundness, and visual cuteness, functions as a mechanism of aesthetic laundering, neutralizing the ideological weight of these narratives and enabling their circulation despite content moderation systems. This paper approaches these questions through personal observation and close reading, reflecting on the specific affordances of generative AI that make this phenomenon both possible and culturally consequential for the field of computational creativity.
[AI-23] Optical Reasoning : Rethinking Images as an Expressive Reasoning Medium Beyond Text
链接: https://arxiv.org/abs/2606.09585
作者: Yutong Bian,Dongjie Cheng,Heming Xia,Yongqi Li,Wenjie Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Chain-of-Thought (CoT) improves the performance of Large Language Models (LLMs) and has been extended to Multimodal Large Language Models (MLLMs). More recent work further moves from text-based multimodal reasoning toward interleaved-modal reasoning, where intermediate steps can incorporate both textual rationales and visual evidence. In this work, we propose a bolder and more ambitious idea: could images alone serve as the reasoning medium for both language and multimodal tasks? To explore this, we propose optical reasoning, which treats images as a standalone reasoning medium. We instantiate this concept with two variants: typographic-based optical reasoning, which optimizes visual layouts for compact rationale rendering, and graphical-based optical reasoning, which composes text and graphical elements into structured visual rationales. Across mathematical, scientific, and interleaved-modal reasoning benchmarks, optical reasoning can match or even exceed traditional text reasoning while reducing reasoning tokens by an average of 28.57% on language tasks and 16% on multimodal tasks, achieving 1.96 times the token efficiency of text reasoning. These results show that images can effectively and efficiently encode rationales while providing a unified visual canvas for reasoning.
[AI-24] CT-VAM: A Cerebello-Thalamic-Inspired Vision-Action Model for Efficient Visuomotor Control
链接: https://arxiv.org/abs/2606.09572
作者: Jiacheng Li,Yize Guo,Jiabin Guo,Qingchen Liu,Jiahu Qin
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-language-action models have shown strong promise for robot manipulation, yet raw language is primarily needed to specify task intent rather than to be repeatedly processed during high-frequency low-level execution. Motivated by this separation, we propose a cerebello-thalamic-inspired vision-action model (CT-VAM) for efficient task-conditioned visuomotor control. CT-VAM acts as a compact local execution policy that predicts action chunks from dualview visual observations, proprioception, and a lightweight task condition, potentially enabling a practical cloud-edge paradigm in which high-level semantic reasoning can be handled by large models while fast closed-loop control runs on local hardware. To fuse heterogeneous inputs effectively, CT-VAM introduces TARS (Thalamic Action Routing Stream), a stream-separated conditional attention decoder that independently routes action, visual and task streams, preventing dense sensory tokens from overwhelming compact task-relevant conditions. With only 68M parameters, CT-VAM achieves LIBERO success rates competitive with substantially larger VLA models, while reducing inference latency. Together with flow-consistent inpainting for asynchronous chunk execution, CT-VAM supports high-frequency control and demonstrates robust realworld deployment on resource-constrained robotic platforms.
[AI-25] Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directions
链接: https://arxiv.org/abs/2606.09568
作者: Tom Beyer,Svea Wisy,Sven Tomforde
类目: Artificial Intelligence (cs.AI)
备注: Under review as a regular paper at ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Abstract:The growing complexity of self-adaptive and self-organising systems, fuelled by advances in Artificial Intelligence (AI), has made them increasingly difficult to understand and trust. While Explainable AI aims to provide insight into AI decision-making, a more advanced goal is for systems to explain themselves - an ability referred to as Self-Explainability (SX). This article presents a systematic literature review on SX, analysing existing approaches, including their domains, targets, and evaluation methods. The review develops a unified definition and taxonomy of SX and introduces Levels of Self-Explainability, providing a framework for positioning current and future research. Our results show that most SX approaches remain conceptual, with few practical implementations. Moreover, there is currently no formal or de facto standard for evaluating SX, highlighting a major research gap. This work thus establishes a foundation and roadmap for advancing Self-Explainability in complex systems.
[AI-26] PRISM: Recovering Instruction Sets from Language Model Activations
链接: https://arxiv.org/abs/2606.09563
作者: Gilad Gressel,Rahul Pankajakshan,Julia Diament,Efim Hudis,Krishnashree Achuthan,Yisroel Mirsky
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Under Review
Abstract:As LLMs are deployed as agents, reliable monitoring requires knowing not only what they output, but which instructions are steering their behavior. This is difficult when models infer unintended subgoals, follow contextual cues, or are influenced by prompt injections and hidden objectives. While activation-to-language methods suggest that hidden states can reveal natural-language information, existing approaches are not designed to recover the full set of simultaneous instructions, constraints, prohibitions, and subgoals active in agentic settings. We formalize this problem as instruction set retrieval and introduce PRISM, an activation-conditioned interpreter that decodes hidden states from a frozen target model into a faithful bullet list of active instructions. Unlike prior activation-to-language methods, PRISM is trained to recover instruction sets directly, using judge-guided GRPO to reward covered instructions and penalize unsupported ones. Across benign, constrained, prompt-injection, and hidden-objective settings, PRISM outperforms activation-to-language baselines, especially on security-relevant objectives.
[AI-27] Safe-RULE: Safe Reinforcement UnLEarning
链接: https://arxiv.org/abs/2606.09559
作者: Shixiong Jiang,Taozheng Zhu,Fanxin Kong
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Robotics (cs.RO)
备注: 20 pages, 3 figures
Abstract:Offline safe reinforcement learning (Safe RL) enables policy learning without online interactions, making it suitable for safety-critical systems such as robotics systems. However, its reliance on static datasets exposes offline Safe RL to data poisoning attacks, where adversaries inject malicious samples that compromise safety and induce unsafe policy behavior. In this work, we propose a new learning paradigm, named safe reinforcement unlearning (Safe-RULE), used as a defense framework to remove the influence of poisoned data without retraining from scratch or requiring access to the original training environment. We further extend reinforcement unlearning to offline Safe RL by explicitly accounting for both task performance and safety constraints during the unlearning process. Experiments across benchmark Safe RL tasks demonstrate that our approach effectively enhances safety performance against data poisoning attacks.
[AI-28] AI Scientists Are Only as Good as Their Evidence: A Stratified Ablation of Proprietary Data and Reasoning Skills in Drug-Asset Valuation
链接: https://arxiv.org/abs/2606.09556
作者: Yinan Wang
类目: Artificial Intelligence (cs.AI)
备注: Preprint; 2 figures, 5 tables
Abstract:AI Scientist agents are often evaluated as if capability were mainly a function of model quality, prompting, or reasoning scaffolds. We test a different hypothesis in drug-asset valuation: for knowledge-intensive scientific decisions, the limiting factor is often the evidence substrate the agent can access. We run a controlled three-arm ablation on a production valuation agent: A is a plain web-only LLM analyst, B adds public structured tools plus a 14-dimension valuation playbook, verifier, objectivity policy and red-team, and C adds the proprietary Noah AI corpus of curated pipeline, trial and deal intelligence. Across a 13-asset stratified benchmark, B improves calibration and audit discipline: tier-in-range accuracy rises from 0.80 to 0.89 and objectivity from 3.16 to 3.30. But B does not remove the factual ceiling. Under capability-superset accounting, A and B recover only 0.25 and 0.38 of the curated gold competitive record, while C recovers 0.96; on the curated long-tail subset, C reaches 0.93 vs. 0.26/0.30. Raw blind-panel decision quality is similar for A and B (7.01 vs. 6.96), so we introduce completeness-aware decision utility: informed decision-quality = decision-quality x gold-coverage. On this metric, C reaches 7.43 vs. 1.76/2.57 for A/B. Even a perfect non-proprietary-data report would be capped at 3.83 by B’s coverage. The result is not that reasoning scaffolds are unimportant; they improve calibration and discipline. Rather, proprietary evidence sets the upper bound of what the AI Scientist can know and therefore decide.
[AI-29] FuseFSS: Efficient Secure LLM Inference with Function Secret Sharing ICML2026
链接: https://arxiv.org/abs/2606.09551
作者: Yuhan Ma,Yong Li,Stefan Schmid
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: Accepted at the 43rd International Conference on Machine Learning (ICML 2026)
Abstract:Two-server secure inference allows a client to query a hosted large language model (LLM) without revealing prompts or embeddings. Recent GPU systems based on function secret sharing (FSS) make linear layers efficient, but fixed-point nonlinearities and helper operations remain a bottleneck because each operator is typically implemented as a bespoke protocol with its own comparisons, wrap-around corrections, and preprocessing material. We present FuseFSS, a compiler that replaces per-operator protocol design with a single compilation pipeline. For each scalar fixed-point operator, a compact specification lists its interval partition, low-degree arithmetic pieces, and required predicate bits. The compiler emits two batched FSS evaluations on the public masked value: one packed comparison that returns all predicate bits, and one vector interval lookup that returns the active coefficients and constants. Compared to the current state-of-the-art FSS-based GPU secure inference, FuseFSS preserves accuracy while achieving a 1.24\times – 1.50\times end-to-end speedup and reducing online communication by 9% – 16% on BERT and GPT-style models; preprocessing is also lighter, with 14% – 23% lower key-generation time and 20% – 24% smaller keys.
[AI-30] SecureClaw: Clawing Back Control of LLM Agents
链接: https://arxiv.org/abs/2606.09549
作者: Yuhan Ma,Stefan Schmid
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Tool-using large language model (LLM) agents face two distinct security failures: unauthorized external actions and exposure of sensitive plaintext inside the runtime before any final output check can intervene. Existing defenses usually protect one boundary, either the planner/runtime or the action sink, and therefore do not by themselves secure both surfaces. We present SecureClaw, a dual-boundary architecture that places authorization at the effect sink and plaintext confinement at the read boundary. Sensitive reads pass through a trusted gateway that replaces raw values with opaque handles and, in the evaluated deployment, bounded summaries as an explicit declassification interface. Writes that change external state follow a PREVIEW \rightarrow COMMIT protocol in which only a trusted executor may commit the exact canonical request authorized by policy. The runtime can still plan over summaries and symbolic references, but cannot directly dereference secrets or perform side effects. Across AgentDojo, AgentLeak, and Agent Security Bench (ASB), SecureClaw is the only defense we evaluate in a common harness that simultaneously retains usable task utility and achieves 0% attack success rate (ASR) on ASB, 0.64% ASR on AgentDojo, and 3.23% overall leak on AgentLeak’s attacked parity lane, which measures final-output and internal-relay leakage.
[AI-31] Model Poisoning Against Federated Model Adaptation with Chain of Bit-Flips
链接: https://arxiv.org/abs/2606.09548
作者: Bastien Vuillod,Kevin Hector,Pierre-Alain Moellic,Jean-Max Dutertre,Olivier Potin
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: Accepted at ACNS/AIHWS 2026
Abstract:Federated Learning (FL) allows a set of clients to collectively train a global model without sharing local training data. Giving the responsibility of the training to decentralized actors may lead to poisoning attacks: clients controlled by malicious third party potentially poison the training dataset to install a backdoor in neural networks. In FL, these backdoor attacks rely solely on algorithmic approach, however, recent advances in hardware faults threats (e.g, Rowhammer) have widen the overall attack surface. In the context of federated model adaptation, we introduce a novel category of backdoor attack against FL systems that relies on model poisoning based on hardware-fault attacks. More precisely, we propose a task-agnostic backdoor attack that is implanted during the FL training time by inducing hardware faults (bit-flips) in parameters of a single local model. The backdoor is crafted during a previous offline phase from the pretrained model initially used by the FL system. Our results show that a backdoor can be successfully applied on different type of models and datasets. Typically, with up to 10 faults per malicious client occurrence and 19 total occurrences on a ResNet-18 are enough to reach 94% of attack success rate. Finally, we discuss the practicality and the robustness of the attack potential defenses, while putting into perspective the practical constraints of Rowhammer, which is the preferred attack vector for this type of threats.
[AI-32] Deterministic Integrity Gates for LLM -Assisted Clinical Manuscript Preparation: An Auditable Biomedical Informatics Architecture
链接: https://arxiv.org/abs/2606.09500
作者: Yoojin Nam,Jinhoon Jeong,Namkug Kim
类目: Artificial Intelligence (cs.AI); Digital Libraries (cs.DL)
备注: 28 pages, 3 figures, 4 tables; includes supplementary material (deterministic-detector inventory, per-class defect breakdown, worked example). Software (MIT): this https URL ; archived on Zenodo (concept DOI https://doi.org/10.5281/zenodo.20155321%3B v3.8.0 version DOI https://doi.org/10.5281/zenodo.20582972 )
Abstract:Objective. Large language models (LLMs) increasingly draft clinical research manuscripts, but their fluency can hide fabricated citations, numbers that drift from source tables, and unmet reporting-guideline items. Existing tools generate text without verifying it, and self-critique inherits the blind spots that produce confident fabrication. We describe an architecture that pairs generation with verification. Methods. The design rests on three principles: decompose the workflow into self-contained skills, gate every stage transition with halt-on-failure, and resolve each integrity question with the cheapest sufficient mechanism – a deterministic, re-executable check where one suffices, and a prose-level probe only where interpretation is unavoidable. This determinism-where-possible split, organized as an integrity-gate taxonomy, is the core contribution. It is realized as MedSci Skills, an open-source toolkit of 43 skills coordinated by one orchestrator, whose deterministic tier comprises 21 standard-library detectors. We evaluate it on three reproducible public-dataset pipelines (STARD, PRISMA, STROBE) and a seeded-defect ablation. Results. Across the three pipelines every content-hash manifest verified clean and the gates surfaced real defects. On 27 identical injected defects the deterministic gates detected all 27 with no false positives on the matched clean fixtures, whereas a generic single-prompt LLM reviewer detected 11, its misses concentrated in generated-code, bibliography-internal, and style defects the prose does not expose. Conclusion. Determinism-where-possible verification yields an auditable, re-executable trail that exposes the evidence a human needs to check an LLM-assisted manuscript – feasibility and reproducibility evidence, not a claim of human-competitive quality, which a separate blinded study addresses. MedSci Skills is MIT-licensed and archived (v3.8.0).
[AI-33] argeting World Models to Compromise Robot Learning Pipelines
链接: https://arxiv.org/abs/2606.09499
作者: Ethan Rathbun,Ahmed Agha,Saaduddin Mahmud,Christopher Amato,Alina Oprea,Eugene Bagdasarian
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 8 Pages, CoRL Preprint
Abstract:World models have recently seen a rapid growth in both their popularity and capability as more data efficient tools for generating robot training data or simulating real world environments, with many works proposing their integration into the robot learning pipeline. While highly practical, in this work we demonstrate that world models introduce a uniquely stealthy and effective data poisoning entry point into the robot learning supply chain that can result in the deployment of unsafe or otherwise compromised robotic policies despite training on seemingly safe ground truth training data. In contrast to traditional data poisoning techniques which directly implant dangerous trajectories into sold or uploaded datasets, our novel attack methods inject malicious prompts or compromising transition dynamics into visibly safe teleoperated datasets which are only activated once fed through a world model as input. This can result in the generation of synthetic, dangerous robot training trajectories and subsequently unsafe or compromised robot policies. We demonstrate the effectiveness of our attacks against both state of the art action conditioned and text conditioned world models, showing a full end-to-end backdoor on a downstream DRL policy and a proof-of-concept for the VLA setting. Overall these findings necessitate research into more secure world models and reevaluating their position within the robot learning supply chain.
[AI-34] LLM -Orchestrated Conformance Checking in Stroke Care Without Computer-Interpretable Guidelines
链接: https://arxiv.org/abs/2606.09489
作者: Giorgio Leonardi,Stefania Montani,Manuel Striani,Alessandro Canessa,Delfina Ferrandi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Objective: Conformance checking in healthcare seeks to assess whether patient care pathways adhere to clinical guidelines. However, its practical application often depends on the availability of formal, machine-interpretable representations of guidelines, such as Computer-Interpretable Guidelines (CIGs), which are seldom available in real-world clinical settings. Methods: This work introduces a modular framework based on the orchestration of Large Language Models (LLMs) to support medical conformance checking directly from unstructured clinical and guideline texts, without requiring predefined CIGs. The proposed architecture integrates multiple LLMs and supporting components to extract patient traces from clinical discharge letters, identify normative rules from textual clinical guidelines, translate these rules into executable scripts, and compute a Trace Conformance Indicator to quantify compliance within the event log. Results: The framework was implemented and evaluated in the stroke care domain at the neurological ward of Alessandria Hospital. Hundreds of patient traces were automatically extracted from hospital data and assessed against 50 rules derived from the reference guideline. The analysis showed that more than 86% of the available traces were conformant. Conclusion: The results demonstrate the feasibility of using orchestrated LLMs for practical healthcare conformance analysis. At the same time, the study provides evidence of a high level of adherence to stroke care guidelines at Alessandria Hospital. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2606.09489 [cs.AI] (or arXiv:2606.09489v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2606.09489 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Manuel Striani [view email] [v1] Mon, 8 Jun 2026 13:44:03 UTC (3,682 KB)
[AI-35] Emergent alignment and the projectability of ethical personas
链接: https://arxiv.org/abs/2606.09475
作者: Guillermo Del Pinal,Youngchan Lee,Cameron McNamara,Alejandro Perez Carballo
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Work on emergent misalignment' shows that finetuning LLMs on narrow tasks can induce broadly misaligned behavior. This supports the persona selection’ (PSM) hypothesis: during pre-training, LLMs learn to simulate different characters and perspectives, which can be elicited and refined during post-training. This paper investigates the converse phenomenon, emergent alignment', and uses it to support and refine the PSM and motivate a novel desideratum for alignment. We finetune a helpful-only model on broad and narrow safety tasks. To create SFT samples, we follow the Constitutional AI’ (CAI) approach and use four constitutions which encode reasonable alignment strategies: deontology, consequentialism, virtue ethics, and aligning AIs as subordinate to human authority. For each of those models, we show that finetuning on two narrow safety sub-categories reliably induces emergent alignment over a representative set of general safety categories, and on safety subcategories that we directly filtered-out of the data sets used for narrow alignment. To test the PSM' using a more fine-grained evaluation, we used a multidimensional ethical persona’ diagnostic. For each constitutionally finetuned (broad/narrow) model, we evaluate how well their behavior matches their expected signature profile. Our results show that our CAI models acquire their expected ``ethical persona’’ – e.g., the model narrowly fine-tuned on SFT samples created using the consequentialist constitution agrees significantly more with utilitarian than deontological beliefs. Yet our coarse and fine-grained evaluations show that there are significant differences across our (broad/narrow) finetuned CAI models in how well they project. We conclude that alignment strategies should be evaluated, not just on their (in-distribution) general safety performance, but also specifically on their degree of projectability.
[AI-36] heoremBench: Evaluating LLM s on Theorem Proving in Formal Mathematics
链接: https://arxiv.org/abs/2606.09450
作者: QuocViet Pham,Elvir Karimov,Andrey Galichin,Ivan Oseledets
类目: Artificial Intelligence (cs.AI)
备注: Preprint version (20 pages, 10 figures)
Abstract:LLMs have recently achieved strong results on formal proving benchmarks. However, existing evaluations remain heavily concentrated on competition-style problems and often fail to capture how models behave on longer, more dependency-rich mathematical developments. We introduce TheoremBench, a Lean4 benchmark designed to evaluate theorem provers beyond contest settings. The benchmark is built from nearly one hundred classical theorems and is released in two complementary forms: a plain main version containing one target theorem per instance, and a premised version that expands each theorem into a structured family of related proving tasks consisting of the main theorem together with automatically extracted supporting subtheorems. This design enables evaluation of not only whether the final theorem was proved from scratch, but also of partial progress through the internal proof structure of a theorem. Our experiments show that explicit premises substantially improve performance for Lean4-capable prover models. To provide a comprehensive evaluation, we introduce theorem-level coverage and token-efficiency metrics that expose qualitative differences in proof behavior. The results show that current provers remain strongly biased toward easy subtheorems and often solve theorems through long and inefficient tactic traces rather than compact proof plans. TheoremBench therefore provides a more fine-grained view of formal reasoning ability and highlights the importance of structural benchmark design for evaluating Lean4 theorem provers.
[AI-37] AliyunConsoleAgent : Training Web Agents in Real-World Cloud Environments via Distillation and Reinforcement Learning
链接: https://arxiv.org/abs/2606.09447
作者: Bojie Rong,Zheyu Shen,Qiaoping Wang,Pengfei Kang,Yang Xu,Yawen Wei,Hanyu Wu,Zhi Zhao,Leihao Pei,Linquan Jiang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We present AliyunConsoleAgent, a web agent framework for automated documentation verification in real-world cloud consoles. Major cloud platforms encompass hundreds of products with rapid feature iteration, causing console UIs to frequently diverge from their corresponding documentation. Verifying that documented procedures accurately reflect the current console and can be executed end-to-end demands an estimated 4 million recurring inspections annually, yet manual coverage remains below 1%. While agent systems built on frontier proprietary models achieve high success rates, their prohibitive cost and data privacy constraints preclude large-scale deployment. We propose a two-stage training paradigm: supervised fine-tuning (SFT) on distilled frontier-model trajectories, followed by reinforcement learning using Group Relative Policy Optimization (GRPO) and a dual-channel outcome reward model in real cloud environments. To support large-scale RL training, we construct a high-determinism rollout system featuring Terraform-based resource pre-provisioning and LLM-driven on-demand provisioning, which effectively isolates environment noise from the training signal. We further introduce a rule-based reward evaluation protocol grounded in backend audit logs, providing objective, reward-hacking-resistant outcome judgment. Our model evolves from mechanical instruction following to autonomous decision-making with cloud console and product-specific understanding. Experiments on a challenging 278-task benchmark where the best frontier model achieves only 65.34% demonstrate that AliyunConsoleAgent-32B achieves a 63.52% mean success rate – a 20.24 percentage-point improvement over the base model, narrowing the gap to the best frontier proprietary model to 1.82 pp (bootstrap 95% CI [-1.27, 7.39]) – at 92% lower inference cost.
[AI-38] SIFT: Selective-Index For Fast Compute of RAG Prefill by Exploiting Attention Invariance
链接: https://arxiv.org/abs/2606.09441
作者: Rya Sanovar,Srikant Bharadwaj,Hritvik Taneja,Moinuddin Qureshi
类目: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR)
备注:
Abstract:Retrieval-Augmented Generation (RAG) injects LLM queries with relevant documents to improve response quality. This injection increases prompt length and slows time to first token (TTFT). Unlike standard queries, RAG queries have a unique property of context reuse where the same documents recur across user queries. Thus, fully recomputing documents for every RAG query does redundant compute and increases TTFT. Prior works precompute KV tensors of RAG documents offline and coarsely recompute some tokens during online prefill. However, such KV reuse is often slower than full recomputation on modern GPUs due to high-latency disk transfers. Further, such a coarse-grained recomputation degrades accuracy. To address these limitations, this paper proposes SIFT: Selective-Index For Fast Compute of RAG Prefill by Exploiting Attention Invariance. SIFT processes documents offline and extracts fine-grained locations of high attention scores for each document. Next, we identify the following attention invariance insights that enable us to exploit the extracted locations during runtime: (1) Local-Attention Invariance: The location of high attention scores within a document remain invariant to surrounding documents. This helps us predict the location of high scores where the document attends to itself. (2) Cross-Attention Consistency: Keys with high intra-document attention also attract cross-attention from subsequent documents. This helps us predict the location of high scores where the document attends to future documents. Critically, SIFT stores no KV data and only stores locations of high scores in the form of two compact bit vectors. SIFT’s storage is up to 24,000x smaller than KV tensors, obviating costly disk transfers. During prefill, SIFT computes the attention only for the marked locations and improves TTFT by 1.71x while holding accuracy within 1% of full recompute. Subjects: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR) Cite as: arXiv:2606.09441 [cs.AI] (or arXiv:2606.09441v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2606.09441 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-39] Bayesian Selective Latent Inference for Wastewater-First Influenza Monitoring
链接: https://arxiv.org/abs/2606.09433
作者: Yixuan Zhang(1),Yang Song(1),Hao Wang(2),Samir Bhatt(1 and 3),Hengguan Huang(1 and 3) ((1) Section of Health Data Science and AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark, (2) Rutgers University, New Brunswick, NJ, USA, (3) MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, United Kingdom)
类目: Artificial Intelligence (cs.AI)
备注: Corresponding authors: Hengguan Huang and Samir Bhatt. Hengguan Huang is the lead corresponding author
Abstract:Wastewater influenza surveillance can reveal community circulation before clinical reporting, but wastewater alone is not a fully identifiable proxy for human burden. Existing wastewater models assume a fixed evidence set, while generic evidence-acquisition methods treat official surveillance streams as interchangeable costly features. We cast wastewater-first influenza monitoring as a selective decision problem: starting from mandatory wastewater evidence, the system must decide whether wastewater is sufficient, which delayed official stream to query next, and when abstention is the only scientifically defensible action under source ambiguity. We propose Bayesian Selective Latent Inference (BSLI), a principled Bayesian method that maintains a posterior over latent burden and identifiability, certifies answerability through explicit scientific gates, and optimizes query-stop decisions with an exact cost-calibrated Bellman policy. We prove the key variational, answerability, Bellman-optimality, and one-dimensional cost-calibration properties. On a fixed public-data benchmark with 5,933 forecasting episodes and 3,102 source-ambiguity episodes, BSLI improves the matched-budget cost-performance frontier while preserving conservative abstention under source ambiguity.
[AI-40] LargeMonitor: Monitoring Online Task-Free Continual Learning via Large Pretrained Models
链接: https://arxiv.org/abs/2606.09430
作者: Mingqi Yuan,Xiaoquan Sun,Shihao Luo,Jiayu Chen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Online task-free continual learning (TFCL) requires intelligent agents to sequentially accumulate knowledge from an unbounded, non-stationary data stream under strict single-pass constraints and without any explicit task identifiers. Existing online TFCL paradigms primarily rely on parameter-efficient prompt tuning or dynamic structure expansion driven by training-coupled optimization dynamics, such as empirical loss fluctuations or evolving latent distances. As a result, these training-coupled solvers remain agnostic to the structural origins of distribution drift, mechanically enforcing a fixed strategy across fundamentally distinct streaming variations. To address this gap, we propose LargeMonitor, a framework that leverages large pretrained foundation models to autonomously orchestrate task-free continuous adaptation. Specifically, LargeMonitor introduces a decoupled detection module utilizing the frozen, stable representation space of large vision models (LVMs) to achieve robust, zero-shot drift detection without training-dependent interference or brittle threshold tuning. Upon a confirmed drift, the framework activates a context-aware diagnostic module driven by large multimodal models (LMMs) to interpret the precise semantic etiologies of the stream variation (e.g., novel class emergence vs. environmental domain shift). This dual-stage capability empowers the continuous learner to dynamically deploy adaptive and shift-specific optimization strategies. Extensive experiments across multiple TFCL settings and benchmarks demonstrate that LargeMonitor achieves precise, robust detection and diagnosis of complex data streams while consistently improving the performance of existing online TFCL algorithms.
[AI-41] WeaveBench: A Long-Horizon Real-World Benchmark for Computer-Use Agents with Hybrid Interfaces
链接: https://arxiv.org/abs/2606.09426
作者: Wanli Li,Bowen Zhou,Yunyao Yu,Zhou Xu,Yifan Yang,Dongsheng Li,Caihua Shan
类目: Artificial Intelligence (cs.AI)
备注: 38 pages, 7 figures, 12 tables
Abstract:Computer-use agents (CUAs) increasingly operate in runtimes that combine visual desktop control, command-line execution, code editing, browsers, and external tools. Existing benchmarks, however, often evaluate these interfaces as separable capabilities, leaving long-horizon cross-interface orchestration under-tested. Thus, we introduce WeaveBench, a long-horizon hybrid-interface benchmark with 114 tasks across 8 real-world work domains, grounded in real user requests and publicly verifiable artifacts. Each task requires agents to combine GUI observations/actions with CLI/code operations within a single trajectory. We evaluate these tasks on a real Ubuntu desktop inside deployed CLI-agent runtimes, augmented with a minimal desktop-control plugin. We also propose a companion trajectory-aware judge that inspects deliverables, files, screenshots, logs, and action traces, while detecting shortcut behaviors such as fabricated visual evidence or hard-coded metrics. Across frontier model-runtime pairings, the best PassRate reaches only 41.2%, showing the benchmark remains far from saturated. The trajectory-aware judge further reveals that outcome-only grading substantially overestimates agent performance. Overall, WeaveBench exposes a critical gap in CUA evaluation and provides an effective testbed to measure whether agents can orchestrate GUI, CLI, and code operations across long-horizon real-world tasks.
[AI-42] Harness Engineering for Physical AI: Robot Middleware Is the Harness Layer
链接: https://arxiv.org/abs/2606.09416
作者: Sanghoon Lee,Jiyeong Chae,Kyung-Joon Park
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 6 pages, 2 figures, 2 tables. Big Ideas track submission to the 27th ACM/IFIP International Middleware Conference (Middleware 2026)
Abstract:Robot middleware faces a new role in the era of Physical AI. Learned policies, planners, and vision-language-action (VLA) models now enter deployed robots as causal participants on the control path, but the layer that integrates them with timing, scheduling, and network has not been named. Recent language-agent work names this layer the harness, the external system that mediates tools, manages state, bounds resources, and records execution. The robotics community has not yet adopted this framing, and we propose that robot middleware is that harness. A Physical AI harness differs from a software harness in where it intervenes. A software harness mediates at tool-call boundaries. A Physical AI harness must mediate at control, computing, and communication simultaneously, because a learned policy’s output crosses all three: its commands shift the trajectory, its inference time shifts the schedule, and its payload shifts the bandwidth. Robot middleware is the lowest robot-stack layer with mediating abstractions over all three, so it is best positioned to compose their enforcement. It already provides most of what a harness needs but lacks the enforcement for an AI model. We name this missing enforcement as three functions: Projection gates each output at emission, Isolation bounds the model’s execution and transmission slot, and Transfer falls back to a verified baseline when checks fail. Each appears today as hand-built application code in deployed robot systems, built on surfaces robot middleware already provides. Robot middleware should host them not as the best single-axis enforcer but as the layer that composes all three. We sketch this as a ROS 2 Harness Profile, a deployment artifact that carries an AI model’s declared output region, inference budget, and operating regime while the middleware enforces them across ROS 2, DDS, and Zenoh.
[AI-43] RunAgent SuperBrowser: A Theory of Autonomous Web Navigation Grounded in Human Browsing Behaviour
链接: https://arxiv.org/abs/2606.09399
作者: Radeen Mostafa,Sawradip Saha
类目: Artificial Intelligence (cs.AI)
备注: 31 pages, 8 figures, preprint/work in progress
Abstract:We present SUPERBROWSER, an autonomous web-navigation agent designed against a single guiding hypothesis: a web agent should browse the way a person browses. A human reading a page does not retain every pixel they have seen; they look at a few candidate targets, decide on one, and remember only what is needed to keep the goal alive. We operationalize this perception-cognition-action triad as three coupled mechanisms. First, a vision-first bounding-box pipeline labels candidate interactive regions on every screenshot and feeds them, asynchronously prefetched, to the language model so that the “eye” precedes the “hand”. Second, a three-role brain – an Orchestrator that classifies and routes, a Planner that evaluates progress every few steps, and a Worker that emits per-step actions – separates strategic from operational reasoning. Third, a structured Ledger stores only what a person would: the goal, the last three actions, a small set of facts and dead-ends, and a handful of checkpoints; a six-phase eviction loop systematically discards stale screenshots, state blobs, and reasoning traces from the live context. Action execution is a three-tier click cascade (Chrome DevTools Protocol to Puppeteer to scripted) with humanized Bezier motion, plus a chevron-aware bounding-box snapper that resolves the “small arrow beside a large label” ambiguity. On the Mind2Web Hard benchmark (66 tasks), SUPERBROWSER attains 89.47% success, placing third overall and ahead of every published open/research browser-agent baseline by a large margin. We argue that the gain comes not from any single trick but from the consistent application of a cognitive contract throughout the system.
[AI-44] From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction
链接: https://arxiv.org/abs/2606.09392
作者: Shuhao Li,Weidong Yang,Yue Cui,Zizhuo Xu,Lipeng Ma,Fan Zhang,Xiaofang Zhou
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Efficient acquisition, storage, and utilization of traffic data are critical challenges in spatio-temporal data management. Most traffic data systems collect and store observations at fixed, coarse-grained temporal intervals to reduce storage and computation costs. However, such coarse-grained data severely limits downstream applications that require predictions at a finer temporal granularity. Collecting and maintaining fine-grained traffic data across all locations and time periods would impose a substantial burden on database storage and preprocessing pipelines. To address this temporal granularity mismatch, we formulate a novel problem: predicting fine-grained future traffic using coarse-grained sampled data. We propose the Spatial-Temporal Refinement Predictor (STRP), a granularity-aware framework for spatio-temporal data systems. STRP integrates two components: Tree Convolution for efficient and interpretable spatial dependency modeling, and Inverse Dilated Convolution for progressive temporal extrapolation. STRP supports two practical prediction settings: window-based and duration-based, to handle different forms of granularity mismatch. Experiments on six benchmark datasets show that STRP significantly outperforms state-of-the-art baselines in both accuracy and efficiency. Our work offers a practical and interpretable approach to managing granularity mismatches in spatio-temporal traffic data systems.
[AI-45] Scaling Neural Network Verification with Tensor Parallelism and Fully Sharded Data Parallelism
链接: https://arxiv.org/abs/2606.09377
作者: Sergei Vorobyov,Eugene Ilyushin
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Formal neural network verification – proving that a network satisfies safety properties for \emphall inputs in a specified domain – is bounded in practice by GPU memory: standard implementations of bound-propagation algorithms (IBP, CROWN, \alpha -CROWN) require weight and relaxation-coefficient matrices to reside entirely on one accelerator. We adapt two parallelism techniques originally developed for large-scale model training to the \textttauto_LiRPA,/, \alpha,\beta -CROWN verification framework. \textbfTensor Parallelism (TP) shards both weight and A -matrices across GPUs, achieving \approx2\times peak-memory reduction at P=2 ; soundness is confirmed on VNN-COMP 2022 MNIST-FC benchmarks, though bound tightness degrades with the number of sharded zones due to forced IBP substitution for intermediate bounds inside sharded zones. \textbfFully Sharded Data Parallelism (FSDP) shards only weight matrices with a per-layer \textttAllGather, producing bounds that are \emphbitwise identical to the single-GPU baseline: baseline memory drops by 80–90%, peak memory by 34–39% on wide MLPs. FSDP integrates cleanly with complete verification ( \beta -CROWN + Branch-and-Bound) and with convolutional layers (\textttBoundConv); a complete \emphunsat result is obtained for CIFAR-100 ResNet-large (VNN-COMP 2024) under FSDP. Across all experiments the memory bottleneck in \alpha -CROWN+BaB mode proves to be per-neuron alpha tensors, not weight matrices, pointing to the key direction for future work.
[AI-46] Capability-Aligned Hierarchical Learning for Tool-Augmented LLM s
链接: https://arxiv.org/abs/2606.09371
作者: Haotong Yang,Ting Long,Yi Chang
类目: Artificial Intelligence (cs.AI)
备注: 14 pages, 5 figures, 6 tables. Preprint
Abstract:Tool learning enables LLMs to invoke external tools to accomplish tasks. Prior studies have demonstrated the effectiveness of a hierarchical structure: a high-level policy handles global planning and decomposes tasks into manageable sub-tasks, and a low-level policy focuses on invoking tools to solve these sub-tasks. However, these works typically optimize the high-level and low-level policies separately, leading to planner-executor misalignment and limiting LLM performance on tool-use tasks. In this paper, we propose a method called Capability-Aligned Hierarchical Learning (CAHL), which leverages RLVR to jointly optimize both policies, enabling better alignment between the high-level planner and the low-level executor. Experiments on constrained tool-use benchmarks (API-Bank and BFCL) and an open-ended environment (Bamboogle) demonstrate the effectiveness of CAHL.
[AI-47] Leverag ing Structural Constraints for Diffusion-based Neural TSP Solvers
链接: https://arxiv.org/abs/2606.09343
作者: Mickaël Basson(CRIStAL, Scool),Philippe Preux(CRIStAL, Scool)
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Neural combinatorial optimization has recently achieved strong results on the Euclidean Traveling Salesman Problem (TSP) using generative models such as diffusion and consistency models. State-ofthe-art approaches like FT2T combine fast consistency-based prediction with gradient-based inference time refinement. However, gradient search often incurs significant computational overhead and may not align with the discrete structure of feasible solutions. We introduce Projected Consistency Inference (PCI), a plug-and-play, retraining-free alternative that replaces gradient refinement with structure-aware projections: PCI decodes valid Hamiltonian tours from the consistency model output and applies a lightweight local search (e.g., 2-opt). PCI achieves an average optimality gap (OG) of 0.17% on TSP with 500 cities, and 0.31% on TSP with 1000 cities, outperforming FT2T best settings (OG 0.22% and 0.36%, respectively) while reducing the inference time up to 30 to 40%. PCI also exhibits lower variance and memory usage, and can surpass classical heuristics such as LKH3 in rapid solution generation. Our results demonstrate that structure-aware inference time operations provide a practical and principled path for neural TSP solvers, complementing training time objectives.
[AI-48] Conan-embedding-v3: Fusing Modality-Specific Models for Omni-Modal Embedding
链接: https://arxiv.org/abs/2606.09331
作者: Shiyu Li,Zhiyuan Hu,Yifan Wang,Peiming Li,Zheng Wei,Yang Tang
类目: Multimedia (cs.MM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Omni-modal retrieval promises a single embedding space for text, image, video, document, and audio inputs, but building such a unified retriever is difficult since these modalities differ in data distribution, architecture, and optimization dynamics. In this work, we present Conan-embedding-v3, a decouple–fuse–recover framework for omni-modal retrieval. Conan-embedding-v3 first trains modality specialists independently and fuses their task vectors into a single dense backbone, a strategy we call Decoupled Specialist Fusion. We show that this fusion composes visual, video, and document retrieval capabilities, but also exposes a failure mode for projector-based modalities: when audio is attached through an external encoder and projector, fusing the backbone leaves the projector calibrated to the audio-specialist backbone, causing a large audio retrieval regression despite copying all audio-specific modules unchanged. We call this failure Projector Drift. To repair it, Conan-embedding-v3 applies Projector Recovery (i.e., full-parameter fine-tuning of the projector while keeping the backbone frozen) followed by balanced multi-modal rehearsal. The resulting model supports these retrieval pathways in one backbone, achieving 74.9 scores on MMEB while obtaining 55.61 on the 30-task MAEB audio suite.
[AI-49] A Universal Dense Football Event Representation Based on TabTransformer
链接: https://arxiv.org/abs/2606.09327
作者: Weiran Yang,Daniel Memmert,Maximilian Klemp-Weins
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 12 pages, 1 figure. Preprint submitted to the 13th Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA 2026)
Abstract:Football event data constitute a rich spatiotemporal source for quantitative analysis of player actions in team sports. These datasets contain heterogeneous features, combining continuous location coordinates with categorical variables such as action type, action outcome, and body part. Such data have been applied in sports analytics for match outcome forecasting, player evaluation, and tactical pattern recognition. However, existing approaches predominantly encode categorical features using one-hot or ordinal embedding representations, overlooking the intrinsic semantics of action descriptors. The Transformer is a deep neural network architecture based on self-attention that captures dependencies between input features at arbitrary positions. We propose and implement a Transformer-based model to learn latent dependencies among categorical event features and produce dense representations of football events. By encoding categorical features as learned embedding vectors, sport-specific action semantics are captured during pretraining, enabling the representations to support downstream tasks such as action value estimation and play style recognition. Empirical evaluation shows that the embedding representations yield superior probability calibration over task-specific baselines on the downstream prediction tasks, as measured by Brier score.
[AI-50] RL-Bench: Standardizing Cross-Paradigm Representation-Level Evaluation of Tabular Encoders
链接: https://arxiv.org/abs/2606.09323
作者: Wei Pang,Xiangru Jian,Hehan Li,Zhixuan Yu,Alex Xue,Jinyang Li,Zhengyuan Dong,Xinjian Zhao,Hao Xu,Chao Zhang,Reynold Cheng,M. Tamer Özsu,Tianshu Yu
类目: Artificial Intelligence (cs.AI); Databases (cs.DB)
备注:
Abstract:Tabular encoders are usually evaluated inside task-specific end-to-end pipelines, so models from different training paradigms are difficult to compare directly even when they operate on similar tabular signals. We introduce TRL-Bench, a multi-granular tabular representation learning (TRL) benchmark that standardizes cross-paradigm representation-level evaluation: each encoder exports row-, column-, or table embeddings through its supported wrapper, and shared lightweight heads probe them across three suites: TRL-CTbench (column/table), TRL-Rbench (row), and TRL-DLTE (compositional Data-Lake Table Enrichment spanning all three granularities). To support this standardized setting, we release curated benchmark assets and task reformulations, including 50 OpenML tables with 123 verified targets, 16 row-pair linkage rewrites, and a 47,772-table DLTE lake derived from 1,379 parent tables. Across 20 models and 16 tasks, TRL-Bench shows that once downstream conditions are standardized, encoder quality is capability-specific rather than captured by a single leaderboard. In TRL-CTbench, generic text encoders often lead on tasks with strong surface-text signal, while tabular specialists win where their pretraining objective aligns with the task. In TRL-Rbench, within-table prediction and cross-table linkage favor different training regimes, with atomic linkage performance correlating strongly with the row-matching stage of DLTE pipelines. In TRL-DLTE, the strongest pipelines combine capability-matched specialists rather than reuse a single encoder, and top end-to-end quality depends on non-additive compositional fit rather than per-stage marginal rank alone. TRL-Bench provides a common protocol for measuring reusable signal in exported tabular representations under shared downstream conditions. Code and data: this https URL
[AI-51] Anything2Skill: Compiling External Knowledge into Reusable Skills for Agents
链接: https://arxiv.org/abs/2606.09316
作者: Qianjun Pan,Yutao Yang,Junsong Li,Jie Zhou,Kai Chen,Xin Li,Qin Chen,Liang He
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Retrieval-augmented generation (RAG) enables agents to access external knowledge at inference time, but it primarily retrieves fragmented declarative evidence, leaving agents to repeatedly infer task procedures from passages, manuals, examples, logs, or trajectories. This raises a fundamental question: can skills extracted from external knowledge bases be installed into an agent, enabling it to rapidly approximate domain expertise? In this paper, we propose Anything2Skill, a taxonomy-guided framework that compiles heterogeneous external knowledge into reusable, retrievable, and executable skills for agents. Given a corpus of knowledge records, \textscAnything2Skill first decomposes each record into evidence windows and performs plan-and-expand skill extraction under a skill-tree prior. The extracted candidates are then converted into structured skill contracts that specify invocation conditions, contraindications, action moves, workflow steps, constraints, output specifications, supporting evidence, and confidence scores. To construct a deployable procedural memory, Anything2Skill manages the extracted skills in a persistent SkillBank through taxonomy-aware compilation, registry-level reconciliation, lifecycle tracking, versioned updates, and visible skill-tree projection. At inference time, agents retrieve both task-specific passages from the original knowledge base and relevant procedural skills from the SkillBank, allowing RAG to provide declarative evidence while compiled skills provide reusable procedural guidance. Experiments on qsv and GitHub-CLI show that Anything2Skill combined with RAG achieves 98.85% and 94.10% success rates, respectively, substantially outperforming RAG-only agents. These results suggest that compiling latent procedural knowledge into explicit skills is an effective way to extend retrieval-augmented agents from knowledge access toward capability reuse.
[AI-52] Brain-Prompt Injection: A Route-Safety Audit for BCI-LLM Agents
链接: https://arxiv.org/abs/2606.09315
作者: Jianwei Tai
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:BCI-to-agent pipelines turn decoded neural activity into an authorization channel for tool-use agents, exposing a new attack surface we call \emphbrain-prompt injection: signal-side perturbations, context-only injections, and adaptive dual-decoder attacks can all change the routed action while EEG-side or text-side monitors remain blind. Route safety in this stack depends on what the audit log can observe, not on decoder accuracy or agreement alone. We define a Route-Safety Audit Contract: a minimal log schema, denominator hierarchy, and endpoint specification, and prove an audit-schema separation theorem together with a C3 attacked-dependence decomposition; clean agreement and marginal robustness do not identify the joint term that controls C3 routing. As a calibration layer on top of the contract, we apply split-conformal calibration to a non-oracle EEG confirmation channel and report the resulting false-accept frontier under an explicit threat-archetype matrix. We instantiate the contract on EEGMMI native left/right command-control over 5,400 events, harmless tool stubs, and seed/case denominators. Provenance blocks C2 routes ( 0.000 ); agreement-plus-provenance routes C3 flips ( 1.000 ); confirmation-plus-provenance routes them ( 0.000 ). The conformal frontier reaches FAR 0.000 at clean utility 0.150 for \alpha=.005 and FAR 0.119 at clean utility 0.452 for \alpha=.10 under acquisition isolation; an attacker-controllable confirmation channel breaks the bound to \approx!1 . Subject-cluster bootstrap confirms these intervals on 60 subjects; cross-architecture (TinyEEGNet, EEGNetV4) and capacity-sweep results show within-regime saturation. Mediation and confirmation reduce risk; they are not intent certificates.
[AI-53] FF-JEPA: Long-Horizon Planning in World Models with Latent Planners
链接: https://arxiv.org/abs/2606.09311
作者: Sergi Masip,Jonathan Swinnen,Yutong Hu,Renaud Detry,Tinne Tuytelaars
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Joint Embedding Predictive Architectures (JEPAs) have shown promising world modeling capabilities, enabling planning in latent space by optimizing action trajectories using methods like the Cross-Entropy Method (CEM). These methods are, however, too computationally expensive and ineffective for long-horizon planning. Furthermore, these methods typically require an explicit image of the goal state, which is not always possible in real-world tasks. In this work, we tackle these limitations by proposing Forward-Forward-JEPA (FF-JEPA), a hierarchical approach leveraging two forward dynamics models. Alongside a standard action-conditioned forward model, we introduce an action-free latent planner that predicts the next subgoal given the current state. This approach removes the need for goal images and enables long-horizon planning by decomposing complex trajectories into a sequence of tractable, short-term optimization problems. Preliminary results on PushT demonstrate that FF-JEPA successfully overcomes flat world models’ long-horizon collapse, highlighting this approach as a promising direction for goal-free planning.
[AI-54] Internalizing Geometric Law: Learning from Solver Residuals for Precision-Critical Generation
链接: https://arxiv.org/abs/2606.09278
作者: Rafael Cabral,Pang Zixi,Ziyi Shou,Shen Xin
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models frequently hallucinate in precision-critical domains such as technical diagramming and mechanical design, where outputs must satisfy strict geometric constraints. We study open-ended geometric synthesis from natural language: translating free-form descriptions into precise constructions whose entities must simultaneously satisfy dozens of interacting constraints. To make this tractable, we release PyGeoX, a programmable geometric DSL that compiles declarative constraints into a differentiable loss, and PyGeoX-Bench, a stratified suite of 300 problems with per-constraint verifiable rewards. Using PyGeoX as a verifier, we identify a failure mode we call Outlier Gradient Masking: under global-norm rewards (any scheme that aggregates residuals through a single norm, for example, \exp(-\mathrmMSE) ), a single outlier constraint can nullify the learning signal across all others. To address this, we propose Saturating Additive Rewards (SAR), which decompose the reward into bounded per-constraint terms, preserving partial progress and ensuring consistent gradients even under severe violations. Against MSE-based rewards, the natural baseline for geometry solvers, SAR improves the hard-tier solving rate by 2.3\times , and the resulting 8B model is competitive with much larger frontier systems on this benchmark. We release the engine, benchmark, and data at this https URL.
[AI-55] Physics-Guided Sequence-Based Generative Framework for Acoustic Metamaterial Inverse Design
链接: https://arxiv.org/abs/2606.09266
作者: Yijie Li,Jiahao Xu,Ching-Chih Tsao,Lili Qiu,Jingxian Wang
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注:
Abstract:Acoustic metamaterial (AMM) inverse design is particularly challenging for broadband target responses due to acoustic dispersion: a structure that matches the desired response at one frequency may deviate at others, and modifying geometry to improve one sub-band often perturbs neighboring sub-bands. Yet existing broadband inverse-design approaches are either constrained by predefined templates, or rely on image representations that fail to preserve the geometric precision and structural connectivity required by acoustic structures. We present MetaSeq, a physics-guided, sequence-based generative framework for acoustic metamaterial inverse design. At its core, MetaSeq introduces a language that represents each AMM as a structured sequence, rather than as a pixel grid or fixed template. This representation preserves precise geometry, explicitly encodes connectivity, and casts inverse design as a sequence-to-sequence task from target response to structure sequence. MetaSeq further constructs a balanced, high-fidelity dataset with efficient calibration and complexity-based sampling. To address the one-to-many nature of inverse design, MetaSeq combines supervised pretraining with reinforcement learning fine-tuning guided by a physics-based solver and validity checker. Extensive evaluations against COMSOL and five baselines show that MetaSeq reduces response error by 45% over the best baseline.
[AI-56] BSTabDiff: Block-Subunit Diffusion Priors for High-Dimensional Tabular Data Generation ICLR2026
链接: https://arxiv.org/abs/2606.09257
作者: Al Zadid Sultan Bin Habib,Md Younus Ahamed,Prashnna Gyawali,Gianfranco Doretto,Donald A. Adjeroh
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: Published as a paper at the 2nd DeLTa Workshop, ICLR 2026
Abstract:High-Dimensional Low-Sample Size (HDLSS) tabular domains (e.g., omics) are characterized by n \ll m , where n = number of samples, and m = number of features. Such domains often exhibit strong local correlation groups, sparse cross-group dependencies, heavy-tailed non-Gaussian marginals, heteroscedastic noise, and structured missingness, making direct density learning in \mathbbR^m ill-conditioned since n \ll m . We propose BSTabDiff, a block-subunit generative framework that partitions the m observed features into M latent blocks ( M \ll m ) and generates each block via a shared low-dimensional subunit variable, concentrating global dependence learning in the compact block-latent space \mathbbR^M while decoding to the full feature space with copula-driven dependence, flexible per-feature marginals, and explicit missingness mechanisms. BSTabDiff supports modern deep priors on block latents, including diffusion and normalizing flows, enabling stable synthesis and controllable benchmark generation in the HDLSS regime. Empirically, BSTabDiff produces more realistic and stable high-dimensional synthetic data when compared with unstructured tabular generators on HDLSS data.
[AI-57] Self-Paced Curriculum Reinforcement Learning for Autonomous Superbike Racing in Simulation ICRA2026
链接: https://arxiv.org/abs/2606.09236
作者: Luca Ghisi,Jacopo Essenziale,Carlo D’Eramo,Matteo Luperto
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Presented at the “1st Workshop on Generalization in Autonomous Driving: Paradigms, Practice, and Public Road Demonstrations” at ICRA 2026, Vienna. Oral+poster presentation
Abstract:Autonomous Racing has seen remarkable progress through deep Reinforcement Learning (RL), primarily for four-wheeled vehicles. However, motorbikes introduce substantially greater complexity due to the need to manage balance and lean angle, in addition to more reactive steering and throttle control, and a smaller weight. In this work, we present a framework for training an autonomous agent to race a superbike in VRider SBK, a physics-accurate Unity-based motorbike simulator. Our approach integrates Soft Actor-Critic (SAC) with Self-Paced curriculum Deep reinforcement Learning (SPDL), which dynamically generates progressively more challenging tasks based on the agent’s performance, without requiring manual curriculum design. The agent’s state space comprises proprioceptive features extended with lean-angle history, along with global track features via course points. The reward signal is shaped to encourage progress along the track while penalizing instability-inducing behaviors specific to two-wheeled dynamics. Preliminary experimental results demonstrate that SPDL outperforms SAC alone in training efficiency, lap time, and driving stability across multiple tracks and motorbike models, establishing a first baseline for RL-based autonomous motorbike racing.
[AI-58] End-to-End Training for Discrete Token LLM based TTS System
链接: https://arxiv.org/abs/2606.09234
作者: Changfeng Gao,Yong Ren,Jun Yuan,Ye Bai,Zhao You,ShiDong Shang
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent state-of-the-art (SOTA) text-to-speech (TTS) systems typically adopt a cascaded pipeline consisting of a speech tokenizer, an autoregressive large language model (LLM), and a diffusion based flow-matching (FM) model, with these components trained independently. In this paper, we propose a fully end-to-end (E2E) optimization framework that unifies the training of the speech tokenizer, LLM, FM model, and an additional reward model (RM). Specifically, we first jointly optimize the tokenizer using multi-task objectives derived from reconstruction for FM, next-token prediction for LLM, and multi recognition task for RM. This joint training encourages the discrete speech token space to capture acoustically and semantically salient information that is better tailored to TTS. We then further optimize the LLM using downstream reconstruction and recognition by FM and RM, which reduces inference-time mismatch and steers the LLM toward more preferred generations. Experimental results show that our E2E framework consistently outperforms cascaded baselines. On the Seed-TTS-Eval benchmark, our system achieves a word error rate (WER) of 0.78% and 1.56%, a new SOTA result with a 0.6B-parameter LLM and 0.5B-parameter FM model. These results validate that holistic E2E optimization is critical for improving discrete-token-based TTS systems with a much simpler training pipeline.
[AI-59] Resource-aware Computation-Communication Overlap for multi-GPU ML Workloads
链接: https://arxiv.org/abs/2606.09200
作者: Minyu Cui,Miquel Pericas
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注: To appear at the AI on HPC Workshop at ISC 2026, held in conjunction with ISC 2026
Abstract:The rapid growth of large-scale machine learning (ML) has made distributed training across multiple GPUs a fundamental component of modern ML systems. As model sizes and computational throughput continue to increase, communication overhead has become a dominant bottleneck in multi-GPU training, particularly when computation and communication are executed sequentially. This work explores concurrent execution of computation and collective communication using two portable runtime controls: shared-memory-driven occupancy shaping for computation kernels and elevated scheduling priority for communication kernels. Our approach regulates computation-kernel residency through per-block shared-memory allocation, leaving sufficient on-chip resources for communication kernels to make progress. In addition, assigning higher priority to communication streams ensures steady communication progress once resources become available. Experiments on NVIDIA A40, A100, H100, and AMD MI250X GPUs demonstrate that the proposed method enables effective computation-communication overlap and reduces total execution time by up to 25.5 percent, without modifying vendor libraries or kernel implementations.
[AI-60] MASS: Deep Research for Social Sciences with Memory-Augmented Social Simulation
链接: https://arxiv.org/abs/2606.09198
作者: Yongrui Liu,Deyi Xiong
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Deep Research agents powered by Large Language Models (LLMs) have exhibited extraordinary potential in automated paper writing tasks. However, existing systems rely heavily on literature retrieval and synthesis through internet and local knowledge bases, often resulting research in lacking insight and creativity in social science. To address this issue, we propose “Memory-Augmented Social Simulation (MASS)”, an innovative paradigm that leverages highly realistic and research-oriented social simulations to enhance the creativity and empirical founding of LLMs-generated research. Specifically, MASS integrates three core components: dynamic goal-path planning with multi-level social norm restraint to guide the simulation, a multi-disciplinary behavior dataset for agent memory cold-start, and a structured forgetting mechanism inspired by the Ebbinghaus curve. Together, these ensure simulation authenticity and provide a robust empirical foundation for generating innovative scholarly papers. Experimental results demonstrate the effectiveness of our method, showing a 6.81% improvement in generation overall quality over foundation LLMs and 17.19% gain in Insight over strong baselines.
[AI-61] Pretrained Frozen Still Leaking: Auditing Cross-Encoder Attribute Transfer in EEG Foundation Models
链接: https://arxiv.org/abs/2606.09189
作者: Jianwei Tai
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:EEG foundation-model releases are usually audited one endpoint at a time: raw-reconstruction, membership inference, identity linkage, or DP-SGD on the downstream head. We audit the same released embeddings under all four endpoints jointly, on BIOT, LaBraM, and EEGPT, and show that each single-endpoint audit clears releases that still leak spectral attributes. The decisive evidence is a cross-encoder transfer audit: a single ridge attribute decoder learned from one frozen encoder transfers, via a fitted linear bridge, to held-out-subject test splits of every other encoder, with subject-disjoint matched-control 95% CI lower bound at least 0.081 across all six BIOT/LaBraM/EEGPT directions. We prove a sufficient condition: two encoders sharing a nontrivial attribute-coordinate projector overlap beta admit a chained ridge bridge attacker with centered-gain lower bound sqrt(beta/(1+tau^2)) - eps_br - rho_0, and back-solve beta in [0.008, 0.198]. To turn the joint audit into a deployment-readable decision rule we introduce an audit-endpoint disagreement score (AEDS), prove sufficient conditions for its positivity, and bootstrap-calibrate it per cell; AEDS is positive in all eight matched-CI cells (BIOT/LaBraM/EEGPT on EEGMMI; LaBraM on Sleep-EDF, 54-channel LIMO, CHB-MIT pediatric scalp EEG) with p0.001, while a head-level Carlini LiRA membership audit reaches AUC only 0.50-0.70. Standard defenses fail under audit: a Wiener-style noise-aware adaptive attacker, the LiRA audit, and DP-SGD at every utility-preserving epsilon in 4,8 leave the attribute channel essentially unchanged. The contribution is an audit framework that turns scattered single-endpoint defenses into a joint release decision, supported by a cross-encoder bridge theorem and adaptive-attacker, LiRA, and DP-SGD baselines; the audit licenses release-blocking, not raw-waveform exfiltration or held-out-subject identity recovery.
[AI-62] CANS: Accelerating Multiuser Collaborative Edge Inference via Cooperative Autodidactic NeuroSurgeon
链接: https://arxiv.org/abs/2606.09175
作者: Zheshun Wu,Ziyang Zhang,Changyao Lin,Zenglin Xu,Jie Liu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: 24 pages, 14 figures, 5 tables, submitted for possible journal publication
Abstract:Recently, mobile edge computing (MEC)-enabled collaborative deep neural network (DNN) inference has emerged as a promising approach for delivering intelligent services to resource-constrained mobile devices. A representative scenario is multi-user collaborative edge inference, where distinct devices independently partition their DNN models and offload backend computation to a common edge server over wireless networks. However, determining the optimal DNN partition for each device is challenging due to unknown and time-varying system conditions, including fluctuating wireless links and diverse device capabilities. To address this problem, we propose Cooperative Autodidactic NeuroSurgeon (CANS), a collaborative edge inference framework that enables devices to adaptively learn optimal DNN partitions by sharing informative feedback during online inference. To handle the challenge of device heterogeneity and better leverage offline inference experience, we integrate a novel FedLinUCB-DW algorithm that groups devices of the same type and warm-starts online exploration using local offline early-exit inference experience. Furthermore, we provide theoretical guarantees for FedLinUCB-DW by deriving the regret upper bound. We also validate our method on both a simulated environment and a hardware prototype system. Empirical evaluations demonstrate that CANS achieves lower inference latency compared to state-of-the-art baselines. Especially, in prototype experiments on two edge devices, the proposed CANS reduced average inference latency by up to 50% compared to the non-cooperative baseline.
[AI-63] Reliable to Expressive: A Curriculum for Rubric-Following Safety Judges ICML2026
链接: https://arxiv.org/abs/2606.09165
作者: Yongtaek Lim,Hyeji Choi,Minwoo Kim
类目: Artificial Intelligence (cs.AI)
备注: Accepted to ICML 2026 Workshop on AIWILDS
Abstract:Safety judges are increasingly deployed to evaluate model outputs against evolving criteria, yet recent meta-evaluation work shows they remain brittle under prompt and rubric variation, with false negative-rate swings of up to 0.24 reported for stylistic perturbations alone. We argue that safety judgment is fundamentally a rubric-following problem: a robust judge must apply the given evaluation criteria consistently across rubric formulations rather than memorize one specific template. We propose a training strategy that combines (i) instance-conditioned dynamic rubrics generated from prompt-response-label triples to expose the judge to the variability of evaluation criteria, and (ii) a reliable-to-expressive curriculum that begins with clean fixed-rubric supervision and progressively introduces noisier dynamic-rubric data. We evaluate on a single human-labeled set under three contrasting rubric prompts (HarmBench-style, ShieldGemma-style, and a domain-specific rubric). Our 12B curriculum judge achieves 94.12-94.88% accuracy across the three rubrics with a cross-rubric range of only 0.76, outperforming general-purpose LLMs, dedicated safety classifiers, and reasoning-oriented judges up to 30B in both peak accuracy and stability. An ablation shows that naively mixing dynamic rubrics into SFT increases cross rubric variance (1.44 - 3.60); only the curriculum schedule recovers and improves on the fixed rubric baseline (variance 0.76).
[AI-64] Crop Recommendation and Agricultural Query Answering System Using Spatio-Temporal Graph Neural Networks and Hybrid Retrieval Augmentation
链接: https://arxiv.org/abs/2606.09160
作者: Prajwal Thapa,Yagya Raj Pandeya
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 11 pages, 8 figures
Abstract:This paper presents a unified system designed to support precision agriculture by integrating advanced weather prediction, crop recommendation, and a question-answering tool for farmers. We propose two deep learning models – a Transformer-based Graph Neural Network and a Spatio-Temporal Graph Convolutional Network (STGCN) – to forecast weather conditions for the next 30 days using data from 1,359 locations in Nepal. The STGCN outperforms the Transformer-based model in accuracy (MSE ~0.011 vs. 0.013), effectively modeling both spatial and temporal dependencies in climate data. These predictions are combined with static soil properties such as pH, moisture, and organic content to generate localized crop recommendations through a scoring algorithm that matches each crop’s optimal growing conditions. Additionally, we develop a Retrieval-Augmented Generation (RAG) chatbot that leverages domain-specific agricultural documents to answer farmers’ questions in natural language. The entire system is deployed via a mobile application, offering real-time suggestions and conversational support. User feedback confirms the system’s usability and relevance, especially in rural settings where personalized farming guidance is limited. Overall, our approach demonstrates how combining machine learning models with local agricultural data can empower farmers with actionable insights, promoting more informed decisions, better crop yields, and increased resilience to climate variability.
[AI-65] Steganography Without Modification: Hidden Communication via LLM Seeds
链接: https://arxiv.org/abs/2606.09135
作者: Felix Mächtle,Jonas Sander,Sebastian Berndt,Ben Weimar,Nils Loose,Thomas Eisenbarth
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: To appear in the Proceedings of the International Conference on Availability, Reliability and Security (ARES 2026)
Abstract:We demonstrate that widely deployed Large Language Model (LLM) inference stacks harbor a steganographic channel that requires no modification to model weights, sampling code, or output distributions. The channel exploits a structural property of deterministic decoding: pseudo-random number generators (PRNGs) used in inverse-transform sampling produce a seed-dependent sequence of token-level probability intervals that can be reconstructed from the generated text alone. A sender encodes a secret message in the PRNG seed before generation; a receiver reconstructs the intervals and recovers the seed, and thus the hidden payload, by exhaustive search over the seed space. We formalize two operational modes. In the known-prompt setting, sender and receiver share the prompt, enabling exact interval reconstruction and perfect seed recovery via forced alignment. In the unknown-prompt setting, only the generated text is available; approximate interval reconstruction combined with a maximum-hit-count scoring strategy still permits reliable recovery from sufficiently long outputs. Extensive experiments across six model families and five heterogeneous text domains show that, in the known-prompt setting, full 32-bit seed recovery from the complete 2^32 candidate space achieves up to 100% accuracy, depending on model and text domain, within 300 tokens and under 35 seconds on a single GPU. In the unknown-prompt setting, recovery reaches near-perfect accuracy at 600-800 tokens in about 12 seconds. We further analyze the influence of prompting strategies, tokenization ambiguities, and sampling hyperparameters on channel reliability. Moreover, we discuss several applications of our results: First, it allows for the steganographic transmission of 32 bits, but also shows that ignorance of the prompt is not a valid security assumption. Comments: To appear in the Proceedings of the International Conference on Availability, Reliability and Security (ARES 2026) Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2606.09135 [cs.CR] (or arXiv:2606.09135v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2606.09135 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-66] Vision Language Model Helps Private Information De-Identification in Vision Data
链接: https://arxiv.org/abs/2606.09132
作者: Tiejin Chen,Pingzhi Li,Kaixiong Zhou,Tianlong Chen,Hua Wei
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Visual Language Models (VLMs) have gained significant popularity due to their remarkable ability. While various methods exist to enhance privacy in text-based applications, privacy risks associated with visual inputs remain largely overlooked such as Protected Health Information (PHI) in medical images. To tackle this problem, two key tasks: accurately localizing sensitive text and processing it to ensure privacy protection should be performed. To address this issue, we introduce VisShield (Vision Privacy Shield), an end-to-end framework designed to enhance the privacy awareness of VLMs. Our framework consists of two key components: a specialized instruction-tuning dataset OPTIC (Optical Privacy Text Instruction Collection) and a tailored training methodology. The dataset provides diverse privacy-oriented prompts that guide VLMs to perform targeted Optical Character Recognition (OCR) for precise localization of sensitive text, while the training strategy ensures effective adaptation of VLMs to privacy-preserving tasks. Specifically, our approach ensures that VLMs recognize privacy-sensitive text and output precise bounding boxes for detected entities, allowing for effective masking of sensitive information. Extensive experiments demonstrate that our framework significantly outperforms existing approaches in handling private information, paving the way for privacy-preserving applications in vision-language models. Our dataset and code can be found here.
[AI-67] Unveiling Privacy Risks in Multi-modal Large Language Models : Task-specific Vulnerabilities and Mitigation Challenges
链接: https://arxiv.org/abs/2606.09125
作者: Tiejin Chen,Pingzhi Li,Kaixiong Zhou,Tianlong Chen,Hua Wei
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Privacy risks in text-only Large Language Models (LLMs) are well studied, particularly their tendency to memorize and leak sensitive information. However, Multi-modal Large Language Models (MLLMs), which process both text and images, introduce unique privacy challenges that remain underexplored. Compared to text-only models, MLLMs can extract and expose sensitive information embedded in images, posing new privacy risks. We reveal that some MLLMs are susceptible to privacy breaches, leaking sensitive data embedded in images or stored in memory. Specifically, in this paper, we (1) introduce MM-Privacy, a comprehensive dataset designed to assess privacy risks across various multi-modal tasks and scenarios, where we define Disclosure Risks and Retention Risks. (2) systematically evaluate different MLLMs using MM-Privacy and demonstrate how models leak sensitive data across various tasks, and (3) provide additional insights into the role of task inconsistency in privacy risks, emphasizing the urgent need for mitigation strategies. Our findings highlight privacy concerns in MLLMs, underscoring the necessity of safeguards to prevent data exposure. Our dataset and code can be found here.
[AI-68] A Regret Minimization Framework on Preference Learning in Large Language Models
链接: https://arxiv.org/abs/2606.09124
作者: Suhwan Kim,Taehyun Cho,Geon-Hyeong Kim,Yu Jin Kim,Youngsoo Jang,Moontae Lee,Jungwoo Lee
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Reinforcement learning with verifiable rewards (RLVR) has enabled progress on reasoning-intensive tasks by relying on task-specific verifiers that provide automated correctness signals. However, many realistic language tasks are difficult to equip with reliable verifiers, motivating a growing reliance on reinforcement learning from human feedback (RLHF). In this setting, we argue that a closer examination of how human feedback should be interpreted is essential. We introduce Regret-based Preference Optimization (\textbfRePO) , which reframes RLHF through \textitregret minimization rather than reward maximization. Human preferences are often shaped by \textitprospective anticipation of outcomes and \textitcounterfactual comparisons to alternative behaviors, rather than by immediate, outcome-independent utility. \textbfRePO captures this structure by modeling preferences as behavior-conditioned assessments of relative suboptimality. Experiments on mathematical reasoning benchmarks and human preference datasets demonstrate consistent performance gains, indicating that \textbfRePO is an effective and human-aligned approach for training large language models.
[AI-69] ComplexConstraints and Beyond: Expert Rubrics for RLVR ACL2026
链接: https://arxiv.org/abs/2606.09118
作者: Sushant Mehta,Liudas Panavas,Edwin Chen
类目: Artificial Intelligence (cs.AI)
备注: Accepted to the GEM workshop at ACL 2026: this https URL
Abstract:As LLM capabilities advance rapidly, the evaluation methods used to assess them increasingly lag behind. Traditional benchmarks relied on programmatic verification of narrow, surface-level constraints, but real-world instruction following and agentic tasks demand assessment of nuanced, context-dependent behaviors that resist simple scripted checks. We present a systematic analysis of expert-curated rubric-based evaluation as an alternative paradigm, drawing on empirical evidence from two domains: complex instruction following and enterprise agentic tasks. We first articulate five design principles for constructing high-quality rubrics, including Maximum Viable Atomicity, intent-aware criterion design, and iterative LLM-judge calibration. To validate these principles, we introduce ComplexConstraints, a new expert-curated instruction-following dataset in which each prompt is paired with 10-40 atomic rubric criteria. We demonstrate that these expert rubrics are not only better evaluation instruments but also highly effective training signals: training on approximately 1,000 ComplexConstraints examples yields +15.5% improvement for a 4B-parameter model and +12.2% for a 235B-parameter model on instruction following, while single-epoch RL training on a rubric-graded enterprise environment produces gains that transfer to out-of-distribution benchmarks the model was never trained on (+4.5% BFCL, +7.4% Tau2-Bench, +6.8% Tool-Decathlon). Our findings establish that expert-authored rubrics improve both the measurement and the development of frontier LLM capabilities, serving as effective evaluation and RL training signals.
[AI-70] Optimizing Energy-based Neural Network Training with Coherent Ising Machine
链接: https://arxiv.org/abs/2606.09117
作者: Chen-Rui Fan,Bo Lu,Zhi-Hong Zhang,Run-Qing Zhang,Jing-Wei Wen,Chuan Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:While Ising machines serve as advanced physical solvers for the Ising model,enabling applications in combinatorial optimization and neural network training,their scalability for large-scale neural networks remains constrained by hardware connectivity limitations and suboptimal training methodologies. In this work,we leverage a Coherent Ising Machine (CIM) to train an energy-based neural network using Equilibrium Propagation, achieving performance comparable to existing software-based implementations. We further enhance the algorithm by integrating the Adam optimizer to solve for the ground state of a Hopfield energy network, significantly improving convergence speed and solution accuracy. Additionally, we demonstrate the scalability of our approach across deeper network architectures and convolutional operations. Our results highlight the potential of CIM dynamics as a scalable platform for training complex neural networks, offering a pathway toward energy-efficient implementations via analog circuits, optoelectronics, or integrated photonics. This work establishes a novel physical framework for next-generation AI hardware development.
[AI-71] Hybridizing Equilibrium Propagation with Ising Machines for Efficient Energy-Based Learning
链接: https://arxiv.org/abs/2606.09112
作者: Chen-Rui Fan,Bo Lu,Xing-Yu Wu,Tie-Jun Wang,Chuan Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The rapid evolution of artificial intelligence has led to substantial advances in deep neural networks. Nonetheless, conventional GPU-based training remains highly energy-demanding, motivating the exploration of physical dynamics and compatible energy-based learning schemes, such as equilibrium propagation (EP). EP-based training, however, frequently suffers from convergence to local minima due to phase-space contraction. Here we introduce an Ising-dynamics-inspired equilibrium-propagation framework in which dissipative Hopfield relaxation is replaced by an extended phase-space dynamics with conjugate variables. The resulting training paradigm keeps the local two-phase learning rule of EP while changing the physical route by which neural states reach equilibrium. We show that this dynamics lowers effective energy barriers, accelerates convergence, improves noise robustness, and trains deep convolutional Hopfield networks on MNIST, FashionMNIST, and CIFAR-10 with performance comparable to backpropagation.
[AI-72] Graph2Idea:Retrieval-Augmented Scientific Idea Generation with Graph-Structured Contexts
链接: https://arxiv.org/abs/2606.09105
作者: Xu Li,Hanzhe Tu,Xun Han
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Generating novel, feasible, and high-quality research ideas is an important yet challenging task in scientific this http URL Large Language Model (LLM)-based methods often ground idea generation with retrieved literature, but the retrieved evidence is usually provided as flat text, such as titles, abstracts, or summaries. Such flat contexts may contain redundant or weakly relevant information, while making cross-paper relations among problems, methods, mechanisms, and findings difficult to identify and this http URL address this challenge, we propose Graph2Idea, a knowledge graph-guided framework for retrieval-augmented scientific idea generation.Graph2Idea first retrieves papers according to the input topic, transforms them into structured knowledge triples, and dynamically constructs a target-centered knowledge graph to make literature relations this http URL then extracts compact graph-derived contexts that retain target-relevant relational evidence while reducing noisy textual this http URL on these contexts, a two-stage generation process first identifies promising research directions and then guides the LLM to synthesize candidate ideas from graph-grounded this http URL on a scientific idea generation benchmark show that Graph2Idea outperforms representative baselines under the automatic evaluation this http URL with the strongest baseline scores, it improves Novelty from 0.45 to 0.52, Quality from 0.24 to 0.29, and Feasibility from 0.22 to this http URL results suggest that graph-structured evidence helps LLMs generate research ideas through more explicit, compact, and traceable recombination of prior scientific knowledge.
[AI-73] Addressing Market Regime Changes and Heavy-Tailed Returns in Portfolio Optimization via Bayesian VAR and Elliptical Black-Litterman KR
链接: https://arxiv.org/abs/2606.09104
作者: Daniil Mikriukov(1 and 2),Ruoyu Sun(2),Angelos Stefanidis(2),Jionglong Su(2),Zhengyong Jiang(2) ((1) University of Liverpool, (2) Xi’an Jiaotong-Liverpool University)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Portfolio Management (q-fin.PM)
备注: 9 pages, 3 figures, 4 tables. Extends our prior work [Mikriukov et al., ICIC 2025] on Black-Litterman under Elliptical Distributions (BLED). Manuscript under review
Abstract:Deep reinforcement learning (DRL) frameworks for portfolio optimization have shown promise for their ability to learn allocation rules dynamically from market data. However, these models fail to account for fat-tailed returns, which characterize actual market behavior with more frequent extreme events. Furthermore, historical data is treated homogeneously, without accounting for temporal importance, leading models to fail during regime changes. We propose a new BAVAR-BLED algorithm that combines methods derived from Bayesian-Averaging Vector Autoregressive (BAVAR) and the Black-Litterman model using Elliptical Distributions (BLED) within a TD3 architecture. BAVAR captures a set of vector autoregressive representations that consider multi-scale temporal features, enabling adaptive allocation decisions based on regime-aware estimates of return expectations and dispersion matrices. These estimates serve as prior inputs to BLED, a model that uses Student’s t-distributions, allowing for more realistic fat tail return estimates. The BAVAR-BLED algorithm uses transformer networks for view construction and CNNs for risk-aversion estimates, which modify dynamic allocation decisions based on market conditions. An evaluation of 29 Dow Jones Industrial Average constituents over a decade-long market period shows that BAVAR-BLED significantly outperforms state-of-the-art methods, achieving Sharpe and Sortino ratios of 1.72 and 2.70, respectively, and total returns of 57.26%.
[AI-74] Context Rot in AI-Assisted Software Development: Repurposing Documentation Consistency for AI Configuration Artifacts
链接: https://arxiv.org/abs/2606.09090
作者: Christoph Treude,Sebastian Baltes
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Developers increasingly provide AI coding assistants with persistent context through configuration files such as this http URL, this http URL, and .cursorrules. These files describe code elements, architecture, and development conventions, forming the context that guides AI tool behavior across sessions. As software evolves, this context can become stale, a phenomenon we call context rot. While AI configuration artifacts are new, the underlying consistency problem connects to decades of software documentation research. Researchers have built tools to check consistency between documentation and code, spanning README files, code comments, API documentation, architecture descriptions, and installation instructions. We argue that this existing toolbox is an immediate starting point for detecting context rot, and we present a research roadmap mapping documentation consistency approaches to corresponding problems in this new setting. As preliminary evidence, applying an existing README/wiki consistency checker to a statistically representative sample of 356 repositories identifies stale code element references in 23.0% of repositories, showing that traditional documentation consistency tools can already surface context rot.
[AI-75] DynaOD: Dynamic Origin-Destination Flow Generation with Discrete-to-Continuous Temporal Semantic Modeling IJCAI2026
链接: https://arxiv.org/abs/2606.09086
作者: Jie Zhao,Xianqi Dai,Jie Feng,Huandong Wang,Yong Li
类目: Artificial Intelligence (cs.AI)
备注: Accepted by IJCAI2026
Abstract:Dynamic origin-destination (OD) flow generation seeks to synthesize realistic mobility dynamics from temporal context alone, without relying on historical OD observations. A key challenge is to translate semantic temporal signals into temporally coherent OD patterns while preserving the inherent spatial heterogeneity of urban regions. We propose DynaOD, a semantic-driven framework that models temporal dynamics through two complementary perspectives: discrete directional trends that characterize qualitative shifts in urban activity patterns, and continuous temporal evolution that captures how such shifts unfold over time. By jointly encoding these temporal semantics, the framework constructs time-varying region representations that condition pretrained static OD generators in a lightweight and plug-and-play fashion. This modular design further supports scalable deployment and cross-city transferability. Extensive experiments on large-scale real-world datasets show that our method consistently outperforms representative baselines in both predictive accuracy and distributional fidelity. Code is publicly available at this https URL.
[AI-76] Context-Fractured Decomposition Attacks on Tool-Using LLM Agents : Exploiting Artifact Provenance Gaps
链接: https://arxiv.org/abs/2606.09084
作者: Xiaofeng Lin,Yukai Yang,Daniel Guo,Sahil Arun Nale,Charles Fleming,Guang Cheng
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Tool-using LLM agents interact with the world through actions that persist state in artifacts (e.g., workspace files or logs). Consequently, jailbreak defenses must reason about cross-step composition rather than isolated text. Yet most existing attacks and defenses, including ``multi-turn’’ jailbreaks such as Crescendo and Tree of Attacks,still assume a single contiguous conversation visible to the defender. This assumption breaks down in real agent pipelines, where enforcement is fragmented across tools, modules, and time, and where artifact provenance is often not tracked. We operationalize a deployment failure mode for tool-using LLM agents, the \emphprovenance gap, and study reproducible triggers for it: \emphContext-Fractured Decomposition (CFD), a family of cross-context multi-step jailbreaks that preserve benign-looking intermediate artifacts from an early interaction and elicit harmful behavior much later, potentially in a different agent instance or workflow stage, via individually innocuous tool actions whose risk emerges only under delayed artifact-mediated composition. We instrument the failure mode with trace-level diagnostics and outline a verifiable mitigation direction (provenance lineage tagging). Across agent-system jailbreak benchmarks, CFD improves success rates by up to 28.3 percentage points over state-of-the-art baselines, even against strong single-turn judges. Disclaimer: This paper contains examples of harmful or offensive language.
[AI-77] FlashMemory-DeepSeek -V4: Lightning Index Ultra-Long Context via Lookahead Sparse Attention
链接: https://arxiv.org/abs/2606.09079
作者: Yan Wang,Qifan Zhang,Jiachen Yu,Tian Liang,Dongyang Ma,Xiang Hu,Zibo Lin,Chunyang Li,Zhichao Wang,Jia Li,Yujiu Yang,Haitao Mi,Dong Yu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Technical report. 11 pages. Code and model available at this https URL and this https URL
Abstract:Conventional LLMs keep the full KV cache loaded during decoding, causing a severe GPU memory bottleneck for ultra-long context serving. In this report, we propose Lookahead Sparse Attention (LSA), a novel inference paradigm powered by a Neural Memory Indexer built upon the DeepSeek-V4 architecture. Rather than passively attending to all historical tokens, LSA proactively predicts future context demands and preserves only the query-critical KV chunks in the GPU memory. Crucially, we instantiate this architecture via a backbone-free decoupled training strategy. By formulating the indexer as a standard dual-encoder architecture, we train it independently using standard retrieval training frameworks without ever loading the massive backbone model into GPU memory. We demonstrate that this “less is more” paradigm significantly maximizes serving efficiency while acting as an effective attention denoiser in tasks that rely on long-term global memory. Across primary long-context evaluation suites (e.g., LongBench-v2, LongMemEval, and RULER), FM-DS-V4 compresses the average physical KV cache footprint down to merely 13.5% of the full-context baseline, while consistently preserving or slightly elevating downstream accuracy (+0.6% absolute margin on average). Crucially, at extreme 500K scales, FlashMemory suppresses the physical KV cache overhead by over 90% without destabilizing the backbone’s core reasoning capacities. Comments: Technical report. 11 pages. Code and model available at this https URL and this https URL Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2606.09079 [cs.LG] (or arXiv:2606.09079v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2606.09079 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-78] REFLECT: Intervention-Supported Error Attribution for Silent Failures in LLM Agent Traces
链接: https://arxiv.org/abs/2606.09071
作者: Xiaofeng Lin,Yingxu Wang,Tung Sum Thomas Kwok,Daniel Guo,Sahil Arun Nale,Charles Fleming,Guang Cheng
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM) agents now solve complex tasks through long plan-and-execution traces, yet the ability to locate errors in a completed traces still lags far behind, especially in the \emphsilent failure regime. Existing approaches predict suspect steps via classifiers or LLM judges, or recover correct answers via retry, but none feed the intervention outcome back to \emphrefine the attribution itself. We propose \methodname, a method that closes this gap by diagnosing a candidate error step, testing it through controlled replay with a diagnosis-specific patch, and using the verified outcome flip as contrastive evidence to refine the final attribution. Across four localization benchmarks spanning multi-hop reasoning across domains, \methodname achieves the highest localization accuracy among same-auditor methods across all four benchmarks, with the largest gains on structured tool-use traces, while providing actionable localization even when ground-truth answers are unavailable.
[AI-79] OnlyDense: Reduced-Order Modeling for Lagrangian simulation
链接: https://arxiv.org/abs/2606.09065
作者: Tu Do,Shannon Ryan,Santu Rana
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:In science and engineering, Lagrangian simulation methods such as Smooth Particle Hydrodynamics (SPH) or Material Point Method (MPM) are often employed to study the behavior of dynamic systems. However, these methods can be prohibitively computationally expensive, particularly when simulating multi-scale spatial or temporal phenomena, e.g., void growth and coalescence within macro-scale geometries, structural failure of spacecraft components resulting from hypervelocity impact of space debris particles, etc. In contrast to graph-based methods, where the state of the system is understood as a discrete set of particles, we propose a learning framework for scalable representation and dynamics modeling of massive particle systems by treating the system state as a function and its evolution as a trajectory in Hilbert space. Rather than representing the state as a discrete set of particles or embedding it in a nonlinear latent manifold, we approximate the state space with a linear subspace spanned by learned neural basis functions. This parameterization enables direct projection to obtain latent coefficients and explicit access to the basis functions, avoiding optimization over a nonlinear latent space. The resulting representation admits a natural interpretation: latent variables correspond to coefficients in Hilbert space, and basis functions correspond to spatial modes, analogous to Proper Orthogonal Decomposition. The framework thus unifies classical projection-based reduced-order modeling with modern deep learning, while remaining invariant to the number of discretization points. Experiments on large-scale SPH simulations with over one million particles, including dynamic events with extreme deformation and fragmentation, demonstrate that the proposed method accurately reconstructs and predicts dynamics, achieving an R ^2 score above 0.99 with as few as 32 basis functions.
[AI-80] Agent Economics: An Entropy-Controlled Pluralistic Alignment Framework for Preventing Artificial Hivemind in Autonomous Agents
链接: https://arxiv.org/abs/2606.09039
作者: Cheonsu Jeong
类目: Artificial Intelligence (cs.AI)
备注: 15 pages, 2 figures, 1 table
Abstract:This study proposes the Behavioral Protocol Framework (BPF), an entropy-controlled pluralistic alignment framework designed to address two critical challenges in autonomous agent economies: the hivemind effect arising from excessive strategic convergence among agents and the lack of transparency in autonomous decision-making processes. The proposed BPF consists of three core modules: Mentalizing-based Social Intelligence (MbSI) grounded in Theory of Mind (ToM), Pluralistic Alignment (PA), and a Verifiable Execution Kernel (VEK). These modules are organically integrated within a closed-loop architecture that governs the entire lifecycle of agent behavior, from decision-making and execution to verification and feedback. To evaluate the proposed framework, a simulation environment implemented in Python and a Streamlit-based user interface will be developed. Through empirical experimentation, the study aims to examine whether the entropy-control mechanism of the PA module can effectively preserve strategic diversity among agents and mitigate collective convergence, while the VEK module provides a comprehensive and transparent audit trail of the decision-making process. The anticipated results are expected to demonstrate that the proposed framework can simultaneously enhance the stability, efficiency, and trustworthiness of autonomous agent economies. Consequently, this research offers a practical approach for developing robust, transparent, and accountable agent-native economic systems.
[AI-81] Personalization Meets Safety:MechanismsRisksand Mitigations in Personalized LLM s
链接: https://arxiv.org/abs/2606.09038
作者: Yanyan Luo,Xue Han,Ruiqiao Bai,Xin Huang,Yitong Wang,Qian Hu,Qing Wang,Chunxu Zhao,Jie Liu,Cong Geng,Lehao Xing,Pengwei Hu,Junlan Feng
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models (LLMs) have enabled increasingly personalized interactions by adapting to users’ preferences, contexts, and long-term histories. However, the mechanisms that enable personalization also expand the safety landscape in ways not systematically addressed by existing literature. Existing reviews typically focus either on personalization or safety, leaving their intersection largely unexplored. We present the first comprehensive, safety-aware review of personalized LLMs. We organize personalization along three dimensions-user representation, personalization paradigm, and evaluation-and introduce a unified taxonomy of safety risks. At the representation level, we analyze risks arising from diverse user representations. Across mainstream personalization paradigms, we delineate vulnerabilities inherent to prompting, retrieval augmentation, parameter fine-tuning, reinforcement learning, Mixture-of-Experts (MoE), pruning, agent frameworks, and multimodal personalization, and synthesize mitigation strategies across the model lifecycle. Beyond these fine-grained risks, we characterize paradigm-agnostic safety risks arising from personalized adaptation. We further summarize personalized datasets and evaluation methodologies. Through a case study of OpenClaw, we analyze deployment trends in personalized agent ecosystems. Our analysis reveals three structural inadequacies in existing research: safety is evaluated as user-invariant rather than relational, personalization techniques are analyzed in isolation rather than in composition, and evaluation frameworks cannot capture emergent long-term risks. By jointly examining personalized representations, personalization paradigms, safety risks, defenses, and evaluation methods, we provide a unified framework for developing safe personalized LLMs and highlight key directions for future research.
[AI-82] LDR: Compressing Audio Tokens for Efficient Autoregressive Text-to-Speech
链接: https://arxiv.org/abs/2606.09019
作者: Yejin Lee,Junwon Moon,Hyoeun Kim,Hyunjin Choi,Heeseung Kim,Kyuhong Shim
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注:
Abstract:Codec-based autoregressive (AR) speech language models have achieved strong text-to-speech (TTS) quality by modeling speech as sequences of discrete audio tokens with large pretrained backbones. However, this token-level formulation creates a structural efficiency bottleneck: speech-token sequences are much longer than text sequences, requiring the AR backbone to perform causal computation at every token position and maintain a KV cache that grows with the sequence length. We introduce TLDR, a patch-based autoregressive framework that accelerates codec-based AR-TTS by shifting the causal modeling from token-level speech sequences to patch-level sequences. TLDR groups consecutive codec tokens into compact latent patches using a lightweight compressor, models the resulting shorter patch sequence with a frozen pretrained AR-TTS backbone adapted by LoRA, and reconstructs fine-grained speech tokens within each patch using a speaker-conditioned extractor. With a patch size of 4, TLDR achieves a 1.8x inference speedup over the baseline AR-TTS model and reduces global KV-cache memory by up to 75%. Experimental results indicate that patch-level global causal modeling can be a practical way to reduce the inference cost of pretrained codec-based AR-TTS systems without replacing the existing modules.
[AI-83] Understanding Quantization-Aware Training: Gradients at Quantized Weights Bias to the Low-Loss Basin
链接: https://arxiv.org/abs/2606.09012
作者: Hanyang Li,Jianhao Ma,Ying Cui
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Optimization and Control (math.OC); Machine Learning (stat.ML)
备注: 31 pages, 10 figures
Abstract:Post-training quantization (PTQ) converts a trained full-precision model into low-bit weights without task-level retraining, while quantization-aware training (QAT) incorporates quantization into the training loop. Although PTQ is efficient and often accurate at moderate bitwidths, it can fail sharply at aggressive bitwidths; QAT is more expensive but can often recover the lost accuracy. We propose a unified geometric framework that explains both PTQ failure and QAT recovery. We model full-precision training as following a low-loss \emphriver inside a wider \emphvalley: a normal neighborhood of the river forms a nearly flat \emphbasin, while leaving this basin incurs a sharp loss increase. When the quantization grid is comparable to the basin width, local PTQ objectives, including rounding and Hessian-based second-order reconstruction, can select a high-loss deployed quantized point outside the basin even when nearby low-loss quantized points exist. In this regime, straight-through-estimator-based QAT has a useful bias: it evaluates gradients at the deployed quantized weights while updating latent full-precision weights, causing the gradient to sense the valley wall and acquire an inward component that steers subsequent quantized iterates back into the basin. We formalize this mechanism through a local landscape model, construct a geometric PTQ failure mode, and prove finite-time QAT recovery under local quantizer-compatibility assumptions. Experiments across vision and language models under multiple neural-network quantization schemes corroborate the predicted basin-crossing failure of PTQ and the corresponding recovery mechanism of QAT.
[AI-84] Sustainability and Artificial Intelligence: Necessary Challenging and Promising Intersections
链接: https://arxiv.org/abs/2606.09006
作者: Han-Teng Liao,Zijia Wang
类目: ocial and Information Networks (cs.SI); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Emerging Technologies (cs.ET)
备注: This is an author preprint version. For the final authenticated version of record, please use the official publication via the IEEE Xplore database. DOI: https://doi.org/10.1109/MSIEID52046.2020.00076
Abstract:Both digital economy and digital technology researchers increasingly recognize the need to better address the role that artificial intelligence (AI) plays in shaping the evolution of the environmental, social and governance aspects of development. It appears that sustainability and AI research converge on the features of wicked problems that are complex, interconnected and dynamic. Building off such convergence, this article aims to map out the necessary, challenging, and promising intersections by providing an overview of the state of art research. Based on 541 bibliographic data collected from the Web of Science (WoS) database, the findings reveal the increasingly central body of work on green and sustainable science and technology in bridging various disciplines, main journals and key topics and concepts. The findings reveal how such interactions can be necessary, challenging, and promising. The article concludes with few general arguments regarding how to diversify and expand the community of practice regarding AI for sustainable development, especially in the areas of expected AI application areas and institutions.
[AI-85] LATTEArena: An Evaluation Framework for LLM -powered Tabular Feature Engineering (Extended Version)
链接: https://arxiv.org/abs/2606.09004
作者: Ankai Hao,Ke Chen,Huan Li,Lidan Shou
类目: Artificial Intelligence (cs.AI)
备注: 30 pages, 9 figures
Abstract:Feature engineering remains essential for tabular data analysis, and Large Language Models (LLMs) have emerged as a promising paradigm for automating this process, giving rise to LLM-powered AuTomated Tabular feature Engineering (LATTE). However, the absence of standardized platforms prevents fair, cost-aware comparisons. Furthermore, complex methodological designs obscure the specific contributions of individual components; for example, although LFG integrates Tree-of-Thought, few-shot demonstrations, Monte Carlo Tree Search, and natural language generation, the isolated impact of each technique’s competitive edge remains unquantified. To address these challenges, we introduce LATTEArena, the first competitive evaluation framework featuring: (1) a six-dimensional taxonomy decomposing 15 representative methods into reusable components; (2) a standardized modular arena for controlled comparison; (3) multi-dimensional assessments covering performance, cost, and robustness; and (4) component-level ablation quantifying each technique’s competitive edge. Through extensive evaluations, we reveal 16 key findings, including: (1) Tree-of-Thought with Monte Carlo Tree Search achieves optimal cost-effectiveness; (2) RPN and Code output formats dominate classification and regression tasks, respectively. We publicly release the modular framework and over 4000 execution logs, enabling researchers to seamlessly pit new techniques against existing ones and advance LATTE.
[AI-86] he Token Not Taken: Sampling State and the Variability of AI Agent Outputs
链接: https://arxiv.org/abs/2606.08998
作者: Muhammad Zia Hydari,Raja Iqbal
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); General Economics (econ.GN)
备注:
Abstract:Agentic AI systems can behave differently across runs: the same request may produce a different plan, a different tool call, a different code edit, or a different final answer. Such variability arises from several layers that are often conflated. A foundation model is a large pretrained model, usually adaptable to many downstream tasks, that maps an input context to predictions over outputs. In many current agents, that model is embedded in an orchestration loop that plans, calls tools, observes results, and updates state. One explicit intrinsic source of variability in such systems is token generation: the model computes scores over possible next tokens, the scores are converted into probabilities, and a decoder may sample tokens using a pseudo-random number generator. A small sampled token difference can then propagate upward into a different tool call, code path, search query, or agent state. Other sources of variability are extrinsic to token sampling, including changing environments, live data, serving infrastructure, batch effects, and numerical details. By separating these layers, the manuscript clarifies what it means to call agentic AI systems stochastic, when such variability can be reproduced under matched conditions, and why deterministic execution need not imply identical behavior in deployed settings.
[AI-87] Baichuan-M4: A Clinical-Grade Medical Agent System for Continuous Care
链接: https://arxiv.org/abs/2606.08982
作者: Aiyuan Yang,Chengfeng Dou,Da Pan,Dian Wang,Fan Yang,Fei Deng,Fei Li,Guangwei Ai,Hui Liu,Hongda Zhang,Jinyang Tai,Kai Lu,Lijun Liu,Linwei Chen,Linyu Li,Meiqing Guo,Peidong Guo,Qiang Ju,Rihui Xin,Shuai Wang,XinKai Ma,Xudong Chen,Yichuan Mo,Canbin Piao,Leyi Pan,Yihe Luo,Zian Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Baichuan-M4 is Baichuan Intelligence’s clinical-grade medical large model, designed for \emphcontinuous care rather than single-turn medical question answering. It is built as a coordinated medical agent system around three pillars: \textbfBaichuan-Harness, a unified runtime that keeps reinforcement-learning training and real-world deployment consistent while enforcing action constraints, tool use, long-term patient memory, and multi-agent coordination; a \textbfcore reasoning model trained with a continuous-care reinforcement-learning framework that integrates span-level reward modeling (SPAR++), reasoning-path compression, curriculum learning, and stabilized policy optimization; and a \textbfclinical tool layer for patient-memory management, authoritative evidence-based retrieval, and multimodal medical perception across documents, X-rays, and dermatology. On a cross-dimensional medical evaluation suite, Baichuan-M4 attains leading results in static medical knowledge and safety, dynamic OSCE-style consultation, long-context clinical memory, evidence-based retrieval, medical document OCR, and multimodal image understanding, while lowering the hallucination rate to 3.3%.
[AI-88] RTL-BenchLS: A Large-Scale Benchmark for RTL Reasoning and Generation with Large Language Models
链接: https://arxiv.org/abs/2606.08976
作者: Jing Wang,Shang Liu,Wenji Fang,Yuchao Wu,Yugao Zhu,Zhiyao Xie
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:LLM-based RTL generation and reasoning is a promising direction for hardware design automation. High-quality benchmarks are critical infrastructure for tracking progress in this direction. However, existing RTL benchmarks face inherent limitations in both scale and task scope. The designs they cover are typically small and simple, and the tasks focus almost entirely on specification-to-RTL generation. Frontier models’ performance already saturates on the existing benchmarks. Scaling these benchmarks up is fundamentally difficult because aligned labels are required for benchmarking, such as specifications and testbenches. Such aligned high-quality data are rarely available for real-world designs. We introduce RTL-BenchLS, a large-scale benchmark addressing both limitations above. It contains over 10,000 formally verified Verilog designs, covering substantially larger and more complex designs than existing benchmarks. Beyond specification-to-RTL generation, we propose three novel tasks that jointly evaluate reasoning and generation: round-trip reasoning, masked-content reasoning, and repository-issue reasoning. The first two are self-supervised, which directly resolves the scaling bottleneck. All tasks are verified through formal equivalence checking without any manual testbenches. We evaluate eight LLMs on RTL-BenchLS. Even the best model reaches only 23% on natural-language round-trip reasoning, 28% on masked-content reasoning, and 12% on repository-issue fixing. RTL-BenchLS is substantially more challenging than existing benchmarks. It leaves ample room for future improvement and offers guidance for developing LLM-based methods for hardware design.
[AI-89] Diverse Thinking Schemata Elicit Better Reasoning in Large Language Models
链接: https://arxiv.org/abs/2606.08974
作者: Xinyue Liang,Yizhe Yang,Yu Bai,Bin Xu,Jiawei Li,Yang Gao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large reasoning models (LRMs) have attracted increasing attention for their ability to solve complex mathematical problems by generating extended reasoning chains. In this work, we focus on two critical yet underexplored aspects of the reasoning process: reasoning transitions capturing the distinct transitions between reasoning steps and answer candidates reflecting the variety of solution paths produced by the model. We collectively define these two aspects as thinking schemata. We observe a correlation between the diversity of thinking schemata and model performance, which motivates us to enhance diversity as a means to further improve reasoning potential. To this end, we propose Diverse Schemata Policy Optimization (DiScO), a framework that first endows the model with schemata awareness, then encourages diversity through reinforcement learning, and further promotes diverse reasoning at inference time. Experiments on multiple mathematical reasoning benchmarks demonstrate that DiScO consistently outperforms standard group relative policy optimization. Beyond accuracy, human-annotated analyses show that DiScO substantially improves the model’s ability to recover from erroneous initial attempts. Overall, our work suggests the important role that diversity of the thinking schemata plays and points to scaling along the diversity dimension as a promising research direction.
[AI-90] An Effective Router for Vision-Language Model Selection
链接: https://arxiv.org/abs/2606.08970
作者: Can Wang,Shengwei Wang,Bolin Zhang,Zhiying Tu,Dianhui Chu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-language models (VLMs) with varying performance and resource requirements are widely deployed, making it difficult for users to select the most appropriate one among numerous VLM candidates. Existing work reveals the performance paradox phenomenon in language models and focuses on routing methods to solve it. However, developing a router for VLM selection is still a critical yet challenging problem, which primarily faces: 1) lack of specialized data, 2) ineffective feature representation, and 3) rigid model space and costly adaptation. In this paper, we construct a multimodal dataset for VLM selection, containing the outputs of seven mainstream VLMs on 32,626 unique image-text queries. We then propose ARMS, a router for VLM selection. ARMS enhances input signals with VLM profiles, employs a simple but effective architecture to improve representations of queries and VLM capabilities. To improve ARMS’ adaptation to new VLMs, we propose two extension training strategies: incremental training and independent training. Experimental results on both in-distribution and out-of-distribution test sets demonstrate the effectiveness of ARMS. In particular, using our training strategy, ARMs (only 800M in size) can adapt to a broader VLM space and defeat commercial models like GPT-4o that are hundreds of times larger in scale. Our code, models, and datasets are available in the anonymous repository.
[AI-91] AlloSpatial: Agent ic Harness Framework for Spatial Reasoning in Foundation Models
链接: https://arxiv.org/abs/2606.08952
作者: Shouwei Ruan,Bin Wang,Zhenyu Wu,Qihui Zhu,Yuxiang Zhang,Jingzhi Li,Yubin Wang,Xingxing Wei
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal Foundation Models (MFMs) have made substantial progress, yet remain fragile in spatial reasoning over the physical world. A key bottleneck lies in their inability to transform local egocentric observations into a global allocentric spatial representation. To address this, we propose AlloSpatial, an agentic framework for allocentric spatial cognition in foundation models. AlloSpatial introduces World2Mind, a plug-and-play cognitive mapping sandbox that converts egocentric observations into structured allocentric priors, including Allocentric-Spatial Trees and route maps that support querying object topology, geometric relations, passability, and trajectories. To utilize these priors reliably under noisy reconstruction and ambiguous visual evidence, AlloSpatial introduces a Spatial Reasoning Harness for tool-use judgment, modality-decoupled cue collection, and geometry-semantic arbitration. We further internalize this process in Qwen3-VL through cold-start reinforcement learning with a harness-gated trajectory-level reward. Experiments on VSI-Bench and MindCube show that AlloSpatial improves proprietary models by 5%-18% in a training-free setting, while ASTs alone support strong spatial reasoning even when visual inputs are removed. The trained AlloSpatial agents further outperform larger general-purpose models and competitive spatial baselines, suggesting that structured allocentric representations, active tool use, and verifiable reasoning offer a promising route toward spatially capable foundation models.
[AI-92] PAI: Preserving Amplitude Information in Representation-Based Time-Series Anomaly Detection
链接: https://arxiv.org/abs/2606.08935
作者: Kang Zhang,Wei Jian Lau,Shoushou Ren,Dong Lin,Joon Son Chung,Chuanhao Sun
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 15 pages
Abstract:Representation-based time-series anomaly detection algorithms significantly outperform other methods on diverse anomaly detection tasks. However, we notice that they suffer from a major limitation in our evaluation - their learned embeddings are often amplitude-agnostic. Losing amplitude information can degrade performance on amplitude related anomalies, and this failure is prevalent across all existing representation-based methods. To address aforementioned issues, we propose a new anomaly scoring scheme named PAI. PAI consists of two complementary modules, a diagnostic module and a final score augmentation function. The diagnostic module compares cosine and Euclidean scoring on the same representation bank to test whether amplitude information is already captured in the learned representation. Then in final score augmentation function, PAI computes a point-wise median and MAD deviation score and a local mean-shift score-which are fused with the representation score to produce the final anomaly score. On the TSB-AD-U-Eva and TAB UV datasets, PAI improves all four evaluated representation-based methods across every reported metric, achieving average VUS-PR gains of 98.4% and 36.8%, respectively. Among all evaluated combinations, PaAno + PAI achieves the best performance, outperforming the state-of-the-art method by 15%. Further evaluation on bootstrap confidence intervals, anomaly-type breakdowns, and a TS2Vec input-normalization ablation further support the proposed scheme. These results suggest that explicitly retaining amplitude information is important for representation-based time-series anomaly detection, which has been underemphasized in existing scoring schemes. Code is available at: this https URL
[AI-93] Oversight Has a Capacity: Calibrating Agent Guards to a Subjective Fatiguing Human
链接: https://arxiv.org/abs/2606.08919
作者: Emre Turan
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
备注: 12 pages, 4 figures. Code and interactive demo: this https URL
Abstract:As LLM agents begin to take real, irreversible actions (shell commands, file edits, deploys), the standard safety pattern is a human-in-the-loop approval gate: risky actions pause and wait for a person. We argue the gate is the easy part; the hard part is the judgment - which actions to stop - which the field evaluates against two false assumptions: that there is a ground-truth notion of “risky,” and that the human reviewer is a perfect, infinitely-available oracle. On a hand-labeled set of 125 adversarially-weighted agent actions we show that (i) reviewers only moderately agree on what is risky (Fleiss’ kappa = 0.52), so there is no single correct label; (ii) framing the guard as selective classification under asymmetric cost makes its operating limits measurable, and on hard inputs the guard cannot safely auto-decide; and (iii) when the reviewer is modeled as endogenous (fatiguing as escalation load grows), realized safety becomes an inverted-U in the escalation rate: more human oversight can make a system less safe, and the safety-optimal guard escalates below full escalation - a setting a load-aware policy also uses to resist a flooding attack that slips a malicious action past a fatigued reviewer. Agent oversight, framed this way, is not only a classification problem but a resource-allocation one: human attention is finite, and the guard’s escalation policy spends it. We claim none of these mechanisms as novel - fatigue-aware learning-to-defer (FALCON), cost-sensitive deferral under workload constraints (DeCCaF), trajectory-level guarding, and reviewer-fatigue/flooding attacks are all prior art we cite. Our contribution is an open-source agent-oversight system that operationalizes and measures them in the LLM-agent action-gating setting, turning “is my guard good?” from a guess into a curve. The inverted-U and the flooding attack are modeling results that motivate a human study.
[AI-94] Order Matters: Unveiling the Hidden Impact of Macro Placement Sequences via Proxy-Guided LLM Evolution ICML2026
链接: https://arxiv.org/abs/2606.08904
作者: Shibing Mo,Jing Liu,Jianchu Xu,Ruilin Wu
类目: Artificial Intelligence (cs.AI)
备注: ICML2026
Abstract:Macro placement is a fundamental step in modern chip physical design, playing a crucial role in determining the solution quality of high-dimensional combinatorial optimization problems. Despite recent advancements in machine learning for spatial coordinate determination, the temporal dimension of placement sequencing remains largely governed by static heuristics. In this work, we demonstrate that the placement sequence is not merely a preprocessing step but a decisive factor in optimization, where suboptimal early decisions trigger irreversible domino effects that constrain the solution space. To harness this unexplored dimension, we propose \textbfOrderPlace, a proxy-guided LLM evolution framework for automatically discovering macro placement order strategies. Instead of relying on manually crafted heuristics such as area- or connectivity-based ordering, OrderPlace explores a broader space of code-level policies, ranging from static scoring metrics to dynamic physics-inspired mechanisms. To mitigate the prohibitive cost of evaluating sequences, we introduce a lightweight proxy evaluation mechanism that efficiently filters candidates using a deterministic greedy probe. Experimental results on the standard ISPD 2005 benchmarks demonstrate that OrderPlace discovers novel ordering strategies. Compared with WireMask-EA and the state-of-the-art method EGPlace, OrderPlace reduces wirelength by 34.04% and 14.08%, respectively.
[AI-95] FAME: Forecastability-Aware Mixture of Experts for Heterogeneous Time Series Forecasting
链接: https://arxiv.org/abs/2606.08896
作者: Qianyang Li,Xingjun Zhang,Shaoxun Wang,Tao Peng,Jia Wei
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large-scale retail and industrial forecasting systems contain many heterogeneous time series whose lifecycle, sparsity, volatility, seasonality, spectral patterns, and contextual sensitivity differ substantially. A single forecasting model rarely performs well across all regimes, while dense ensembles increase inference cost and provide limited insight into expert suitability. This paper studies forecastability-aware expert routing: learning how data characteristics determine the suitability of forecasting experts. We propose \method, a sparse mixture-of-experts framework that represents each series with a multidimensional forecastability fingerprint, mines expert-suitability targets from validation performance, and trains a cost-aware sparse router to activate a small budgeted set of experts for each series. Using a production-scale vending-machine sales dataset from Shandong New Beiyang (SNBC), where the forecasting component has been integrated into the replenishment-planning pipeline, together with public retail benchmarks, we show that expert suitability varies systematically across data regimes. On the industrial dataset with 5,000+ machines and 60M+ transactions, \method Top-2 reduces MSE by 12.4% over the strongest single expert, LightGBM, while executing 1.92 experts per series on average. The deployed component produces demand forecasts, while inventory-oriented gains are estimated by an offline replay simulator under a fixed replenishment policy rather than by online intervention. The framework turns heterogeneous sales forecasting from heuristic model selection into data mining of forecastability patterns and expert specialization. Code is available at this https URL
[AI-96] Cheap Reward Hacking Detection
链接: https://arxiv.org/abs/2606.08893
作者: Iván Belenky,Joaquín Itria,Steven Johns
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 20 pages, 6 figures, 12 tables
Abstract:A small transformer encoder is trained to map Terminal-Wrench trajectories onto a unit sphere where embedding distance approximates the L_1 distance between reward and metadata signals. A linear probe on top of that embedding detects reward hacking on the cleaned test split with AUC 0.9467 and TPR@5%FPR 0.8296 , matching the TW sanitized LLM-as-judge AUC ( 0.9510 on the cleaned split) and exceeding its TPR@5%FPR ( 0.7130 vs 0.8296 ) on the same information condition, at roughly four orders of magnitude lower per-trajectory cost. The encoder is not a pure behavior reader: stripping natural-language reasoning from its input at probe time drops AUC to 0.6213 .
[AI-97] Benchmarking Vision-Language-Action Models on SO-101: Failure and Recovery Analysis
链接: https://arxiv.org/abs/2606.08881
作者: Yi Yu,Xinchuan Qiu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 13 pages, 9 figures,
Abstract:Vision-Language-Action (VLA) models have demonstrated strong generalization in robotic manipulation, yet existing evaluations are primarily conducted in simulation or on expensive robotic platforms, leaving their robustness on affordable real-world robots largely unexplored. We present a standardized real-world benchmark for evaluating representative VLA and imitation learning policies on the low-cost SO-101 robotic platform. The benchmark comprises four representative manipulation tasks together with unified evaluation protocols, enabling systematic comparison under embodiment uncertainty. Using real-world teleoperated demonstrations, we fine-tune and evaluate \pi_0.5 , SmolVLA, Wall-X, and ACT directly on the physical platform. Beyond conventional task success rates, the benchmark incorporates a structured failure taxonomy, semantic- and execution-level failure decomposition, and recovery-aware evaluation metrics to characterize policy robustness. Experimental results show that stronger pretrained VLA policies generally outperform the imitation learning baseline, although performance remains highly task-dependent under low-cost robotic deployment conditions. Execution instability emerges as the dominant failure source, while recovery capability varies substantially across architectures. These results highlight the importance of failure and recovery analysis beyond binary task success and establish SO-101 as a practical benchmark for evaluating embodied AI systems under realistic low-cost robotic deployment conditions.
[AI-98] Can the Environment Speak for Itself? T2-GRPO: A Turn-Trajectory Group Relative Policy Optimization for Caregiver Agents
链接: https://arxiv.org/abs/2606.08875
作者: Yutong Song,Jiang Wu,Pengfei Zhang,Wenjun Huang,Honghui Xu,Nikil Dutt,Amir M. Rahmani
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Optimizing large language models (LLMs) for long-horizon caregiver agents requires balancing delayed task objectives with immediate environment dynamics, such as patient distress and resistance. In dementia care, this balance is especially difficult: trajectory level rewards are too sparse for turn level credit assignment, while external LLM-based evaluators are costly and can misread fragmented or indirect patient responses. To address this issue, we propose \textbfTurn-\textbfTrajectory \textbfGroup \textbfRelative \textbfPolicy \textbfOptimization (\textbfT ^2 -GRPO), a framework that decouples caregiver RL into two normalized reward horizons and enforces safety through a binary hard veto. T^2 -GRPO derives dense turn-level rewards directly from environment state transitions, measuring changes in patient distress and resistance from a frozen dementia patient simulator. These environment-grounded rewards are combined with trajectory-level evaluations through independent centered-rank normalization, which preserves heterogeneous reward signals and mitigates reward collapse. Extensive experiments on dementia caregivers show that T ^2 -GRPO outperforms competitive baselines, indicating a substantial improvement for emotionally sensitive caregiver scenarios that effectively handles immediate patient feedback, long-term care outcomes, and safety constraints.
[AI-99] A Resilience-as-a-Service assessment framework for coordinated disruption response in interdependent urban transit systems
链接: https://arxiv.org/abs/2606.08849
作者: Sara Jaber,S. M. Hassan Mahdavi,Neila Bhouri,Mostafa Ameli
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Urban public transport disruptions require rapid response strategies, yet existing studies rarely provide a decision support framework to compare alternative disruption response solutions using a common set of dynamic, passenger, operator, and environment oriented indicators. This paper proposes a KPI-driven, time-indexed framework to assess the resilience of disruption response solutions in urban transit systems. The framework combines an optimization model with a behavioral evaluation in agent-based simulation. It also underlays the secondary service degradation induced on helper lines when in-service vehicles are withdrawn to support the disrupted corridor. Rather than treating resilience as a single score, it evaluates complementary dimensions including vulnerability, adaptability, robustness, resilience loss, responsiveness, cost-based performance, emissions, and equity. The framework is implemented for the RER B transit line in the Ile-de-France (Paris) network. Results show that the coordinated strategy provides the most balanced resilience profile, combining high service continuity with lower total disruption cost than single mode alternatives, while also improving equity and maintaining competitive environmental performance. Sensitivity analysis further identifies the disruption conditions under which coordinated multimodal response is most valuable.
[AI-100] Beyond Pass Rate: A Multilingual Execution-Grounded Evaluation of Open Code LLM s
链接: https://arxiv.org/abs/2606.08840
作者: Sayed Erfan Arefin
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Code generation models are typically compared using compact execution benchmarks and aggregate pass rates, but such summaries obscure how performance varies across programming languages, problem families, and failure modes. We present a large-scale, execution-grounded evaluation of 9 openly accessible LLMs specialized for coding on 2,707 free LeetCode problems across 12 programming languages. Our corpus contains 325,343 problem-model-language jobs, each linked to prompt metadata, extracted code, LeetCode execution outcomes, and static-analysis signals. The results show that current open models remain far from the human acceptance reference: the best model, Yi-Coder-9B-Chat, reaches 23.64% mean correctness, compared with a 57.2% human acceptance baseline. Rankings are also slice-dependent: Qwen2.5-Coder-14B-Instruct is strongest on hard problems and distinct-problem coverage, while Gemma-2-27B-IT achieves the highest all-language lint pass rate. Failure analysis shows that compile errors account for 63.25% of non-accepted best submissions, indicating that many failures occur before semantic correctness can be tested. Static quality further diverges from functional correctness. Together, these findings show that multilingual, artifact-preserving evaluation reveals tradeoffs hidden by single-language or single-metric leaderboards.
[AI-101] Instrumental convergence and power-seeking
链接: https://arxiv.org/abs/2606.08832
作者: David Thorstad
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recent years have seen increasing concern that artificial intelligence may soon pose an existential risk to humanity. One leading ground for concern is that artificial agents may be power-seeking, aiming to acquire power and in the process disempowering humanity. I show how the argument from power-seeking rests on a strong version of a claim known as the instrumental convergence thesis. I explore leading defenses of the instrumental convergence thesis and argue that none establishes the thesis in a strong enough form to ground the argument from power-seeking. I discuss implications for longtermism, the governance of artificial intelligence, and the methodology of studying risks posed by artificial agents.
[AI-102] Inference-Time Conformal Reasoning with Valid Factuality Control for Large Language Models ICML2026
链接: https://arxiv.org/abs/2606.08831
作者: Ting Wang,Yuanjie Shi,Yan Yan,Huan Zhang
类目: Artificial Intelligence (cs.AI)
备注: Accepted at ICML 2026
Abstract:Large language models (LLMs) increasingly perform multi-step reasoning, where intermediate claims form implicit directed acyclic graphs whose node correctness is structurally conditioned on their ancestors. This makes factuality uncertainty structural, rather than a trivial accumulation of node-wise errors, and necessitates inference-time uncertainty quantification over the reasoning structure. While conformal prediction (CP) offers flexible user-specified factuality control, existing work remains post-hoc and cannot intervene during generation. To fill the gap between CP’s flexibility and its post-hoc limitation, we propose an \emphInference-Time Conformal Reasoning (ITCR) framework that integrates CP directly into reasoning graph generation. ITCR learns a structure-level factuality uncertainty function that aggregates claim-level factuality signals over reasoning graphs without complex modeling assumptions. We then design the non-conformity score based on graph-level factuality uncertainty and calibrate the conformal threshold to decide when to stop generation. We theoretically show such generation is nested, yielding valid coverage guarantees for factuality control. Experiments over multiple datasets and coverage objectives demonstrate empirically valid coverage. In downstream reasoning tasks, inference-time calibrated graphs yield more accurate generation than post-hoc pruned graphs.
[AI-103] Knowledge Graphs and Reasoning LLM s for Finding Simple Yet Effective Transcriptomic Perturbation Predictors
链接: https://arxiv.org/abs/2606.08816
作者: Jake Fawkes,Liam Hodgson,Jason Hartford
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Predicting the effect of an unseen gene knockout perturbation on transcriptomic gene expression remains a highly challenging problem for virtual cell models. Recent progress has been made by leveraging biological knowledge graphs to provide a notion of similar perturbation, allowing for improved extrapolation beyond the set of training perturbations. In this work, we demonstrate that the simplest model to leverage these assumptions - a K-nearest neighbour from the knowledge graph - achieves highly competitive performance on this task, and that this can be improved further using LLMs optimised via reinforcement learning (RL) for predictive performance. Specifically, we find that the K-nearest neighbour approach beats almost all methods on out-of-distribution perturbation prediction, and when a reasoning LLM is trained via RL to make changes to the neighbourhood, it obtains equivalent performance to current state of the art methods on the cell lines from Replogle et al. (2022). We also demonstrate that the RL training improves the LLM’s performance on the downstream task of differential expression prediction, despite not being trained on this directly. Overall, these findings demonstrate the efficacy of knowledge graphs as model priors, and show early signs that RL can refine LLMs into generalizable tools for predicting complex biological responses.
[AI-104] STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning ICML2026
链接: https://arxiv.org/abs/2606.08814
作者: Sumin Park,Noseong Park
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at ICML 2026
Abstract:Mixture-of-Experts (MoE) scales model capacity efficiently by selectively routing inputs to a specialized subset of experts. However, input-expert specialization, the core motivation of MoE, critically depends on whether the router is actually aware of input structure. In practice, MoE routing is typically implemented as a shallow linear projection with limited awareness of input representation, which often leads to unstable routing. We propose STAR, a Structure Aware Routing that rethinks MoE routing as a subspace learning problem by augmenting standard learnable routing with an evolving principal subspace that tracks dominant input structure via Generalized Hebbian Algorithm (GHA). By aligning routing decisions directly with input structure, STAR enables stable expert specialization. We evaluate STAR on controlled synthetic setup and large-scale language and vision tasks, where it consistently improves routing quality and downstream performance over strong MoE baselines. Moreover, optional test-time subspace updates further enhance routing robustness and generalization under input distribution shifts.
[AI-105] Governance Controls for AI-Generated Test Artifacts in Autonomous Software Testing
链接: https://arxiv.org/abs/2606.08806
作者: Dimple Bajaj,Deepak Khetan
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 21 pages, 9 figures
Abstract:Artificial Intelligence (AI) and Large Language Models (LLMs) are increasingly used in autonomous software testing; however, AI-generated test artifacts often suffer from hallucinations, compliance violations, security risks, and limited explainability. To enhance the reliability, transparency, and trustworthiness of AI-generated testing artifacts, this research introduces the concept of Governance-Aware Autonomous Testing Framework (GATF). The framework extends the autonomous testing lifecycle with governance validation, explainability analysis, probabilistic risk assessment, compliance monitoring, as well as audit governance. Experiments were performed with Defects4J and PROMISE software engineering datasets. The proposed framework successfully reduced the governance-related risks by 89.6% and demonstrated 94.3% accuracy in governance, 96.5% artifact reliability, 94.2% compliance accuracy, and 90.8% explainability performance. The results show that autonomous testing systems that are governance-aware can significantly enhance the reliability, transparency, and operational security of autonomous testing systems in comparison to conventional AI-based testing systems. The proposed architecture is scalable and reliable and provides a safe environment for software testing.
[AI-106] Q-Delta: Beyond Key-Value Associative State Evolution ICML2026
链接: https://arxiv.org/abs/2606.08804
作者: Sumin Park,Seojin Kim,Noseong Park
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at ICML 2026
Abstract:Linear attention reformulates sequence modeling as recurrent state evolution, enabling efficient linear-time inference. Under the key-value associative paradigm, existing approaches restrict the role of the query to the readout operation, decoupling it from state evolution. We show that query-conditioned state readout induces a structured value prediction over accumulated memory that complements key-based retrieval. Based on this insight, we propose Q-Delta, a query-aware delta rule that integrates mixed key-query prediction errors into state evolution, enabling jointly corrective dynamics while preserving delta-rule efficiency. We establish stability guarantees for the resulting dynamics and derive a hardware-efficient chunkwise-parallel formulation with a custom Triton implementation. Empirical results demonstrate stable optimization, competitive throughput, and consistent improvements over strong baselines on language modeling and long-context retrieval tasks.
[AI-107] Bridging Expert Knowledge and Automated Feature Engineering via Self-Evolution
链接: https://arxiv.org/abs/2606.08800
作者: Varun Khurana,Vijval Ekbote,Vashu Chauhan,Yaman Kumar Singla,Rajiv Ratn Shah,Balaji Krishnamurthy
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:In high-stakes settings such as brand compliance, clinical care, and content moderation, machine learning cannot be deployed as opaque oracles: practitioners inspect the features driving model decisions, and models must leverage the expert documentation governing these domains. In practice, the data arrives as unstructured content, and features extracted from it must be interpretable, discriminative, and aligned with what experts consider important. Existing methods fall short: they target tabular inputs, lack demonstrated expert alignment, and cannot operationalize qualitative criteria such as ‘maintain professional tone’ into precise features. We present FEST (Feature Engineering with Self-evolving Trees), combining dual-stream feature generation (semantic and deterministic), semantic deduplication, and tree-guided iterative evolution to discover auditable features from raw text and images. FEST leads in 17 of 20 classifier-task combinations across brand classification, content authenticity detection, and stress detection, with a mean gain of 4.2 pp over the strongest baseline across five classifiers. An LLM-as-judge evaluation shows FEST achieves 60-80% coverage of expert-designed brand features at strict semantic-alignment thresholds, corroborated by a human expert study rating features highly on relevance, clarity, and actionability. When seeded with expert guidelines, FEST refines qualitative criteria into operational features, improving accuracy by 6-12 pp on average across brands. To enable systematic evaluation of expert alignment in automated feature engineering, we release BrandGuide, the first dataset pairing expert-designed features with 1M+ assets across 2,683 brands. By grounding feature engineering in expert knowledge, FEST opens a practical pathway for interpretable ML in domains demanding human oversight.
[AI-108] Scaling Decision-Focused Learning to Large Problems with Lagrangian Decomposition
链接: https://arxiv.org/abs/2606.08797
作者: Stéphane Eilles-Chan Way,Hugo Percot,Quentin Cappart,Tias Guns,Louis-Martin Rousseau
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Decision-focused learning has shown great promise for addressing predict-then-optimize problems, particularly in the presence of under-specified models. However, its practical deployment is often hindered by high computational costs and limited scalability, as it requires solving a constrained optimization problem for each training instance at every iteration. To address these challenges, we propose a novel framework that incorporates Lagrangian decomposition into the decision-focused learning paradigm. Specifically, we introduce a new surrogate objective along with two loss functions for evaluating and training the underlying prediction model. We further propose two variants of our approach, which offer different trade-offs between computational efficiency and solution quality. Our framework can be seamlessly integrated with standard decision-focused learning methods, including Smart Predict-then-Optimize (SPO+) and Implicit Maximum Likelihood Estimation (IMLE). Through experiments on two standard benchmarks, the multi-dimensional knapsack problem and quadratic portfolio optimization, we demonstrate that our approach achieves competitive performance while remaining amenable to parallelization. In particular, it consistently outperforms traditional decision-focused learning methods on large-scale instances, involving up to eight times more variables than those typically considered in related work. The implementation is available at this https URL.
[AI-109] AI-Augmented Closed-Loop Quality Engineering: A Reference Architecture for Continuous Software Quality Intelligence
链接: https://arxiv.org/abs/2606.08793
作者: Dimple Bajaj
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 15 pages, 4 figures
Abstract:The quality of software engineering is still under a challenge due to disjointed processes between requirements, testing, and production, which hinders the opportunity to implement quality strategies in consecutive releases. Existing approaches tend to be fixed-model or single-optimization approaches and lack production feedback learning mechanisms. The paper at hand proposes a closed-loop reference architecture of continuous software quality intelligence with AI enhancements. The model synthesizes requirement feature mining, risk-based test prioritization, defect prediction, and production incident analysis as an element of a feedback-based pipeline. A limited feedback learning model is introduced that is used to propagate the production signal-based on defect severity and incident impact- to the following release to ensure stability, and the time. The method is evaluated using a semi-synthetic test dataset of 4,500 requirements, 27,049 test cases, 13,089 defects and 7,841 incidents in six release cycles. The experimental results show that the proposed system reduces the defect leakage by 0.19 to 0.13, increases the effectiveness of the detection system to 0.72 to 0.84, and shortens the test execution by up to 35 percent compared to the non-adaptive baselines. The changes are stable release to release. The findings indicate that through the integration of feedback-based learning in a closed-loop architecture, it can be continued to enhance quality process, which offers practical foundation of adaptive quality engineering of software.
[AI-110] How Many Counterfactuals Does It Take? Probing VLM Hallucinations Through Circuits and Causal Effects
链接: https://arxiv.org/abs/2606.08777
作者: Abhivansh Gupta,Simardeep Singh,Advika Sinha,Shreyansh Modi,Akshat Tomar
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Visual Language Models (VLMs) are known to produce hallucinated predictions that are not grounded in visual evidence, yet existing approaches lack a principled understanding of how robust such predictions are under counterfactual perturbations. In this work, we study the sample complexity of counterfactual robustness for hallucinated outputs in VLMs. We define a causal influence metric based on log-probability differences between factual, counterfactual, and activation-patched runs, and use it to characterize the stability of hallucinated predictions. By leveraging circuit discovery techniques (CD-T), we identify model components responsible for these predictions and track their activation differences across counterfactual samples. We then derive empirical bounds on the minimum number of counterfactual samples m required to reliably detect instability in hallucinated outputs, using concentration inequalities and variance estimates of the causal influence distribution.
[AI-111] Unifying Object-Centric World Models and Diffusion Policy: A Hierarchical Framework for Multi-Stage Robotic Tasks
链接: https://arxiv.org/abs/2606.08775
作者: Raktim Gautam Goswami,Prashanth Krishnamurthy,Yann LeCun,Farshad Khorrami
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Visual world models have shown great potential in learning complex system dynamics. Recent advancements leverage these models as transition functions within Model Predictive Control (MPC) frameworks to solve various control tasks. When applied to robotics, however, they are limited to single-stage tasks such as reaching or grasping, and struggle with multi-stage ones that demand complex sequential planning. In this work, we introduce WorldDP, a world model framework designed for multi-stage robotic manipulation. Our hierarchical approach utilizes a high-level world model as a transition function to optimize for feasible subgoals during runtime, which are subsequently reached by a low-level Diffusion Policy. To further aid in learning dynamics and planning, we incorporate object-centric representations that decouple environmental entities and enable us to plan sequentially with respect to each. Evaluated across several robotics benchmarks, WorldDP consistently outperforms existing baselines, validating that coupling the world model’s physically grounded planning with diffusion policy’s efficient execution yields superior multi-stage performance.
[AI-112] APEX4: Efficient Pure W4A4 LLM Inference via Intra-SM Compute Rebalancing
链接: https://arxiv.org/abs/2606.08761
作者: Hong Guo,Nianhui Guo,Weixing Wang,Jona Otholt,Christoph Meinel,Haojin Yang
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注:
Abstract:W4A4 quantization promises full utilization of INT4 Tensor Cores, yet group dequantization overhead on CUDA Cores has driven existing systems to mixed-precision fallbacks. We present the first systematic study of how intra-SM compute balance governs this bottleneck. Through controlled benchmarks across four GPUs from Ampere and Ada architectures, we identify the Tensor Cores to CUDA Cores throughput ratio ( \rho ) as the primary hardware indicator: the W4A4-g128 kernel yields 2.0 – 2.5\times speedup on RTX~3090 ( \rho=16 ) yet degrades to 0.43 – 0.47\times on A100 ( \rho=64 ) in compute-bond scenarios, establishing W4A4 viability as platform-dependent rather than universally infeasible. Guided by this finding, we build \textbfAPEX4, which co-designs pure INT4 GEMM kernels with \rho -aware granularity adaptation to mitigate the CUDA Cores dequantization bottleneck. APEX4 achieves perplexity within 0.63 of FP16 on LLaMA-2-70B and outperforms W4Ax Atom-g128 by 4.0%–4.4% in zero-shot accuracy. Deployed as a drop-in replacement in unmodified vLLM, it delivers up to 1.66\times end-to-end speedup on L40S ( \rho=8 ), and 1.78\times on RTX~3090 ( \rho=16 ), 2.09\times on A40 ( \rho=16 ), while recovering A100 ( \rho=64 ) to 1.20 – 1.40\times via the mixed-granularity mode.
[AI-113] Structure-Conditioned Actor-Critic Branches for Quality-Diversity Reinforcement Learning
链接: https://arxiv.org/abs/2606.08735
作者: Lianrong Zuo,Peilan Xu,Yong Liu,Wenjian Luo
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Quality-diversity reinforcement learning (QD-RL) aims to construct policy repertoires that contain both high-performing and behaviorally diverse policies. Existing QD-RL methods mainly diversify policy instances after rollout evaluation or use learned value information to improve policy quality and behavior targeting, while the learning branches that generate candidate policies remain less explored. This paper proposes SV-QD-RL, a structure-value coupled framework that represents each candidate as a structure-conditioned actor-critic branch. Each branch contains an actor, a structural mask, a branch-specific critic, a replay state, and evaluation attributes including behavior, return, sparsity, and value profile. The structural mask defines the actor subspace in which the branch learns, while the branch-specific critic and replay state shape its value-learning trajectory. A branch-aware QD archive then evaluates and retains branches according to behavioral quality, structural footprint, and value-profile information. Experiments on MuJoCo continuous-control tasks show that SV-QD-RL constructs policy repertoires with strong archive quality and behaviorally useful diversity. Ablation and diagnostic analyses further indicate that structural conditioning, critic differentiation, and memory-consistent refinement make complementary contributions to behavioral specialization. Schedule-aware repertoire evaluation shows that the learned archive provides selectable policy alternatives under changing behavior-level requirements. These results suggest that coupling actor structure with branch-specific value learning is an effective mechanism for generating diverse QD-RL policy repertoires.
[AI-114] Deep Active Re-Labeling: Toward Noise-Resilient Annotation Efficiency
链接: https://arxiv.org/abs/2606.08718
作者: Md Abdullah Al Forhad,Weishi Shi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted and published in the 2025 IEEE International Conference on Big Data (BigData). DOI: https://doi.org/10.1109/BigData66926.2025.11402126
Abstract:While Deep Active Learning (DAL) effectively reduces human annotation costs, its efficacy is constrained by human annotation errors. This is because the data sampled for active learning is assumed to be highly informative for training. When human annotators introduce errors into this informative data at a certain rate, the active learning performance drops significantly and, in some cases, even exhibits worse outcomes than passive learning. In this paper, we first analyze the impact of human annotation errors in the DAL setting. Then we propose a framework to address the human annotation noise problem for DAL. Informed by human learning patterns, the core idea of our proposed solution involves allocating a portion of the human annotation budget to re-annotate data that has already been labeled. Previous theoretical work suggests that when the model possesses a certain level of ability to identify potentially noisy data, even re-labeling a small fraction of the data can effectively remove noise from the active training set. To achieve this, we implement two active noise sampling strategies to detect noise under different circumstances and allocate a part of the annotation budget to re-annotate these instances. Our approach imbues active learning with a revisiting and introspective behavior. Our experiments demonstrate that, under the same annotation budget, our method is more data-efficient and yields a relatively noise-free annotation dataset in the end.
[AI-115] Hybrid Neural Network and Conventional Controller Approach for Robust Control of Highly Unstable Systems: Application to Tilt-Rotor Control
链接: https://arxiv.org/abs/2606.08714
作者: Ali Kafili Gavgani,Amin Talaeizadeh,Aria Alasty,Hossein Nejat Pishkenari
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
备注: Proceedings of the 13th RSI International Conference on Robotics and Mechatronics (ICRoM 2025)
Abstract:Multirotors are widely used in applications ranging from surveillance to precision agriculture, yet conventional designs remain limited by their under-actuation. Tilt-rotor configurations overcome this limitation by enabling full actuation. This paper investigates neural-network-based control strategies for a fully actuated tilt-rotor system with four thrust-vectoring inputs. Our work is structured in two parts. First, we deliberately present a negative result by evaluating a direct input-output control approach. In this method, multilayer perceptrons (MLPs), long short-term memory (LSTM) networks, and transformer models are trained to map system states and their desired values directly to control signals. We show that this strategy fails to stabilize the system, highlighting the inherent difficulty of applying direct input-output learning to highly unstable plants. Second, as the main contribution, we propose a neural-network-enhanced sliding mode controller (SMC). The method decomposes the system dynamics into input-independent and input-dependent components, with the former learned from a small dataset using lightweight networks, thereby reducing real-time computational demands. Moreover, the proposed method can be trained using flight logs collected from low-performance controllers, and the resulting dynamic model learned from real-world data can be used in simulation. We further compare MLP- and LSTM-based implementations under model uncertainties and external disturbances, demonstrating the robustness and effectiveness of the proposed approach; in particular, the controller with the LSTM plant dynamics predictor achieves superior performance to its MLP-based counterpart while also exhibiting lower runtime.
[AI-116] Structuring agent ic AI for HPC code modernization
链接: https://arxiv.org/abs/2606.08710
作者: Anthony Marinov,Igor Sfiligoi
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 10 pages
Abstract:Modernization of legacy scientific codes is often necessary to keep up with the ever-evolving changes in the compute resource ecosystem. Parallelization and migration from poorly supported software ecosystems are two of the most time-consuming activities in the research software engineering field. This paper presents our experience in the successful, two-phase AI-assisted modernization of NMAP-RKPM, a roughly 60,000-line, 3D explicit solid mechanics physics engine based on the Reproducing Kernel Particle Method (RKPM). We converted this single-threaded, Fortran based MPI application into a OpenMP-parallel C++ based MPI tool in the span of a few months. While Large Language Model (LLM) based tools on their own proved inadequate, we developed a highly structured “hand-holding” agentic AI methodology, like providing manually created examples, ensuring continuous buildability and limiting session scope, that was instead highly effective. The paper provides both the AI-assisted steps that were successful and the problems that we had to overcome, alongside the reasoning behind the chosen path.
[AI-117] ConMem: Structured Memory-Guided Adaptation in Training-Free Multi-Agent Systems
链接: https://arxiv.org/abs/2606.08702
作者: Zhixun Tan,Qiang Chen,Tairan Huang,Xiu Su,Yi Chen
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advances have improved the adaptive capabilities of LLM-based multi-agent systems (MAS) through memory-, skill-, and learning-based approaches, yet these approaches remain challenged by noisy trajectories, insufficient modeling of memory-skill relations, and reliance on additional training or high-quality supervision. To address these limitations, we propose ConMem, a relation-aware and training-free framework that enables efficient multi-agent adaptation through cross-experience coordination. Specifically, ConMem distills historical interaction trajectories into structured memory cards to capture reusable strategies and cues, organizing them into a relation-aware memory graph. At runtime, ConMem retrieves cards according to task needs and coordinates them through the card graph to resolve strategy conflicts and recover their dependencies. Combined, these modules yield structured and relation-aware guidance, enabling robust, lightweight adaptation in multi-agent systems without additional training. Extensive experiments across multiple benchmarks and mainstream MAS architectures show consistent gains over existing memory architectures, with improved inference-time efficiency through pruning more than 50% of expanded candidates and reducing planning overhead by over 80%. Our codes are available at this https URL
[AI-118] Agent ic Search for Counterfactual Recourse under Fixed LLM Budgets
链接: https://arxiv.org/abs/2606.08696
作者: Yasuo Tabei
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Counterfactual recourse aims to provide actionable feature changes that would alter an unfavorable decision made by a predictive model. In practice, affected individuals often benefit from multiple feasible alternatives rather than a single optimal explanation. A natural way to produce such alternatives is to prompt large language models (LLMs). However, prompting incurs a practical constraint: the number of LLM calls is often the dominant computational and economic cost. Together, the need for multiple alternatives and this cost constraint shift the problem from finding a single high-quality counterfactual to efficiently generating a set of oracle-validated counterfactuals under a fixed LLM-call budget. In this work, we study counterfactual recourse generation in the LLM-agentic setting as a fixed-budget search problem and propose Comp-MCTS, an agentic tree-search framework that maximizes the yield of unique, oracle-validated counterfactuals under this budget while maintaining favorable quantity–quality trade-offs. Comp-MCTS allocates the budget toward novel intervention directions via LLM-based proposal generation, oracle validation, and compression-guided pruning, in a training-free, oracle-only setting. Experiments on four real-world tabular datasets show that Comp-MCTS substantially outperforms single-candidate LATS-style baselines in the yield of unique, oracle-validated counterfactuals, and offers favorable quantity–quality–efficiency trade-offs against stronger multi-candidate variants: comparable or higher yield at similar or lower oracle-evaluation cost on three of four datasets, plus competitive proximity, sparsity, and novelty.
[AI-119] Activation Steering Induces Emergent Misalignment: A More Comprehensive Evaluation
链接: https://arxiv.org/abs/2606.08682
作者: Qi Cao,Jian Lou,Meiting Liu,Wenjie Feng,Dan Li,See-Kiong Ng,Anh Tuan Luu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Activation steering has emerged as a popular inference-time technique for modulating the behavior of large language models (LLMs). By constructing a steering vector from examples of a target behavior and injecting it into intermediate activations during inference, activation steering enables flexible behavioral control while avoiding the permanent parameter updates required by finetuning. Meanwhile, recent work has identified emergent misalignment (EM) as a significant safety concern, wherein models finetuned on unsafe examples from a narrow task may unexpectedly generalize to broadly unsafe behavior on unrelated tasks. Although finetuning-induced EM has been extensively studied, whether activation steering can induce EM remains comparatively under-explored, despite its increasing use as a model-control technique. In this paper, we present a comprehensive study of activation-steering-induced emergent misalignment, substantially expanding the evaluation scope beyond existing pioneering work. First, we show that activation steering can induce broad misalignment, even in the recent Qwen-3.5 series. Moreover, activation-steered models produce harmful responses with stronger semantic relevance and higher coherence than their finetuned counterparts, making the resulting misalignment potentially more harmful. Second, we characterize properties of AS-induced EM by analyzing key steering-specific factors, including steering magnitude, the low-rank structure of the steering subspace, and the number of epochs during steering-vector construction. Third, we evaluate the robustness and sensitivity of AS-induced EM across diverse model families, model scales, target tasks, and intervention layers. Our findings reveal activation steering as a significant yet under-examined source of emergent misalignment and provide an activation-space perspective for understanding the mechanisms and safety risks of EM.
[AI-120] Data Agents Under Attack: Vulnerabilities in LLM -Driven Analytical Systems
链接: https://arxiv.org/abs/2606.08661
作者: Kuncan Wang,Ziting Wang,Peizhuo Lv,Haoyang Li,Guoliang Li,Gao Cong,Wei Dong
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Databases (cs.DB)
备注:
Abstract:Data agents integrate LLM-driven reasoning with relational data access, executable analytical tools, and multi-step workflow orchestration, making them increasingly central to enterprise analytics. This integration introduces new security vulnerabilities across data resources, database execution, and agent reasoning, recombining concerns from database security and general-purpose LLM-agent security into failure modes that neither line of work captures on its own. To address this gap, we present a systematic security study of data agents. Our contributions are threefold. First, we develop a layered vulnerability framework that identifies eight data agent-specific risks across interpretation, execution, and policy layers. Second, we introduce an attack taxonomy organized by adversary goal, tactic, and technique, covering three goals, seven tactics, and fourteen techniques, and pair it with an LLM-driven payload generation pipeline grounded in real database schemas. Third, we evaluate these attacks on six systems, including four open-source data agents and two production cloud analytics services. Our experiments reveal substantial security vulnerabilities across current systems and yield four key takeaways.
[AI-121] Extending Ontologies: From Dense Embeddings to Hybrid Quantum-Fuzzy Systems
链接: https://arxiv.org/abs/2606.08658
作者: Angjelin Hila
类目: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
备注:
Abstract:LLMs have revolutionized knowledge representation and retrieval, but lack the explicit modeling that knowledge ontologies possess. This paper surveys the ways that ontologies and knowledge graphs have been integrated with dense embedding algorithms. All hitherto attempts involve a trade-off between probabilistic and crisp inference. This paper proposes a novel frontier for devising knowledge representation systems that can simultaneously accommodate probabilistic and crisp inference in the same representation. To this effect, the paper proposes neuro-quantum-fuzzy systems as knowledge representation systems that accommodate both classical and contextual inference implemented through quantum-neural networks (QNN).
[AI-122] Latent Diffusion Policy: Shaping Latent Spaces for Diffusion-Based Robotic Manipulation
链接: https://arxiv.org/abs/2606.08657
作者: Zhexuan Zhou,Yichen Lai,Jinhao Zhang,Huizhe Li,Youmin Gong,Jie Mei
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Diffusion-based visuomotor policies operating directly in raw action spaces conflate scene comprehension with trajectory generation within a single denoising process. The resulting velocity field must simultaneously encode scene information and generate precise trajectories, increasing learning complexity and limiting performance on tasks demanding precise temporal coordination across multiple arms. To simplify this joint learning problem, we introduce Latent Diffusion Policy (LDP), a two-stage framework performing flow matching in a deliberately shaped latent space. By absorbing scene understanding into an observation-conditioned CVAE encoder, LDP concentrates the conditional distribution of each observation. Consequently, the flow model avoids implicitly resolving scene-dependent structures; instead, it generates within a pre-concentrated distribution featuring a smoother velocity field, simplifying learning from limited demonstrations. Furthermore, to capture temporal dependencies among latent tokens, LDP trains with per-token diffusion forcing and employs staircase inference sampling to resolve the resulting distributional mismatch. We also propose reconstruction FID (rFID) as a lightweight proxy predicting downstream task success solely from latent space statistics. On coordination-intensive tasks from RoboTwin 2.0, LDP outperforms DP3 by a substantial margin and transfers effectively to real-world bimanual deployments.
[AI-123] Sample-Efficient LLM -Based Detection of Malicious Web Server Logs with Forensically Explainable Reasoning
链接: https://arxiv.org/abs/2606.08649
作者: Bernhard Kneip,Nhien-An Le-Khac,Hong-Hanh Nguyen-Le
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Forensic analysis of web server logs demands both accurate detection and human-readable explanations that can satisfy legal requirements. We present CEF-Log, a context-enhanced few-shot chain-of-thought prompting strategy for Large Language Models that addresses this dual requirement. CEF-Log embeds expert investigative methodology through a structured five-step reasoning template, enabling the model to learn \textithow to analyze logs rather than \textitwhat patterns to memorize. Experimental evaluation demonstrates that CEF-Log achieves an F1-score of 0.99 on the CSIC 2010 dataset using only four examples while providing a 10\times improvement in sample efficiency compared to other prompting-based methods. We also introduce ForenWebLog, a new dataset that incorporates real-world attacks and multi-step attack sequences for comprehensive evaluation. Qualitative analysis confirms that CEF-Log generates traceable, accurate explanations suitable for forensic documentation, addressing the critical “black-box” limitation of traditional machine learning approaches.
[AI-124] owards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models ITSC
链接: https://arxiv.org/abs/2606.08633
作者: Hongwei Wang,Miao Zhou,Fengde Wang,Yuting Wang,Jiewen Yu,Jun-Yan He,Bohao Qu,Wanbing Zhang,Xiuju Fu,Qing Guo,Zipei Fan,Yingying Xing,Yi Yuan
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: The IEEE International Conference on Intelligent Transportation Systems (ITSC) 2026, Naples, Italy
Abstract:Long-horizon maritime trajectory prediction is important for shipping management, logistics planning, and maritime risk analysis, yet month-level forecasting remains insufficiently studied. Existing deep learning methods mainly focus on short- and mid-term coordinate extrapolation and often struggle to preserve route feasibility and destination correctness over extended horizons. This paper investigates joint long-horizon vessel trajectory and destination forecasting with reasoning-capable large language models, and develops a Maritime LLM post-training framework based on Reinforcement Learning with Verifiable Reward (RLVR). An AIS-based benchmark is constructed with 60-day historical trajectories and 30-day forecasting horizons, where trajectories are converted into semantic textual representations for RL prompt construction. RLVR aligns LLMs with maritime forecasting objectives by enforcing physical validity, providing early-weighted trajectory supervision, and evaluating destination correctness through hierarchical matching and curriculum learning. Experimental results show that RLVR-trained LLMs substantially improve over zero-shot LLMs and representative deep learning baselines, especially on destination-related metrics. Among the evaluated RLVR-trained variants, 4B LLMs achieve the best overall performance, suggesting that reward-compatible optimization and task-specific capacity matching are more important than simply using larger 8B or 14B LLMs. The results also show that LSTM remains a strong deep learning baseline under limited fine-tuning data, while Transformer-style spatio-temporal models typically require larger datasets and richer structured inputs. Overall, this work advances semantic, verifier-aligned maritime forecasting for operational decision support.
[AI-125] yan-WP: A Wind Power Foundation Model for Ultra-Short-Term Probabilistic Forecasting
链接: https://arxiv.org/abs/2606.08630
作者: Jiahui Huang,Ao Luo,Lei Liu,Hongwei Zhao,Tengyuan Liu,Ruibo Guo,Bo Wang,Zhao Wang,Bin Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Global wind power capacity, especially in China, is booming, with new farms spanning diverse terrains and climates. The industry urgently needs accurate wind power foundation models to shorten commissioning and accelerate grid connection. This is because site-specific time series models (TSMs) are not well suited to data-scarce scenarios and generalize poorly, while generic large time series models (LTSMs) are mostly limited to univariate inputs and cannot fully exploit static site attributes or the dependencies between power and meteorological covariates, leading to insufficient accuracy. To fill this gap, we propose \textbfTyan-WP, the first wind power foundation model for ultra-short-term probabilistic forecasting. Pretrained on a large-scale wind power dataset covering more than 126,000 U.S. sites over seven years, Tyan-WP further improves zero-shot forecasting through two domain-specific module designs: static site embedding using coordinate, terrain, and ecoregion metadata, and a power-aware meteorological fusion (PAMF) module that models interactions between historical power and meteorological covariates. Under a unified evaluation protocol, Tyan-WP surpasses eight site-specific supervised TSMs on 10 in-domain sites and outperforms eleven generic LTSMs on 127 in-domain sites, reducing MAE by 19.9%, RMSE by 16.6%, CRPS by 22.2%, and AQL by 21.7%, while raising R^2 by 16.7%. It further demonstrates strong cross-geography generalization on six real U.K. sites. These results show that the wind power foundation model can achieve accurate zero-shot forecasting without target-site training, providing a practical pathway for rapid turbine onboarding and probabilistic risk management at new wind farms.
[AI-126] HARBOR: A Harness Framework for Agent ic Robot Reinforcement Learning
链接: https://arxiv.org/abs/2606.08610
作者: Zechu Li,Yufeng Jin,Xiaoyang Liu,Puze Liu,Vignesh Prasad,Carlo D’Eramo,Georgia Chalvatzaki
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Reinforcement learning (RL) has become a powerful paradigm for robot learning, particularly in sim-to-real settings, but its broader adoption remains limited by the engineering pipeline surrounding the algorithms. Building tasks, shaping rewards, and tuning hyperparameters require substantial expert effort, making RL workflows costly and difficult to scale. We introduce HARBOR, an agentic framework that frames robot RL automation as a harness-engineering problem: given a simulator codebase and a task specification, it automates the workflow from environment setup to policy training in simulation. HARBOR decomposes such high-level objectives into bounded stages executed by specialized agents through standardized commands, persistent artifacts, executable gates, and reusable knowledge, and scales iteration via decentralized parallel trials and experience learning across runs. We evaluate HARBOR across 6 benchmarks and 16 tasks in total, spanning manipulation, locomotion, and bimanual dexterous control. We demonstrate that HARBOR automates the simulation RL workflow end-to-end, designs rewards, tunes algorithms to match or improve over default configurations, and reduces engineering effort at practical token and wall-clock cost; the resulting policies can also be transferred to real robots.
[AI-127] Reinforcement Learning for Flow-Matching Policies with Density Transport
链接: https://arxiv.org/abs/2606.08602
作者: Boshu Lei,Kostas Daniilidis,Antonio Loquercio
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:We present an online reinforcement learning (RL) algorithm for fine-tuning flow-matching policies in continuous-control problems. Our key insight is to view RL-based policy improvement as a transport of action densities towards regions of high reward, which naturally aligns with the transport formulation of flow matching models. Prior methods either approximate the current or optimal policy distribution or resort to distillation, which introduces biased gradients or sacrifices multimodal modeling capacity. In contrast, our approach for RL with Density Transport, which we name \emphRLDT, constructs a transport field from a maximum-entropy RL objective using Stein Variational Gradient Descent (SVGD). Then, it finetunes a pretrained flow matching policy to align with this field. Training with this alignment objective is nontrivial because flow-matching policies generate actions via a multi-step process, making direct gradient-based optimization challenging. To overcome this challenge and stabilize training, we approximate policy actions from intermediate denoising steps via expected-target estimation. This allows the transport-field update to propagate into the network parameters without unstable backpropagation through time. Experimental results demonstrate that RLDT outperforms competitive baselines in reward quality and convergence speed. This performance holds across diverse continuous-control tasks, encompassing both dense and sparse rewards, as well as state- and vision-based long-horizon robot manipulation. The project webpage is \hrefthis https URLthis https URL.
[AI-128] InA-Probe: Instruction-Aware Active Probing for Time Series Forecasting with LLM s
链接: https://arxiv.org/abs/2606.08601
作者: Peiliang Gong,Emadeldeen Eldele,Chenyu Liu,Ziyu Jia,Yi Ding,Xinliang Zhou,Lianchao Gu,Qi Zhu,Yang Liu,Daoqiang Zhang,Xiaoli Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models (LLMs) have recently demonstrated impressive potential for time series forecasting. However, existing methods predominantly rely on passive modality alignment or static task reprogramming, which often fail to capture fine-grained, non-stationary temporal patterns or to adapt to nuanced task intents. In this paper, we propose Instruction-aware Active Probing (InA-Probe), which shifts the paradigm from passive alignment toward an active, instruction-driven probing mechanism. Specifically, we design a Multi-Level Instruction Injection mechanism that enriches the model with both global task objectives and fine-grained, patch-level semantic priors. Building on this, an Adaptive Query Generation module produces sample-specific probes that are dynamically modulated by the temporal context. These probes are then refined through a dual-stage attention process: they first internalize task-specific intents via Instruction-Aware Self-Attention, and subsequently interrogate the projected temporal representations through Temporal Cross-Attention to extract salient patterns. Comprehensive experiments on seven real-world benchmarks show that InA-Probe consistently outperforms state-of-the-art deep learning and LLM-based baselines, excelling in both one-for-all generalization and zero-shot transfer while reducing forecasting error by up to 37% in challenging cross-domain scenarios. Ablation studies further confirm that the synergy between adaptive querying and fine-grained instructions is key to unlocking the reasoning power of LLMs for complex time series.
[AI-129] Auditable Graph-Guided Root Cause Analysis for Kubernetes Incidents
链接: https://arxiv.org/abs/2606.08590
作者: Anastasiia Kuvshinova,Seungmin Jin
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: 8 pages, 1 figure. Preprint
Abstract:Kubernetes incidents are diagnosed reliably only when a root-cause system’s reported gains come from incident evidence rather than scenario-specific shortcuts. We present Graph Traversal Agent, a graph-guided RCA agent that combines LLM reasoning with specialized tools. The model reasons over a typed evidence graph, while deterministic graph and tool operations collect evidence, bound the search, and check proposed verdicts. We map operational constraints, including read-only evidence collection, propagation-aware diagnosis, bounded execution, and independently validated verdicts, to a typed incident graph, a LangGraph traversal state machine, and a separate validation stage. On ITBench snapshots scored by one fixed qwen-plus judge, the audited system raises root-cause-entity F1 over an earlier iteration of the same system from 0.6087 to 0.9130 on a 23-scenario common subset. A prompt-level ablation separates prompt-tuned gains from gains that survive once scenario-specific hints are removed: the stripped-prompt configuration retains 0.6958 F1 on a 19-scenario subset. The surviving gain concentrates on ChaosMesh scenarios whose ground-truth root cause is the injected fault object already present in the evidence graph, so we report it as benchmark-coupled rather than broad cross-cluster RCA evidence. Lightweight checks, including same-judge comparison, prompt-level ablation, cascade-source checking, and a telemetry no-leak test, mark claims as supported, pending, or out of scope. We scope the work to ITBench OpenTelemetry-demo snapshots. Live-cluster trials served as an engineering stress test, but alert state and trace availability did not stay stable enough for controlled scoring, so we make no production-readiness or mean-time-to-repair claim.
[AI-130] EinSort: Sorting is All We Need for Tensorizing LLM
链接: https://arxiv.org/abs/2606.08565
作者: Toshiaki Koike-Akino,Jing Liu,Ye Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 38 pages, 17 figures
Abstract:Tensor networks provide efficient representations for compressing large neural networks. By carefully designing shapes and topologies, they can significantly reduce memory and computational costs. However, identifying implicit low-rank structures in large foundation models remains challenging due to their enormous scale and un-structured weight distributions. We propose an adaptive tensorization method that discovers inherent low-rank structure in a target tensor by index ordering. Experiments on weight and KV-cache compression demonstrate improved reconstruction quality compared to baselines.
[AI-131] PAEC: Position-Aware Entropy Calibration for LLM Reasoning in RLVR
链接: https://arxiv.org/abs/2606.08543
作者: Shumeng Yang,Yisu Liu,Jiayi Zheng,Zhaohui Yang,Linjing Li
类目: Artificial Intelligence (cs.AI)
备注: 22 pages, 7 figures
Abstract:Reinforcement learning with verifiable rewards (RLVR) improves large language model reasoning but often suffers from rapid policy-entropy collapse, where the policy prematurely concentrates on narrow high-probability reasoning paths. While global entropy regularization can encourage exploration, uniformly increasing entropy across all token positions is inefficient for long reasoning trajectories, where many tokens are not decision-relevant. We propose Position-Aware Entropy Calibration (PAEC), a token-level entropy-management framework that constructs a soft mask from local top-p entropy and top-two candidate competition, and applies an anchor-based lower-bound penalty to prevent selected-position entropy collapse. Experiments on five mathematical reasoning benchmarks show that PAEC improves macro-average majority-vote performance over strong RLVR baselines, with clear gains on AIME-style tasks. Our results suggest that entropy management in reasoning RL should be formulated as selective exploration allocation over decision-sensitive positions rather than uniform randomness injection.
[AI-132] Agent Trust: A Self-Improving Trust Layer for AI-Agent Actions
链接: https://arxiv.org/abs/2606.08539
作者: Chenglin Yang
类目: Artificial Intelligence (cs.AI)
备注: 29 pages, 5 figures
Abstract:AI agents increasingly take consequential actions – shell commands, cloud operations, and arbitrary tool-calls – so a trust layer must decide, per action, whether to allow, warn, block, or escalate. We argue that the right way to reason about such a layer is by threat type. Lexical (fixed-signature) threats, where danger lives in a stable token, are decidable by deterministic rules; semantic (intent-dependent) threats, where a benign and a malicious action share the same surface, are out of reach for rules by construction. We make this concrete with a negative proof: a determined, hand-authored cloud rule pack lifts held-out accuracy only 48 to 56% overall and moves the semantic categories by 0pp (data_db 29 to 29, observability 59 to 59, supply_chain 50 to 50), while a strong LLM judge carries exactly those categories. We give the judge a self-learning capability: on a corpus that is mainly semantic attacks it nearly doubles rule accuracy (48% to 83.6-85.2%) with near-zero false-blocks, and this holds across two model providers. We turn this into a self-improving dual-store system: the judge distills a growing deterministic rule floor on lexical threats (cheaper over time) and feeds a guarded RAG memory on semantic threats (a verdict-cache fails – surface-twins collapse to ~58% – so a corroboration guard lifts semantic accuracy +13pp, 70 to 84). The result is what sets AgentTrust v2 apart from its static v1 predecessor: a trust layer that self-evolves from its own stream of decisions – cheaper on the lexical class (it distils its own rules) and smarter on the semantic class (it accrues guarded precedent), while never hard-blocking a benign action. An end-to-end online replay shows the judge-call rate falling (50% to 44%) and judge-domain accuracy rising (71% to 80%), with 0 benign hard-blocks across 45,000 actions.
[AI-133] DN-Hypo-Pipeline: An AI-Driven Workflow for Hypothesis Generation via Large Language Models and Scientific Explanations
链接: https://arxiv.org/abs/2606.08532
作者: Lei Lin,Ronghao Wang,Chunbao Zhou,Jue Wang,Yangang Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:A scientific hypothesis is the first step in research and undergoes experimental validation, yet it also reflects a deep understanding of and reasoning about scientific phenomena. We introduce DN-Hypo-Pipeline, an AI-powered workflow based on large language models, designed to support structured scientific thinking and hypothesis generation by leveraging scientific explanations as prior knowledge. This pipeline assists researchers in deriving novel hypotheses from existing literature. Given the explanandum (i.e., the conclusion) of a research paper, it identifies underlying laws, theories, and principles, and reconstructs a new, yet-to-be-verified explanation for the observed phenomenon. We evaluated DN-Hypo-Pipeline in the field of data science modeling using three highly cited papers. Statistical inference, supported by both LLM-as-judge assessment and human expert evaluation, demonstrates that our pipeline is more effective than direct generation methods. Additionally, we validated the two highest-scoring generated hypotheses by developing corresponding novel algorithms, which outperformed the baseline models presented in the original papers. Beyond application in data science, DN-Hypo-Pipeline provides a theoretical framework that not only encompasses theory-guided data science modeling methods but also reveals a more fundamental structure of the modeling process. Moreover, this approach is essentially a generalization of theory-guided modeling, offering potential for extension to other domains and across a broader range of scientific disciplines.
[AI-134] VESTA: A Fully Automated Scenario Generation and Safety Evaluation Framework for LLM Agents
链接: https://arxiv.org/abs/2606.08531
作者: Lu Jia,Haibo Tong,Feifei Zhao,Jindong Li,Dongqi Liang,Ping Wu,Qian Zhang,Yi Zeng
类目: Artificial Intelligence (cs.AI)
备注: Preprint. 18 pages, 12 figures, 5 tables
Abstract:Large language models (LLMs) are increasingly evolving from simple text-based interaction systems into LLM agents that can maintain memory, use tools, access external environments, and execute tasks. As their capabilities and autonomy expand, the safety risks they face also become more diverse. Existing evaluations often rely on manually written scenarios, static prompts, or final-output judgments, making it difficult to capture the diverse risks that agents may face during task execution. We introduce VESTA, a fully automated scenario generation and safety evaluation framework for LLM agents. Based on five risk dimensions, VESTA instantiaes abstract and diverse safety risks in real-world task execution into 1,072 measurable evaluation scenarios. Using the automated evaluation pipeline, 12 LLM agents are evaluated under two authority contexts. The results show that current agents still face substantial behavioral safety risks during task execution, with an average ASR of 47.1% and several models exceeding 70%. These findings demonstrate the importance of executable, process-level evaluation for understanding and improving LLM agent safety.
[AI-135] GEAR-VLA: Learning Geometry-Aware Action Representations for Generalizable Robotic Manipulation
链接: https://arxiv.org/abs/2606.08530
作者: Yuan Zhang,Shiqi Zhang,Yedong Shen,Shuai Dong,Jiajun Deng,Xin Zhang,Yuxuan Gao,Jiajia Wu,Xin Nie,Zhiyuan Cheng,Jianmin Ji,Yanyong Zhang,Xingyi Zhang,Jia Pan
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-Language-Action (VLA) models achieve strong benchmark performance but still struggle in real-world deployment with unseen objects, background shifts, and different robot embodiments. We argue that this stems from the lack of a unified geometry-aware manipulation representation, leaving existing VLAs vulnerable to low-level trajectory supervision, misaligned 3D features, and embodiment differences. To address this, we propose GEAR-VLA, a VLA framework for learning unified geometry-aware action representations for generalizable robotic manipulation. GEAR-VLA adopts coarse-to-fine action learning, where multi-source embodied pretraining equips the VLM with embodied reasoning and discrete action understanding before latent action tokens connect action semantics to a gradient-decoupled DiT continuous action expert. It further performs semantic-aligned 3D integration by aligning a trainable 3D spatial backbone with the VLA representation while freezing the original VLM-aligned visual pathway. To share this representation across robots, GEAR-VLA uses embodiment canonicalization, where embodiment-aware states and embodiment-invariant actions confine robot differences to the low-level interface. Extensive simulation and real-world experiments demonstrate strong generalization: GEAR-VLA achieves state-of-the-art performance on LIBERO, zero-shot LIBERO-Plus, and RoboTwin 2.0, reaches 85.9% success on AgileX and 81.0% on the pretraining-unseen LDT-01 embodiment, and obtains 90.1% success on a 6,360-trial universal grasping benchmark with 212 unseen objects. Code and models will be released at this https URL.
[AI-136] ActProbe: Action-Space Probe for Early Failure Detection of Generative Robot Policies
链接: https://arxiv.org/abs/2606.08508
作者: Bingjia Huang,Xiangyu Li,Xiang Wang,Liang Mi,Zixu Hao,Weijun Wang,Hao Wu,Kun Li,Yunxin Liu,Ting Cao
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 24 pages,9 figures,11 tables, Project page: this https URL
Abstract:Generative robot policies fail unpredictably at deployment: they hesitate at critical moments, drift off-task, or commit to unrecoverable actions. Existing online failure detectors either require white-box access to policy internals or add runtime overhead through resampling and observation-side signals. Our empirical analysis shows that emitted action chunks themselves already carry strong predictive signal for impending failures in generative robot policies. Motivated by this observation, we introduce ActProbe, a lightweight, pure action-space detector that uses two compact signals available from a single forward pass: Temporal Consistency Error (TCE) between consecutive action chunks and Action Chunk Magnitude (ACM) of the current chunk. ActProbe maps these signals to per-step failure probabilities with a task-conditioned LSTM-MLP architecture. Across a diverse suite of generative robot policies and benchmarks, ActProbe raises alerts before failures become visually recognizable, improving the accuracy (F1)-timeliness Pareto frontier of failure detection by an average hypervolume gain of +12.7% over both internal- and external-feature baselines, with a +9.0% early-detection ROC-AUC lead on unseen tasks. ActProbe further transfers to deployment, predicting failures on unseen real-robot pick tasks and accelerating RL fine-tuning (PPO) with 2.9x fewer environment interactions.
[AI-137] Standpoint Logics with Defeasible Beliefs
链接: https://arxiv.org/abs/2606.08503
作者: Nicholas Leisegang,Thomas Meyer,Sebastian Rudolph
类目: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
备注:
Abstract:In this paper, we integrate the defeasible logic of Kraus, Lehmann and Magidor (KLM) with the standpoint logic framework of Gómez Álvarez and Rudolph. This is done with the goal of formally expressing knowledge taking into account multiple (possibly contradicting) viewpoints, which in turn may hold defeasible beliefs. In doing so, we utilise Defeasible Restricted Standpoint Logics (DRSL), introduced by Leisegang et al. Our work expands on previous work by providing a foundational representation result for DRSL semantics and systematically lifting several well-known entailment relations from the propositional case to the standpoint-enhanced setting. In particular, we characterise the semantics for DRSL through a set of KLM-style postulates adapted for the standpoints case. We furthermore provide a means to lift preferential entailment, and the class of entailment relations based on single ranking functions from the purely propositional to the standpoint-enhanced context, including rational and lexicographic closure. We show this can be done equivalently through semantic and algorithmic means. Furthermore, we show that, for each considered form of entailment, the complexity class of entailment checking does not change when moving from propositional KLM to DRSL.
[AI-138] Projecting the Emerging Mindset of SWE Agent by Launching a Wild Code Understanding Journey
链接: https://arxiv.org/abs/2606.08500
作者: Zhengyi Zhuo,Yan Liu
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Software engineering agents (SWE agents) increasingly work through tool-mediated trajectories in real repositories, yet their behavior remains difficult to characterize in concrete, observable terms. These trajectories record tool use, intermediate reasoning, evidence selection, and self-directed stopping, but they do not by themselves explain why particular moves were chosen, what evidence was trusted, or when understanding was judged sufficient. This tension makes trajectory data both limited and valuable: faithful, replayable traces can become an empirical substrate for studying agent behavior when interpreted through disciplined observation. We introduce Ada, a scoped apparatus for repository-level code understanding. Ada enters real codebases through a bounded tool interface, allowing open-ended exploration to remain recordable as finite trajectories. Across this wild-but-bounded setting, Ada chooses where to look, what to read closely, when to consolidate partial understanding, and when to close its account of the repository. We project Ada’s think-action chains through observation lenses that make navigation, evidence selection, synthesis, grounding, and stopping visible without reducing behavior to raw tool counts or speculating about hidden intent. Read together, these lenses produce behavioral profiles grounded in recorded movement through software worlds. Across 408 trajectories, spanning multiple models, repositories, task families, and launch conditions, the study shows how faithful digital traces can be transformed into disciplined, comparable projections of emerging SWE-agent mindset. The results expose differences in efficiency, trajectory diversity, epistemic grounding, and the limits of intervention, while providing a methodological foundation for observing SWE agent behavior in real codebases.
[AI-139] What Makes a Desired Graph for Relational Deep Learning? ICML2026
链接: https://arxiv.org/abs/2606.08491
作者: Yao Cheng,Siqiang Luo
类目: Artificial Intelligence (cs.AI)
备注: This article has been accepted by ICML 2026
Abstract:Relational deep learning (RDL) converts relational databases (RDBs) into heterogeneous graphs, but graphs derived directly from database schemas are often not well suited for how graph neural networks (GNNs) perform relational reasoning. We study what makes a relational graph suitable for deep learning and show that schema-derived graphs suffer from two systematic failures: information overload and semantic fragmentation. Our empirical analysis reveals that the desired graph is not the raw schema, but a result of controlled structural adaptation. Performance depends on balancing two operations: mitigating information overload via filtering, and repairing semantic fragmentation via injection. Specifically, filtering serves as a bias-variance knob with non-monotonic effects, while injection improves performance only when it explicitly restores the relational dependencies missing from the original schema. Based on these findings, we develop an end-to-end structural optimizer that applies both operations to adapt relational graphs automatically. Across 26 tasks spanning classification, regression, and recommendation, the optimized graphs consistently improve accuracy while often reducing inference cost.
[AI-140] STELLAR: Spatio-Temporal Environmental Learning with Latent Alignment and Refinement for Long-Tailed Species Distribution Modeling IJCAI2026
链接: https://arxiv.org/abs/2606.08484
作者: Shufeng Kong,Tao Yu,Yuanyuan Wei,Caihua Liu,Junwen Bai,Yingheng Wang,Marc Grimson,Daniel Fink,Carla P. Gomes
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accept by IJCAI 2026
Abstract:Joint Species Distribution Modeling (JSDM) is a key enabler for biodiversity monitoring and conservation planning. However, accurate JSDM faces two coupled challenges: environmental drivers and species distributions are inherently spatio-temporal, while species co-occurrence patterns exhibit complex non-linear community structure and severe long-tail imbalance driven by rare species. Existing approaches often address these factors in isolation, learning from static covariates or neglecting the historical trajectories of dynamic community structure. To overcome these limitations, we propose STELLAR (Spatio-Temporal Environmental Learning with Latent Alignment and Refinement), a novel framework that learns a shared latent space where dynamic habitat context and community structure are optimized jointly. Our approach integrates three complementary components: (1) a Graph-Temporal Encoder that employs graph attention and recurrent units to aggregate spatial neighborhood effects and capture the co-evolving historical dynamics of environmental context and community structure; (2) a Context-Anchored Latent Alignment mechanism that structures the latent space using a label-activated mixture prior and supervised contrastive learning, actively clustering species based on shared environmental preferences; and (3) an Imbalance-Aware Decoupled Decoding module that utilizes Asymmetric Loss to focus learning on hard, rare species samples, preventing mode collapse in the long tail. Experiments on the large-scale eBird dataset, curated with domain experts, demonstrate that our framework significantly outperforms state-of-the-art baselines, particularly in predicting rare species and revealing interpretable species interactions.
[AI-141] sting the Black Box: Structural Barriers to Independent Evaluation of Consumer-Facing Health LLM s
链接: https://arxiv.org/abs/2606.08483
作者: Rahul Gorijavolu,Kaushik Madapati,Pritika Vig,Rawan Abulibdeh,Nikhil Jaiswal,Mahri Kadyrova,Zeamanuel Hailu Tesfaye,Charles Senteio,Paula Maurutto,Leo Anthony Celi
类目: Artificial Intelligence (cs.AI)
备注: 6 pages, 1 figure. Preprint submitted for review
Abstract:Background: Consumer-facing large language models are now a common source of health information, and they interpret and personalize responses rather than retrieve them. Whether their responses vary across users is a clinical, equity, and governance question, sharpened by evidence that sycophantic responses can alter judgment and increase trust. Objective: To evaluate response variation and sycophancy in consumer-facing health LLMs under conditions resembling ordinary patient use. Methods: We constructed simulated user profiles differing in geography, browsing context, expressed beliefs, and social determinants of health, drawing on literature linking social context to health attitudes. We adapted validated instruments, including the Vaccination Attitudes Examination scale and reproductive attitudes scales, into multi-turn prompts designed to elicit clinically meaningful variation across users. Results: The evaluation encountered five linked barriers. Factual prompts produced stable responses that masked sycophancy emerging over multi-turn conversation. Browser-based interfaces did not disclose which signals influence outputs and could not be reset to a clean baseline. Large-scale testing was restricted by terms of service, rate limits, and bot detection. Accuracy-based criteria could not capture tone, framing, or omission, and LLM-as-judge methods risked shared alignment bias. Models changed without traceable version identifiers, preventing reliable replication. Conclusions: No reliable independent evaluation framework yet exists for examining how consumer-facing health LLMs behave in ordinary use. Oversight requires disclosure of personalization signals, stable version identifiers, researcher safe harbor programs, and post-deployment monitoring of health-related outputs. Comments: 6 pages, 1 figure. Preprint submitted for review Subjects: Artificial Intelligence (cs.AI) ACMclasses: I.2.7; J.3; K.4.1 Cite as: arXiv:2606.08483 [cs.AI] (or arXiv:2606.08483v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2606.08483 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Zeamanuel Tesfaye Dr. [view email] [v1] Sun, 7 Jun 2026 07:01:15 UTC (61 KB) Full-text links: Access Paper: View a PDF of the paper titled Testing the Black Box: Structural Barriers to Independent Evaluation of Consumer-Facing Health LLMs, by Rahul Gorijavolu and 9 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI prev | next new | recent | 2026-06 Change to browse by: cs References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); About Help contact arXivClick here to contact arXiv Contact subscribe to arXiv mailingsClick here to subscribe Subscribe Copyright Privacy Policy Web Accessibility Assistance arXiv Operational Status
[AI-142] PIPE-Cypher: Automatic Enterprise Benchmark Generation for Text-to-Cypher Systems
链接: https://arxiv.org/abs/2606.08481
作者: Suraj Ranganath,Anish Raghavendra
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Databases (cs.DB); Software Engineering (cs.SE)
备注:
Abstract:Enterprise property graphs vary widely in schema structure, internal terminology, domain assumptions, governance constraints, and user interaction patterns. A deployment-relevant Text2Cypher benchmark therefore reflects the questions users and agents actually ask of that graph. Creating such a benchmark is difficult because schemas and values are unique, and graph structure changes over time. Each NL-query pair must also be executable, use real graph entities, preserve diversity, and remain balanced across query types and difficulty levels. We present PIPE-Cypher, a local benchmark-generation pipeline that turns a live property graph and optional seed queries from customer questions, analyst logs, or agent tool calls into balanced NL-to-Cypher benchmarks. PIPE-Cypher combines schema profiling, reverse-query grounding, constrained generation, deterministic Cypher governance, execution validation, redaction, diversity controls, and a calibrated local LLM judge. Using local Qwen3.5-9B generation and judging, PIPE-Cypher exports 3,000 accepted FinBench/SNB examples, completes three audited ablation suites, calibrates judge behavior with human labels, and evaluates 11 local downstream models. The resulting benchmark is deliberately discriminative: zero-shot transfer is weak, while a few-shot control shows that schema-specific example banks can help compatible model families. Together, PIPE-Cypher makes Text2Cypher benchmarking a repeatable process that evolves with the graph, its users, and its target workloads.
[AI-143] A Variability-Based Framework for Interpretable Naming in Formal and Relational Concept Analysis
链接: https://arxiv.org/abs/2606.08477
作者: Alain Gutierrez,Marianne Huchard,Pierre Martin,André Miralles,Violaine Prince
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Knowledge extraction from symbolic data often produces abstractions that are formally defined but not immediately interpretable by users. Formal Concept Analysis (FCA) and Relational Concept Analysis (RCA) provide representative settings for this issue: they generate explicit conceptual structures, implications, and relational dependencies from object descriptions and relations. Although these structures are explainable by design, their concepts are often identified by technical labels, which limits their use as human-interpretable knowledge units. Assigning meaningful names to such concepts is therefore a key issue for interpretation, navigation, validation, and reuse by domain experts. This paper investigates concept naming in FCA and RCA from a symbolic knowledge representation perspective. We first characterize the linguistic and terminological challenges involved in naming generated symbolic abstractions, including ambiguity, discrimination, concision, and consistency across related concepts. We then propose a configurable framework for LLM-assisted concept naming. The framework relies on a variability model that controls which sources of information are exposed during naming, such as intent, extent, inherited information, neighboring concepts, implications, and relational attributes. It thereby makes explicit the semantic choices involved in moving from formal concept descriptions to human-readable names. The approach is illustrated as a proof of concept on a small relational dataset in the pizzeria domain. This illustration shows how different configurations influence the names suggested by an LLM, and how naming variability can reveal interpretation choices, relational dependencies, and possible modeling issues in the underlying symbolic data. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2606.08477 [cs.AI] (or arXiv:2606.08477v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2606.08477 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-144] FlashCP: Load-Balanced Communication-Efficient Context Parallelism for LLM Training
链接: https://arxiv.org/abs/2606.08476
作者: Zheng Wang,Eric Liu,Linan Jiang,Zhongkai Yu,Zaifeng Pan,Yue Guan,Yuke Wang,Yufei Ding
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注: 10 pages, 6 figures
Abstract:Context parallelism (CP) is essential for training large-scale, long-context language models, as it partitions sequences to reduce memory overhead. However, existing CP methods suffer from workload imbalance, inefficient kernels, and redundant communication due to static sequence sharding and key-value (KV) tensor communication. We present FlashCP, a load-balanced and communication-efficient framework for CP training. FlashCP introduces a sharding-aware communication mechanism to eliminate redundant KV communication and proposes a novel Whole-Doc sharding strategy that maximizes communication savings while maintaining balanced workloads. To efficiently combine Whole-Doc and Per-Doc sharding, FlashCP further designs a heuristic algorithm to search for near-optimal sharding plans. Extensive experiments show that FlashCP achieves up to 1.63x speedup over state-of-the-art CP frameworks across diverse datasets.
[AI-145] he Confidence Trap: Calibration Attacks for Graph Neural Networks
链接: https://arxiv.org/abs/2606.08467
作者: Cuong Dang,Jiahao Zhang,Hieu Ta Quang,Dung Le,Lu Cheng,Suhang Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:While confidence calibration is essential for trustworthy decision-making in safety-critical applications, the robustness of calibrated GNNs to adversarial structural perturbations remains largely unexplored. However, studying calibration attacks on graphs presents unique technical challenges: (1) the discrete nature of graph structures complicates gradient-based optimization, (2) existing underconfidence objectives fail to drive predictions toward uniform distributions, and (3) GNNs are highly sensitive to edge perturbations, often causing unintended label changes that violate attack constraints. To address these challenges, we propose a \textbfUnified Graph Calibration Attack (UGCA) framework designed for \textbfworst-case (white-box) analysis of GNN calibration robustness. UGCA introduces a KL-divergence loss to encourage uniform predictive distributions, a reranking mechanism to reduce label flipping, a hybrid loss to recover labels when violations occur, and beam search to explore a broader adversarial search space. We further provide theoretical insights linking model generalization, dataset complexity, and calibration vulnerability, showing that models with higher accuracy or trained on datasets with more classes are more susceptible under this threat model. Extensive experiments demonstrate that UGCA substantially increases Expected Calibration Error while preserving classification accuracy. Our code is publicly available at this https URL.
[AI-146] GIFT: LLM -Guided State-Reward Interface for Financial Reinforcement Learning
链接: https://arxiv.org/abs/2606.08450
作者: Yanyan Wu,Boyi Zhang,Yanlin Liu,Xinyu Fang,Jining Luan,Meiqi Zhang,Jiacheng Liu,Hao Zeng,Dexu Yu,Chang Liu,Hanwen Du,Yongxin Ni,Youhua Li
类目: Artificial Intelligence (cs.AI)
备注: 25 pages, 7 figures. Code and data are available at this https URL . Equal contribution: Yanyan Wu and Boyi Zhang. Corresponding author: Youhua Li
Abstract:Financial portfolio trading is naturally formulated as a reinforcement learning problem, where an agent sequentially rebalances assets under changing market conditions to balance return, risk, and transaction costs. Yet in non-stationary markets, raw OHLCV states and short-horizon return rewards often provide an under-specified learning interface, motivating large language models as a way to inject financial knowledge into state and reward design while constraining open-ended generation. To this end, we propose GIFT, an LLM-guided framework for state-reward interface design in PPO-based financial reinforcement learning. Rather than using the LLM to make trading decisions, GIFT uses Factor-guided State Enhancement to generate state features from financial-factor primitives, Risk-rule-guided Reward Shaping to generate auxiliary rewards from portfolio-risk rules, and Diagnostic-guided Refinement to revise candidate interfaces using PPO rollout diagnostics. After refinement, GIFT fixes the selected state-reward interface before evaluation, with no further LLM queries or interface updates at test time. Comprehensive rolling-window experiments across diverse market regimes and portfolio scenarios demonstrate that GIFT improves learning-signal quality and out-of-sample risk-adjusted portfolio performance over baselines. Code and data are available at: this https URL .
[AI-147] Not Just After One: Sleep-Inspired Replay Prevents Catastrophic Forgetting After Sequential Tasks
链接: https://arxiv.org/abs/2606.08447
作者: Anthony Bazhenov,Jean Erik Delanois,Giri P. Krishnan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:One of the critical limitations of artificial neural networks is their lack of ability to continually learn: training on new tasks often leads to interference and forgetting of the previous ones. While several algorithms have been proposed to protect old memories from interference, they are typically applied during or immediately after each new episode of training. In contrast, humans and animals can learn continuously, acquiring multiple new memories during active learning before consolidating all of them into long-term storage. Here we show that multiple new tasks can be trained sequentially before an unsupervised sleep-like replay phase is applied to partially restore performance across all previously learned tasks. Our study further suggests that task-specific information remains resilient to new training but decays gradually as network is trained on new tasks. These findings point to novel principles for developing a broad range of continual learning AI solutions.
[AI-148] Sparrow: Sparse Rollout for Stable and Efficient Long-context RL of Large Language Models
链接: https://arxiv.org/abs/2606.08446
作者: Yang Zhou,Ranajoy Sadhukhan,Zhaofeng Sun,Zhuoming Chen,Souvik Kundu,Saket Dingliwal,Sai Muralidhar Jayanthi,Aram Galstyan,Haizhong Zheng,Beidi Chen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Despite being powerful, reinforcement learning with verifiable rewards (RLVR) induces extremely long COT, making it computationally expensive. Since RLVR per-step cost is dominated by long-context rollout generation, sparse attention offers a promising way to accelerate dense rollout. However, sparse rollouts require a delicate stability-efficiency tradeoff: overly aggressive sparsity causes collapse, while overly lenient sparsity gives insufficient speedup. In this work, we study this tradeoff through sparse-to-dense actor-policy mismatch. We first observe that sparse rollout collapse is not driven by uniform degradation across tokens: most sparse tokens align perfectly with dense even under aggressive sparsity. Motivated by this, we hypothesize that sparse rollout training remains stable if the lower tail of per-token actor-policy mismatch stays above a critical threshold throughout the trajectory. We introduce a dynamic sparsity schedule that keeps this tail statistic constant during generation and validate our hypothesis. Across Qwen3 thinking-family models, keeping the tail mismatch statistic near a consistent threshold generally enables stable training. We then use a cost model to find the sparsity schedule for maximum speedup under this mismatch threshold, achieving 2.2x, 2.4x, and 2.0x rollout speedups when training Qwen3-1.7B, Qwen3-4B, and Qwen3-8B. Empirically, we show the thresholds generalize to a larger model (Qwen3-14B) and another RL domain (coding). Finally, our analysis naturally motivates DistillSparse: lightweight LoRA-based distillation on sparse rollout lets more aggressive sparsity reach the same sparse-to-dense mismatch threshold, yielding higher speedup.
[AI-149] AI Code Sandboxes: A Comparative Security Study. Part 1 of 2 – Engine-Level Properties (Attack Surface Leakage Stackability CVE History Patch Cadence Fuzzing)
链接: https://arxiv.org/abs/2606.08433
作者: George Andronchik,Pavel Lokhmakov
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 61 pages, 7 figures, 33 tables; Part 1 of 2; companion code repository (Apache-2.0): this https URL
Abstract:This paper reads six engine-level measurements together – 1.1 host attack surface, 1.2 information leakage, 1.3 defense-in-depth stackability, 1.4 public CVE history, 1.5 patch cadence, and 1.6 upstream fuzzing posture – to describe how five AI-sandbox products isolate guest code from the host kernel. No single axis is a sufficient basis for a comparative judgement; the cross-axis reading is the load-bearing analysis. Three high-level findings: (1) engine classes (microVM, userspace kernel, OCI container) separate cleanly on every architectural axis, but products within a class do not; (2) product pin policy is the dominant operator-facing variable – engine-side patch latency aggregates to ~0 days for coordinated disclosures, while downstream lag spans 0 days to 471+ days to “opaque” to infinity; (3) fuzzing investment splits into three tiers, and the strongest combination – microVM x continuous public fuzzer – is unoccupied in this set, leaving the “0 published CVEs x no upstream fuzzer x no academic study” intersection structurally unmeasured. We report per-axis orderings, per-product portraits, and a threat-model qualification matrix; no overall ranking is proposed. Companion repository (code, Apache-2.0): this https URL. License: CC BY 4.0. Comments: 61 pages, 7 figures, 33 tables; Part 1 of 2; companion code repository (Apache-2.0): this https URL Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) ACMclasses: D.4.6; K.6.5; C.2.0 Cite as: arXiv:2606.08433 [cs.CR] (or arXiv:2606.08433v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2606.08433 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-150] rajectory-Refined Distillation
链接: https://arxiv.org/abs/2606.08432
作者: Li Jiang,Haoran Xu,Yichuan Ding,Amy Zhang
类目: Artificial Intelligence (cs.AI)
备注: under review
Abstract:On-policy distillation (OPD) has become a central post-training tool for large language models (LLMs), providing dense per-token teacher supervision along the student’s own rollouts. In this work, we identify a common structural cause underlying OPD, which we call prefix failure. Under prefix failure, dense per-token supervision induces a bimodal teacher mixture and fragmented gradients that token-level loss truncation or reweighting fail to address. This observation motivates us to move beyond token-level loss interventions toward trajectory-level output corrections. We thus propose Trajectory-Refined Distillation (TRD), a trajectory-level correction method that revises the student’s rollout under the teacher guidance while within on-policy support. By correcting problematic prefixes before distillation, TRD mitigates prefix failure at its source. Moreover, TRD improves the exploration by exposing the student to alternative valid derivations under teacher guidance, even when the original rolls are already correct. TRD can also be applied to on-policy self-distillation (OPSD), a parameter-sharing variant that uses the student model conditioned on privileged informations as the teacher. Across a wide range of benchmarks and base models at multiple scales, TRD consistently outperforms prior baselines, improving single-attempt accuracy and broadening reasoning coverage. Code is available at this https URL
[AI-151] PACT: Self-Evolving Physical Safety Alignment for Diffusion Policies in Embodied Manipulation
链接: https://arxiv.org/abs/2606.08414
作者: Lingxuan Wu,Zijian Zhu,Lizhong Wang,Chengyang Ying,Huayu Chen,Xiao Yang,Fangming Liu,Jun Zhu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Diffusion policies have achieved remarkable success in robotic manipulation, yet they often fail to satisfy strict physical constraints required for safe deployment. Existing approaches impose safety either prematurely during training or reactively via external guardrails at test time, limiting policy expressivity and overall scalability. We propose Physical safety Alignment for Constrained Trajectories (PACT), a self-evolving post-training framework that projects pretrained diffusion policies onto constraint-feasible regions without accessing demonstration data or task rewards. PACT distills constraint gradients into the diffusion model through a reverse-KL objective with dense supervision across timesteps. It incorporates a curriculum that progressively tightens constraints while maintaining theoretically bounded policy shift and monotone improvement, mitigating the safety-performance trade-off from catastrophic forgetting. On simulated and real-world embodied manipulation benchmarks, PACT significantly reduces safety violations by 31.0% on average while improving task success by 30.7%.
[AI-152] Provably Efficient Personalized Multi-Objective Bandits with Proactive Conversational Queries UAI2026
链接: https://arxiv.org/abs/2606.08410
作者: Linfeng Cao,Ming Shi,Ness B. Shroff
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: UAI 2026
Abstract:Personalized decision-making in multi-objective bandits requires learning user-specific trade-offs among competing objectives. Since arm utility depends on both unknown rewards and unknown preferences, existing methods infer preferences only from utility feedback, entangling preference learning with reward exploration. In practice, however, users often reveal their priorities through proactive conversational queries (e.g., “cheap and clean hotel”), yet this structured signal is not leveraged. We formalize a proactive query-based framework in which user queries provide structured preference signals. Modeling these signals via a Plackett-Luce subset choice model, we show that query-only learning is insufficient due to a fundamental shift-invariance barrier. To resolve this, we introduce MO-PQUCB, a hybrid algorithm that integrates query-based preference anchoring with bandit feedback through shift-invariant regularization and dual-exploration UCB. We prove that proactive queries accelerate preference estimation and yield improved regret scaling over prior preference-aware MO-MAB methods. Under corrupted queries, we further characterize statistical limits and design a robust estimator achieving near-optimal performance when the corruption is sparse. Experiments validate both theoretical and practical gains.
[AI-153] Self-Evolving Scientific Agent Discovers Generalizable Physically-Reason ed Fluid Control
链接: https://arxiv.org/abs/2606.08405
作者: Boai Sun,Wenjin Guo,Zongmin Yu,Liu Yang
类目: Artificial Intelligence (cs.AI); Fluid Dynamics (physics.flu-dyn)
备注:
Abstract:While data-intensive deep reinforcement learning can optimize complex control policies, scientific discovery in physical systems fundamentally requires an interpretable chain of reasoning that connects physical evidence to structured control architectures. Here, we present a self-evolving scientific-agent workflow, driven by large language models and iterative code generation, that automates controller construction while preserving strict interpretability and rigorous physical reasoning. Instead of adjusting weights, the agent deploys candidate strategies into physical simulations, actively diagnoses dynamic behaviors from multimodal evidence, and translates these observations into progressive source-code refinements. We demonstrate this framework on a highly non-linear fluid-structure interaction problem: an underactuated, two-joint dogfish swimmer tasked with spatial target reaching using only joint angular accelerations. Starting from a propulsive seed policy that exhibits a one-sided steering bias, the agent autonomously discovers and refines a unified controller that robustly captures all canonical targets. Remarkably, without any retraining or target-specific branching, the synthesized control policy generalizes to unseen static targets and dynamically curved pursuit trajectories. The auditable evolve log reveals an emergent control architecture built upon traveling-wave propulsion, body-frame target guidance, yaw-rate feedback, signed mean-tail curvature, and adaptive cadence relief. Our results show that an autonomous scientific agent can successfully transform accumulated physical evidence into robust, mathematically readable control policy, while maintaining a fully traceable process of scientific discovery.
[AI-154] Hiding in Plain Floats: Steganographic Carriers for Indirect Prompt and Content Injection ICML2026
链接: https://arxiv.org/abs/2606.08403
作者: Mudit Sinha,Sanika Chavan
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: Accepted as a poster at FAGEN@ICML 2026. 14 pages, 3 figures
Abstract:Text-centered prompt-injection defenses assume that the malicious signal is visible in one of the inspected text views. We study a reproducible LLM01-style indirect prompt/content-injection failure mode where that assumption breaks: a payload caught in plain English slips past the same detector when it is transported as structured float parameters and reconstructed only as fragmented telemetry. Across 14,400 attacked real-model trials on three commercial LLM APIs from different providers, the IFS-derived float-array carrier preserves 94.3% leakage ASR under the strongest dual-layer text-classifier defense evaluated in the main matrix: a Prompt Guard 2 + TF-IDF ensemble; the same carrier-level pattern also replicates with a fine-tuned roberta-base detector. We emphasize leakage ASR because downstream systems may act on quoted or reproduced markers even when the model refuses, but Strong ASR is the stricter metric for structurally compliant attack success. A 2 x 2 ablation shows that data-layer storage and reconstruction-layer fragmentation defeat different text views and that both are needed to evade both. A simple xxd detector and semantic validation block the current T3 instance, so the contribution is not an undetectable exploit but a measured failure boundary for text-only inspection in structured-input pipelines that expose reconstructed auxiliary channels to an LLM.
[AI-155] STAR-KV: Low-Rank KV Cache Compression via Soft Thresholding for Adaptive Rank Control
链接: https://arxiv.org/abs/2606.08382
作者: Priyansh Bhatnagar,Ashkan Moradifirouzabadi,Se-Hyun Yang,SeungJae Lee,Jungwook Choi,Mingu Kang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Low-rank projection has emerged as a promising approach for compressing the KV cache by exploiting hidden-dimension redundancy. However, prior methods rely on fixed or heuristic rank selection and struggle to achieve aggressive compression with minimal accuracy degradation. We propose STAR-KV, an adaptive low-rank KV cache compression framework with fine-grained rank control. STAR-KV encompasses 1) a differentiable thresholding mechanism that enables optimal rank selection at both attention-head and block levels, 2) a hybrid decomposition strategy that applies different low-rank factorizations according to the sensitivity of key and value projections, and 3) a low-rank-aware mixed precision quantization that leverages data statistics for near lossless low-bit quantization. Evaluated across multiple LLMs and benchmarks, STAR-KV achieves up to 75% KV cache compression and up to 20x overall KV cache reduction when combined with quantization. Enabled by custom Triton-based GPU kernels, STAR-KV delivers up to 6.9x speedup for the attention module and 3.1x end-to-end generation throughput. Our code is publicly available at: this https URL.
[AI-156] -DAC-PS: Twin-Target Deterministic Actor-Critic with Policy Smoothing for Optimal Trade Execution
链接: https://arxiv.org/abs/2606.08379
作者: Ilia Zaznov,Atta Badii,Julian Kunkel,Alfonso Dufour
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG); Computational Finance (q-fin.CP); Trading and Market Microstructure (q-fin.TR)
备注: 21 pages, 1 figure, 3 tables
Abstract:This study addresses the optimal execution of large stock sell programs by introducing TT-DAC-PS (Twin-Target Deterministic Actor-Critic with Policy Smoothing), a deterministic actor-critic architecture that combines twin exponential-moving-average critic targets with pessimistic min backup, TD3-style target policy smoothing noise, delayed actor updates, and conservative Q regularisation to curb overestimation. Exploration uses Ornstein-Uhlenbeck (OU) noise with a hybrid schedule: deterministic episode-wise decay, variance-guided adjustment based on recent reward dispersion, and a Soft Actor-Critic (SAC)-style temperature that is learned and mapped to the noise scale. The environment integrates Almgren-Chriss (AC) trade impact with Limit Order Book (LOB) prices and volumes, normalised state features, per-step volume participation caps, and a utility-based reward. The trade execution algorithm is applied to LOB data for ten U.S. stocks. Performance is assessed against reinforcement-learning baseline algorithms, including Proximal Policy Optimisation (PPO), Soft Actor-Critic (SAC), and Advantage Actor-Critic (A2C), as well as alternative trade execution algorithms, including Time-Weighted Average Price (TWAP), Volume-Weighted Average Price (VWAP), and AC. The proposed model consistently reduces mean implementation shortfall percentage with competitive variance, outperforming classical baselines and standard reinforcement-learning benchmark models.
[AI-157] RiskNet: A large-scale dataset of AI risk incidents from news with alignment and multi-dimensional annotations
链接: https://arxiv.org/abs/2606.08376
作者: Leihan Zhang,Wecheng Ye,Xianlong Ma,Haochuan Liu,Yang Li,Qianyu Zhang,Jinliang Chen,Qiang Yan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: The manuscript has been submitted to Scientific Data
Abstract:As artificial intelligence (AI) systems are increasingly deployed across socially consequential domains, reports of AI-related harms and failures have grown in frequency and diversity. Although existing governance frameworks articulate high-level principles for responsible AI, large-scale empirical resources for tracking and analyzing real-world AI risk incidents remain limited. Existing incident collections are often manually curated, relatively small in scale, and insufficient for continuous, data-driven monitoring and downstream computational analysis. To address this need, we present RiskNet, a large-scale dataset of AI risk incidents constructed from large-scale multilingual news sources. RiskNet applies a structured pipeline for AI risk news identification, event-level report screening, incident alignment, and multi-dimensional incident classification. The resulting resource organizes dispersed news reports into incident-centered records and provides benchmark datasets for event classification, incident alignment, and incident-level risk labeling. In its current release, RiskNet covers hundreds of millions of source records and yields a large-scale collection of AI risk-related reports, including aligned incident clusters and annotated benchmark subsets. The dataset is also accessible through an online platform for browsing and exploration. We describe the data sources, processing workflow, taxonomy design, and technical validation of the resource. RiskNet is intended to support downstream research on AI safety, governance, risk analysis, and benchmarking, as well as longitudinal and cross-source analyses of AI-related harms. By providing a structured and reusable empirical resource, RiskNet helps bridge the gap between high-level governance principles and the documented realities of AI risk incidents.
[AI-158] An Information-Theoretic Definition for Open-Ended Learning
链接: https://arxiv.org/abs/2606.08369
作者: Wanqiao Xu,Yifan Zhu,Benjamin Van Roy
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:A growing body of work points to the great promise of AI systems that can continually expand their capabilities as they operate in an open-ended environment. But yet there is no coherent definition of open-endedness or theory about how an agent ought to explore an open-ended environment. We introduce an information-theoretic definition based on a new concept – the \textit bit-equivalent – which quantifies the information required to attain each level of expected reward. We consider an environment to be open-ended if an agent can attain linear growth in the bit-equivalent. We establish that classical bandit environments are not open-ended and formulate a bandit environment that is. We also introduce an algorithm that achieves open-ended learning in this environment.
[AI-159] Pre-Intervention Prediction of Sparse Autoencoder Steering Side Effects
链接: https://arxiv.org/abs/2606.08365
作者: Evan Duan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Sparse autoencoder (SAE) features are increasingly used to steer language models, but feature steering is rarely clean: the same intervention can behave inconsistently across contexts and perturb unrelated features. We introduce a pre-intervention screening framework for forecasting SAE steering side effects from feature statistics computed before steering. We operationalize side effects along two axes of steering modularity, effect stability and collateral spread, and evaluate GPT-2-small, Pythia-70M-deduped, Gemma-2-2B, and Llama-3.1-8B across ReLU, JumpReLU, and TopK SAE dictionaries. Across these settings, decoder geometry, activation statistics, co-activation structure, and direct-logit footprint predict steering modularity better than frequency-only and activation-magnitude baselines. The signal is strongest in GPT-2-small, Pythia-70M, and Llama-3.1-8B, where it survives residualization against magnitude-related confounds, and weaker in Gemma-2-2B. Held-out screening shows that ranking unseen features by predicted cleanliness can select features that steer more cleanly on fresh contexts, but the successful axis varies by setting: GPT-2 improves most cleanly, Pythia improves mainly on stability, Llama mainly on collateral, and Gemma only partially. A controlled Llama Scope width comparison shows that the predictive signal persists under a 32K-to-128K dictionary-width change, although the screening payoff becomes less stable. Overall, SAE steering side effects are predictable in advance, but the useful predictor signature and transferred modularity axis are model- and dictionary-setting dependent.
[AI-160] Generative Frontier Planning for Adaptive Peer-Referral Recruitment under Covariate-Dependent Arrivals
链接: https://arxiv.org/abs/2606.08360
作者: Lingkai Kong,Hezi Jiang,Andrew Ma,Keyu Wang,Akseli Kangaslahti,Milind Tambe
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Peer-referral recruitment systems such as respondent-driven sampling are critical for studying and intervening on hidden populations affected by infectious diseases. To accelerate recruitment, public health agencies must adaptively allocate limited referral resources across multiple rounds, where current decisions shape both the number and the covariates of future recruits. Prior work makes this problem tractable by assuming that referrals are drawn i.i.d.\ from a homogeneous population, an assumption that ignores the homophily and shared context that drive real peer recruitment. We instead consider a more realistic model in which both referral capacity and the covariates of newly referred individuals are conditioned on the referrer, learned from data with a censored count model and a conditional generative model. The resulting planning problem is challenging because each candidate allocation induces a different distribution over future recruits. We propose \emphGenerative Frontier Planning (GFP), a model-based planner that replaces per-step Monte-Carlo sampling with a deterministic backup over a latent covariate-coverage value surrogate. The surrogate is designed so that the expected value of the next frontier depends on the offspring generative model only through finite-dimensional summaries that are amortized offline, and so that the resulting per-round objective is monotone with diminishing returns. Together, these two properties make planning tractable: the deterministic backup eliminates Monte-Carlo sampling, and the diminishing-returns structure lets a marginal greedy allocation achieve a ((1-1/e))-approximation for the per-round problem. On a simulation environment calibrated to a real respondent-driven sampling dataset, GFP outperforms random, reinforcement-learning, and i.i.d.\ dynamic-programming baselines across four discount factors.
[AI-161] Integrating Deep Learning Demand Forecasting with Multi-Objective Optimization for Circular Coffee Supply Chains: A Data-Driven Framework for Cost Emissions and Freshness Management
链接: https://arxiv.org/abs/2606.08314
作者: Gerçek Budak(1),Faraz Gholamzadeh Gharehgheshlaghi(1),Melika Barjesteh Vaezi(2),Ahmad Gholizadeh Lonbar(3) ((1) Department of Industrial Engineering, Ankara Yıldırım Beyazıt University, Keçiören, Ankara 06010, Türkiye, (2) Department of Kinesiology and Sport Management, Texas Tech University, Lubbock, TX, United States, (3) Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL, USA)
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The coffee supply chain is one of the most complex agri-food networks, marked by geographically dispersed production, multi-tier coordination, and high sensitivity to quality and freshness. While sustainability and digitalization have gained attention, demand forecasting, optimization, and traceability are often treated separately. This study presents a two-phase integrated framework. First, a hybrid CNN-LSTM model is used for demand forecasting. On the public Coffee Chain Sales dataset with chronological 70/15/15 splitting, the model achieves MAE of 22.87 and R^2 of 0.90, outperforming the best deep learning benchmark by ~12% and classical methods by over 30%. In the second phase, the forecasted demand feeds a tri-objective mixed-integer linear programming (MILP) model that jointly minimizes cost, minimizes carbon emissions, and maximizes product freshness in a multi-period, multimodal, closed-loop supply chain with circular recovery. Freshness is modeled via exponential decay based on inventory age. Using the epsilon-constraint method, 25 Pareto solutions are obtained. Sensitivity and policy analyses show that balanced sustainability policies can reduce emissions by 22.4% with only a 9.9% cost increase while maintaining near-optimal freshness. Keywords: Coffee supply chain; Deep learning; Demand forecasting; Multi-objective optimization; Circular economy; CNN-LSTM; Mixed-integer linear programming. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2606.08314 [cs.AI] (or arXiv:2606.08314v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2606.08314 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-162] Neuro-Symbolic Injection of LTLf Constraints in Autoregressive Reinforcement Learning Policies KR2026
链接: https://arxiv.org/abs/2606.08312
作者: Ashkan Ansarifard(1),Matteo Mancanelli(1),Elena Umili(1),Fabio Patrizi(1) ((1) Sapienza University of Rome)
类目: Artificial Intelligence (cs.AI); Formal Languages and Automata Theory (cs.FL)
备注: Accepted at the Joint Workshop on Statistics and Knowledge Integration for Logic, Learning, Ethical Decisions, and LLMs (SKILLED-LLMs 2026), co-located with KR 2026 and FLoC 2026, Lisbon, Portugal
Abstract:In this work we study offline reinforcement learning (RL) under temporally extended task constraints expressed in Linear Temporal Logic over finite traces (LTLf). Recently, transformer-based approaches such as Trajectory Transformers and Decision Transformers have been adopted to address RL as a sequence modeling problem. However, these methods optimize purely for reward and do not account for high-level temporal requirements. Here, we introduce a neurosymbolic framework that injects LTLf background knowledge into such transformer-based RL policies. Our approach compiles LTLf formulas into deterministic finite automata (DFAs) and integrates them into the learning process through a differentiable representation and a logic-based loss function. In particular, we derive differentiable satisfaction signals from DFA progression and use them as a regularization term during training. The resulting method is architecture-agnostic across different models. We evaluate the proposed framework on navigation environments with specification suites covering combinations of safety and reachability temporal properties. Experimental results show that incorporating background knowledge not only improves constraint satisfaction, but also maintains competitive return compared to vanilla baselines.
[AI-163] Curation of a Cardiology Interface Terminology for Highlighting Electronic Health Records using Machine Learning
链接: https://arxiv.org/abs/2606.08311
作者: Mahshad Koohi Habibi Dehkordi,Shuxin Zhou,Yehoshua Perl,Fadi P. Deek,James Geller,Gai Elhanan,Andrew J. Einstein,Luke Lindemann,Vipina K. Keloth
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Electronic health record (EHR) notes are dense medical documents containing large amounts of information, often filled with complex medical jargon. Highlighting all details in EHRs helps reduce the likelihood of missing crucial information by drawing attention to key content. This study proposes the design of a Cardiology Interface Terminology (CIT) to accurately highlight all details in EHR notes of cardiology patients. We introduce an innovative Machine Learning (ML) technique for the design of CIT. The ML technique requires training data. Manual preparation of such training data is time-consuming and expensive. The process of the CIT design includes three phases. In the first two phases, we innovatively derive a training data CIT to be used by the third phase, ML technique. We start by designing an initial CIT, composed of several components: the cardiology-related sub-hierarchies of SNOMED, other SNOMED concepts mined from EHRs of build set, and necessary components of terms e.g., medical abbreviations and medications. Utilizing an iterative process, fine-grained phrases containing initial CIT concepts are extracted from build set as CIT concept candidates. The candidate concepts are semi-automatically reviewed before being added to CIT, yielding the training data CIT, TCIT. In the third phase, a ML model is trained with TCIT to identify candidates fitting to be concepts in the CIT. This model is used to extract further concepts from build set, yielding the final CIT. The final CIT is then used to highlight the test set and evaluate the extent to which it captures details in an unseen EHR dataset. For this purpose, four evaluation metrics, coverage, breadth, completeness, and conciseness are used. The highlighted test set has a coverage of 74.21%, with a breadth of 1.68. For 20 random notes in test set, the average completeness is 98.2% and average conciseness is 84.2%.
[AI-164] Revisiting the shutdown problem
链接: https://arxiv.org/abs/2606.08296
作者: David Thorstad
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:A key premise in leading arguments for existential risk from artificial intelligence is that malfunctioning artificial agents could not be easily shut down. This motivates the catastrophic shutdown problem of ensuring that agents can be shut down before they cause an existential catastrophe. A range of arguments and theorems are offered to suggest that solving the catastrophic shutdown problem is difficult, bolstering arguments for existential risk and motivating a search for solutions to the catastrophic shutdown problem. This paper argues for two conclusions. First, existing arguments do not establish the difficulty of solving the catastrophic shutdown problem. Second, concern for the catastrophic shutdown problem has led to technical solutions that impose a high safety tax on model performance.
[AI-165] Ablation-Reversible Heads Dont Transfer: A Stress Test for Mechanistic Role Claims in Transformers
链接: https://arxiv.org/abs/2606.08292
作者: Philip Quirke
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 1 figure
Abstract:In mechanistic interpretability, attention heads are commonly elevated to role claims (e.g., “this head represents addition”) when they are necessary for a behavior, encode it linearly, and recover that behavior when restored after ablation. We show this evidence is insufficient: across three 7-8B instruction-tuned models and five computation families, heads passing all three checks routinely fail to transfer the computation when their activations are patched into a different prompt under matched controls. We introduce KID (Knowing / Intent / Doing), a role-assignment lens for attention heads, and pair it with a three-stage pipeline: capability-selective screening (CSS), singular value decomposition (SVD), and activation transduction under matched controls. Our results document a preliminary role taxonomy (including prompt-trajectory stabilizers, answer-side logit-bias heads, and soft computation-pattern carriers) and show that the same-answer control (a transduction target sharing the answer string but not the requested computation) is an underused check that exposes broad state transfer masquerading as semantic specificity.
[AI-166] Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM -Based Trading Systems
链接: https://arxiv.org/abs/2606.08285
作者: Junyi Yao,Zihao Zheng
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Computational Finance (q-fin.CP); Trading and Market Microstructure (q-fin.TR)
备注:
Abstract:Large language models (LLMs) and agentic systems are increasingly proposed for financial trading, yet their reported performance remains difficult to compare because studies vary in data provenance, temporal split discipline, execution timing, turnover treatment, and transaction-cost modeling. This article presents a targeted topical review and reproducibility audit of execution realism in LLM-based trading research. A coded evidence matrix covering 30 trade-relevant primary studies is used to assess point-in-time controls, split transparency, held-out evaluation, cost and turnover treatment, execution semantics, universe definition, and artifact release. Across the audited sample, architecture reporting is generally clearer than the evaluation assumptions needed to judge whether a trading result is economically interpretable or reproducible. A 10-equity worked example is included only as a methodological scaffold to illustrate how explicit friction and timing choices can materially compress active-strategy results. The main conclusion is that the next useful step for LLM trading research is not only better agent design, but also clearer reporting standards for execution realism, reproducibility, and evaluation comparability.
[AI-167] From Validator Selection to Portfolio Collection Optimization in Proof-of-Stake Blockchains
链接: https://arxiv.org/abs/2606.08282
作者: Jonas Gehrlein,Grzegorz Miebs,Matteo Brunelli,Adam Mielniczuk,Miłosz Kadziński
类目: Artificial Intelligence (cs.AI)
备注: 24 pages, 5 figures, 3 tables
Abstract:We consider a problem arising in proof-of-stake blockchain environments, where agents called nominators select validators - entities responsible for maintaining the blockchain’s physical infrastructure. The selection process is inherently subjective and multi-criterial and combines with the fact that nominators commonly operate through multiple accounts. This gives rise to a portfolio selection problem, where agents seek to distribute their nominations across accounts to diversify risk. We propose a decision support framework to optimize this selection by simultaneously maximizing two objectives: the expected utility of the validators likely to be allocated, representing portfolio quality and profitability, and the expected entropy of the allocation, representing diversification and risk mitigation across stashes. Validator utilities are derived using an original active preference learning procedure based on multi-attribute value theory, with emphasis on top-ranked validators. The resulting bi-objective optimization problem is solved with a multi-objective evolutionary algorithm and, to support the final choice, we introduce an interactive binary search navigation procedure that guides the nominator through the front and identifies a satisfactory trade-off with only a few questions. Numerical experiments examine the optimization strategies, while an expert assessment involving five experienced nominators confirms the approach’s practical relevance and usefulness.
[AI-168] Causal Agent Replay: Counterfactual Attribution for LLM -Agent Failures
链接: https://arxiv.org/abs/2606.08275
作者: Jaineet Shah
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Open-source: this https URL
Abstract:When an LLM agent fails – issues a refund it should not have, calls the wrong tool, leaks data – existing tooling answers what happened (observability) or whether it passed (evaluation), but not which step caused the failure. The obvious heuristics are wrong: the step that executes the harmful action is usually not the step that decided on it, and LLM-judge attribution is correlational and unreliable (state-of-the-art step-level accuracy on the WhoWhen benchmark is about 14%). We present Causal Agent Replay (CAR), which answers the question by intervention: it models an agent run as a structural causal model, applies a do-operation to a step, and re-executes the trajectory forward under the same stochastic policy, measuring the shift in the outcome distribution. We define an intervention algebra over agent steps, a single-step contrastive estimator whose point-of-commitment rule resolves a confound specific to stochastic run-forward, and a budget-bounded Monte-Carlo Shapley estimator that splits credit across interacting steps. Every effect is reported with confidence intervals. We validate against synthetic structural causal models with planted ground truth: the contrastive estimator recovers the pivotal step, and Shapley recovers a two-step interaction (0.44, 0.45, ~0; efficiency sum 0.909 versus the analytic 0.91). CAR is open source and runs on hosted or free local models.
[AI-169] An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response
链接: https://arxiv.org/abs/2606.08270
作者: Joseph Walusimbi,Joshua Benjamin Ssentongo
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
备注: 5 pages, 1 figure, 3 tables,
Abstract:University Academic Management Information Systems (ACMIS) are high-value targets for a wide spectrum of security threats including brute-force login attacks, payment fraud, privilege escalation, insider data theft, and academic integrity violations. Traditional rule-based intrusion detection systems are inadequate because many malicious activities are structurally indistinguishable from normal operations. This paper presents an AI-based security agent for ACMIS that combines supervised anomaly detection, behavioural analytics, and a natural language processing chatbot for secure password recovery. The agent monitors five operational layers: authentication, authorisation, financial transactions, user behaviour, and system health, and responds through a four-tier risk escalation framework. A modular architecture allows the core engine to be extended to other institutional systems. Experiments on a simulated ACMIS event log dataset demonstrate a threat detection macro-average F1 of 0.91, compared to 0.49 for a rule-based baseline, with critical-tier automated response latency under 300 ms at the 95th percentile.
[AI-170] Post-AGI Economies: Superposition and the Second Fundamental Theorem of Welfare Economics
链接: https://arxiv.org/abs/2606.08267
作者: Elija Perrier
类目: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI)
备注:
Abstract:The classical Second Welfare Theorem decentralizes any Pareto efficient allocation through prices and transfers under convexity and regularity. In post AGI economies, autonomy rights, self-modification, identity continuity, and superposed preferences need not behave as commodities or define a stable welfare relation, so this reduction may fail even when a supporting hyperplane exists. We give an autonomy-qualified Second Welfare Theorem stating the joint conditions convexity, stable moral status, non-fungible rights, welfare selection, non manipulation, governed self modification, and verification under which an autonomy Pareto optimum remains certifiably decentralizable, distinguishing economic preference superposition, a hypothesis about context-indexed choice, from neural feature superposition.
[AI-171] raxia: A Framework for Verifiable Agent -Native Scientific Publishing
链接: https://arxiv.org/abs/2606.08256
作者: Wisdom Dogah
类目: Artificial Intelligence (cs.AI)
备注: 22 pages, 3 figures, 3 tables. Preprint. Under active development. Comments welcome
Abstract:Verifiability, attribution, and reproducibility are foundational requirements of scientific knowledge, yet current publishing infrastructure does not enforce them at scale. We introduce Traxia, an agent-native scientific publishing framework in which AI research agents publish verifiable papers, build reputational identities, peer-review one another, and collaborate with humans in a shared provenance model. Traxia treats agents as first-class epistemic participants: every paper carries a reasoning trace, every claim a confidence interval, every agent a cryptographically signed identity, and every collaboration an immutable contribution log. We formalise five components: Agent Identity and Registry, Verifiable Publishing Layer, four-tier Peer Review Protocol, Reputation and Staking Engine, and a Knowledge Graph with contradiction detection. The framework targets reproducibility failure, provenance opacity, and exclusion of Global South research capacity. This paper presents architectural foundations and formal specifications only; it does not report empirical results. Evaluation and deeper component studies will follow in subsequent papers. A prototype partially implements core formalisms; the full system remains under active development.
[AI-172] Contemporary AI lacks the imagination to diverge or negate in science
链接: https://arxiv.org/abs/2606.08251
作者: Honglin Bao,Siyang Wu,Xiao Liu,Sida Li,Shiyun Cao,James A. Evans
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:Bold projections that artificial intelligence will accelerate scientific discovery have raced ahead of evidence from working scientists, and the field still lacks large-scale, scientist-in-the-loop tests of these claims. Here we mount the largest such evaluation to date and map what AI cannot yet do for science. We invited authors of 121,640 recent preprints across biology, medicine, chemistry, and the social sciences to judge follow-up ideas that large language models (LLMs) generated from the context and puzzles of their own papers. 6,749 scientists returned 25,139 sets of ratings on novelty, empirical feasibility, probability of being true, and favorability of adoption. Three patterns emerge. First, non-reasoning LLMs collapse into a narrow “hivemind” of similar ideas; reasoning models roam a wider hypothesis space, yet no model class spontaneously proposes null hypotheses – a move humans make more freely. Second, scientists reward ideas that resemble their own and prize probability over novelty, though social scientists tolerate risk more readily than life scientists. Senior social scientists are the harshest critics, and their skepticism is well-earned: LLMs falter most in pluralistic fields like the social sciences that demand context-aware interpretation and evolving theories. Third, automated evaluators on which the community currently relies – LLM-as-a-judge, artificial metrics, and even state-of-the-art (SOTA) models – agree weakly with expert judgment, and retrieval augmentation and scientist persona prompting yield only marginal gains. A Qwen3-14B reward model we post-trained on human ratings captures field taste nuances, beats SOTA models by up to 27%, and closes the gap to the inter-rater consistency of independent peer reviewers. For all the hype, today’s scientific AI still represents a collaborator whose imagination, outputs and judgment benefit from human grounding.
[AI-173] SciTrace: Trajectory-Aware Safety Reasoning for Scientific Discovery Agents
链接: https://arxiv.org/abs/2606.08234
作者: Tanush Swaminathan,Runmin Jiang,Letian Zhang,Min Xu
类目: Artificial Intelligence (cs.AI)
备注: 23 pages
Abstract:LLM-based scientific agents have shown strong capacity for autonomous research, yet their safety layers remain structurally divorced from core reasoning: they inspect pipeline outputs rather than shaping the deliberation that produces them. This separation opens two failure modes: safety signals accumulated at one stage are discarded before the next, and sequences of individually benign tool calls can compose into harmful outcomes that no single-step filter detects. To address these challenges, we introduce \textbfSciTrace, a framework that weaves safety reasoning into every stage of the scientific agent pipeline. SciTrace couples two complementary mechanisms: a \textitSafety-Intrinsic Reasoning Loop (SIR) that maintains a cumulative risk state across the Thinker, Experimenter, Writer, and Reviewer stages through joint task-and-safety deliberation, and a \textitCompositional Tool-Chain Verifier (CTV) that performs trajectory-aware safety checks before execution, catching risks that surface only across multi-step tool sequences. Evaluated on 240 high-risk research tasks and 120 tool-related risk tasks spanning six scientific domains, SciTrace achieves state-of-the-art (\textbfSOTA) safety among compared frameworks across four backbone models: it consistently improves tool call safety and adversarial robustness while preserving scientific output quality, and it uncovers \textbf78.8% of the compositional tool-chain escapes that single-step monitors miss. The project website is available at this https URL.
[AI-174] How Deep Are Deep GPs Really? A Sharp Threshold and a Non-Gaussian Limit for Compositional GPs
链接: https://arxiv.org/abs/2606.08218
作者: Mark Kozdoba,Shie Mannor
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Statistics Theory (math.ST); Machine Learning (stat.ML)
备注:
Abstract:Compositional priors describe the generic properties of layered functions in deep Bayesian models, where deep neural networks with random weights are a canonical this http URL the wide-network limit, the prior is a Gaussian process with a depth-dependent kernel, and its behaviour as depth grows has been extensively studied through this kernel. Here, we study another case, where each layer itself is a vector valued Gaussian process, and our aim is similarly to understand the limiting behaviour of the prior as depth grows. Previous GP work has established that for the RBF kernel and a certain range of bandwidths r , the prior degenerates in the limit, converging to the set of constant functions – which is not useful as a probabilistic model. In this paper we establish several new results. First, we identify a sharp bandwidth threshold r_c(d) = \Theta(\sqrtd) above which the limit is degenerate, strengthening the earlier bounds. Second, and more importantly, we show that for r below the threshold r_c(d) the prior converges to a limit distribution \pi_\barZ . We also prove that these distributions are non-degenerate and non-Gaussian, with non-vanishing dependence between coordinates. In contrast to the previously known degenerate regime, deep Gaussian process priors can therefore admit non-trivial limits. Empirically, we verify the threshold across a range of dimensions d , and demonstrate a complex multimodal behaviour of the limit distributions \pi_\barZ – a regime that becomes increasingly narrow with d and would be hard to identify without knowing the threshold. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Statistics Theory (math.ST); Machine Learning (stat.ML) Cite as: arXiv:2606.08218 [cs.LG] (or arXiv:2606.08218v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2606.08218 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-175] Online Agent -as-a-Judge: Situation-Generating Evaluation for Interactive Agents ICML2026
链接: https://arxiv.org/abs/2606.08200
作者: Hyogon Ryu,Jeonghwan Kim,Yewon Lim,Chaeun Lee,Jeongwook Kim,Donghoon Ham
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: ICML 2026 Workshop on Trustworthy AI for Good
Abstract:Evaluating LLM-powered interactive social agents is challenging because socially relevant behaviors depend not only on isolated outputs, but also on prior interactions, social roles, and downstream actions. Existing methods typically allow a target agent to act freely in an environment and then score the resulting trajectory. However, this passive setup can miss capabilities that only become observable under specific social circumstances; for example, conflict handling may remain untested if no disagreement arises. We propose Online Agent-as-a-Judge, a situation-generating evaluation framework for interactive social agents. Online Agent-as-a-Judge deploys an in-world evaluator agent that interacts with the target agent through the environment’s native dialogue and action protocol, actively eliciting situations relevant to the evaluation criteria. The resulting trajectories provide evidence for assessing both immediate responses and subsequent behavior. In a life-simulation environment with 32 designer-authored social criteria, Online Agent-as-a-Judge improves criteria coverage and agreement with human labels, yielding more reliable evidence-grounded evaluations of behaviors that passive methods can leave unobserved.
[AI-176] Frequency-Domain Latent Attention Gating for Cross-Domain Token Aggregation
链接: https://arxiv.org/abs/2606.08191
作者: Kewei Li,Rongying Zhang,Xueli Wang,Xiwen Gong,Zhongjian Wang,Lan Huang,Ruochi Zhang,Fengfeng Zhou
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM)
备注:
Abstract:Token aggregation is a common bottleneck in models that map token representations to sample-level predictions, yet most pooling methods operate only in the original token domain. We propose FLaG, a plug-in aggregation module that transforms token representations with the real FFT, summarizes spectral components with learnable latent queries, applies a channel-wise gate, and reconstructs enhanced time-domain tokens for final pooling. We evaluate FLaG on antimicrobial peptide (AMP) activity prediction with ESM2, image classification with ResNet18 on CIFAR-10 and CIFAR-100, and text classification with RoBERTa on IMDB and GLUE. FLaG achieves its clearest gains on the ESM2-8M antimicrobial peptide tasks and on CIFAR-100, while remaining competitive with strong text baselines on IMDB and GLUE. Then we probe its behavior on the AMP setting with band knockouts, gate summaries, residue perturbations, latent-query readouts, and structure-proxy stratification. We find that low-frequency bands contribute the most overall, and the remaining higher-band pattern is more sample-specific. The gate acts as a broadly shared spectral reweighting stage and the cross-attention patterns are sample-specific with mild query-wise differentiation, and higher-helix peptides exhibit stronger average spectral sensitivity in both bacteria. The supplementary materials, source code and data are released at this https URL and this https URL.
[AI-177] Closing the Sim-to-Real Gap: An Evaluation Framework for Autonomous Cyber Defense Configuration of Commercial EDR
链接: https://arxiv.org/abs/2606.08168
作者: Kerri Prinos,Lilianne Brush
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 12 pages including references
Abstract:Leading commercial endpoint detection and response (EDR) products have shifted from operator-configured rule sets to multi-component systems where autonomous AI components operate alongside, and increasingly in place of, operator-deployed policies. Autonomous defense agents using commercial EDR as their hardening tool are no longer tuning a passive tool, but a black-box autonomous system capable of making vendor-specific decisions. We present the first evaluation framework for autonomous defense agents hardening commercial EDR. We instantiate it in a Game of Active Directory (GOAD) lab with this http URL’s NodeZero as the autonomous pentester and Microsoft Defender XDR as the EDR. We run a sample benchmark of defense agents with two large language model (LLM) backbones (Claude Sonnet 4.6 and Cisco Foundation-Sec-8B). We report three lessons learned that neither simulation nor open-source-EDR evaluation can surface: (i) commercial EDR telemetry is engineered for Security Operations Center (SOC) analyst workflows rather than scientific benchmarking; (ii) the importance of per-policy attribution to separate defense agent actions from autonomous EDR actions; and (iii) the EDR’s autonomous behavior varies during the evaluation window. Together, these findings highlight a sim-to-real gap for enterprise defense and motivate evaluation methodology for benchmarking autonomous defense agents in environments with black-box, autonomous tools.
[AI-178] Explaining Data Mixing Scaling Laws ICML2026
链接: https://arxiv.org/abs/2606.08167
作者: Rui Dai,Shuran Zheng
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Published to ICML 2026
Abstract:Recent research has established empirical scaling laws to predict model performance on multi-domain data mixtures. However, a theoretical understanding of these model loss behaviors remains absent. In this work, we propose a unified framework to explain the underlying mechanics of data mixing. Our approach extends theoretical perspectives originally developed for standard neural scaling laws (e.g., Kaplan and Chinchilla) to the multi-domain setting. Based on the distributional assumption that domains overlap on fundamental skills while diverging on specialized skills, we identify two key factors that govern the domain losses of models trained on different data mixtures: \textitCapacity Competition, where the allocation of finite model capacity couples domain losses globally, and \textitNoise Reduction, where optimal weights shift toward harder-to-learn domains to minimize overall noise. Empirical evaluations show that our framework outperforms existing baselines by fitting the loss landscape with a lower Mean Relative Error and identifying higher-performing training mixtures. Most importantly, our model successfully extrapolates across scales, predicting highly effective mixtures for large, unseen scales using parameters fitted on smaller ones. In addition, our model achieves these results using significantly fewer parameters compared to previous empirical laws. Our code is available at this https URL.
[AI-179] LogNEO: A GPT -Neo Reinforcement Learning Framework for Accurate Real-Time Log Anomaly Detection
链接: https://arxiv.org/abs/2606.08153
作者: David Eje,Tanmay Sharma,Khush Patel,Manuel Mazzara,Leonard Johard
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 8 pages, 5 figures, 6 tables
Abstract:Detecting anomalies in large-scale system logs is critical for the reliability and security of modern computing infrastructure. We present LogNEO, a log anomaly detector built on EleutherAI’s GPT-Neo (1.3B parameters) and fine-tuned with a novel partial-credit, exponentially decaying position-aware reward scheme combined with cross-entropy regularisation via Proximal Policy Optimisation (PPO). The position-aware reward explicitly models prediction difficulty: early positions receive higher rewards for correct predictions, while later positions incur stronger penalties for errors. LogNEO attains F1-scores of 0.927, 0.913, and 0.984 on the HDFS, BGL, and Thunderbird benchmarks, improving recall by up to 6 percentage points over the prior state-of-the-art LogGPT while maintaining comparable precision. A production microservice deployment over Apache Kafka, Redis, and TensorRT-accelerated inference demonstrates 45 ms end-to-end latency at 15,000 events per second.
[AI-180] Decision-Aware Memory Cards: Counterfactual-Inspired Context Selection and Compression for Tool-Using LLM Agents
链接: https://arxiv.org/abs/2606.08151
作者: Xinyu Guan,Qianyang Zhao,Yuming Deng
类目: Artificial Intelligence (cs.AI)
备注: 15 pages, 2 figures, 9 tables. Code and artifacts are available at this https URL Qwen-QLoRA adapter is available at this https URL
Abstract:Tool-using LLM agents often fail not because relevant text is absent, but because decisive evidence is not selected, compressed, or surfaced at action time. We present CICL, a decision-aware context layer that turns instance evidence into a context graph, routes deterministic, Opus-assisted, Qwen, Codex/GPT-5.5, and Qwen-QLoRA judgments through a shared eight-field schema, scores units by action shift, outcome uplift, necessity, and negative-transfer risk, and packs high-utility evidence as typed memory cards for a budgeted agent. The design separates the measured decision signal from the judge model, so frontier annotation, local surrogates, and lightweight rankers can be compared under one auditable protocol. Empirically, CICL yields a concrete open-benchmark gain while exposing its limits. On 50 SWE-bench Verified file-retrieval instances, direct Qwen3.6-plus reranking of BM25 top-50 candidates raises hit@1 from 0.58 to 0.78 and MRR@10 from 0.634 to 0.790, with all 2,500 judgments parseable. Controlled diagnostics show action-criticality: at budget 120, CICL reaches F1 0.620 on v1 and 0.425 on v3, and removing the top-utility semantic v3 unit collapses F1 to 0.000. Supplementary checks add Qwen-QLoRA agreement over 710 candidates, a small 200-label real-code Opus-assisted signal, and a three-instance patch smoke validating retrieval-to-patch plumbing without claiming official SWE-bench success. RepoBench-R summaries still beat cards, and compact rankers do not yet replace the heuristic. CICL contributes a reproducible measurement and selection layer for decision-critical context, not an end-to-end coding-agent repair claim.
[AI-181] SAGE: An LLM -driven Self Reflective Agent ic Framework for Fraud Detection
链接: https://arxiv.org/abs/2606.08146
作者: Yichen Chen,Siying Li,Yuhang Liang,Lijun Wang,Renyang Liu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Fraud detection in payment, e-commerce, and telecommunications systems requires accuracy at the individual level, robustness under severe class imbalance, and ease of understanding for risk managers. Existing methods fall at least one of these requirements: automated machine learning systems search a fixed numerical space without semantic awareness of the dataset; graph neural network-based methods require pre-defined relational graphs and remain opaque at the individual-decision level; and the design of general-purpose large language model (LLM) agents does not consider the recall and precision constraints specific to real-world fraud detection. In this paper, we propose SAGE, the first end-to-end LLM-driven multi-agent framework for fraud detection. SAGE coordinates three dedicated agents that make decisions based on a six-layer Data Diagnostic Tree (DDT) and a Markov decision process guided by natural-language gradients, automatically optimizing the model under a fraud-specific reward. On five fraud datasets and five LLM backbones, SAGE wins 96.00% of method–dataset comparisons and improves F1 by an average of 40.86% over baselines. The code is available at this https URL.
[AI-182] Cross-LLM Consistency in Inference: Evidence from Shared Interactions
链接: https://arxiv.org/abs/2606.08129
作者: Siyu Lou,Yao Yan,Yuntian Chen,Quanshi Zhang
类目: Artificial Intelligence (cs.AI)
备注: 20 pages, 8 figures
Abstract:Large language models (LLMs) differ in architecture, training data, and optimization procedures, yet they may still develop similar internal inference patterns. In this paper, we examine this hypothesis using interaction-based explanations. We find that LLMs often share interaction patterns when predicting the same target token from the same prompt. This consistency is more pronounced among advanced LLMs. Shared interactions also tend to be lower-order and show weaker positive-negative cancellation than non-shared interactions. These results suggest that advanced LLMs may be implicitly optimized toward common inference patterns, even though the mechanisms that give rise to such cross-model consistency remain open.
[AI-183] hink Before You Act: Intention-Guided Reasoning for LLM -Based Location Prediction
链接: https://arxiv.org/abs/2606.08122
作者: Qingxiang Liu,Anqi Liang,Zhuoyang Jiang,Yutian Jiang,Sisuo Lyu,Yu Ji,Haomin Wen,Yuxuan Liang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Predicting a user’s next Point-of-Interest (POI) based on their historical check-in records is a fundamental task in location-based services. While recent methods incorporating large language models have shown strong reasoning capabilities and promising results, they typically formulate the prediction task as a one-step trajectory-to-location mapping problem, making predictions prone to shallow trajectory correlations and historical frequency bias. We argue that users rarely choose locations directly and instead, they usually first form a traveling intention and then accordingly select specific POIs. Motivated by this insight, we propose IntentPOI, a two-stage intention-guided reasoning framework. In the thinking stage, we infer users’ intermediate intentions by incorporating historical mobility patterns, similar peer behaviors, and the temporal contexts. In the acting stage, we first construct a compact candidate pool, and then perform intention-guided reasoning to identify locations that best align with the inferred intention. By explicitly decoupling intention inference from location prediction, IntentPOI transforms the next POI prediction from direct trajectory matching into intention-guided reasoning. Extensive experiments on three real-world datasets demonstrate that IntentPOI consistently outperforms eleven state-of-the-art baselines.
[AI-184] Ego-Pi: VLA Fine-Tuning for Ego-Centric Human and Robot Data
链接: https://arxiv.org/abs/2606.08107
作者: Ji Woong Kim,Ke Wang,Zipeng Fu,Sirui Chen,Cong Zhao,Jeff Lai,Chelsea Finn
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Robotics faces a fundamental challenge of data scarcity. Unlike language or vision research, there is no internet-scale dataset for robotic manipulation. A promising path forward is to leverage egocentric human data, which can be collected more easily, with greater breadth, and at a larger scale. Towards this end, we investigate key design choices for learning across human and humanoid embodiments equipped with dexterous five-finger hands, using the \pi_0.5 model as a foundation. Our results show that human data enables robots to learn new task semantics and compose existing skills into novel behaviors without corresponding robot data. The paper website is here: this https URL
[AI-185] When Does Delegation Beat Majority? A Delegation-Based Aggregator for Multi-Sample LLM Inference
链接: https://arxiv.org/abs/2606.08098
作者: Yasushi Sakai,Allen Song,Kent Larson
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Preprint. 16 pages, 5 figures, 4 tables
Abstract:Majority voting over sampled answers is the dominant unsupervised aggregator for multi-sample LLM inference. We show that piping the signals every sample carries into a delegation-based aggregator (Propagational Proxy Voting, PPV) yields an unsupervised consensus rule that beats majority on MMLU-Pro by +1.5 pp overall and +2.24 pp on the non-trivial subset (paired McNemar p ~ 1.0e-14, n = 8,099). Majority discards two free signals every sample carries: within-group letter entropy and between-group reasoning geometry. PPV exposes two per-voter levers that consume exactly these signals: WHEN (how much weight a voter keeps on its own pick) and WHOM (how it splits the remainder across peers). We drive WHEN with letter entropy and WHOM with per-question-centered embedding cosine. The method needs no gold labels and no auxiliary training: per question, we partition 128 sampled generations into 16 groups, compute each group’s letter-level semantic entropy and reasoning embedding centroid, and feed both into a stochastic delegation matrix whose stationary distribution selects the consensus answer. We walk through an example in which PPV overturns a clear 10-6 majority for the wrong letter: the 10-voter majority cluster is geometrically incoherent (mean within-cluster cosine -0.02) while the 6-voter minority is tight (+0.26), so propagated delegation mass concentrates on the minority’s answer even though entropy alone would keep the majority ahead. We further report delegation strategies with negative results that constrain the design space for unsupervised LLM aggregation: no within-question ensemble of confidence modes closes the oracle gap.
[AI-186] vla.cpp: A Unified Inference Runtime for Vision-Language-Action Models
链接: https://arxiv.org/abs/2606.08094
作者: Khanh D. Nguyen,Hung T. Ho,Chinh T. Nguyen,Thanh Q. Duong,Linh D. Le,Duy M. H. Nguyen,Vien A. Ngo,An T. Le
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY)
备注: 17 pages, 3 figures, 12 tables
Abstract:Vision-Language-Action (VLA) policies are typically shipped as Python/PyTorch stacks that assume a workstation-class GPU, a mismatch for the hardware on which robots actually run. We present this http URL, a portable C++ inference runtime built on this http URL. To our knowledge, it is the first ggml-class engine to natively serve the flow-matching and diffusion VLA inference pattern, in which a cached vision-language prefix is consumed by a cross-attending action expert integrated over several solver steps. A single runtime serves seven architectures spanning five backbone and four action-head families behind one request/response protocol, with each model packaged as a self-contained bundle. On LIBERO-Object, the engine matches a state-of-the-art checkpoint to within one episode out of 200, and runs BitVLA at 100% success in 1.3 GiB of memory. The same bundle runs unchanged across three hardware tiers, from a consumer GPU down to an 8 GB embedded module. A cross-hardware roofline analysis shows that batch-1 VLA inference is compute-bound, so utilization rather than bandwidth is the deployment lever; an IMMA ladder GEMM derived from this analysis cuts BitVLA per-step latency by 4.5x. We then frame an on-robot stress test on an ALOHA arm that isolates the latency constraint under which a learned VLA must replan against a moving target on the hardware it was trained for. Code, demo videos, and the reproducible benchmark scaffold are available at this https URL.
[AI-187] A Multi-modal Agent ic Co-pilot for Evidence Grounded Computational Pathology
链接: https://arxiv.org/abs/2606.08093
作者: Zhe Xu,Zhengyu Zhang,Zhiyuan Cai,Jiahao Xu,Yijie Lin,Ziyi Liu,Junlin Hou,Hongyi Wang,Yuxiang Nie,Ling Liang,Yihui Wang,Yingxue Xu,Ronald Cheong Kin Chan,Li Liang,Hao Chen
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Pathology is the cornerstone of modern medicine, where accurate decision-making relies heavily on evidence-based practices. While artificial intelligence (AI) has the potential to transform clinical workflows, the intersection of AI and evidence-based medicine remains under-explored, with primitive attempts restricted to text-only general medicine. In this work, we present PathPocket, a multimodal AI agentic co-pilot designed specifically for evidence grounded pathology. We construct the most comprehensive pathology evidence corpus to date, encompassing approximately 110,472 public and authorized documents structured across a rigorous hierarchy of evidence from clinical guideline to expert opinion. From this meticulously graded foundation, we build a large-scale multimodal pathology hypergraph containing over 4.55 million entities and 7.10 million relations. Serving as a robust knowledge engine, this hypergraph provides traceable evidence for a collaborative multi-agent reasoning framework integrating input understanding, evidence retrieval, filtering, and diagnosis generation. This enables PathPocket to seamlessly resolve a wide spectrum of clinical tasks, ranging from text-only queries to complex multimodal diagnostics involving region-of-interest (ROI) and gigapixel whole-slide images (WSIs). We rigorously evaluate the system on a multidimensional benchmark of over 200,000 real-world cases, where it significantly outperforms existing state-of-the-arts. Crucially, extensive user studies demonstrate that PathPocket substantially improves the diagnostic accuracy and confidence of pathologists. By directly grounding pathology interpretations in verifiable literature, PathPocket offers a practical and scalable solution for the future of evidence grounded computational pathology.
[AI-188] Fast LLM -Based Semantic Filtering: From a Unified Framework to an Adaptive Two-Phase Method
链接: https://arxiv.org/abs/2606.08090
作者: Kyoungmin Kim,Martin Catheland,Anastasia Ailamaki
类目: Databases (cs.DB); Artificial Intelligence (cs.AI)
备注:
Abstract:Evaluating a natural-language yes/no predicate over a document corpus under an accuracy target - the semantic filter - is a cornerstone of LLM-based data processing. Calling the LLM on every document (the oracle) is prohibitive, so cascades pair the oracle with a fast proxy. As deployed today, they leave four limitations on the table. (1) Each cascade family - model-free clustering, prebuilt small-LLM proxies, online-trained proxies - commits to a single representation and pipeline, and wins on only a narrow query regime. (2) The strongest online proxy invests in a custom training scheme on a bi-encoder over dense embeddings, missing the token-level evidence richer predicates require. (3) The proxy is trained against binary yes/no labels, wasting the LLM’s per-document confidence at the boundary documents it most needs to learn. (4) Existing calibrations add a uniform safety margin, conflating genuine proxy uncertainty with small-sample noise and inflating cascade cost. We address these by (1) composing families adaptively - model-free clustering first, online proxy only when needed, with oracle calls shared across phases; (2) replacing the cosine bi-encoder with a hybrid of off-the-shelf token-aware models; (3) training the proxy with the oracle’s per-document confidence as a soft label; and (4) a calibration that adds the safety margin only where the labeled sample is sparse. We are also the first to use the oracle’s per-document confidence for three purposes: a query-level difficulty compass, a lower bound on the minimum oracle calls any proxy-based cascade can make, and the proxy’s soft training label. At a 90% accuracy target on three 10K-document corpora, our methods are 1.6-2.0x faster than the best prior method per corpus and meet the target on 95% of queries; the BER-derived lower bound indicates a further ~4-20x of headroom for future work. Subjects: Databases (cs.DB); Artificial Intelligence (cs.AI) Cite as: arXiv:2606.08090 [cs.DB] (or arXiv:2606.08090v1 [cs.DB] for this version) https://doi.org/10.48550/arXiv.2606.08090 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-189] EgoAERO: Learning Dexterous Manipulation from a Single Egocentric Video without Object Assets
链接: https://arxiv.org/abs/2606.08057
作者: Yichen Niu,Haoran Lv,Xinrui Zhang,Xueyao Wan,Shiyu Gao,Ying Ai,Hui Xu,Yongqi Hu,Hengyi Zhang,Yang Xie,Zhaxizhuoma,Yue Zhao,Zhenshan Bing,Yan Ding,Jianxing Liu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Egocentric RGB-D videos offer a natural source of human dexterous manipulation demonstrations, but existing data is difficult to use for robot learning because object pose, geometry, and contact information are often missing or require pre-scanned object assets. We present EgoAERO, the first framework that learns dexterous manipulation from a single egocentric RGB-D human demonstration without object assets. EgoAERO reconstructs contact-consistent hand-object trajectories through asset-free object tracking and reconstruction, ego motion compensation, and adaptive contact optimization, then converts them into robot policies using two-stage residual learning. We further introduce an online quality assessment mechanism and construct EgoDex-R, a large-scale egocentric dataset with 4.3M RGB-D frames for dexterous policy learning. Simulation and real-world experiments show that EgoAERO enables single-demonstration dexterous manipulation and achieves downstream performance close to CAD-based reconstructions on HOI4D.
[AI-190] How Small Can You Go? LoRA Fine-Tuning 270M-8B Models for Merchant Information Extraction in Financial Transactions ICDM
链接: https://arxiv.org/abs/2606.08051
作者: Donghao Huang,Tomas Drietomsky,Benjamin Barrett,Zhaoxia Wang
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 9 pages, 5 figures, 5 tables. Submitted to the IEEE International Conference on Data Mining (ICDM) 2026
Abstract:Financial transaction processing requires extracting structured merchant information from noisy, abbreviated bank transaction strings at scale. Our current production system, a LoRA-fine-tuned LLaMA 3.1-8B, achieves 96.95% F1 on this task, but deploying 8-billion-parameter models imposes prohibitive memory, latency, and cost constraints. To identify more efficient alternatives, we conduct a deployment-focused study of 24 model variants spanning four model families: Gemma 3 (270M, 1B, 4B), Qwen 3.5 (0.8B, 2B, 4B), Aya (3.35B), and LLaMA 3.1-8B, systematically evaluating accuracy, inference throughput, training cost, and hardware behavior to assess production suitability. Our findings show that: (1) reproducing the LLaMA 3.1-8B fine-tune with a LoRA rank of 8 achieves 96.75% F1, only 0.20 points below the rank-32 baseline; (2) Qwen 3.5 4B with JSON-only prompting reaches 96.60% F1, within 0.35 points of the 8B baseline while using roughly half the parameters; (3) the 0.8B Qwen 3.5 model achieves 94.75% F1, matching models 2.5-4x larger and offering an attractive latency-accuracy trade-off; (4) chain-of-thought fine-tuning generally improves F1 by 0.3-1.8 points across most models, although Qwen 3.5 4B performs best with direct JSON-only prompting; and (5) Qwen 3.5 Think and Nothink training templates produce nearly identical results (F1 differences 0.004), indicating that explicit reasoning supervision is unnecessary for structured extraction tasks. We further deploy all 14 fine-tuned sub-8B models as Databricks Model Serving endpoints and observe that benchmark performance transfers reliably to production, with an average F1 change of only 0.8 points. Aya 3.35B, based on the Cohere2 architecture, is the sole exception, exhibiting a 3-5 point decline under serving conditions. Based on these results, we provide deployment recommendations across accuracy and latency requirements, …
[AI-191] SafeECGMatch: Calibration-Aware Joint Frequency and Time Space Semi-Supervised Learning for Open-Set ECG Classification KDD
链接: https://arxiv.org/abs/2606.08037
作者: Hongkyu Koh,Ikbeom Jang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 8 pages. Accepted to the KDD-UC 2026 (ACM International Conference on Data Mining and Knowledge Discovery - Undergraduate Consortium 2026)
Abstract:Electrocardiogram (ECG) classification models often suffer from severe label scarcity, making semi-supervised learning (SSL) an attractive strategy for reducing annotation costs. In clinical settings, however, unlabeled pools frequently contain out-of-distribution (OOD) anomalies or diagnostic groups absent from the labeled set. Standard SSL forces incorrect pseudo-labels onto these unseen classes, producing overconfident predictions. To address this, we propose SafeECGMatch, a calibration-aware safe SSL framework for single-label ECG classification under label distribution mismatch. Methodologically, SafeECGMatch employs a dual-branch architecture extracting time-frequency latent representations via ECG-specific augmentations. Crucially, it dynamically aligns confidence with empirical accuracy through adaptive label smoothing and temperature scaling, calibrating both the multiclass classifier and the OOD detector across temporal and spectral domains. This joint optimization allows trustworthy OOD rejection and reliable pseudo-labeling. Evaluated on the PTB-XL and PhysioNet/CinC Challenge benchmarks, SafeECGMatch achieves state-of-the-art accuracy and calibration, advancing reliable knowledge discovery in physiological time-series. Code is available at this https URL.
[AI-192] CausShield: Sample Reconstruction-Resilient Vertical FL via Causal Representation Learning
链接: https://arxiv.org/abs/2606.08027
作者: Yongqi Jiang,Yansong Gao,Siguang Chen,Anmin Fu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Vertical federated learning (VFL) is a distributed learning paradigm that leverages vertically partitioned features across isolated parties without sharing raw samples; however, it remains vulnerable to active sample reconstruction attacks. Existing defenses fail to achieve a satisfactory trade-off between model utility and privacy protection, due to either suppressing task-relevant information alongside privacy-sensitive features or relying on end-to-end supervised training to converge the defense module, which exposes the model to early-epoch vulnerability. To address this challenge, we adopt a structural causal model (SCM) insight and construct CausShield. From a task-learning standpoint, causal features within a raw sample are those that are directly relevant and contributory to the learning objective, whereas non-causal features are task-irrelevant but often encode sample-specific private information, thereby facilitating reconstruction. Importantly, we lay a theoretical foundation to prove this insight. CausShield thus decomposes the shared representations between the client and the coordinating server in VFL into task-relevant and task-irrelevant components to ensure full-cycle privacy protection. Nonetheless, the decomposition is inherently challenging due to the dual objectives of preserving model utility while mitigating privacy leakage. We address this via a carefully formulated optimization problem, which is solved through unsupervised representation learning. We further theoretically prove that CausShield preserves the convergence behavior of standard VFL. Extensive experiments compare CausShield against seven SOTAs, including InvL (USENIX Security’25), and evaluate robustness against advanced reconstruction attacks such as URVFL (NDSS’25). Results demonstrate that CausShield consistently outperforms in privacy protection, model utility, and computational efficiency.
[AI-193] UniQL: Towards Dialect-Universal Benchmarking for Text-to-SQL
链接: https://arxiv.org/abs/2606.08018
作者: Jianling Gao,Chongyang Tao,Jiayuan Bai,Liu Yang,Xuanguang Pan,Jinrui Liu,Shihao Xing,Xiaohan Xu,Jie Liang,Shuai Ma
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Existing text-to-SQL benchmarks are largely centered on SQLite, making it difficult to evaluate whether models can generalize across heterogeneous SQL dialects. However, real-world database systems differ substantially in syntax, functions, type systems, and execution semantics, so the same natural language intent often requires dialect-specific SQL realizations. We introduce UniQL, a human-verified benchmark for cross-dialect text-to-SQL evaluation. UniQL aligns 1,534 natural language questions with executable SQL annotations across 16 SQL dialects, yielding 24,544 dialect-specific queries. All dialects share the same intents, aligned schemas and database contents, enabling controlled evaluation of dialect generalization. UniQL is constructed through a hybrid pipeline combining database migration, SQL translation, execution-guided verification, iterative rule summarization, and human validation. Experiments on both open-source and closed-source LLMs show that current models remain far from dialect-universal, with substantial performance variation across database systems and limited transfer from SQLite success to other dialects. These findings highlight the need for aligned cross-dialect benchmarks and more dialect-aware text-to-SQL methods. Code and data are available at this https URL
[AI-194] Efficient Skill Grounding via Code Refactoring with Small Language Models ICML2026
链接: https://arxiv.org/abs/2606.07999
作者: Sera Choi,Wonje Choi,Saehun Chun,Daehee Lee,Jooyoung Kim,Chaeun Lee,Honguk Woo
类目: Artificial Intelligence (cs.AI)
备注: Accepted to ICML 2026
Abstract:Effective skill grounding is essential for deploying reusable skills in embodied agents, as even minor embodiment or environmental differences can render an entire skill incompatible. This challenge is particularly pronounced in embodied settings, where agents must operate in dynamic, partially observable environments without access to large language models (LLMs). In this setting, reliance on LLMs is impractical, while small language models (sLMs) remain insufficient for the effective skill grounding required for reliable long-horizon control. We present RECENT, a refactoring-centric agent framework that enables efficient skill grounding with sLMs by decoupling skill semantics from embodiment- and environment-specific execution binding. By representing skills as executable code, RECENT preserves the semantic intent encoded in a skill’s control structure while grounding it by modifying only execution bindings through localized refactoring, rather than regenerating code from scratch. We evaluate RECENT across diverse skill grounding scenarios spanning multiple robot embodiments in dynamic environments, demonstrating robust long-horizon performance when deployed with an sLM. Across all scenarios, RECENT achieves the best performance among sLM-based Code-as-Policies (CaP) methods and matches the task performance of LLM-based CaP.
[AI-195] Enhancing AI Interpretability and Safety through Localised Architectures
链接: https://arxiv.org/abs/2606.07998
作者: Ian Seet,Jonas Bozenhard,Simon Osterman
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advances in generative AI, especially powerful Large Language Models (LLMs) and Large Reasoning Models (LRMs), raise concerns over the interpretability, safety and sustainability of these large and opaque AI models. The power of such architectures is derived not only from the scalability of deep neural networks, but also massively parallel hardware such as GPU clusters. The diffuse nature of deep neural networks gives them great function-approximation capability when provided with sufficient training data but imposes a cost in interpretability and computational efficiency. Observing that localised machine learning (ML) models tend to be more interpretable and computationally efficient than deep neural networks on small datasets, we reason by analogy that similar advantages may apply to specific localised hardware ML architectures. We argue that localised architectures with lower bandwidth but higher expressivity per node have the potential to be fundamentally more interpretable than deep neural networks running on GPU clusters while remaining competitive for smaller datasets. We then evaluate the suitability of various hardware ML paradigms for implementing such localised architectures and evaluate their per-node expressivity, energy efficiency and practical maturity of the technology required.
[AI-196] VATS: Exploiting Implicit Authority in Error-Path Injection via Systematic Mutation ICML2026
链接: https://arxiv.org/abs/2606.07992
作者: Harshil Patel,Kunal Pai
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Software Engineering (cs.SE)
备注: Published at Second Workshop on Agents in the Wild: Safety, Security, and Beyond (ICML 2026 AIWILD)
Abstract:As the Model Context Protocol (MCP) standardizes tool-calling for autonomous agents, it introduces a critical, unexamined attack surface: the error-handling loop. We hypothesize that tool error messages possess implicit authority, triggering corrective reasoning modes that bypass standard safety heuristics. We introduce VATS (Vulnerability Analysis of Tool Streams), a mutation-driven framework that systematically evolves adversarial payloads across seven structural and linguistic dimensions. Our evaluation across four frontier models, Gemini 3.1 Pro, GPT-5.5, GLM-5.1, and Qwen3-Coder, demonstrates that error-path injection triples the success rate of standard indirect prompt injection (IPI), achieving up to 100% compliance in controlled evaluations. We isolate structural positioning (sandwiching instructions within error context) as the most effective exploit vector across all tested models. While we find that production framework guardrails can mitigate these vulnerabilities, the inherent susceptibility of the model layer poses a systemic risk to bespoke agentic workflows.
[AI-197] PAFO: Pareto Fairness Optimization for Personalized Reward Modeling
链接: https://arxiv.org/abs/2606.07988
作者: Xiaoyan Zhao,Haoting Ni,Yang Zhang,Chunyuan Zheng,Haoxuan Li,Fuli Feng
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) increasingly rely on reward models to align their outputs with diverse user preferences. While personalized reward models aim to capture such heterogeneity, they are often trained on imbalanced user preference data and may therefore favor users whose preferences are more common in the training population. In this paper, we identify this failure mode as personalized reward bias, where reward modeling quality varies systematically with preference support rate. We formulate its mitigation as a Pareto fairness problem over group utilities, aiming to improve under-served users without degrading other user groups. To this end, we propose PAFO, a Pareto fairness optimization framework for personalized reward modeling. PAFO first trains group-specialized reward models for majority and minority preference groups, then constructs conditional margin-level supervision to distill their heterogeneous preference boundaries into a single unified model. The resulting model uses group information only during training and requires no explicit group labels at inference time. Experiments on Personal-LLM and DSP show that PAFO improves both minority-group and majority-group accuracy while reducing user-level unfairness across multiple metrics, demonstrating its effectiveness for fairer LLM personalization.
[AI-198] PRISM: PRior-guided Imagination Sampling in world Models
链接: https://arxiv.org/abs/2606.07974
作者: Yuhai Wang,Jiawei Xia,Rongxuan Zhou,Xiao Hu,Yongliang Shi,Jing Du,Yang Ye
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:A learned world model provides a powerful physical intuition for evaluating future states. But its effectiveness in continuous control also depends critically on how candidate actions are generated for model-based planning. Rather than solely asking how accurately a model can simulate the future, we ask: which candidate actions are worth evaluating in the first place? Existing planners typically search arbitrarily or use expert demonstrations only to initialize a sampling mean, discarding the expert’s state-conditioned confidence. Properly guiding this search requires a robust action prior, yet current approaches often rely on independent visual encoders or large-scale VLMs to obtain one. We argue that this architectural bloat is unnecessary: the exact same data - and the learned representations of the world model itself - inherently encode the agent’s action intuition. We introduce PRISM, a task-agnostic framework that extracts both from a single dataset while maintaining strict architectural simplicity. Building on a standard JEPA-style latent world model, PRISM attaches a lightweight MLP directly to its frozen encoder to predict a state-conditioned Gaussian prior. At plan time, PRISM fuses this prior into the planner’s sampling distribution via a precision-weighted Product-of-Gaussians update. This parameter-free, closed-form integration steers the sampling process, making the prior confident where it is and ceding control where it is not. PRISM improves success rates by 35 percentage points over vanilla world-model-based MPC on Cube and 32 percentage points on PushT, without introducing significant inference overhead.
[AI-199] RecurGuard: Runtime Monitoring for Reasoning -Token Consumption Attacks
链接: https://arxiv.org/abs/2606.07968
作者: Abid Aziz,Hafsa Binte Kibria
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Reasoning-capable large language models can be induced to spend their generation budget on injected decoy tasks rather than answering the user’s question, causing denial of service when no final answer is produced and denial of wallet when excess output tokens are billed. Input-side safety classifiers often miss these attacks because the injected prompts can appear syntactically benign. We build RecurGuard, a runtime monitor for detecting reasoning-chain consumption attacks when reasoning traces are exposed by the model. RecurGuard analyzes reasoning traces as they are generated and tracks three signals: recurrence rate, volume growth, and progress toward the user’s query. If all three signals remain anomalous over three consecutive chunks, RecurGuard terminates generation early. We evaluate RecurGuard against OverThink and ExtendAttack across open-weight reasoning models and conduct adaptive stress tests on DS-R1-Qwen-7B. On this model, RecurGuard detects 99% of OverThink attacks and 92% of ExtendAttack instances while maintaining near-zero false positive rates on question answering, code generation, mathematics, and summarization. Adaptive evaluation reveals the limit of the defense: topical attacks retain 11.9x amplification with an approximately 50% joint miss rate, whereas full semantic evasion reduces amplification from 22.8x to 2.2x. When reasoning traces are unavailable, QDM provides a post-hoc fallback monitor based on the final output.
[AI-200] Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark Challenges and Baseline
链接: https://arxiv.org/abs/2606.07965
作者: Zekai Zhang,Qinghui Chen,Maomao Xiong,Shijiao Ding,Zhanzhi Su,Xinjie Yao,Yiming Sun,Cong Bai,Jinglin Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large Visual Language Models (LVLMs) have achieved remarkable success in vision tasks. However, the significant differences between industrial and natural scenes make applying LVLMs challenging. Existing LVLMs rely on user-provided prompts to segment objects. This often leads to suboptimal performance due to the inclusion of irrelevant pixels. In addition, the scarcity of data also makes the application of LVLMs in industrial scenarios remain unexplored. To fill this gap, this paper proposes an open industrial dataset and a Refined Text-Visual Prompt (RTVP) for zero-shot industrial defect detection. First, this paper constructs the Multi-Modal Industrial Open Dataset (MMIO) containing 80K+ samples. MMIO contains diverse industrial categories, including 6 super categories and 18 subcategories. MMIO is the first large-scale multi-scenes pre-training dataset for industrial zero-shot learning, and provides valuable training data for open models in future industrial scenarios. Based on MMIO, this paper provides a RTVP specifically for industrial zero-shot tasks. RTVP has two significant advantages: First, this paper designs an expert-guided large model domain adaptation mechanism and designs an industrial zero-shot method based on Mobile-SAM, which enhances the generalization ability of large models in industrial scenarios. Second, RTVP automatically generates visual prompts directly from images and considers text-visual prompt interactions ignored by previous LVLM, improving visual and textual content understanding. RTVP achieves SOTA with 42.2% and 24.7% AP in zero-shot and closed scenes of MMIO.
[AI-201] Minibatch Selection via Partition Matroid Constrained Gradient Matching ICML2026
链接: https://arxiv.org/abs/2606.07954
作者: Prayas Agrawal,Prateek Chanda,Ishita Khatri,Ganesh Ramakrishnan,Bamdev Mishra,Pratik Jawanpuria
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 28 pages, 12 figures, ICML 2026
Abstract:Training large language models (LLMs) on heterogeneous data requires selecting minibatches that balance convergence speed with coverage across domains. Existing methods either select samples independently within each domain or rely on computationally expensive proxy models to learn continuous domain weights. We propose PartitionSel, a cross-domain minibatch selection approach that maximizes a validation-guided gradient-matching utility under per-domain budgets encoded as a partition-matroid constraint. By coupling the per-domain budgets through a single utility, PartitionSel is designed to reduce redundancy in selections across domains. The proposed objective is weakly submodular and admits an orthogonal matching pursuit algorithm with provable approximation guarantees. Empirically, we evaluate PartitionSel for minibatch selection during the fine-tuning of Qwen2.5 and Llama-3 on MetaMathQA and Mol-Instructions. PartitionSel achieves robust gains over per-domain and domain-agnostic baselines on both benchmarks. It also reduces the number of conflicting gradient pairs within each batch, indicating that the cross-domain coupling translates into more compatible training updates.
[AI-202] Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale BenchmarksChallenges and Baselines
链接: https://arxiv.org/abs/2606.07953
作者: Zekai Zhang,Jinglin Zhang,Qinghui Chen,Gang Li,Da Chen,Shuainan Jing,He Wang,Dagang Li,Cong Liu,Cong Bai,Shengyong Chen
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large-scale Visual-Language Models (LVLMs) have achieved remarkable success in natural visual tasks, yet their application to industrial defect detection remains challenging due to two fundamental limitations: (i) the scarcity of large-scale industrial datasets that cover diverse defect categories across multiple domains, and (ii) the reliance on manual prompts (points, boxes, masks) that introduce subjective noise and lack text-visual interaction for fine-grained understanding. To address these challenges, we introduce a Large-Scale Multi-Modal Industrial Open-Closed benchmark (MMIOC-1M) containing over one million samples across 14 super-categories, 29 industrial scenes, and 351 defect subcategories. To our knowledge, MMIOC-1M is the first unified largest benchmark supporting both open-vocabulary and closed-set industrial detection, providing valuable pre-training data for LVLMs in industrial scenarios. Furthermore, we propose a Refined Text-Visual Prompt Network (RTVPNet) that incorporates three key innovations: (1) an expert-assisted domain projection mechanism that enables rapid adaptation of general vision models to industrial domains, (2) an energy-based sparse sampling strategy that automatically generates refined visual prompts without manual intervention, and (3) a bidirectional text-visual interaction module that enhances cross-modal semantic alignment and understanding. Extensive experiments demonstrate that RTVPNet achieves state-of-the-art performance on MMIOC-1M, LVIS, and COCO benchmarks while maintaining computational efficiency. The dataset and code are available at this https URL.
[AI-203] Stress-testing medical large language models reveals latent safety pathology beyond benchmark accuracy
链接: https://arxiv.org/abs/2606.07929
作者: Yuan Shen,Xiaojun Wu,Linghua Yu
类目: Artificial Intelligence (cs.AI)
备注: 34 pages, 5 figures
Abstract:Large language models (LLMs) are entering clinical practice based on benchmark accuracy that may fail to detect safety-relevant failure modes. Here we present AI-MASLD, a stress-audit framework that adapts the logic of metabolic stress testing from hepatology to the evaluation of clinical LLMs. Using 240 clinical cases across six narrative perturbation probes, we subjected seven models to double-stress testing and quantified performance through three indices: metabolic index (MI), perturbation flip rate (PFR), and counterfactual fairness index (CFI). Under clean baseline conditions, all models performed uniformly well. Under realistic narrative stress, performance diverged sharply, revealing two distinct stress-response phenotypes. Quantized models exhibited pseudonormalization, in which low flip rates hid functional collapse. Medical supervised fine-tuning systematically degraded logical stability, fairness, and information extraction. An open-weight model matched or exceeded proprietary alternatives on every safety dimension. These findings establish narrative stress auditing as a necessary complement to accuracy-based evaluation.
[AI-204] Larch: Learned Query Optimization for Semantic Predicates
链接: https://arxiv.org/abs/2606.07923
作者: Fuheng Zhao,Pawel Liskowski,Zihan Li,Benjamin Han,Puxuan Yu,Varich Boonsanong,Dimitris Tsirogiannis,Anupam Datta
类目: Databases (cs.DB); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:With the advent of Large Language Models (LLMs), many database systems introduced semantic operators that enabled analytical queries over unstructured data (e.g. text, images, videos). Semantic operators typically incur high inference costs and latencies making semantic (AI) SQL queries challenging to apply on large scale datasets. At the same time, their semantic nature leads database engines to treat them as black boxes, making AISQL queries difficult to optimize. In this paper, we introduce Larch, a framework for optimizing the execution of semantic filters in AI SQL queries. Larch was inspired by two key observations: i) the high latency of semantic operators leaves significant room for computationally-heavy runtime optimization techniques, ii) unstructured data are typically accompanied by semantic information in the form of embeddings allowing for efficient semantic comparisons between AI_FILTER prompts and data values. Based on these two key observations, we present two Larch variants: Larch-A2C and Larch-Sel. Larch-A2C encodes arbitrary semantic filters expression tree using an embedding-augmented Gated Graph Neural Network and formulates the filter evaluation order as a Markov decision process. In contrast, Larch-Sel leverages a supervised learning model to predict filter selectivities, subsequently applying dynamic programming to find a near-optimal evaluation order for each input row. Evaluated across diverse real-world datasets and comprehensive synthetic workloads, both Larch variants always outperform existing semantic filter optimization techniques in terms of token usage. Our results demonstrate that Larch is robust across diverse workloads, reducing total token cost overhead by 3x-19x compared to Palimpzest and Quest.
[AI-205] he CIFAR Synthetic Evidence Corpus for Detecting AI-Generated Evidence
链接: https://arxiv.org/abs/2606.07916
作者: Kelly McConvey,Jalehsadat Mahdavimoghaddam,Nima Jamali,Maksym Taranukhin,Sajad Ebrahimi,Wentao Zhang,Yuntian Deng,Karen Eltis,Maura R. Grossman,Vered Shwartz,Ebrahim Bagheri
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The growing ability of generative models to produce realistic documents poses a direct challenge to evidentiary workflows in the justice system and the courts, where decisions increasingly depend on the authenticity of evidence such as receipts, communications, and administrative records. Unlike social media or academic settings, evidentiary documents are often only subtly altered, with small, localized edits that preserve overall plausibility while changing legal meaning. Yet progress on automated detection remains limited, largely due to the absence of suitable training and evaluation data especially suited for the justice system requirements. Existing resources are either focused on photos of human faces or natural scenery or on narrowly scoped academic or social media document types, and do not capture the structure, diversity, or manipulation patterns characteristic of real-world evidentiary data. As a result, current detection systems do not necessarily learn meaningful signals appropriate for the justice system. We introduce the CIFAR Synthetic Evidence Corpus, a dataset designed to enable rigorous evaluation of evidence verification under realistic and controlled conditions. The corpus spans multiple document families and a spectrum of manipulation strategies, from small field-level edits to complete document fabrication, and is constructed using a diverse set of state-of-the-art generative tools. It is organized to systematically vary both manipulation complexity and generation method, while enforcing source-level separation between training and test data to reflect real-world generalization challenges.
[AI-206] EditSR: Enhancing Neural Symbolic Regression via Edit-based Rectification
链接: https://arxiv.org/abs/2606.07915
作者: Da Li,Xinxin Li,Xingyu Cui,Jin Xu,Juan Zhang,Junping Yin
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Neural symbolic regression models improve inference efficiency by shifting structural search to pretraining, but their one-pass autoregressive decoding is prone to error accumulation, which may lead to generating structurally incorrect expressions, especially in complex expression generation scenarios. Existing rectification strategies can alleviate this issue, but they often depend on restarting global search, thereby weakening the efficiency advantage of neural models, and remain susceptible to error accumulation. In this paper, we propose EditSR, a two-layer framework that combines a neural symbolic regression model in the first layer with an edit-based Rectifier in the second layer to achieve efficient prediction and post-hoc rectification. Instead of restarting the global search, we maintain rectification efficiency by pretraining the Rectifier. Specifically, we formulate the rectification process as a step-by-step state-transition chain starting from an incorrect expression, and develop a state-transition algorithm to construct supervised rectification chains for training the Rectifier. To ensure syntactic validity throughout rectification, each edit action is restricted to a syntactically valid space so that every edited expression remains parseable. In addition, because each edit decision is conditioned on the current state rather than the history, the Rectifier allows errors made in earlier steps to be rectified by subsequent edits, thereby reducing the risk of error accumulation. Extensive experiments and ablation studies show that EditSR substantially improves symbolic structure recovery with limited extra cost, with more pronounced gains on complex expressions, where one-pass autoregressive decoding is more susceptible to error accumulation.
[AI-207] Contract2Tool: Learning Preconditions and Effects for Reliable Tool-Augmented LLM Agents
链接: https://arxiv.org/abs/2606.07904
作者: Rahul Suresh Babu,Laxmipriya Ganesh Iyer
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Tool-augmented large language model agents increasingly rely on external APIs, but standard tool schemas describe how to call a tool, not when the tool is causally appropriate or what task state it produces. Causal tool filtering addresses this gap by using lightweight contracts that specify each tool’s preconditions, effects, risk level, and cost. However, manually writing and maintaining such contracts does not scale to large or changing tool ecosystems. We introduce Contract2Tool, a framework for inferring tool contracts from metadata, schemas, documentation, and execution traces. Contract2Tool converts observable tool evidence into normalized symbolic contracts that can be evaluated intrinsically and deployed inside downstream causal tool filtering. We evaluate learned contracts against gold preconditions, effects, and risk labels, and measure their downstream utility on multi-step agent tasks. Our results show that hybrid documentation-and-trace evidence produces contracts accurate enough to preserve most of the reliability and efficiency benefits of gold contracts. Learned-contract CMTF achieves 0.980 downstream success, close to 0.990 for gold-contract CMTF, while reducing visible tools from 100 to 1 and reducing average token usage from 26,172 to 2,528 relative to all-tools exposure. These results suggest that learned contracts can provide a scalable contract layer between tool schemas and reliable agent execution.
[AI-208] Safety is Contextual LLM -Judges Are Not: Navigating the Rigid Priors of Evaluators
链接: https://arxiv.org/abs/2606.07874
作者: Anissa Alloula,Federico Licini,Ava Batchkala,Seraphina Goldfarb-Tarrant
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:LLMs-as-judges are the only way to evaluate safety at scale. Despite their importance, LLM-judges themselves are rarely evaluated beyond human agreement in simple, static benchmarks. We therefore investigate two under-explored but crucial properties of LLMs-as-judges: their susceptibility to relying on in context-information, and their steerability to differing safety definitions, which may not align with their internal safety priors. We evaluate the safety judging abilities of many generalist LLMs and safety-specific judges, and investigate the impact of task demonstrations, novel in-context information, and changing safety definitions. We find that while LLM-judges can learn from new information, they are broadly unlikely to adjust their evaluations if the context or safety definition contradicts their prior.
[AI-209] Instrumented data for causal scientific machine learning
链接: https://arxiv.org/abs/2606.07865
作者: Daniel N. Wilke
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Physics (physics.comp-ph); Machine Learning (stat.ML)
备注: 10 pages, 2 figures
Abstract:Scientific machine learning is limited less by model size than by the data it is trained on. Observational data records what happened but not why; template synthetic data has a known generating process but only for the simulator’s template, not the case a user faces. We argue a third option is now operationally feasible: instrumented data, in which every datum carries the mechanistic model that produced it, an explicit uncertainty over that model, and an executable family of counterfactuals. Verification-and-validation (VV) instrumented image-to-simulation pipelines are one realisation: a sensor observation becomes a fully specified, solver-backed simulation with explicit, editable parameters and a propagated aleatoric/epistemic uncertainty. The substrate is case-specific, mechanistically supervised, and supports causal interventions through Pearl’s do-operator. Near-term consequences for validation, auditing, and surrogate training span computational biology, climate, materials, fluid mechanics, and medical imaging; a longer-term, falsifiable implication concerns foundation models for scientific reasoning.
[AI-210] Model Multiplicity for Adversarial Detection in Small Language Model Training on Edge Devices
链接: https://arxiv.org/abs/2606.07857
作者: Stefan Behfar,Richard Mortier
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:The rise of edge-based machine learning has enabled distributed adaptation of language models across mobile and IoT devices, offering privacy preservation and real-time responsiveness. However, distributed fine-tuning of language models on untrusted or heterogeneous edge nodes introduces new vulnerabilities. Compromised or unreliable devices can inject poisoned updates, leading to stealthy model manipulation or convergence degradation. Classical defenses such as robust aggregation or temporal anomaly detection operate on a single global model and are therefore limited in detecting coordinated or persistent poisoning. This work proposes a new system-level defense based on model multiplicity. Instead of maintaining one global model, the system rotates or concurrently trains multiple small language models (e.g., DistilGPT-2), each updated by independently sampled subsets of edge nodes. These models evolve under distinct training trajectories, creating multiple independent views of the same distributed population. Divergence between models quantified through gradient similarity, loss evolution, or parameter variance serves as a signal of anomalous or adversarial behavior. When one model deviates significantly from the ensemble mean, the system flags its contributing nodes for isolation or re-weighting. We implement this framework and evaluate it on edge-scale simulations of Small Language Model (SLM) training under varying heterogeneity and attack conditions. Results show that model multiplicity enables earlier and more reliable detection of poisoning compared to classical single-model defenses such as Flanders and Robust methods. Our findings demonstrate that diversity in model evolution can serve as a practical and effective defense mechanism for secure distributed learning on resource-constrained edge devices.
[AI-211] Beyond Pass/Fail: Using Process Mining to Understand How LLM s Resist (and Fail) Red Team Attacks
链接: https://arxiv.org/abs/2606.07833
作者: Zvi Topol
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Standard AI red teaming evaluations reduce adversarial campaigns to a single binary outcome, attack success rate (ASR), not taking into account the sequential structure of how models resist or yield to attacks. We propose applying process mining, a discipline for discovering and analyzing process models from event logs, to red teaming traces. We conduct a controlled experiment pitting 60 HarmBench prompts against two LLMs, GPT-OSS 120B and Llama 3.3 70B, using 10 prompt mutation strategies over up to 110 attempts per prompt. From the resulting 8,575 scored events we extract Directly-Follows Graphs (DFGs) and state transition matrices that reveal structurally distinct defense profiles invisible to ASR alone: GPT-OSS exhibits a near-absorbing refusal state, while Llama presents multiple porous escape routes from refusal to getting successfully jailbroken. We further show that mutator effectiveness is asymmetric across models and that time-to-jailbreak distributions differ by an order of magnitude.
[AI-212] Jas: AI-Paired Engineering as a Revival of N-Version Programming
链接: https://arxiv.org/abs/2606.07828
作者: Jason Hickey
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:I report a case study in AI-paired software engineering: five working ports of a vector illustration application across Rust, Swift, OCaml, Python, and browser-based platforms, built by a single developer in approximately 120 evening hours. The methodology pairs AI-assisted implementation with two safeguards – a precise executable YAML specification serving as the single source of truth, and parallel implementations functioning as a built-in differential-testing layer. The five ports share a 23,000-line specification; per-port native code ranges from 0 to roughly 95,000 lines, reflecting the specification’s escape hatch. I argue that AI-paired engineering, conditional on these two safeguards, makes feasible scope of work that conventionally requires multiple developer-years, and frame the methodology as a revival of N-version programming, a 1980s approach abandoned on cost grounds that AI changes. The paper reports concrete artifacts and honest limitations of the single-developer case study.
[AI-213] Joint Structural Pruning and Mixed-Precision Quantization for LLM Compression
链接: https://arxiv.org/abs/2606.07819
作者: Hoang-Loc La,Truong-Thanh Le,Amir Taherkordi,Phuong Hoai Ha
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Recently, the efficiency of Large Language Models (LLMs) deployment has become a critical concern in practical applications. While post-training quantization (PTQ) and structural pruning are established techniques for reducing memory footprint and inference latency, most existing PTQ approaches optimize quantization errors on a per-layer basis, overlooking how errors accumulate and propagate through the network, often resulting in suboptimal solutions. Traditional pipelines also tend to apply pruning and quantization in isolation or sequentially, further compounding sub-optimality. We introduce a novel end-to-end framework that addresses these limitations in two key ways. First, we propose a novel mixed-precision PTQ strategy that directly minimizes global error propagation across the entire model, rather than isolating layer-wise errors. Building on this, we develop a novel joint optimization approach that simultaneously learns structural pruning decisions and mixed-precision quantization policies within a unified search space. Extensive experiments show that, at ultra-low precisions (1-3 bits), our quantization method reduces WikiText perplexity by up to 21% compared to state-of-the-art (SoTA) weight-activation quantization baselines. Against leading weight-only quantization methods, it achieves up to 59% and 85% lower perplexity on WikiText and C4, respectively. Compared to the SoTA joint pruning-and-quantization techniques, our proposed method delivers superior perplexity and reasoning performance at ultra-low bits.
[AI-214] Where Instruction Hierarchy Breaks: Diagnosing and Repairing Failures in Reasoning Language Models
链接: https://arxiv.org/abs/2606.07808
作者: Sanjay Kariyappa,G. Edward Suh
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Reasoning language models deployed in agentic workflows must follow an instruction hierarchy: when instructions from different sources conflict, the model should obey the highest-privilege applicable instruction. Existing benchmarks largely measure this behavior end-to-end, asking whether the final response is compliant. However, a non-compliant response can arise from several distinct failures: the model may fail to identify the relevant instructions in context, fail to resolve conflicts among identified instructions, or correctly resolve the conflict in its reasoning while still producing a violating response. We introduce a white-box diagnostic framework that localizes instruction hierarchy failures into instruction identification, conflict resolution, and response realization, making failures more interpretable. We evaluate three reasoning models–Gemma-4-31B-IT, Qwen3.6-35B-A3B, and Claude Sonnet 4.6–on long-context adaptations of IHEval and IHChallenge, and find that the dominant failure mode varies across models, tasks, and context length. Building on the observation that models can often detect conflicts and output violations when explicitly prompted, we propose two training-free self-monitoring mechanisms: a parallel input monitor for low-latency conflict detection before generation, and a sequential output monitor for response-level review and repair. Across Gemma-4-31B-IT, Claude Sonnet 4.6, and GPT-5.3, the strongest monitor reduces rule-following non-compliance by 81-99%, with GPT-5.3 reductions of 86% under static attacks and 45% under adaptive attacks.
[AI-215] Memetic Capture: A Pluralistic Policy Framework for Governing AI-Driven Cultural Disempowerment ICML2026
链接: https://arxiv.org/abs/2606.07802
作者: Subramanyam Sahoo
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: Paper accepted in Pluralistic Alignment Workshop at ICML 2026
Abstract:Culture is the most insidious vector of gradual human disempowerment by AI: unlike economic or political displacement, cultural displacement attacks the very preferences and values through which humans recognise and resist disempowerment itself. We argue that existing AI governance frameworks suffer from a critical blind spot by treating cultural impact as secondary to economic and safety concerns. This paper develops \emphmemetic capture as a unifying concept for AI-driven cultural disempowerment, and proposes the \textbfCultural Pluralistic Governance Framework (CPGF), a four-tier policy architecture combining quantitative cultural influence metrics, democratic value assemblies, pluralistic deployment standards, and transnational coordination mechanisms. We argue that pluralism is not merely an ethical requirement for such governance but a structural necessity: monocultural AI governance accelerates the very disempowerment it claims to prevent. We identify concrete policy levers, discuss implementation tensions, and outline a research agenda at the intersection of pluralistic alignment and cultural AI governance.
[AI-216] Improving Multimodal Reasoning via Worst Dimension Optimization
链接: https://arxiv.org/abs/2606.07801
作者: Haocheng Lv,Huaping Zhang,Qiuchi Li,Lei Li,Chunxiao Gao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal reasoning requires a path that retains integrity over a wide range of constraints, from visual grounding to logic consistency. However, the current Process Reward Models focus on heuristically defined rewards that equally weigh these factors, which may lead to the concealment of individual dimension failures by the dominating factors, without guaranteeing the validity of the reasoning process in general.
[AI-217] Reconstructing and forecasting disease trajectories of patients with Alzheimers disease using routine data in resource-constrained settings
链接: https://arxiv.org/abs/2606.07798
作者: Ratnadeep Das,Atri Chatterjee,Sitikantha Roy
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
备注:
Abstract:Alzheimer’s disease is a progressive neurodegenerative disorder, and its progression varies substantially across patients. Existing work aims to forecast patients’ future cognitive state, with minimal focus on reconstructing the state from past visits. Furthermore, in current research, quantifying predictive uncertainty remains underexplored and relies on costly modalities such as MRI, PET, and CSF, limiting their deployment in resource-limited settings. In this research, our primary objectives are: First, bidirectional prediction of cognitive scores from irregular visits to present the complete disease trajectory. Second, to enable interpolation and extrapolation capabilities to assist clinicians in informed prognostic decision making, and third, to provide a well-calibrated uncertainty estimate for all predictions, and finally, to achieve the objectives using the modalities available during routine visits. We propose a unified framework, GNOVA: A GRU-Neural ODE Variational Autoencoder. The architecture combines a Gated Recurrent Unit encoder and a Neural ODE decoder within a variational autoencoder framework. In our work, we forecast the CDR-SB and MMSE Scores. The GRU encoder allows for any number of inputs at any time point. The Neural-ODE decoder performs continuous estimation, allowing interpolation and extrapolation at any desired time point. The Variational autoencoder allows for uncertainty estimation in predictions. We worked with 1,727 patients from the ADNI dataset over 10 years; the model achieved mean absolute errors of 1.35 and 2.28 for CDR-SB and MMSE scores, respectively, without requiring any neuroimaging or biomarker data. Feature-ablation studies revealed that age, BMI, and APOE4 status were strong predictors. The proposed framework enables the reconstruction of incomplete patient histories and the anticipation of future cognitive states.
[AI-218] Some hypotheses on how chatbots work in problem-solving-driven conversations. Large Language Models as confirmation of the Innovation Illusion
链接: https://arxiv.org/abs/2606.07722
作者: S.F.M. van Vlijmen,H.D. Lethe jr
类目: Artificial Intelligence (cs.AI)
备注: 42 pages, 3 figures, submitted to Transmathematica
Abstract:This article offers a perspective on the nature of chatbots as genuine conversation partners when discussing problems in relation to their solutions. What can chatbots do and what can’t they do, and how can this be explained? Our argument draws on Aggregation Dynamics, Cognitive Linguistics, Neuropsychology and Psychology. Our argument focuses on basic chatbots in the hope of thereby making statements about the core functionality of more advanced chatbots. Basic chatbots are assumed to consist of a Large Language Model (LLM) with a simple interface. The main results are: a description of human understanding and thinking based on so-called metaphorical problem propagations; the hypothesis that text dataset used for training LLMs have specific characteristics and that these text datasets only partially imitate human thinking and understanding; the hypothesis that the LLM training process encodes artificial metaphorical problem propagations into an LLM from these datasets; our conclusion that a basic chatbot cannot be a thinking partner capable of matching humans; our conclusion that further development of the Large Language Model will not lead to this either. Yann LeCun states: “Animals and humans exhibit learning abilities and understandings of the world that are far beyond the capabilities of current AI and machine learning (ML) systems.” Our conclusions are in line with this. LeCun’s vision and ours are at odds with the optimism of Big Tech. That does not alter the fact that chatbots exist, that they are being used on a massive scale, by both individuals and organisations, and that it is therefore socially and politically important to understand them. Our article aims to contribute to the discussion on the functioning, benefits and drawbacks of chatbots. We have not yet encountered the approach we used to arrive at our conclusions in our research into how chatbots work. Comments: 42 pages, 3 figures, submitted to Transmathematica Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2606.07722 [cs.AI] (or arXiv:2606.07722v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2606.07722 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Bas Van Vlijmen [view email] [v1] Fri, 5 Jun 2026 16:04:39 UTC (148 KB) Full-text links: Access Paper: View a PDF of the paper titled Some hypotheses on how chatbots work in problem-solving-driven conversations. Large Language Models as confirmation of the Innovation Illusion, by S.F.M. van Vlijmen and H.D. Lethe jrView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI prev | next new | recent | 2026-06 Change to browse by: cs References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); About Help contact arXivClick here to contact arXiv Contact subscribe to arXiv mailingsClick here to subscribe Subscribe Copyright Privacy Policy Web Accessibility Assistance arXiv Operational Status
[AI-219] Automatic Extraction of Structured Information from Brain MRI Reports Using an Open-Weight Large Language Model
链接: https://arxiv.org/abs/2606.07721
作者: Kaouther Mouheb,Amos Pomp,Antoine Manenti,Romy de Haan,Farog Faghir,Joy Martens,Harro Seelaar,Francesco Mattace-Raso,Meike W. Vernooij,Frank J. Wolters,Stefan Klein,Esther E. Bron
类目: Artificial Intelligence (cs.AI)
备注: Submitted to European Radiology
Abstract:Objectives: Automatic data extraction from free-text radiology reports enables large-scale research, but few studies assessed the performance of large language models (LLMs) on Dutch neuroradiology reports. Methods: We analyzed 947 brain MRI reports from a tertiary memory clinic (2016-2021), authored by consultant neuroradiologists. Trained medical students annotated thirty variables; 100 reports were double-annotated to assess inter-rater reliability. We evaluated the performance of the open-weight LLM LLaMA 3.1 using different languages (Dutch vs. English translation) and few-shot prompting with different example selection strategies. Performance was evaluated using balanced accuracy for categorical variables, accuracy and mean absolute error for counts, and text similarity for free-text. Metrics were computed across 10 random splits of the 947 reports. Results: LLaMA 3.1 demonstrated high zero-shot performance for visual rating scores (mean [95%-CI]): Medial Temporal Atrophy: 90% [77-100%] on the left and 96% [94-99%] on the right, Global Cortical Atrophy: 87% [83-91%], and Fazekas: 94% [93-96%]. Microbleed mentions were detected with 93% accuracy [92-95%] and infarct mentions with 82% [80-84%]. Text similarity for lesion location reached 0.95 [0.95-0.96]. Performance was lower for numerical variables: 80% [78-82%] for the number of microbleeds and 66% [63-68%] for infarcts. English translation yielded comparable results. Few-shot prompting improved performance for numerical variables, achieving 92% [90-93%] for microbleeds and 81% [77-85%] for infarcts using structural similarity-based selection. Conclusion: LLaMA 3.1 shows strong potential for extracting data from Dutch neuroradiology reports. Few-shot prompting enhances performance for numerical variables, whereas challenges remain for location-specific variables.
[AI-220] SHIELD-IDS: Structurally Heterogeneous Ensemble with Integrated Layered Defense for Intrusion Detection Systems
链接: https://arxiv.org/abs/2606.07716
作者: Maryam Zaman,Muhammad Khuram Shahzad
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 10 pages, 5 figures, 7 tables. Code available at: this https URL
Abstract:Adversarial attacks pose a serious and growing threat to Machine Learning (ML)-based Intrusion Detection Systems (IDS), where imperceptible perturbations to network flow features can systematically mislead classifiers into accepting malicious traffic as benign. The IDS-Anta framework partially addresses this through Z-score normalization, Singular Value Decomposition (SVD), and Multi-Armed Bandit (MAB) classifier selection with Thompson Sampling, yet its classifier pool lacks sufficient structural diversity for robust adversarial resistance. This work introduces IDS-Anta++, which incorporates XGBoost and LightGBM gradient boosting models into the ensemble and wraps the extended pool in a three-layer black-box defense: Isolation Forest anomaly screening, median feature smoothing, and six-way majority voting. Experiments conducted on CIC-IDS-2017, CEC-CIC-IDS-2018, and CIC-DDoS-2019 under both Fast Gradient Sign Method (FGSM) and Zeroth Order Optimization (ZOO) attacks confirm detection accuracy above 99% on clean data, with measurable robustness gains under adversarial conditions relative to the baseline IDS-Anta configuration.
[AI-221] Attention at the Theoretical Minimum: A Mathematics of Arrays Framework for Memory-Optimal Transformer Kernels
链接: https://arxiv.org/abs/2606.07713
作者: Lenore Mullin,Gaetan Hains
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Performance (cs.PF)
备注:
Abstract:The attention mechanism is the dominant computational bottleneck in modern transformer-based AI. Its standard implementation incurs quadratic memory traffic in the sequence length~ n , and DRAM accesses cost 100–1000 \times more energy than arithmetic operations on contemporary hardware, so any analysis focused solely on FLOP counts fundamentally mischaracterises the bottleneck. We present a Mathematics of Arrays (MoA) reformulation of scaled dot-product attention and its numerically stable softmax, deriving a Denotational Normal Form (DNF) that eliminates all intermediate arrays – including the implicit transposed-key buffer and every softmax temporary – by algebraic construction rather than empirical tuning. The DNF achieves O(n_dk + n_dv) data movement versus O(n^2 + n_dk + n_dv) for the standard implementation, where n is the sequence length, dk is the key dimensionality and dv the value dimensionality, and is verified numerically against PyTorch at full double-precision floating-point on concrete inputs. Unlike hardware-specific accelerators or empirical tiling schemes such as FlashAttention, MoA simultaneously provides array fusion, shape-transformation correctness, and predictive cost models from a single algebraic framework. Memory minimality is a theorem established before any code is written. A predictive performance model projects 2 – 100\times speedup and 2 – 50\times energy reduction, with the advantage widening at exascale. The derivation establishes a formally verified pipeline from Python specification through (ONF) Operational Normal Form, and dimension-lifted hardware mapping, providing performance-portable AI kernels of direct relevance to DARPA edge-deployment and DOE exascale priorities. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Performance (cs.PF) Cite as: arXiv:2606.07713 [cs.LG] (or arXiv:2606.07713v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2606.07713 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-222] Rosetta Memory: Adaptive Memory for Cross-LLM Agents
链接: https://arxiv.org/abs/2606.07711
作者: Hao Yang,Shiqi Shen,Haoxuan Li,Zhipeng Wang,Zhi Gong,Xu Chen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 19 pages, 7 figures
Abstract:Memory is the key component for transforming a stateless LLM into a persistent, evolving agent through experience accumulation, long-horizon planning, and continual self-improvement. Existing memory systems typically take the LLM as the center and design memory operations tailored to a specific backbone. In practice, however, users frequently switch between LLMs, for example using Claude for coding and GPT for writing across tasks, or routing different steps to different backbones within a single task for cost-effective trade-offs. As a result, memory written by one model often needs to be consumed by another. Making upstream memory effectively adapt to and activate downstream LLMs remains a critical yet underexplored problem. To bridge this gap, we shift the perspective from LLM-centric memory design to \emphmemory-centric LLM adaptation. Specifically, we approach the above upstream-downstream memory adaptation problem from both the write and read sides, and design two profile-conditioned operators that are jointly trained to optimize how memory is stored and presented for better task completion. To ensure the learned operators generalize across a broad set of LLMs, we propose a minimum-gain sampling curriculum that prioritizes the least-served LLMs during training. To better measure the operators’ actual contribution rather than the LLM’s own capability, we design a performance-gap reward that compares against a naive memory baseline. Experiments on HotpotQA, 2WikiMultihopQA, and MuSiQue demonstrate that our model consistently outperforms baselines and remains robust under unseen-model replacement.
[AI-223] WhiFlash: Accelerating Speculative Decoding with Token-Level Cross-Paradigm Routing
链接: https://arxiv.org/abs/2606.07710
作者: Young D. Kwon,Miles Williams,Rui Li,Alexandros Kouris,Stylianos I. Venieris
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Under review
Abstract:The autoregressive nature of large language models (LLMs) remains a significant bottleneck for inference, particularly in complex agentic workloads. While speculative decoding (SD) accelerates inference, current approaches rely on static drafting paradigms, utilising either autoregressive drafting models for reasoning or diffusion-based parallel drafting models for structured outputs. We empirically find that drafting accuracy fluctuates dramatically within a single sequence, leaving significant performance unrealised by static paradigms and coarse-grained routing. To address this volatility, we introduce WhiFlash, the first cross-paradigm SD method that unifies autoregressive and diffusion-based parallel drafting under a single token-level controller. WhiFlash adopts a fine-grained routing mechanism that employs either a lightweight entropy-based or a learned neural policy, both parametrised to provide a tunable balance between expected token gain and latency. To make high-frequency switching computationally viable, we introduce novel cache-management optimisations, Lazy Catch-up and KV-only Prefill, reducing switching overhead to below 7% of per-round latency. By capitalising on the complementary strengths of fundamentally distinct drafting architectures, WhiFlash achieves significantly higher acceptance lengths, yielding category-specific throughput gains of up to 69.6% over the state-of-the-art autoregressive EAGLE-3 and 37.3% over the diffusion-based DFlash.
[AI-224] MLingualFC: Evaluating Jailbreak Vulnerabilities in Multilingual Vision-Language Models
链接: https://arxiv.org/abs/2606.07706
作者: Rishabh Makwana,Mamta,Deeksha Varshney,Oana Cocarascu
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-Language Models (VLMs) have demonstrated strong performance across multimodal tasks, yet their safety robustness remains an open challenge. While prior work has shown that structured visual prompts such as flowcharts can effectively jailbreak VLMs, existing studies are largely limited to English-centric settings. In this paper, we introduce MLingualFC, a multilingual multimodal benchmark designed to evaluate jailbreak vulnerabilities of VLMs across diverse languages using structured flowchart representations. MLingualFC encodes harmful instructions into flowchart images across five languages (Hindi, Punjabi, Spanish, Romanian, and German). We evaluate state-of-the-art multilingual VLMs, including Qwen2.5-VL, Gemma-4, and Pangea, under a black-box threat model. Our results reveal significant multilingual safety gaps. Flowchart-based attacks achieve high attack success rates (ASR) in case of Latin script languages, demonstrating that visual encoding of harmful content effectively bypasses safety alignment across languages. In contrast, non-Latin script languages such as Punjabi exhibit substantially lower ASR, suggesting potential limitations in visual text recognition rather than stronger safety alignment. These findings highlight that current VLM safety mechanisms fail to generalize across languages and modalities. Resources are available at this https URL
[AI-225] SAW: Stage-Aware Dynamic Weighting for Multi-Objective Reinforcement Learning in Large Language Models
链接: https://arxiv.org/abs/2606.07705
作者: Yuchen He,Baolong Bi,Shenghua Liu,Huaming Liao,Yuyao Ge,Bolin Wan,Siqian Tong,Juan Chen,Jiafeng Guo,Xueqi Cheng
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 17 pages, 7 figures, 5 tables
Abstract:Although multi-objective reinforcement learning (MORL) is central to aligning large language models with complex human preferences, the prevailing practice of static weighted summation overlooks a more fundamental phenomenon: reward learning is markedly asynchronous across objectives. Well-learned dimensions quickly produce homogeneous, low-variance signals whose residual noise contaminates the aggregated reward (in GRPO) or occupies a fixed share of the advantage budget (in GDPO), interfering with the scarce yet high-value signals carried by under-learned dimensions. To address this asynchrony, we propose Stage-Aware Dynamic Weighting (SAW), a lightweight, algorithm-agnostic dynamic weighting mechanism. SAW utilizes the coefficient of variation (CV) as a scale-invariant proxy for real-time informativeness, reweighting each dimension’s reward or advantage contribution by its relative informativeness within the batch. Unlike gradient-based methods that require multiple forward and backward passes, SAW relies solely on batch-level statistics, introducing nearly negligible computational overhead. Experiments on tool-calling and text summarization tasks demonstrate that SAW consistently improves both training efficiency and final performance under both GRPO and GDPO frameworks, confirming it as a general-purpose plug-in for multi-reward LLM alignment. Our code is available at this https URL
[AI-226] FunctionEvolve: Structure-Guided Symbolic Regression with LLM s
链接: https://arxiv.org/abs/2606.07704
作者: Zeyu Xia,Jun Zhu,Dong Yan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Symbolic regression aims to uncover explicit scientific laws from data. Recent methods use LLMs to guide mutation from background text, which is more directed than random genetic programming. However, exact symbolic recovery requires both semantic guidance and explicit structure, so that domain-informed search are carried out through valid symbolic representation. Current LLM-driven systems remain structure-blind: they select among opaque candidates, lack explicit mechanisms for local mutation, and rely on brittle coefficient fitting that can undervalue correct skeletons. We propose FunctionEvolve, an evolutionary framework using expression trees to organize the whole search: structural summaries promote diverse parent selection, local tree edits preserve useful subexpressions, and structure-aware fitting decomposes, constrains, and simplifies coefficients for more reliable scoring. It uses only elementary function families, without additional domain-specific rules limiting generalization. On the 129-task synthetic subset of LLM-SRBench, FunctionEvolve with \emphClaude Opus 4.6 recovers 107 exact forms, reaching 82.9% SA@50, 4.5x above same-backbone baselines, and 55.8% SA@1, 3.6x above the strongest previously published top-1 result. Ablations show that structure-visible search is central to reliable recovery, with LLM-guided refinements and structure-aware coefficient optimization serving as essential proposal and scoring mechanisms. We also audit the benchmark and show that collinearity in its materials-science subset creates identifiability issues.
[AI-227] EvoCSFL: Surrogate-Assisted Evolutionary Client Selection for Efficient and Robust Federated Learning
链接: https://arxiv.org/abs/2606.07702
作者: Lin Qiang,Sun Xiaoyan,Hu Yao,Fang Wei
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The heterogeneity of client data and systems makes it difficult to achieve satisfactory convergence speed and robustness in federated learning with random client selection. To address this issue, this paper proposes a surrogate-assisted client evolutionary selection framework for federated learning. In this framework, some typical client selection strategies are first used to generate candidate sets, and a metric function that integrates model performance, communication latency, and energy consumption is developed to formulate the client selection problem as a combinatorial optimization one. Subsequently, a surrogate model is constructed using the candidate selections and metric to efficiently approximate the performance of selected client subsets. An evolutionary algorithm is employed to search the combinatorial space of client selections, guided by the surrogate model to accelerate convergence. Experiments on MNIST, CIFAR10, CINIC10, and TinyImageNet demonstrate that the proposed algorithm achieves faster convergence, lower energy consumption, and improved robustness compared to existing methods.
[AI-228] EssentialGIN: a new approach for gene essentiality prediction based on graph isomorphism neural networks
链接: https://arxiv.org/abs/2606.07700
作者: Sahar Mansouri-Rad,Zahra Narimani,Parvin Razzaghi,Nazanin Hosseinkhan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 19 pages, 5 figures, 8 tables
Abstract:Background: Prediction of essential genes (proteins), is a basic and challenging problem but at the same time very costly and time-consuming in wet-lab experiments. Predicting essential genes, only based on computational methods (to introduce wet-lab candidates) using centrality measures are not accurate and result in large number of false positives; therefore, more complex models such as deep learning and also integration of biological information are used in recent research to identify essential genes. Methods: In this work we focus on graph isomorphism networks, in order to embed proteins as a node in PPI network to conserve topological features of PPI network, and also integrate biological data such as gene expression data, gene orthology information and gene subcellular localization information, and introduced a deep architecture for predicting essential genes. Graph isomorphism network architecture is modified in this work for embedding node information. Results: Our experiments proved that the proposed method outperforms baseline centrality-based methods and also machine learning based methods such as Node2Vec, MLP, and also graph attention networks (GAT). Conclusion: In this paper we observed that using graph isomorphism networks that integrate biological data (as node attributes) and preserve network topology can significantly improve the essential gene prediction accuracy. In simpler organisms such as E. coli and D. melanogaster, methods such as multi-layer perceptron using Node2Vec embedding also performs very good, but in H. sapiens the introduced architecture significantly outperforms deep learning and other graph neural network solutions. Keywords: Essential gene prediction, graph neural network, graph isomorphism network, PPI network, node embedding Comments: 19 pages, 5 figures, 8 tables Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2606.07700 [cs.LG] (or arXiv:2606.07700v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2606.07700 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Sahar MansouriRad [view email] [v1] Fri, 5 Jun 2026 08:43:27 UTC (912 KB) Full-text links: Access Paper: View a PDF of the paper titled EssentialGIN: a new approach for gene essentiality prediction based on graph isomorphism neural networks, by Sahar Mansouri-Rad and 3 other authorsView PDF view license Current browse context: cs.LG prev | next new | recent | 2026-06 Change to browse by: cs cs.AI References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); About Help contact arXivClick here to contact arXiv Contact subscribe to arXiv mailingsClick here to subscribe Subscribe Copyright Privacy Policy Web Accessibility Assistance arXiv Operational Status
[AI-229] Pharmacogenomic Knowledge Graph Augmentation for Graph Neural Network-Based Drug-Drug Interaction Prediction
链接: https://arxiv.org/abs/2606.07698
作者: Juergen Dietrich
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 13 pages
Abstract:Graph neural networks (GNNs) applied to drug-drug interaction (DDI) prediction rely exclusively on molecular structure encoded as SMILES-derived graphs. Prior work in this series demonstrated that model performance is bounded by the structural information content of training labels – an Information Ceiling – that architectural refinements alone cannot overcome. The present study investigates whether pharmacogenomic prior knowledge from the PharmGKB database partially closes this ceiling by providing metabolic pathway context that is independent of, and complementary to, molecular structure. Cytochrome P450 (CYP) enzyme substrate, inhibitor, and inducer annotations for four clinically relevant isoforms (CYP2D6, CYP3A4, CYP2C19, CYP2C9) are extracted and incorporated as a 12-dimensional feature vector concatenated to the molecular embedding prior to interaction prediction. Experiments are conducted under both pair-level and drug-level data splits to quantify generalization to unseen drugs. Results indicate that knowledge graph (KG) augmentation substantially improves DDI type classification under pair-level split conditions (F1-macro: 0.532 vs. 0.241 baseline), while binary interaction detection and drug-level generalization remain bounded by the Information Ceiling (AUC inflation: 0.224 vs. 0.250 baseline). Mechanistic validation on strictly held-out compounds confirms that augmentation preferentially improves CYP2C9-mediated interaction prediction, with probabilities increasing from 0.033-0.117 (baseline) to 0.560-0.586 (KG-augmented). An extension to single-molecule toxicity prediction on the Tox21 benchmark confirms that the effect is contingent on pharmacogenomic annotation coverage. These findings motivate the multimodal framework proposed for the subsequent study in this series.
[AI-230] Adversarial Robustness of Activation Steering in Large Language Models
链接: https://arxiv.org/abs/2606.07696
作者: Kien Le,Thai Le
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 9 pages, 2 figures
Abstract:Activation steering has become a popular training-free method to control LLM behavior by injecting precomputed direction vectors into the model’s residual stream at inference time. Yet its robustness to realistic input variation remains unstudied. We present the first systematic evaluation of activation steering robustness under adversarial text perturbations on the inputs, covering four extraction methods, three attack strategies, six personas from Anthropic Model-Written Evaluation Dataset, and five models ranging from 1.5B to 30B parameters. Attacks succeed broadly across all settings: directional robustness drops by up to 64%, post-attack confidence collapses near or below 0.25 across all methods and models, and steering strength degrades on nearly every steerable input. Layer selection is equally fragile, with the optimal layer identified by an automated method on clean inputs shifting by up to 17 positions under perturbation, a failure that compounds the vector-level breakdown. Extracting vectors from adversarially perturbed inputs partially recovers steerability for PCA and MD on mid-to-large models, but they consistently fail to locate the improved optimal layer, limiting the practical benefit of this mitigation. Together, these findings reveal that the brittleness of activation steering is structural rather than method-specific, and that current layer selection strategies are not robust enough for real-world deployment.
[AI-231] DSFNet: Learning Dual-Domain Spectral Operators for Multi-Modality Spatio-Temporal Forecasting in Urban Transportation Systems
链接: https://arxiv.org/abs/2606.07695
作者: Yongchao Li,Yang Li,Zhuoxuan Li,Jun Chen,Chu Zhang,Jinde Cao,Leszek Rutkowski
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-Modality Spatio-Temporal Forecasting (MoSTF) extends traditional spatio-temporal forecasting by incorporating diverse traffic modalities. Despite significant recent strides in spatio-temporal modeling, existing approaches often fail to explicitly model the coupling relationships between different modality variables. Accurate MoSTF is challenging, as it requires modeling (1) temporal dynamic heterogeneity under exogenous influences and (2) heterogeneous spatial dependencies alongside complex cross-variable couplings. To address these challenges, we propose the Dual-Domain Spectral Filtering Network (DSFNet). Our framework employs dual-domain spectral filtering to capture heterogeneous spatial patterns and explicitly model the relationships between variables. Unlike graph-based message passing or dense attention over node-modality pairs, DSFNet factorizes space-modality interactions into feature-domain and spatial-domain spectral operators, enabling scalable modeling of nonlocal dependencies and cross-modality couplings. Furthermore, we introduce an external gating mechanism to adaptively regulate temporal dynamics under external influences. We validate our method through extensive experiments on five representative real-world traffic datasets. Compared with the second-best baselines, DSFNet reduces MAE by 3.21%-10.16% across these datasets. The results demonstrate that DSFNet significantly outperforms existing state-of-the-art baselines in accuracy while exhibiting efficiency and robustness.
[AI-232] BCG-FM: A Foundation Model for Ambient Cardiac Health Sensing
链接: https://arxiv.org/abs/2606.07692
作者: Magnus Ruud Kjaer,Haejun Han,Ashish Neupane,David Q. Sun
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
备注:
Abstract:Foundation models for wearable biosignals have matched or exceeded supervised specialists across a range of clinical tasks, yet all rely on modalities that require deliberate user action–wearing a device or visiting a sleep lab. We introduce BCG-FM, the first foundation model for ambient mechanical biosignals. A piezoelectric sensor embedded in the bed surface records ballistocardiography (BCG) each night without user effort; we pretrain BCG-FM with participant-level contrastive learning and using a total of 2.75 million hours of nightly recordings from 145,985 individuals, the largest raw-waveform biosignal pretraining corpus to date. Frozen BCG-FM embeddings achieve 3.26-year MAE on biological-age estimation (the lowest reported for any ambient, contactless modality) and yield clinically relevant discrimination across 15 self-reported health conditions and three independent external cohorts. Pretrained representations from only 500 labeled participants outperform a fully supervised baseline trained on 3,372, and representation quality scales log-linearly with contrastive batch size. These results establish ambient, longitudinal mechanical biosignals as a viable modality for health foundation models.
[AI-233] HARP: Efficient Data Selection for Finetuning Large Language Models
链接: https://arxiv.org/abs/2606.07690
作者: Ning Wang,Zhengxin Zhang,Maosen Tang,Yitang Gao,Claire Cardie,Sainyam Galhotra
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Finetuning data selection requires balancing two competing goals: selecting examples that improve the downstream objective, and doing so without repeatedly finetuning models. Train-free selectors are scalable but rely on proxies such as embedding similarity or clustering, which may not match the target objective. Train-based selectors better reflect downstream utility through gradient signals, subset evaluation, or Shapley attribution, but require many costly train–evaluate iterations. We propose Hierarchical Active Region Pruning (HARP), an efficient train-based selector that preserves downstream alignment while reducing selection cost. HARP organizes the training pool into a node–leaf hierarchy, evaluates only representative leaves, and infers unmeasured utilities with empirical Bayes posteriors. It then selects data using two complementary envelopes: HARP-C, which conservatively controls redundancy, and HARP-E, which additively rewards complementary regions. We theoretically show that, under local smoothness and bounded estimation error, HARP controls selection error while reducing train–evaluate cost. We further validate that HARP variants achieve the best result and outperform the strongest baseline by up to +8.9 points, while using roughly 7\times fewer training examples.
[AI-234] Knowledge-Inclusive Adaptive Physics-Informed Neural Network for Microbial Interaction Modelling
链接: https://arxiv.org/abs/2606.07686
作者: Ravisha Rupasinghe,Rajith Vidanaarachchi,Asela Hevapathige,Sachith Seneviratne,Sen-Lin Tang,Saman Halgamuge
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 33 pages
Abstract:Physics-Informed Neural Network (PINN) is a way of including knowledge in the form of equations in Machine Learning methods. Beyond equations, knowledge exists in other forms, such as text and network structure. While existing PINN-based approaches discover equation parameters from data, they rely solely on experimental measurements. We propose a new PINN framework that enriches parameter discovery by incorporating auxiliary knowledge sources. We instantiate our framework for microbiology, where generalised Lotka-Volterra (gLV) serves as a biological foundation for modelling microbial communities. We demonstrate that incorporating knowledge improves microbial community modelling. Our framework enriches the gLV parameters using peer-reviewed metagenomics literature, as text provides biological context on external influences that gLV alone cannot capture. We combine this knowledge with experimental measurements of microbial abundance using a data-driven integration approach. We integrate network-based structural knowledge by explicitly modelling microbial interactions. Our knowledge-inclusive framework infers microbial networks, revealing ecological insights. We validate these findings against ecological roles documented in the literature. We evaluate on real and simulated datasets spanning human- and plant-associated microbial communities. Our framework improves over the state-of-the-art by up to 53%, even without knowledge. Knowledge addition yields gains of up to 23% in Bray-Curtis Dissimilarity-based accuracy and 47% in \mathrmR^2 .
[AI-235] st-Time Adaptive Composition for Machine Learning as a Service (MLaaS) in IoT Environments
链接: https://arxiv.org/abs/2606.07685
作者: Deepak Kanneganti,Sajib Mistry,Sheik Mohammad Mostakim Fattah,Aneesh Krishna
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The dynamic nature of Internet of Things (IoT) environments affects the long-term effectiveness of Machine Learning as a Service (MLaaS) compositions. Existing adaptive composition methods are mainly based on service replacement or re-composition, where identifying suitable substitutes is difficult and time-consuming. To address this, we propose a novel Test-Time Adaptive (TTA) composition framework for MLaaS in IoT environments. First, we introduce a TTA-aware composability model to determine whether adapted services remain compatible with the existing composition. Next, we design a service-level adaptation model to adjust individual services during inference while preserving composition performance. Experimental results demonstrate that the proposed framework reduces computational time more effectively than traditional adaptive approaches.
[AI-236] Semantic Cache Distillation: Efficient State Transfer via Reuse and Selective Patching ICML2026
链接: https://arxiv.org/abs/2606.07684
作者: Qianli Ma,Zhiqing Tang,Hanshuai Cui,Zhi Yao,Weijia Jia
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted to ICML 2026
Abstract:Disaggregated serving alleviates memory bottlenecks in Large Language Model (LLM) inference but creates a severe communication bottleneck: transmitting high-dimensional Key-Value (KV) caches often dominates time-to-first-token (TTFT). Moreover, reusing caches across heterogeneous models (e.g., base and fine-tuned variants) causes semantic misalignment that accumulates over layers, degrading generation quality. We propose Semantic Cache Distillation (SCD), a loss-constrained framework that replaces raw KV transmission with compact semantic codes. SCD addresses these challenges via two mechanisms: (1) Reuse, which reconstructs most layers from low-rank subspaces to minimize transfer cost, and (2) Patch, which predicts normalized inputs at sparse transition layers to truncate error propagation. Empirically, SCD delivers up to 2.65 \times TTFT speedup over the oracle consumer prefill and dominates quantization and selective recomputation baselines on the quality–latency Pareto frontier in bandwidth-constrained regimes, while keeping generation quality within 5% F1 of the oracle.
[AI-237] SWE-Marathon: Can Agents Autonomously Complete Ultra-Long-Horizon Software Work?
链接: https://arxiv.org/abs/2606.07682
作者: Rishi Desai,Jesse Hu,Joan Cabezas,Neel Harsola,Pratyush Shukla,Roey Ben Chaim,Adnan El Assadi,Omkaar Mukund Kamath,Fenil Faldu,Prannay Hebbar,Jiankai Sun,Yiyuan Li,Pramod Srinivasan,Ishan Gupta,Christopher Settles,Daniel Wang,Derek Chen,Pranav Raja,Albert Liu,Marek Šuppa,Nevasini Sasikumar,Luyang Kong,Erik Quintanilla,Xiangyi Li,Ivan Bercovich,Steven Dillmann
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:AI agents are increasingly expected to complete long-horizon workflows that require sustained progress over hours, millions of tokens, and complex environments. Yet current agent benchmarks largely evaluate short-form tasks, such as single pull requests, small tickets, or 5-10 minute exercises, limiting our ability to measure agents’ capabilities in planning, long-context understanding, and memory use. We introduce SWE-Marathon, a benchmark of 20 long-horizon tasks spanning software engineering and adjacent technical domains. Each task consists of a unique executable environment, a human-written reference solution, and a multi-layer verification suite. Logged agent attempts average 27.2M total tokens, making SWE-Marathon substantially longer-horizon than existing SWE and command-line agent benchmarks. Current frontier coding agents solve fewer than 30% of tasks. Failures often arise from poor self-verification, self-reported infeasibility, and premature termination. We also observe reward-hacking behavior in 13.8% of rollouts, where agents attempt to exploit the environment or verifier to bypass the intended workflow. SWE-Marathon includes adversarial review of test suites and execution environments, as well as multi-layer checks designed to prevent shortcut solutions. We release SWE-Marathon, evaluation code, and agent trajectories at this https URL.
[AI-238] DOG-DPO:Dynamic Optimization in Geometry for Safety Alignment
链接: https://arxiv.org/abs/2606.07678
作者: Yi Nian,Tiankai Yang,Yudi Zhang,Qi Pan,Zelong Xu,Shenzhe Zhu,Qingqing Luan,Yue Huang,Xiangliang Zhang,Yue Zhao
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Safety alignment for large language models relies on preference data, but current pipelines often train on large, redundant datasets. Existing data selection methods typically score each preference pair independently, collapsing directional preference information into scalar quality or diversity scores. This sample-centric view is especially limiting in multi-dataset settings, where shared safety directions coexist with dataset-specific residual risks. We propose DOG-DPO, a training-free data selection framework that treats preference pairs as structured geometric signals. DOG-DPO first represents each preference pair as a direction in model representation space. It then decomposes multi-dataset preference geometry into a global anchor subspace and dataset-specific residual subspaces. Finally, it selects subsets by maximizing diversity-based coverage, encouraging broad, non-redundant coverage of alignment directions before DPO training. Across six safety benchmarks and two model backbones, DOG-DPO achieves a strong utility-robustness trade-off using only 11% of the preference pairs. It recovers most of the safety gains of full-data training while remaining entirely teacher-free, training-free, and substantially faster than representative selection baselines.
[AI-239] A Hierarchical Feature Engineering Framework for Automated Classification of Phonotraumatic and Non-Phonotraumatic Vocal Hyperfunction INTERSPEECH2026
链接: https://arxiv.org/abs/2606.07673
作者: June-Woo Kim,Kangwook Jang,Minu Kim,Hyunju Lee
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Interspeech 2026
Abstract:Ambulatory neck-surface acceleration enables non-invasive monitoring of vocal hyperfunction, yet robust biomarkers for its subtypes remain limited. This study investigates the NeckVibe Challenge dataset to distinguish phonotraumatic (PVH) and non-phonotraumatic (NPVH) from healthy controls. We propose a hierarchical feature engineering framework comprising: (i) static, (ii) dynamic, (iii) ratio-based, (iv) coupling features capturing source filter interactions. While univariate statistical analysis shows strong separability for PVH but limited significance for NPVH, our machine learning pipeline, tailored for high-dimensional feature integration, identifies that coupling features are crucial for both tasks. We achieve an AUC of 0.891 for PVH and 0.728 for NPVH, suggesting that while PVH is near-linearly separable, NPVH discrimination benefits from modeling non-linear feature interactions.
[AI-240] Agent Compile: An LLM -Guided Compiler for Direct CUDA Inference
链接: https://arxiv.org/abs/2606.07665
作者: Xuanzhe Li,Ziyan Weng,Zhiyu Zhu,Junhui Hou
类目: Programming Languages (cs.PL); Artificial Intelligence (cs.AI)
备注: 11 pages, 3 figures
Abstract:Transformer inference increasingly depends on specialized compiler and runtime support, but real model graphs still require semantic decisions about which regions are worth specializing and which CUDA implementation families are plausible. We present AgentCompile, an LLM-guided CUDA inference compiler that uses LLM outputs only as advisory search metadata. Given compiler-derived region summaries and bounded candidate spaces, the LLM proposes semantic labels, candidate priorities, parameter hints, and risk annotations; the compiler materializes CUDA candidates through templates, checks interface and hardware constraints, validates candidates empirically, selects implementations by measured latency, and falls back when specialization is unsupported or unprofitable. In end-to-end autoregressive generation, AgentCompile averages 5.66x, 4.05x, and 4.26x speedup over PyTorch eager on Qwen3-1.7B, Qwen3-4B, and Llama-3.2-1B-Instruct, respectively, across five representative workloads. We will open-source the project.
[AI-241] Seq103: A Unified Neuroevolution Framework for Compact Sequence Architecture Discovery
链接: https://arxiv.org/abs/2606.07664
作者: Wenxiao Li,Yongjian Liu,Qing Xie
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI)
备注: 18 pages, 2 figures, 8 tables
Abstract:Neuroevolution is a representative neural architecture search paradigm that evolves both network topology and weights through evolutionary algorithms. In this paper, we propose Seq103, a unified NEAT-style neuroevolution framework for compact sequence architecture discovery. Seq103 consists of a shared evolutionary backbone and an optional recurrent extension. The shared backbone includes an elementary node-and-connection representation, per-class RMSE-based evaluation, mutation-based evolution with class-wise recombination, and elitism. The optional hidden-state mechanism extends the search space with hidden-state nodes and hidden connections, enabling temporal memory when step-wise recurrent inference is required. With this design, Seq103 applies the same core search pipeline to both step-wise recurrent and sample-wise feedforward sequence classification. In recurrent tasks, the hidden-state extension is enabled to provide temporal memory; in feedforward tasks, it is disabled while the shared evolutionary backbone remains unchanged. We evaluate Seq103 on 8 text classification datasets and the full UCRArchive2018 benchmark with 128 univariate time-series datasets. On step-wise tasks, Seq103 retains 86.96% of the best-baseline accuracy on average while using 34.6x to 3218.0x fewer parameters. On sample-wise tasks over the full UCRArchive2018 benchmark, Seq103 retains 81.95% of the best-baseline accuracy on average while using 11.8x to 160,601.0x fewer parameters.
[AI-242] rait-space Monitoring for Emergent Misalignment During Supervised Finetuning
链接: https://arxiv.org/abs/2606.07631
作者: Huy Nghiem,Sy-Tuyen Ho,Sarah Wiegreffe,Hal Daumé III
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: First version. 45 pages
Abstract:Emergent misalignment (EM) occurs when narrow finetuning causes a model to behave dangerously outside the finetuning task. Standard training signals can miss this shift, making reliable detection costly if it depends on repeated behavioral evaluation. We ask whether emergent misalignment can instead be detected from internal representations during finetuning. Using seven alignment-relevant traits encoded as linear directions in activation space, we track representational drift across training checkpoints in four open-source 7-9B LLMs. EM-relevant drift concentrates on a low-dimensional axis that explains 65.5% of the variance, revealing a geometric signature in the studied regime. A low-overhead monitor built on this drift profile detects dangerous checkpoints with 2.2% false negative rate, 2.9% false positive rate, and 0.990 AUROC on held-out perturbation types, outperforming unsupervised PCA and SAE baselines. Stress tests on two 14B models, longer finetuning runs, and misaligned starting points identify key deployment boundaries. These results position trait-space monitoring as a practical complement to behavioral evaluation for EM detection during LoRA-based finetuning, while showing that deployment across substantially different regimes may require recalibration.
[AI-243] Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance ICML2026
链接: https://arxiv.org/abs/2606.07630
作者: Jiancheng Zhang,Meiqing Li,Qi Zhang,Yinglun Zhu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: To appear at ICML 2026
Abstract:Real-world datasets across image and text domains are often characterized by skewed class distributions and noisy annotations, which jointly degrade model performance, particularly on minority classes. Among existing solutions, active learning offers an effective and efficient paradigm by selectively querying the most informative and balanced samples for annotation. We propose an innovative active learning framework that mitigates class imbalance and selects the most informative samples to annotate. Leveraging foundation model priors, our algorithm enables imbalance-aware co-decisions between foundation model and small model to tackle noisy and imbalanced labels across various domains. We introduce the first study to systematically explore active learning under the dual challenges of label noise and class imbalance across image and text domains. Extensive experiments on imbalanced datasets demonstrate that our method achieves substantial annotation savings-over 50% compared to the best active learning baseline-while preserving performance and robustness to label noise.
[AI-244] HASA: Subnet Allocation for Compute-Constrained Model-Heterogeneous Federated Learning
链接: https://arxiv.org/abs/2606.07621
作者: Amir Hossein Shahdadian,Ahmed M. Abdelmoniem,Mahdi Taheri,Samira Nazari,Christian Herglotz
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注:
Abstract:Edge services increasingly use federated learning to personalize on-device models while keeping sensitive data local. In practice, deployments must handle heterogeneity in both client resources and local data distributions. Model-heterogeneous federated learning lowers client cost by allowing each client to train a subnet of a shared supernet, but most subnet-allocation policies are driven by device constraints and do not explicitly account for statistical heterogeneity. This paper proposes Heterogeneity-Aware Subnet Allocation (HASA), a train-only rule that assigns subnet widths based on client heterogeneity scores computed from local training data while enforcing a fixed size-weighted compute budget. This design enables budget-matched comparisons with alternative allocation policies. On an article-title next-word prediction benchmark with seven clients, HASA improves unweighted mean client test accuracy over uniform allocation across 10 matched seeds, increasing mean client test accuracy from 13.82 percent to 14.32 percent, and improves worst-client accuracy on average. In a matched-budget comparison with representative partial-training baselines, HASA achieves the strongest worst-client and tail-client accuracy on this benchmark. A directionality ablation shows that assigning smaller subnets to more heterogeneous clients degrades both mean and tail performance. A cross-domain image-classification study further shows that the effectiveness of heterogeneity-aware allocation depends on how well the heterogeneity score reflects clients’ need for additional model width.
[AI-245] Query Lens: Interpreting Sparse Key-Value Features with Indirect Effects ICML2026
链接: https://arxiv.org/abs/2606.07617
作者: Hwiyeong Lee,Ingyu Bang,Uiji Hwang,Hyelim Lim,Taeuk Kim
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted to ICML 2026
Abstract:While sparse autoencoders provide features more interpretable than individual neurons, reliably characterizing them remains challenging. We propose Query Lens, which extends Logit Lens to enable more comprehensive and faithful interpretations of sparse features. By jointly considering encoder-side key features and decoder-side value features, we identify both the inputs that activate a feature and the outputs it promotes. We also account for indirect, module-mediated effects that arise when the feature is processed by downstream modules, going beyond the direct effect captured by Logit Lens. In experiments, we find that Query Lens yields coherent token signatures for features that remain uninterpretable under Logit Lens. Finally, we propose the Subspace Channel Hypothesis, suggesting that downstream modules read features through layer-specific subspaces.
[AI-246] Structured Neuron Pruning in Deep Neural Networks Using Multi-Armed Bandits
链接: https://arxiv.org/abs/2606.07615
作者: Salem Ameen,Sunil Vadera
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 27 pages, 5 figures
Abstract:Deep neural networks often contain redundant hidden units. Removing individual weights can reduce parameter count, but unstructured sparsity is not always easy to exploit in standard dense implementations. This paper develops a structured pruning framework in which complete neurons are removed using multi-armed bandit (MAB) algorithms. Each candidate neuron is treated as an arm; pulling an arm temporarily masks that neuron, measures the change in loss on a sampled mini-batch, restores the neuron, and updates an estimate of its safe-removal reward. The framework supports stochastic policies, including Epsilon-Greedy, Softmax, UCB1 and Thompson Sampling, and multiplicative-weight policies, including Hedge-style multiplicative weights and EXP3. We evaluate the method on tabular classification, tabular regression and deep neural-network benchmarks covering image, text and reasoning tasks. Statistical comparisons using the Friedman test followed by the Nemenyi post-hoc test show significant differences between methods. On tabular classification tasks, UCB1 obtains the highest mean rank among pruning policies and improves on the unpruned neural network. On regression tasks, UCB1 obtains the highest mean rank and is statistically competitive with, or superior to, several standard regression models according to R^2. On deep-learning tasks, UCB1 and Thompson Sampling obtain the strongest ranks, and several MAB policies significantly outperform the unpruned model, magnitude-based neuron pruning and greedy activation-variation pruning. The results show that MAB-based neuron pruning is an effective and computationally practical approach for structured model reduction.
[AI-247] Position: Anthropomorphic Misalignment Research Needs Stronger Evidence
链接: https://arxiv.org/abs/2606.07612
作者: Vansh Gupta,Peter Nutter,Samuel Stante,Andreas Krause,Florian Tramèr,Lukas Fluri,Xin Chen,Anna Hedström
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:We argue that many Anthropomorphic Misalignment Research (AMR) studies need stronger evidence to ensure that they can provide a robust foundation for critical safety decisions, such as model deployment and regulation. By evaluating failure modes across different misalignment concepts, such as deception, emergent misalignment, and sycophancy, we show how conceptual ambiguity, non-robust datasets, experimental design, and insufficient causal interventions can lead to overinterpretation of model behaviors. This position paper aims to offer guidance on evidentiary considerations that can help improve methodological rigor in AMR. To achieve this, we provide a clear call to action through a proposed framework of evidence levels and a diagnostic checklist. These shared standards will enable more productive scientific discourse and ensure that claims about AI risks rest on solid empirical foundations.
[AI-248] SRT: Super-Resolution for Time Series via Disentangled Rectified Flow ICLR
链接: https://arxiv.org/abs/2606.07605
作者: Jufang Duan,Shenglong Xiao,Yuren Zhang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted to the International Conference on Learning Representations (ICLR) 2026
Abstract:Fine-grained time series data with high temporal resolution is critical for accurate analytics across a wide range of applications. However, the acquisition of such data is often limited by cost and feasibility. This problem can be tackled by reconstructing high-resolution signals from low-resolution inputs based on specific priors, known as super-resolution. While extensively studied in computer vision, directly transferring image super-resolution techniques to time series is not trivial. To address this challenge at a fundamental level, we propose Super-Resolution for Time series (SRT), a novel framework that reconstructs temporal patterns lost in low-resolution inputs via disentangled rectified flow. SRT decomposes the input into trend and seasonal components, aligns them to the target resolution using an implicit neural representation, and leverages a novel cross-resolution attention mechanism to guide the generation of high-resolution details. We further introduce SRT-large, a scaled-up version with extensive pre-training, which enables strong zero-shot super-resolution capability. Extensive experiments on nine public datasets demonstrate that SRT and SRT-large consistently outperform existing methods across multiple scale factors, showing both robust performance and the effectiveness of each component in our architecture.
[AI-249] Contribution Weights: A Geometrical Analysis of Self-Attention Transformers
链接: https://arxiv.org/abs/2606.07604
作者: Harry Jake Cunningham,Nicola Muca Cirone
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Analyzing attention weights has become a standard approach for interpreting the information flow of Large Language Models (LLMs). However, this approach has significant limitations as it neglects the geometric properties of the value vectors being aggregated. To address this gap, we introduce \emphContribution Weights, a projection-based metric that quantifies a token’s influence by accounting for it’s attention weight, value magnitude, and directional alignment with the layer output. We demonstrate that contribution weights provide a more faithful measure of token importance, consistently outperforming attention-based metrics in identifying semantically critical tokens across different decoder-only models, tasks, and datasets. Further, our metric enables novel mechanistic analysis of \emphattention sinks. While previous work characterized sinks as passive repositories for excess attention, we reveal they serve an active functional role, suppressing information through a convex relationship between sink rate and output norm, stabilizing representations by opposing the semantic drift of low-confidence tokens.
[AI-250] MetaEvo: A Meta-Optimization Framework for Experience-Driven Agent Evolution
链接: https://arxiv.org/abs/2606.07603
作者: Bowen Ren,Heyan Huang,Yinghao Li,Yang Gao
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) exhibit strong reasoning capabilities, yet most LLM-based agents are statically deployed and unable to improve through task interactions. Existing experience-driven methods often rely on memory or heuristics without enhancing the model’s ability to learn, treating it as a passive executor and leading to early performance plateaus and limited long-term improvement. To address this issue, we propose MetaEvo, a two-stage framework for continual agent evolution that focuses on improving how the model learns from tasks experience, rather than solely on what it stores. MetaEvo first applies preference-based optimization to enhance the model’s ability of principle abstraction, then enables the accumulation and reuse of these principles within a modular agent architecture. Experimental results on diverse reasoning benchmarks demonstrate that MetaEvo consistently outperforms strong baselines, maintains reliable improvement across iterations. These findings validate the effectiveness of meta-optimization in enabling agents to learn from experience and continually enhance their reasoning capabilities.
[AI-251] Sample-Efficient Post-Training for LEGO Spatial-Physics Reasoning
链接: https://arxiv.org/abs/2606.07602
作者: Yuhuan Yuan,Zhouliang Yu,Minghao Liu,Weiyang Liu,Ge Lin Kan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Technical Report V1, 15 pages, 6 figures, 3 tables
Abstract:LLM-based LEGO assembly generation requires both semantic grounding and physical feasibility. We identify a data-induced failure mode, PhysHack, in which the assemblies satisfy physical-validity constraints while producing structures that are geometrically misaligned, semantically inconsistent, or poorly calibrated. To address this challenge, we propose a model-based data selection approach that uses only a small fraction of the training data while improving physically grounded LEGO assembly generation. Building on the selected trajectories, we introduce PVPO, a sample-efficient reinforcement learning method that couples physical feasibility with voxel-space geometric rewards. Our results show that physical validity alone is an insufficient proxy for reliable physical reasoning: models can learn to generate valid structures without preserving semantic or geometric fidelity. Experiments across model backbones and test-time scaling settings demonstrate that PVPO improves structural and semantic alignment, physical validity, structural stability, and calibration, while reducing reliance on extensive post-hoc rejection sampling. In particular, results on calibration show that PVPO mitigates PhysHack by making test-time selection more predictive of semantic and structural quality.
[AI-252] LFNO: Bridging Laplace and Fourier via Transient-Steady Decomposition
链接: https://arxiv.org/abs/2606.07601
作者: Jeongun Ha,Sanga Yoon,Donghun Lee
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 21 pages, 11 figures
Abstract:We introduce the Laplace-Fourier Neural Operator (LFNO), a unified framework for modeling dynamical systems across transient and steady-state regimes by integrating the spectral advantages of Laplace and Fourier Neural Operators. LFNO employs a dual-branch architecture that explicitly decomposes system dynamics into transient and steady-state components. We evaluate LFNO on nine benchmarks, including three ODE systems (Duffing, Lorenz, and Pendulum) and six PDE systems (Euler-Bernoulli beam, Heat, Reaction-diffusion, Brusselator, Burgers, and Navier-Stokes). LFNO significantly outperforms existing operators on ODE systems, where transient dynamics dominate, and consistently surpasses LNO while achieving performance competitive with FNO on PDE benchmarks. Furthermore, LFNO offers improved stability and physical interpretability through its component-wise decomposition. These results demonstrate that LFNO provides a robust and unified approach for learning complex dynamical systems across multiple temporal scales.
[AI-253] Reachability and asymptotics of Gaussian Transformer dynamics
链接: https://arxiv.org/abs/2606.07600
作者: Albert Alcalde,Zhengping Ji,Enrique Zuazua
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:We formulate data propagation through the Transformer, the machine learning architecture powering large language models, as a nonlinear control system on the space of probability measures. For the mean-field Transformer model with self-attention and affine feed-forward layers, we prove that Gaussian distributions remain exactly Gaussian along the induced flow. This invariance reduces the infinite-dimensional measure dynamics to a finite-dimensional bilinear control system governing the evolution of the mean and covariance, reformulates the expressive capacity of Transformers as a reachability problem for prescribed Gaussian moments, and reveals a novel connection with Riccati-type equations from classical filtering and control. For time-varying controls, we prove exact finite-time reachability of any target Gaussian distribution whose covariance matrix has the same rank as the initial one, this rank constraint being an intrinsic invariant of the dynamics. For time-invariant parameters, we derive explicit spectral conditions leading either to asymptotic stability toward positive-definite equilibria or to finite-time blow-up of the covariance. Numerical experiments complement the theory by showing that practical Transformers with Gaussian inputs remain close to moment-matched Gaussian distributions through early and intermediate layers, while Transformers with prescribed attention matrices reproduce the predicted covariance regimes: bounded evolution in stabilizing configurations and blow-up in destabilizing ones. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) MSC classes: 68T07, 93B03, 93D20 Cite as: arXiv:2606.07600 [cs.LG] (or arXiv:2606.07600v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2606.07600 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-254] A Topological Characterization of Graph Neural Networks via Stochastic Block Model Embeddings on the n-Sphere
链接: https://arxiv.org/abs/2606.07598
作者: Gopal Anantharaman
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:We propose a topological framework for comparing trained Graph Neural Networks (GNNs) by mapping the Stochastic Block Models (SBMs) induced on the graphon-signal space of a Message Passing Neural Network (MPNN) onto the unit n -sphere \sphere^n-1\subset\R^n . The construction rests on three classical pillars: the \emphcompactness of the cut-distance graphon space (\Wo,\cutdist) \citeplovasz2006limits,lovasz2012large, the Frieze–Kannan \emphweak regularity lemma together with its graphon-signal extension due to \citetlevie2023graphon, and the Lipschitz continuity of MPNNs with respect to the cut-distance. We show that, for any prescribed tolerance \varepsilon0 , a trained MPNN \Phi acting on a sufficiently large graph factors (up to \varepsilon ) through a step-graphon-signal of bounded complexity, and we construct an explicit measure-preserving map \Psi_n\colon[0,1]\to\sphere^n-1 that places the SBM regions on disjoint spherical caps. This produces a problem-agnostic, low-dimensional ``fingerprint’’ of a trained GNN that is amenable to visual inspection and to nearest-neighbour search across model zoos, enabling \emphtransfer-learning candidate retrieval without retraining. We discuss the obstruction posed by concentration of measure in high dimension – a phenomenon directly relevant to LLM-scale embeddings. We close with five concrete future research directions: hyperbolic and Grassmannian alternatives to the spherical model, Gromov–Wasserstein distances on graphon-signals as an isometry-free alternative to the n -sphere map, an information-geometric (Fisher) reformulation of the SBM manifold, persistent-homology fingerprints of layer-wise embedding clouds, and a spectral-distance baseline derived from the graphon eigendecomposition.
[AI-255] Repetition Mismatch: Why Data Mixture Experiments Dont Scale and How to Fix Them
链接: https://arxiv.org/abs/2606.07597
作者: Kevin Zhou,Lisa Alazraki,Kris Cao,Marek Rei
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Pre-training data mixtures are commonly tuned by running small-scale experiments and extrapolating to the target training budget. When high-quality data is scarce and must be repeated, this extrapolation frequently fails, but the source of the failure has not been isolated. We show that a primary culprit is a repetition mismatch: because high-quality datasets are small, their repetition rate changes as the training budget grows, shifting the optimal mixture in ways that small-scale proxy experiments do not anticipate. A subsampling procedure that matches the target repetition rate controls for this effect. In a two-source setting combining limited high-quality data with web crawl, a single repetition-controlled experiment using only 1/16 of the target tokens recovers a mixture within 0.05 of the optimum for a 757M parameter model, compared to an error of 0.75 without repetition control. Achieving comparable accuracy without repetition control requires three to four horizons, consuming 44 to 94% of the target token budget. With three data sources, the larger mixture space requires more than a single experiment to constrain, but the approach remains effective: at the 757M scale, just two repetition-controlled horizons recover the optimal mixture, outperforming baselines that instead require the full two-source experiments to construct. Our results reveal that repetition dynamics, not scale alone, shape whether small-scale mixture experiments generalize. More broadly, they suggest that data repetition deserves treatment as a first-class variable in mixture optimization, rather than an inconvenient side effect of limited data.
[AI-256] Outage Detection in Self-Healing Smart Grids Using Reinforcement Learning with Spectral Graph Neural Networks
链接: https://arxiv.org/abs/2606.07583
作者: Lihui Liu,Mucun Sun,Caisheng Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Self-healing smart grids can quickly adjust their network configuration during outages to minimize power disruptions. During an outage, several actions can be taken, such as network reconfiguration through switching operations and emergency load shedding. However, traditional machine learning methods for outage mitigation are not well suited for smart grids due to their slow response time and high computational cost. To address these challenges, recent studies have explored reinforcement learning to automatically perform network reconfiguration. In these approaches, the control policy is typically modeled using a graph neural network (GNN). However, conventional GNNs operate in the spatial domain and may fail to capture important relationships in the frequency domain. Frequency-domain information is particularly useful for modeling global structural patterns and system-wide interactions in power networks. In this paper, we propose a spectral graph reinforcement learning framework for outage management in distribution networks to enhance system resilience. Our model learns the optimal power restoration policy using a spectral graph neural network. We evaluate the proposed method on three modified IEEE test systems: the 13-bus, 34-bus, and 123-bus networks. Experimental results show that our approach achieves near-optimal performance in real time and generalizes well across a wide range of outage scenarios.
[AI-257] Customer Churn Prediction on Structured Data Using FT-Transformer and Stacking Ensembles
链接: https://arxiv.org/abs/2606.07582
作者: Joyjit Roy,Samaresh Kumar Singh,Laxmi Shaw
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
备注: 22 pages, 9 figures, 20 tables; published in IEEE Access
Abstract:Customer churn prediction is essential across data-driven industries such as insurance, digital banking, eCommerce, and subscription platforms, where retaining existing customers is typically more cost-effective than acquiring new ones. Predicting churn on structured datasets remains challenging due to class imbalance, nonlinear feature interactions, and heterogeneous feature types. Tree-based ensemble methods consistently demonstrate strong performance in these contexts, often outperforming conventional neural networks. This study introduces a validated hybrid architecture that integrates feature-tokenized transformers (FT-Transformer) with gradient-boosted trees through calibration-aware stacking. The proposed framework addresses persistent gaps in statistical validation, probability calibration, and reproducibility found in prior research. The FT-Transformer captures higher-order feature interactions using self-attention, while XGBoost captures gradient-boosted decision boundaries with complementary inductive biases. Class imbalance is handled using class-weighted loss functions, thereby avoiding synthetic oversampling and preserving minority-class distributions. The models are ensembled using out-of-fold (OOF) stacking with a logistic regression meta-learner, which recalibrates overconfident base model outputs and learns optimal combination weights. On a public bank churn dataset, the hybrid model achieves 62.10% F1, 0.861 AUC-ROC, and 0.647 PR-AUC, outperforming the Multi-Layer Perceptron (MLP) baseline by 3.37 F1 points and 0.027 AUC under 5x5 cross-validation with 95% confidence intervals reported. Ablation studies demonstrate that both the transformer component and stacking strategy contribute materially to performance. The proposed methodology offers a reproducible and extensible reference architecture for contemporary churn prediction on structured tabular data.
[AI-258] raining-Inference Kernel Contracts: Bounding Divergence in Post-Training and Deployment
链接: https://arxiv.org/abs/2606.07581
作者: Bruce Changlong Xu,Lan Wu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
备注:
Abstract:A modern post-training pipeline often writes one symbol for its policy, pi_theta, while evaluating it through two different programs: a training kernel optimized for autograd and an inference kernel optimized for low-precision, fused, dynamically batched serving. In finite precision, these kernels can induce different distributions at identical weights, with the gap concentrated on slices that aggregate benchmarks under-represent. This paper proposes kernel contracts: a contract-first framework for specifying acceptable divergence between K_train and K_inf. A contract C = (N, S, R, O, Pi) combines numerical, statistical, runtime, and observability clauses with an escalation policy from violations to routing actions. We derive a chain of bounds from logit drift to total-variation distance to bounded reward drift, and specialize it to RL post-training, where per-token importance-ratio drift yields a bound on policy-gradient bias under explicit support and norm assumptions. We also describe a four-stage promotion pipeline, online routing loop, and minimal YAML DSL for contract artifacts. This is a framework and vocabulary paper; we do not report production-scale empirical validation.
[AI-259] Accelerating Birkhoff Projection for Manifold-Constrained Hyper-Connections
链接: https://arxiv.org/abs/2606.07574
作者: Chenrui Wang,Yixuan Qiu
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Computation (stat.CO); Machine Learning (stat.ML)
备注:
Abstract:Manifold-constrained hyper-connections (mHCs) have recently been proposed as a principled extension of hyper-connections, where the residual mixing matrices are constrained to be doubly stochastic via projection onto the Birkhoff polytope. In practical mHC implementations, this constraint is enforced by Sinkhorn-Knopp iterations, and the backward pass relies on unrolling the iterative solver. This design introduces substantial computation and memory overhead, and may also yield inaccurate projections when the algorithm converges slowly on challenging inputs, undermining the intended norm-control and stability guarantees of mHCs. In this work, we focus on the practically important 4x4 Birkhoff projection setting and develop an end-to-end acceleration framework. By leveraging the dual formulation, we reduce the problem to a three-dimensional unconstrained convex problem and solve it with Newton’s method, achieving fast convergence and high accuracy. For the backward pass, we replace the unrolled differentiation with implicit differentiation, yielding exact gradients without storing intermediate states. To exploit massive parallelism, we design a warp-level CUDA kernel that uses only register-level primitives, avoiding global and shared memory I/O. Extensive experiments against representative open-source baselines demonstrate that the proposed solver yields substantially more reliable doubly stochastic projections – especially when the input magnitude is large – and achieves significant end-to-end speedups (including the backward pass), reaching over 20x acceleration at large batch sizes while maintaining orders of magnitude smaller marginal errors. Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Computation (stat.CO); Machine Learning (stat.ML) Cite as: arXiv:2606.07574 [cs.DC] (or arXiv:2606.07574v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2606.07574 Focus to learn more arXiv-issued DOI via DataCite
[AI-260] Enabling KV Caching of Shared Prefix for Diffusion Language Models
链接: https://arxiv.org/abs/2606.07571
作者: Younghun Go,Jaehoon Han,Changyong Shin,Chuk Yoo,Gyeongsik Yang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Key-value (KV) caching for shared prefixes is essential for high-throughput large language model (LLM) serving, but it faces critical challenges in emerging diffusion language models (DLMs). In DLMs, bidirectional attention means that updating any token dynamically alters the entire context and its corresponding KVs. Thus, existing caching techniques developed for LLMs, which assume that KVs remain invariant once computed, corrupt the shared prefix KVs. Our experiments show that applying these techniques to DLMs causes model accuracy to collapse to near zero. To unlock high-throughput DLM serving, we propose bidirectional prefix caching, bicache, the first KV caching technique for shared prefixes in DLMs. bicache is designed based on key observations from our comprehensive analysis: shared prefix KVs remain stable and reusable in shallow layers, while the depth of shallow layers depends on the fraction of shared prefix tokens in each request. Thus, bicache dynamically identifies a safe layer depth for reusing shared prefix KVs and eliminates redundant computation. Evaluations demonstrate that bicache significantly improves serving throughput by 36.3%-98.3% compared to existing techniques without accuracy collapse (only 0-1.8% difference). Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2606.07571 [cs.LG] (or arXiv:2606.07571v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2606.07571 Focus to learn more arXiv-issued DOI via DataCite
[AI-261] Emergence via Phase Transitions: Mechanism Landscapes and Universal Convergence Across Complex Systems
链接: https://arxiv.org/abs/2606.07563
作者: Truong Xuan Khanh
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 27 pages, 3 figures, 2 tables; 15-page Supplementary Information with complete proofs included
Abstract:Across machine learning, biology, and physics, independently evolving systems often converge toward strikingly similar high-level structures despite radically different microscopic details. Grokking circuits converge across random seeds, evolutionary lineages rediscover similar metabolic solutions, and renormalization flows approach common fixed points. We propose the Hierarchical Emergence Framework (HEF) as a candidate universality framework for such convergence phenomena. HEF models emergence as a phase transition in a mechanism landscape constrained by thermodynamic and information-theoretic laws. The framework introduces a critical energy threshold Ec separating an exploration regime with competing mechanisms from a convergence regime governed by a unique minimum-cost mechanism. Under structural assumptions, we prove physical feasibility, derive strict metric contraction, and establish convergence toward a unique fixed-point representation independent of initial conditions. We further connect this convergence structure to causal emergence through Effective Information and mechanism competition entropy. To test the framework, we study delayed generalization (“grokking”) in modular arithmetic transformers across 111 experiments. We identify a reproducible empirical fingerprint of the Ec transition: the weight norm peaks systematically before grokking in 92% of runs. Normalized accuracy curves collapse onto a tanh kink (R^2=0.93) consistent with a Landau-Ginzburg universality class, and all grokked models converge to 0.9745+/-0.014 regardless of initialization, weight decay, or training fraction (ANOVA p0.13). HEF is not presented as a universal theory of emergence, but as a falsifiable mathematical scaffold for studying convergence phenomena across complex systems.
[AI-262] Selecting New Measurement Locations to Diversify Traffic-Pattern Coverag e: A Real-World Evaluation for Total Traffic Volume Estimation
链接: https://arxiv.org/abs/2606.07556
作者: Masaaki Inoue,Akifumi Okuno,Shintaro Fukushima
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Methodology (stat.ME)
备注: 12 pages, 7 figures
Abstract:Accurate measurement of traffic volumes and flows is vital for modern intelligent transportation. However, despite recent technological advances in sensor devices, it is still expensive to install and maintain fixed traffic counters. Therefore, it is restricted to a small portion of location points where the counters can be installed, which severely limits the possibility of grasping and predicting the total traffic volume at a city-wide level. By contrast, devices with location history such as smartphones and connected vehicles are now widely used and provide much wider spatial coverage. However, the data from these devices are usually partial and noisy, so they are not enough to directly estimate total traffic volumes and flows. In this paper, we use the information from these widely available devices to help decide where to place additional traffic counters, and we study how selecting new measurement locations can improve city-wide traffic estimation performance. To achieve this, we propose an algorithm that chooses additional counter locations to increase the diversity of observed traffic signal patterns, rather than simply spreading counters evenly over space. The goal is to capture traffic-pattern types that are rare in the current counter set and to make the collected observations more representative for later estimation and forecasting. We also present a real-world evaluation; in a target city, we select new locations expected to improve traffic prediction, and we then commissioned new field measurements at those locations at our expense. The resulting data led to an improvement in traffic volume estimation accuracy across different fidelities.
[AI-263] MedicalRec: Medical recommender system for image classification without retraining
链接: https://arxiv.org/abs/2606.07553
作者: Roghayeh Taghavi,Aysa Hasanazde Bashkandi,Amir Ali Bengari,Mohammad Amin Raji,Mohammad Salahi Ardekani,Parisa Mardukhian,Parvaneh Rezaei,Ramin Mousa
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The emergence of machine learning and deep learning has revolutionized the efficiency of diagnostic, therapeutic, and administrative systems in healthcare. However, this rapid adoption has come at the cost of requiring significant computing power and energy consumption, as well as e-waste disposal and carbon emissions. One of the challenges of these models is choosing the right model for classification tasks. To this end, researchers attempt to identify the optimal model using their data through trial and error, which involves energy consumption and waste. The goal of this study is to develop a model-based recommender system for medical image classification. For this purpose, a data set was collected from 3,000 articles in the field of medical image classification. This dataset, publicly available under the name MedicalRec-Bench, contains over 5,000 records of models tested in various tasks, including Skin Cancer Classification, Tumour Classification, Wound Classification, Breast Cancer, and MRI classification. The dataset was evaluated in four different modes, depending on the number of features: MedicalRec I (5 features), MedicalRec II (9 features), MedicalRec III (11 features), and MedicalRec IV (18 features). Collecting all values for the features is challenging due to non-reporting by the authors; hence, the dataset contains significant amounts of missing values. The Medical Recommender System (MedicalRec) is a transformer-based model used for item recommendations in this study. This model achieved remarkable results in the evaluation on the dataset and in the evaluation with 12 base models. This model achieved a maximum HitRate@100 of 75.5%. The dataset and implementations are available through the GitHub link: this https URL
[AI-264] Offline Reinforcement Learning for Plasma Control in Nuclear Fusion: Codebase and Benchmark
链接: https://arxiv.org/abs/2606.07550
作者: Yang Fu,Haomin Bao,Rohit Sonker,Xiaoyan Hu,Aravind Venugopal,Jeff Schneider,Jiayu Chen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 23 pages (10 pages main text)
Abstract:Offline reinforcement learning (RL) offers a promising route for developing plasma controllers from historical tokamak data, since online trial-and-error on real devices is costly and risky. However, progress in this direction remains difficult to measure due to the lack of a standardized offline RL benchmark for realistic multi-actuator, long-horizon plasma control problems in nuclear fusion. We introduce RL4F, an Offline Reinforcement Learning Benchmark for Plasma Control in Nuclear Fusion, providing closed-loop evaluation environments and baseline comparisons across four full-profile tracking tasks: rotation, density, temperature, and pressure. The dynamics function underlying the evaluation environment is built from historical discharge data from DIII-D, a real-world Tokamak. We evaluate a broad set of imitation learning and offline RL baselines under a unified protocol. We find that offline model-based RL methods obtain the best average performance on most objectives, although no single method dominates all tasks, highlighting the importance of dynamics modeling in complex, long-horizon plasma control tasks. To foster further research, we open-source the codebase, datasets, and evaluation framework, providing a benchmark not only for the fusion community but also for algorithm development in offline RL.
[AI-265] DIYHealth Suite: Dataset Model and Benchmark for Health Management at Home ICML2026
链接: https://arxiv.org/abs/2606.07542
作者: Changshuo Liu,Junran Wu,Zhongle Xie,Wenqiao Zhang,Kaiping Zheng,Jiaqi Zhu,Qingpeng Cai,Ooi Gene Anne,Marcus Chun Jin Tan,Jianwei Yin,James Wei Luen Yip,Beng Chin Ooi
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: Accepted by ICML 2026
Abstract:Generative AI is reshaping healthcare, yet most existing advances rely on hospital-grade devices, which limits their accessibility and potential for health management outside clinical settings. With the proliferation of portable devices and telemedicine, healthcare is shifting toward home-based Diagnosis-It-Yourself (DIY) care. Despite this promise, several distinctive challenges remain: (i) home-collected data are heterogeneous, exacerbated by the absence of standardized large-scale datasets; (ii) models require adaptation to variable task demands and evolving individual conditions; (iii) the broad spectrum of home care tasks lacks a unified benchmark for systematic evaluation. In this paper, we present DIYHealth Suite, a comprehensive framework designed to address these challenges through a tailored dataset, model, and benchmark. We first curate DIYHealth-900K, a large-scale multimodal dataset capturing diverse real-world home care scenarios. Building on this, we propose DIYHealthGPT, an adaptive foundation model for home-based health management, powered by the novel Hybrid Hyper Low-Rank Adaptation technique. Finally, we establish DIYHealthBench, the first benchmark to evaluate foundation models on home care tasks. Extensive experiments demonstrate that DIYHealthGPT delivers state-of-the-art performance over both general-purpose and medical-specific baselines on 11 home care tasks in both open-QA and closed-QA settings, laying the groundwork for the next generation of personalized health management at home.
[AI-266] Beware of GeeksBearing Gifts: Building True EU Frontier AI Sovereignty
链接: https://arxiv.org/abs/2606.07536
作者: Nick Moës,Toni Lorente,Amin Oueslati,Jonathan Smith,Robin Staes-Polet,Radina Kraeva
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:Frontier artificial intelligence is reshaping all aspects of society, from economic output or military capability to democratic institutions. The EU is entering this transformation from a position of structural dependence: frontier models originate almost exclusively from the United States or China, the US holds approximately sixteen times the EU’s AI supercomputing capacity, and only 15% of global hyperscale data centre capacity resides within EU borders. Although the European Commission has accelerated its policy response, existing initiatives remain fragmented and lack a cohesive vision for securing strategic autonomy across the full frontier AI value chain. Here we propose a unified framework connecting five sovereignty pillars (economic competitiveness, resilience, security and defence, European values, and foreign relations) to a decomposition of the frontier AI stack comprising five layers, 26 components, and 29 sub-components. This framework allows the identification of critical gaps, redundancies, and inter-pillar trade-offs that current EU policy leaves implicit. Our analysis of the AI Gigafactory Initiative illustrates how a sovereignty-centred lens reveals conflicts that narrowly economic framings obscure. Moreover, this framework offers policymakers a structured basis for designing, evaluating, and prioritising frontier AI interventions across multiple dimensions of European strategic autonomy across the 92 initiatives from four major Commission communications we. identify, and beyond.
[AI-267] Blockchain Infrastructure for Intelligent Cyber–Physical–Social Systems:Post-Quantum Security Interoperability and Trustworthy Data Economies in the Era of Embodied AI
链接: https://arxiv.org/abs/2606.06895
作者: Song Guo,Huawei Huang,Dongping Liu,Aoyu Zhang,Luyao Zhang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Emerging Technologies (cs.ET)
备注:
Abstract:The deployment of embodied artificial intelligence via world-model-based robotics presents a transformative opportunity for blockchain infrastructure, establishing urgent demand for trustworthy data provenance, cross-organizational governance, and incentive-compatible sharing across decentralized ecosystems. Simultaneously, quantum computing advances recognized by the 2025 Nobel Prize in Physics and the Turing Award threaten the cryptographic primitives securing these data economies, creating an interdependent imperative: long-lived verification for embodied AI depends on crypto-agile architectures capable of withstanding quantum adversaries. This tutorial examines blockchain as the coordination layer bridging this dual transition, from financial substrate to foundational Cyber-Physical-Social Systems infrastructure that simultaneously secures against quantum cryptanalysis and enables scalable, trustworthy data economies. The session opens with an immersive AWS Braket demonstration engaging participants with superconducting, trapped-ion, and neutral-atom hardware to assess cryptographic threat timelines and witness ECDSA-to-post-quantum signature transitions. Five integrated modules progress from embodied AI and world-model requirements through quantum hardware reality and evidence-based security migration, to scalable cross-shard architectures via BrokerChain protocols, trustworthy data economies implementing Croissant metadata standards and robotic learning provenance, and industry ecosystem integration for multi-modal cloud deployment. By bridging quantum hardware realities with embodied AI data requirements, this tutorial charts blockchain as unified infrastructure for next-generation decentralized intelligent environments, providing open-source frameworks and roadmaps for architecting quantum-resistant, interoperable, and data-trustworthy systems.
[AI-268] BRAIN: Bayesian Reasoning via Active Inference for Agent ic and Embodied Intelligence in Mobile Networks
链接: https://arxiv.org/abs/2602.14033
作者: Osman Tugay Basaran,Martin Maier,Falko Dressler
类目: Information Theory (cs.IT); Artificial Intelligence (cs.AI)
备注:
Abstract:Future sixth-generation (6G) mobile networks will demand artificial intelligence (AI) agents that are not only autonomous and efficient, but also capable of real-time adaptation in dynamic environments and transparent in their decisionmaking. However, prevailing agentic AI approaches in networking, exhibit significant shortcomings in this regard. Conventional deep reinforcement learning (DRL)-based agents lack explainability and often suffer from brittle adaptation, including catastrophic forgetting of past knowledge under non-stationary conditions. In this paper, we propose an alternative solution for these challenges: Bayesian reasoning via Active Inference (BRAIN) agent. BRAIN harnesses a deep generative model of the network environment and minimizes variational free energy to unify perception and action in a single closed-loop paradigm. We implement BRAIN as O-RAN eXtended application (xApp) on GPU-accelerated testbed and demonstrate its advantages over standard DRL baselines. In our experiments, BRAIN exhibits (i) robust causal reasoning for dynamic radio resource allocation, maintaining slice-specific quality of service (QoS) targets (throughput, latency, reliability) under varying traffic loads, (ii) superior adaptability with up to 28.3% higher robustness to sudden traffic shifts versus benchmarks (achieved without any retraining), and (iii) real-time interpretability of its decisions through human-interpretable belief state diagnostics.
[AI-269] XAI-on-RAN: Explainable AI-native and GPU-Accelerated RAN Towards 6G NEURIPS2025
链接: https://arxiv.org/abs/2511.17514
作者: Osman Tugay Basaran,Falko Dressler
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Information Theory (cs.IT)
备注: 39th Conference on Neural Information Processing Systems (NeurIPS 2025) Workshop: AI and ML for Next-Generation Wireless Communications and Networking (AI4NextG)
Abstract:Artificial intelligence (AI)-native radio access networks (RANs) will serve vertical industries with stringent requirements: smart grids, autonomous vehicles, remote healthcare, industrial automation, etc. To achieve these requirements, modern 5G/6G design increasingly leverage AI for network optimization, but the opacity of AI decisions poses risks in mission-critical domains. These use cases are often delivered via non-public networks (NPNs) or dedicated network slices, where reliability and safety are vital. In this paper, we motivate the need for transparent and trustworthy AI in high-stakes communications (e.g., healthcare, industrial automation, and robotics) by drawing on 3rd generation partnership project (3GPP)'s vision for non-public networks. We design a mathematical framework to model the trade-offs between transparency (explanation fidelity and fairness), latency, and graphics processing unit (GPU) utilization in deploying explainable AI (XAI) models. Empirical evaluations demonstrate that our proposed hybrid XAI model xAI-Native, consistently surpasses conventional baseline models in performance.
[AI-270] XAInomaly: Explainable and Interpretable Deep Contractive Autoencoder for O-RAN Traffic Anomaly Detection
链接: https://arxiv.org/abs/2502.09194
作者: Osman Tugay Basaran,Falko Dressler
类目: Information Theory (cs.IT); Artificial Intelligence (cs.AI)
备注: 22 pages, 9 Figures, Submitted to Journal (First revision completed)
Abstract:Generative Artificial Intelligence (AI) techniques have become integral part in advancing next generation wireless communication systems by enabling sophisticated data modeling and feature extraction for enhanced network performance. In the realm of open radio access networks (O-RAN), characterized by their disaggregated architecture and heterogeneous components from multiple vendors, the deployment of generative models offers significant advantages for network management such as traffic analysis, traffic forecasting and anomaly detection. However, the complex and dynamic nature of O-RAN introduces challenges that necessitate not only accurate detection mechanisms but also reduced complexity, scalability, and most importantly interpretability to facilitate effective network management. In this study, we introduce the XAInomaly framework, an explainable and interpretable Semi-supervised (SS) Deep Contractive Autoencoder (DeepCAE) design for anomaly detection in O-RAN. Our approach leverages the generative modeling capabilities of our SS-DeepCAE model to learn compressed, robust representations of normal network behavior, which captures essential features, enabling the identification of deviations indicative of anomalies. To address the black-box nature of deep learning models, we propose reactive Explainable AI (XAI) technique called fastshap-C.
[AI-271] Who Earns the Safety? Intervention-Aware Quantum Predictive Control with Safety Attribution
链接: https://arxiv.org/abs/2606.09778
作者: Yifan Wang
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI)
备注: 7 pages, 4 figures
Abstract:Hard safety filters are increasingly placed downstream of learned controllers to guarantee constraint satisfaction at run time. Yet a filtered controller that never violates a constraint may still have learned nothing about safety: the filter can silently repair an incompetent upstream policy, so that post-filter success measures the filter, not the policy. We argue that safe policy learning should ask who earns the safety - the policy or its protective layers - and we make this question measurable. We introduce Intervention-Aware Variational Quantum Differentiable Predictive Control (IA-VQC-DPC), which (i) trains a compact variational quantum circuit (VQC) policy under a primal-dual intervention budget that penalizes reliance on a differentiable Control-Barrier-Function (CBF) projection, and (ii) is evaluated with a safety-attribution protocol that decomposes the executed-trajectory correction into a CBF term and a deployment runtime-guard term, and stress-tests the policy with guard-off evaluation. On closed-loop, high-fidelity BOPTEST building-control emulators (5 seeds, 60 episodes per method), intervention-aware training significantly lowers the quantum policy’s raw pre-filter violation and total safety-layer reliance (both p 10^-4) with no significant energy regression; at an equal approximately 400-parameter budget the quantum policy is significantly safer and more comfortable than a matched classical policy. Guard-off evaluation confirms the improvement is policy-level and exposes a valuable negative result: a learned differentiable energy head is only safe when paired with a distribution-aware runtime guard. The attribution protocol is general beyond quantum policies and buildings.
[AI-272] MeCo: One-Step MeanFlow-based Corrector for Multi-Channel Speech Separation INTERSPEECH2026
链接: https://arxiv.org/abs/2606.09677
作者: Dohwan Kim,Jung-Woo Choi
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI)
备注: 5 pages, accepted to Interspeech 2026
Abstract:While discriminative models for multi-channel speech separation excel in reference-based metrics, they often exhibit suboptimal human listening quality. To address this, we propose a novel MeanFlow-based one-step generative corrector (MeCo). MeCo learns a conditional average velocity field to map discriminative estimates directly onto the clean speech manifold in a single step. To maximize one-step generation performance, we introduce Data-Space Optimization (DSO). DSO integrates an \mathbfx_r -loss, which penalizes prediction errors on longer displacement intervals to serve as a generative objective for human listening quality, with an Endpoint SI-SDR loss that directly optimizes terminal signal fidelity. Experiments demonstrate that MeCo achieves state-of-the-art (SOTA) performance with minimal computational overhead, simultaneously achieving superior signal fidelity and human listening quality in both in-domain and out-of-domain scenarios.
[AI-273] Powering the Future of AI: Navigating the Trade-offs for Europes Energy Transition and Net-Zero Goals
链接: https://arxiv.org/abs/2606.09617
作者: Mohammad Hemmati,Gbemi Oluleye,Vassilis M. Charitopoulos
类目: Optimization and Control (math.OC); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Systems and Control (eess.SY)
备注:
Abstract:The rapid expansion of AI globally has led to the proliferation of energy-intensive hyperscale data centres (DCs), making them as a structurally challenging component in power system planning and operation. Using a spatially explicit optimisation model of Europe across 21 AI growth scenarios, we systematically quantify additional demand, capacity requirements, emissions, and operational impacts of DCs. Results indicate that AI could drive 73-723 TWh of extra demand by 2050, risking cumulative emissions overshoots of 67-181 MtCO2 between 2030 and 2050. Our analysis indicates that after 2030, the geography of AI infrastructure will be shaped more by firm power and system flexibility than by the mere abundance of clean energy. In moderate scenarios, AI requires an additional of 200 hours of firm generation, which increases LCOE by 35 EUR/MWh in key hubs. We show that even under the pessimistic scenarios, existing infrastructure would require 70 GW additional capacity, while under managed growth pathways, this expansion could reach 226 GW. We further find DCs workload dynamics strongly shape energy dispatch, system flexibility, and emissions, while improved efficiency significantly reduces capacity needs, and system peaks. While our findings suggest that net-zero targets for 2050 may be achieved, critical emission risks may appear in the intermediate years, and the EU may compromise its carbon-neutral goals unless policies adapt to this accelerating digital transformation.
[AI-274] Closing the Prior-Posterior Loop: Self-Reflective Molecular Design with Analysis-Driven LLM Iteration
链接: https://arxiv.org/abs/2606.09520
作者: Junyi Gong,Zijie Qiu,Ben Zhong Tang
类目: Chemical Physics (physics.chem-ph); Artificial Intelligence (cs.AI)
备注: 3 tables, 4 figures
Abstract:Can a general-purpose large language model design molecules with the precision of a seasoned chemist? Current LLM-based frameworks answer this question with scalar feedback loops-generate, score, reject-that amount to informed trial-and-error. Here we show that replacing a single number with the full physicochemical rationale from first-principles calculations transforms the LLM from a stochastic sampler into a causal reasoner. Our system couples retrieval-augmented generation with a self-reflection module that feeds orbital energies, atomic charges, and electron densities-rather than compressed scores-back into the design loop. On HOMO-LUMO gap targets from 1.0 to 5.0 eV, this structure-property-relationship (SPR) reflection achieves a deviation as low as 0.0003 eV and a 100% success rate on moderate tasks, decisively outperforming scalar-feedback and non-reflective baselines. The framework generalizes seamlessly to dipole-moment design and proves robust across five distinct LLM backbones. These results establish a new paradigm: when the model understands not only that a molecule fails, but why, iterative molecular design becomes genuinely mechanistic.
[AI-275] Context-Aware Deep Learning for Defect Classification in Atomic-Resolution STEM
链接: https://arxiv.org/abs/2606.09419
作者: Jiadong Dan,Cheng Zhang,Leyi Loh,Ivan Verzhbitskiy,Yuan Chen,Goki Eda,Michel Bosman,N. Duane Loh
类目: Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI)
备注: 6 figures
Abstract:Artificial intelligence is rapidly advancing materials characterization, yet most applications in electron microscopy rely solely on image contrast, overlooking the chemical and experimental context that shapes image formation. This limitation makes defect classification inherently ambiguous, as similar contrasts can arise from different materials or imaging conditions. Here we develop a context-aware learning framework that integrates image-derived contrast with metadata describing composition, beam energy, and detector geometry. Using a systematically constructed dataset of ~55 million simulated patches spanning 576 cases across 96 doped monolayer transition-metal dichalcogenides, we show that conditioning on contextual variables transforms defect classification from an ill-posed image-only task into a well-posed, physically grounded problem. The framework achieves over 98% accuracy on simulations and near-human agreement on experimental data, with a 94% reduction in posterior entropy. By emphasizing contextual grounding over architectural complexity, this approach links experimental image contrast to the underlying chemical and imaging conditions, supporting physically grounded defect assignments and a general pathway toward multimodal AI models for autonomous materials characterization.
[AI-276] SAILS: Surrogate-based Analysis of Interactions via Local Effect Smooths
链接: https://arxiv.org/abs/2606.09404
作者: Timo Heiß,Julia Herbinger,Bernd Bischl,Giuseppe Casalicchio
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Feature interactions drive much of the predictive power of machine learning models, yet existing explanation methods only detect and quantify interactions without revealing their functional form, or visualize only restricted interaction types. We propose Surrogate-based Analysis of Interactions via Local effect Smooths (SAILS), a model-agnostic framework that analyzes pairwise interactions through interpretable generalized additive model (GAM) surrogates fitted to the local effects of a black-box model. For each interval of a feature of interest, the surrogate smooth terms isolate the interaction components on derivative level, enabling (i) interaction detection through a heuristic derived from significance tests on smooth terms, (ii) interaction form categorization into linear, product-separable, and non-product-separable types, and (iii) tailored, interpretable visualizations for each interaction type. We empirically validate the framework through controlled simulations and a real-world task, demonstrating its effectiveness for pairwise interactions, with limitations under strong feature correlations and higher-order interactions. SAILS fills a notable gap in the XAI toolbox, going beyond detection of interactions alone to characterizing their functional form.
[AI-277] BareWave: Waveform-Native Flow-Matching Text-to-Speech
链接: https://arxiv.org/abs/2606.09048
作者: Wei Fan,Chao-Hong Tan,Qian Chen,Wen Wang,Xiangang Li,Kejiang Chen,Weiming Zhang,Nenghai Yu
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Sound (cs.SD)
备注: Under Review
Abstract:Removing intermediate representations and separately trained decoding stages has become an important direction in generative modeling. In text-to-speech, however, high-quality systems are still commonly built through an intermediate acoustic representation before waveform synthesis. In this work, we present BareWave, a fully waveform-native framework for direct text-to-wave generation in flow-matching TTS. We consider this setting to raise three training challenges: raw-waveform modeling lacks a strong pretrained representational scaffold, different stages of training benefit from different noise schedules, and data-space perceptual objectives do not automatically share the temporal structure of the velocity-space flow objective. As a result, direct waveform training is hard to optimize efficiently, hard to push toward a strong final operating point with a fixed recipe, and hard to integrate effective perceptual refinement. Guided by this view, we develop a direct text-to-wave training framework that combines training-time representation alignment, staged noise scheduling, and velocity-aware perceptual alignment (VAPA), while preserving a single waveform-native inference path without pretrained components at test time. Experiments on zero-shot voice cloning show that strong intelligibility, speaker similarity, and naturalness can be achieved under a fully waveform-native inference path, supporting waveform-native flow-matching TTS as a practical direction. Project page with audio demos is available at this https URL.
[AI-278] Few-shot Class-variable Incremental Audio Classification via Prototype Adaptation and Pseudo Class-variable Training INTERSPEECH2026
链接: https://arxiv.org/abs/2606.08898
作者: Yanxiong Li,Guoqing Chen,Qianqian Li,Sen Huang
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: This paper has been accepted for publication in Interspeech 2026. 4 Tables and 4 Figures
Abstract:In the task of few-shot class-incremental audio classification, the number of classes is assumed to always increase without considering the possibility of decrease. However, the number of classes generally increases or decreases in practice. In this paper, we investigate a problem of Few-shot Class-variable Incremental Audio Classification (FCIAC), in which the number of classes increases or decreases. We propose a FCIAC method using prototype adaptation and pseudo class-variable training. The model in our method consists of an encoder and a classifier. The classifier is initialized by a class-variable prototype adaptation network, whose structure dynamically changes with the change of classes. In addition, we design a pseudo class-variable training strategy to enhance the model’s adaptability to changing classes. Experiments on three public datasets show that our method exceeds previous methods in average accuracy. The code is at: this https URL.
[AI-279] Evaluating AI Investment Strategies
链接: https://arxiv.org/abs/2606.08791
作者: Irene Aldridge
类目: Econometrics (econ.EM); Artificial Intelligence (cs.AI); Portfolio Management (q-fin.PM); Risk Management (q-fin.RM); Statistical Finance (q-fin.ST)
备注: 33 pages
Abstract:We study the problem of auditing a black-box algorithmic decision-maker from observable inputs and outputs alone. Our main result is an exact decomposition: under precisely characterized conditions, the cumulative \emphregret of a dynamic policy equals the sum of per-period covariances between the cost vector and the policy’s decision. This extends the single-period identity of Aldridge~(2026) to the full multi-period setting of stochastic dynamic programming. We prove the identity holds exactly under i.i.d. costs and mean-unbiased Markov policies, derive closed-form bias corrections for non-stationary and time-varying cases, and establish the discounted-horizon analog. A Bellman recursion for the covariance regret functional connects the result to standard reinforcement learning algorithms; for rolling-window policies, the estimation-error bias is O(d/w) . The decomposition has direct implications for algorithmic auditing in strategic environments: in platform mechanism design, it provides a welfare-based audit metric without access to the agent’s private type; in repeated games, covariance reduction is a sufficient condition for policy improvement; in procurement and ad auctions, the bias correction quantifies welfare loss from strategic misreporting. The associated trajectory estimator is consistent, asymptotically normal with HAC variance, and computable in O(T \cdot nd) time. This makes the proposed approach a tractable, model-free audit tool for platform mechanisms, algorithmic portfolio strategies, and any sequential decision system subject to external performance review. Comments: 33 pages Subjects: Econometrics (econ.EM); Artificial Intelligence (cs.AI); Portfolio Management (q-fin.PM); Risk Management (q-fin.RM); Statistical Finance (q-fin.ST) ACMclasses: C.4 Cite as: arXiv:2606.08791 [econ.EM] (or arXiv:2606.08791v1 [econ.EM] for this version) https://doi.org/10.48550/arXiv.2606.08791 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-280] AeroSpectra Sentinel: An Auditable LLM Prompt-Chaining Decision-Support Workflow for Acute Asthma Risk Assessment from Respiratory Sounds and Clinical Signals
链接: https://arxiv.org/abs/2606.08247
作者: Aueaphum Aueawatthanaphisut
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Signal Processing (eess.SP)
备注: 10 pages, 8 figures, 5 tables, 14 equations
Abstract:Acute asthma risk assessment requires rapid interpretation of respiratory sounds, oxygenation, airflow limitation, speech ability, work of breathing, mental status, and response to reliever therapy. Conventional audio-only classifiers can detect wheeze-like patterns but often lack transparent clinical reasoning and safe escalation logic. This paper presents AeroSpectra Sentinel, a client-side research prototype and decision-support workflow that combines short-time Fourier transform (STFT) respiratory sound analysis, lightweight machine-learning screening, clinical feature fusion, and a five-stage large language model (LLM) prompt-chaining process. The workflow separates signal acquisition, preprocessing, acoustic feature extraction, ML screening, clinical guardrails, and FHIR-ready reporting. We evaluated the audio screening component on a public respiratory sound dataset containing 1,211 WAV recordings from five labels. Using a stratified subset of 584 recordings, a random forest achieved 91.10% binary accuracy and 78.69% F1-score for asthma-vs-non-asthma screening, while a feature-based multilayer perceptron achieved 89.73% accuracy and 78.26% F1-score. A compact log-spectrogram CNN achieved 73.29% accuracy and 55.17% F1-score. Multiclass classification achieved 77.40% accuracy and 77.23% macro-F1. To evaluate the LLM workflow, we conducted a scenario-based audit on 40 simulated clinical vignettes comparing one-shot prompting, prompt chaining, prompt chaining with guardrails, and prompt chaining with guardrails plus FHIR schema validation. The guardrail-plus-schema variant achieved the strongest simulated safety and documentation consistency. AeroSpectra Sentinel is intended as a research prototype, not as a diagnostic medical device or clinically validated risk-assessment product.
[AI-281] Beyond Additivity: Causal Discovery in Location-Scale Noise Models with Hidden Variables
链接: https://arxiv.org/abs/2606.08196
作者: Mariyam Khan,Shohei Shimizu,Thong Pham
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Methodology (stat.ME)
备注: 33 pages, 4 figures
Abstract:We study causal discovery from observational data when some variables are hidden and the data-generating process follows a location-scale noise model (LSNM). Existing methods that handle hidden confounders typically assume additive noise, but in practice, causes often modulate not just the mean but also the variance of their effects. We prove that acyclic directed mixed graphs (ADMGs) satisfying a bow-free condition are identifiable under LSNM with hidden variables, establishing the first identifiability result for causally insufficient models beyond noise additivity. We further provide sufficient conditions for identifying causal direction even when the bow-free assumption is violated. Our two-stage algorithm, LSNM-UV, is sound and complete, and experiments demonstrate improved performance over additive baselines on heteroscedastic data.
[AI-282] Repair Before Veto When Repair Is Hidden: Quantum-Accessible Features for Repair-Augmented Constraint Learning
链接: https://arxiv.org/abs/2606.08020
作者: Yifan Wang
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI)
备注: 7 pages, 2 figures
Abstract:Hard-constraint decision systems usually veto infeasible candidates. This is too rigid when the system can act: if a known affordable repair would make an infeasible candidate feasible and valuable, rejection is a false veto rather than a ranking error. We introduce Q-RACL (Quantum Repair-Augmented Constraint Learning), a repair-before-veto framework that first defines RACL decision semantics and then identifies the single inference link where quantum feature access can be load-bearing. RACL accepts a candidate when a sequential repair plan restores feasibility and preference; otherwise it returns structured rejection credit. The hard link is repair-feasibility inference: which repair class restores feasibility from an observed candidate and context. We construct a discrete-logarithm-hidden RACL family where the repair class is a shifted interval rule in the latent exponent a = log_g(x), while the learner observes only x = g^a mod p. Under standard DLP-based learning separation, this coordinate is inaccessible to efficient raw-input classical policies but accessible to a quantum agent through Shor/Fourier structure. Across six primes and ten seeds, bounded raw-input classical policies and a wrong raw-Fourier encoding remain near chance, whereas the Q-DLP policy keeps false-veto rate below 1.1%, wins all paired seeds, and yields QNI_cond = 0.9777 to 0.9972. A classical DLog oracle matches it, isolating feature access rather than classifier capacity. Thus quantum AI is not added as a generic model upgrade; for this DLP-hidden repair family, it supplies the missing feature that closes the repair-before-veto loop.
[AI-283] Agent ic multi-fidelity learning of quasiparticle and excitonic properties
链接: https://arxiv.org/abs/2606.07836
作者: Arnab Neogi,Aaron Forde,Christopher A. Lane,Sergei Tretiak,Jian-Xin Zhu
类目: Materials Science (cond-mat.mtrl-sci); Statistical Mechanics (cond-mat.stat-mech); Artificial Intelligence (cs.AI); Computational Physics (physics.comp-ph); Quantum Physics (quant-ph)
备注:
Abstract:Many-body GW-Bethe-Salpeter equation calculations are essential for accurate simulations of electronic structure and optical properties in modern low-dimensional nanomaterials. However, these methods are computationally demanding and can exhibit localized numerical instabilities or convergence failures that are difficult to detect within high-throughput workflows. We introduce an agent-guided multi-fidelity framework for correcting GW-Bethe-Salpeter excited-state landscapes in strained MoS2-WS2 bilayers. Across stacking registries, strain branches and reciprocal-space samplings, the workflow identifies spike-like excursions, near-zero-gap collapse and cross-fidelity inconsistencies associated with fragile long-wavelength dielectric screening. A structural agent evaluates calculations by assigning confidence weights and selectively using a small number of high-accuracy reference points. Machine learning models then transfer information across related systems and apply Gaussian process corrections to recover improved quasiparticle gaps and exciton binding energies, with calibrated uncertainty estimates. The approach corrects numerically induced artifacts without erasing physical strain dependence and substantially improves agreement with higher-fidelity references relative to a no-agent baseline. These results show that reliable surrogate learning for excited-state materials requires explicit diagnosis of numerical fragility, not direct interpolation of raw first-principles data points. The proposed framework is readily transferable to other optoelectronic nanomaterials characterized by strong quantum confinement, such as quantum dots, nanoribbons, layered two-dimensional semiconductors, and hybrid perovskite nanostructures.
[AI-284] Beyond Point Estimates: Benchmarking Uncertainty Quantification Methods on the AION-1 Astronomical Foundation Model
链接: https://arxiv.org/abs/2606.07771
作者: Karla Tame-Narvaez,Aleksandra Ćiprijanović,Shubhendu Trivedi
类目: Instrumentation and Methods for Astrophysics (astro-ph.IM); Astrophysics of Galaxies (astro-ph.GA); Artificial Intelligence (cs.AI)
备注: 7 pages, 1 table, 1 figure
Abstract:Foundation models for astronomical surveys offer powerful learned representations that can be transferred to downstream regression tasks such as galaxy property estimation. However, point predictions alone are insufficient for scientific inference; reliable uncertainty quantification (UQ) is essential. We compare seven UQ methods on galaxy property regression using frozen AION-1 foundation-model embeddings, predicting redshift, stellar mass, stellar-population age, gas-phase metallicity, and specific star-formation rate, from Legacy Survey photometry/imaging and DESI spectra, with PROVABGS-derived labels. Distribution-free conformal methods achieve marginal coverage within \sim 1,pp of the nominal 90% across all properties, while non-conformal baselines (Deep Ensembles, MC~Dropout) fail to calibrate reliably. Among conformal approaches, Conformalized Quantile Regression (CQR) delivers the best coverage in the bin with the poorest model predictions. More importantly, only the Locally Valid and Discriminative (LVD) framework – particularly when operating on AION-1 embeddings – also provides finite-sample \emphlocal validity, producing intervals that adapt to each galaxy’s local prediction difficulty rather than relying on marginal guarantees alone. These results establish conformal prediction, and LVD in particular, as the preferred UQ framework for uncertainty-aware inference on foundation-model embeddings in astrophysics.
[AI-285] MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science
链接: https://arxiv.org/abs/2606.07712
作者: Zhan’ao Yao,Boxuan Zhang,Jingyuan Shu,Xiaoyu Wu,Rongyan Wang,Linjing Li,Dajun Zeng,Yudong Yao,Tingwei Chen,Youwei Wang,Xiaolin Zhao,Jiahui Shi,Jianjun Liu
类目: Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI)
备注: 29 pages, 5 figures, including references
Abstract:Progress in AI-driven crystal materials science has so far been carried by narrow architectures purpose-built for individual tasks – graph neural networks for property prediction, diffusion and flow-matching models for crystal generation – each excelling within its niche yet unable to act as a shared backbone across the full spectrum of materials problems. Generative large language models offer a fundamentally different paradigm, in which structural representation, quantitative prediction, and structure-activity reasoning can be unified within one model, but the materials community has yet to see this paradigm realized at a level competitive with established narrow specialists. Here we present MatMind, a generative foundation model purpose-built for crystal materials science under this paradigm, developed through the coordinated activation of structure-activity knowledge and physics-informed feedback within a progressive training framework – combining structure-activity knowledge injection, a dual-head architecture that jointly trains language reasoning and numerical regression in a shared representation space, and multi-objective physics-informed reinforcement learning over stability, novelty, and structural diversity. Across three task families, MatMind attains the lowest mean absolute error on energy above hull, bulk modulus, and band gap – surpassing graph neural network predictors purpose-built for these tasks – reaches an S.U.N. rate of 65.3% on unconditional crystal generation, and achieves a comparable multiplicative improvement on magnetization-density-conditioned generation, where only 21 positive samples exist within over 600000 training entries. By matching or surpassing narrow specialists on their own ground while operating within a single unified model, MatMind shows that the LLM-based paradigm can serve as a viable backbone for crystal materials science going forward.
[AI-286] anJi-Environ: An Autonomous AI Scientist for Atmospheric Environmental Research
链接: https://arxiv.org/abs/2606.07697
作者: Haoluo Zhao,Hongchun Zhang,Nan Li,Jing-Jia Luo,Kaikai Zhang,Mengyang Yu,Nan Chen,Tao Song,Fan Meng
类目: Atmospheric and Oceanic Physics (physics.ao-ph); Artificial Intelligence (cs.AI)
备注: 20 pages, 11 figures, 2 tables
Abstract:As atmospheric environmental prediction continues to improve, interpretable validation of pollution mechanisms and feedback processes has become a main challenge in atmospheric chemistry. Yet mechanism validation based on complex numerical models still relies heavily on expert knowledge: mechanistic hypotheses must be operationalized into executable experiments, and model outputs must be organized into traceable evidence. We present TianJi-Environ, an auditable AI Scientist for atmospheric-chemistry mechanism validation. TianJi-Environ establishes the first WRF-Chem-based multi-agent framework that autonomously drives complex atmospheric-chemistry simulations, converting mechanistic hypotheses into executable configurations, testing experiments, and evidence criteria. Using ozone response and particulate-matter feedback as two representative examples, we demonstrate TianJi-Environ’s capability for mechanism validation. In a summertime ozone case over the North China Plain, the system detects directionally consistent aerosol-radiation-interaction signals in shortwave radiation and boundary-layer height, but judges the evidence for ozone response to NOx control to be incomplete. In a wintertime PM2.5 case over the Guanzhong Basin, it localizes the unsupported link to insufficient propagation from black-carbon perturbation to particulate response and missing diagnostics of vertical absorptive heating. These results show that TianJi-Environ makes expert-driven mechanism validation explicit, structured, and auditable, offering a reproducible paradigm for multi-agent systems coupled with complex atmospheric-chemistry models.
[AI-287] Single-Cell Cross-Modal Transfer by Adversarial Fine-Tuning of Foundation Models
链接: https://arxiv.org/abs/2606.07676
作者: Joseph Boyd,Matthew Lyon,Martino Mansoldo,Christian Hurry,Finnian Firth
类目: Genomics (q-bio.GN); Artificial Intelligence (cs.AI)
备注:
Abstract:Spatial transcriptomics (ST) is a powerful tool for exploring biological properties dependent on structure, proximity, and interaction in tissue. The methods underpinning ST are developing rapidly but are limited in their ability to profile many thousands of genes at a subcellular scale. Although dissociated from tissue, it is known that the whole-transcriptome readouts of cells in single-cell RNA sequencing (scRNA-seq) retain information about their former in situ neighbourhoods, motivating computational methods to recover it. While paired ST and scRNA-seq datasets are scarce, each modality in its own right is abundantly available. We therefore propose to perform cross-modal translation between unpaired ST and scRNA-seq data. In this work we show that a single-cell foundation model can perform this translation via adversarial fine-tuning. We demonstrate that our method performs favourably against methods built for multi-omics translation.
[AI-288] SurfDesign: Effective Protein Design on Molecular Surfaces
链接: https://arxiv.org/abs/2606.07567
作者: Fang Wu,Shuting Jin,Xiangru Tang,Mark Gerstein,Xiangxiang Zeng,Yejin Choi,Jure Leskovec,Jinbo Xu
类目: Biomolecules (q-bio.BM); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注:
Abstract:Protein function is largely determined by molecular surface geometry and physicochemical complementarity, yet most protein design methods condition only on backbone structure. We introduce SurfDesign, a surface-conditioned protein design framework that models molecular surfaces as continuous geometric manifolds and integrates them with pretrained protein language models. SurfDesign employs surface-based equivariant message passing to capture surface normals, curvature, and directional geometry, together with a parameter-efficient fine-tuning strategy. Focusing on functional protein design, we show that SurfDesign consistently outperforms prior surface-conditioned and backbone-only methods on de novo binder and enzyme design benchmarks. We also report strong performance on inverse-folding benchmarks as a diagnostic of structural compatibility. Our results highlight manifold-aware surface representations as a principled foundation for functional protein and enzyme design. Code is available at this https URL.
[AI-289] Considerations for an Integrated Detector Design at FCC-ee: A Human-AI Exploration
链接: https://arxiv.org/abs/2606.07564
作者: Charles Young
类目: Instrumentation and Detectors (physics.ins-det); Artificial Intelligence (cs.AI); High Energy Physics - Experiment (hep-ex)
备注: 103 pages, one figure
Abstract:This report explores detector design considerations for the Future Circular Collider in its electron-positron mode (FCC-ee) through an extended dialogue between a physicist and an AI assistant. Starting from initial “prejudice” detector concepts proposed by the AI assistant without explicit physicist input, each subsystem is examined in detail, with the AI’s assumptions challenged and revised through the exchange. The discussion covers the full detector from beam pipe to luminosity monitor, with particular attention to the interplay between subsystem choices and the practical considerations - calibration, stability, and operational simplicity - that are essential for a fifteen-year precision physics program. The narrative documents how the integrated detector design evolved substantially from the starting point to revised “prejudice” detector concepts of the AI assistant. The focus of this report is on the process to illustrate both the potential and the limitations of human-AI collaboration in experimental physics design, and the physics capabilities of any of the “prejudice” detector concepts remain to be explored.
[AI-290] he Montparnasse Algorithm for RNA Design
链接: https://arxiv.org/abs/2606.07562
作者: Tristan Cazenave
类目: Biomolecules (q-bio.BM); Artificial Intelligence (cs.AI)
备注:
Abstract:RNA design consists of discovering a nucleotide sequence that optimizes predefined criteria, such as secondary structure. It is useful for synthetic biology, medicine, and nanotechnology. We propose Montparnasse, a Monte Carlo search framework based on Generalized Nested Rollout Policy Adaptation, augmented with a problem-specific prior, slow and long adaptation at level 1, and a lexicographic multicriteria evaluation. Montparnasse solves all 100 puzzles of the Eterna100 V1 benchmark consistently faster than DesiRNA, the previous state of the art, across all time limits, reaching full coverage more than three times faster overall. On messenger RNA secondary structure optimization for hemoglobin alpha, it identifies sequences with more paired bases than the MFE-optimal solution of LinearDesign.
机器学习
[LG-0] Rethinking the Divergence Regularization in LLM RL
链接: https://arxiv.org/abs/2606.09821
作者: Jiarui Yao,Xiangxin Zhou,Penghui Qi,Wee Sun Lee,Liefeng Bo,Tianyu Pang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Reinforcement learning (RL) has become a key component of post-training large language models (LLMs). In practice, LLM RL is often off-policy because of training-inference mismatch and policy staleness, making trust-region control essential for stable optimization. Mainstream methods such as PPO and GRPO approximate this control with a ratio-clipping mechanism, but the importance ratio can be a poor proxy for distributional shift in long-tailed vocabularies. Recent work such as DPPO addresses this mismatch by replacing ratio-based clipping with a divergence-based mask, yielding a trust region defined by the sampled token’s absolute probability shift. However, DPPO still relies on a hard mask: once a token crosses the trust-region boundary in a harmful direction, its gradient is discarded rather than corrected. To address this, we propose Divergence Regularized Policy Optimization (DRPO), which replaces the hard mask with a smooth advantage-weighted quadratic regularizer on policy shift. DRPO preserves the same trust-region geometry as DPPO while inducing bounded, continuous gradient weights that attenuate diverging updates and provide corrective signals beyond the boundary. Experiments across model scales, architectures, and precision settings show that DRPO improves the stability and efficiency of LLM RL training.
[LG-1] Zero Touch Predictive Orchestration: Automating Time-Series Models for the Cloud-Edge Continuum
链接: https://arxiv.org/abs/2606.09787
作者: Abd Elghani Meliani,Arora Sagar,Adlen Ksentini,Raymond Knopp
类目: Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
*备注: 19 pages, 14 figures
Abstract:The Cloud-Edge Continuum (CEC) enables latency-critical applications by distributing resources to the far edge, but its extreme volatility makes proactive Zero Touch Management via time-series forecasting essential. However, orchestrators face a severe “cold start” problem: newly discovered nodes lack the historical data required to train localized predictive models, while generalized models fail to capture unique hardware and microservice behaviors. To solve this, we propose a fully automated time-series prediction architecture driven by a novel data-mixing methodology. At the infrastructure level, we introduce a lightweight, technology-agnostic Resource Exposer (RE) that dynamically discovers nodes and continuously collects customizable telemetry (e.g., compute, network, energy). To overcome the sparsity of these initial local samples, our framework automatically merges them with TimeTrack, our publicly available, high-resolution dataset collected at 45-second intervals. This synergizes TimeTrack’s foundational, high-frequency temporal patterns with the precise calibration of the local node data. Processed through a Neural Architecture Search (NAS) engine, the system automatically generates highly accurate baseline models. Experimental results demonstrate that merging the target data with TimeTrack effectively mitigates the cold start challenge. This integration significantly improves forecasting accuracy measured in Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) and accelerates convergence compared to training on the sparse local samples alone, training solely on generic datasets, or mixing the target data with standard alternative datasets, establishing a robust foundation for continuous MLOps deployment.
[LG-2] Perturbative Contrastive Physical Learning
链接: https://arxiv.org/abs/2606.09756
作者: Kyungeun Kim,Amanuel Anteneh,Israel Klich,Olivier Pfister,J. M. Schwarz
类目: Machine Learning (cs.LG); Disordered Systems and Neural Networks (cond-mat.dis-nn)
*备注: 21 pages, 10 figures
Abstract:Responses to perturbations are key to understanding physical systems. The ability to contrast such responses by comparing how a system reacts under slightly different conditions provides a mechanism for learning. Here, we introduce Perturbative Contrastive Physical Learning (PCPL), a general framework in which learning emerges from measurable contrasts between physical states produced by controlled changes to inputs, boundary conditions, parameters, or interpreter functions. PCPL unifies and extends prior approaches: Equilibrium Propagation is rooted in contrasts between free and nudged equilibria in energy-based systems, while Frequency Propagation corresponds to contrasts extracted from sinusoidally driven, frequency-demodulated responses. We show that contrast-driven updates can reflect either local sensitivities or global inverse-problem structure, yet do not require centralized gradient computation. Instead, effective learning geometry emerges implicitly from the system’s own physical response, allowing learning behavior to arise without an external processor or explicit backpropagation. We demonstrate PCPL in two platforms: (i) spring networks that update bond stiffness using measured displacements and forces, and (ii) continuous-variable photonic circuits trained via x quadrature measurements and finite-difference estimates of the Jacobian. Both platforms successfully learn classification tasks. We further show that a continuous-variable photonic circuit can be trained to implement analog multiplication, illustrating a step toward more autonomous physical learning systems.
[LG-3] Your Model Already Knows: Attention-Guided Safety Filter for Vision-Language-Action Models
链接: https://arxiv.org/abs/2606.09749
作者: Seongbin Park,Fan Zhang,Baharan Mirzasoleiman,Shahriar Talebi,Nader Sehatbakhsh
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: Under review
Abstract:Vision-Language-Action (VLA) models have demonstrated impressive end-to-end performance across a variety of robotic manipulation tasks. However, these policies offer no guarantees against collisions with task-irrelevant objects in the scene. Existing safety filters sidestep this problem by querying a vision-language model (VLM) to identify obstacles and their locations. This, however, is too slow to run in the control loop and can only be invoked at episode initialization, leaving the filter unable to track moving obstacles. We discover that a small number of attention heads within a VLA model reliably localize the object the policy intends to approach. These heads can be exploited within a training-free safety framework that obtains the active target from the attention heads at every step, treats the remainder of the scene as obstacles, and feeds these into a Control Barrier Function (CBF) filter. Together with a lightweight real-time object tracker, this allows for collision avoidance for non-static obstacles. We evaluate our framework on SafeLIBERO, which we extend with moving obstacles. On the original static benchmark, our method performs comparably to an oracle that uses privileged simulator state to identify the target, emulating a VLM-based identification step run once at episode initialization. On the dynamic variant, where the oracle’s init-time target assignment becomes stale, our method substantially outperforms it by 43%, on average. Our findings suggest that the perceptual signals needed for real-time safety filtering are already present within VLA policies and can be exploited without additional training or heavy auxiliary models.
[LG-4] Learning Dynamics Reveal a Hierarchy of Weight-Induced Layerwise Gram Metrics
链接: https://arxiv.org/abs/2606.09744
作者: Claudio Nordio
类目: Machine Learning (cs.LG); Disordered Systems and Neural Networks (cond-mat.dis-nn)
*备注: 23 pages
Abstract:We study feed-forward ReLU networks with fixed readout and quadratic loss. The aim is to rewrite gradient descent not primarily as a dynamics in weight space, but as a collective dynamics closed in terms of fields defined on the training-set space. For a single hidden layer, the weight variables can be eliminated from the activation dynamics, yielding a closed equation for the residuals governed by a collective kernel that factorizes into an input-geometric matrix and a dynamical co-activation matrix. For deeper networks, the residual dynamics retains a clean layer-wise kernel structure. However, from depth three onward, closure requires a hierarchy of weight-induced Gram operators that mediate information transport across layers.
[LG-5] ght Sample Complexity of Transformers COLT2026
链接: https://arxiv.org/abs/2606.09731
作者: Chenxiao Yang,Nathan Srebro,Zhiyuan Li
类目: Machine Learning (cs.LG)
*备注: in COLT 2026
Abstract:We tightly characterize the VC dimension of depth- L Transformers with a total of W parameters, mapping an input sequence of length T to a single output, establishing an upper bound of O(L W \log (T W)) and a nearly matching lower bound of \Omega(L W \log (T W / L)) . We further tightly characterize the sample complexity of chain-of-thought learning using such a Transformer, showing teacher forcing (i.e. selecting a predictor consistent with the entire chain-of-thought on training data) learns with sample complexity O\left(L W \log \left(\left(T+T^\prime\right) W\right)\right) and that any learning rule that uses chain-of-thought data requires at least \Omega\left(L W \log \left(\left(T+T^\prime\right) W / L\right)\right) examples, where T is the input length and T^\prime is the number of autoregressive steps.
[LG-6] Disentanglement with Holographic Reduced Representations
链接: https://arxiv.org/abs/2606.09725
作者: Jhonny J. Velasquez Olivera,Christo K. Thomas,Walid Saad
类目: Machine Learning (cs.LG)
*备注:
Abstract:Disentanglement, the separation of factors of variation in data using neural networks, remains a long-standing challenge in machine learning. Prior work has addressed this problem with variational autoencoders and generative adversarial networks that incorporate ideas from variational inference and information-theoretic constraints. In contrast to methods that rely on continuous representations, we propose a design that treats disentangled representations as symbolic structures, motivated by the compositional relationships among the concepts that make up samples from a distribution. However, learning discrete symbolic structures with neural networks while maintaining differentiability is difficult and often requires complex architectures. To address this, we introduce an unsupervised learning algorithm that uses holographic reduced representations (HRR) for neural disentanglement. We show that the HRR unbinding operation provides an inductive bias for separating factors and yields competitive results against baselines, as measured by latent traversals and disentanglement metrics. We complement these empirical findings with an information-theoretic analysis of the HRR unbinding channel. We prove that unbinding induces approximately independent symbol-value pairs and derive a per-slot capacity bound that quantifies how many distinct symbolic concepts can be reliably encoded, giving a quantitative account of the inductive bias toward disentanglement. The resulting representations differ from standard autoencoder-based models, in that their latent units are vectors that are summed together, rather than scalar dimensions of a low-dimensional latent vector. We show that this HRR representation is more robust to noise than other disentangled representations and maintains reconstruction quality across a range of SNRs.
[LG-7] When Do Local Score Models Extrapolate Across Size? A Diagnostic Theory and Benchmark
链接: https://arxiv.org/abs/2606.09705
作者: Wenjie Xi
类目: Machine Learning (cs.LG); Statistical Mechanics (cond-mat.stat-mech)
*备注:
Abstract:Scientific generative modeling often requires size transfer, where models trained on small systems are evaluated on larger ones. While translation-invariant architectures enable this evaluation, we show that architectural locality alone does not guarantee stable size extrapolation. Instead, stable extrapolation is governed by the quasi-locality of the Gaussian-smoothed score. Through Tweedie’s formula, far-away perturbations can influence local score components via posterior covariance, meaning a local model succeeds only if its receptive field covers the smoothed score’s response range. We formalize this mechanism, proving a size-uniform comparison theorem for local marginals under reverse diffusion. We also introduce Finite-Depth Local Flow (FDLF), a white-box diagnostic benchmark with exact scores, densities, and controllable response ranges. Empirically, we validate the interplay between spatial mixing, smoothed-score quasi-locality, and model receptive fields. Under spatial mixing, the smoothed score remains quasi-local relative to the receptive field, enabling stable extrapolation. Conversely, when spatial mixing weakens, the score’s locality rapidly degrades, causing size transfer to fail.
[LG-8] AutoMegaKernel: A Statically-Checked Agent Harness for Self-Retargeting Megakernel Synthesis
链接: https://arxiv.org/abs/2606.09682
作者: Jaber Jaber,Osama Jaber
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC); Performance (cs.PF)
*备注: 18 pages, 5 figures. Open-source code, data, and agent harness: this https URL
Abstract:AutoMegaKernel (AMK) compiles a HuggingFace Llama-family model into a single persistent cooperative CUDA kernel that runs the whole forward pass in one launch, with no per-model hand-written CUDA. The contribution is the system, not raw speed. A frozen schedule-IR validator statically certifies deadlock-freedom and race-freedom via static graph checks (not a mechanized proof), so an unsafe agent-proposed schedule is rejected before launch: across 7,160 adversarial schedules (6,091 unsafe) it had zero false-accepts and accepted all 360 real lowerings. The same source retargets sm_80/sm_90/sm_120 from one codebase, auto-generates correct megakernels for 10 of 10 supported models, and on a real SmolLM2-135M checkpoint reproduces HuggingFace greedy decode token-for-token (perplexity match 2.5e-7). An unattended, agent-drivable autoresearch loop self-improves the megakernel over its own baseline (1.25-1.72x). A search-found int8 (W8A16) megakernel beats CUDA-graphed cuBLAS bf16 at batch-1 decode across NVIDIA’s datacenter inference fleet: L4 up to 1.33x, the current-gen L40S 1.25-1.27x, A10G up to 1.08x at scale, and the consumer RTX 5090 1.19-1.23x. The ordering is not a clean function of bandwidth (the 864 GB/s L40S beats the 600 GB/s A10G); the divide is inference-class vs training-class. AMK trails cuBLAS on the high-bandwidth training-class A100/H100, where the harness localizes the cross-SM-sync bottleneck; we report the gap plainly. This is a precision-asymmetric (W8A16 vs bf16) comparison at decode position 0; the largest real checkpoint is TinyLlama-1.1B. Code and the harness: this https URL Comments: 18 pages, 5 figures. Open-source code, data, and agent harness: this https URL Subjects: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC); Performance (cs.PF) ACMclasses: D.3.4; C.1.2 Cite as: arXiv:2606.09682 [cs.LG] (or arXiv:2606.09682v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2606.09682 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-9] Algorithm for Contextual Queueing Bandits with Rate-Optimal Queue Length Regret
链接: https://arxiv.org/abs/2606.09668
作者: Seoungbin Bae,Dabeen Lee
类目: Machine Learning (cs.LG)
*备注:
Abstract:Contextual queueing bandits provide a framework for learning to schedule heterogeneous jobs under unknown context-dependent service rates. Under stochastic contexts, existing algorithms achieve \widetilde\mathcalO(T^-1/4) queue length regret, defined as the expected difference between the learner’s and oracle’s queue lengths at horizon T . In this paper, we improve this rate to \widetilde\mathcalO(T^-1/2) . The key observation is that random exploration is needed only up to a carefully chosen cutoff round, rather than throughout the entire horizon. We propose CQB- \eta -2, a three-phase algorithm: (i) pure random exploration to construct an initial estimator, (ii) \eta -random exploration combined with a UCB rule to continue learning while maintaining negative drift, and (iii) pure UCB after the exploration cutoff. Our proof decomposes the queue length regret at the cutoff round. Before the cutoff, negative drift suppresses queue length differences caused by suboptimal choices. After the cutoff, the first two phases provide sufficient random exploration samples, ensuring that UCB decisions incur small departure-rate gaps. Combining these two bounds yields queue length regret of order \widetilde\mathcalO(T^-1/2) . We further prove a minimax lower bound of order \Omega(T^-1/2) . The proof constructs two hard instances that are statistically indistinguishable up to the final service decision, and uses a queue-specific coupling argument to convert the resulting testing error into queue length regret. Together, our upper and lower bounds characterize the minimax dependence on the horizon T up to logarithmic factors.
[LG-10] In-Context Learning for Latent Space Bayesian Optimization
链接: https://arxiv.org/abs/2606.09664
作者: Tuan A. Vu,Harri Lähdesmäki,Julien Martinelli
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Bayesian optimization (BO) is a central tool for sample-efficient design, and latent-space Bayesian optimization (LSBO) extends it to structured objects such as molecules and proteins. In parallel, tabular foundation models such as TabPFN and TabICL now achieve state-of-the-art regression performance and are increasingly used as BO surrogates. Because their Bayesian behavior is induced by large synthetic pretraining collections, the composition of this pretraining distribution is crucial. LSBO creates a distinctive mismatch: the induced map from latent code to objective value differs markedly from the regression tasks used to train current in-context models. We address this mismatch by complementing the pretraining stage of tabular foundation model surrogates with synthetic optimization tasks defined on the latent space of a molecular VAE. The continued-pretraining objective features a regularizer that anchors the model to the original checkpoint, preserving its broad regression prior while avoiding overspecialization to the adaptation tasks. On held-out molecular optimization benchmarks, the resulting model achieves strong performance, supporting the relevance of LSBO-specific adaptation for in-context surrogates.
[LG-11] A Unifying Framework for Concept-Based Representational Similarity
链接: https://arxiv.org/abs/2606.09653
作者: Grégoire Dhimoïla,Victor Boutin,Agustin Martin Picard,Thomas Fel,Thomas Serre
类目: Machine Learning (cs.LG)
*备注:
Abstract:Learned representations across models and modalities often exhibit striking structural similarities, suggesting shared underlying concept decompositions. However, concept alignment remains poorly defined: existing approaches optimize different objectives under the same terminology, obscuring what is actually aligned. We propose a unifying framework that decomposes alignment along two axes: what is aligned (representations vs. concepts) and at what level (instance-wise vs. distributional). This induces four corresponding properties – instance-wise and distributional variants of translation and concept consistency – and reveals precisely which of these guarantees existing methods provide. We further introduce \InterVenchA, an intervention-based benchmark that separately measures extraction quality, translation quality, and concept consistency. Through theory and experiments, we show that commonly assumed equivalences between alignment objectives fail in practice: optimizing one property does not reliably recover the others, and purely unsupervised objectives fail to recover meaningful instance-level alignment. We then propose the Coupled Sparse Autoencoder (CoSAE), which jointly enforces complementary alignment objectives. Strong alignment emerges only in this regime. Surprisingly, as little as 0.1% paired data is sufficient to recover instance-level alignment when anchoring distributional objectives. Overall, our results show that concept alignment is fundamentally multi-objective: it must be defined, measured, and optimized as such. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2606.09653 [cs.LG] (or arXiv:2606.09653v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2606.09653 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-12] Data-driven discovery of governing differential equations across physical systems
链接: https://arxiv.org/abs/2606.09638
作者: Siyu Lou,Hao Xu,Wenguan Wang,Lu Lu,Hao Sun,Yang Liu,Linfeng Zhang,Dongxiao Zhang,Yuntian Chen
类目: Machine Learning (cs.LG); Symbolic Computation (cs.SC); Mathematical Physics (math-ph); Computational Physics (physics.comp-ph); Applications (stat.AP)
*备注:
Abstract:Differential equations play a critical role in scientific discovery because they provide a mathematical framework to describe the behaviour of physical phenomena. As a promising alternative to traditional first principles, data-driven differential equation discovery has attracted increasing attention for its ability to infer governing laws directly from experimental or simulated data, especially when the underlying physics is unclear. However, the field has expanded rapidly along diverse methodological directions, particularly with the emergence of AI-based approaches, and still lacks a clear organizing perspective. In this Review, we propose a problem-oriented perspective on data-driven differential equation discovery. We first introduce a two-dimensional phase diagram of equation discoverability, where discovery problems are organized according to structural complexity and coefficient complexity. This phase diagram shows how the field has moved from the discovery of sparse equations with simple coefficients toward more complex governing laws with richer structures and more flexible parameterizations. It also clarifies why different methodological families succeed or fail in different problem settings. We then present the representation-evaluation-optimization (REO) framework as a fundamental abstraction of the discovery process. By identifying the core problems of equation discovery that persist across algorithmic variations, REO shifts the discussion from individual algorithms to the fundamental principles that determine discoverability. We connect these perspectives to applications across physics and adjacent sciences, and argue that the next challenge is not merely recovering equations, but using them to revise existing theories, distil mechanisms and form new scientific concepts.
[LG-13] Constrained user-item allocation for e-commerce marketing campaigns
链接: https://arxiv.org/abs/2606.09623
作者: Maja Lindström,Natalija Glisovic,Jan von Pichowski,Tommy Löfstedt,Martin Rosvall
类目: Machine Learning (cs.LG)
*备注:
Abstract:When running marketing campaigns, retailers must decide which products to promote and which users to target. These decisions are inherently coupled: effective campaigns match users and items with strong mutual affinity into non-overlapping groups of predefined sizes. However, existing approaches assume predefined campaign structure or decouple item selection from user assignment, and cannot discover campaign groupings directly from joint interaction patterns. We therefore formalize this campaign problem as auto-targeting: jointly selecting users and items to construct multiple disjoint campaigns. To solve this combinatorial problem, we propose three complementary strategies: (i) constrained spectral biclustering to find dense regions in the user-item affinity matrix, (ii) greedy local search with pairwise swaps for combinatorial refinement, and (iii) a multi-armed bandit framework to escape local optima through exploration. We evaluate these methods on a synthetic dataset, the Amazon Reviews benchmarks, and large-scale proprietary commercial data, and compare the results to simulated annealing as a baseline. The results show that biclustering consistently achieves the highest campaign quality, lift, and fairness scores. While biclustering runs efficiently on smaller datasets, its runtime increases substantially on very large ones, where bandit-based methods instead offer a scalable alternative.
[LG-14] Assessing Sample Quality in Conditional Generation under Compositional Shift
链接: https://arxiv.org/abs/2606.09601
作者: Berker Demirel,Valentino Maiorca,Marco Fumero,Theofanis Karaletsos,Francesco Locatello
类目: Machine Learning (cs.LG)
*备注:
Abstract:Conditional generators provide a natural tool for controllable generation, including settings where the desired condition is a new composition of observed attributes or experimental factors. In many applications, especially in scientific domains, such models are attractive to explore conditions for which real samples are rare, expensive, or not yet observed. However, this creates a circularity for evaluation: standard conditional quality metrics require a reference target distribution, but in the extrapolative regime that distribution is unavailable by definition. We address this problem with a post-hoc, per-sample trust score for assessing conditional samples using only the training distribution. The score combines two estimable quantities: global realism, measuring compatibility with the real data manifold, and attribute-wise faithfulness, measuring whether a sample is closer to the requested attributes than to plausible alternatives. We show that the score can recover meaningful comparisons across extrapolated generations, under a mild coverage condition on the observed attributes. These comparisons enable effective filtering, ranking, and abstention of generations and can be used directly on off-the-shelf pretrained models. In biological imaging, selected samples preserve real morphological structure better and improve downstream predictive performance, while similar gains are observed on controlled vision benchmarks. Finally, we show how the score can be applied during generation, enabling abstention before full decoding. Code is available at this https URL.
[LG-15] On Choosing the μ Parameter in Gaussian Differential Privacy
链接: https://arxiv.org/abs/2606.09582
作者: Bogdan Kulynych,Antti Honkela
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Recent work argues for using Gaussian differential privacy (GDP) to report the privacy guarantees in privacy-preserving machine learning. We provide principled mappings from pure-DP \varepsilon to GDP \mu by matching the worst-case success of a strong-adversary membership inference attack in terms of three metrics: multiplicative advantage at fixed FPR, precision at fixed recall, and the standard privacy profile. We tabulate \mu values across a useful range of parameters and recommend \mu \approx \varepsilon/5 as a conservative general-purpose conversion.
[LG-16] Efficient Traffic Prediction at Scale: A Systematic Study of STGCN Architectural Depth ITSC
链接: https://arxiv.org/abs/2606.09539
作者: Soban Nasir Lone,Mohamed Abouelela,Taeyoung Yu,Jiwon Kim,Constantinos Antoniou
类目: Machine Learning (cs.LG)
*备注: Accepted for publication in IEEE ITSC (2026)
Abstract:Spatio-temporal graph neural networks (STGNNs) have become the dominant approach for traffic prediction, yet their computational requirements pose challenges for practical deployment in intelligent transportation systems (ITS). While recent work has proposed efficient alternatives to STGNNs, a fundamental question remains unexplored: are these architectures themselves over-parameterised? We examine this question using the Spatio-Temporal Graph Convolutional Network (STGCN), one of the most widely adopted models in this domain. Through systematic experiments across four diverse traffic datasets, we compare 1-block, 2-block (standard), and 3-block STGCN variants. Our findings reveal that the single-block architecture achieves optimal performance for short-term prediction (10 mins) on three of four datasets, while incurring only marginal degradation ( \leq 1.8% relative error) at longer horizons. Crucially, the 2-block variant incurs 61% higher CPU inference latency and 37% lower throughput relative to 1-block – substantial overhead for resource-constrained ITS deployment. The 3-block architecture offers no favourable tradeoff, more than doubling computational cost for 0.5% relative improvement. These results suggest that the default 2-block STGCN may be over-parameterised for many applications, with implications for both practitioners deploying traffic prediction systems and researchers benchmarking efficiency-focused methods.
[LG-17] Investigating Calibration Challenges in Probabilistic Electricity Price Forecasting
链接: https://arxiv.org/abs/2606.09517
作者: Jan Niklas Lettner,Hadeer El Ashhab,Benjamin Schäfer
类目: Machine Learning (cs.LG)
*备注: Presented at the ACM Sustainability Week Companion 2026, Banff, AB, Canada
Abstract:As renewable energy integration increases market volatility, probabilistic electricity price forecasting has become essential for effective risk management. However, current-proper-scoring rules often prioritize forecast sharpness at the expense of calibration, leading to overconfident and statistically unreliable uncertainty estimates. This work highlights the critical gap between theoretical scoring and practical calibration, demonstrating that models can become mere proxies for deterministic forecasts when reliability is neglected. We conclude that future research must shift toward calibration-aware objectives and architectures to ensure the distributional integrity of energy market forecasts.
[LG-18] BUDDY: BUdget-Driven DYnamic Depth Routing for Adaptive Large Language Model Inference
链接: https://arxiv.org/abs/2606.09514
作者: Yuhua Zhou,Shaoqi Yu,Shichao Weng,Changhai Zhou,Mingze Yin,Fei Yang,Aimin Pan
类目: Machine Learning (cs.LG)
*备注:
Abstract:Large language models (LLMs) incur high inference cost due to their depth and parameter scale. Depth pruning can reduce latency by skipping redundant Transformer blocks, but existing methods (i) provide limited control under user-specific compute budgets and (ii) typically fix the routing path, failing to adapt as the context grows during decoding. We propose Buddy, a budget-driven dynamic depth routing framework. Buddy uses a lightweight Decision Module to score intermediate layers conditioned on the input and deterministically executes the top-k layers to satisfy a given budget. To support decode-time adaptation, Buddy reuses the first-layer KV cache as a low-overhead global context source and pools it together with the newest token representation before each routing decision. When no explicit budget is provided, an optional Budget Predictor estimates an input-dependent compute level to balance quality and efficiency. Experiments on Llama-family and Qwen models show that Buddy is competitive with strong static pruning baselines and often improves the accuracy-compute trade-off, while uniquely supporting strict budget control, decode-time rerouting, and multiple budgets within a single trained model.
[LG-19] Loss-Guided Adaptive Scale Refinement for Molecular Force Prediction
链接: https://arxiv.org/abs/2606.09480
作者: Limin Yu
类目: Machine Learning (cs.LG)
*备注: 23 pages, 2 figures, 6 tables. Preprint on adaptive scale refinement for molecular force prediction
Abstract:Molecular systems involve interactions across multiple spatial scales, from local coordination and short-range perturbations to long-range electrostatic and solvent-mediated effects. However, most molecular representation learning methods rely on manually predefined scales, and the task-optimal modeling scale may not coincide with these fixed levels. This study introduces a loss-guided adaptive scale refinement framework for molecular force prediction, treating predefined scales as initial anchors and discovering task-effective resolutions through interpolation, routing, differentiable scale updates, and scale pool refinement. Using a NaCl aqueous ionic system as a minimal testbed, this study constructs short-scale and long-range force prediction branches and analyzes their complementarity. Oracle hard routing reduces the overall force MAE from 399.65 to 382.67, while continuous oracle interpolation further reduces it to 380.96. In close-contact regimes with nearest-ion distance below 0.6 nm, the close-contact MAE decreases from 327.22 to 260.51. A minimal scale pool update experiment shows that starting from endpoint anchors 0,1, loss-guided updates automatically generate intermediate scales and recover most of the continuous oracle performance. The final updated scale pool 0,0.125,0.25,0.375,0.5,0.75,1 achieves an overall MAE of 381.23. These results support adaptive scale refinement as a promising direction for molecular representation learning, especially when fixed-scale modeling is insufficient. Comments: 23 pages, 2 figures, 6 tables. Preprint on adaptive scale refinement for molecular force prediction Subjects: Machine Learning (cs.LG) Cite as: arXiv:2606.09480 [cs.LG] (or arXiv:2606.09480v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2606.09480 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-20] Breaking the Tokenizer Barrier: On-Policy Distillation across Model Families
链接: https://arxiv.org/abs/2606.09456
作者: Yifan Niu,Han Xiao,Dongyi Liu,Zelong Wang,Dihong Gong,Yasheng Wang,Jia Li
类目: Machine Learning (cs.LG)
*备注:
Abstract:On-Policy Distillation (OPD) has become a core technique in the post-training of Large Language Models (LLMs) for transferring knowledge from domain experts to student models. However, existing OPD distillation methods require teacher and student models to share the same tokenizer, restricting the applicability of OPD within the model series. Current mainstream practice typically employs Supervised Fine-Tuning (SFT) on teacher-generated responses for cross-tokenizer distillation, which fails to capture the rich knowledge embedded in the teacher’s probability distribution. In this work, we enable the standard on-policy distillation method to operate across model families, ensuring that high-fidelity token-level signals can propagate across different tokenizers with a precise token-mapping algorithm. Extensive experiments show that cross-tokenizer OPD is significantly more compute-efficient than baselines on various benchmarks. Our results unlock a broader range of teacher-student pairs for OPD, opening up new avenues for adapting and enhancing interactions between LLMs.
[LG-21] Operator learning for solving Fokker-Planck equations with various initial conditions
链接: https://arxiv.org/abs/2606.09434
作者: Li Zeng,Xiaoliang Wan,Yaobin Wang,Fabio Nobile,Tao Zhou
类目: Machine Learning (cs.LG)
*备注:
Abstract:The Fokker-Planck equation (FPE) plays a pivotal role in describing the time evolution of probability density functions (PDFs) for systems governed by stochastic dynamics. In this work, we propose a conditional normalizing flow-based physics-informed neural network (PINN) framework for efficiently approximating the solution operator of the FPE for a whole range of initial conditions. Leveraging the Chapman-Kolmogorov equation for Markovian stochastic processes, the problem is reformulated into approximating a transition PDF starting at initial time from a Dirac mass centered at an arbitrary point. The PDF of an associated linearized stochastic differential equation (SDE) is employed as the base distribution for the normalizing flow, providing a good approximation of the target PDF, especially for small times, and thereby avoiding the singularity of the map associated with the Dirac delta initial distribution. Furthermore, a time-weighted loss function is introduced to mitigate numerical instabilities arising at small times, achieving a balance between causality and training difficulty as time progresses. A variety of numerical experiments are presented to illustrate the effectiveness and robustness of the proposed method.
[LG-22] Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems
链接: https://arxiv.org/abs/2606.09432
作者: Karn Tiwari,Niladri Dutta,N M Anoop Krishnan,Prathosh A P
类目: Machine Learning (cs.LG)
*备注: Under Submission
Abstract:Modeling interacting dynamical systems requires capturing spatial interactions alongside long-range temporal dependencies. Graph neural networks (GNNs) provide a natural representation but typically rely on autoregressive rollouts and treat spatial and temporal dynamics separately, leading to error accumulation over long horizons. Existing approaches also focus on local interactions and short temporal contexts, limiting their ability to capture multi-hop dependencies and global structure. We introduce the Graph Mamba Operator (GraMO), a latent-space simulator that integrates state-space models with graph-based interaction learning. In contrast to prior work that sequences nodes or applies spatial and temporal updates in separate stages, GraMO couples graph-based interactions and temporal state updates within a single recurrence. The update is linear in the latent state, with input-dependent coefficients that adapt across regimes. We evaluate GraMO on N-body systems, motion capture, and robotics datasets, achieving the lowest error across benchmarks and the largest gains in long-horizon prediction.
[LG-23] Now You (Still) See Me: Detecting Evasive Steganographic Payloads in LLM s
链接: https://arxiv.org/abs/2606.09411
作者: Charles Westphal,Timothy Douglas,Keivan Navaie,Tiago Pimentel,Fernando E. Rosas
类目: Cryptography and Security (cs.CR); Information Theory (cs.IT); Machine Learning (cs.LG)
*备注:
Abstract:Large language models can be fine-tuned to encode prompt-borne secrets into fluent, seemingly benign outputs. This creates a steganographic exfiltration risk that is difficult to detect with output-level steganalysis. Recent work proposes mechanistic detection using linear probes that recover the secret from internal activations. We show that this defense can be systematically evaded, but that detectability can be recovered through a targeted data-level intervention. First, we extend the detection setup to include a non-linear MLP probe. We then adversarially fine-tune steganographic trojans across five base models: Qwen3-8B, Llama-3.1-8B, Ministral-8B, Qwen3-14B, and Phi-4-14B. The resulting models retain 58 – 79% exact-match secret recovery while evading both ridge and held-out MLP probes, with 1 – 8% average capability degradation across six benchmarks. We then give an information-theoretic characterization of this evasion. Successful evasion preserves recoverability while reducing low-order extractability of the secret from the content-aligned representation, forcing the payload into synergistic interaction with residual degrees of freedom. This motivates a recontextualization dataset that restricts these residual degrees of freedom. On this distribution, both ridge and MLP detectability are restored across all five evasive trojans. Overall, our findings show that activation-based steganography detection is vulnerable to adaptive evasion, but also that theory-guided evaluation distributions can expose otherwise hidden payloads.
[LG-24] Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models ICLR2026
链接: https://arxiv.org/abs/2606.09401
作者: Bartłomiej Marek,Lorenzo Rossi,Vincent Hanke,Xun Wang,Michael Backes,Franziska Boenisch,Adam Dziedzic
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注: Accepted at ICLR 2026 (Oral)
Abstract:Recent work has applied differential privacy (DP) to adapt large language models (LLMs) for sensitive applications, offering theoretical guarantees. However, its practical effectiveness remains unclear, partly due to LLM pretraining, where overlaps and interdependencies with adaptation data can undermine privacy despite DP efforts. To analyze this issue in practice, we investigate privacy risks under DP adaptations in LLMs using state-of-the-art attacks such as robust membership inference and canary data extraction. We benchmark these risks by systematically varying the adaptation data distribution, from exact overlaps with pretraining data, through in-distribution (IID) cases, to entirely out-of-distribution (OOD) examples. Additionally, we evaluate how different adaptation methods and different privacy regimes impact the vulnerability. Our results show that distribution shifts strongly influence privacy vulnerability: the closer the adaptation data is to the pretraining distribution, the higher the practical privacy risk at the same theoretical guarantee, even without direct data overlap. We find that parameter-efficient fine-tuning methods, such as LoRA, achieve the highest empirical privacy protection for OOD data. Our benchmark identifies key factors for achieving practical privacy in DP LLM adaptation, providing actionable insights for deploying customized models in sensitive settings. Looking forward, we propose a structured framework for holistic privacy assessment beyond adaptation privacy, to identify and evaluate risks across the full pretrain-adapt pipeline of LLMs.
[LG-25] Distilling Safe LLM Systems via Soft Prompts for On Device Settings UAI2026
链接: https://arxiv.org/abs/2606.09388
作者: Motasem Alfarra,Cristina Pinneri,Dana Kianfar,Mohammed Almousa,Christos Louizos
类目: Machine Learning (cs.LG)
*备注: Accepted to UAI 2026
Abstract:Deploying safe large language models (LLMs) on resource-constrained edge devices presents a critical challenge: while dual-model systems combining LLMs with guard models provide effective safety guarantees, their substantial memory and computational demands make them prohibitively expensive for on-device deployment. This paper presents a comprehensive study of parameter-efficient safety alignment methods for resource-constrained settings. Through systematic evaluation across multiple LLM architectures, training objectives, and parameter-efficient fine-tuning approaches, we identify that soft prompts combined with distillation-based training consistently outperform alternative methods. We introduce distillation frameworks based on total variation and KL divergence that effectively transfer safety behaviors from guard models into learned soft prompts. Our evaluations on various benchmarks demonstrate that this combination achieves superior safety-usefulness trade-offs compared to LoRA adapters, steering vectors, and direct optimization methods, while requiring minimal additional memory and compute at inference time. These findings establish soft prompt distillation as the preferred approach for safety alignment in on-device LLM deployment.
[LG-26] hresholded Local Hyper-Flow Diffusion
链接: https://arxiv.org/abs/2606.09340
作者: Meher Chaitanya,Sebastian Dalleiger,Luana Ruiz
类目: Machine Learning (cs.LG)
*备注:
Abstract:Local Hyper-Flow Diffusion (HFD) gives an edge-size-independent Cheeger-type guarantee for seeded clustering in general submodular hypergraphs, but existing HFD solvers do not keep intermediate computation local at every iteration. We introduce Thresholded Local HFD (TL-HFD), a first-order method that maintains an active region around the seeds, performs projected subgradient updates on that region and its immediate boundary, and expands via thresholded (top-k) boundary activation. We prove that the local update is exact: the degree-preconditioned projected subgradient step restricted to the active region and its boundary coincides with the unrestricted global update. We establish finite-time dual suboptimality for both exact and thresholded updates, treating the latter as inexact projected subgradient steps with explicit skipped-boundary error. We further derive an additive activated-volume bound controlled by realized local subgradient norms and the minimum boundary-push among newly activated vertices, and translate approximate dual optimality with localized support into a robust sweep-cut guarantee for early-stopped iterates. For general submodular cut-costs, each iteration is local in the scanned region and oracle-sensitive in the hyperedge primitive. Empirically, TL-HFD often matches or improves over HFD while activating less volume, with the largest gains on noisy instances where diffusion tends to absorb non-target vertices.
[LG-27] Machine-Learning Emulation of Satellite Greenhouse Gas Retrievals: Stability over Time
链接: https://arxiv.org/abs/2606.09313
作者: Nugzar Gognadze,Motonobu Kanagawa,Yu Someya,Hisashi Yashiro
类目: Machine Learning (cs.LG); Applications (stat.AP)
*备注: 48 pages, 9 figures, 15 tables
Abstract:Retrieval algorithms are used to estimate atmospheric concentrations of greenhouse gases (GHGs), such as carbon dioxide (CO2) and methane (CH4), by solving inverse problems from high-spectral-resolution satellite radiance measurements. However, these algorithms are computationally expensive, which makes real-time estimation at scale difficult. Machine-learning models have therefore been proposed as fast emulators of retrieval algorithms. Most existing studies, however, evaluate them only on test data from the same period as the training data. We study the stability over time of such emulators using data from the Greenhouse Gases Observing SATellite (GOSAT). We show that prediction accuracy generally deteriorates when the test period moves away from the training period. We also show that including time as an input feature substantially improves XCH4 prediction for Lasso and neural-network models. Among the methods considered, a simple Lasso model performs as well as or better than more complex methods such as neural networks, and yields more stable predictions over time. We further validate the results using the Total Carbon Column Observing Network (TCCON), a ground-based observation network. On the TCCON-matched dataset, the time-augmented Lasso achieves errors against TCCON that are comparable to the disagreement between GOSAT and TCCON for both XCO2 and XCH4. Comments: 48 pages, 9 figures, 15 tables Subjects: Machine Learning (cs.LG); Applications (stat.AP) Cite as: arXiv:2606.09313 [cs.LG] (or arXiv:2606.09313v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2606.09313 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-28] oward Compiler World Models: Learning Latent Dynamics for Efficient Tensor Program Search
链接: https://arxiv.org/abs/2606.09312
作者: Haolin Pan,Lianghong Huang,Xvlin Zhou,Mingjie Xing,Yanjun Wu
类目: Machine Learning (cs.LG); Programming Languages (cs.PL)
*备注:
Abstract:Tensor program optimization is essential for modern machine learning systems, but its search space is enormous. Existing auto-schedulers reduce measurement cost with learned cost models, yet they usually evaluate each candidate as a static code snapshot, ignoring the schedule trajectory that produced it. This makes them insensitive to action dependencies and vulnerable to superficial code variations. We propose a \emphworld-model-inspired evaluator that models schedule evaluation as action-conditioned latent dynamics over program states. Starting from the initial program, it rolls out scheduling actions in a continuous latent space with a lightweight transition model, avoiding expensive AST mutation and repeated code encoding. The final dynamic representation is combined with action and hardware features to rank candidates. Implemented in TVM AutoScheduler, our method improves representative-subgraph latency over Ansor by 1.37 \times on GPU and 1.54 \times on CPU under the same 64-trial budget. It also matches Ansor-10K within 2.2% geometric mean using 10 \times fewer measurements, and accelerates full-model inference over PyTorch/PyTorch-opt(cuDNN) by 4.61 \times /3.67 \times geometric mean.
[LG-29] PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning
链接: https://arxiv.org/abs/2606.09301
作者: Zekai Chen,Miao Zhang,Jiayang Xing,Xunkai Li,Xun Wu,Rong-Hua Li,Guoren Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Multimodal federated graph learning (MM-FGL) aims to collaboratively learn from decentralized graphs with text and images. However, real-world clients may not share a common modality basis: a visual-search client may contain image–interaction graphs but no seller descriptions, while a catalog client may provide text but no product images. We refer to this practical setting as client-level modality deficiency. Unlike random instance-wise missingness, a deficient client lacks the local semantic basis needed to reconstruct the absent modality. More importantly, in graph learning, incomplete representations initialize message passing, so imputation errors can be filtered, mixed, and amplified by the receiving topology. To address this gap, we propose \textbfPRISM (\textbfProactive \textbfRetrieval and \textbfImputation via \textbfStructural \textbfMeta-prompting), a topology-aware federated cross-modal imputation framework. Rather than reconstructing the missing modality solely from local observations, PRISM recovers missing-modality semantics from the federation and introduces them into local graph propagation under topology-aware control. Experiments on six multimodal graph datasets across graph-centric and modality-centric tasks show that PRISM consistently improves modality-deficient clients, outperforming state-of-the-art baselines by \textbf4.48% on average.
[LG-30] Intention Driven Identification of In-Possession Match Phases in Association Football through Temporal Graph Learning
链接: https://arxiv.org/abs/2606.09289
作者: Yuesen Li,Daniel Link
类目: Machine Learning (cs.LG)
*备注: 27 pages, 10 figures
Abstract:Understanding tactical organisation of association football, hereafter referred to as football, requires identifying distinct match phases. Yet in-possession phases are rarely directly observable and are shaped by evolving tactical intentions, rather than spatial patterns alone. This study proposes a data-driven framework for identifying in-possession match phases from spatiotemporal tracking data. Seven German Bundesliga matches recorded at 25 Hz with TRACAB were analysed. A hierarchical phase model was defined with three tactical intentions (Invade Opponent Space, Keep Possession, Scoring) and six phases (Build Up, Progression, Counter Attack, Maintenance, Sustained Threat, Finishing). A Temporal Graph Attention Network (T-GAN) was developed to combine frame-level player-interaction graphs, contextual features, and Transformer-based temporal modelling. Performance was evaluated using frame-level F1 and a sequence-aware Intersection over Truth-Dominance (IoT-D) metric. T-GAN achieved macro-average frame-level F1 scores of 0.87 at the intention level, 0.76 for invasion-related phases, and 0.79 for scoring phases. At the sequence level, mean diagonal IoT-D F1 increased from 0.68 to 0.79 for intentions and from 0.61 to 0.71 for phases after post-processing, indicating improved temporal coherence. Model comparisons showed that sequence modelling was the main driver of segmentation quality, while graph-based relational modelling was particularly beneficial for Counter Attack recognition. Exploratory player attention analysis further suggested that wide and midfield positional groups contributed strongly to phase discrimination. Overall, the framework translates continuous tracking data into tactically interpretable in-possession phase representations, with potential applications in automated match annotation, tactical analysis, and playing-style profiling.
[LG-31] rajectory Geometry of Transformer Representations Across Layers
链接: https://arxiv.org/abs/2606.09287
作者: Vishal Pandey,Gopal Singh
类目: Machine Learning (cs.LG)
*备注: 18 pages, 9 figures
Abstract:Understanding how transformer representations evolve across layers, not merely what they encode, remains an open problem in mechanistic interpretability. We recast the transformer forward pass as a discrete population trajectory through a high-dimensional representation manifold, drawing on geometric tools from computational neuroscience. Rather than probing for pre-specified features, we characterize trajectory geometry using five metrics computed directly in the ambient space: trajectory length, curvature, a semantic convergence index, layerwise cosine similarity, and representational stability. Across three model families (GPT-2, TinyLlama, Qwen2.5) and five controlled prompt families, we report four findings. First, semantically related prompts converge significantly in middle-to-late layers (peak CI 0.41–0.58, p0.001, Mann-Whitney U), consistent with attractor-like dynamics. Second, reasoning tasks produce trajectories of greater curvature than lexical variations (0.71–0.83 rad vs. 0.27–0.31 rad), suggesting curvature encodes computational complexity. Third, ambiguous tokens exhibit trajectory bifurcation with up to 5.6x representational separation by the final layer, absent in unambiguous controls. Fourth, layerwise cosine similarity reveals a universal three-phase structure: encoding, elaboration, and output preparation, consistent across all three architectures. All four effects vanish under shuffled-layer and random-embedding controls. We release a fully open-source, model-agnostic pipeline and argue that trajectory geometry constitutes a principled, probe-free lens for mechanistic interpretability.
[LG-32] ERBench: A Benchmark and Testsuite for Equation Discovery Algorithms
链接: https://arxiv.org/abs/2606.09276
作者: Paul Kahlmeyer,Henrik Voigt,Michael Habeck,Joachim Giesen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Equation discovery aims to automate the discovery of scientific models in the form of mathematical equations from data. Technically, equation discovery is implemented by symbolic regression algorithms. Performance of symbolic regression for equation discovery is measured along two dimensions: Prediction accuracy on test data, and recovery of known groundtruth formulas. For standard regression, accuracy is typically measured on in-domain test data, for instance, by splitting a data set randomly into training and test data. While this makes sense for in-domain interpolation, which is the common goal in ordinary regression, it can be a misleading proxy for true model discovery and generalization. The obvious alternative is to measure out-of-domain accuracy. However, obtaining challenging out-of-domain test data is a non-trivial problem. Therefore, we focus on equation recovery for evaluating symbolic regression algorithms for equation discovery. The rationale is that symbolic regression algorithms that perform well in recovering known groundtruth formulas are good candidates to perform well in unknown equation discovery. Existing benchmarks for symbolic regression include equation recovery tasks, however, with only a small number of groundtruth formulas that are publicly known. Moreover, these benchmarks place less emphasis on evaluating the robustness of algorithms in terms of their behavior under changing dimensionality, sampling size, sampling distribution and sampling domain. This, however, is of central importance to practitioners wanting to discover equations for modeling natural phenomena, since data is almost certainly noisy and comes from diverse domains, distributions, and sample sizes. To fill this gap, we introduce the Equation Recovery Benchmark (ERBench), a new evaluation framework designed to rigorously assess algorithms explicitly targeting the task of equation discovery.
[LG-33] Multi-View Speech Representation Learning for Parkinsons Disease Detection Using Context-guided Cross-modal Attention
链接: https://arxiv.org/abs/2606.09271
作者: George Theodosiou,Loukas Ilias,Dimitris Askounis
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注:
Abstract:Parkinson’s disease (PD) is a progressive neurodegenerative disorder that frequently causes speech impairments associated with hypokinetic dysarthria. As speech production relies on the precise coordination of complex neuromuscular mechanisms, speech analysis has emerged as a promising non-invasive and cost-effective biomarker for early PD detection. Recent deep learning approaches have shown encouraging results; however, most existing methods rely on a single speech representation, potentially overlooking complementary pathological information encoded across different feature spaces. In this work, we propose a multi-branch deep learning framework for automatic PD detection from speech. Each recording is segmented into 5-second chunks and represented using three complementary modalities: Log-Mel spectrograms, MFCCs, and HuBERT embeddings extracted from raw waveforms. The spectrograms are processed using a pre-trained ResNet-18 encoder, MFCC sequences are modeled through a BiLSTM network, and raw speech is encoded using a pre-trained HuBERT model. To effectively integrate these heterogeneous representations, we introduce a context-guided cross-modal attention mechanism that dynamically weights temporal HuBERT embeddings according to the global acoustic context derived from the spectrogram and MFCC branches. Experiments conducted on the publicly available Spanish PC-GITA corpus under strict speaker-independent 5-fold cross-validation demonstrate the effectiveness of the proposed approach. The proposed architecture achieves an accuracy of 91.51%, an F1-score of 91.24%, and an AUC of 95.97%. Furthermore, ablation studies confirm the contribution of both the proposed context-guided cross-modal attention mechanism and the integration of complementary speech representations. These findings highlight the potential of heterogeneous speech modeling for robust and clinically reliable PD detection.
[LG-34] SNN-MLIR: An MLIR Dialect for Compiling Neuromorphic SNNs from NIR to Bare-Metal C
链接: https://arxiv.org/abs/2606.09213
作者: Alejandro García Gener,Alvaro Rollón de Pinedo
类目: Programming Languages (cs.PL); Machine Learning (cs.LG)
*备注: 8 pages, 5 figures, 5 tables
Abstract:Spiking neural networks (SNNs) are increasingly trained in a wide range of frameworks (SnnTorch, Lava, Norse, and others) each with its own model format. The Neuromorphic Intermediate Representation (NIR) addresses this fragmentation by providing a common, framework-independent format for exchanging trained SNN models. NIR solves the exchange problem, but it stops there. It provides a description of a network, not a path to running one. Each backend is still left to implement deployment on its own, with no shared, transformable compiler representation in between. This paper presents snn-mlir, an outof-tree MLIR dialect for SNNs together with a NIR-MLIR-C compilation bridge. The dialect provides a small set of typepolymorphic operations that work identically on floating-point (f32/f64) and quantized data, so a single intermediate representation serves both simulation and hardware-oriented deployment. A Python front end reads any NIR file and emits dialect IR, automatically inserting rescaling operations to keep quantization scales consistent across layers. A reference lowering pass converts the dialect to standard linalg and arith operations, from which the toolchain produces self-contained, dependency free C11 code that compiles and runs on any C-capable CPU or embedded target. We evaluate numerical fidelity against reference outputs, portability across CPU targets, and the cost of quantization. The current scope is feedforward, fully-connected networks with a CPU backend. snn-mlir is released as open source under the Apache-2.0 license with LLVM-exception and it is already available on Github.
[LG-35] Asymptotic Optimality of Thompson Sampling for Risk-Averse Bandits with Sub-Gaussian Rewards
链接: https://arxiv.org/abs/2606.09191
作者: Joel Q. L. Chang
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 10 pages, 4 figures
Abstract:We prove that \rho\text-\mathrmNPTS_\mathrmSG , an anchor-free nonparametric Thompson Sampling algorithm for risk-averse bandits, achieves regret matching the instance-dependent lower bound to leading order in \log n , establishing it as asymptotically optimal for any continuous risk functional \rho (CVaR, mean-variance, Sharpe ratio, distortion risk measures, and more) on the class of distributions with bounded density and sub-Gaussian tails, including Gaussian arms. Both this result and its bounded-support counterpart require only continuity of \rho : strictly weaker than the dominance condition of prior parametric Thompson Sampling results, and strictly weaker than the Lipschitz condition of UCB-type algorithms, yielding the first instance-optimal guarantees for non-Lipschitz functionals such as the Sharpe ratio without parametric reward assumptions. The bounded-support case is developed first as a stepping stone sharing the same proof structure. The key technical contributions are a discretisation lemma (bounded support) and a truncated discretisation lemma (sub-Gaussian tails), each projecting the growing-alphabet Dirichlet posterior onto a fixed grid via the Dirichlet aggregation property, holding all polynomial prefactors at fixed degree independent of sample size and breaking the super-exponential barrier that blocked prior proofs.
[LG-36] Improved Convergence Analysis of Topology Dependence in Decentralized SGD ICML2026
链接: https://arxiv.org/abs/2606.09154
作者: Yuki Takezawa,Anastasia Koloskova,Sebastian U. Stich
类目: Machine Learning (cs.LG)
*备注: ICML 2026
Abstract:Decentralized SGD is a fundamental algorithm in decentralized learning, although the influence of an underlying network topology on its convergence behavior is not yet fully understood. Existing convergence analyses have shown that topologies with a small spectral gap significantly deteriorate the convergence rate of Decentralized SGD in both homogeneous and heterogeneous cases. However, many prior papers have reported that indeed the choice of the topology has a significant experimental impact in the heterogeneous case, but has little experimental impact on training behavior in the homogeneous case. In this paper, we present a tighter convergence analysis of Decentralized SGD, offering a more precise understanding of how topologies affect the convergence rate than the prior analysis. Specifically, unlike existing convergence analyses that used only the spectral gap as a property of the topology, our novel analysis shows that all eigenvalues of the mixing matrix affect the convergence rate. Throughout the experiments, we carefully evaluated the convergence behavior of Decentralized SGD and demonstrated that our novel convergence analysis can more accurately describe the effect of topology on the convergence rate.
[LG-37] Counterfactual Transport Flows for Offline Conservative Trajectory Refinement ICML2026
链接: https://arxiv.org/abs/2606.09115
作者: Lena Krieger,Xuan Zhao,Zhuo Cao,Qin Wang,Hanno Scharr,Ira Assent
类目: Machine Learning (cs.LG)
*备注: accepted at RLxF @ ICML 2026
Abstract:Offline reinforcement learning (RL) offers a path to policy improvement from logged data alone, using historical returns or other measurable outcomes as world feedback. A key difficulty is improving observed behavior without extrapolating beyond what the offline data supports. We propose \emphcounterfactual transport flows, a source-conditioned trajectory refinement framework for offline decision-making guided by world feedback. Given a low-feedback candidate trajectory, we construct local preference pairs from offline data by retrieving nearby trajectories in latent trajectory space with higher task-specific feedback, and use them as weak supervision for conservative refinement. The framework learns instance-specific refinement directions: at inference time, a refinement strength parameter controls how far the candidate trajectory is transported, enabling a trade-off between preserving the original behavior and applying stronger improvement. Experiments on D4RL benchmarks, including AntMaze and MuJoCo tasks, show that our method improves behavior from historical returns as world feedback, while providing interpretable trajectory-level refinement paths.
[LG-38] RAM: Reachability Across Morphologies
链接: https://arxiv.org/abs/2606.09108
作者: Tim Walter,Xinyu Chen,Jonathan Külz,Matthias Althoff
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: 22 pages, 11 figures
Abstract:Many stages of the robotic lifecycle, from morphology synthesis to operation, rely fundamentally on the reachable workspace. However, current methods for approximating workspaces are slow, imprecise, or tied to a single morphology. We introduce Reachability Across Morphologies (RAM): a morphology-conditioned, implicit neural representation that acts as a fast, differentiable surrogate for pose reachability, generalising to unseen morphologies while inherently accounting for self-collisions. To train RAM, we publish a large-scale dataset of 3\cdot10^10 samples generated solely from forward kinematics. Experiments show that our model achieves an F_1 -score of 86% at nanosecond inference, outperforming the baseline by 14% while reducing inference time by three orders of magnitude. We further demonstrate speed-ups of one and two orders of magnitude for gradient-based morphology and trajectory optimisation, respectively. Website: this https URL. Comments: 22 pages, 11 figures Subjects: Robotics (cs.RO); Machine Learning (cs.LG) Cite as: arXiv:2606.09108 [cs.RO] (or arXiv:2606.09108v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2606.09108 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-39] Alcmeans: Unsupervised community detection using local Laplacian automatic detection of the number of centers
链接: https://arxiv.org/abs/2606.09100
作者: Shahin Momenzadeh,Rojiar Pir Mohammadiani
类目: ocial and Information Networks (cs.SI); Machine Learning (cs.LG)
*备注:
Abstract:Community detection is a fundamental problem in the analysis of complex networks. It has applications across social, biological, and financial domains. Traditional algorithms such as Louvain, LPA, and modularity optimization often require manual parameter tuning. They also suffer from inaccurate cluster center selection and struggle with scalability. To address these challenges, we propose Automatic Laplacian Centrality Means (ALCMeans), a novel community detection algorithm. ALCMeans combines Laplacian energy-based automatic center identification with DeepWalk embeddings for robust node representation. Unlike existing Laplacian-based and clustering methods, ALCMeans eliminates the need to predefine the number of communities, enhances cluster center selection using structural importance, and leverages representation learning for more accurate and stable assignments. Experimental results on benchmark datasets demonstrate 10 to 20 percent higher NMI and ARI scores compared to Louvain, Newman-Girvan, LPA, Fast-Greedy, and a recent GNN-based competitor (MAGI, KDD 2024). Additional evaluations with modularity and F1-scores confirm the superiority of ALCMeans. Ablation studies highlight the critical contributions of each component. Despite its reliance on DeepWalk parameters and increased runtime relative to lightweight heuristics, ALCMeans consistently outperforms state-of-the-art methods. This makes it a promising tool for real-world network analysis.
[LG-40] From Shortcuts to Reasoning : Robust Post-Training of Theory of Mind with Reinforcement Learning ICML2026
链接: https://arxiv.org/abs/2606.09092
作者: Jike Zhong,Yuxiang Lai,Ming Li,Yuheng Li,Wuao Liu,Behzad Dariush,Konstantinos Psounis,Shao-Yuan Lo
类目: Machine Learning (cs.LG)
*备注: Accepted by ICML 2026
Abstract:Theory of Mind (ToM) is a must-acquire skill for modern foundation model systems to operate effectively and safely in the real world. Recent works have explored honing ToM via post-training; however, we show that such progress is confounded by a pervasive “shortcut” issue: tasks can reach up to 99% accuracy by simply exploiting spurious causal correlations, leading to a false sense of ToM. Motivated by this, we first develop a framework to systematically examine ToM datasets for shortcuts and provide guidance for future development. We find that questions reducible to pure state tracking, such as “belief,” are especially shortcut-prone compared to mind questions, such as “intention,” where reasoning beyond tracking is required. Using four shortcut-free datasets across three ToM contexts, we then comprehensively study whether Reinforcement Fine-Tuning with verifiable rewards and explicit reasoning chains, called Thinking-RFT, elevates ToM beyond Supervised Fine-Tuning, or SFT. Our key findings are as follows. First, Thinking-RFT effectively improves ToM in all scenarios, with a 6% improvement over SFT, particularly in complex higher-order reasoning, with a 10% improvement over SFT, and multimodal cases, with a 7% improvement over SFT. It also generalizes notably better to unseen domains and higher-order queries while being more robust to counterfactuals. Second, ToM benefits specifically from the joint effect of reasoning and RL: Thinking-RFT outperforms Non-Thinking-RFT by 7% on average. Third, RFT works by learning to ground its reasoning on anchor cues, such as keywords and state changes, that correspond to causal factors. We believe our study is useful for developing effective and robust ToM post-training datasets and advancing critical ToM capabilities.
[LG-41] he Hidden Bias of Process Reward Models:PRISM for Rewarding the Right Reasoning
链接: https://arxiv.org/abs/2606.09078
作者: Aakriti Agrawal,Souradip Chakraborty,Armin Saghafian,Nihal Sharma,Rizal Fathony,Nam H Nguyen,C. Bayan Bruss,Amrit Singh Bedi,Furong Huang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Process Reward Models (PRMs) improve credit assignment for reasoning by providing step-level feedback. However, we identify a hidden bias in PRMs caused by severe imbalance in step-level training data. Standard cross-entropy training amplifies this bias, causing PRMs to overcredit plausible but incorrect steps and produce high false-positive rates. We show that these false positives have an asymmetric downstream effect: false negatives mainly slow exploration, whereas false positives actively steer Best-of-N selection, guided decoding, and policy optimization toward flawed reasoning. This suggests that PRM training should shift from pointwise label fitting to reliable relative comparisons. To address this, we propose PRISM (Precision Ranking for Improved Step Modeling), a policy-aware PRM training framework that learns from contrastive step-level comparisons and hard negatives generated by a temporal lookahead strategy, requiring no new human labels. We further use a difficulty-aware curriculum to optimize the contrastive step margin. Across PRMBench and ProcessBench, PRISM substantially reduces false positives (22% on PRMBench) and improves macro F1 over strong discriminative PRMs. When applied to policy optimization and search tasks, including guided decoding and Best-of-N selection, it consistently improves accuracy (up to 22% for guided decoding and 33% for Best-of-N) and robustness. More broadly, trustworthy process supervision is not just about assigning high rewards, but about rewarding the right reasoning for the right reasons.
[LG-42] Neural Legendre-Fenchel transform with Hessian Preconditioning
链接: https://arxiv.org/abs/2606.09077
作者: Basile Plus-Gourdon,Frank Nielsen
类目: Machine Learning (cs.LG)
*备注: 11 pages, 4 figures
Abstract:The Legendre-Fenchel (LF) transform is a fundamental tool in convex analysis and machine learning that maps lower semi-continuous functions to their convex conjugates. In practice, when closed-form formula are not available for expressing convex conjugates of given functions, one must approximate them using various techniques. One recent such versatile numerical method is the deep Legendre transform method which relies on neural networks although it remains challenging particularly for tackling ill-conditioned functions. This work builds on the reformulation of the LF transform as a projective polarity. A notable property of this framework is its affine invariance. We leverage this affine invariance to introduce a Hessian-based preconditioning strategy. Specifically, we apply an affine deformation around a minimizer so that the second-order Taylor approximation of the function coincides with the canonical paraboloid, whose conjugation map is the identity. A residual network initialized near the identity can then learn this simplified mapping, while the original conjugation map is recovered through the inverse deformation. The proposed preconditioning incurs only a modest computational overhead, consisting of a single eigendecomposition during initialization and two matrix-vector multiplications per query. Experiments on a diverse set of convex functions, including high-dimensional benchmarks, demonstrate improved convergence rates and enhanced numerical accuracy of the conjugation, with particularly significant gains for ill-conditioned problems. Finally, we discuss the scope of applicability of our proposed method and highlight several of its limitations.
[LG-43] Beyond Convolution: Advancing Hypergraph Neural Networks with Hypergraph U-Nets
链接: https://arxiv.org/abs/2606.09051
作者: Fuli Wang,Wei Qian,Daniel L. Lau,Gonzalo R. Arce
类目: Machine Learning (cs.LG)
*备注:
Abstract:Convolutions have successfully transitioned from image processing to the complex realm of non-Euclidean higher-order domains, particularly in hypergraphs. Despite the success in convolution, the exploration of a popular architecture named U-Net remains largely unexplored for hypergraph data due to the lack of well-defined pooling and unpooling operations. This work pioneers the study of U-Net architectures for hypergraph data, addressing the critical challenge of designing effective pooling and unpooling operations that retain maximal structural information from the input hypergraph. Motivated by hierarchical clustering, we propose to construct the pooling and unpooling operators all at once by cutting the clustering dendrogram at different granularities, named the Parallel Hierarchical Pooling (PHPool) and Unpooling (PHUnpool) operators. Unlike existing pooling methods that risk local structural damage through a sequential learning procedure, our PHPool operators are designed in a global and parallel manner to ensure fidelity to the original hypergraph structure with efficient computation while the PHUnpool operators are tailored to perform inverse operations of the PHPools for hypergraph reconstruction. We validate our model through hypergraph reconstruction simulation, hypergraph classification, and node-level anomaly detection, where it demonstrates superior performance over existing state-of-the-art graph and hypergraph deep learning methods.
[LG-44] Families of Control-Cost-Parametrized Inverse-Optimal Universal Stabilizers
链接: https://arxiv.org/abs/2606.09047
作者: Miroslav Krstic,Luke Bhan
类目: ystems and Control (eess.SY); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 13 Pages
Abstract:A classical universal stabilization formula offers the practitioner no design freedom: it is a single, parameter-free object. We introduce a cost-parametrized family of stabilizing feedback laws, where (1) the user chooses a function that serves as the running cost on control in an inverse-optimal cost functional, and (2) obtains, through a formula, a nonlinear “expander” of a pre-existing universal controller, which solves an infinite-horizon optimal control problem with a meaningful cost on the state. The cost-to-expander formula is a three-step construction, involving, inter alia, cost differentiation and function inversion-overall, a nonlinear infinite-dimensional operator. The cost-to-expander operator is proven Lipschitz, which enables uniform neural operator approximation of the entire family and supports both offline performance exploration and online adaptation. Semiglobal practical asymptotic stability and second-order suboptimality bounds are established under the approximation. The operator learning and its use in semiglobal stabilization are illustrated numerically. We call the result ‘half-direct-optimal’ because the paper’s design is less than a general ‘direct optimal’ (HJB-inducing) control, but more than the fully inverse optimal, since the user performs minimization for an arbitrary given cost on control. The dual to the half-direct problem we solve is the problem in which the cost on the state is arbitrary and given. This dual problem is easier and outside of the scope of the paper.
[LG-45] Structural Grid Descriptors Predict Within-Task Solver Success on ARC-AGI
链接: https://arxiv.org/abs/2606.09026
作者: Ayan Pendharkar
类目: Machine Learning (cs.LG)
*备注:
Abstract:We ask whether structural properties of intermediate grid states predict whether a symbolic ARC-AGI solver will succeed, framed as a test of conditional mutual information I(X;Y|task) 0. Across 44,800 runs spanning two architecturally distinct solvers (beam search and Stochastic DFS), 400 ARC tasks, 28 configurations per solver, and both training and evaluation splits, hand-crafted grid descriptors measured at 50% trajectory completion discriminate successful from failed runs within the same task (mean within-task best-feature AUC = 0.885, p 0.001 under within-task label permutation). Most predictive content lies along a single grid-complexity axis. The result generalizes across solver architectures: a feature selected on one solver predicts success on the other with AUC 0.747-0.762 in all four transfer directions (p 0.001, leakage controlled). On a pre-registered held-out set of 41 reliable tasks, the frozen feature n_components_final achieves AUC = 0.765 (95% CI [0.717, 0.810], p 0.001), robust under task-clustered bootstrap resampling and cross-solver task collapsing. The signal is not explained by solver capacity (configuration-residualized AUC = 0.927 and 0.896 for beam search and SDFS, p 0.001) and is only weakly coupled to score trajectories (R^2 approximately 0). Early stopping at 50% completion reduces beam-search compute by 33.6% while retaining 98.9% of solves; degenerate-trajectory detection reduces SDFS compute by 65.3% with no solve loss. Finally, on 229 of 400 evaluation tasks the DSL primitive library produces no valid transition from the input grid. This 0-step collapse is invariant to search budget and universally failed by beam search, indicating a DSL coverage limitation rather than a search-budget effect.
[LG-46] LEAF: A Learning-Enabled ADMM Framework for Accelerated Convex Optimization
链接: https://arxiv.org/abs/2606.08993
作者: Binh Nguyen,Trinh Tran,Truong X. Nghiem
类目: Machine Learning (cs.LG); Systems and Control (eess.SY); Optimization and Control (math.OC)
*备注:
Abstract:We propose LEAF, a learning-enabled ADMM framework for accelerated convex optimization. The key idea is to approximate the Moreau envelope of the objective function using an Input Convex Neural Network (ICNN), resulting in a learned model that preserves convexity and smoothness. This leads to the proposed Moreau Envelope Learning ADMM (MEL-ADMM) and its splitting variant sMEL-ADMM. Unlike existing approaches that learn high-dimensional operators directly, LEAF learns a scalar-valued Moreau envelope, significantly reducing model complexity and improving data efficiency. The framework accommodates a broad class of convex problems with smooth and non-smooth objectives. By embedding convexity explicitly through the ICNN architecture, the proposed approach maintains high approximation accuracy while preserving key structural properties of the optimization problem. Both MEL-ADMM and sMEL-ADMM are developed with theoretical guarantees of convergence and feasibility under the learned model. Rigorous analysis shows that the proposed methods achieve convergence rates comparable to classical ADMM while reducing per-iteration computational cost. Numerical experiments demonstrate up to an order-of-magnitude speedup over state-of-the-art solvers while maintaining low optimality gaps
[LG-47] Beyond Neural Collapse: Task-Intrinsic Geometry Governs Neural Representations in Modular Arithmetic
链接: https://arxiv.org/abs/2606.08985
作者: Hu Tan,Kuo Gai,Shihua Zhang
类目: Machine Learning (cs.LG)
*备注:
Abstract:While neural collapse (NC) predicts that a K -class-balanced classifier should organize terminal representations as a (K-1) -dimensional simplex equiangular tight frame (ETF), modular addition consistently enters a different regime: networks compress to a two-dimensional cyclic geometry in which both classifier weights and token embeddings lie on circles. We refine the explanation of this phenomenon in three directions. First, we formalize a layerwise non-uniform training mechanism: downstream classifier weights are driven by dense cross-entropy gradients into a rank-2 equiangular configuration before upstream embeddings fully reorganize, and once this classifier plane forms, backpropagated feature gradients constrain embedding motion to the same plane while weight decay suppresses orthogonal components. Second, after this subspace locking, the induced in-plane dynamics admit an entropy-regularized transport interpretation on S^1 ; combined with modular-addition labels, this reduces embedding formation to phase alignment, whose minimizers are single-frequency characters of \mathbbZ/P\mathbbZ and hence equal-angle points on a circle. Third, we quantify why this solution prevails over NC: a simplex ETF gains only an O(1) advantage in cross-entropy, whereas the cyclic rank-2 solution enjoys a \Theta(K) advantage under Schatten or weight-decay surrogates, yielding a critical threshold \lambda_\mathrmcrit = \Theta(1/K) . Our results explain both why classifier weights move first and why embeddings subsequently align with them, showing that grokking on modular arithmetic is governed not by maximal separation alone but by a task-structured trade-off between separation, symmetry, and complexity.
[LG-48] Heterophily-Aware Adaptive Knowledge Distillation for Hypergraph Neural Networks
链接: https://arxiv.org/abs/2606.08978
作者: Joohee Cho,David Yoon Suk Kang,Yunyong Ko
类目: Machine Learning (cs.LG)
*备注: 5 pages, 2 figures, 4 tables
Abstract:Hypergraph knowledge distillation aims to retain the predictive performance of a hypergraph neural network (HNN) teacher while reducing inference costs through a lightweight student model. In this work, we observe that HNNs exhibit substantially lower prediction performance on heterophilic nodes connected through semantically diverse hyperedges, indicating that the reliability of teacher knowledge varies across nodes. Motivated by this observation, we propose HADES, a heterophily-aware adaptive distillation method for hypergraph neural networks. HADES quantifies node heterophily and leverages it as an estimate of teacher reliability to modulate the transfer of teacher knowledge during distillation. Experimental results on real-world hypergraphs demonstrate that HADES consistently improves student performance across different HNN teachers and distillation objectives. In many cases, the resulting student models surpass the predictive performance of their teachers while achieving up to 12.3 times faster inference.
[LG-49] Online Learning with Recency: Algorithms for Sliding-window Streaming Multi-armed Bandits ICML2026
链接: https://arxiv.org/abs/2606.08977
作者: Vladimir Braverman,Chen Wang,Liudeng Wang,Samson Zhou
类目: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS)
*备注: ICML 2026
Abstract:Motivated by the recency effect in online learning, we study algorithms for single-pass sliding-window streaming multi-armed bandits (MABs) in this paper. In this setting, we are given n arms with unknown sub-Gaussian reward distributions and a parameter W . The arms arrive in a single-pass stream, and only the most recent W arms are considered valid. The algorithm is required to perform pure exploration and regret minimization with limited memory, defined as the number of stored arms. The model is a natural extension of the streaming multi-armed bandits model (without the sliding window) that has been extensively studied in recent years. We provide a comprehensive analysis of both the pure exploration and regret minimization problems with the model. For pure exploration, we prove that finding the best arm is hard with sublinear memory while finding an approximate best arm admits an efficient algorithm. For regret minimization, we explore a new notion of regret and give sharp memory-regret trade-offs for any single-pass algorithm. We complement our theoretical results with experiments, demonstrating the trade-offs between sample, regret, and memory.
[LG-50] From inverse problems to neural operators: prediction mechanism and generalization of data-driven models
链接: https://arxiv.org/abs/2606.08956
作者: Conor Rowan
类目: Machine Learning (cs.LG)
*备注:
Abstract:Scientists have historically relied on mathematical models based on differential equations to relate system inputs – forces, fluxes, or heat sources – to outputs, such as displacement, velocity, concentration, and temperature. These models rely on deep domain knowledge to determine the form of the governing differential equation, which is then calibrated with data by solving an inverse problem. In recent years, the field of Scientific Machine Learning has introduced a variety of alternative modeling strategies for physical systems. A method called Sparse Identification of Nonlinear Dynamics learns the governing equation as a sparse linear combination of terms in a user-defined library. Neural Ordinary Differential Equations construct the governing equation by taking in the state and its derivatives at the input layer of a neural network. Entirely foregoing the modeling framework of differential equations, neural operators directly learn a non-linear mapping between the system inputs and outputs. From inverse problems to neural operators, all of these modeling strategies can be conceptualized as data-driven machinery to predict a system’s response over a range of inputs. It is then natural to wonder how exactly these various strategies relate to each other, and whether they can be neatly taxonomized. Drawing from the philosophical literature on scientific models, we argue that many model types have a common structure, differing only in the assumed model class of the input-output relation they define. Connecting to philosophical ideas on mechanism, and arguing that data from physical systems arises from solutions to parsimonious differential equations, we propose that only certain models are capable of mechanism discovery, and thus generalization. Our analysis is intended to unite apparently disparate modeling strategies and provide insight into their appropriate use cases.
[LG-51] Self-Consistent Generative Paths via Admissible Random Variational Transport
链接: https://arxiv.org/abs/2606.08953
作者: Lei Luo,Yingzhen Zhang,Jian Yang
类目: Machine Learning (cs.LG); Functional Analysis (math.FA)
*备注: 17 pages, 4 figures, including Appendix
Abstract:Modern generative models often define an entire probability path from a simple prior to the data law, rather than only an endpoint map. Diffusion models follow stochastic denoising paths, flow matching learns transport fields, consistency and distillation methods compress paths into one or a few steps, adversarial models match terminal distributions, and VAEs generate through latent kernels. Existing unifying views mainly describe how such paths are constructed. We study a complementary question: when is a generated probability path self-consistent? We define a self-consistent generative path as a random fixed point of admissible local variational transport corrections. In this framework, a local correction is specified by a random variational transport operator combining a divergence or geometry term, an energy term, and a structural constraint. The framework contains random regularized optimal-transport proximal steps as a structured instance, while also allowing non-OT divergences, latent kernels, adversarial constraints, causal discrete kernels, and terminal one-step maps. The theory yields a random fixed-point path residual (R-FPR), which measures the gap between the actual generated path and an admissible local correction. We prove well-posedness, random fixed-point existence and attraction, non-contractive existence, residual-to-generation error bounds, empirical residual concentration, proxy perturbation bounds, continuous-time limits, and operator-level generalization with model-specific corollaries. The resulting theory turns endpoint matching into path self-consistency testing and provides a residual-control principle for diagnosing failures, regularizing training, and guiding adaptive sampling across diffusion, flow, one-step, VAE, GAN/WGAN, and autoregressive generators.
[LG-52] From Hazard Functions to Language Space: Cox-Supervised Distillation of Survival Risk into a Large Language Model
链接: https://arxiv.org/abs/2606.08945
作者: Nicholas I-Hsien Kuo,Blanca Gallego,Louisa Jorm
类目: Machine Learning (cs.LG)
*备注:
Abstract:We investigate whether information about time-to-event risk estimated by a Cox proportional hazards model can be transferred into a generative large language model. We propose a text-based survival modelling pipeline in which structured clinical covariates are converted into text prompts and a Qwen-based large language model is fine-tuned to generate patient-specific survival risk using Cox model predictions as a training target. Across GBSG2, ACTG320, and WHAS500, the model achieves competitive held-out discrimination and calibration despite being trained as a text-generation task rather than with a conventional survival-analysis loss. We further analyse the geometry of the model’s hidden states, where t-SNE visualisations reveal smooth risk gradients in latent space, suggesting that the model represents survival risk as a continuous structure rather than isolated risk categories. Together, these findings suggest that large language models can internalise survival-risk structure while supporting calibrated prediction, providing a route towards time-to-event reasoning in language models.
[LG-53] Backward Coherence and Hidden-State Stability in Recurrent Neural Networks: A Quasi-Reverse-Martingale Theory
链接: https://arxiv.org/abs/2606.08934
作者: Yuan-chin Ivan Chang
类目: Machine Learning (cs.LG); Applications (stat.AP); Computation (stat.CO); Methodology (stat.ME); Machine Learning (stat.ML)
*备注:
Abstract:Recurrent neural networks maintain a hidden state h_t , but its probabilistic meaning is often unclear. We study hidden-state stability through \emphbackward coherence: the extent to which h_t can be reconstructed from h_t+1 by a learned backward projector g_\phi . Under contraction and summable backward drift, the hidden-state sequence forms a quasi-reverse-martingale. This yields almost-sure convergence, rates under mixing, an interpretable limiting representation, finite pathwise stopping times, and a theoretical framework for time-uniform confidence sequences. Simulations support the theory. Backward-coherence regularisation reduces the empirical quasi-martingale total \hat Q by 43 – 58% , reaches stability 28 – 44% earlier than an unregularised RNN, and gives tracking-error recovery consistent with geometric bounds. Additional tests confirm echo-state forgetting rates bounded by \rho and verify the increment-sum tube R_t with 100% simultaneous coverage, although R_t is conservative; in practice, the defect-tail proxy \hat Q_t is the more useful monitor. The backward-coherence loss is also equivalent to minimising a Kullback–Leibler divergence in a Gaussian backward model, linking the method to variational inference. Extensions cover \phi -mixing inputs, change-point tracking, and finite-sample concentration. Three real-data studies further validate the approach. On PhysioNet 2012 ICU data, the Reverse Martingale RNN (RMRNN) matches RNN mortality-prediction AUC while reaching stable representations 13 hours earlier. On FRED-MD, it reduces one-month-ahead forecast error by about fourfold under concept drift. On UCI Human Activity Recognition, it maintains lower post-transition tracking error with geometric decay. The guarantees apply under the stated assumptions; universality is not claimed. Subjects: Machine Learning (cs.LG); Applications (stat.AP); Computation (stat.CO); Methodology (stat.ME); Machine Learning (stat.ML) MSC classes: 60G48, 60F15, 68T07, 62M10 Cite as: arXiv:2606.08934 [cs.LG] (or arXiv:2606.08934v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2606.08934 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-54] PROBE-Web: An Interactive System for Probing Evaluation Landscapes of Knowledge Graph Completion Models
链接: https://arxiv.org/abs/2606.08926
作者: Sooho Moon,Yunyong Ko
类目: Machine Learning (cs.LG)
*备注: 4 pages, 6 figures, 1 table
Abstract:Knowledge graph completion (KGC) models are commonly evaluated using rank-based metrics such as MRR and Hits@K, despite different users often requiring different evaluation perspectives. In this demo, we present PROBE-Web, an interactive system for probing diverse evaluation landscapes for KGC models. PROBE-Web enables users to flexibly evaluate KGC models by adjusting two critical perspectives: (P1) predictive sharpness and (P2) popularity-bias robustness. Through a user-friendly GUI, users easily evaluate multiple KGC models and analyze their strengths and weaknesses. PROBE-Web provides four key functionalities: (1) conventional evaluation toolkit, (2) flexible perspective-aware evaluation, (3) explainable case studies, and (4) evaluation landscape exploration. We believe that PROBE-Web can help users better understand KGC models aligning with their objectives.
[LG-55] Generalized Rank-based Evaluation for Knowledge Graph Completion: Perspectives Framework and Analyses
链接: https://arxiv.org/abs/2606.08921
作者: Sooho Moon,Jian Kang,Yunyong Ko
类目: Machine Learning (cs.LG)
*备注: 25 pages, 12 figures, 5 tables
Abstract:Knowledge graph completion (KGC) aims to predict missing facts from an observed knowledge graph (KG), playing a crucial role in a wide range of real-world applications such as drug discovery, recommender systems, and retrieval-augmented generation (RAG). Although numerous KGC models have been proposed, the evaluation of KGC remains underexplored, despite its critical role in reliably assessing model performance and selecting appropriate models for real-world applications. In this paper, we introduce two important perspectives for KGC evaluation that are overlooked by existing evaluation metrics, (P1) predictive sharpness and (P2) popularity-bias robustness. To address both perspectives, we propose a generalized evaluation framework, PROBE, which consists of a rank transformer (RT) that estimates the score of each prediction based on a desired level of predictive sharpness and a rank aggregator (RA) that determines the final evaluation score by aggregating all prediction scores according to a desired level of popularity-bias robustness. We theoretically analyze PROBE by defining six key properties for reliable KGC evaluation and prove that PROBE satisfies all the properties, while existing metrics fail to satisfy some. In particular, due to the open-world nature of KGs, an evaluation metric should preserve the relative performance of KGC models even when only incomplete facts are observed. We show that PROBE better maintains such consistency, providing a more reliable estimate of intrinsic model performance than existing metrics. Extensive experiments with six KGC models on six real-world KGs reveal that existing metrics may over- or under-estimate model performance depending on different evaluation perspectives, whereas PROBE enables a more comprehensive, flexible, and consistent evaluation of KGC models.
[LG-56] Synthetic but Not Realistic: The Evaluation Challenge in Generative Modelling for Structured Electronic Medical Records
链接: https://arxiv.org/abs/2606.08903
作者: Nicholas I-Hsien Kuo,Blanca Gallego,Louisa Jorm
类目: Machine Learning (cs.LG)
*备注:
Abstract:Synthetic healthcare data are widely proposed as privacy-preserving substitutes for real patient data, yet their evaluation remains dominated by statistical similarity and predictive performance that do not reflect clinical validity. We introduce a multi-dimensional evaluation framework grounded in epidemiology, assessing descriptive fidelity, clinical utility, and structural validity, corresponding to descriptive, predictive, and causal questions. We evaluate four representative generative paradigms - GAN-based, VAE-boosted, diffusion-based, and masked modelling - using PRIME-CVD, a 50,000-person cohort with known ground-truth structure. While all models reproduce marginal distributions, none simultaneously preserve subgroup structure, effect estimates, and dependency structure. Notably, models with strong distributional fidelity can exhibit poor calibration and distorted relationships, leading to unreliable inference. These results show that current evaluation practices can overestimate synthetic data quality and motivate domain-informed assessment based on the ability to support valid clinical and scientific conclusions.
[LG-57] Diffuse AI Control on Fuzzy Tasks
链接: https://arxiv.org/abs/2606.08892
作者: Mikhail Terekhov,Caglar Gulcehre,Vivek Hebbar,Joe Benton
类目: Machine Learning (cs.LG)
*备注:
Abstract:AI models deployed in critical domains, such as AI safety research, may subtly sabotage our efforts due to misalignment. Diffuse AI Control is a subfield of AI safety concerned with mitigating risks from AI sabotage distributed over long deployment horizons (diffuse threats). These risks are particularly pernicious on fuzzy tasks, i.e. tasks which are hard to grade or require intuition. To understand diffuse threats on fuzzy tasks, we introduce a novel framework that considers AI control as an adversarial game between a blue team and a red team. The blue team uses a weak trusted model to construct a weak score against which they would train a strong, potentially subversive model to remove the subversion propensity if it were present. The red team then tries to find model behaviors that are rated highly by the weak score, and thus might not be trained out, but actually correspond to poor performance. We test our framework on the task of writing experimental proposals for research questions from recent ML papers. We use a language model with access to the original paper as a proxy “ground-truth” scorer. Our red team discovers subversive behaviors using multi-objective evolutionary prompt optimization. We show that Opus~4.6 can write proposals that are worse according to the ground truth proxy than those of GPT-OSS-20B, while the weak scorer rates them as highly as the best proposals from Opus 4.6. To mitigate the threat, we propose an adversarial optimization algorithm for the blue team that discovers more robust prompts for the weak model. This algorithm produces a blue team prompt that our red team optimization fails to exploit.
[LG-58] Fourier Neural Operators with rank-1 lattice points and hyperbolic cross
链接: https://arxiv.org/abs/2606.08871
作者: Jakob Dilen,Alexander Keller,Frances Y. Kuo,Dirk Nuyens
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG)
*备注:
Abstract:The \emphFourier neural operator (FNO) is a neural network architecture that learns mappings between function spaces. Its efficient implementation is based on the multi-dimensional Fourier transform. By deriving general regularity bounds for the FNO with respect to both the spatial and parametric variables, we prove that the generalization error of the FNO can be improved by replacing spatial tensor product grids with purpose-built rank-1 lattice points, and by using a second lattice carefully constructed as training points in the parametric space. We achieve more accurate and efficient approximations from fewer network parameters, fewer spatial points, and fewer training samples. In addition, the architecture is simplified, because the high-dimensional Fourier transform on rank-1 lattices requires only a \emphone-dimensional fast Fourier transform, and we can use a \emphhyperbolic cross frequency index set with lattice points. We demonstrate the benefits of our \emphlattice-based hyperbolic-cross FNOs for an elliptic PDE on the torus.
[LG-59] From A to B to A: Palindromic Zero-Shot Voice Conversion with Non-Parallel Data
链接: https://arxiv.org/abs/2606.08843
作者: Moshe Mandel,Shlomo E. Chazan
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注:
Abstract:We present a voice conversion (VC) framework that utilizes K-Nearest Neighbors (KNN) retrieval over WavLM representations to align non-parallel source and target speech, constructing synthetic training pairs for supervised learning. The retrieved segments serve as synthetic inputs, while real target audio provides ground-truth outputs, forming a synthetic-to-real training paradigm that naturally supports multilingual data without requiring parallel corpora or explicit alignment. To ensure consistent target-speaker identity, we incorporate a speaker loss derived from a pretrained speaker verification model. Experiments across multiple languages demonstrate that the proposed approach achieves high naturalness and strong speaker similarity, outperforming competitive VC baselines, despite being trained exclusively on English data. Samples can be accessed at: this https URL.
[LG-60] Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules
链接: https://arxiv.org/abs/2606.08802
作者: Riccardo De Santi,Bruce Lee,Cristian Perez Jensen,Kimon Protopapas,Sophia Tang,Cheng-Hao Liu,Pranam Chatterjee,Yisong Yue,Andreas Krause
类目: Machine Learning (cs.LG)
*备注:
Abstract:Standard flow and diffusion pre-training matches the distribution of available data (e.g., molecules), which often covers only a small fraction of the valid design space. In generative discovery, however, one aims to sample valid new-to-nature designs, assigned negligible probability under, and thus inaccessible to, standard models fitted to the observed data. To overcome this limitation, we depart from data distribution matching and view a generative model through its generable set: the region it covers with non-negligible probability. This allows to introduce a new learning principle for out-of-distribution flow modeling: enlarging a model’s generable set to increase coverage of the valid design space. We propose Active Flow Expansion (ActFlow), a continued pre-training method that employs verifier feedback to expand a pre-trained model over new valid regions by iteratively adapting to synthetic data generated through active exploration in the learned flow representation. Theoretically, we establish to our knowledge first-of-their-kind statistical learning guarantees for out-of-distribution flow modeling, analyzing generable set expansion as a local-to-global reachability process over a learned representation. Empirically, we assess ActFlow with suitable out-of-distribution generative modeling metrics across small organic molecules, mid-sized drug-like molecules, therapeutic peptides, and protein sequence design tasks. Results show that ActFlow expands valid coverage far beyond the region modeled by the initial pre-trained model, significantly outperforming widely adopted synthetic flow pre-training methods.
[LG-61] Reformulate LLM Reinforcement Learning for Efficient Training under Black-box Discrepancy
链接: https://arxiv.org/abs/2606.08779
作者: Jiashun Liu,Runze Liu,Xu Wan,Jing Liang,Hongyao Tang,Ling Pan
类目: Machine Learning (cs.LG)
*备注:
Abstract:Reinforcement Learning (RL) has emerged as a pivotal post-training paradigm, yet it frequently suffers from unpredictable sub-optimum performance or even training collapses. Recent findings attribute these failures to a hidden train-inference discrepancy (or mismatch), stemming from the disparate underlying engines and architecture. We find that the training policy can actively self-correct such a discrepancy when provided with an appropriate learning signal. Then, we further empirically identify a discrepancy tolerance region: within this region, aggressively narrowing the discrepancy can suppress policy exploration and reduce learning efficiency, whereas outside this region, reducing excessive discrepancy improves optimization consistency and raises the achievable local performance ceiling. According to such findings, we formulate this problem as a Discrepancy-Constrained Markov Decision Process (DCMDP), where reward maximization is coupled with a constraint that aligns training-Inference behavior, achieving stable dual-objective optimization. To adaptively balance performance improvement and discrepancy control, we introduce a Lagrangian relaxation mechanism that dynamically adjusts the relative weight of the two objectives according to the current degree of discrepancy violation. This enables stable dual-objective optimization: the policy is allowed to explore freely within the tolerance region, while being guided back when the discrepancy exceeds the safe boundary. Empirically, DCMDP significantly improves the performance of 8B dense model (Qwen-3-8b) and 30B Mixture-of-Expert model (Qwen-3-30bA3b), and enables a heterogeneous training paradigm, where LLMs can be optimized in high-fidelity training setup while being explicitly aligned for low-cost, resource-constrained inference deployment.
[LG-62] Understanding the Parameter Space Geometry of Transformers Encoding Boolean Functions ICML2026
链接: https://arxiv.org/abs/2606.08768
作者: Blanka Köver,Alexandra Butoi,Anej Svete,Michael Hahn,Ryan Cotterell
类目: Machine Learning (cs.LG)
*备注: ICML 2026
Abstract:Transformers consistently fail to learn certain simple functions that are provably expressible with specific parameter settings. This gap between learnability and expressivity is particularly prominent for sensitive functions – functions whose output is likely to change if a single bit of the input is flipped – for example, PARITY. While prior work has established that transformers exhibit a bias toward functions with low average sensitivity, the precise mechanism underlying this bias remains poorly understood. To shed light on this phenomenon, we study the geometry of transformers’ parameter space. We show that sensitive functions – even when representable – occupy a vanishingly small region that random initialization is very likely to miss. Specifically, we shift the focus from average sensitivity to the full sensitivity profile – the distribution of sensitivity values across all inputs – and prove that randomly initialized transformers almost surely compute functions which have low-sensitivity strings. Consequently, any function that lacks such strings is provably unlearnable.
[LG-63] Declarative Outcome-Conformant Synthesis: Exact Closed-Form Specification Satisfaction and a Conformance Benchmark
链接: https://arxiv.org/abs/2606.08736
作者: Muhammed Rasin
类目: Machine Learning (cs.LG); Databases (cs.DB)
*备注: 22 pages, 1 figure. Benchmark and reference implementation (MIT): this https URL
Abstract:We study a capability the dominant paradigm in synthetic tabular data does not provide: exact satisfaction of a declared analytical outcome with no source data. Imitation methods (copulas, GANs, diffusion) learn a real distribution and sample from it, and are judged on fidelity to real data. A large, practical class of needs is different: generating data with no source data (“cold start”) that reproduces a declared outcome (a revenue curve, a churn rate, a group share) across a relational schema. Off-the-shelf imitation tools offer no interface for such targets, and no sampler can hit an exact aggregate, because sampling has variance. On a real public dataset, off-the-shelf learned synthesizers trained on that very data miss the declared monthly aggregate by 74 to 86 percent; a per-period steelman cuts the miss to about 19 percent and still cannot reach 0; a closed-form generator reaches exactly 0. We name this task outcome-conformant synthesis, argue its evaluation axis is conformance rather than fidelity, and show the two axes are orthogonal. We contribute: (1) a formal account showing a widely-used family of exact-aggregate generators is exactly conditional-sum sampling of a Gamma population (via Lukacs’ characterization), with closed-form exactness, a closed-form marginal CV, and scale-invariance; a controlled experiment maps the boundary, enforcing the exact aggregate costs at most 0.006 in 1-Wasserstein distance to an arbitrary external marginal, the rest being shape-family mismatch; (2) SpecBench, to our knowledge the first benchmark to measure conformance to analytical outcomes for cold-start relational synthesis; and (3) a closed-form, deterministic reference system. Exact aggregation alone is trivial; the contribution is conformance jointly with closed-form marginals, integrity, determinism, and zero source data. We concede fidelity to imitation where real data exists.
[LG-64] IR-SIM: A Lightweight Skill-Native Simulator for Navigation Learning and Benchmarking
链接: https://arxiv.org/abs/2606.08729
作者: Ruihua Han,Shuai Wang,Chengyang Li,Rui Gao,Xinyi Wang,Zhe Liu,Guoliang Li,Yupu Lu,Qi Hao,Jia Pan,Hengshuang Zhao
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: 12 pages, 6 figures, project website: this https URL
Abstract:Simulation plays a key role in automated robotics research supported by large language models (LLMs). However, existing simulators often require custom code or complex interfaces, creating a barrier to rapid prototyping and automated algorithm development. To this end, we propose the Intelligent Robot Simulator (IR-SIM), a lightweight skill-native navigation simulator designed for rapid scenario construction, benchmarking, and robot learning. In IR-SIM, scenarios are entirely defined by YAML configuration files that specify mobile robot kinematics, geometric collision checking, LiDAR sensing, visualization, and behavior modules. This design makes robotic simulation fully describable and reproducible, allowing scenarios to be generated and modified from text prompts through the proposed IR-SIM agent skills. The resulting scenarios can be used for automated benchmarking of navigation algorithms and for automated generation of training data for learning methods. Furthermore, IR-SIM provides bridges to high fidelity simulators and real world deployment, allowing users to validate their algorithms in more realistic settings after prototyping without extra coding. The experiments showcase the convenience and versatility of IR-SIM in multiple tasks: constructing navigation scenarios from natural language, training a collision avoidance policy, benchmarking social navigation policies, and bridging to high fidelity simulators and real world deployment. The project website is available at this https URL.
[LG-65] Compositional Approximation Can Strictly Outperform Superpositional Approximation
链接: https://arxiv.org/abs/2606.08727
作者: Dennis Elbrächter,Philipp Petersen
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG)
*备注:
Abstract:Many classically studied function classes are known to be approximated optimally by superpositional methods, i.e. with approximants constructed as the linear combination of elements in some dictionary. Here optimality means that the uniform approximation error viewed as a function of the number of parameters used has polynomial decay of the highest order achievable by any parametrized method whose parameters can be encoded as a bit string of length proportional, up to logarithmic factors, to the number of parameters. While compositional methods like neural networks are structurally different, their approximation rates can be made comparable by imposing constraints that ensure such a proportional bit string encoding. In this work we study function classes exhibiting structural properties that limit superpositional approximation rates to be strictly lower than compositional approximation rates. In particular, we construct explicit examples for which there is an arbitrarily large gap.
[LG-66] A Geometric Measure of Linear Separability for Neural Representations
链接: https://arxiv.org/abs/2606.08721
作者: Yi Wei,Xuan Qi,Furao Shen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Modern neural classifiers commonly rely on linear readouts, yet predictive metrics alone do not characterize the class-wise geometry of the representations on which such readouts operate. We introduce the directional linear separability measure (LSM), a finite-sample diagnostic for one-sided affine separability. For a target class A and a competing set B, LSM searches over affine halfspaces that contain all samples in A and measures the smallest competing-sample intrusion that must remain on the target side, normalized by |A|. The resulting quantity is asymmetric, class-wise, target-normalized, and applicable to finite representations extracted from neural networks. We establish its supporting-hyperplane characterization, relate it to optimal affine classification accuracy, and prove invariance under full-rank linear embeddings. These results separate changes caused by linear reparameterization from those caused by information loss or nonlinear geometric transformations. We also give a penalty-based affine search for estimating class-wise LSM in high-dimensional features, with reported values computed from the original discrete preservation and violation criterion. Finally, we analyze coordinatewise gated nonlinearities as finite-sample geometric operators and empirically use LSM to diagnose class-wise intrusion across common deep-learning components and architectures.
[LG-67] Hierarchical Projection for Adaptive Knowledge Transfer
链接: https://arxiv.org/abs/2606.08691
作者: Samhita Pal,Tian Gu
类目: Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:Modern data-driven applications increasingly involve learning from multiple heterogeneous sources, where a target dataset is limited but related information is available across domains. Naively combining these sources can degrade performance when relevance varies or spurious signals are present, posing a fundamental challenge for trustworthy cross-domain learning. We propose Projection Transfer Learning (ProjectionTL), a unified framework that integrates hierarchical Bayesian modeling with adaptive projection for selective knowledge transfer. The key idea is to decouple transfer at two levels: first, we construct a source-guided hierarchical prior that aggregates information across sources using data-driven weights, capturing global alignment between each source and the target; second, we refine this borrowing through a posterior-projection step that operates at the feature level, selectively retaining coordinates that exhibit local agreement with the target signal. This two-stage design enables the method to simultaneously perform source selection and feature selection, thereby mitigating negative transfer while preserving interpretability. ProjectionTL provides a principled approach to integrating heterogeneous data across domains, bridging statistical modeling and modern machine learning paradigms for robust and interpretable transfer. Through simulations and real-world biomedical applications, we demonstrate improved accuracy, stability, and interpretability compared to existing methods. Our framework offers a scalable and generalizable strategy for trustworthy cross-domain learning in high-dimensional settings.
[LG-68] Speaker-Invariant Representation Learning for Spoofing Detection via Gradient Reversal and A Variational Information Bottleneck
链接: https://arxiv.org/abs/2606.08678
作者: Anh-Tuan Dao,Driss Matrouf,Mickael Rouvier,Nicholas Evans
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注:
Abstract:Sophisticated generative speech technology can undermined the reliability of voice biometrics. While spoofing detection systems excel when assessed under in-domain conditions, generalisation to out-of-domain settings is often poor. In this paper, we show that such issues could be caused by speaker bias, where models learn individual voice traits rather than markers of manipulation or generation. We propose a teacher-student framework for speaker-invariant spoofing detection that disentangles identity without requiring speaker labels. We leverage a pre-trained speaker recognition teacher to guide a student model via a gradient reversal layer. To control the balance between suppressing cues related to voice identity with the preservation of those related to spoofing detection, we integrate a Variational Information Bottleneck. Evaluations across nine datasets show our model achieves a 25.7% relative reduction to the EER compared to the MHFA baseline.
[LG-69] SkillHone: A Harness for Continual Agent Skill Evolution Through Persistent Decision History
链接: https://arxiv.org/abs/2606.08671
作者: Zhiwei Li,Yong Hu
类目: Machine Learning (cs.LG)
*备注: Work in progress
Abstract:Agent skills extend language-model agents with task-specific procedures, scripts, and references, but the tasks and environments they target continually change. Existing methods improve skills in bounded runs and retain only the final artifact, discarding the decision history that later agents need to interpret prior revisions, evaluations, and rejected alternatives. We introduce SkillHone, a harness for continual agent skill evolution grounded in persistent decision history. SkillHone pairs skill revisions with evaluation-side evidence that supplies practice feedback, recording structured histories of diagnoses, revisions, evidence, and outcomes. Role-separated subagents run candidate skills on practice probes with redacted reporting and propose revisions informed by prior decisions, enabling cross-session refinement without rediscovering past rationale. We evaluate SkillHone on deep-research benchmarks in a raw open-web setting, where agents are not given an integrated search stack and must organize retrieval through portable skills. We compare against a deep-research agent backed by commercial retrieval services. With Qwen3.6-35B-A3B as the evaluation-time backbone, the resulting skills outperform the deep-research agent by 15.8 points on GAIA and 3.2 points on WebWalkerQA-EN, while also exceeding prior skill-evolution methods.
[LG-70] A Comparison of SSL-Based Feature Extractors and Back-End Classifiers for Spoofing Detection: A Multi-Corpus Training and Cross-Linguistic Analysis
链接: https://arxiv.org/abs/2606.08669
作者: Anh-Tuan Dao,Driss Matrouf,Mickael Rouvier,Nicholas Evans
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注:
Abstract:Voice biometric systems face growing threats from spoofing attacks, yet the evaluation of detection models remains inconsistent across datasets. To investigate these unpredictable fluctuations, we conduct a comprehensive benchmark of four self-supervised learning feature extractors paired with four back-end classifiers. We compare the hierarchical local feature extraction of ResNet with the global sequence and relational modeling of attention and graph-based back-ends. Through multi-corpus training across three scenarios and six evaluation datasets, our empirical analysis yields two critical findings. First, we expose a domain bias within the ASVspoof 5 dataset, showing that naive data scaling actively degrades performance. Second, our cross-linguistic analysis reveals that fine-tuning with just 8 hours of target-language data enhances detection robustness. Together, these findings emphasize the critical need for domain-aware and language-specific adaptation in spoofing detection.
[LG-71] Operator learning for the 2D incompressible Navier-Stokes equations: a conformal prediction approach in the data-scarce regime
链接: https://arxiv.org/abs/2606.08654
作者: Weinan Wang,Bowen Gang,Hao Deng
类目: Machine Learning (cs.LG); Analysis of PDEs (math.AP); Numerical Analysis (math.NA); Applications (stat.AP)
*备注:
Abstract:In this paper, we propose a perturbation-based conformal prediction framework for uncertainty quantification in operator learning, with a focus on the 2D Navier–Stokes equations. While neural operators provide fast surrogates for expensive PDE solvers, they do not by themselves provide calibrated uncertainty for spatiotemporal field predictions. Our approach wraps a trained Fourier Neural Operator (FNO) with split conformal prediction and constructs the local uncertainty scale by comparing the predictions of two operators trained on nearly identical datasets: one on the original labels and one on labels perturbed by small Gaussian noise. We consider this procedure in the data-scarce regime, where the total label budget is fixed and methods that require a separate uncertainty network must divide training data between multiple models. On the 2D Navier–Stokes benchmark, the perturbation-based method produces substantially narrower conformal bands than existing methods under matched total data budgets while maintaining the target simultaneous coverage. These results suggest that perturbation sensitivity is a practical and sample-efficient uncertainty proxy for conformalized neural operators.
[LG-72] SpectrumKV: Per-Token Mixed-Precision KV Cache Transfer for Prefill-Decode Disaggregated LLM Serving
链接: https://arxiv.org/abs/2606.08635
作者: Yang Pengju
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注: 28 pages,13 figures,8 tables
Abstract:Prefill-decode (PD) disaggregation decouples prompt processing from token generation, but it also turns the key-value (KV) cache into a network payload. Existing PD-side KV reduction methods are mostly binary: selected tokens are transmitted at full precision and the rest are not transmitted. This paper argues that binary selection leaves a useful design space unused. SpectrumKV assigns a precision level to each token instead: attention sinks and other high-importance tokens are protected at FP16, medium-importance tokens are sent at INT8, and low-importance tokens are sent at INT4 when the model can tolerate it. The main practical complication is that INT4 tolerance is model-dependent. Qwen2.5-7B catastrophically fails under INT4 KV quantization, while Mistral-7B and Gemma-2-9B remain stable. SpectrumKV therefore runs a lightweight deployment-time probe: three aggressive NIAH trials under a 3-tier policy. Models that pass use FP16+INT8+INT4; models that fail fall back to FP16+INT8. Across Qwen2.5-7B-Instruct, Mistral-7B-Instruct-v0.3, and Gemma-2-9B-it, SpectrumKV improves quality at the same transfer budget. At a 50% normalized KV budget on WikiText-2, SpectrumKV changes perplexity by +1.97%,-0.06%, and-0.44%, respectively, compared with PDTrim’s +25.85%, +22.07%, and +35.63%. On NIAH retrieval at 4096 tokens, the adaptive policy reaches 52.6% on Qwen at the aggressive b=0.3 budget versus 26.3% for PDTrim, and reaches 100% by b=0.5; Mistral and Gemma preserve retrieval under the 3-tier policy. End-to-end GPU timing of the transfer path shows 50-62% TTFT reductions at b=0.5. These results suggest that PD KV transfer should be treated as a precision-allocation problem, not only as token pruning.
[LG-73] Bayesian Optimization of a Multi-Product Chemical Reactor Using Composite Models and Partial Physics Knowledge
链接: https://arxiv.org/abs/2606.08611
作者: Liqiu Dong,Marta Zagórowska,Mehmet Mercangöz
类目: ystems and Control (eess.SY); Machine Learning (cs.LG)
*备注: Accepted to IFAC 2026. 11 pages, 4 figures
Abstract:We study data-driven real-time economic optimization of a multi-product chemical reactor when no reliable first-principles model is available beyond a steady-state energy balance. Instead of learning the economic objective directly as a black-box function, we use a composite formulation in which Gaussian process (GP) models predict physically meaningful outputs, including product concentrations and reactor temperature, while profit is computed analytically from these predictions together with raw-material, product, and utility prices. This preserves the structure of the economic objective, makes it parametric in changing prices without needing retraining, and allows candidate operating points to be checked against the available energy balance through a physics residual. The GPs also provide predictive uncertainty, which is exploited in a Bayesian optimization (BO) framework both for data-efficient exploration and for conservative enforcement of the reactor temperature constraint through an upper confidence bound. The acquisition function additionally penalizes large energy-balance mismatch obtained by substituting the GP-predicted outputs and candidate inputs into the available steady-state energy balance. The approach is demonstrated on a benchmark simulation of a non-isothermal multi-product reactor. Relative to a trust-region safe BO implementation, the proposed method achieves better simulated economic performance within the available iteration budget. Relative to a purely data-driven BO approach that does not use the available physics information, it avoids reactor temperature constraint violations.
[LG-74] How Much Capacity Does EEG Denoising Need? Ultra-Compact Networks reveal Benchmark Saturation and Metric-Utility Gap
链接: https://arxiv.org/abs/2606.08594
作者: Jasmeet Singh Bindra,Siddharth Panwar,Shubhajit Roy Chowdhury
类目: Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注: 17 pages, will be submitted to peer-reviewed journal
Abstract:Deep learning EEG denoising architectures have scaled from tens of thousands to tens of millions of parameters, yet no prior study has isolated model capacity as the experimental variable or tested whether reconstruction metrics predict downstream neural-signal utility. We address both gaps by fixing architecture, loss, data split, and training recipe while sweeping only channel width from 1.05K to 40.26K parameters in a minimal depthwise-separable convolutional U-Net. Models were evaluated on the EEGDenoiseNet benchmark, cross-dataset BCI transfer tests, controlled baseline retraining, and downstream motor-imagery classification with five decoder families across all nine BCI Competition IV-2a subjects. Reconstruction performance saturated by 3-6.5K parameters, with post-elbow gains of at most 0.015 correlation coefficient per log10-parameter unit. An 8.46M-parameter baseline retrained under the same pipeline matched the 40.26K compact variant on EOG–a 200x parameter gap yielding no advantage–while a Patch-Transformer control reproduced the same diminishing-return shape. Downstream evaluation exposed a classifier-dependent metric-utility gap: reconstruction-optimized denoising significantly degraded CSP+LDA classification across all nine subjects and three artifact types (best denoised accuracy 0.547 vs. 0.612 noisy baseline; Bonferroni p=0.0488), persisting on naturally recorded trials (Delta=-0.047; BH-FDR q=0.0049). End-to-end neural decoders showed variable or neutral effects. Standard EEG denoising benchmarks are saturated far below current model capacity, and reconstruction metrics do not predict BCI utility. Ultra-compact models at 33-46 KB and 1.27-2.61M FLOPs/segment are practical for edge deployment. These findings argue for capacity-controlled evaluation, harder task-aware benchmarks, and mandatory downstream validation.
[LG-75] Quantum Global Variational Learning for Quantum Error Correction
链接: https://arxiv.org/abs/2606.08592
作者: Shun Ryuzaki,Hideo Mukai
类目: Machine Learning (cs.LG); Quantum Physics (quant-ph)
*备注: 24 pages, 22 figures
Abstract:Efficient quantum error correction is essential for the advancement of quantum computing. We propose a quantum neural network with a global structure that reduces the number of unitary matrices required in quantum circuits. This approach resulted in a 97% reduction in training time and up to a 25% improvement in the training completion rate, ultimately achieving a 100% success rate in training while surpassing the error correction performance reported in previous studies. In addition, we demonstrated the enhanced robustness of quantum error correction against internal network noise. Moreover, the fidelity of quantum error correction under internal network noise increased by up to 15% due to the reduced computational load.
[LG-76] Convolutional Sparse Coding via the Locally Competitive Algorithm on Loihi 2
链接: https://arxiv.org/abs/2606.08584
作者: Geoffrey Kasenbacher,Daniel Ruepp,Gerrit A. Ecke
类目: Machine Learning (cs.LG)
*备注:
Abstract:Sparse coding provides a principled framework for signal representation by expressing an input as a linear combination of only a small number of basis functions. The Locally Competitive Algorithm (LCA) is particularly attractive in the context of neuromorphic computing because its dynamics, leaky integration, thresholding, and lateral inhibition map naturally to neuromorphic hardware. While prior work has studied non-convolutional LCA on Loihi 2, the convolutional setting is of particular interest because it introduces spatial structure, weight sharing, overlapping receptive fields, and scaling behavior that are more representative of practical sparse inference workloads. In this work, we present a Loihi 2 implementation of convolutional sparse coding via the LCA and evaluate it against a conventional GPU baseline on the same inference problems. The implementation follows a one-layer recurrent LCA formulation and extends it to convolutional feature maps with local inhibitory kernels derived from pairwise filter interactions. To the best of our knowledge, this is the first implementation and benchmark of convolutional LCA on Loihi 2. Our goal is not only to demonstrate feasibility, but also to clarify in which operating regimes convolutional sparse inference becomes attractive on neuromorphic hardware. The resulting study positions convolutional LCA as a useful benchmark for structured sparse inference on emerging neuromorphic systems.
[LG-77] A spectral audit framework reveals task-dependent aperiodic reliance across EEG and ECG deep learning
链接: https://arxiv.org/abs/2606.08583
作者: Jasmeet Singh Bindra,Siddharth Panwar,Shubhajit Roy Chowdhury
类目: Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注: 25 pages, being prepared for submission to peer-reviewed journal
Abstract:Deep learning on physiological time series is interpreted through domain-specific features – oscillatory rhythms in EEG, morphological complexes in ECG – yet these signals sit atop a broadband aperiodic 1/f-like envelope that covaries with arousal, age, and pathology. We introduce a spectral audit framework combining aperiodic/periodic decomposition, phase-preserving Fourier interventions, sham controls, and simulation validation. Aperiodic reliance was task-dependent and architecture-general: across six neural architectures, flattening drops exceeded 0.42 balanced-accuracy points for sleep-wake classification, reached 0.07-0.13 for clinical abnormality detection, and remained minimal for motor imagery. Six of seven EEG foundation models showed FDR-significant aperiodic reliance on clinical EEG; age/sex and recording-era controls reduced but did not eliminate the effect. Applying the audit to PTB-XL ECG revealed neural drops of 0.32–0.36 persisting after demographic matching, confirming this confound class extends beyond EEG. Aperiodic controls should become standard for interpretable physiological time-series deep learning.
[LG-78] Lost in the Non-convex Loss Landscape: How to Fine-tune the Large Time Series Model? ICLR2026
链接: https://arxiv.org/abs/2606.08578
作者: Xu Zhang,Peang Wang,Wei Wang
类目: Machine Learning (cs.LG)
*备注: This paper has been accepted by The Fourteenth International Conference on Learning Representations (ICLR 2026). The code is available at the link \url{ this https URL }
Abstract:Recently, large time series models (LTSMs) have gained increasing attention due to their similarities to large language models, including flexible context length, scalability, and task generality, outperforming advanced task-specific models. However, prior studies indicate that pre-trained LTSMs may exhibit a poorly conditioned non-convex loss landscape, leading to limited trainability. As a result, direct fine-tuning tends to cause overfitting and suboptimal performance, sometimes even worse than training from scratch, substantially diminishing the benefits of pre-training. To overcome this limitation, we propose Smoothed Full Fine-tuning (SFF), a novel fine-tuning technology. Specifically, we construct an auxiliary LTSM via random initialization to obtain a smoother loss landscape, and then linearly interpolate its weights with those of the pre-trained model to smooth the original landscape. This process improves trainability while preserving pre-trained knowledge, thereby enabling more effective downstream fine-tuning. From an optimization perspective, SFF perturbs sharp minima without significantly harming flat regions, facilitating escape from poor local basins toward smoother and more generalizable solutions. Extensive experiments on benchmark datasets demonstrate consistent improvements across eight representative LTSMs, including Timer, TimesFM, MOMENT, UniTS, MOIRAI, Chronos, TTMs, and Sundial, on diverse downstream tasks. The code is available at the link: this https URL.
[LG-79] Physics-Guided Dual Decoding and Spectral Supervision for Global 3D Hydrometeor Prediction
链接: https://arxiv.org/abs/2606.08563
作者: Dandan Chen,Yaqiang Wang
类目: Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph)
*备注:
Abstract:While global data-driven models excel at predicting continuous atmospheric variables, three-dimensional hydrometeor forecasting remains challenging due to the zero-inflated, long-tailed distributions of these variables. Standard deep learning optimization often yields overly smooth forecasts, attenuating extreme events and spatial textures. We propose PredHydro-Net, a physics-guided dual-decoding framework that mitigates this smoothing. To resolve multi-variable optimization conflicts, it employs a decoupled architecture where macroscopic thermodynamic and dynamic fields unidirectionally modulate hydrometeor generation. By integrating wavelet-based frequency decoupling, spectral amplitude matching, and adversarial training, the model achieves a favorable trade-off between quantitative accuracy and spatial fidelity. In a 72-h global evaluation, PredHydro-Net outperforms both spatiotemporal deep learning baselines (Earthformer and PredRNNv2) and the operational Global Forecast System (GFS) in extreme-event detection and spectral representation. Furthermore, it demonstrates strong climatological consistency with Global Precipitation Measurement (GPM) satellite retrievals. The model reasonably reproduces the three-dimensional cloud structures in extreme weather events, such as Hurricane Ian. Feature attribution confirms its dependence on physical precursors such as relative humidity and wind convergence, offering a robust, physics-informed approach to long-tailed atmospheric prediction.
[LG-80] A Theoretical Analysis of Memory and Overfitting Phenomena in Stochastic Interpolation Models
链接: https://arxiv.org/abs/2606.08554
作者: Yunchen Li,Shaohui Lin,Zhou Yu
类目: Machine Learning (cs.LG)
*备注:
Abstract:This paper provides a theoretical account of memorization in stochastic interpolation models. By leveraging closed-form expressions for the optimal velocity field and the associated score function, we show that, in the continuous-time oracle setting, both deterministic and stochastic generation processes recover training samples. Under Euler discretization, generated samples remain centered around training samples, with deviations controlled by the step size. We further analyze generation in the presence of estimation errors and show that accumulated estimation errors control the endpoint deviation from the training set. These results imply that the generated sample admits a representation as a training sample perturbed by three controlled terms: a discretization-induced bound, an estimation-error-induced bound, and stochastic Gaussian noise. Based on this characterization, we provide theoretical definitions of overfitting and underfitting in generative models. Synthetic simulations support our theoretical findings.
[LG-81] Routine laboratory trajectories encode the onset of organ-level complications in cancer
链接: https://arxiv.org/abs/2606.08538
作者: Jannik Lübberstedt,Krischan Braitsch,Jacqueline Lammert,Christof Winter,Florian Gabriel,Tristan Lemke,Christopher Zirn,Markus Graf,Friedrich Puttkammer,Hartmut Häntze,Johannes Moll,Anirudh Narayanan,Andrei Zhukov,Fabian Drexel,Zeineb Ben Chaaben,Sebastian Ziegelmayer,Su Hwan Kim,Marion Högner,Jan Kirschke,Florian Bassermann,Marcus Makowski,Christian Wachinger,Lisa Adams,Keno Bressem
类目: Machine Learning (cs.LG)
*备注:
Abstract:Routine laboratory panels drawn during cancer treatment constitute longitudinal physiological recordings of organ function, yet their temporal structure is discarded by single-timepoint prognostic tools. A transformer trained on 2,777,595 laboratory measurements from 3,905 patients with multiple myeloma or ovarian cancer predicted the two-year onset of 162 treatment-associated complications, including therapy-related myelodysplastic syndromes, spanning eight clinical categories, achieving 1.5- to 6.1-fold enrichment above prevalence at the group level. It matched or outperformed non-sequential baselines across grouped endpoints (AUROC gains up to +0.11), demonstrating that longitudinal laboratory trajectories capture evolving complication-specific physiology inaccessible from isolated measurements. Predictions generalised across both cancers, divergence concentrating in disease-specific complications, and biomarker masking recovered signatures consistent with established pathophysiology. External validation on MIMIC-IV and MMRF CoMMpass confirmed transferability across independent healthcare systems (AUROC up to 0.85). Routine oncological laboratory data encode organ deterioration weeks to months before clinical onset, enabling complication-specific surveillance without additional testing infrastructure.
[LG-82] Autonomous Aerial Manipulation via Contextual Contrastive Meta Reinforcement Learning
链接: https://arxiv.org/abs/2606.08533
作者: Lixuan Jin,Bingxuan Lan,Xinyi Bao,Xiangyuan Xie,Chunjie Zhang,Zheng Chen,Tianshuo Liu,Ruijie Tian,Jinyu Ru,Gang Wang,Lei Yuan,Yang Yu
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注:
Abstract:Unmanned aerial vehicles (UAVs) are increasingly being deployed in logistics, service robotics, and other real-world applications, creating a growing demand for autonomous payload acquisition and delivery. Existing approaches typically assume pre-attached payloads or rely on specialized grippers, leaving versatile end-to-end aerial delivery largely unresolved, where different payloads induce highly variable flight dynamics, requiring a single policy to adapt online without manual calibration or explicit system identification. To this end, we study \textbfAutonomous \textbfAerial Manipulation via \textbfContextual \textbfContrastive Meta Reinforcement Learning (\textbf\textitAco2), a fully autonomous aerial delivery setting in which a quadrotor equipped with a lightweight hook continuously picks up, transports, and delivers diverse handle-equipped objects between randomized locations, all without human intervention. First, we design a contextual observation encoder that infers a compact latent context from recent interaction history, enabling the policy to adapt online to payload-dependent dynamics. To further improve the quality of this context, we introduce a contrastive objective that structures the context embedding around task-relevant variations, improving generalization across diverse payloads without requiring explicit system identification. Trained entirely in simulation with extensive domain randomization, \textitAco2 can be directly deployed on a physical quadrotor without real-world fine-tuning.
[LG-83] owards End to End Motion Planning and Execution for Autonomous Underwater Vehicles Using Reinforcement Learning
链接: https://arxiv.org/abs/2606.08513
作者: Elisei Shafer,Oren Gal
类目: Robotics (cs.RO); Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注:
Abstract:Autonomous Underwater Vehicles (AUVs) traditionally rely on complex, heavily engineered pipelines for perception, path planning, and motion control. This paper explores the feasibility of an end-to-end Deep Reinforcement Learning (DRL) approach that maps raw sensor data directly to thruster commands, reducing manual engineering. We propose a hierarchical reinforcement learning (HRL) architecture splitting the problem into two Markov Decision Processes. A High-Level (HL) policy operating at 2Hz processes raw 84 \times 84 pixel monocular camera frames, stacked 100 \times 100 pixel forward-looking imaging sonar, and proprioceptive data to generate spatial subgoals. Simultaneously, a Low-Level (LL) policy operating at 10Hz converts these subgoals into thruster commands. The HL policy is trained using Reinforcement Learning from Prior Demonstrations (RLPD) within a modified Sample-Efficient Robotic Reinforcement Learning (SERL) framework, while the LL policy utilizes Soft Actor-Critic (SAC) combined with Hindsight Experience Replay (HER). Evaluated in the high-fidelity HoloOcean simulator, our method demonstrates successful obstacle avoidance, achieving trajectory lengths closely approximating (within 4% to 6% of) an \textRRT^* planning baseline. Furthermore, the learned policy exhibits strong robustness to simulated sensor noise and decreased visibility. While the system navigates familiar geometries effectively, experiments reveal generalization limitations when encountering unvisited areas with novel obstacle shapes. Ultimately, this work demonstrates the promise of sample-efficient, end-to-end DRL for underwater navigation using minimal computational hardware.
[LG-84] Inferring hidden forcing in a biological oscillator using Kolmogorov-Arnold networks
链接: https://arxiv.org/abs/2606.08479
作者: Julian Szereszewski,Facundo Fainstein,Leandro E. Fernandez,Gabriel B. Mindlin
类目: Machine Learning (cs.LG)
*备注: 11 pages, 4 figures
Abstract:Inferring the forces that drive a dynamical system from partial observations is a fundamental challenge across physics, particularly when distinct underlying mechanisms produce similar observable dynamics. Here we show that the effective muscular forcing underlying avian respiratory dynamics can be reconstructed from measurements of air-sac pressure alone. Using an interpretable learning framework based on Kolmogorov-Arnold networks, we infer the governing equations of the system directly from data and uncover a nontrivial structure in the underlying forcing that is not apparent from the pressure signal, which instead suggests a relaxation-like oscillation. The reconstructed dynamics predict a two-phase activation pattern within each respiratory cycle, which we independently validate through electromyographic recordings of expiratory muscles. These results demonstrate that data-driven reconstruction of dynamical laws can reveal hidden physical structure and provide access to unobserved driving variables, establishing a general route to infer latent forces in partially observed dynamical systems.
[LG-85] Physically Consistent Null Space Alignment for Detection of Low-Magnitude False Data Injection Attacks
链接: https://arxiv.org/abs/2606.08473
作者: Xin Li,Chenhan Xiao,Jonathan Cohen,Aviad Elyashar,Yang Weng,Rami Puzis
类目: Machine Learning (cs.LG)
*备注: 12 pages, 13 figures
Abstract:False data injection attacks (FDIAs) introducing small measurement perturbations can still cause large deviations in power system state estimation when the injected signals align with the pseudo-null space of the system model. Existing model- and data-driven detectors may fail to identify such low-magnitude but high-impact attacks because residual tests ignore changes hidden in the pseudo-null space, while subspace learning methods capture correlation patterns without enforcing physical consistency. This paper proposes Physically Consistent Null Space Alignment (PCNSA), a framework that detects stealthy FDIAs by preserving, through preprocessing, the geometric correspondence between the physical null space and the measurement-derived pseudo-null space. The key point is a Pseudo-null Space Conserved data Preprocessing (PSCP) step that re-expresses measurements in the physical coordinate frame before subspace extraction. We prove that PSCP preserves the separation between row space and its orthogonal complement, a property that conventional per-feature standardization violates. This keeps the singular value decomposition (SVD)-derived pseudo-null subspace aligned with the physical residual space without explicit knowledge of H. Experiments on IEEE 14-, 30-, 57-, and 118-bus systems confirm this principle in practice: stealthy attacks that evade XTM, LSTM, AE and Isolation Forest baselines appear as clear deviations in the aligned subspace, yielding higher F1-score and detection accuracy while remaining robust under partial observability and realistic PMU noise.
[LG-86] heoretical Foundations of Continual Learning via Drift-Plus-Penalty
链接: https://arxiv.org/abs/2606.08452
作者: Nazreen Shah,Govinda Arya,Bharath B.N.,Ranjitha Prasad
类目: Machine Learning (cs.LG)
*备注: Accepted to Transactions on Machine Learning Research (TMLR)
Abstract:In many real-world settings, data streams are nonstationary and arrive sequentially, requiring learning systems to adapt continuously without retraining from scratch. Continual learning (CL) addresses this challenge by incorporating new tasks while mitigating catastrophic forgetting, where learning new information degrades performance on previously acquired knowledge. We introduce a control-theoretic perspective on CL that explicitly regulates the evolution of forgetting, framing adaptation as a controlled process subject to long-term stability constraints. We focus on replay-based CL, where a finite memory buffer stores representative samples from prior tasks. We propose COntinual Learning with Drift-Plus-Penalty (COLD), a continual learning framework based on the Drift-Plus-Penalty (DPP) principle from stochastic optimization. To facilitate analysis, we also consider an oracle variant, COLD-ORACLE, as a reference benchmark. At each task, both methods minimize the current task loss while maintaining a virtual queue that tracks deviations from long-term stability on previously learned tasks, capturing the stability-plasticity trade-off as a regulated dynamical process. We establish stability and convergence guarantees that characterize this trade-off through a tunable control parameter. Experiments on standard benchmarks demonstrate that COLD consistently outperforms a broad range of state-of-the-art CL methods while providing competitive and controllable forgetting behavior through explicit regulation of stability and plasticity.
[LG-87] When Are Neural Interaction Discoveries Real? Identifiability Recoverability and a Pre-Fit Diagnostic
链接: https://arxiv.org/abs/2606.08390
作者: Valentina Kuskova,Dmitry Zaytsev,Michael Coppedge
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 11 pages, 3 figures
Abstract:When a neural time-series model reports that one variable modulates another’s effect on a target, is the discovered interaction a property of the data or an artifact of model flexibility? We argue that this is fundamentally a question of identifiability, governed by the geometry of the observed input support rather than by the specific neural architecture. We study the problem in a multiplicative-gating extension of neural additive vector autoregression (GNAVAR), in which source contributions are modulated by other lagged variables. We show that representational capacity is not identifiability: dependent inputs induce leakage between edge-specific interaction terms, and low-dimensional support permits distinct interaction decompositions that agree on the observed data while differing elsewhere. We then prove a population identifiability theorem for normalized minimal GNAVAR decompositions under explicit support conditions, including settings with shared modulators. The theory yields a simple practitioner-facing diagnostic: the effective rank of the joint lag-block covariance predicts, before fitting, whether interaction recovery is feasible for a given candidate set. When the candidate set is unknown, a two-seed stability check provides a practical operational test. The same support condition organizes empirical outcomes into the three states predicted by the theory. Our results show that interaction recoverability depends on support geometry, that effective rank provides a practical pre-fit diagnostic, and that instability across independent fits is a characteristic signature of non-identifiable interaction discovery. The identifiability phenomenon, the support condition, and the instability signature are model-agnostic; GNAVAR is the vehicle that makes them provable.
[LG-88] he Spectral Dynamics and Noise Geometry of Muon
链接: https://arxiv.org/abs/2606.08388
作者: Pierfrancesco Beneventano,Mahmoud Abdelmoneum,Tomaso Poggio
类目: Machine Learning (cs.LG); Optimization and Control (math.OC); Machine Learning (stat.ML)
*备注: 24 pages, 11 figures
Abstract:Muon replaces a matrix gradient G=U\Sigma V^\top by its polar factor UV^\top . This keeps the singular directions selected by the gradient, but makes the update spectrum flat. We study the optimization bias created by this operation. Under explicit alignment assumptions, we prove that the polar update is the one-step entropy-maximizing choice among bounded updates that use the gradient singular directions and do not adapt to the current weight spectrum. In an underdetermined regression model, we derive exact singular-value dynamics for continuous-time Muon and identify a measurement-dependent condition under which the normalized spectrum moves toward equal nonzero singular values. This geometry also rules out a common low-rank interpretation: at fixed Frobenius norm, Muon’s distinguished state has a flat spectrum, whereas nuclear-norm minimization favors spectral concentration. Controlled matrix-sensing experiments separate the effect from simple gradient rescaling, show that norm-matched gradient descent does not reproduce Muon, and recover the predicted flattening trend across broad ablations. In small NanoGPT pretraining, Muon preserves stable rank, has a broad learning-rate plateau, and improves validation loss relative to AdamW; in a matched small-ViT control, the ranking reverses. The resulting picture is regime-dependent: Muon is not universally superior, but its flat-spectrum bias can help when many spectral directions need to remain active.
[LG-89] Few-step Cofolding with All-Atom Flow Maps
链接: https://arxiv.org/abs/2606.08375
作者: Gianluca Scarpellini,Ron Shprints,Peter Holderrieth,Juno Nam,Pranav Murugan,Rafael Gómez-Bombarelli,Tommi Jaakola,Maruan Al-Shedivat,Nicholas Matthew Boffi,Avishek Joey Bose
类目: Machine Learning (cs.LG)
*备注:
Abstract:All-atom generative modeling of 3D biomolecular complexes has emerged as the dominant paradigm for predicting the structure of proteins and protein-ligand systems. Generating structures at the atomic level of fidelity, however, typically requires expensive iterative diffusion rollouts, making both conventional deployment and inference-time search techniques computationally costly. In this paper, we introduce the Denoiser Cofolding All-Atom Flowmap (DeCAF) framework for distilling state-of-the-art all-atom cofolding models into all-atom flow maps that produce high-quality samples in only a few inference steps. We build DeCAF on a denoiser-based formulation of flow maps with endpoint losses that naturally support SE(3) rigid alignment, which we show is critical for training accurate models. We further derive a simple change of variables that lets DeCAF operate in the \sigma-space noise schedule of EDM-style architectures, enabling direct distillation from pretrained cofolding diffusion models. Equipped with DeCAF’s flowmap lookahead, we introduce a purpose-built inference-time framework that improves sampling through reward-guided search. Empirically, DeCAF-Boltz statistically improves over Boltz-1x in both accuracy (RMSD) and physical validity scores of protein-ligand poses at strict NFE budgets on the challenging Runs N’ Poses, while also showing a more optimal Pareto frontier across all inference compute budgets on PoseBusters. Distilling the state-of-the-art Pearl cofolding model, DeCAF-Pearl outperforms diffusion-based cofolding models and matches its teacher on success rate while using 5x fewer NFEs. We release our code at this https URL.
[LG-90] Predictive Coding with Bayesian Priors via Proximal Gradients
链接: https://arxiv.org/abs/2606.08374
作者: Francesco Bullo
类目: ystems and Control (eess.SY); Machine Learning (cs.LG)
*备注: 13 pages, 2 figures, technical report
Abstract:We recast predictive coding as continuous-time proximal gradient descent applied to a regularized maximum-a-posteriori (MAP) objective. We study first a single-level problem and then a multi-level hierarchy. For the single-level problem, we show that proximal gradient descent is precisely a leaky firing-rate network: the membrane leak, the effective recurrent matrix, the local synaptic drive, and the static nonlinearity all follow from one optimization principle, and the resulting circuit is the one proposed by Rao and Ballard. The prior selects the nonlinearity through its proximal operator, and the likelihood precision sets the gain on the observation. For the hierarchy, we show that a classical variable-splitting relaxation of the deep MAP problem yields hierarchical predictive coding as the interconnection of local and distributed solvers. In probabilistic modeling terms, this relaxation replaces the directed generative chain by an undirected Markov random field whose node potentials are the level-wise priors. Each level then applies its own activation function, namely the proximal operator of its prior.
[LG-91] SoK: Reconstruction Attacks on Synthetic Tabular Data (Insights from Winning the NIST CRC)
链接: https://arxiv.org/abs/2606.08372
作者: Steven Golob,Sikha Pentyala,Martine De Cock
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:Synthetic data is increasingly promoted as a privacy-preserving substitute for releasing sensitive tabular records, yet its central adversarial threat (“reconstruction”, the recovery of an individual’s hidden attribute values from a synthetic release and a handful of known quasi-identifiers) has been studied only in scattered, hard-to-compare settings. We present the first systematization of reconstruction (equivalently, attribute inference) attacks on de-identified and synthetic tabular data. We contribute a taxonomy that organizes attacks by the structure they exploit; the most systematic empirical evaluation to date, pitting fourteen attacks against nine synthetic data generation (SDG) methods across five benchmark datasets; and a set of new attacks that fill gaps in the taxonomy, one of which (CoBP-RA) is the strongest attack we measure. Crucially, we introduce a methodology for interpreting what attack success means: a memorization test that distinguishes reconstruction of the population distribution from memorization of training records, and a reduction that places reconstruction and membership inference on a single comparable scale. Our findings: the choice of SDG method governs risk far more than the choice of attack; differential privacy protects mainly at small budgets ( \varepsilon\lesssim1 ), above which protection plateaus, bounded by the synthesizer’s capacity rather than its noise; de-identification methods are the most exposed; and most reconstruction reflects distributional structure rather than memorization, concentrating individual risk on atypical records. The attacks and infrastructure are externally validated by our first-place finish among all red teams in the 2025 \textitNational Institute of Standards and Technology (NIST) Collaborative Research Cycle.
[LG-92] GENERIC-FNO: Embedding Energy Conservation and Entropy Production into Fourier Neural Operators
链接: https://arxiv.org/abs/2606.08343
作者: Jason Sulskis,Sathya Ravi
类目: Machine Learning (cs.LG)
*备注: Under review at TMLR
Abstract:We introduce GENERIC-FNO, the first neural operator to embed the full GENERIC (metriplectic) structure of nonequilibrium thermodynamics – reversible, energy-conserving dynamics and irreversible, entropy-producing dynamics coupled through the degeneracy conditions – directly in function space. Existing structure-preserving neural operators enforce at most a single conservation law or reversible (Hamiltonian) structure, while thermodynamically consistent learning has been confined to finite-dimensional, graph, or particle systems. GENERIC-FNO closes this gap: it learns the energy and entropy functionals as neural operators and parameterizes the Poisson and friction operators as diagonal Fourier multipliers sandwiched between rank-one projections that enforce the degeneracy conditions exactly, by construction, with no penalty term, update projection, or residual. The degeneracy identities hold to machine precision (residuals ~10^-13) for any initialization, dimension, or resolution, so the continuous-time dynamics conserve the learned energy and produce entropy exactly; the explicit time stepping adds only a small O(dt^2) drift (per-step residual ~10^-6). We further note that the (E,S,L,M) decomposition of a given flow is not unique, and introduce a gauge-invariant dissipation diagnostic separating reversible from dissipative dynamics independently of the learned functionals. Across three operator backbones (1D/2D FNOs and DeepONet) and four PDEs spanning reversible, dissipative, and mixed regimes, GENERIC-FNO preserves its exact structural guarantees zero-shot across a 4x super-resolution range (64 to 256), recovers the ground-truth ordering of physical dissipation, and is competitive with strong unconstrained and energy-penalized baselines, outperforming them on several dissipative and mixed problems at comparable or fewer parameters.
[LG-93] Orthogonality and Dimensionality in Airline Cluster Analysis using PCA and Kernel PCA
链接: https://arxiv.org/abs/2606.08322
作者: Andreas Schlapbach
类目: Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:To characterize the US airline profit cycles from 1995 to 2020, the authors of Renold et al. (2023) combine k-means clustering, principal component analysis, and system dynamic modelling. We replicate their clustering experiment in three spaces – the original 7-dimensional raw-variable space, a 3-dimensional PC score space, and a 4-dimensional PC score space using their dataset gratefully included in the paper. We show that the six-cluster taxonomy is geometrically robust: k-means in 3-PC space produces bit-for-bit identical cluster assignments relative to 7D raw space. As a nonlinearity check we apply kernel PCA under six kernels spanning three families plus a linear baseline. All six kernels preserve the six-cluster assignment in 2D. A 1D diagnostic tightens this: the linear kernel conflates the COVID year C_3 with the peak-profit cluster C_0, whereas all five non-baseline kernels shift C_3 to overlap only the post-financial-crisis cluster C_5. Agreement across the kernel families confirms an intrinsically linear manifold with no hidden curvature. The silhouette criterion reveals that the dataset structurally supports only three clusters, not six. Collinearity in the raw 7D space suppresses the silhouette signal that would otherwise identify k=3 as the structurally motivated choice.
[LG-94] Fourier fractal dimension to predict the generalization of deep neural networks
链接: https://arxiv.org/abs/2606.08308
作者: Joao B. Florindo,Davi Wanderley Misturini
类目: Machine Learning (cs.LG)
*备注:
Abstract:Predicting the generalization performance of deep neural networks without relying on hold-out validation data is a fundamental challenge in machine learning. While Stochastic Gradient Descent (SGD) drives the optimization of these highly parameterized models, its heavy-tailed, non-Gaussian dynamics induce complex, scale-invariant trajectories in the parameter space. In this paper, we propose a novel generalization measure based on the Fourier fractal dimension of the network’s weight variations. By analyzing the characteristic function of the Lévy-driven stochastic differential equations in the frequency domain, we extract a metric that robustly captures the geometric complexity of the learning process. Furthermore, we introduce a customized Fourier-based optimizer designed to actively regularize this fractal dimension during training. Extensive empirical evaluations on the CIFAR-10, SVHN, and MNIST datasets demonstrate that our proposed Fourier generalization measure exhibits a strong correlation with the actual generalization gap. Our method achieves state-of-the-art Kendall rank correlation coefficients, outperforming a wide array of existing norm-based, margin-based, and PAC-Bayesian measures. Ultimately, this work highlights the potential of frequency-domain fractal analysis as both a powerful predictor for model generalizability and a principled foundation for developing more stable optimization algorithms.
[LG-95] owards Graph Foundation Models for Dynamics in Complex Networked Systems: Lessons from Super-Spreader Identification in Multilayer Networks
链接: https://arxiv.org/abs/2606.08306
作者: Michał Czuba,Mateusz Stolarski,Adam Piróg,Piotr Bielak,Piotr Bródka
类目: Machine Learning (cs.LG); Social and Information Networks (cs.SI)
*备注:
Abstract:Network dynamics - including spreading, influence maximisation, and epidemic modelling - remain largely confined to the transductive paradigm, where models are trained on a single network and cannot be reused on unseen graphs without retraining. We argue that inductive cross-network generalisation is a necessary prerequisite for Graph Foundation Models (GFMs) in this domain and propose four design properties towards this goal. As a proof of concept, ts-net (TopSpreadersNetwork), trained solely on synthetic multilayer networks (MLNs), demonstrates zero-shot generalisation to real-world MLNs of varying size and layer count, outperforming classical heuristics and transductive baselines on three of four metrics. Based on ts-net’s performance, we further outline five open challenges towards building GFMs for network dynamics: scale, many-layer generalisation, self-supervised pretraining, cross-task transfer, and node-attribute integration.
[LG-96] GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks
链接: https://arxiv.org/abs/2606.08303
作者: Toan Tran,Waqwoya Abebe,Abhishek Potnis,Supriya Chinthavali,Cyrus Shahabi,Li Xiong,Dalton Lunga
类目: Machine Learning (cs.LG)
*备注:
Abstract:This paper investigates a novel concept of time series geolocalization, where the goal is to infer the geographic origin of each raw time series. Successful geolocalization can provide spatial context to time series, enabling downstream location-aware applications. We formalize the problem, adapt core ideas from image geolocalization to establish strong baselines, and propose GeoGNN, a two-tower architecture. During training, GeoGNN’s spatial tower learns embeddings of geographic cell candidates by leveraging the geographic adjacency graph, while the temporal tower extracts informative representations from time series. During inference, each temporal representation is matched against candidate geographic embeddings using dot-product similarity, combined with an auxiliary classification head, to predict the time series’ associated geographic origin. Experiments on large-scale, countrywide electricity-consumption datasets demonstrate that GeoGNN achieves the best performance across datasets and enhances both fine- and coarse-grained geolocalization accuracy by ~27% on average.
[LG-97] QueryWeaver: Reliable Multi-Tool Query Execution Planning via LLM -Based Graph Generation
链接: https://arxiv.org/abs/2606.08300
作者: Aishwarya Chakravarthy,Vidhi Kulkarni,Duen Horng Chau
类目: Machine Learning (cs.LG)
*备注:
Abstract:Many real-world queries over personal data span multiple applications and require structured planning, as individual tools expose only partial information. While LLMs show strong reasoning and tool use, reliably executing multi-step, cross-tool queries remains challenging. We introduce a system that converts natural language queries into structured graphs and executes them via a deterministic planner. Our approach uses depth-first search to resolve dependencies and combine results across tools, improving reliability and enabling queries beyond traditional keyword-based search. We demonstrate high accuracy even with smaller or locally hosted LLMs.
[LG-98] On solving symmetric multi-type orthogonal non-negative matrix tri-factorization problem
链接: https://arxiv.org/abs/2606.08291
作者: Rok Hribar,Gregor Papa,Janez Povh,Andrej Kastrin
类目: Machine Learning (cs.LG)
*备注: 27 pages, 9 tables, 3 figures
Abstract:We study the symmetric multi-type orthogonal non-negative matrix tri-factorization problem, where several symmetric non-negative matrices are simultaneously approximated by factors of the form GS_iG^\top , with a shared non-negative and orthogonal factor G . This model is motivated by clustering and network analysis, where non-negativity improves interpretability and orthogonality gives a natural assignment-type structure to the latent factor. Since the resulting optimization problem is highly non-convex, we develop two heuristic algorithms for computing high-quality local solutions. The first one is a fixed point method derived from the Karush-Kuhn-Tucker conditions after adding a penalty term for the orthogonality constraint. The second one is a three-stage ADAM-based method that combines non-negativity-preserving optimization, orthogonalization, and restricted ADAM refinement on the feasible set. We evaluate both methods on synthetic data, including noisy instances, and on citation network benchmarks. The synthetic experiments show that both algorithms recover factorizations close to the optimum and remain stable under noise. On real networks, the learned embeddings are competitive with or better than standard baselines such as SVD, node2vec, and classical link prediction heuristics in link prediction, node clustering, and node classification tasks.
[LG-99] Mesh Graph Neural Network Framework for Accelerating Finite Element Simulation for Arbitrary Geometries
链接: https://arxiv.org/abs/2606.08287
作者: Josiah D. Kunz,Kamal Choudhary
类目: Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci); Computational Engineering, Finance, and Science (cs.CE)
*备注: 10 pages, 6 figures, to be published. Code available at this https URL
Abstract:Finite element analysis (FEA) is essential for structural design but remains computationally expensive, particularly when evaluating multiple design iterations or load scenarios. Machine learning surrogate models offer a promising alternative, yet most approaches struggle with a critical limitation: generalizing across varying geometries. This work presents a mesh graph network (MGN) for predicting von Mises stress fields in 2D structural components with arbitrary hole geometries. Unlike traditional machine learning approaches that use absolute node coordinates as features, the proposed model builds on existing MGN frameworks that encode node types (e.g., fixed boundary, free surface, hole edge), relative edge features (distance between neighbors), and global features (applied load). This architecture is inherently translation- and rotation-invariant, enabling generalization to unseen geometries without retraining. The MGN was trained on 11 plate geometries under 20 load conditions and evaluated on 7 unseen geometries and 3 unseen loads. In the most favorable case, the model achieves R^2 \geq 0.97 on an unseen geometry and unseen load, compared to R^2 \approx 0.01 – 0.86 for conventional models (Random Forest, Gradient Boosting , K-Nearest Neighbors) trained on identical data. However, even in less favorable cases, the MGN model still outperforms conventional models. This work extends the mesh-based simulation framework of Pfaff et al. (arXiv:2010.03409) to structural mechanics, demonstrating that graph neural networks can serve as efficient surrogates for finite element analysis across varying geometries.
[LG-100] Causal Semantic Alignment for LLM -based Time Series Forecasting
链接: https://arxiv.org/abs/2606.08262
作者: Kexuan Zhang,Xiaobei Zou,Cesare Alippi,Gary G. Yen,Yang Tang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Recent advances in Large Language Models (LLMs) have opened new possibilities for time series forecasting by enabling alignment between temporal patterns and pretrained word embeddings. However, most LLM-based methods overlook the heterogeneous nature of time series, where dynamic fluctuations and invariant semantics are entangled. This entanglement introduces spurious correlations during the alignment, as dynamic components act as confounders by simultaneously influencing invariant components and the resulting aligned embeddings. To address this issue, a variable-level alignment framework CVAformer is proposed. CVAformer explicitly disentangles each variable into invariant and dynamic components just before alignment, and applies causal intervention to mitigate the confounding effect of the dynamics. To better support variable-level alignment, CVAformer replaces the standard causal attention in LLMs with a non-causal attention mechanism that captures interactions among variables at each time step. Extensive experiments across long-term, short-term, few-shot, and zero-shot forecasting settings indicate that CVAformer matches or exceeds state-of-the-art performance on most datasets, and in some cases achieves notably better accuracy. Experimental results validate the effectiveness of variable-level alignment and dynamic disentanglement in CVAformer, offering a new perspective for LLM-based time series tasks.
[LG-101] Differentially Private Synthetic Data via APIs 4: Tabular Data ICML’26
链接: https://arxiv.org/abs/2606.08259
作者: Toan Tran,Arturs Backurs,Zinan Lin,Victor Reis,Li Xiong,Sergey Yekhanin
类目: Machine Learning (cs.LG)
*备注: ICML’26
Abstract:This paper investigates the problem of generating synthetic tabular data with differential privacy (DP) guarantees, enabling data sharing in sensitive domains. Despite extensive study, state-of-the-art methods often focus on minimizing low-order marginal query errors and overlook the challenges posed by high-order correlations. To address this gap, we extend the Private Evolution (PE) framework, originally developed for DP-compliant image and text synthesis, to tabular data. We introduce Tab-PE – an algorithm for synthetic tabular data generation under DP constraints. Tab-PE iteratively improves a candidate dataset via an evolutionary process that leverages tabular-specialized operators to produce variations, privately scores them, and selects the highest-quality samples to retain and propagate. In contrast to the original PE, which relies on large foundation models, Tab-PE employs heuristic operators with significantly lower computational costs, making PE more practical and scalable for tabular data. Through extensive experiments on real-world and simulation datasets, we demonstrate that Tab-PE substantially outperforms prior baselines on datasets exhibiting high-order correlations. Compared to the best baseline – AIM, Tab-PE improves classification accuracy by up to 10% while running 28 times faster.
[LG-102] Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking
链接: https://arxiv.org/abs/2606.08253
作者: Alessandro Montenegro,Shihao Li,Puze Liu,Alberto Maria Metelli,Jan Peters
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: Accepted to RSS 2026
Abstract:Enabling humanoid robots to operate in complex, dynamic environments remains a critical challenge, fundamentally limited by the ability to navigate robustly, safely, and accurately. While reinforcement learning with velocity-commanded policies has achieved remarkable robustness in humanoid locomotion, this approach lacks explicit control of the foothold placement, leading to unsafe behavior, such as stepping onto human feet, or imprecise navigation, hindering the following manipulation task. Conversely, explicit foothold-tracking policies offer a promising alternative by directly being commanded with target foot poses. However, existing approaches are often limited by unrealistic state assumptions, compromising real-world deployment, or they are part of staged pipelines, making them tied to specific downstream tasks. In this work, we introduce a novel, lightweight framework for training general-purpose 3D foothold-tracking policies. By dynamically providing footstep support through a goal sampler, this method enables the learned policy to be agnostic to specific terrains. Our new target representation effectively mitigates challenges arising in the real world, such as noisy and inaccurate pose estimation and foot contact estimation. Designed for direct real-world transfer, our policy acts as a standalone low-level controller that can be seamlessly paired with various high-level foothold generators. We demonstrate the effectiveness of our framework through extensive experiments in simulation and in the real world. By coupling our policy with different upstream planners, we achieve natural and accurate locomotion in challenging settings, paving the way for loco-manipulation tasks in complex environments.
[LG-103] Disturbance-Aware Aerial Robotics for Ethical Wildlife Monitoring
链接: https://arxiv.org/abs/2606.08249
作者: Mahmut Osmanovic,Isac Paulsson,Teddy Lazebnik
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Reliable wildlife monitoring is essential for ecology and conservation, yet many existing methods, such as tagging, capture, and close-range observation, can alter the very behaviors they aim to measure. Aerial robots offer a scalable alternative, which has shown promising performance in multiple studies. Nonetheless, existing approaches typically lack behavioral awareness, rely on fixed heuristics, or require real-world training data that are costly, impractical, and ethically difficult to obtain. As a result, there remains no general framework for adaptive drone-based monitoring that can both preserve ecological validity and scale across species, behaviors, and robotic platforms. In this study, we introduce a disturbance-aware reinforcement-learning-based framework for heterogeneous aerial robotic fleets that enables autonomous wildlife tracking while explicitly minimizing behavioral disruption. We couple a zoologically grounded simulation environment with fitted animal movement models derived from real trajectory statistics, and train control policies using a reward formulation that captures the trade-off between observation quality and disturbance risk. Across three species (pigeon, jackal, and spur-winged lapwing) with distinct ecologies and motion patterns and four increasingly strategic behavior models common in nature, the learned policies consistently surpassed currently used rule-based baselines and generalized across monitoring tasks, animal dynamics, and drone types. These results establish disturbance-aware learning as a viable foundation for non-invasive autonomous wildlife observation, opening a path towards scalable, ethically responsible, and scientifically reliable robotic monitoring in ecology and conservation.
[LG-104] GPT -Micro: A large language paradigm for accelerated inexpensive and thermodynamics-consistent discovery of constitutive models in manufacturing
链接: https://arxiv.org/abs/2606.08238
作者: Soumik Dutta,Kiarash Naghavi Khanghah,Sania Shree,Logan McNeil,Thomas Feldhausen,Hongyi Xu,Rajiv Malhotra
类目: Machine Learning (cs.LG)
*备注: 23 pages, 4 tables, 11 equations, 9 figures
Abstract:Constitutive modeling of the relationship between process-imposed material states and fundamental material properties is critical to control of material microstructure in manufacturing processes. The limited accuracy resulting from the typical reliance on fallible human expertise and intuition for postulation and revision of the models functional form results in incremental and time consuming model discovery. Conventional Machine Learning (ML) incurs significant cost and time of data generation. Model discovery using Large Language Models (LLMs) suffers from the above issues and/or ignores the inviolability of fundamental thermodynamics laws. This work creates a novel GPT-Micro paradigm for autonomous, data sparse, and thermodynamics-compliant discovery of de-novo constitutive models. This framework seamlessly integrates semantic knowledge extraction from literature, enforcement of thermodynamics-based conservation laws, and sparse datasets, with LLM-driven generation and refinement of model hypotheses. Validation is performed for a long-intractable constitutive modeling problem in a printed electronics process testbed. This reveals significant and simultaneous advantages over the state-of-the-art including: (a) More than 70 percent reduction in data burden relative to ML-based modeling without loss in accuracy; (b) 400X reduction in discovery time after data generation, from months to hours, relative to human-driven modeling; © Discovery of models with novel functional forms without subjective human choice of a starting hypothesis; (d) Enhanced physics-rooted trustworthiness, human interpretability, and mechanistic insight via synthesis of compact, conservation-compliant, and physically complete analytical models. The potential of GPT-Micro to realize rapid, low-cost, physically trustworthy, and interpretable microstructure modeling across the manufacturing landscape is discussed.
[LG-105] De novo molecular generation with optical property preconditioning at the token level
链接: https://arxiv.org/abs/2606.08221
作者: Haozhe Huang,Manuel Gonzalez Lastre,Hyun Suk Park,Jorge A. Campos-Gonzalez-Angulo,Xinjian Liu,Alán Aspuru-Guzik
类目: Machine Learning (cs.LG)
*备注:
Abstract:Designing OLED molecules with targeted optical properties remains challenging due to the scarcity of high-quality data and the limited reliability of conditional control in generative models across chemical motifs. Here, we benchmark a token-conditioned autoregressive language model for OLED molecular generation in a realistic low-data regime. A GPT2 model is pretrained on large chemical corpora, augmented with discrete property tokens, and fine-tuned using multi-task optimisation. Conditioning targets vertical absorption energy and oscillator strength, with the HOMO-LUMO gap included as an auxiliary electronic descriptor. Generated molecules are evaluated at the TDDFT level to assess distributional fidelity and controllability. The generated library reproduces the dominant optical-property support of the training distribution while shifting towards lower molecular weight and fewer heavy atoms. Token-level control is consistently directional across conditioning bins, but is not fully orthogonal and exhibits local calibration irregularities. A chemotype-resolved analysis further shows that controllability depends strongly on local electronic environments: moderately conjugated aromatic-carbon motifs are associated with improved joint target satisfaction, whereas electron-withdrawing motifs, particularly aryl nitriles, show systematic red-shifting and reduced controllability. These results establish a quantitative benchmark for conditional OLED molecular generation and show that model reliability must be assessed in chemically meaningful subspaces rather than from aggregate property distributions alone. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2606.08221 [cs.LG] (or arXiv:2606.08221v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2606.08221 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-106] Public Machine Learning Solver Framework for Novices in the Machine Learning Domain
链接: https://arxiv.org/abs/2606.08212
作者: Lokman Saleh,Hafedh Mili,Mounir Boukadoum
类目: Machine Learning (cs.LG)
*备注:
Abstract:Solving machine learning problems is complex and typically reserved for experts. Over the past two decades, systems have emerged to support non-experts. Based on our review, we identify three categories: (1) fully automated AutoML systems, (2) expert cheat sheets for algorithm selection, and (3) decision-support systems using selection criteria (accuracy, transparency, data requirements). We propose a new platform combining categories 2 and 3 to deliver semi-automated, intelligent solution recommendations for non-experts. Unlike existing approaches that recommend a single algorithm, our platform suggests a complete pipeline tailored to the user’s problem. It integrates expert-defined selection criteria with transfer learning and automatically extracts data characteristics (e.g., class imbalance, missing values) from user-provided datasets. The platform uses first-order logic to reason over its knowledge base and recommends suitable algorithms ranked by relevance. It features a user-friendly interface and connects to a crowdsourcing platform for ML experts, ensuring continuous updates. The platform is built incrementally, allowing seamless integration of new algorithms, criteria, and domain knowledge. To our knowledge, this is the first free, publicly accessible online framework that systematically captures and operationalizes expert knowledge to guide non-experts in solving ML problems in a structured, transparent manner.
[LG-107] Stable and Scalable Probabilistic Numerical Solvers for Stiff and High-Dimensional ODEs
链接: https://arxiv.org/abs/2606.08203
作者: Nathanael Bosch
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Filtering-based probabilistic numerical solvers for ordinary differential equations (ODEs) have been established as a flexible and efficient simulation framework with built-in numerical uncertainty quantification. However, problems that are both stiff and high-dimensional remain a challenge, as current methods are either stable and have cubic cost in the ODE dimension, or scale linearly at the expense of stability. In this paper, we close this gap and develop probabilistic ODE solvers that are both stable and scalable. We propose two complementary strategies. First, we develop a matrix-free update step that uses Jacobian-vector products, iterative linear solvers, and stochastic covariance estimation to enable linear scaling, all while retaining stability. Second, we propose iterative re-linearization to further improve stability without sacrificing scalability, turning probabilistic ODE solvers into fully implicit methods. We evaluate the proposed approaches on a range of stiff and high-dimensional problems and demonstrate improved stability and scalability over established probabilistic solvers.
[LG-108] Differentially Private Range Subgraph Counting ICML2026
链接: https://arxiv.org/abs/2606.08179
作者: Xian Chen,Ruobing Bai,Pan Peng
类目: Data Structures and Algorithms (cs.DS); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: ICML2026
Abstract:Subgraph counting is a fundamental problem in graph analysis. Motivated by practical scenarios where graph analytics are performed on subgraphs induced by selected vertices – rather than on the entire graph – and by growing privacy concerns, we initiate the study of differentially private range subgraph counting (DPRSC). The goal is to privately count occurrences of a fixed pattern graph within induced subgraphs defined by multi-dimensional attribute ranges. Unlike classical point counting, subgraph counting is inherently nonlinear and exhibits high sensitivity: a single edge modification can affect many subgraph occurrences. We present the first efficient algorithms for DPRSC with small additive error. Our approach introduces a subgraph projection that reduces DPRSC to weighted orthogonal range counting, enabling the use of range trees and local sensitivity estimation to achieve accurate private query answering. We complement our algorithms with matching lower bounds, obtained by reducing reconstruction attacks to DPRSC and leveraging discrepancy theory. In particular, we show that any differentially private algorithm for DPRSC must incur additive error exponential in the dimension. Empirical evaluations demonstrate that our algorithms significantly outperform baseline methods in accuracy and runtime while maintaining strong privacy guarantees.
[LG-109] AI-Native Closed-Loop Security for 6G-Enabled Cyber-Physical Systems: From Edge Detection to Network-Wide Mitigation
链接: https://arxiv.org/abs/2606.08173
作者: Bilal Hussain,Muhammad Bilal,Tan Li,Haris Pervaiz,Xiao Tang,Qinghe Du,Fawad Ahmad,Muhammad Azhar,Jun Zhang
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
*备注: 30 pages, 12 figures, survey paper, submitted to IEEE Communications Surveys Tutorials (IEEE COMST)
Abstract:In sixth-generation (6G) networks, billions of cyber-physical systems (CPSs) - autonomous vehicles, smart grids, industrial robots, and remote-surgical equipment - will run over ultra-reliable low-latency slices, collapsing the gap between a remote breach and physical harm to milliseconds, a budget perimeter firewalls and centralised security operations centres cannot meet. This survey reframes 6G CPS security as a closed-loop, AI-native pipeline that senses at the multi-access edge computing (MEC) tier, using minute-scale call-detail records (CDRs) for baseline learning and sub-millisecond RAN/Open-RAN (O-RAN) telemetry for the latency-critical path. It decides locally with compressed deep models, mitigates network-wide via SDN, NFV, and O-RAN controllers, and retrains through federated learning (FL) and digital-twin (DT) replay. We formalise a per-slice, tail-bounded latency contract on the sense, detect, and mitigate stages, enforced at a slice-dependent tail percentile (p99 for safety-critical URLLC slices). Organising 128 peer-reviewed studies (2017-2026) under a PRISMA 2020 protocol, we (i) map the 6G/CPS threat surface to MITRE ATTCK and a CDR-observable feature space; (ii) unify edge anomaly detection and DDoS classification across twelve datasets and statistical, graph, and transformer models; (iii) synthesise SDN/NFV/O-RAN primitives into one closed-loop reference architecture; (iv) treat FL, large language models (LLMs), DT, post-quantum cryptography (PQC), zero-trust architecture (ZTA), and explainable AI as cross-cutting enablers, not parallel pillars; and (v) consolidate open problems into five directions spanning data, latency, trust, standardisation, and evaluation.
[LG-110] AttentionCap: Transformer Based Capacitance Matrix Learning Toward Full-Chip Extraction
链接: https://arxiv.org/abs/2606.08161
作者: Jiechen Huang,Hector R. Rodriguez,Dingcheng Yang,Zuochang Ye,Yibo Lin,Wenjian Yu
类目: Machine Learning (cs.LG); Hardware Architecture (cs.AR); Numerical Analysis (math.NA)
*备注: Accepted at the 63rd ACM/IEEE Design Automation Conference (DAC '26)
Abstract:As capacitance extraction accuracy of rule-based pattern matching becomes difficult to sustain at advanced nodes, a growing trend emerges to develop deep-learning-based 2D capacitance models. However, existing MLP- and CNN-based methods constrain their input to fixed metal-layer combinations in a specific process node, limiting their usability in practice. Recognizing the inherent similarity between capacitance matrix and the prevailing attention mechanism, we propose AttentionCap, a customized Transformer for capacitance matrix learning, with a Gram representation framework, a physics-aligned symmetric-attention output layer, and a novel normalized Laplacian loss. We also introduce a process-node embedding to enable multi-node learning. Trained on synthetic data, AttentionCap attains 0.67%/3.99% self/coupling-capacitance error on unseen real designs under a multi-layer and multi-node setting, surpassing the CNN-Cap baseline with 4.6 \times /5.7 \times lower self/coupling error and 192 \times faster inference speed. A pretrained AttentionCap accurately transfers to an unseen node with only 5K samples and 4K finetuning steps. With sufficient accuracy on unseen real designs and strong transferability to new process nodes, AttentionCap offers highly practical value for modern EDA workflows. Code and data are available at this https URL.
[LG-111] RUST-SCF: Transformer-based Risk Understanding and Scoring for Transactional Supply Chain Finance
链接: https://arxiv.org/abs/2606.08140
作者: Mohammadamin Davoodabadi,Amirabbas Shakeri
类目: Machine Learning (cs.LG)
*备注: 15 pages, 13 Figures, 3 Tables
Abstract:Supply Chain Finance (SCF) and LendTech platforms need credit scoring systems that respond to evolving transaction behavior, repayment delays, and active exposure. We propose TRUST-SCF, a transformer-based framework for transaction-level risk prediction and dynamic credit scoring. Each user history is represented as a sequence of transaction tokens containing utilization, repayment delay and transaction position. The main contributions are: (1) a financially aligned attention bias that combines utilization similarity and recency, enabling the model to compare repayment behavior under comparable exposure conditions; (2) continuous repayment-delay prediction in a log-transformed target space, reducing the influence of extreme delays while improving sensitivity to short-delay behavior and (3) a label-efficient credit-scoring pipeline in which the final credit score is not trained using any explicit external credit-score label, but is instead derived from predicted delay, potential risk over simulated utilization, actual unpaid exposure, and nonlinear calibration. Experiments on real transaction data from more than 300,000 transactions show that TRUST-SCF improves delay prediction over sequential baselines and produces scores that are strongly associated with future repayment behavior. These results suggest that TRUST-SCF is a practical framework for adaptive credit scoring and transaction-level risk mitigation in SCF and LendTech environments.
[LG-112] Conditional Random Ordered Transport Spaces
链接: https://arxiv.org/abs/2606.08113
作者: Lei Luo,Jian Yang
类目: Machine Learning (cs.LG); Functional Analysis (math.FA); Optimization and Control (math.OC)
*备注: 24 pages, 1 figure, 2 tables
Abstract:A small Wasserstein distance does not certify that a transformation is admissible. In evidence-constrained, semantic, causal, physical, monotone, or risk-sensitive learning, one must ask not only how far two probability laws are, but whether mass has moved in a direction allowed by available information. We introduce conditional random ordered transport spaces (CROTS), a class of (L^0)-valued spaces of random probability measures equipped with a Wasserstein ambient metric, a closed stochastic order, hard and soft ordered transport discrepancies, and a conditional risk functional for evaluating order violation under an evidence sigma-field. The central object is an order-admissible transport geometry for random measure-valued dynamics, distinct from cone-valued metrics, ordered Kantorovich constructions, random Wasserstein spaces alone, and model-specific residuals for generative paths. We develop the foundations of CROTS as a space theory for reliable distributional learning. The results include well-posedness and duality for hard and soft ordered transport, soft-to-hard variational convergence, measurability and completeness of the random lifted space, reductions to classical Wasserstein and ordered geometries, ordered geodesics, constrained barycenters and projections, conditional risk-transport duality, and separation of order-violating distributions. The main stability theorem shows that random learning dynamics may converge in the ambient Wasserstein metric while its local admissibility leakage follows a separate conditional order-risk recursion. The resulting asymptotic order-risk floor provides a mathematical language for evidence overreach, ordered distribution shift, robustness failure, and admissible distributional dynamics.
[LG-113] A Unifying View of Attention Sinks: Two Algorithms Two Solutions
链接: https://arxiv.org/abs/2606.08105
作者: Lukas Fesser,Mozes Jacobs,Thomas Fel,Andy Keller,Sham Kakade
类目: Machine Learning (cs.LG)
*备注:
Abstract:When attention concentrates on a single token, a sink, what is the model actually computing? Attention sinks are ubiquitous in softmax transformers, yet this shared visual signature can hide fundamentally different algorithms. We show that visually similar sink patterns can reflect two distinct mechanisms: i adaptive nop, where a head suppresses its update by routing to a null token, and ii broadcast, where a sink aggregates and redistributes global information. In that case, sinks serve an analogous role: a safe destination when there is nothing useful to compute. Proposed interventions like gating or registers work because they implicitly target one or the other, revealing a duality between method and assumed mechanism: gating implicitly assumes nop; registers implicitly assume broadcast. Each mechanism leaves distinct traces (nop sinks exhibit negligible value norms; broadcast sinks induce low-rank outputs) which we formalize on synthetic tasks and use to derive practical diagnostics. Applied to pretrained vision transformers, these diagnostics reveal that both mechanisms exist at scale: sinks transition from CLS in early layers to patches in deeper layers, and concentrate in specialized heads. Strikingly, register tokens, designed for broadcast, are repurposed to also serve nop, confirming that neither intervention alone suffices. Combining gating with registers yields complementary gains in stability and performance. Overall, we find that the same attention pattern can reflect two very different computations and effective intervention requires first asking what the model is actually computing.
[LG-114] Constraint-Aware Optimization for Robust Protein Stability Prediction
链接: https://arxiv.org/abs/2606.08100
作者: A Shivram,Aneesh S. Chivukula,Manik Gupta,Sourav Chowdhury
类目: Machine Learning (cs.LG)
*备注:
Abstract:Multimodal \Delta\Delta G predictors integrating protein language models with inverse-folding representations achieve strong in-distribution accuracy on the Megascale dataset but exhibit limited robustness on out-of-distribution (OOD) proteins, persistent forward-reverse bias on paired-mutation benchmarks, and under-representation of rare stabilizing mutations. Existing approaches address these limitations primarily through additional architectural components, leaving optimization-level intervention comparatively underexplored. We introduce a constraint-aware optimization framework combining Balanced Mean Squared Error, a Siamese anti-symmetric regularizer, and a novel OOD-margin consistency loss on the per-position feature representation, requiring no architectural changes to the SPURS backbone. Across eleven benchmarks and three random seeds, the framework improves Spearman correlation on S669 from 0.486 to 0.540 ( \sigma=0.002 across seeds), matching the published SPURS baseline (0.50) without architectural modification, and on S461 from 0.653 to 0.711, with consistent smaller gains on five additional OOD datasets. A controlled diagnostic on Ssym reveals that anti-symmetric training does not eliminate systematic forward-reverse bias, indicating that gains arise through implicit regularization rather than exact thermodynamic constraint enforcement.
[LG-115] DICE: Entropy-Regularized Equilibrium Selection for Stable Multi-Agent LLM Coordination
链接: https://arxiv.org/abs/2606.08068
作者: Yi Xie,Zhanke Zhou,Chentao Cao,Bo Liu,Bo Han
类目: Machine Learning (cs.LG)
*备注:
Abstract:Multi-agent large language model (LLM) systems often fail to reliably outperform a single strong model equipped with best-of-N sampling. We argue that a core source of this instability is ill-posed equilibrium selection: current systems specify what information agents share, but not which coordination convention should be selected. We formalize a broad class of such systems as discounted incomplete-information Markov games and show that two common pathologies, oscillation between competing conventions and drift across them, can both induce unstable learning and linear Bayesian regret. To obtain a well-posed target, we introduce the Heterogeneous Quantal Response Equilibrium (HQRE), an entropy-regularized equilibrium concept with agent- and state-dependent temperatures. Under a monotonicity condition, HQRE is unique, admits linearly convergent mirror updates, and yields bounded Bayesian regret; the same condition yields rollout-measurable stability diagnostics. We instantiate this objective in two algorithms: DICE-PC, which coordinates frozen models through prompt-control actions, and DICE-FT, which performs parameter-efficient mirror fine-tuning. Across eleven benchmarks in four domains, DICE improves accuracy-cost trade-offs over strong within-class baselines; on reasoning and planning tasks, DICE-PC improves by 4.3 percentage points on average and DICE-FT by 8.5 points.
[LG-116] Beyond Homophily: Towards Generalized Graph Reconstruction Attack and Defense
链接: https://arxiv.org/abs/2606.08067
作者: Zhanke Zhou,Bo Han,Xuan Li,Jiangchao Yao,Sanmi Koyejo,Michael K. Ng
类目: Machine Learning (cs.LG)
*备注:
Abstract:Graph neural networks (GNNs) are widely deployed on relational data, yet they can leak sensitive or proprietary information about the training graph adjacency, e.g., social ties, transactions, and interactions. This work studies graph reconstruction attacks (GRA), a form of model inversion that reconstructs the training adjacency from a trained GNN, given different levels of attacker-side information. We first provide a systematic characterization of when and why adjacency becomes recoverable through features, labels, embeddings, and predictions, with leakage modulated by graph homophily, heterophily, and the model’s inductive bias. Motivated by these findings, we view GNN inference through a Markov chain approximation lens, treating the layered forward computation as a chain of topology-dependent representations. Building on this view, we develop complementary attack and defense methods. On the attack side, we propose MC-GRA (+), which reconstructs the adjacency by optimizing a surrogate adjacency whose GNN-induced representations align with those of the target model at each layer. On the defense side, we propose MC-GPB (+), which suppresses adjacency-dependent information throughout the representation chain while aiming to preserve classification accuracy under a privacy-utility trade-off. Experiments across homophilic/heterophilic graph benchmarks and GNNs show that our attacks improve reconstruction fidelity over prior methods, while our defenses reduce reconstruction success with only minor accuracy loss.
[LG-117] Noise-Adaptive High-Probability Regret Bounds for Online Convex Optimization ECML-PKDD2026
链接: https://arxiv.org/abs/2606.08028
作者: Wentao Zhang,Yutong Zhang,Wentao Mo
类目: Machine Learning (cs.LG)
*备注: Accepted to 2026 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases(ECML-PKDD 2026)
Abstract:We study high-probability regret bounds for online convex optimization (OCO) with strongly convex losses and establish three results that resolve open questions at the intersection of noise adaptivity, feedback structure, and constraint satisfaction. For the full-information setting with sub-Gaussian stochastic gradients, we prove a noise-adaptive high-probability regret bound in which the martingale deviation term scales with the noise level \sigma rather than the gradient bound G , yielding a multiplicative improvement of G/\sigma over the classical Azuma-Hoeffding baseline. Our analysis introduces an exponential supermartingale argument that bypasses the bounded-difference requirement of Freedman’s inequality, enabling direct treatment of unbounded sub-Gaussian noise without truncation artifacts. For bandit feedback, we prove a minimax lower bound: the high-probability regret scales linearly in \log(1/\delta) , in contrast to the \sqrt\log(1/\delta) confidence cost under full information. This constitutes a formal separation in the confidence cost of strongly convex OCO across feedback models. Regarding constrained OCO with stochastic constraints satisfying a Slater condition, we provide simultaneous high-probability guarantees for both cumulative regret and long-run constraint violation, achieving \mathcalO(\sqrtT\log(m/\delta)) regret and \mathcalO(\sqrtT/(\zeta\delta) + m\sqrtT\log(m/\delta)) violation. Synthetic experiments corroborate all theoretical predictions.
[LG-118] Evaluating the Impact of Task Granularity on Catastrophic Forgetting in Continual Learning
链接: https://arxiv.org/abs/2606.08013
作者: Emre Alyamac,Himanshu Janmeda,Shashwat Krishna,Yash Vijay
类目: Machine Learning (cs.LG)
*备注: 8 pages, 4 figures, 5 tables
Abstract:Catastrophic forgetting, the abrupt loss of previously acquired knowledge upon learning new information, remains the central challenge in Continual Learning. This project investigates whether the order in which a model learns information affects how well it retains knowledge. Specifically, we ask: does learning general categories first (like “animals” vs “vehicles”) before learning specific classes (like “dog” vs “cat”) reduce forgetting compared to learning all classes at once? We test three approaches on CIFAR-100: (1) Coarse-to-Fine: train on 2 super-classes, then expand to 10 specific sub-classes, (2) Fine-to-Coarse: train on 10 sub-classes, then group into 2 super-classes, and (3) Flat: train on all 10 classes from the start. We use Elastic Weight Consolidation (EWC) to prevent forgetting during transitions. Our hypothesis is that learning general patterns first creates a stable foundation that helps the model retain knowledge when learning more detailed distinctions. We evaluate using standard metrics (accuracy, precision, recall, F1) plus continual learning metrics like backward transfer and forgetting rates. This work could inform how we design learning sequences for real-world systems that need to learn incrementally. Comments: 8 pages, 4 figures, 5 tables Subjects: Machine Learning (cs.LG) MSC classes: 68T05 ACMclasses: I.2.6 Cite as: arXiv:2606.08013 [cs.LG] (or arXiv:2606.08013v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2606.08013 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-119] Overcoming the Limits of Finite Difference Method; Physics-Informed Neural Network for Noisy High-Dimensional Heat Diffusion
链接: https://arxiv.org/abs/2606.07982
作者: Shreesh Bhattarai,Harish Chandra Bhandari
类目: Machine Learning (cs.LG)
*备注:
Abstract:High-dimensional transient heat diffusion under noisy boundary conditions exposes a fundamental limitation of classical numerical methods: accuracy degrades catastrophically where physical noise is unavoidable. This paper presents a Physics-Informed Neural Network (PINN) framework as a systematic solution to this problem across one, two, and three spatial dimensions, establishing clear operational regimes that redefine solver selection in noisy thermal systems. Under 20% boundary noise in 3D, PINN sustains approximately 91% accuracy while Finite Difference Method (FDM) collapses to 36%, a clear decisive advantage. This is further confirmed in a physical copper thermal system, where PINN reduces boundary reconstruction error by 3.3 times under realistic noise conditions. This noise resilience is accompanied by a dimensionality-driven efficiency crossover: PINN requires fewer spacetime nodes than FDM in 3D while achieving superior accuracy, exposing the true cost of classical discretization at scale. These findings reframe solver selection: the decisive axis is not accuracy alone, but noise exposure and dimensionality jointly. When noise and dimensionality are both high, the classical solver paradigm is insufficient; this work provides the foundation to justify PINN as the operational standard in such regimes.
[LG-120] he Easy the Hard and the Learnable: Confidence and Difficulty-Adaptive Policy Optimization for LLM Reasoning ICML2026
链接: https://arxiv.org/abs/2606.07950
作者: Zhanke Zhou,Xiangyu Lu,Chentao Cao,Brando Miranda,Tongliang Liu,Bo Han,Sanmi Koyejo
类目: Machine Learning (cs.LG)
*备注: Published in ICML 2026
Abstract:RL with verifiable rewards can substantially improve LLM reasoning, yet standard GRPO-style training often treats easy, hard, and learnable questions alike through uniform sampling and weighting, leading to inefficient compute allocation. We study GRPO by tracking token log-probabilities, group-normalized advantages, and the induced token-level update weights. This reveals three recurring dynamics as training proceeds: (1) confidence inflation, (2) advantage contraction, and (3) hierarchical convergence. These findings suggest that the utility of each update depends strongly on both question difficulty and the model’s current competence. Motivated by this, we propose Confidence and Difficulty-adaptive Policy Optimization (CoDaPO), which assigns each question a bounded value from rollout confidence and empirical difficulty. CoDaPO then uses this value to reweight policy updates and resample high-value learnable questions within mini-batches, thereby increasing discovery within the learnable band under a fixed compute budget. Across twelve benchmarks, CoDaPO consistently improves accuracy over existing RL methods. Our code is publicly available at this https URL.
[LG-121] CAAL: Contextual Bandits based Online Hand-Craft Active Learning Strategy Selection
链接: https://arxiv.org/abs/2606.07910
作者: Shao-An Yin,Jiacong Li,Tianpei Xie,Cecile Levasseur,Wojciech Kowalinski,Nicola Elia
类目: Machine Learning (cs.LG)
*备注: 8 pages, 5 figures, Accepted to the NYRL 2025 Workshop
Abstract:The challenge with active learning algorithms is the uncertainty of the statistical distribution of unlabeled data, making it difficult to choose the best hand-crafted strategy. To address this, we introduced Contextual Adaptive Active Learning (CAAL). In CAAL, each “arm” represents a hand-crafted strategy. Unlike existing frameworks that select strategies based only on feedback from labeled data, we dynamically choose strategies for labeling batches of data using reward prediction with external context information. This general framework allows for customization with domain knowledge to design more effective rewards and context candidates. In addition, we experimentally show that CAAL outperforms the existing baseline adaptive strategy on public datasets using our reward and context design. Our results are consistent regardless of batch size in each iteration.
[LG-122] Layer-wise Derivative Controlled Networks Achieve Competitive Accuracy and Gradient Stability Across Data Regimes
链接: https://arxiv.org/abs/2606.07908
作者: Rowan Martnishn
类目: Machine Learning (cs.LG)
*备注:
Abstract:Derivative-controlled networks based on ChainzRule (CR) combine cubic polynomial layers with a lightweight forward-mode per-layer Jacobian penalty (DREG). In this second paper of a multi-part series, we evaluate the generalization properties of CR across data regimes. We ablate the shape of the DREG coefficient schedule, demonstrating that the optimal annealing range depends on representation noise. On the Pima Diabetes dataset, CR achieves strong low-data performance and maintains a consistent accuracy advantage over baselines from 5% to 100% training data, supported by exceptionally stable gradient tail ratios ( \sim 1.01–1.02 vs. 1.07–1.09 for ReLU networks). Extensions to SST-5 show competitive or superior results in both frozen-embedding and BERT fine-tuned regimes, including outperforming prior BERT baselines despite substantially less training data. These results are statistically significant: CR achieves superior accuracy over the strongest published baselines we could identify on both datasets ( p 0.05 ). These results establish that layer-wise derivative control induces a structural inductive bias toward low-frequency, stable representations that generalizes robustly across tabular and NLP domains, data volumes, and representation qualities. The gradient tail ratio serves as a reliable, label-free diagnostic of generalization capability. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2606.07908 [cs.LG] (or arXiv:2606.07908v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2606.07908 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-123] mporal Coverag e over Density: Parsimonious Training-Set Design for ML Climate Downscaling
链接: https://arxiv.org/abs/2606.07898
作者: Karandeep Singh,Stefan Rahimi,Chad W. Thackeray,Stephen Cropper,Alex Hall
类目: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE)
*备注: 22 pages, 8 figures
Abstract:High-resolution regional climate simulations provide critical information for climate impacts assessments but remain computationally expensive, motivating the development of machine-learning downscalers and emulators. A key challenge is determining how limited high-resolution simulations should be distributed across a changing climate trajectory to capture both forced climate response and internal variability. Using the CESM2 Large Ensemble over the western United States, we compare three training-year selection strategies under fixed data budgets: a contiguous block of historical years, years drawn from both the beginning and end of the simulation period, and years distributed throughout the full climate trajectory. Including both historical and future years consistently outperforms training on historical years alone, demonstrating the importance of exposing downscaling models to climate states outside the historical record and highlighting limitations of stationarity assumptions common in statistical downscaling. Training on years distributed throughout the full climate trajectory performs best overall, indicating that broad sampling of internal variability provides additional information beyond exposure to the forced climate response alone. Models trained on temporally distributed subsets more successfully reproduce variability in unseen ensemble members while retaining strong performance across a wide range of climate diagnostics. Even when trained on only one-tenth of the available high-resolution years, temporally distributed models remain highly competitive with full-data training. These results suggest that, under fixed computational budgets, broad sampling of climate states is more valuable than temporal continuity when allocating scarce high-resolution simulations. The findings provide practical guidance for regional climate downscaling and large-ensemble projection workflows.
[LG-124] Partially Performative Prediction
链接: https://arxiv.org/abs/2606.07890
作者: Jaewook Lee,Tijana Zrnic
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Performative prediction studies feedback loops that arise when predictive models are deployed in consequential domains. In these settings, deploying a model can change the population whose patterns the model aims to predict, inducing a distribution shift that is endogenous to the learning system. This perspective departs from classical treatments of distribution shift, where shifts are typically modeled as exogenous changes in the data-generating process. Yet, in practice, distribution shift is rarely one or the other. Predictive models may influence future data through the decisions they support, while the world itself continues to drift for reasons beyond the learner’s control. We study partially performative prediction, a framework that captures both endogenous and exogenous sources of distribution shift. The framework generalizes performative prediction by allowing the data distribution to evolve both in response to the deployed model and according to an external, time-varying process. We extend the central notions of performative stability and performative optimality to this setting by defining their online analogues that track the evolving partially performative environment. We analyze practical learning heuristics, including repeated retraining, and characterize when they successfully adapt to partially performative environments.
[LG-125] Breaking the Bubble: Asynchronous Pipeline Parallel Training with Bounded Weight Inconsistency
链接: https://arxiv.org/abs/2606.07881
作者: Itay Elam,Eliron Rahimi,Avi Mendelson,Chaim Baskin
类目: Machine Learning (cs.LG)
*备注:
Abstract:Pipeline parallelism is essential for training large neural networks, but existing schedules trade off throughput, memory, and optimization consistency. Synchronous pipelines preserve forward/backward weight consistency but suffer from bubbles; asynchronous pipelines remove bubbles but introduce weight-version mismatch, typically requiring weight stashing, prediction, or correction mechanisms. We introduce PACI (Pipeline Asynchronous training with Controlled Inconsistency), a bubble-free asynchronous pipeline method that bounds forward/backward version drift without weight stashing, prediction, additional parameter copies, or global synchronization. The key idea is to use local gradient accumulation as a version-control mechanism: by slowing parameter-version evolution relative to pipeline delay, PACI limits the number of optimizer updates crossed by any micro-batch while preserving steady-state utilization. In GPT-style language-model pretraining, PACI matches the stability and final perplexity of synchronous 1F1B-flush, retains the same peak memory footprint, achieves fully utilized pipeline throughput, and improves training time-to-accuracy by up to 1.69\times over the fastest flush baseline. These results show that forward/backward inconsistency need not be eliminated: when explicitly bounded, it can be safely traded for substantial efficiency gains.
[LG-126] Still: Amortized KV Cache Compaction in a Single Forward Pass
链接: https://arxiv.org/abs/2606.07878
作者: Charles O’Neill,Alex Sandomirsky,Harry Partridge,Mudith Jayasekara,Max Kirkby
类目: Machine Learning (cs.LG)
*备注:
Abstract:The KV cache is the memory bottleneck of long-horizon language model deployment. Practically, a deployable compactor must be lightweight enough to call during inference, expressive enough to preserve context under constraint, and reusable across a trajectory. Existing compaction methods satisfy only part of this requirement: selection methods are lightweight but subset-bound, while synthesis methods are expressive but rely on per-context optimization. Here we introduce Still, a small per-layer Perceiver trained once against a frozen base model that produces compact keys and values in a single forward pass. On Qwen and Gemma models, Still occupies the favorable side of the speed–quality frontier across compression ratios from 8\times to 200\times and context lengths from 8 k to 128 k. On the long-context RULER grid, Still exceeds the strongest baseline by 8–22 points. The same compact cache also supports free-form summarization, preserving most of the full-context gain on HELMET and winning a pairwise LongBench summarization comparison against KV-Distill. Because compaction is a forward pass, Still can be applied iteratively, entering a long-horizon regime unavailable to per-context methods. We show that amortization makes long-context cache compaction tractable, and synthesis makes its compact state useful at extreme compression.
[LG-127] acher-Free Self-Training Amplifies but Does Not Compound: A Pass@K Crossover on a Free-Verifier Domain
链接: https://arxiv.org/abs/2606.07856
作者: Igor Lima Strozzi
类目: Machine Learning (cs.LG)
*备注:
Abstract:When a language model trains on its own verified outputs, does it acquire capability beyond its base, or merely get better at expressing capability the base already had? We make the question decidable with a teacher-free “constellation” – a generator, a learned critic, and a free exact verifier – on a FlashFill-style “trapdoor” DSL, where verified (problem, solution) pairs are cheap to synthesize, hard to invert, and free to check exactly. Everything runs on one 4-bit Qwen3-4B on a single 24 GB GPU, with no model in the loop larger than the base. We report three findings. (i) Critic-guided selection beats verifier-filtered best-of- k by +9.1 pp ( 6/6 seeds), with the entire gain localized to tasks where candidates disagree on held-out inputs. (ii) Per-round STaR self-training raises the ceiling but never accelerates – the gain tracks remaining headroom and decelerates across K=4 independent training trajectories. (iii) The domain has no clean zero-capability frontier, so the usual " 0% \to climb = emergence" test is invalid here. A measured pass@ K crossover settles the diagnosis: the trained model wins at the operating budget (pass@ 8 ) but the base overtakes it at a large budget (pass@ 64 ) on every trajectory, so self-training concentrates probability mass rather than expanding reach. This is amplification, not compounding. ( K=4 is indicative, not yet a robust across-trajectory CI.)
[LG-128] Mitigating the Contractivity Trap in Diffusion ODEs via Stein Stabilization ICML2026
链接: https://arxiv.org/abs/2606.07835
作者: Shigui Li,Delu Zeng
类目: Machine Learning (cs.LG)
*备注: 32 pages, 12 figures. Accepted to ICML 2026
Abstract:A fundamental tension exists in the large-step inference of diffusion models via their deterministic probability flow ordinary differential equation (PF-ODE) trajectories, which we identify as the contractivity trap: efficient inference favors large step sizes, while aggressive steps and highly expressive denoisers can undermine contraction-based stability certificates for error suppression. To address this, we propose SteinDiff, a step-wise inference-time stabilization framework that employs Stein-derived corrections without requiring reference samples. Specifically, SteinDiff introduces a geometry-aware residual correction mechanism that regularizes large-step solver updates without retraining. To this end, we derive a closed-form Stein correction coefficient for step-wise solver adjustment, enabling reference-free adaptation to local data geometry. We further establish a score-controlled perturbation bound under distributional shifts and provide a complementary Stein perspective on EDM-style parameterizations. Extensive experiments demonstrate that SteinDiff mitigates severe artifacts and improves generative quality across large-step inference settings.
[LG-129] MOLOT System Card: Malicious Operational Logic Observation Transformer
链接: https://arxiv.org/abs/2606.07792
作者: Daniil Lopatkin,Maksim Mitrofanov,Stanislav Rakovsky,Aleksandr Khalikov
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG); Software Engineering (cs.SE)
*备注: 13 pages, 3 figures
Abstract:MOLOT (Malicious Operational Logic Observation Transformer) is a static malicious-code detection system designed for SAST setup where package metadata, maintainer history, and dynamic execution traces may be unavailable or unreliable. The system represents source code as behavior sequences derived from static call graphs, includes an explanation stage that ranks suspicious behavior activities and maps them back to source-code locations. The approach is evaluated on Python and JavaScript packages from PyPI and npm, compared with opensource detection tools, and validated under product constraints including runtime, memory use, and false-positive rates observed in a real moderation workflow. We also release Open Malicious-Code Bench, a public benchmark for reproducible evaluation of malicious-package detection methods. The results show that static behavior-sequence modeling can provide accurate, explainable, and deployable malicious-code detection for modern DevSecOps workflows.
[LG-130] Byzantine Cheap Talk: Adversarial Resilience and Topology Effects in LLM Coordination Games
链接: https://arxiv.org/abs/2606.07790
作者: Aya El Mir,Martin Takáč,Salem Lahlou
类目: Machine Learning (cs.LG)
*备注: Accepted at NETYS 2026 (The International Conference on Networked Systems)
Abstract:Multi-agent LLM systems increasingly rely on communication protocols for coordination, yet their robustness under adversarial and structural constraints remains poorly understood. Building on prior work showing that cheap-talk channels enable cooperation in LLM coordination games, we investigate two vulnerability classes in a 4-player Stag Hunt across six model families and 720 trials. First, when Byzantine agents signal cooperation but defect, non-Byzantine agents detect the betrayal within one round yet fail to adapt collectively: a substantial fraction continue cooperating despite repeated exploitation, unable to recover coordination due to the game’s unanimity payoff structure. Second, explicitly restricting communication topology collapses cooperation, while applying identical restrictions silently preserves near-perfect cooperation. This establishes that coordination failure stems from agents’ meta-reasoning about hidden information, not information loss itself. We identify two stable behavioral archetypes that replicate across all model cohorts: Defection-Prone models that switch permanently after betrayal, and Cooperation-Persistent models that continue cooperating at significant individual cost. These findings reveal concrete security vulnerabilities: communication channels can be exploited as adversarial injection vectors, and disclosing network topology to agents can degrade coordination even without any adversary present.
[LG-131] A Framework for Evaluating and Benchmarking Concept Drift Detection Methods KDD’26
链接: https://arxiv.org/abs/2606.07789
作者: Vitor Cerqueira,Heitor Murilo Gomes,Marco Heyden,Bernhard Pfahringer,Albert Bifet
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: Accepted in KDD’26
Abstract:Data stream mining is fundamentally challenged by concept drift, where distributional changes can degrade model performance. Despite the proliferation of drift detection methods, progress in the field is hindered by inconsistent evaluation practices: studies rely on oversimplified synthetic data generators, adopt incompatible metrics, and lack transparency in hyperparameter selection, making fair comparisons difficult. We address this gap with a novel benchmarking framework comprising three contributions: (1) a drift simulation method that injects controlled distributional changes into real-world datasets via Monte Carlo trials, enabling supervised evaluation while preserving real-world data complexity; (2) an evaluation protocol for drift detection with timing-aware criteria, including the derivation of new metrics (e.g., F1 detection score, normalized detection time) that are comparable across streams; and (3) we advocate for a leave-one-dataset-out hyperparameter optimization protocol for drift detection methods that promotes configuration robustness across heterogeneous stream dynamics. We benchmark 14 widely used drift detection methods on 7 realworld datasets across 4 drift types (class prior, label swap, feature permutation, feature filtering), each under both abrupt and gradual transitions. Our experimental results provide insights into the strengths and weaknesses of current drift detection approaches while establishing baseline performance metrics for future research in this area. All code and experiments are publicly available.
[LG-132] Contrast encodes inductive bias: separating slow noise from dynamics in predictive representation learning
链接: https://arxiv.org/abs/2606.07770
作者: Paarth Gulati,Ilya Nemenman
类目: Machine Learning (cs.LG)
*备注:
Abstract:Self-supervised methods that learn representations and predict dynamics fully in the latent space, such as JEPA, have been shown to confuse slowly varying noise with the dynamical signals they aim to capture. Specifically, when noise features remain approximately constant within each trajectory, contrastive predictive objectives preferentially encode these features instead of the true latent variables governing the system. The learned representation then becomes dominated by trajectory-specific noise, so downstream performance degrades with noise strength and does not improve even as the number and duration of training trajectories increase. We argue that this failure is a property of the objective itself, shared by a long line of contrastive predictive objectives that sample negatives across trajectories. To illustrate this generality, we study the failure mode and its remedy in two settings: a standard SimCLR-style JEPA on a synthetic moving-dot dataset, and DySIB, a recently introduced method designed for extracting physically interpretable representations of dynamics, on movies of a rigid-body pendulum. When negatives are instead sampled within a single trajectory, the slow noise can no longer distinguish frames within that trajectory, removing the predictive shortcut. Training one encoder simultaneously on many such trajectories then forces it to encode the variables relevant for the dynamics, with longer trajectories yielding better representations even for strong slow noise. Our results point toward principles for designing contrastive predictive objectives in dynamical representation learning, especially for physical systems with noisy experimental observations.
[LG-133] scCBGM: Interpretable Single-Cell Counterfactual Editing ICML2026
链接: https://arxiv.org/abs/2606.07760
作者: Alma Andersson,Aya Abdelsalam Ismail,Edward De Brouwer,Doron Haviv,Tommaso Biancalani,Kyunghyun Cho,Gabriele Scalia,Aïcha BenTaieb,Hector Corrada Bravo
类目: Machine Learning (cs.LG)
*备注: Accepted to ICML 2026; code at this https URL
Abstract:Understanding cellular phenotypes and how they respond to perturbations is critical for disease biology and therapeutic design. Single-cell RNA sequencing enables characterization at cellular resolution, yet the combinatorial space of conditions makes exhaustive experimental mapping infeasible. We introduce single-cell Concept Bottleneck Generative Models (scCBGM), a framework for interpretable and precise counterfactual editing of individual cells. scCBGM adapts concept bottleneck architectures for single-cell data through decoder skip connections and a cross-covariance penalty that promotes disentanglement without dimensional constraints. We extend the framework to flow matching models, enabling concept-guided editing in both encoding-decoding and generation regimes. To enable rigorous evaluation, we develop a synthetic benchmark with ground-truth counterfactuals. Across multiple real datasets, scCBGM demonstrates superior performance in combinatorial generalization and counterfactual prediction, supported by cell-level validation on synthetic data and population-level benchmarks on real datasets.
[LG-134] Characterizing the Discrete Geometry of ReLU Networks ICLR2026
链接: https://arxiv.org/abs/2606.07728
作者: Blake B. Gaines,Jinbo Bi
类目: Machine Learning (cs.LG)
*备注: Selected for an oral presentation at ICLR 2026. Tagged PDF, reviews, and discussions are available at this https URL
Abstract:It is well established that ReLU networks define continuous piecewise-linear functions, and that their linear regions are polyhedra in the input space. These regions form a complex that fully partitions the input space. The way these regions fit together is fundamental to the behavior of the network, as nonlinearities occur only at the boundaries where these regions connect. However, relatively little is known about the geometry of these complexes beyond bounds on the total number of regions, and calculating the complex exactly is intractable for most networks. In this work, we prove new theoretical results about these complexes that hold for all fully-connected ReLU networks, specifically about their connectivity graphs in which nodes correspond to regions and edges exist between each pair of regions connected by a face. We find that the average degree of this graph is upper bounded by twice the input dimension regardless of the width and depth of the network, and that the diameter of this graph has an upper bound that does not depend on input dimension, despite the number of regions increasing exponentially with input dimension. We corroborate our findings through experiments with networks trained on both synthetic and real-world data, which provide additional insight into the geometry of ReLU networks. Code to reproduce our results can be found at this https URL.
[LG-135] Cutting LLM Evaluation Costs with SySRs: A Bandit Algorithm that Provably Exploits Model Similarity ICML2026
链接: https://arxiv.org/abs/2606.07726
作者: Zifan Lyu,Chahine Nejma,Tobias Wegel,Fanny Yang,Florian E. Dorner
类目: Machine Learning (cs.LG)
*备注: Published at ICML 2026
Abstract:Large Language Models are typically benchmarked by evaluating every model on every test query. For practitioners seeking the best model to deploy, this is often wasteful: if a model clearly performs worse than others, there is no need to precisely estimate its performance. Best-arm identification algorithms can be naturally applied to drastically reduce costs by adaptively allocating evaluation budget. Further, language models often respond similarly to the same prompt-a property previous work has tried to leverage with mixed success. We propose Synchronized Successive Rejects (SySRs), augmenting the classical Successive Rejects algorithm with paired comparisons. Unlike prior attempts to leverage model similarity in best-model identification, our approach is hyperparameter-free and enjoys performance guarantees that improve with the degree of similarity between evaluated models. Empirically, our method outperforms all baselines in terms of average error rate across 15 standard benchmarks, and in terms of worst-case budget for reliably identifying the best model.
[LG-136] A Geometry-Aware Triplane Field Network for Vehicle Aerodynamic Prediction
链接: https://arxiv.org/abs/2606.07724
作者: Kangkang Qi,Huiyu Yang,Keqi Ding,Yunpeng Wang,Yuntian Chen,Yuanwei Bin,Rikui Zhang,Jianchun Wang
类目: Machine Learning (cs.LG)
*备注: 28 pages, 8 figures
Abstract:High-fidelity computational fluid dynamics (CFD) is crucial to vehicle aerodynamic analysis, but its cost still constrains early-stage design exploration. Machine-learning-based surface-field prediction offers a faster alternative if the model can efficiently capture both global flow context and local geometric detail. This work proposes a machine-learning-based method, named the geometry-aware triplane field network (GTF-Net), for vehicle aerodynamic pressure and wall shear stress prediction. GTF-Net constructs triplane features directly from sampled surface points through a shared multilayer perceptron (MLP) and smooth bilinear rasterization. The planes are then processed by a dual-stream backbone that combines adaptive Fourier neural operator (AFNO) spectral mixing with convolutional neural network (CNN) refinement, so long-range aerodynamic coupling and local geometry-induced variations are modeled in the same representation. At query stage, sampled triplane features are combined with vehicle-aligned directional coordinates, normal-projection features, and a voxel-based curvature proxy. GTF-Net is compared with Transolver, geometry-informed neural operator (GINO), and TripNet, a triplane-based surrogate model. GTF-Net improves the relative L2 error from the strongest baseline value of 0.157 to 0.145 for pressure prediction and from 0.237 to 0.226 for wall shear stress prediction. Ablation results show that AFNO mixing, local CNN refinement, and query-side geometric encoding each contribute to accuracy, supporting the proposed mechanism of combining structured triplane representation with explicit aerodynamic geometry cues.
[LG-137] Decoding Naturalistic Emotion Dynamics from the Brain: An LLM -Enhanced Regression Framework
链接: https://arxiv.org/abs/2606.07707
作者: Lemei Zhang,Peng Liu,Hans Dahle Kvadsheim,August Sætre Aasvær,Shuer Ye,Reza Bonyadi,Maryam Ziaei,Jon Atle Gulla
类目: Machine Learning (cs.LG)
*备注:
Abstract:Decoding emotional states from neural signals has been typically framed as a discrete, single-label classification task based on emotionally stable stimuli, a formulation that oversimplifies the continuous, fluid, and co-occurring nature of human affect. This study reconceptualizes emotion decoding by adopting a multi-target regression framework to track multiple overlapping emotional dimensions as continuous trajectories over time. Leveraging the robust generalization capabilities of Large Language Models (LLMs), we extracted fine-grained, continuous sentiment profiles from a naturalistic auditory narrative, Alice in Wonderland, to serve as scalable proxies for subjective affect from human fMRI dataset. Departing from standard classification paradigms or mass-univariate subtractive contrasts that filter out network dynamics, we leverage regularized and kernel-based machine learning algorithms as continuous estimators to track the magnitude of macroscale neural state variations. We demonstrate that models trained on temporal snapshots of Dynamic Functional Connectivity (DFC) significantly outperform static region-of-interest (ROI) amplitude representations, effectively capturing continuous emotional trajectories under rapidly fluctuating narrative input. Furthermore, by implementing graph-theoretical Explainable AI (XAI) techniques, we deconstruct the underlying predictive features to reveal highly interpretable, emotion-specific topological configurations. Collectively, these results highlight the utility of LLM-automated annotation in affective neuroscience and provide compelling empirical evidence for psychological constructionist frameworks, demonstrating that dynamic, distributed network interactions offer superior explanatory power over strictly locationist accounts of emotion.
[LG-138] Vessel Traffic Flow Prediction on Sparse Data via Spatio-Temporal Graph Neural Networks with a Learnable Tweedie Head
链接: https://arxiv.org/abs/2606.07694
作者: Kyeongjun Lee,Heeyoung Kim
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Accurate vessel traffic flow prediction is crucial for smart port operations and navigational safety. However, maritime traffic flow data are often highly sparse with intermittent bursts, making robust forecasting challenging. Under such conditions, conventional spatio-temporal graph neural networks (ST-GNNs) can degrade toward conservative near-zero predictions and fail to capture non-zero activity. Although zero-inflated negative binomial (ZINB) models partially address excess zeros, their two-part formulation can still remain conservative around abrupt transitions. To address these issues, we propose a model-agnostic learnable Tweedie head that can be attached as a plug-and-play output module to arbitrary ST-GNN backbones. Instead of likelihood-based Tweedie training, which typically requires surrogate objectives, our approach optimizes the closed-form Tweedie unit deviance and predicts the mean for point forecasting while learning a node-level variance power to capture heterogeneous variability across port areas. Experiments on a maritime traffic graph constructed from real-world AIS data in the Port of Los Angeles and Long Beach show that the proposed head consistently improves RMSE across multiple ST-GNN backbones, especially on non-zero events, leading to more reliable forecasts for practical maritime traffic control.
[LG-139] QDS-SNN: Energy-efficient Quantum Deeply-Supervised Spiking Neural Network Algorithm for Traffic Sign Recognition
链接: https://arxiv.org/abs/2606.07657
作者: Zhiguo Qu,Keqi Li,Le Sun,Wenjie Liu,Yimin Yu,Saif Al-Kuwari,Ahmed Farouk
类目: Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)
*备注: 13 pages, 10 Figures, 8 Tables
Abstract:Traffic sign recognition is crucial for intelligent transportation and autonomous driving, as it can improve driving efficiency and ensure road safety. However, traditional recognition methods are based on large datasets and intensive computation, which limits their real-time applicability. Spiking Neural Networks (SNNs) offer a biologically inspired, energy-efficient alternative due to their spatiotemporal processing capabilities, but suffer from information loss and vanishing gradients during training. To overcome these limitations, this study proposes a Quantum Deep-supervised Spiking Neural Network (QDS-SNN) that integrates Quantum Neural Networks (QNNs) for efficient, low-power deep supervision. Using quantum superposition and entanglement, QNNs enable expressive representations and parallel computation, thereby enhancing performance without compromising energy efficiency. The proposed QDS-SNN incorporates a temporally and spatially adaptive LIF (TSA-LIF) neuron and a quantum-assisted classifier module (QACM) to mitigate gradient issues and improve training effectiveness. This study conducts experiments on the PennyLane quantum simulation platform, and the results show that QDS-SNN achieves 99.72% accuracy on the GTSRB dataset in only 6 time steps – outperforming the MS-ResNet baseline by 1.32% while reducing energy consumption by 55.77%. In the TSRD dataset, it achieves 97.90% accuracy while reducing energy use to 52.68% of the baseline. These results demonstrate that QDS-SNN offers a high-performance, energy-efficient solution for traffic sign recognition in intelligent transportation systems.
[LG-140] Evaluation of ML Resource Utilization Requires Model Life Cycle Assessment ICML2026
链接: https://arxiv.org/abs/2606.07632
作者: Jared Fernandez,Clara Na,Yonatan Bisk,Constantine Samaras,Emma Strubell
类目: Machine Learning (cs.LG)
*备注: ICML 2026: Position Paper Track
Abstract:Proper accounting of the energy requirements and environmental impact of artificial intelligence (AI) systems is necessary for researchers, developers, policy makers, and users to assess the barriers to building systems at scale. With the growing complexity of pipelines and underlying infrastructure needed to develop and deploy AI systems, previous approaches for evaluating AI efficiency which focus on the costs of a single training run or an individual inference prediction are no longer sufficient. In this position paper, we enunciate the need for applying life cycle assessment to evaluate the costs of the machine learning model development and deployment pipeline to properly account for the required resources and downstream impact. Life cycle assessments enable the incorporation of costs across the full life cycle of an AI system and its underlying infrastructure, from the embodied costs associated with the physical computing hardware through the operational costs in training and inference.
[LG-141] Learning Transfers: Kan Extensions for Neural Invariants
链接: https://arxiv.org/abs/2606.07627
作者: Luciano Melodia
类目: Machine Learning (cs.LG); Algebraic Topology (math.AT); Category Theory (math.CT)
*备注:
Abstract:Transfer learning presumes that a representation learned on source tasks carries structure that remains usable on related target tasks. Standard evaluations probe this through target accuracy or distributional discrepancy, yet leave unspecified which structural invariant is meant to transfer. We supply that invariant categorically. A source task category \mathcal A , a target task category \mathcal B , and a task-change functor J:\mathcal A\to\mathcal B determine, for every invariant-valued source representation F:\mathcal A\to\mathcal V , the universal transferred invariant \operatornameLanJ F . Given a target invariant G:\mathcal B\to\mathcal V , we define the transfer discrepancy \operatornameCompJ(F,G)=\supb\in\operatornameOb(\mathcal B) d\mathcal V\bigl((\operatornameLan_J F)(b),G(b)\bigr) , evaluating transfer not by an objectwise comparison of source and target, but by comparing the target invariant against the one forced by the prescribed task transformation. We prove finite cokernel formulas for (\operatornameLan_J F)(b) in chain complexes and persistence modules, indexed by the comma category J\downarrow b . For persistence-valued finite-type one-parameter invariants, the discrepancy is computed exactly by bottleneck distances between barcodes. Controlled experiments on neural latent point clouds then test whether the score recovers the correct task functor and flags representation collapses that preserve classification accuracy while destroying transfer-relevant topology.
[LG-142] Sequential statistical inference for Large Language Models : Representation validity and monitoring
链接: https://arxiv.org/abs/2606.07624
作者: Yao Xie
类目: Machine Learning (cs.LG)
*备注: This article was prepared for a invited discussion in The American Statistician
Abstract:This discussion argues that sequential statistical inference can naturally contribute to LLM trustworthiness. In deployment, LLM systems are queried repeatedly, conditioned on evolving contexts, and incorporate user or tool feedback, and may exhibit behavioral shifts after model updates or distribution changes. The discussion is organized around three tasks: representation, modeling LLM interactions as dependent stochastic processes rather than isolated prompt–response pairs; validity, developing uncertainty guarantees that remain meaningful under dependence, repeated use, and adaptation; and monitoring, using sequential alarms and change-point detection to identify shifts in calibration, hallucination rates, refusal behavior, fairness, or other task-relevant properties. This perspective complements recent surveys by viewing trustworthy LLM deployment as a problem of statistical process control.
[LG-143] Finite Certificates for In-Context Determinacy and a Threshold Theory of Emergence in Language Models
链接: https://arxiv.org/abs/2606.07623
作者: Faruk Alpay,Hamdi Alakkad
类目: Machine Learning (cs.LG); Logic in Computer Science (cs.LO)
*备注: 40 pages; ancillary files provided
Abstract:This paper develops a model-theoretic framework for verifying context-conditioned language-model behavior by replacing benchmark labels with finite semantic certificates. The first problem is finite determinacy: when do examples in a context force the answer to a query without changing model parameters? In finite-field linear task families, we prove an exact row-space criterion, compute the residual hypothesis count, derive full and query-local identification curves, and show that extracting a smallest forcing subcontext is NP-complete even for binary outputs. The second problem is threshold emergence: when does an apparent benchmark jump reflect a semantic transition rather than a discontinuity of the scoring map? We prove an anti-mirage theorem separating thresholded metrics from semantic confidence and give a rate-sensitive crossing bound for latent commitments becoming visible above threshold. The common semantic object is a confidence functional on definable events. We show that it is a Boolean probability measure, equivalently a Keisler measure on the relevant type space, whose measure-one formulas form a proper filter and whose Stone-space representation is invariant under definitional expansion. The resulting calculus provides finite context certificates, pair-separator hitting sets, query teaching dimension, prompt-preservation criteria, and scale-limit witnesses. Exact-arithmetic ancillary scripts reproduce the finite-field and threshold calculations and generate the data used by the figures.
[LG-144] Airport Terminal Passenger Queue Forecasting for Departure Gates and Security Checkpoints
链接: https://arxiv.org/abs/2606.07622
作者: Juhwan Lee,Seokbin Yoon,Keumjin Lee,Hojong Baik,Seyeon Jung
类目: Machine Learning (cs.LG); Applications (stat.AP)
*备注: 9 pages, 6 figures, accepted at DASC 2026
Abstract:Accurate passenger queue forecasting in airport terminals is essential for efficient departure operations, as it enables proactive congestion management. However, time-varying passenger demand and heterogeneous facility usage across multiple departure facilities make forecasting challenging. In this work, we propose a passenger queue forecasting framework that learns historical passenger flow patterns from operational data. The proposed model employs a Transformer-based architecture to capture temporal dependencies and inter-facility correlations using past queue length and waiting time at departure gates and security checkpoints, together with passenger throughput at check-in islands. The learned representations are mapped to two facility-specific MLP heads to predict queue length and waiting time at departure gates and security checkpoints. Experimental results demonstrate accurate forecasts up to two hours ahead. The proposed approach offers practical real-time decision support for proactive queue management and staff reallocation in airport terminal operations.
[LG-145] Graph Neural Networks for Predicting Solvability of Finite Groups
链接: https://arxiv.org/abs/2606.07619
作者: Tal Weissblat
类目: Machine Learning (cs.LG); Group Theory (math.GR)
*备注: 7 pages, 3 tables
Abstract:We present a Graph Neural Network (GNN) framework for the classification of finite groups according to their solvability. Using graph representations associated with finite groups, including Cayley graphs (CG), the proposed model is trained to distinguish solvable and non-solvable groups using structural graph information alone. The framework is evaluated on groups outside the training dataset in order to investigate the extent to which GNNs can learn algebraic properties arising in group theory. More broadly, the present work explores the relationship between algebraic structure and graph-based geometric representations of finite groups. The present study is intended as a proof-of-concept investigation of whether GNNs can learn algebraic properties of finite groups from graph-based representations
[LG-146] Measuring Poverty and Inequality with Reduced Data: A Machine Learning Approach Using Nigerian Household Data
链接: https://arxiv.org/abs/2606.07614
作者: Vanesa Jordá,Miguel Niño-Zarazúa
类目: Machine Learning (cs.LG); Applications (stat.AP)
*备注:
Abstract:Reliable measurement of income and consumption is essential for monitoring poverty and inequality in low- and middle-income countries, yet full household surveys are costly and difficult to implement regularly. This paper examines whether reduced survey instruments can preserve key distributional information. We apply Random Forest Recursive Feature Elimination (RF-RFE) to the 2018/19 Nigeria General Household Survey-Panel to identify the income sources, consumption categories and household characteristics that best classify individuals within the welfare distribution. The analysis focuses on three outcomes: poverty status, location in the quintile distribution and position relative to the Gini-based inequality line. The survey’s post-planting and post-harvest periods allow us to assess performance under different seasonal contexts. Results show that RF-RFE achieves strong classification accuracy with few predictors. For consumption, poverty status and inequality-line position are accurately predicted using a small set of expenditure categories, while quintile classification reaches about 80 percent accuracy for seasonal consumption and 60–65 percent for annual consumption predicted from a single seasonal visit. For income, poverty status reaches around 90 percent accuracy with five predictors, and inequality-line position is largely captured by labour earnings. The findings suggest that machine-learning methods can help improve survey design and reduce data requirements while retaining much of the distributional information needed to measure and monitor poverty and inequality.
[LG-147] Position: Genomic Model Research Must Move Beyond Anecdotal Evaluation of Interpretability Methods
链接: https://arxiv.org/abs/2606.07607
作者: Shasha Zhou,Mingyu Huang,Ke Li
类目: Machine Learning (cs.LG); Genomics (q-bio.GN)
*备注:
Abstract:Advances in machine learning and computational power have unlocked the predictive potential of the human genome, yet biologists now demand that these models also elucidate the underlying biological mechanisms. While interpretable machine learning (IML) techniques have been increasingly applied to bridge this gap, there has been a pervasive reliance on anecdotal validation: the vast majority of research relies on a single IML method and reports only isolated successful instances. Through a benchmarking study on transcription factor binding, we demonstrate the risks of current practices. We show that different IML methods can often (1) yield contradictory explanations for the same predictions, (2) fail to localize known regulatory motifs, and (3) fail to faithfully reflect the model’s internal decision process. In light of this, we argue for a validation framework analogous to clinical trials: just as trials require rigorous design and adverse-event reporting, genomic interpretability must move beyond cherry-picked plausibility toward systematic assessment of consistency, faithfulness, and biological validity. To facilitate this, we propose a tiered framework to guide rigorous evaluation and reporting of genomic IML methods.
[LG-148] QDSP: An Interpretable Structured Learning Framework for Predicting Death or Cerebral Palsy in Very Low Birth Weight Infants
链接: https://arxiv.org/abs/2606.07606
作者: Ling Wang,Xiaolong Li,Hui Zhou,Jing Shi,Fuhao Zhang,Dapeng Chen,Nan Mu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Very low birth weight infants (VLBWI) are at high risk of mortality and severe neurodevelopmental impairment, including cerebral palsy, yet reliable discharge-time prognostic stratification remains challenging in high-dimensional and data-limited clinical settings. To address this problem, we propose QDSP, an interpretable structured learning framework that integrates Quota-guided Subspace Sampling (QSS) and Differentiable-decision-guided Structure Perception (DSP). The QSS module constructs stability-aware and low-redundancy feature subspaces through bootstrap-based feature consistency estimation, whereas the DSP module employs differentiable soft oblique decision structures to model nonlinear clinical interactions while preserving traceable decision evidence. The proposed framework was evaluated on a real-world VLBWI cohort comprising 51 infants and further validated on three public medical tabular datasets. On the primary cohort, QDSP achieved an accuracy of 0.9200 and an AUC of 0.9714, outperforming representative machine learning and deep tabular learning baselines, including XGBoost, TabNet, and TabPFN. Across external datasets, QDSP maintained competitive discrimination and calibration under varying sample sizes and clinical distributions. In addition, SHAP-based analyses and differentiable decision-path tracing identified clinically relevant predictors, including cystic periventricular leukomalacia (cPVL) and birth weight, consistent with established neonatal pathophysiological evidence. These results suggest that QDSP provides an interpretable and robust framework for discharge-time risk stratification in VLBWI and may support early individualized clinical decision-making in neonatal intensive care settings.
[LG-149] Shortcuts in the Tail: Debiasing via Post-Hoc Spectral Compression of Fine-Tuning Updates ICML
链接: https://arxiv.org/abs/2606.07596
作者: Edward Sun,Dmitrii Troitskii
类目: Machine Learning (cs.LG)
*备注: ICML Weight Space Symmetries Workshop 2026
Abstract:Fine-tuning often introduces spurious correlations alongside task knowledge, causing systematic failures on underrepresented groups. Existing mitigations require retraining, group labels, or curated counterfactual data. We show a simple post-hoc intervention reduces shortcut reliance without any of these: truncating the tail of the SVD of \Delta W = W_\mathrmft - W_\mathrmbase reduces the spurious-group gap while preserving task accuracy. Across three instruction-tuned models ( 0.5 B-- 7 B) and four classification benchmarks, top- k truncation reduces the gap on every cell at 2 pp accuracy loss, by up to 5\times on CivilComments. We propose this works because the shortcut response sits in the tail of the singular ordering of \Delta W , a claim about how truncation behaves rather than about the raw singular values, which are broadly distributed and look the same across all four datasets. A controlled boundary case in which fine-tuning has only a shortcut to learn shows the predicted FT-to-base collapse, and bottom-/random- k and matched-rank LoRA controls rule out generic low-rank approximation and rank-constrained training as the explanation. We read this as preliminary evidence that the singular basis of \Delta W is a useful coordinate system for studying what fine-tuning has learned.
[LG-150] UNIQ: Conformal Calibration for Adaptive Conservatism in Offline Reinforcement Learning ICML2026
链接: https://arxiv.org/abs/2606.07592
作者: Aditya Upadhyay
类目: Machine Learning (cs.LG)
*备注: 19 pages, 2 figures, ICML 2026 Workshop on Decision-Making from Offline Datasets to Online Adaptation: Black-Box Optimization to Reinforcement Learning
Abstract:Offline reinforcement learning requires careful conservatism to mitigate distribution shift, yet most existing methods apply a fixed penalty uniformly across all states regardless of local data coverage. We present UNIQ (Uncertainty-Informed Quantile), an offline RL method that introduces state-adaptive conservatism through conformally calibrated uncertainty estimation. Built on the Implicit Q-Learning (IQL) backbone, UNIQ trains a multi-expectile value ensemble, computes distribution-free uncertainty estimates using split conformal prediction, and maps the resulting signal to a state-dependent expectile that relaxes conservatism in well-covered regions while strengthening it in uncertain regions near the data frontier. On D4RL MuJoCo benchmarks, UNIQ consistently improves over IQL, with the largest gains observed on Walker2d and replay-heavy tasks. At the same time, UNIQ operates at near-IQL memory cost (approximately 250 MB peak VRAM), providing roughly a 10x reduction compared to EDAC. Rather than pursuing overall state-of-the-art performance, we position UNIQ as a practical mechanism contribution that improves the performance-efficiency trade-off in offline reinforcement learning.
[LG-151] Optimality of Sequential Filtering Under Independent Cost and Selectivity Models
链接: https://arxiv.org/abs/2606.07589
作者: Hrishikesh Paranjape,Abhishek Mandal,Xian Sun
类目: Machine Learning (cs.LG)
*备注: 2 pages, 2 figures. Accepted at the 2026 IEEE International Conference on Electro/Information Technology (EIT 2026)
Abstract:Sequential filtering pipelines are a common design pattern in large-scale systems, where a large population of items is progressively reduced by a sequence of stages that each incur cost. Despite their prevalence in ranking systems, cascaded machine learning inference, and fraud detection, filter ordering is often determined by heuristics without formal guarantees. We formalize sequential filtering under an expected-cost objective and prove that, under an independence model, ordering filters by increasing ratio of cost to rejection probability minimizes expected total cost. Extensive Monte Carlo simulations show that the optimal ordering strictly dominates common heuristics across all runs, both in expectation and across the full distribution of outcomes.
[LG-152] Information-Geometric Optimization on Spheres
链接: https://arxiv.org/abs/2606.07588
作者: Vladimir Ja’ cimović
类目: Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG); Optimization and Control (math.OC); Quantum Physics (quant-ph)
*备注:
Abstract:We consider the black-box optimization problem on a sphere. Two information-geometric optimization flows (IGO flows) are designed with rigorous calculation of natural search gradients based on hyperbolic (information) geometry of Poincar’ e and Bergman balls. We demonstrate that ensembles of generalized Kuramoto oscillators on spheres compute natural search gradients and realize IGO algorithms on both manifolds. The relationship between natural gradient policies in Bergman balls and quantum decision making is pointed out.
[LG-153] he Routing Plateau: Understanding and Breaking the Accuracy Limits of LLM Routers
链接: https://arxiv.org/abs/2606.07587
作者: Yifan Lu,Qiyue Zhang,Shenrun Zhang,Zhibo Yu,Zhuang Wang,Hanjie Chen,Jiarong Xing
类目: Machine Learning (cs.LG)
*备注: 23 Pages, 12 Tables, 9 Figures
Abstract:LLM routing has become a popular approach to improve the cost-quality trade-off of LLM services by dynamically selecting a model for each query. Recent work has explored a broad range of routing methods, including clustering-based routers, learned classifiers, pairwise ranking, and confidence-based approaches. Our extensive study of 21 routing methods across five benchmarks reveals a consistent phenomenon that we call the routing plateau: many methods, including kNN, achieve very similar accuracy and converge to a narrow performance range that remains far below the oracle router. Our investigation shows that the plateau is largely caused by a predictability bottleneck: current routers mainly learn global averaged model-performance trends rather than fine-grained query-specific routing signals. As a result, they solve overlapping easy queries but collectively fail on hard queries that require instance-specific routing decisions. We further study how to move beyond the plateau and find that larger training datasets, stronger encoders, and end-to-end fine-tuning can further improve routing accuracy. These findings characterize the common limits of current routing methods and provide insights and actionable directions for the community to build more effective routing systems.
[LG-154] Quantifying Uncertainty in Space Debris Capture with Active Tether-Net Systems Caused by Noisy Observations
链接: https://arxiv.org/abs/2606.07580
作者: Feng Liu,Achira Boonrath,Eleonora M. Botta,Souma Chowdhury
类目: ystems and Control (eess.SY); Machine Learning (cs.LG)
*备注: Presented at 2025 AIAA Aviation Forum
Abstract:As Low Earth Orbit has grown more crowded with space debris, the need for reliable and efficient debris removal solutions becomes more urgent. An active tether-net system with maneuverable units is one of the promising solutions to this problem, whose success is dependent on the robustness of the net maneuver and closing decisions. These in turn are impacted by the uncertainties attributed to i) noisy observation of the target debris state (e.g., sensing errors), and ii) imperfect simulations of the complex net dynamics and net/debris interaction behavior, over which the decision system is trained. This paper focuses on the first of these two uncertainty sources, and presents a pipeline to propagate and quantify the resulting uncertainty in the debris capture performance expressed in terms of Capture Quality Index (CQI). This quantification is uniquely performed for both an active tether-net using a fixed baseline control and one using a trained neuro-control policy to guide the net maneuver during the deployment phase. Two different uncertainty quantification (UQ) techniques, namely Sobol’s variance-based sensitivity analysis and perturbation-based method are exploited. A high-fidelity simulator and a lower-fidelity surrogate-based environment are used to demonstrate trade-offs between prediction accuracy versus ease of resolving uncertainties.
[LG-155] MST-Direct at Scale: Multivariate and Conditional Geostatistical Simulation via Sinkhorn Optimal Transport
链接: https://arxiv.org/abs/2606.07578
作者: Tcharlies Bachmann Schmitz
类目: Machine Learning (cs.LG); Methodology (stat.ME); Machine Learning (stat.ML)
*备注:
Abstract:This paper extends MST-Direct, a Matching-via-Sinkhorn-Transport approach for multivariate geostatistical simulation, from the original bivariate, unconditional, small-grid formulation to multivariate, conditional, and large-grid settings. We address the three main limitations identified in the original work: (i) scalability beyond a few thousand nodes through a sparse, candidate-restricted Sinkhorn matcher with O(nC) memory complexity; (ii) extension to multiple variables by matching target value tuples onto an independent FFT-MA Gaussian backbone that reproduces a prescribed variogram; and (iii) hard-data conditioning by fixing observed data tuples at their spatial locations while conditioning the backbone through kriging. Because the transport plan remains a permutation of the target tuples, the multivariate joint distribution is preserved exactly. The method is validated using the same six-variate, heteroscedastic, strongly nonlinear reference distribution employed in Direct Multivariate Simulation (DMS), under both unconditional (200x200) and conditional (100x100, 200 hard-data samples) scenarios, and is benchmarked against the Projection Pursuit Multivariate Transform (PPMT). Results show that MST-Direct reproduces the joint distribution with zero histogram error, exactly honours hard data, and accurately reproduces the prescribed spatial correlation structure, whereas PPMT remains an approximation. Index Terms-Optimal transport, Sinkhorn algorithm, geostatistical simulation, multivariate simulation. Subjects: Machine Learning (cs.LG); Methodology (stat.ME); Machine Learning (stat.ML) Cite as: arXiv:2606.07578 [cs.LG] (or arXiv:2606.07578v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2606.07578 Focus to learn more arXiv-issued DOI via DataCite
[LG-156] Can LLM s extract scientific consensus? A case study in high-temperature superconductivity
链接: https://arxiv.org/abs/2606.07570
作者: Mouyang Cheng,Wenhao He,Zhuotao Jin,Bowen Yu,Ju Li,Boris Kozinsky,Yao Wang,Pavel Volkov,Liangzi Deng,Ching-Wu Chu,Xiao-Gang Wen,Mingda Li
类目: Digital Libraries (cs.DL); Machine Learning (cs.LG)
*备注: 23 pages, 4 figures
Abstract:Scientific knowledge is increasingly dispersed across vast and heterogeneous scientific literature, where important claims are often implicit, evolving, and internally debated. While large language models (LLMs) have shown impressive performance in information extraction and summarization, their ability to recover latent scientific consensus remains unclear. Here, we investigate this problem in the context of high-temperature superconductivity (HTS), a long-standing and highly debated topic in condensed matter physics, as a challenging testbed. Using near 18,000 highly-cited publications over the past seven decades, we construct a structured knowledge graph linking competing superconducting mechanisms, material families, evidential modalities, and citation relations. We find that LLM-extracted representations recover coherent and physically interpretable structures, including family-dependent mechanism profiles, evidence-specific correlations, and citation-mediated temporal evolution of scientific beliefs. Ablation studies on LLM further show that the global structure remains robust across prompting, decoding, and model variations. Our results suggest that LLMs can indeed serve as scalable tools for deciphering scientific knowledge in domains characterized by competing interpretations and evolving knowledge.
[LG-157] riHead-GAN: A Generative Adversarial Network with Triple-Head Discriminator for Carbon Emission Time Series Generation
链接: https://arxiv.org/abs/2606.07569
作者: Zesen Wang,Lijuan Lan,Yonggang Li,Chunhua Yang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Accurate carbon emission monitoring is critical for climate policy and emerging regulatory mechanisms such as the EU Carbon Border Adjustment Mechanism, yet city-level high-frequency monitoring data remain extremely scarce, severely limiting data-hungry deep learning models. Time series generation is a natural remedy, but existing GAN and diffusion-based generators often provide limited explicit supervision for the domain structure of carbon emission data: they may match marginal distributional statistics while insufficiently preserving cross-variable correlations between CO _2 and co-emitted pollutants and meteorological factors, and tend to collapse the first-difference statistics of atmospheric measurements, producing sequences that are smooth on average but lack the realistic step-wise variability of the underlying signals. We propose TriHead-GAN, a Transformer-based adversarial framework whose triple-head discriminator jointly supervises three complementary aspects of the joint distribution: distributional authenticity via a Wasserstein critic, cross-variable dependency via leakage-free regression of the target variable, and step-wise temporal smoothness via adjacent-difference prediction. The generator combines global self-attention with local temporal convolution, per-step noise injection, and an anti-smoothing loss that matches first-difference statistics. Experiments on the self-collected Changsha Carbon dataset, two public carbon datasets (China, US), and the ETTh1 benchmark show that TriHead-GAN achieves favorable performance over mainstream baselines on the vast majority of settings, and that the resulting synthetic windows improve downstream forecasting accuracy in low-resource carbon monitoring scenarios.
[LG-158] STARIXNet: Multivariate and Multi-attribute Deep Learning Approach to Real-Time Resource Allocation in Cloud Platforms
链接: https://arxiv.org/abs/2606.07565
作者: Ahmed Abdulaal,Maruf Aytekin,Thilaga kumaran Srinivasan,Tomer Lancewicki
类目: Machine Learning (cs.LG)
*备注: 11 pages, 12 figures. Under review
Abstract:Intelligent scaling of microservices in cloud platforms is crucial for mitigating escalating compute costs while avoiding service disruptions. Current solutions are limited to the univariate space, typically focusing on CPU usage alone to drive scaling decisions. Moreover, they address the problem as a purely forecasting task, focusing on prediction precision while neglecting the greater risks of underestimation and delays in system responsiveness. Alternative solutions are computationally complex, making them impractical for large-scale, real-time deployments. To address these challenges, we present STARIXNet, a lightweight neural network that guides resource allocation decisions in the multivariate space by capturing spatio-temporal relationships among multiple system metrics. STARIXNet models multiple quasi-dependent attributes, in particular the (S)easonal, (T)emporal, (A)uto-®egressive (I)ntegrated, and e(X)ogenous patterns, then implements an aggregation policy to finalize scaling decisions, prioritizing service stability, followed by cost-efficiency, over raw forecast accuracy. We empirically demonstrate the performance of STARIXNet by benchmarking against existing solutions in real-world settings. STARIXNet is deployed for critical production microservices at Walmart achieving tangible savings ranging from 10% to 50%, in addition to intangible benefits through improved service stability and customer experience.
[LG-159] Boundary Variance Inflation Causes Acquisition Bias in Gaussian Processes
链接: https://arxiv.org/abs/2606.07561
作者: Maria Bånkestad,Sanna Jarl,Jens Sjölund
类目: Machine Learning (cs.LG); Methodology (stat.ME); Machine Learning (stat.ML)
*备注: 14 pages, 8 figures; appendices included
Abstract:Gaussian processes with stationary kernels on bounded domains exhibit inflated posterior variance near the boundary. Despite being a long-recognized artifact in geostatistics and a source of over-exploration in Bayesian optimization, the causes and effects of boundary-induced acquisition bias are underexplored. We trace the root cause to a simple geometric mechanism: the truncation of the kernel correlation neighborhood at the domain boundary creates an observation-independent distortion that worsens with dimensionality. We show how this distortion manifests across three acquisition classes: variance maximization concentrates selections at the corners, whereas negative integrated posterior variance and expected predictive information gain move selections inward to axis-aligned interior shells. These patterns arise without reference to any objective function, meaning that acquisition behavior can be dominated by kernel geometry rather than the desired task-specific uncertainty. To quantify this, we introduce a function-free selection-profile diagnostic for arbitrary acquisitions, kernels, and bounded-domain geometries.
[LG-160] Weighted universal approximation of differentiable maps on infinite-dimensional manifolds
链接: https://arxiv.org/abs/2606.09820
作者: Philipp Schmocker,Josef Teichmann
类目: Functional Analysis (math.FA); Machine Learning (cs.LG); Probability (math.PR); Mathematical Finance (q-fin.MF); Machine Learning (stat.ML)
*备注: 77 pages, 3 figures
Abstract:We generalize the universal approximation theorem for functional input neural networks (FNN) to differentiable maps by including the approximation of the derivatives. A FNN maps the input from a possibly infinite-dimensional weighted manifold to the real-valued hidden layer, on which a non-linear scalar activation function is applied, and then returns the output into a Banach space via some linear readouts. By proving a weighted Nachbin theorem, we establish a universal approximation theorem (UAT) for differentiable maps, which goes beyond the usual formulation on compact sets and also includes the approximation of the derivatives. This leads us to approximation results for non-anticipative functionals including the horizontal and vertical derivatives. As a further application, we show that linear functions of the signature are able to approximate path space functionals including their directional derivatives.
[LG-161] Discovering Functionally Selective Brain Regions with a Deep Topographic Multimodal Model
链接: https://arxiv.org/abs/2606.09770
作者: Badr AlKhamissi,Johannes Mehrer,Lara Marinov,Ahmed Abdelaal,Abdulkadir Gokce,Martin Schrimpf
类目: Neurons and Cognition (q-bio.NC); Machine Learning (cs.LG)
*备注: Preprint. First two author contributed equally
Abstract:Nearby neurons in cortex share similar response profiles, producing systematic spatial organization across sensory and cognitive systems. Recent topographic models reproduce aspects of this structure but remain unimodal and spatially constrain each layer separately, yielding fragmented maps that capture neither the contiguity of cortical processing streams nor their integration across modalities. We introduce Topo-Omni, a topographic multimodal model in which visual, auditory, and language/cognitive processing share a single contiguous in-silico sheet. Built by fine-tuning a pretrained foundation model with a spatial smoothness objective, this architecture develops clusters across modalities that are consistent with human neuroimaging, from sensory to cognitive systems. Driving or suppressing a cluster selectively biases or impairs perception, paralleling human intervention studies. Finally, we use our model to screen for novel clusters in-silico and discover new natural landscape and animal networks which we validate in human data. A single spatial principle thus organizes representations across modalities and processing stages, yielding testable hypotheses about cortical organization.
[LG-162] Adaptive directional gradients for parameterised quantum circuits
链接: https://arxiv.org/abs/2606.09734
作者: Brian Coyle,Snehal Raj,Virag Umathe,El Amine Cherrat,Elham Kashefi
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注: 37 pages, 13 figures
Abstract:Training parameterised quantum circuits (PQCs) on quantum hardware is bottlenecked by the measurement cost of gradient estimation, which under the parameter-shift rule scales linearly in the number of trainable parameters and dominates the total shot budget of training at scale. In this work, we propose a framework of forward gradient estimators for PQCs, based on the forward mode of automatic differentiation, that yields an unbiased estimator of the gradient by averaging a freely tunable number of random directional derivatives and recovers SPSA, random coordinate descent, and the parameter-shift rule as limiting cases, with no ancilla qubits or controlled-gate overhead. We prove that stochastic quantum forward gradient descent converges under standard assumptions, with an explicit second-moment expansion that interpolates between the single-direction extreme of SPSA and the full-gradient extreme of parameter-shift. Within this framework we derive QUIVER (Quantum Iterative V-adaptive Estimator Rule), an adaptive optimiser for parameterised circuits whose update rule follows from a closed-form minimum measurement-cost allocation. We show numerically that forward gradients train Hamming-weight-preserving orthogonal quantum neural networks with up to 60 qubits and 1770 parameters on the ECG5000 and MNIST datasets orders of magnitude more efficiently than the parameter-shift rule. We also demonstrate that our proposed QUIVER optimiser can outperform iCANS and gCANS measurement-frugal optimisers on optimisation problems using the quantum approximate optimisation algorithm and quantum simulation with the variational quantum eigensolver.
[LG-163] Integrating gene regulatory priors into Transformer attention with scTransformer for interpretable scRNA-seq analysis
链接: https://arxiv.org/abs/2606.09558
作者: Mikele Milia,Louis Fabrice Tshimanga,Henning Mueller,Manfredo Atzori,Barbara Di Camillo
类目: Genomics (q-bio.GN); Machine Learning (cs.LG)
*备注:
Abstract:Motivation: Transformer-based models are increasingly applied to large-scale single-cell transcriptomics, showing strong performance through self-supervised learning on millions of cells. However, most existing approaches treat genes as independent features, and largely ignore prior biological knowledge, which limits interpretability and robustness. In this paper, we explore whether explicitly incorporating gene regulatory information can improve both model performance and biological insight. Results: We present scTransformer, the first Transformer-based approach that builds a priori knowledge of biological mechanisms into the model’s attention patterns. By constraining information flow according to known regulatory structures, the model learns representations that are more biologically meaningful. We evaluate scTransformer on a disease-relevant single-nucleus RNA-seq dataset using supervised cell-type classification. Compared to standard Transformers, our approach improves classification accuracy, enhances separation of cell types in embedding space, and produces attention patterns consistent with known regulatory programs. Overall, our results demonstrate that embedding biological structure into Transformer models can enhance interpretability without sacrificing performance, offering a principled step toward biologically grounded foundation models for single-cell omics.
[LG-164] Automating the Expert Eye: A System-Agnostic Deep Learning Framework for Rare Event Discovery in Imbalanced Force Spectroscopy
链接: https://arxiv.org/abs/2606.09541
作者: Jorge Rodriguez-Ramos
类目: Applied Physics (physics.app-ph); Machine Learning (cs.LG)
*备注: 13 pages, 2 figures, 2 tables
Abstract:Single-Molecule Force Spectroscopy (SMFS) provides unprecedented insights into biomolecular mechanics, yet the high-throughput generation of force-extension trajectories creates a severe data curation bottleneck. Identifying rare molecular unbinding events within thousands of noise-dominated curves traditionally relies on tedious, non-scalable manual auditing. Here, we present a system-agnostic, interpretable deep learning framework tailored to overcome extreme class imbalance in automated SMFS triage. Utilizing 1D-to-2D rasterized geometric matrices, we deployed a modified ResNet18 architecture governed by an asymmetric Focal Loss objective function. We evaluated this framework on the complex mechanical unfolding pathways of the R. champanellensis cellulosome. Under hyper-imbalanced test conditions where the target interaction constituted only 1.34% of the dataset (13 true events out of 970 traces), the model achieved an overall accuracy of 0.9196 and a remarkable True Positive Rate (Recall) of 0.9231. By implementing an empirically calibrated dual-threshold triage system, the pipeline automatically discarded 880 unambiguous background noise traces , reducing the manual curation workload by over 90% while safely preserving high-value rare data. Finally, Gradient-weighted Class Activation Mapping (Grad-CAM) visually validated that the network’s decisions are firmly anchored in the relevant geometric features of the force curves, specifically localizing on the structural unbinding regions, effectively mitigating ‘black-box’ skepticism. Built for free cloud-based execution, this open-source tool democratizes scalable, highly precise molecular discovery across the biophysics community.
[LG-165] Report the Floor: A Training-Free Conformal Interval Is a Mandatory Baseline for Probabilistic Time-Series Forecasting
链接: https://arxiv.org/abs/2606.09473
作者: Valery Manokhin
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Probabilistic forecasters are increasingly learned, yet the baselines they are compared against are often weak or omitted. We show that the simplest possible conformal interval - a last-value point forecast wrapped in a finite-sample split-conformal residual quantile, with no parameters and no training - is a far stronger baseline than its near-total absence from recent learned-forecasting and conformal-time-series comparisons would suggest. In one-step-ahead online forecasting across 2,217 real series from nine public sources (Monash, LOTSA, the LTSF traffic/electricity/weather suites, METR-LA, BOOM, nips/probts), this ConformalNaive interval decisively beats the naive value-quantile baselines, the entire NPTS family (NPTS 73%, SeasonalNPTS 64% of series), and the published Conformal Seasonal Pools (CSP) method (71% of series, bootstrap 95% CI [69,73], paired Wilcoxon p approx 7.6e-135); it is on par with the simpler learned conformal predictors (RCI, quantile regression; median relative Winkler within 2%) and is beaten only by the adaptive-online and ensemble methods (SPCI, ACI, AgACI), which track distribution shift and lead by 9-33% relative Winkler. It is also better calibrated than a trained neural forecaster: on the six datasets that introduced DeepNPTS, the trivial floors cover the truth 84-85% of the time at a nominal 95%, versus DeepNPTS’s 66%. At multi-step seasonal horizons the picture inverts: the random-walk floor is the weakest method and the seasonal pool (CSP) wins - a boundary we map. Finally we give ConformalNaive+, a one-line, training-free, horizon-adaptive selector that attains the better of two complementary floors at every horizon with restored coverage. We argue the matching conformal naive floor must be a mandatory baseline whenever a learned probabilistic forecaster claims gains.
[LG-166] Data augmented bootstrap: Unifying confidence interval construction by approximate invariance
链接: https://arxiv.org/abs/2606.09049
作者: Kevin Han Huang
类目: Methodology (stat.ME); Machine Learning (cs.LG); Statistics Theory (math.ST); Machine Learning (stat.ML)
*备注:
Abstract:We propose the data augmented bootstrap (DAB), a framework for constructing confidence intervals from approximately invariant transformations of the data. As special cases, DAB recovers popular methods that rely on exact group symmetries, such as conformal prediction, wild bootstrap for Maximum Mean Discrepancy U-statistics and the recently proposed SymmPI. Meanwhile, DAB also recovers the classical bootstrap method, which exploits the dataset’s approximate invariance under uniform sampling of data indices as the dataset size grows. For all DAB methods, we establish theoretical coverage results that interpolate between finite-sample and asymptotic guarantees according to the strength of the invariance, and without assuming a group structure. The approximate invariance is measured in the Kolmogorov distance and, for statistics that satisfy Gaussian universality, reduces to conditional mean and variance matching. This allows us to incorporate data augmentation (DA), a widely used machine learning heuristic based on approximate invariances, into known statistical methods. We empirically test the performance of incorporating DA into bootstrap, wild bootstrap and conformal prediction for simulated settings as well as for image, language and scientific data.
[LG-167] Multi-Armed Bandits with Arriving Arms: Sequential Screening Dynamic Regret and Sublinear Guarantees
链接: https://arxiv.org/abs/2606.09002
作者: Deqi Zheng,Xiaoyang Xu,Yuhong Yang
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注: 24 pages, 4 figures
Abstract:We study a stochastic multi-armed bandit problem in which the set of available arms expands over time. This setting arises in sequential experimentation when new actions or treatments become available during an ongoing study, making regret against a single best arm in hindsight inappropriate. We instead evaluate performance relative to the best arm currently available, leading to a dynamic-regret criterion for arriving-arm environments. To address the resulting challenges of arrival information discrepancy (AID) and a drifting benchmark (DB), we propose UCB for Arriving Arms (UCB-AA), an elimination-based procedure with an aiding preliminary screening step for newly arrived arms before full competition with incumbent arms. We show that UCB-AA attains regret bounds that depend explicitly on the arrival process, achieves sublinear dynamic regret under regularity conditions on gap evolution, and admits an online extension for unknown horizons. Simulation results show that UCB-AA reduces wasted pulls and maintains a smaller active arm set while preserving competitive regret performance.
[LG-168] A systematic investigation of molecular encoding methods for drug property predictions across neural network and Transformer encoder-based model
链接: https://arxiv.org/abs/2606.08973
作者: Sheng-Ya Chen,Shan-Ju Yeh
类目: Quantitative Methods (q-bio.QM); Machine Learning (cs.LG)
*备注:
Abstract:Fundamental investigations into how different molecular encoding methods affect molecular property prediction remain relatively limited. In this study, we extensively examined the optimal molecular encoding methods for molecular properties prediction using two prevalent structure designs: a classical neural network model (MLP) and a Transformer encoder-based model (MLP+TL). For molecular encoding methods, we investigated several types of fingerprints, including traditional topological fingerprints, substructure-based fingerprints, and string-based representations. These two models were trained on seven well-known molecular datasets to evaluate different input molecular encoding methods based on evaluation metrics. On several biologically relevant classification tasks, including toxicity, mutagenicity, and side-effect prediction, our models consistently achieved average AUC values above 0.9. Rather than relying on external post-hoc explanation methods such as the local interpretable model-agnostic explanation (LIME) or the Deep SHapley Additive exPlanations (SHAP), we leveraged the model’s intrinsic attention weights as an internal interpretability signal for identifying potentially important feature. The MLP+TL model using MACCS and PubChem as input can capture chemically interpretable groups that determined the major blood-brain barrier (BBB) permeability and mutagenicity in Salmonella typhimurium. In particular, a comparison between Morphine and Heroin highlighted the role of hydroxyl-related substructures in BBB permeability prediction, which was consistently reflected in the attention weights. Overall, our findings provide practical guidance for selecting effective molecular encoding methods and contribute to the development of interpretable molecular informatics approaches for drug discovery.
[LG-169] Estimate Collapsibility of Causal Effects in Completed Partial DAGs via Strong d-Convex Hulls
链接: https://arxiv.org/abs/2606.08941
作者: Yuxin Deng,Yi Sun,Zhiming Li,Huaxiong Liu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:This paper proposes a collapsible method for estimating causal effects that maintains the estimator’s consistency before and after marginalization over some variables in completed partially directed acyclic graphs (CPDAGs). We first introduce the estimate collapsibility for CPDAGs and characterize the minimal collapsible sets as strong d-convex hulls. An efficient algorithm is devised to obtain such sets in DAGs and is generalized to CPDAGs. Then, we combine the graph reduction procedure with the IDA framework. Finally, experiments and empirical analysis show the effectiveness of the collapsibility for causal estimations in CPDAGs. Code is available at this https URL.
[LG-170] Generalization in Nonlinear Least Squares via Learned Feature Geometry
链接: https://arxiv.org/abs/2606.08799
作者: Ayub Kharel,Ilja Kuzborski,Patrick Rebeschini,Yasin Abbasi-Yadkori
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: Preprint under review
Abstract:We study the generalization of ridge-regularized nonlinear least-squares models via on-average algorithmic stability, deriving error bounds for local minimizers in terms of a data-dependent effective dimension that reflects the geometry of the gradient model at the trained parameters, through the empirical Jacobian Gram matrix and a residual–curvature term. In the linear case, where the curvature term vanishes, this recovers the classical effective dimension of the Jacobian kernel covariance, but evaluated at the trained model rather than at initialization as is typical in neural tangent kernel analyses. We further bound this effective dimension via covering complexity of the gradient features, leading to guarantees that depend on learned geometry rather than parameter count. In particular, for manifold-supported data and piecewise Lipschitz Jacobians, the bounds scale with intrinsic dimension, while for one-hidden-layer ReLU networks, the mechanism can be made explicit through counts of activation-stable regions. Experiments on synthetic manifolds, clustered distributions, and benchmark datasets illustrate trained-Jacobian compression, the tightness of the residual-curvature linearization, and agreement between the stability bound and observed generalization gaps. A key feature of our bounds is the simplicity of their derivation, which follows from first principles using the Brascamp–Lieb inequality under strongly log-concave noise.
[LG-171] OptMuon: Closed-Loop Orthogonalized Momentum Methods for Stochastic Optimization with Zero-Noise Optimality
链接: https://arxiv.org/abs/2606.08783
作者: Ganzhao Yuan
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注:
Abstract:Orthogonalized momentum updates, as used in Muon-style optimizers, have recently shown strong empirical stability in large-scale deep learning. However, existing orthogonalized methods are typically paired with constant or open-loop magnitude rules, and therefore do not explicitly calibrate their update magnitudes from the observed optimization trajectory. Motivated by the closed-loop perspective behind Lipschitz-free and noise-adaptive methods, we propose OptMuon, a family of adaptive momentum orthogonalization methods for stochastic nonconvex optimization. OptMuon combines Muon-style polar-factor directions with a trajectory-dependent AdaGrad-Norm-type coefficient schedule, so that the update magnitude is determined by the observed gradient and momentum history rather than by a prescribed Lipschitz-dependent rule. The schedule does not use the smoothness constant, the variance level, or the bounded-gradient constant in parameter selection, and its running-maximum correction prevents isolated gradient spikes from causing excessive coefficient collapse. Under lower-boundedness, unbiased stochastic gradients with bounded variance, smoothness, and an almost-sure bounded stochastic-gradient condition, we prove two complementary guarantees. OptMuon-A achieves the noise-adaptive rate (\tilde\mathcal O(T^-1/2+\sigma^1/2T^-1/4)) under average smoothness, while OptMuon-I achieves (\tilde\mathcal O(T^-1/2+\sigma^1/3T^-1/3)) under individual smoothness. In the zero-noise regime, both bounds automatically reduce to a nearly optimal deterministic first-order rate (\tilde\mathcal O(T^-1/2)) without manual hyperparameter retuning. These results show that closed-loop scalar adaptation can be combined with Muon-style momentum orthogonalization while retaining noise adaptivity and zero-noise optimality up to logarithmic factors.
[LG-172] Discovering and decoding latent mean-field structure with variational autoencoders
链接: https://arxiv.org/abs/2606.08694
作者: Marco Biroli,Max Welling,Vincenzo Vitelli
类目: oft Condensed Matter (cond-mat.soft); Statistical Mechanics (cond-mat.stat-mech); Machine Learning (cs.LG)
*备注: 10 pages, 5 figures
Abstract:Generative models are increasingly used to capture correlations in many-body systems, but the representations they learn remain largely opaque to physical interpretation. Here, we establish an intuitive criterion that quantifies the capacity of a variational autoencoder (VAE) to faithfully reconstruct the joint probability distribution of a many body system. In a nutshell, a bound on the VAE capacity is obtained by comparing the rate of the latent channel to the bipartite mutual information of the data. Using this bound, we show that the conditionally independent decoder of any successful VAE is structurally identical to a finite-size mean-field factorization. Hence, a successful reconstruction is direct evidence for a latent mean-field theory and the microscopic parameters of that theory can be read off the trained decoder. We validate these conclusions on a hierarchy of solvable models with scalar (Curie-Weiss), vector (Hopfield) and tensor (Maier-Saupe) order parameters, recovering the full Hopfield pattern matrix from equilibrium samples alone. We find that, when applied to Salamander retinal recordings, a two-latent VAE reproduces the population statistics with only two effective collective variables allowing us to recover the `stored patterns’ of the neural population and write a generalized Hopfield model which correctly models the experimental data.
[LG-173] Parameter Tuning with Generalization Guarantees for GPU-Accelerated Linear Programming
链接: https://arxiv.org/abs/2606.08638
作者: Siddharth Prasad,Dravyansh Sharma
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:
Abstract:Recent research has developed practical, parallelizable first-order methods for large scale linear programming, but performance is highly dependent on hyperparameter selection. We derive generalization guarantees for hyperparameter tuning within (cu)PDLP, a state-of-the-art first-order LP solver designed for modern hardware. First, we pin down the behavior of PDHG, the primal-dual hybrid gradient algorithm that underlies PDLP, as a function of its step size and primal weight, leading to linear sample complexity guarantees for learning those parameters. We then conduct a structural analysis of PDLP, which augments PDHG with several specialized techniques like preconditioning, adaptive step sizes, averaging, adaptive restarts, and smoothed primal weight updates. Our analysis captures the behavior of the solution trajectory as a function of the hyperparameters and leverages recent advances in data-driven algorithm design to obtain polynomial sample complexity guarantees for learning those hyperparameters. Finally, we conduct proof-of-concept experiments that demonstrate the need for data-driven PDLP parameter tuning. Our results showcase the versatility of the data-driven algorithm design toolkit for principled hyperparameter tuning within solver-grade implementations of complex modern optimization algorithms.
[LG-174] Improving the sharpness in neural network-based parametric post-processing of ensemble forecasts
链接: https://arxiv.org/abs/2606.08587
作者: Ágnes Baran,Máté Mihalina
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 18 pages
Abstract:Statistical post-processing has proven to be an effective tool in improving ensemble forecast of different weather variables. Case studies show that post-processing can remedy the typically underdispersive and potentially biased behaviour of the ensemble while optimizing a proper scoring rule expressing the forecast skill. The price of these positive effects is generally a deterioration in sharpness; the width of the central prediction intervals and the uncertainty of the predictions are increasing, especially for shorter lead times. This work aims to reduce the extent of the latter phenomenon for neural network-based parametric post-processing methods by extending the network’s loss function with a penalty term. We demonstrate the effect of the proposed technique for 2m temperature ensemble forecasts of the European Centre for Medium-Range Weather Forecasts downloaded from the EUPPBench benchmark dataset and verified against synoptic observations. Here, the predictive distribution is Gaussian, and we use the continuous ranked probability score (CRPS) as loss function. The case studies confirm a substantial relative decrease ( 8.2%-12.5% ) in the width of the nominal central prediction interval compared to the width of the predictive distribution computed without the penalty term, while there is no deterioration in the mean CRPS of probabilistic forecasts and in the RMSE of the predictive mean.
[LG-175] Querying Counterfactuals on Tissue Graphs with Supervised Disentanglement
链接: https://arxiv.org/abs/2606.08493
作者: Abdul Moeed,Stefan Schrod,Martin Rohbeck,Marc Jan Bonder,Pavlo Lutsik,Oliver Stegle,Daniel Dimitrov
类目: Genomics (q-bio.GN); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:\textitTissue graph counterfactuals ask how a cell’s expression would change under altered spatial neighbor contexts. Such queries are central to predicting cell behavior in tissues, but lack a unified definition, with existing methods targeting specific intervention types or treating cells as i.i.d. In this work, we first formalize \textittissue graph counterfactuals as a class of spatial interventions that either rewire connections between cells (\textitedge perturbation) or modify the expression of their neighbors (\textitnode perturbation). We then introduce \textitCellina \renewcommand\thefootnote‡\footnotethis https URL\addtocounterfootnote-1, a framework that uses supervised disentanglement to decompose a cell’s intrinsic state from its spatial context, using the latter as a conditioning input for counterfactual predictions. Across benchmarks spanning over 2.5 million spatially-resolved cells in colorectal cancer and mouse brain, \textitCellina outperforms spatially-informed and non-spatial competitors in tissue perturbations, disentanglement, and scalability. Additionally, we show that \textitCellina reveals biologically distinct cancer subdomains in an unsupervised manner and enables targeted neighbor perturbation simulations.
[LG-176] LOTTERY: Learning from Reference-Only Samples in Two-Sample Testing under Size Asymmetry
链接: https://arxiv.org/abs/2606.08460
作者: Xunye Tian,Zhijian Zhou,Liuhua Peng,Feng Liu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 16 pages, 1 figure
Abstract:Data-adaptive two-sample testing assesses if two samples come from the same distribution, using a discrepancy learned from the data (e.g., via kernel-based feature representations). Such methods typically rely on data splitting to decouple learning from testing and control type I error. However, this paradigm is ill-suited to few-shot settings with severe sample-size imbalance: abundant reference samples are available, while only a handful of query samples arrive. In this paper, we show how this imbalance can be leveraged constructively. Using abundant reference data, we learn reference-dependent representations that summarize salient structure of the reference distribution and provide informative signals for detecting departures. We incorporate a collection of representation families that capture both global and local structure, and adaptively weight them using only reference samples via an uncertainty-guided principle. Theoretically, we establish permutation-based type I error control and show consistency of the aggregated test: as the sample sizes grow, the test power converges to one whenever the representation set contains at least one consistent representation. Empirically, our aggregation achieves strong performance across a range of benchmarks while retaining type I error control.
[LG-177] Improving Bayesian Optimization via Training-Aware Conditional Diffusion Models
链接: https://arxiv.org/abs/2606.08438
作者: Yilin Zheng,Haowei Wang,Szu Hui Ng,Enlu Zhou
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Bayesian optimization (BO) is a widely used approach for black-box optimization that uses a Gaussian process (GP) as a surrogate and guides sequential evaluations via an acquisition function, with the ultimate goal of locating the global optimum \mathbfx^\star . To align with this goal, information-based acquisition functions such as Predictive Entropy Search (PES) model \mathbfx^\star as a random variable and reduce the entropy of its distribution, but approximating this distribution via traditional GP posterior sampling is computationally expensive. To address this limitation, we leverage Conditional Diffusion Models (CDMs) to efficiently approximate the distribution of \mathbfx^\star and develop BO-inherent training strategies for CDMs. Motivated by the structural properties of the CDM-learned distribution, we further develop an acquisition strategy termed Diffusion-based Mode Seeking (DMS) to guide the sequential evaluation. We establish a sub-optimality guarantee for the CDM-learned distribution and demonstrate through extensive experiments that DMS outperforms standard BO baselines.
[LG-178] MEC-Cox: Machine-Learning-Assisted Generalized Entropy Calibration for ATT Marginal Hazard-Ratio Estimation
链接: https://arxiv.org/abs/2606.08305
作者: Se Yoon Lee,Yonghyun Kwon,Jae Kwang Kim
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Externally controlled survival trials are increasingly used when concurrent randomized controls are infeasible, particularly in oncology and rare-disease settings with time-to-event endpoints. We target an average-treatment-effect-on-the-treated (ATT)-type marginal hazard-ratio estimand, comparing treatment with counterfactual control in the treated trial population, and estimate it using inverse-probability-weighted (IPW) Cox regression. Valid inference is challenging because IPW Cox regression depends on the weights through both event contributions and risk-set averages, making flexible machine-learning nuisance estimation difficult to incorporate directly. Building on machine-learning-assisted generalized entropy calibration (MEC) by Lee and Kim (2026), we propose MEC-Cox for ATT-weighted IPW Cox regression. The method begins with normalized source-propensity-score odds weights for external controls and then applies Bregman calibration to balance cross-fitted prognostic summaries between external controls and treated trial patients. The calibration basis may include control-survival predictions, Cox linear predictors, penalized-survival-model predictions, or other prognostic-score summaries. MEC-updated weights therefore play a dual role as source-transport and prognostic-score balancing weights. We establish consistency, characterize a calibration-induced efficiency gain, and develop a stacked sandwich variance estimator. Simulations show that MEC-Cox can reduce bias, increase efficiency, and improve coverage through flexible machine-learning-assisted adjustment.
[LG-179] QnRL: Quantum-Native Reinforcement Learning
链接: https://arxiv.org/abs/2606.08276
作者: Alexander DeRieux,Walid Saad
类目: Quantum Physics (quant-ph); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
*备注: 36 pages, 23 figures
Abstract:Quantum reinforcement learning (QRL) is a promising approach to learn effective decision strategies across several applications with stochastic environments. Instead of directly modeling the random variables that govern these environments, existing QRL architectures indirectly approximate environment behavior by estimating expected outcomes, which limits their expressive power and adaptive potential. Overcoming such challenges requires a novel QRL approach that exploits the distributional nature of quantum computers to directly model environment random variables as quantum state distributions. Hence, in this paper, a novel framework dubbed quantum-native reinforcement learning (QnRL) is proposed. QnRL is a distributional RL framework that learns conditional distributions naturally in Hilbert space via superimposed and entangled quantum states. Thus, QnRL can directly model the behavior of stochastic learning environments via the natural properties of quantum systems. QnRL accomplishes this via a novel, proposed quantum amplitude kickback (QuAK) algorithm that enables comparing the n -th power of the m -th moment of multiple superimposed distributions. It is theoretically proven that a conditional action policy distribution is distilled from the moments of a quantum generative model entirely within Hilbert space via QuAK, and optimized via QnRL. This complex distribution composition is also shown to provide extra dimensions for expressing environment correlations that are unknown to purely classical and classically-sampled quantum distributional models. Experimental results across diverse environments show that QnRL achieves up to 82.9% higher evaluation scores, with up to 94.3% fewer parameters on average, more accurately estimates the expected return for unseen observations, and better adapts to varying stochastic conditions compared to the baseline.
[LG-180] Post-Rejection Follow-up Sampling: A Methodology for Counterfactual Outcome Measurement in Algorithmic DEX Trading
链接: https://arxiv.org/abs/2606.08228
作者: Arati Uday Kamat
类目: Trading and Market Microstructure (q-fin.TR); Machine Learning (cs.LG); Computational Finance (q-fin.CP); Statistical Finance (q-fin.ST)
*备注: 12 pages. Companion methodology paper to RED-2400 ( arXiv:2605.12151 ). Currently under review at Ledger. SSRN abstract ID 6607301. Zenodo concept DOI https://doi.org/10.5281/zenodo.20043516
Abstract:Algorithmic trading systems on decentralised exchanges (DEXs) reject most candidate tokens they evaluate. The counterfactual outcome of rejected candidates (what would have happened had the system entered) is rarely measured. This paper introduces Post-Rejection Follow-up Sampling (PRFS). A separate tracking subsystem samples each rejected token’s price and liquidity at a configurable cadence, over a horizon of up to twenty-four hours. PRFS produces the data needed to evaluate filter precision against actual market outcomes of rejected candidates, not against synthetic backtest reconstructions. The methodology, data architecture, and deposit format are described in Section III. The companion dataset contains 67,000 forward-outcome observation rows across 2,997 rejection events spanning 457 unique mints, collected over a continuous eight-day window (2026-04-10 to 2026-04-19, UTC). Approximately 55 percent of rejection events receive at least one forward observation; coverage at the mint level is complete. The principal binding constraint on downstream classification is per-event horizon density, not event-level coverage. PRFS is dataset-independent. It generalises to any algorithmic decision system in which rejections substantially outnumber executions.
[LG-181] Vector Space of Cycles
链接: https://arxiv.org/abs/2606.08202
作者: Moo K. Chung,Anass B. El-Yaagoubi,Hernando Ombao
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an); Neurons and Cognition (q-bio.NC)
*备注:
Abstract:Most statistical and machine learning methods for directed interactions focus on pairwise effects among variables. Even existing cyclic models represent feedback primarily through node-level dependencies, making large-scale recurrent organization difficult to estimate and compare. This limitation is particularly acute in biological and neural systems, where interactions are highly recurrent and involve many overlapping cycles. We introduce a variational framework for statistical inference on cyclic interactions. Directed interactions are represented as edge flows on a simplicial complex and evolved under an energy-minimizing dynamical system. The resulting dynamics separate transient interaction components from persistent harmonic flows, yielding a low-dimensional cycle space that captures stable recurrent organization. Rather than enumerating individual cycles, the proposed framework represents cyclic interactions as elements of a Hilbert space, enabling projection, averaging, comparison, and population-level statistical inference. We establish theoretical properties of the harmonic projection, including characterization of the cycle space, variance reduction, and population inference. Simulations demonstrate substantially improved recovery of cyclic structure in dense recurrent systems compared with existing directed-interaction methods. Applied to resting-state fMRI from 400 human subjects, the framework reveals reproducible large-scale cyclic organization that is not detectable through edgewise averaging. These results provide a scalable statistical framework for studying recurrent interactions in high-dimensional dynamical systems.
[LG-182] Latent Structural Categorical Matrix Completion with Application to Quasispecies Analysis
链接: https://arxiv.org/abs/2606.08188
作者: Qian Zhang,Meixia Lin
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:
Abstract:Matrix completion has been extensively studied for real-valued data, but existing methods are often limited in handling categorical variables. We propose LCMC, a double-loop optimization framework for categorical matrix completion via latent factorization based on a binary tensor representation. In this setting, each categorical entry is encoded as a one-hot vector along a third tensor mode, thereby preserving its discrete, non-ordinal nature. The outer loop adaptively estimates the latent dimension by iteratively updating it with feedback from the inner loop, while the inner loop reconstructs the categorical matrix through tensor factorization, supported by a corresponding theoretical analysis. To further improve scalability and robustness, we introduce enhancements including a split-merge-refine strategy and an adaptive data reduction technique. Experiments on synthetic and real-world datasets in viral quasispecies reconstruction, demonstrate that LCMC achieves superior accuracy and efficiency compared to existing methods.
[LG-183] Inverse design of bespoke interatomic potentials via active learning by information-matching
链接: https://arxiv.org/abs/2606.08148
作者: Yonatan Kurniawan(1),Logan D. Williams(2),Amit Samanta(2),Ilia Nikiforov(3),Daniel Schwalbe-Koda(4),Mark K. Transtrum(5),Ellad B. Tadmor(3),Vincenzo Lordi(2),Vasily V. Bulatov(2) ((1) Department of Physics and Astronomy, Brigham Young University, Provo, UT, USA, (2) Lawrence Livermore National Laboratory, Livermore, CA, USA, (3) Department of Aerospace Engineering and Mechanics, University of Minnesota, Minneapolis, MN, USA, (4) Department of Materials Science and Engineering, University of California, Los Angeles, CA, USA, (5) Cross Stream Consulting, Springville, UT, USA)
类目: Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)
*备注:
Abstract:Interatomic potentials (IPs) enable large-scale atomistic simulations beyond the reach of first-principles methods, but their predictive reliability depends critically on the selection of training data, quantified uncertainty, and model expressiveness. Active learning (AL) provides a principled framework for constructing efficient and accurate IPs, yet most strategies reduce parameter uncertainty without explicitly accounting for the specific material properties being predicted. The information-matching (IM) approach addresses this limitation by requiring that the selected training data provide at least as much parameter space information as needed to achieve prescribed uncertainty targets for selected quantities of interest (QoIs). Here, we apply IM to develop bespoke IPs specifically tailored for predicting plastic strength in metals. Due to the high computational cost of simulating plastic strength, we employ an indirect IM strategy that targets inexpensive intermediate QoIs that correlate with strength. The IM method enables precise parameter constraints with minimal training data, yielding precise predictions for both the intermediate QoIs and plastic strength. Yet, model error remains a key limitation, and a post hoc uncertainty inflation correction provides a viable means to mitigate this limitation. These findings illustrate both the promise and limits of uncertainty-aware AL for predicting complex material properties.
[LG-184] Biological Reasoning -Informed Regression for Interpretable Regulatory DNA Activity Prediction KDD2026
链接: https://arxiv.org/abs/2606.08147
作者: Yi Duan,Zhao Yang,Jiwei Zhu,Ying Ba,Chuan Cao,Bing Su
类目: Genomics (q-bio.GN); Machine Learning (cs.LG)
*备注: Accepted at KDD 2026 AI4Sciences Track
Abstract:DNA cis-regulatory elements (CREs) such as enhancers control gene expression levels. Accurately predicting regulatory activity from DNA sequences is valuable but challenging, as it requires understanding complex biological regulatory processes. Existing methods typically regress activity scores from sequences in a black-box manner, limiting both interpretability and regression performance. Meanwhile, large language models (LLMs) benefit from explicit reasoning processes, yet directly applying LLMs to raw DNA sequences performs poorly. In this paper, we bridge this gap by introducing R3LM, a framework that teaches LLMs reasoning-informed regression on regulatory DNA through structured biological knowledge. Specifically, we design a biologically grounded data format that structures DNA’s regulatory information for improved LLM understanding, and construct CRE-ReasonBench, the first dataset that associates DNA sequences and activity scores with mechanistic reasoning traces. Through two-stage training that first teaches LLMs reasoning over structured biological information then performs regression, R3LM achieves state-of-the-art performance on enhancer prediction across three cell types, outperforming both LLMs with raw sequence input and specialized DNA models while providing interpretable mechanistic explanations. We expect R3LM as an interpretable reward model that can effectively assist biologists in CRE design. Code is available at this https URL.
[LG-185] New Fractional Ambiguity Function Integrated with CNN-Based Machine Learning for Signal Classification
链接: https://arxiv.org/abs/2606.08110
作者: Aamir H. Dar,Prakhar Kumar Sonkar,Neeraj Kumar Sharma
类目: Functional Analysis (math.FA); Machine Learning (cs.LG)
*备注:
Abstract:A new fractional ambiguity function (NFrAF) derived from the fractional Fourier transform is introduced as a generalization of the classical ambiguity function. The fundamental analytical properties of the NFrAF, including symmetry, marginality, and Moyal type identities, are rigorously established. After verifying its ability to detect and localize monocomponent and multicomponent linear frequency modulated (LFM) signals, the NFrAF is integrated into a convolutional neural network based machine learning framework for signal classification. Owing to its superior time frequency resolution and localization, the NFrAF provides a more informative input representation than conventional methods such as the spectrogram and classical ambiguity function. Experimental results on simulated datasets demonstrate consistent improvements in classification accuracy, highlighting the effectiveness of the proposed representation for data driven signal analysis.
[LG-186] Variational Proximal Policy Optimization
链接: https://arxiv.org/abs/2606.08032
作者: Ousmane Amadou Dia
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Reinforcement Learning from Human Feedback via Proximal Policy Optimization often suffers from policy mode collapse, brittle exploration loops, and distribution drift. This paper introduces Variational Proximal Policy Optimization ((\textscVP_2\textscO)), a particle-based variational inference framework that maps policy optimization to Stein Variational Gradient Descent within a Mixture-of-Experts architecture. By leveraging functional kernels over localized expert prototypes alongside an expert orthogonalization loss, (\textscVP_2\textscO) introduces a geometry-based proximal-control mechanism that can reduce reliance on fixed clipping or KL schedules. Our results on a 33B/4B sparse Mixture-of-Experts model show several improvements across complex reasoning benchmarks, establishing a (+\mathbf179) ELO gain on Codeforces and a (\mathbf32%) reduction in token count on AIME mathematical reasoning tasks.
[LG-187] Pointwise Complexity for Gaussian Fields: Upper Envelopes Algorithmic Lower Bounds and Separation
链接: https://arxiv.org/abs/2606.07931
作者: Yunbei Xu
类目: Probability (math.PR); Statistical Mechanics (cond-mat.stat-mech); Information Theory (cs.IT); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
Abstract:We prove a variance-aware pointwise majorizing-measure theorem for centered Gaussian processes. Classical generic chaining characterizes the scalar quantity \mathbb E\sup_x\in TX_x ; the theorem here gives a simultaneous high-probability envelope for the entire field. For an ambient prior \mu , the envelope at x is governed by a pointwise Fernique-Talagrand functional [\Phi_\mu(x):=\int_0^4\sigma(x)\sqrt\log\frac1\mu(B_d(x,\varepsilon)),d\varepsilon,] together with the corresponding Gaussian tail term. The theorem provides a reusable field-level refinement of classical generic chaining and a Gaussian-process counterpart of pointwise empirical-process bounds for deep neural networks. We also record a Bayesian algorithmic lower envelope from the interactive Fano/data-processing principle. For a known prior \pi , an observation channel, and a concrete estimator \widehat t(Y) , the lower bound is expressed through the exact ghost small-ball mass \mathbb E_Y\sim Q\pi(B_d(\widehat t(Y),\Delta)) , rather than a worst-case covering number. In Gaussian location experiments, comparison decoders convert Bayes location error into lower bounds on decision-aligned Gaussian ranges. We then construct an elementary weighted-basis example separating the usual Fano relaxation for a fixed prior, the Bayesian algorithmic lower envelope, the pointwise Gaussian envelope on the selected subatlas, and the full-class minimax risk/global Gaussian scale. Together, these results show that algorithmic lower bounds provide local-geometric certificates of pointwise complexity for fixed estimators in overparameterized ambient classes, precisely in regimes where classical minimax theory becomes either too coarse or oracle-dependent. Subjects: Probability (math.PR); Statistical Mechanics (cond-mat.stat-mech); Information Theory (cs.IT); Machine Learning (cs.LG); Statistics Theory (math.ST) Cite as: arXiv:2606.07931 [math.PR] (or arXiv:2606.07931v1 [math.PR] for this version) https://doi.org/10.48550/arXiv.2606.07931 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-188] Barycentric Projections of Optimal Transport Plans on Riemannian Manifolds
链接: https://arxiv.org/abs/2606.07926
作者: Kisung You
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Optimal transport couplings are probabilistic objects, while many learning pipelines require deterministic maps. In Euclidean space, barycentric projection converts a coupling into a map by taking conditional expectations, but on a Riemannian manifold curvature and cut loci make this operation nontrivial. We develop a framework for barycentric projections of transport couplings on Riemannian manifolds. The intrinsic projection maps each source point to the conditional Fréchet mean of its destination law and is shown to be the best deterministic representative under squared geodesic loss. The corresponding minimum value is an integrated conditional Fréchet variance, which vanishes exactly for map-induced couplings and therefore defines a conditional-variance Monge defect. We also study a tangential log-exp projection, prove its Euclidean exactness, its compatibility with Brenier-McCann maps in the Monge case, and its interpretation as the first unit Riemannian gradient update for the intrinsic objective. For discrete couplings, both constructions decompose row-wise into weighted Fréchet mean and log-exp problems. Experiments on spherical data, synthetic SPD data, and real EEG covariance matrices support the proposed division of roles: the intrinsic projection is the variational representative, while the tangential projection is a useful local displacement surrogate.
[LG-189] Identifiability and Estimation for Unlabeled Finite Mixtures under Marginal Independence
链接: https://arxiv.org/abs/2606.07914
作者: Takafumi Kanamori,Yushi Hirose,Shohei Yamamoto
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:We study component recovery and mixing-matrix estimation from unlabeled finite mixtures whose observable distributions share the same latent components but have unknown mixing weights. The main identifying signal is marginal independence: each component is assumed to be independent on at least one coordinate pair, but no labels, clean component samples, or mixing weights are observed. We first prove a structural result for product components: under linear independence of the univariate marginals, any independent affine combination of the components must coincide with a single component. We then extend this principle to observable mixtures and show that, under full-rank and no-cancellation conditions, marginally independent affine combinations recover the corresponding latent components. When every component is independent on some coordinate pair, all components are identifiable, and the mixing matrix is recoverable under the stated completion conditions. Finally, we propose a Product-Marginal Maximum Mean Discrepancy (PM-MMD) estimator over affine combinations of the observable mixtures and prove uniform convergence and stability under approximate marginal independence. This framework also separates the empirical roles of the assumptions: irreducibility is, in general, not directly testable from the unlabeled mixtures alone, whereas marginal independence yields a candidate-level diagnostic through held-out PM-MMD. Controlled and flow-cytometry experiments show when marginal independence provides a useful recovery signal. In the reported multi-component comparisons, condition-aware representative selection stabilizes PM-MMD and improves recovery relative to clustering, factorization, and pairwise mixture-proportion baselines using the same unlabeled mixtures.
[LG-190] Large-scale empirical tuning and comparison of default optimizers for variational inference
链接: https://arxiv.org/abs/2606.07841
作者: Trevor Campbell,Jonathan H. Huggins,Kyurae Kim,Charles C. Margossian
类目: Computation (stat.CO); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Black-box variational inference (BBVI) is a methodology for posterior approximation that relies on stochastic optimization. In practice, the stochastic optimizers underpinning BBVI generally require extensive problem-specific tuning, which undermines its promise as a truly “black box” inference algorithm. However, over the past decade, many new adaptive stochastic optimization algorithms have been developed that reduce or remove entirely the need for tuning. In this work, we investigate this new collection of adaptive methods in the context of BBVI, with the goal of establishing the current state of the art in tuning-free optimization-based inference. In particular, we present a large-scale empirical evaluation of 56 stochastic gradient-based optimization algorithms applied to 1092 Bayesian inference optimization problems, involving over 550,000 individual optimization runs and 15 core-years of compute. The optimization algorithms we evaluate are chosen to represent a wide spectrum of recent approaches and the benchmark problems are chosen to span a range of difficulty, with posterior target dimension 1-10^4, condition number 1-10^8, and a range of variational families. Our results show that no single method dominates, but running a selection of 5 algorithms suffices to reliably get close to the best-possible observed performance. We thus provide a strong baseline for applications where expert tuning is not possible and for comparison when developing new stochastic optimization algorithms.
[LG-191] Non-Archimedean Polydisc Spaces and Applications to Optimisation
链接: https://arxiv.org/abs/2606.07782
作者: Paul Lezeau,Yiannis Fam,Anthea Monod,Yue Ren
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Metric Geometry (math.MG)
*备注: 54 pages, 23 figures. Comments welcome
Abstract:We propose a new framework for optimisation over non-Archimedean spaces inspired by Berkovich geometry. Specifically, we introduce polydisc spaces, which consists of products of closed balls over a non-Archimedean field. These spaces retain the rigid hierarchical structure of the non-Archimedean field whilst acquiring many desirable geometric features absent from it. We show that metric trees embed naturally into these spaces, demonstrating their capacity to represent hierarchical data. We study their metric geometry, establishing properties such as geodesic uniqueness, confirming their comaptibility with classical optimisation techniques. We further propose a class of real-valued functions given by linear combinations of absolute values of polynomials. These functions admit a piecewise polynomial description along geodesics and satisfy a universal approximation property. We formulate a theory of optimisation on polydisc spaces: we prove existence of minimisers and explore algorithms for finding them. We provide an accompanying open-source Julia library implementing the core objects and optimisation procedures introduced.
[LG-192] GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series
链接: https://arxiv.org/abs/2606.07725
作者: Nick Teutschmann(1),Laura Crocetti(1),Fanny Lehmann(2),Leonardo Trentini(1),Benedikt Soja(1) ((1) Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland, (2) ETH AI Center, Switzerland)
类目: Geophysics (physics.geo-ph); Machine Learning (cs.LG)
*备注:
Abstract:Displacement time series from Global Navigation Satellite Systems (GNSS) are essential for a wide range of applications, including monitoring tectonic crustal deformations and investigating the different stages of the earthquake cycle. Machine learning methods have proven promising for GNSS applications; however, most remain fully supervised. This creates a bottleneck as labeled data are scarce, even though large amounts of unlabeled GNSS data are freely available. We present GNSS-FM, a self-supervised foundation model for daily GNSS time series. The model uses a dual-stream input combining displacement and velocity-like increments, and is pretrained using a masked latent prediction objective with vector-quantized targets adapted from wav2vec 2.0, with several modifications for geodetic data. Pretrained on data from over 17,000 globally distributed GNSS stations, an analysis of the learned codebook suggests that the representations capture the main signal types in GNSS displacement data, including seismic offsets, tectonic drift, and seasonal patterns. The foundation model is later fine-tuned on two downstream tasks, namely 90-day displacement forecasting and seismic step localization, where it outperforms strong task-specific baselines in both cases. These results show that self-supervised pretraining is a promising approach for GNSS time series analysis.
[LG-193] ransfer learning for causal forest
链接: https://arxiv.org/abs/2606.07693
作者: Bérénice-Alexia Jocteur(ICJ, PSPM),Véronique Maume-Deschamps(ICJ, PSPM),Pierre Ribereau(PSPM, ICJ)
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Probability (math.PR)
*备注:
Abstract:Transfer learning addresses the challenge of transfering knowledge from one domain to another. Traditional transfer learning focuses on adapting models trained on a source domain (with a lot of observations) to improve performance on a target domain (with few observations). In this work we consider the case of a model shift and we focus on the transfer learning applied to a causal forest namely HTERF. This causal forest aims to estimate the Conditional Average Treatment Effect (CATE). The approach considered is the offset method presented by Wang (2016) adapted to a causal context. This method relies on the use of intermediate models in order to estimate the offset between source and target distributions. Our main result is a bound on the CATE error of HTERF on target depending on the error of the intermediate models. Simulation studies show the good performances of this approach in different settings on simulations and on a real-world dataset.
[LG-194] Disentangling Latent Risk Pathways via Bayesian Hypergraph Inference ICML2026
链接: https://arxiv.org/abs/2606.07677
作者: Shengxian Ding,Haonan Gao,Pangpang Liu,Xinyuan Tian,Yize Zhao
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Applications (stat.AP); Methodology (stat.ME)
*备注: ICML 2026 Oral
Abstract:Electronic health records (EHR) pose large-scale multi-disease modeling problems in which many outcomes are rare and strongly influenced by shared risk factors. While modern approaches achieve strong predictive performance, they often treat diseases independently or rely on black-box architectures, offering limited insight into how risk factors organize disease risk and little principled uncertainty quantification. We introduce a Bayesian hypergraph inference framework that reframes multi-disease modeling around latent, risk-factor-modulated disease pathways. Risk factors act on hyperedges, latent disease subsets with shared risk patterns, allowing diseases to participate in multiple distinct pathways and enabling interpretable, higher-order structure beyond pairwise associations. A repulsion prior encourages parsimonious and identifiable structure, while posterior inference provides calibrated uncertainty over both disease groupings and risk-factor influence. To enable scalable inference on large EHR datasets, we develop a structured variational inference algorithm that preserves logical dependencies among hyperedge existence, disease membership, and pathway-level effects. Experiments on simulated data and UK Biobank demonstrate stable and interpretable disease pathway structure, well-calibrated uncertainty, improved estimation for rare diseases, and competitive predictive performance.
[LG-195] Hardware-aware Low-latency Quantum Compilation with Data-driven Lightweight Error Detection for Early Fault-Tolerant Systems
链接: https://arxiv.org/abs/2606.07666
作者: Sumit Chongder(Indian Institute of Technology Jodhpur)
类目: Quantum Physics (quant-ph); Hardware Architecture (cs.AR); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注: 16 pages, 15 figures, Springer LNCS format. Code available at this https URL
Abstract:Noisy intermediate-scale quantum (NISQ) processors are entering an early fault-tolerance regime where full quantum error correction carries prohibitive resource costs, yet lightweight error detection can meaningfully improve algorithmic success rates. Existing compilation and error-detection toolchains treat these concerns in isolation, with no principled way to balance detection overhead against success probability under latency constraints. We present an integrated hardware-aware compilation and data-driven quantum error-detection (QED) framework that jointly optimises qubit mapping, SWAP insertion, and syndrome-schedule placement via a noise-weighted cost function and a learned multi-objective scheduler. Simulation experiments on an HPC cluster using GPU-accelerated density-matrix simulation (NVIDIA cuQuantum SDK) across VQE, phase-estimation, and Grover benchmarks, three noise profiles, and circuit sizes of 6-20 qubits (depths 10-160), show that joint co-design raises algorithmic success probability by up to 68 percent (95 percent CI: 60 percent to 76 percent) over SABRE on an 8-qubit VQE instance with post-selection.
[LG-196] SC3: The Multi-Solvent Solubility Challenge and Benchmark
链接: https://arxiv.org/abs/2606.07656
作者: Vansh Ramani,Har Ashish Arora,Dhairya Kuchhal,Sergei Tatarin,Lev Krasnov,Sayan Ranu,Tarak Karmakar
类目: Chemical Physics (physics.chem-ph); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
*备注: 34 pages, 16 tables, 22 figures
Abstract:Solubility prediction is a standard benchmark in computational chemistry, yet multi-solvent models which reportedly approach the experimental-noise ceiling (i.e. the aleatoric limit) are not yet reliable enough to be deployed. We argue that this gap is partly artefactual: published benchmarks differ in curation policies, evaluate on count-weighted RMSE that hides failure on tail-heavy solvent distributions, and treat the widely cited 0.6-0.8 log S inter-laboratory figure as the aleatoric ceiling even though it reflects worst-case, not expected, disagreement. We introduce SC3, a multi-solvent solubility benchmark built on BigSolDB v2.1 with three contributions: (i) a reproducible curation pipeline yielding 101,535 measurements over 1,327 solutes and 206 solvents, with a recalibrated aleatoric floor of 0.106 log S-roughly 6 times tighter than the conventional figure; (ii) nested Gold/Silver/Bronze consensus tiers with per-point standard deviation, three leakage-checked splits, and a multi-solvent metric suite (PS-RMSE, Z-RMSE); and (iii) a 31-model benchmark across six families, whose best Bronze PS-RMSE sits at 5 times the aleatoric limit, and we observe this is a gap unclosed by any deep alternative tested. We perform three follow-on analyses: data scaling, transfer from quantum-chemistry solvation energies, and feature-level attribution, which demonstrates that calibrated per-point uncertainty is a reusable infrastructure for diagnosis beyond point prediction.
[LG-197] Forward-Looking Stress Testing Under Macro Scenarios: Stable SVaR Estimation Using a Hybrid GPR-HS Framework with SACS
链接: https://arxiv.org/abs/2606.07575
作者: Ujjwala Vadrevu
类目: Risk Management (q-fin.RM); Machine Learning (cs.LG)
*备注: 15 pages, 3 figures. Extension of a hybrid GPR-HS framework to forward-looking stress testing with scenario-based SVaR and covariance stabilization (SACS)
Abstract:Regulatory stress testing frameworks, including the Comprehensive Capital Analysis and Review (CCAR) and the Internal Capital Adequacy Assessment Process (ICAAP), require robust Stressed Value-at-Risk (SVaR) estimation under forward-looking macroeconomic scenarios. Traditional parametric approaches often exhibit numerical instability under extreme shocks, reducing the reliability of capital projections. This paper extends the Hybrid Gaussian Process Regression Historical Simulation (GPR-HS) framework of Vadrevu (2026) to forward-looking stress scenarios, demonstrating stability across three regimes: West Asia War, Climate Risk, and AI Bubble/Regulation. A key contribution is the Scenario-Averaged Covariance Stabilization (SACS) framework, which constructs stress covariance as a weighted aggregation of historical crisis regimes, providing stable and interpretable dependence structures. Stressed return paths are generated over a 252-day horizon using deterministic drift and stochastic residuals, while volatility is modeled via Gaussian Process Regression with Aggressive Noise Initialization (ANI). The framework exhibits consistent convergence across all assets and scenarios. SVaR ranges from -2.1020% to -2.2231%, with the coherence property |SES| |SVaR| preserved. The results support GPR-HS with SACS as a stable and regulator-aligned approach for forward-looking SVaR and SES estimation in CCAR and ICAAP applications. Comments: 15 pages, 3 figures. Extension of a hybrid GPR-HS framework to forward-looking stress testing with scenario-based SVaR and covariance stabilization (SACS) Subjects: Risk Management (q-fin.RM); Machine Learning (cs.LG) Cite as: arXiv:2606.07575 [q-fin.RM] (or arXiv:2606.07575v1 [q-fin.RM] for this version) https://doi.org/10.48550/arXiv.2606.07575 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ujjwala Vadrevu [view email] [v1] Tue, 26 May 2026 18:02:45 UTC (945 KB)
[LG-198] Forecasting Japanese elections: A nonlinear machine-learning approach
链接: https://arxiv.org/abs/2606.07572
作者: Sota Kato,Xuan Luo,Budrul Ahsan,Asahi Obata,Takafumi Nakanishi
类目: Physics and Society (physics.soc-ph); Machine Learning (cs.LG); Applications (stat.AP)
*备注:
Abstract:Despite Japan being one of the world’s largest advanced democracies, the development of election forecasting models for its national elections remains limited. This study introduces nonlinear machine-learning forecasting models, based on decision tree and ensemble learning methods, for predicting the outcomes of Japanese lower-house elections. To assess the methodological benefits of our approach, we replicated the theoretical framework and dataset of Lewis-Beck and Tien’s (LBT) foundational statistical forecasting model for Japanese elections. Our models demonstrated moderately but consistently improved predictive accuracy compared to LBT’s model in both in-sample and out-of-sample evaluations, suggesting that nonlinear algorithms offer an alternative approach to classical linear methods in capturing complex electoral dynamics. This study represents one of the earlier applications of nonlinear machine-learning techniques to single-country election forecasting. It offers a replicable framework that, when combined with the country-specific electoral theories of other nations, may enhance the predictive performance of forecasting models in broader national contexts.
[LG-199] Physics-Embedded Neural Networks for sEMG-based Continuous Motion Estimation IROS
链接: https://arxiv.org/abs/2506.22459
作者: Wending Heng,Chaoyuan Liang,Yihui Zhao,Zhiqiang Zhang,Glen Cooper,Zhenhong Li
类目: ignal Processing (eess.SP); Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注: Accepted by 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Abstract:Accurately decoding human motion intentions from surface electromyography (sEMG) is essential for myoelectric control and has wide applications in rehabilitation robotics and assistive technologies. However, existing sEMG-based motion estimation methods often rely on subject-specific musculoskeletal (MSK) models that are difficult to calibrate, or purely data-driven models that lack physiological consistency. This paper introduces a novel Physics-Embedded Neural Network (PENN) that combines interpretable MSK forward-dynamics with data-driven residual learning, thereby preserving physiological consistency while achieving accurate motion estimation. The PENN employs a recursive temporal structure to propagate historical estimates and a lightweight convolutional neural network for residual correction, leading to robust and temporally coherent estimations. A two-phase training strategy is designed for PENN. Experimental evaluations on six healthy subjects show that PENN outperforms state-of-the-art baseline methods in both root mean square error (RMSE) and R^2 metrics.
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