本篇博文主要内容为 2026-07-07 从Arxiv.org论文网站获取的最新论文列表，自动更新，按照NLP、CV、ML、AI、IR、MA六个大方向区分。
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目录
概览 (2026-07-07)
今日共更新1519篇论文,其中:
- 自然语言处理共165篇(Computation and Language (cs.CL))
- 人工智能共452篇(Artificial Intelligence (cs.AI))
- 计算机视觉共392篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共427篇(Machine Learning (cs.LG))
- 多智能体系统共23篇(Multiagent Systems (cs.MA))
- 信息检索共34篇(Information Retrieval (cs.IR))
- 人机交互共36篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] LLM -as-a-Verifier: A General-Purpose Verification Framework
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在复杂代理任务中缺乏有效验证机制的问题，即如何准确评估生成解决方案的正确性。传统方法依赖于离散评分的判别器，难以提供细粒度反馈且泛化能力有限。其核心解决方案是提出LLM-as-a-Verifier——一种无需额外训练的通用验证框架，通过计算评分标记（scoring token）logits分布的期望值，将验证结果转化为连续得分，从而实现概率化建模。这一设计使得验证过程可沿多个维度扩展：（1）评分粒度细化，提升正负解之间的区分能力；（2）重复评估以降低方差；（3）标准分解以减少复杂性。实验表明，上述扩展显著提升了验证准确性，并结合高效排序算法实现了最优解选择。该方法在Terminal-Bench V2（86.5%）、SWE-Bench Verified（78.2%）、RoboRewardBench（87.4%）和MedAgentBench（73.3%）上达到当前最佳性能。此外，其细粒度反馈还可作为任务进展的代理指标，支持开发者对代理系统进行监控与优化，并能有效增强强化学习（RL）中的样本效率，尤其在机器人学与数学推理基准上改进了SAC和GRPO算法的表现。
链接: https://arxiv.org/abs/2607.05391
作者: Jacky Kwok,Shulu Li,Pranav Atreya,Yuejiang Liu,Yixing Jiang,Chelsea Finn,Marco Pavone,Ion Stoica,Azalia Mirhoseini
机构: Stanford University (斯坦福大学); UC Berkeley (加州大学伯克利分校); NVIDIA Research (英伟达研究)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Robotics (cs.RO)
备注: Code: this https URL Website: this https URL
Abstract:Scaling pre-training, post-training, and test-time compute have become the central paradigms for improving the capabilities of LLMs. In this work, we identify verification, the ability to determine the correctness of a solution, as a new scaling axis. To unlock this and demonstrate its effectiveness, we introduce LLM-as-a-Verifier, a general-purpose verification framework that provides fine-grained feedback for agentic tasks without requiring additional training. Unlike standard LM judges that prompt LLMs to produce discrete scores for candidate solutions, LLM-as-a-Verifier computes the expectation over the distribution of scoring token logits to generate continuous scores. This probabilistic formulation enables verification to scale along multiple dimensions: (1) score granularity, (2) repeated evaluation, and (3) criteria decomposition. In particular, we show that scaling the scoring granularity leads to better separation between positive and negative solutions, resulting in more calibrated comparisons. Moreover, scaling repeated evaluation and criteria decomposition consistently lead to additional gains in verification accuracy through variance and complexity reduction. We further introduce a cost-efficient ranking algorithm for selecting the best solution among candidates using the verifier’s continuous scores. LLM-as-a-Verifier achieves state-of-the-art performance on Terminal-Bench V2 (86.5%), SWE-Bench Verified (78.2%), RoboRewardBench (87.4%), and MedAgentBench (73.3%). Beyond verification, the fine-grained signals from LLM-as-a-Verifier can also serve as a proxy for estimating task progress. We build an extension for Claude Code, enabling developers to monitor and improve their own agentic systems. Finally, we show that LLM-as-a-Verifier can provide dense feedback for RL, improving the sample efficiency of SAC and GRPO on robotics and mathematical reasoning benchmarks.
[MA-1] OptiAgent : End-to-End Optimization Modeling via Multi-Agent Iterative Refinement
【速读】：该论文旨在解决从自然语言描述自动生成可求解的数学规划模型及可执行代码的问题，尤其针对运筹学（Operations Research）中复杂优化问题建模的自动化与准确性挑战。其核心解决方案在于提出一种多智能体框架OptiAgent，通过专用智能体在数学建模阶段提取决策变量、约束等关键结构，并支持迭代自我修正；同时引入一种新颖的四循环验证架构，集成四种针对性反馈机制，分别应对误理解、结构缺陷、数学不一致、验证失败及代码错误等典型故障模式。该设计不仅显著提升了建模精度，在线性规划（LP）、混合整数线性规划（MILP）及非线性规划（Nonlinear Programming）三类任务上均达到当前最优性能，还在第四项基准测试中保持竞争力，更重要的是，其模块化结构增强了过程透明度，使各智能体的推理与反馈可追溯，从而实现整个建模流程的可审计性。
链接: https://arxiv.org/abs/2607.05346
作者: Adriana Laurindo Monteiro,Nayse Fagundes,Gabriel Mattos Langeloh,Gustavo de Oliveira Kanno,Priscila Louise Aguirre,Thiago Costa Rizuti da Rocha,Victor Leme Beltran
机构: Instituto de Ciência e Tecnologia do Itaú(巴西伊塔乌科学技术研究所); Itaú Unibanco(巴西伊塔乌银行)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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Abstract:We propose OptiAgent, a multi-agent framework that, given a natural language description of an Operations Research problem, is able to output a solver-ready mathematical formulation as well as executable code. Our architecture prioritizes the mathematical modeling step, where dedicated agents extract structures, such as decision variables and constraints, enabling iterative self-correction. We introduce a novel multi-loop validation architecture with four specialized feedback mechanisms, each targeting a distinct failure mode such as misinterpretation, structural defects, mathematical inconsistencies, validation failures, and code errors. Alongside accuracy, our modular design improves the process of solving optimization problems by improving transparency, as each agent exposes its reasoning and feedback, making the full modeling process auditable. Our framework achieves state-of-the-art performance on 3 out of 4 benchmarks across LP, MILP, and Nonlinear Programming tasks, while remaining highly competitive on the remaining dataset.
[MA-2] PiSAs: Benchmarking Contextual Integrity in Multi-User Agent ic Systems
【速读】：该论文旨在解决大型语言模型（Large Language Model, LLM）代理从单用户助手演变为共享组织基础设施过程中产生的新型隐私风险问题。随着代理间通过共享记忆、内部通信及输出交互实现协同，敏感信息可能在不同用户之间发生非故意泄露，而现有基于情境完整性（Contextual Integrity, CI）的隐私评估基准未能充分覆盖此类跨用户场景，因其主要聚焦于单用户环境或独立拥有代理间的交互。为此，论文提出PiSAs（Privacy in Shared Agentic systems）基准，采用双层情境完整性标注机制：一是判断信息是否适合作业任务，二是明确哪些用户有权合法访问该信息。这一设计使系统能够直接量化代理系统各组件与接口（如输出、代理间通信、共享记忆）中跨用户的信息溢出风险。PiSAs具有系统无关性，支持在多种代理拓扑结构和记忆管理策略下进行评估。研究发现，尽管系统架构设计可提升情境完整性合规性，但其性能仍受限于大模型自身判断失误——即使最先进的模型也无法可靠过滤不当内容或限制信息仅向授权用户传播。该结果凸显了亟需发展超越当前研究范畴的隐私保护策略。
链接: https://arxiv.org/abs/2607.05318
作者: Shubham Gupta,Nazanin Mohammadi Sepahvand,Abhinav Kumar,Cem Subakan,Spandana Gella,Pierre-André Noël,Perouz Taslakian,Eugene Bagdasarian,Valentina Zantedeschi
机构: ServiceNow AI Research (ServiceNow人工智能研究); Mila – Quebec AI Institute (Mila –魁北克人工智能研究所); Université Laval (拉瓦勒大学); McGill University (麦吉尔大学); University of Massachusetts Amherst (马萨诸塞大学阿默斯特分校)
类目: Multiagent Systems (cs.MA); Cryptography and Security (cs.CR)
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Abstract:As LLM agents evolve from single-user assistants into shared organizational infrastructure, new privacy risks emerge: inappropriate information may not only be exposed through outputs for external recipients, but also internally across users through inter-agent messages, shared memory and agents. These data spillage risks are not captured by existing privacy benchmarks grounded in contextual integrity (CI) as they focus primarily on either single-user settings or interactions between independently owned agents. We introducePiSAs (Privacy in Shared Agentic systems), a benchmark for assessing unintentional leaks with dual CI annotations: whether an information is appropriate for the task, and which users may legitimately access it. This enables direct measurement of cross-user spillage across agentic system components and interfaces, such as outputs, inter-agent communication, and memory. PiSAsis system-agnostic and supports evaluation across different agent topologies and memory regimes. We find that, although system design improves CI compliance, results are bottlenecked by incorrect LLM judgment calls: even state-of-the-art models fail to reliably filter inappropriate content or restrict transmission to authorized users. Our findings underscore the need for privacy-preserving strategies, beyond those studied in this work.
[MA-3] Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets
【速读】：该论文旨在解决在自由化铁路系统中，运营商在存在部分可观测性环境下如何动态定价的问题。由于运营商保留关于自身目标与绩效的私有信息，且监管约束禁止竞争者之间进行通信或直接信息交换以防止显性合谋，因此各主体必须仅通过可观察的市场数据推断战略互动关系。这一问题对多智能体强化学习（Multi-Agent Reinforcement Learning, MARL）构成了重大挑战，因为传统方法通常将观测视为无结构向量，忽略了决定战略互动的底层市场拓扑结构。为此，论文提出一种基于实体图建模的方法，将环境表示为运营单元构成的图结构，而非决策智能体或静态基础设施，从而显式编码实体间的竞争、协作与连接关系。在此基础上，提出一种基于图表示学习的多智能体双延迟深度确定性策略梯度算法扩展版本，利用多层关系图卷积网络处理实体特征，并通过可学习的注意力机制实现特征聚合。实验结果表明，该框架在不同复杂度递增的铁路定价强化学习环境中，相较代表性关系型与非关系型基线方法，在收益和稳定性方面均表现更优。
链接: https://arxiv.org/abs/2607.05179
作者: Enrique Adrian Villarrubia-Martin,David Muñoz-Valero,Luis Rodriguez-Benitez,Giovanni Montana,Luis Jimenez-Linares
机构: University of Castilla-La Mancha (卡斯蒂利亚-拉曼查大学); University of Warwick (华威大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 46 pages, 14 figures
Abstract:In liberalised railway systems, operators must set prices dynamically in an environment with partial observability, as they retain private information about their objectives and performance, where regulatory constraints prohibit communication or direct information exchange between competitors to prevent explicit collusion. Consequently, agents must learn to infer strategic interactions only from observable market data which presents a significant challenge for multi-agent reinforcement learning, where standard approaches typically treat observations as unstructured vectors, ignoring the underlying market topology that governs strategic interactions. To address this, an entity graph modelling approach is proposed, which represents the environment as a graph of operational units, rather than decision-making agents or static infrastructure, encoding competition, coordination, and connectivity relations between entities. Then, an extension of the multi-agent twin delayed deep deterministic policy gradient algorithm with graph-based representation learning processes the features of the entities through a multi-layer relational graph convolutional network and aggregates them via a learnt attention mechanism. Experimental results in a rail pricing reinforcement learning environment show that this novel framework achieves higher revenue and stability in two different settings of increasing market complexity compared to a representative selection of relational and non-relational baselines. The code is publicly available at: this https URL
[MA-4] ACTIC-KG: Toward Small Agent Teams for Cyber Threat Intelligence Knowledge Graph Construction
【速读】：该论文旨在解决网络安全威胁情报（Cyber Threat Intelligence, CTI）报告中存在大量非结构化、异构且噪声频发的问题，导致其难以直接用于自动化分析与推理。尽管网络安全知识图谱（Cybersecurity Knowledge Graphs, CSKGs）能够以结构化方式表征攻击者实体、行为及关联关系，但如何从自由文本的CTI报告中自动构建高质量的CSKG仍面临挑战。现有方法多依赖于单一的大型语言模型（Large Language Models, LLMs）进行端到端的提取与补全，存在计算成本高、可控性差以及性能不稳定等缺陷。为此，本文提出TACTIC-KG，一种基于智能体（agentic）的框架，通过将任务分解为多个模块化、专业化的LLM智能体，分别负责实体抽取、类型标注、验证与数据清洗等环节。该框架采用轻量级模型（3B–8B参数规模），在保证高性能的同时显著提升了系统的稳定性、召回率与图谱一致性，并降低了部署成本。实验结果表明，在人工标注的CTI报告上，TACTIC-KG在抽取F1分数、类型标注准确率和图谱结构相似性等方面均优于当前最先进的基于上下文学习（In-Context Learning, ICL）的单体模型基线。其解决方案的关键在于通过智能体专业化分工实现任务解耦，从而在保持高效性的同时提升整体系统鲁棒性与可解释性。
链接: https://arxiv.org/abs/2607.05001
作者: Mouhamed Amine Bouchiha,Gregory Blanc
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注: 20 pages, 2 figures, 10 tables
Abstract:Cyber Threat Intelligence (CTI) reports are predominantly unstructured, heterogeneous, and noisy, which limits their direct usability for automated analysis and reasoning. Cybersecurity Knowledge Graphs (CSKGs) provide a structured representation of adversarial entities, actions, and relations, but constructing such graphs from free-text CTI remains a challenge. Recent approaches rely on monolithic Large Language Models (LLMs) to perform end-to-end extraction and completion, leading to high cost, limited controllability, and unstable performance. This paper introduces TACTIC-KG, an agentic framework for CSKG construction that decomposes the task into modular, specialized LLM agents responsible for extraction, typing, verification, and curation. Using lightweight models (3B–8B), TACTIC-KG improves stability, recall, and graph consistency while reducing deployment cost. We implement and evaluate TACTIC-KG against recent state-of-the-art systems. Experiments on human-annotated CTI reports show that agent specialization consistently outperforms larger monolithic in-context-learning (ICL) baselines in extraction F1-score, typing accuracy, and structural graph similarity.
[MA-5] Dynamic Airspace Management for UAVs in Evolving Urban Environments: Collaborative Coordination and Human Safety
【速读】：该论文旨在解决低空经济背景下复杂城市地形与高密度人口环境下多无人机（UAV）协同飞行的安全管理问题，核心挑战在于实现高效避撞与保障人类安全。其解决方案的关键在于提出Pharos系统，该系统介于分布式局部感知与集中式精细化控制之间，通过引入对“人类恐惧”（human fear）的量化建模，创新性地将社会心理因素纳入多无人机空域管理决策过程。系统采用MAPPO（Multi-Agent Proximal Policy Optimization）算法，因其相较于HAPPO和HATRPO等典型多智能体强化学习（MARL）算法具有更快的收敛速度和更高的累积奖励表现。为验证性能，研究构建了基于真实城市数据的三维仿真平台，结果表明，Pharos相较基准方法Ipopt显著降低了52.72%的人类恐惧水平；同时，设计了空间熵（spatial entropy）作为系统评价指标以量化空间利用效率，相比基准方法Ipopt和A-star分别提升了70.82%和2.03%的性能。
链接: https://arxiv.org/abs/2607.04825
作者: Lin Sun,Yuhang Wang,Fan Meng Hong,Haopeng Chen,Yan Jiao,Yongming Xu
机构: Shanghai Jiao Tong University (上海交通大学); Shanghai Shapere Information Technology Co., Ltd. (上海星图信息技术有限公司)
类目: Multiagent Systems (cs.MA)
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Abstract:The low-altitude economy is an emerging industry with significant development potential, in which the safety of unmanned aerial vehicle (UAV) operations is a critical challenge. Particularly within complex urban topographies and human-populated environments, UAV airspace management must prioritize collision avoidance and human safety. We propose Pharos, a collaborative multi-UAV airspace management system. Pharos lies between the distributed local perception paradigm and the centralized fine-grained control paradigm. Pharos coordinates the safe parallel execution of UAVs in shared airspace while innovatively accounting for the impact of human fear. Pharos is implemented using the MAPPO algorithm due to its faster convergence and higher rewards than other typical MARL algorithms (HAPPO and HATRPO). To evaluate Pharos, we developed a 3D simulation system using real urban data. Visualization results demonstrate its effective airspace coordination capability. Regarding performance verification, Pharos reduced human fear by 52.72% compared to the benchmark Ipopt. Moreover, we designed spatial entropy as a system evaluation metric to quantify space utilization, which improved performance by 70.82% and 2.03% compared to the benchmarks Ipopt and A-star, respectively. The source code is available at an anonymized repository: this https URL.
[MA-6] An Exploration of Agent ic Information Fusion for Test Maintenance Prediction
【速读】：该论文旨在解决在快速演化的代码库中，测试用例维护成本高昂且难以高效识别哪些测试需要更新这一关键问题。由于生产代码与测试代码之间存在复杂的依赖关系，传统的测试维护方法往往效率低下或准确率不足。其解决方案的核心在于提出一种多智能体框架MAST，通过整合静态分析、词法分析和语义分析等多种技术手段，并引入智能融合与后置验证机制，实现对测试用例变更需求的精准预测。该方法特别强调在真实工业场景下的适用性，支持标准化输入格式、仓库级别的全局分析，并能够自动推断测试与生产代码之间的关联关系，而无需预设映射。实验基于21个来自Ericsson AB的工业级Java项目进行评估，在包含需维护与无需维护两种情况的真实标注数据上，MAST在精确率、准确率、F1和F2分数上均优于现有最先进基线方法，仅略有召回率损失。消融实验证明了各类分析模块对最终推荐结果的贡献价值，充分体现了多智能体系统在融合多源信息以支持软件测试任务中的潜力。
链接: https://arxiv.org/abs/2607.04786
作者: Jingxiong Liu,Nasser Mohammadiha,Gregory Gay
机构: Chalmers University of Technology and University of Gothenburg(查尔姆斯理工大学和哥德堡大学); Ericsson AB(爱立信公司)
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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Abstract:Test maintenance is a critical, yet costly, activity - particularly as codebases rapidly evolve. To assist, we present MAST, a multi-agent framework that predicts which test cases require maintenance following changes to the production code. This identification task is necessary as a precondition to any subsequent maintenance activities, but remains challenging due to the complex relationships between production and test code. MAST advances the state-of-the-art by integrating multiple analyses – including static, lexical, and semantic analyses - through an intelligent fusion and post-check procedure and by focusing on a realistic use and evaluation setting - i.e., standardized input formats, repository-level analyses, and the ability to infer relations between test and production artifacts rather than assuming a pre-existing mapping. We evaluated MAST on 21 industrial Java repositories from Ericsson AB, considering situations where test maintenance both was and was not required in the ground truth. MAST yielded superior precision to a state-of-the-art baseline - resulting in a higher accuracy, F1, and F2 score - with only some loss in recall. Our ablation study demonstrates the value of each analysis in producing the final recommendations. MAST illustrates the potential of multi-agent systems that can fuse multiple information sources when performing software testing tasks. Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) Cite as: arXiv:2607.04786 [cs.SE] (or arXiv:2607.04786v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2607.04786 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[MA-7] Governed Caste Reassignment in Heterogeneous Swarms: An Asymmetric-Trust Protocol with Audited Operator Countersignature
【速读】：该论文旨在解决异构机器人集群中因电池状态、负载变化及任务优先级调整导致的频繁运行时角色重分配（即“等级重置”，caste reassignment）所引发的治理与审计难题。现有方法将角色重分配视为内部资源调度问题，未向外部监管实体暴露其过程，难以满足受控物理部署场景下的可审计性与授权需求。本文提出一种非对称信任协议：自动收紧型重分配（如降权至更低权限等级）可由系统自主执行，而受限放宽型重分配（如升权至更高权限等级）则需操作员基于各维度预算进行签名确认；所有状态变迁均携带经数字签名的因果链，并提交至基于哈希链结构的默克尔审计日志（Merkle audit log），仅凭操作员签发的身份清单即可实现离线验证。实验评估表明，该方案在真实Ed25519签名环境下支持最多100台机器人规模的集群，自动收紧操作延迟为个位数至十位数毫秒，且协议本身通过设计即抵御四类攻击（等级洗白、重复放松升级、操作员冒用、因果链伪造）。进一步引入分布式审计层，采用N个成员副本的共识机制实现日志复制，结合多数派提交与密码学防分叉机制，证明了协议的一致性与无分叉性，并在模拟环境和基于TCP套接字的真实多进程部署（最高达100个进程，含拜占庭故障节点）中验证了其有效性——所有诚实副本均达成一致，检测到异常行为并成功避免分叉。该架构将单智能体身份变更治理机制扩展至群体层面，实现了对机器人集群等级制度的可审计、可授权的协同治理。
链接: https://arxiv.org/abs/2607.04634
作者: Xue Qin,Simin Luan,Cong Yang,Zhijun Li
机构: 未知
类目: Robotics (cs.RO); Cryptography and Security (cs.CR); Multiagent Systems (cs.MA)
备注: 28 pages, 3 figures, 5 tables
Abstract:In heterogeneous robot swarms, caste reassignment (rebinding a robot to a new capability-bound role) is a high-frequency runtime event driven by battery, payload, and priority changes. Existing approaches treat it as an internal allocation algorithm and do not expose the reassignment to external authority. We argue that for regulated embodied deployments a caste change that elevates a robot’s privilege envelope is a governance event that must be auditable and externally authorised. We propose an asymmetric-trust protocol: auto-tightening reassignments (to safer, lower-privilege castes) are admitted automatically, while bounded relaxation (to higher-privilege castes) requires an operator countersignature against a per-axis budget. Each transition carries a signed cause-chain, committed to a hash-chained Merkle audit log that an offline auditor verifies from an operator-signed identity manifest alone. We evaluate a reference implementation with real Ed25519 signatures over fleets up to 100 robots: auto-tightening completes in single-digit to low-double-digit milliseconds, and the governed protocol refuses four explicit attacks (caste laundering, repeated-relaxation escalation, operator impersonation, cause-chain forgery) by construction, with a partially-governed baseline isolating which gate stops which attack and a randomized fuzz adversary finding no admission. A distributed audit layer replicates the log across N per-member replicas with quorum-committed total order and cryptographic fork exclusion; we prove agreement and fork exclusion and validate them both in simulation and as a real multi-process deployment over TCP sockets (up to 100 real processes) with a Byzantine equivocator, on which every honest replica agrees, detects the equivocation, and commits no fork. The construction generalises a single-agent persona-mutation governance gate to swarm-level caste governance.
[MA-8] ResearchStudio-Reel: Automate the Last Mile of Research from Paper to Poster Video and Blog
【速读】：该论文旨在解决科研成果在传播过程中“最后一公里”的自动化难题，即如何高效、高质量地将学术论文自动转化为海报（poster）、演讲视频（talk video）和博客（blog）等多种形式的传播媒介。现有自动化方法存在诸多局限：各产出物独立处理、重复解析论文内容，导致效率低下；生成结果为单向渲染，作者无法在PowerPoint或Word中进行后续编辑；且质量评估依赖于软性视觉语言模型（VLM）偏好分数，难以持续提升，尤其在关键内容段落仍为空白的情况下。针对此问题，本文提出以“技能组合”为核心的设计范式，构建一个由共享上游提取器与可编辑生成器组成的系统架构，其关键在于：通过统一的论文信息提取器（Paper2Assets）一次性抽取论文内容并形成共享资产包，下游各生成技能（Paper2Poster、Paper2Video、Paper2Blog）基于该共享资产进行确定性生成，并在“测量填充循环”机制中实现硬性通过/失败的渲染门控，确保输出质量可控。进一步地，引入交互式融合层（Paper2Reel），将三类输出整合为自包含的HTML可视化界面，支持按章节点击实现视频、幻灯片、字幕与博客内容的精准联动。实验表明，在Paper2Poster基准测试中，该系统在美学与信息量等子指标上全面超越现有自动化系统及单次生成前沿大模型，且在两位独立VLM评审下优于原作者自动生成的海报，在84%至93%的论文上取得整体领先；能力审计进一步验证，唯有本系统能同时交付可编辑的三类输出，其核心优势在于将讲解内容与幻灯片高亮对齐，并结合布局感知的DOCX修复技术，保障多语言博客的结构完整性。
链接: https://arxiv.org/abs/2607.04438
作者: Lingao Xiao,Yalun Dai,Yangyu Huang,Qihao Zhao,Wenshan Wu,Hugo He,Ruishuo Chen,Jin Jiang,Qianli Ma,Jiahuan Zhang,Xin Zhang,Ying Xin,Yang Ou,Yan Xia,Scarlett Li,Longbo Huang,Zhipeng Zhang,Yang He,Yap Kim Hui,Yan Lu
机构: Microsoft Research (微软研究院); National University of Singapore (新加坡国立大学); Nanyang Technological University (南洋理工大学); Tsinghua University (清华大学); Peking University (北京大学); Shanghai Jiao Tong University (上海交通大学); Westlake University (西湖大学); CFAR, A*STAR (新加坡科技研究局下属的计算与数据科学研究中心)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA); Multimedia (cs.MM)
备注:
Abstract:Research dissemination, turning a paper into a poster, a talk video, and a blog post, is still a manual last mile. Prior automation treats each artifact in isolation that each re-extract the paper from scratch, usually ship one-way renders the author cannot reopen in PowerPoint or Word, and gates quality on soft VLM-preference scores that plateau while load-bearing sections still read as empty. We argue this last mile is best built as a composition of skills: thin agent-readable contracts that share one upstream extractor and wrap deterministic primitives in a measured-fill loop whose exits are hard pass/fail render gates. We instantiate this as ResearchStudio-Reel, five Claude Code and Codex skills organized into one shared extractor (Paper2Assets), three editable generators (Paper2Poster, Paper2Video, Paper2Blog), and one interactive convergence layer (Paper2Reel). Paper2Assets extracts each paper once into a shared bundle that can be reused by every downstream skill; The three generators produce a print-ready poster, a synchronized talk video, and a bilingual blog that stay factually consistent and round-trip through PowerPoint or Word; Paper2Reel then binds all three into a self-contained HTML viewer whose section-level clicks jump the video, slides, captions, and blog to matching content. On the Paper2Poster benchmark, our posters lead every aesthetic and information sub-criterion against both prior automated systems and single-shot frontier LLMs, surpassing the authors’ own on aesthetics under two held-out VLM judges and winning overall on 84% to 93% of papers; capability audits further show that, by uniquely pairing narration-aligned on-slide highlights with a bilingual blog gated by layout-aware DOCX repair, ResearchStudio-Reel is the only pipeline to ship all three editable artifacts. Project is available at this https URL
[MA-9] Agent ic IoT: Architectures Applications and Challenges Toward the Internet of Agents
【速读】：该论文旨在解决当前人工智能物联网（AIoT）系统在智能化水平上的局限性，即现有方案大多依赖于针对特定任务的模型，仅能基于传感器数据进行被动推断，导致系统在实时推理、自适应规划、自主协同、持续学习、工具使用及情境化决策等高级认知能力方面仍存在显著短板。其解决方案的关键在于提出“代理式物联网”（Agentic IoT）这一新一代认知型物联网范式，通过将自主人工智能代理（AI agent）的感知、推理、规划、学习与行动能力深度融合至信息物理系统中，实现从以数据为中心的感知与推断基础设施向跨设备/边缘-雾-云连续体分布式的认知代理生态系统演进。该框架强调系统整体的主动认知与协同智能，为构建具备自主性、适应性与上下文理解能力的下一代智能物联网提供了理论基础与技术架构支撑。
链接: https://arxiv.org/abs/2607.04219
作者: Rümeysa Hilal Sevinç,Bahaeddin Türkoğlu,İbrahim Kök
机构: Ankara University (安卡拉大学)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Networking and Internet Architecture (cs.NI)
备注: 14 pages, 2 figures, Author’s preprint version. The manuscript may be revised based on peer-review feedback and publication requirements
Abstract:The integration of AI into Internet of Things (AIoT) systems has gradually transformed them from passive data collection infrastructures into intelligent systems capable of anomaly detection, predictive maintenance, classification, forecasting, and optimization. However, most existing solutions still rely on task-specific models that infer from sensor data; thus, system-wide capabilities such as real-time reasoning, adaptive planning, autonomous coordination, learning, tool use, and contextual decision-making remain limited. This paper examines Agentic IoT as a next-generation cognitive IoT paradigm that integrates the perception, reasoning, planning, learning, and action capabilities of autonomous AI agents with cyber-physical systems. Agentic IoT aims to transform IoT from data-centric sensing and inference infrastructures into distributed cognitive agent ecosystems operating across the device/edge-fog-cloud continuum. The paper first grounds this transition as a paradigm shift and positions Agentic IoT in relation to AIoT, edge intelligence, multi-agent systems, and the Internet of Agents. It then systematically reviews current studies, presents a holistic architectural framework, discusses domain-specific application potential, and identifies key technical, operational, and research challenges together with future research directions.
[MA-10] EmCom-Diffusion: Probing Visual Reflection in Emergent Languages via Image Generation
【速读】：该论文旨在解决生成式语言（emergent language）在多大程度上编码其输入视觉内容这一开放性问题，核心关注的是“视觉反映性”（visual reflection）——即生成消息在不依赖说话者-听者对的情况下，能否保留可被恢复的原始图像信息。现有评估方法依赖间接代理指标（如人类定义的概念词库、自然语言描述、结构距离相关性或参照游戏准确率），易遗漏真实编码的视觉内容或错误赋予无关内容以高分。本文提出一种直接评估框架EmCom-Diffusion：通过微调预训练文本到图像扩散模型（text-to-image diffusion model），基于（图像, 生成消息）配对实现从消息到图像的生成式重建，并以重建图像与原始图像间的感知相似度作为视觉反映性的量化指标。该方法采用生成式而非判别式范式，避免了对人工标注目标的依赖。在MS-COCO数据集上的参照游戏场景中，该框架在三种预训练视觉编码器下验证了其有效性，相比随机和固定词元基线以及四种现有指标（CBM、监督翻译、TopSim、R@1），EmCom-Diffusion能捕捉到其他方法所遗漏的视觉内容，展现出更高的敏感性和准确性。
链接: https://arxiv.org/abs/2607.03752
作者: Haruumi Omoto,Tadahiro Taniguchi
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注:
Abstract:Measuring the extent to which emergent languages encode the visual content of their inputs is an open problem. We refer to this property as visual reflection: the extent to which emergent messages preserve information about their source images that can be recovered without appeal to the speaker-listener pair that produced them. Existing metrics measure it only indirectly, through proxies such as human-defined concept inventories, natural-language captions, structural distance correlations, or Referential Game accuracy, each of which can either miss visual content the message encodes or credit content it does not. We propose EmCom-Diffusion, an evaluation framework that measures visual reflection directly: it reconstructs each input image from its emergent message and compares the reconstruction with the original image itself, rather than with human-defined targets. Concretely, it finetunes a pretrained text-to-image diffusion model on (image, emergent-message) pairs and scores visual reflection as the perceptual similarity between the reconstructed and original images, operating generatively rather than discriminatively. Instantiating it on MS-COCO with a Referential Game, we validate the metric against random and fixed-token baselines under three pretrained visual encoders, and compare it against four existing metrics (CBM, supervised translation, TopSim, and R@1). EmCom-Diffusion captures visual content the other metrics miss.
[MA-11] Swarm-Driven Multi-Agent Reasoning for Smart City Security
【速读】：该论文旨在解决智能城市中由异构设备构成的网络物理系统所面临的协同攻击检测难题，此类攻击往往表现为低频扫描、异常凭证使用、协议误用或延迟横向移动等分散且微弱的迹象，单个指标难以触发本地告警阈值，导致传统方法在面对部分可观测性、不确定性及对抗性干扰时失效。为此，论文提出基于大语言模型（Large Language Model, LLM）的多智能体框架TPSC-Sec，其核心创新在于通过专业化分工的多个智能体分别分析流量行为、协议交互、身份使用与时间维度上的攻击演进过程，并引入“威胁信息素群集共识”（Threat-Pheromone Swarm Consensus）机制，实现对各智能体独立提出的威胁假设进行聚合：该机制通过强化一致假设、抑制矛盾观点并保持时间一致性，推动不同解释向稳定集体决策收敛。进一步地，为提升推理可靠性，提出自适应验证型TPSC（Adaptive Verified TPSC），引入验证感知校准、上下文敏感加权及分歧自适应控制策略，有效降低不支持的LLM输出和推理不一致性。实验结果显示，TPSC-Sec在500次运行中达成高达0.97±0.02的共识接受率、超过0.99的假设支持集中度、2.08±0.21的共识裕度、仅0.23±0.04的综合风险水平，且智能体间一致性达0.82±0.06，支持质量相关性高达r=0.93；自适应智能体选择机制使活跃智能体数量减少50%，同时系统效能提升11.6%。上述结果表明，该方案在对抗性环境中具备鲁棒性、可解释性与高效性，实现了面向智能城市的稳定安全推理。
链接: https://arxiv.org/abs/2607.03628
作者: Saeid Jamshidi,Kawser Wazed Nafi,Carol Fung,Foutse Khomh
机构: Polytechnique Montréal (蒙特利尔工业大学); Concordia University (康考迪亚大学)
类目: Cryptography and Security (cs.CR); Multiagent Systems (cs.MA)
备注:
Abstract:Modern smart cities are interconnected cyber-physical ecosystems where heterogeneous devices exchange data and control commands. Coordinated attacks may appear as weak and distributed indicators, including low-rate scanning, abnormal credential use, protocol misuse, or delayed lateral movement, with each signal remaining below local alert thresholds. Therefore, smart-city security is not only an anomaly detection task but also a reasoning task under uncertainty, partial observability, and adversarial manipulation. This work presents TPSC-Sec, an LLM-based multi-agent approach for stable security reasoning in smart cities. TPSC-Sec decomposes analysis across specialized agents that inspect traffic behavior, protocol interactions, identity usage, and temporal attack progression. Their independent threat hypotheses are aggregated by the proposed Threat-Pheromone Swarm Consensus mechanism, which reinforces supported hypotheses, suppresses contradictions, and preserves temporal consistency, thereby driving competing interpretations toward a stable collective decision. We further introduce Adaptive Verified TPSC, which adds verification-aware calibration, context-sensitive weighting, and disagreement-adaptive control to reduce unsupported LLM outputs and reasoning inconsistency. Experiments over 500 runs show that TPSC-Sec achieves a high consensus acceptance rate of 0.97 plus or minus 0.02, hypothesis-support concentration above 0.99, a consensus margin of 2.08 plus or minus 0.21, low aggregate risk of 0.23 plus or minus 0.04, high inter-agent agreement of 0.82 plus or minus 0.06, and strong support-quality correlation of r equals 0.93. Adaptive agent selection reduces the number of active agents by 50 percent while improving system fitness by 11.6 percent. These results demonstrate robust, interpretable, and efficient security reasoning for adversary-resilient smart-city environments.
[MA-12] STRATOS: Bridging the Symbolic-to-Numeric Gap in Spatio-Temporal Text-to-SQL for Meteorological Data
【速读】：该论文旨在解决在真实世界气象数据探索中，通用文本转SQL（Text-to-SQL）系统因存在“符号到数值”（Symbolic-to-Numeric）鸿沟而无法有效处理气象领域复杂查询的问题。其核心挑战在于：气象数据具有高度时空维度的特性，且用户自然语言查询常包含空间与语义模糊性，同时数据格式（如NetCDF、GRIB）和访问门槛要求较高的编程能力。为此，论文提出一个端到端的文本转SQL框架，关键创新在于设计了STRATOS（Spatio-Temporal Resolution Agent for Text-to-SQL），通过动态映射模糊概念至局部本体，并利用外部知识库解析空间实体，从而弥合符号与数值之间的鸿沟；此外，引入复杂度感知的查询重写机制，对高开销的空间谓词进行优化，将查询执行时间从数小时缩短至秒级。为验证系统在复杂时空场景下的可扩展性与翻译能力，研究还构建了由领域专家设计的STRATOS评估工作负载，包含7,520个复杂查询对，覆盖此前文本转SQL系统未充分探索的挑战性时空维度。
链接: https://arxiv.org/abs/2607.03501
作者: Yi Zhang,Farhad Nooralahzadeh,Jonathan Fürst,Fabio Scherrer,Antonis Bezes,Vassiliki Kotroni,Kurt Stockinger
机构: Zurich University of Applied Sciences(苏黎世应用科学大学); National Oberservatory of Athens(希腊国家天文台)
类目: Databases (cs.DB); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 8 pages, 4 figures
Abstract:Copernicus, the European Union’s Earth observation program, produces petabytes of Earth observation and climate data, offering immense potential for research, policy, and applications. However, access to these datasets requires advanced programming skills and familiarity with domain-specific formats such as NetCDF or GRIB. Moreover, general-purpose Text-to-SQL systems fail when applied naively to the meteorological domain due to a profound ``Symbolic-to-Numeric’’ gap. To overcome these limitations, we present an end-to-end Text-to-SQL framework specifically engineered for real-world, scalable meteorological data exploration. Our system intercepts natural language to resolve spatial and semantic ambiguities \textitbefore SQL generation. We design STRATOS, a Spatio-Temporal Resolution Agent for Text-to-SQL to dynamically bridge the symbolic-to-numeric gap by mapping fuzzy concepts to a localized ontology and resolving spatial entities via external knowledge bases. Further, our complexity-aware query rewriter rewrites expensive spatial predicates, reducing execution times from hours to seconds. Last, we introduce the STRATOS Evaluation Workload, comprising 7,520 complex query pairs explicitly designed by domain experts to test scalability and symbolic-to-numeric translation across challenging spatio-temporal dimensions previously unexplored by Text-to-SQL systems.
[MA-13] MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination
【速读】：该论文旨在解决多智能体强化学习（MARL）在部分可观测环境下，因通信带宽受限而面临的通信效率与任务性能之间的权衡问题。现有方法通常通过优化信息论代理目标来实现通信压缩，但此类统计代理与实际任务的联合回报目标存在根本性偏差——即某条消息可能具有高信息量却对任务最终收益无关。为此，本文提出了一种名为“目标导向通信精简”（Message Unlearning for Targetd Efficiency, MUTE）的新框架，其核心思想是将通信稀疏化建模为一种基于价值引导的机器遗忘问题。MUTE通过基于注意力机制的估计器严格量化每条消息的反事实消息价值（Counterfactual Message Value），并在此基础上系统性地从无通信约束训练出的策略中“遗忘”低价值消息的传输。该过程采用双目标优化机制，在强制实现通信稀疏的同时保持原始联合策略的性能表现。论文进一步推导了通信稀疏化导致的性能差距理论上限，确保返回值下降可控。在多个复杂多智能体环境中的实证结果表明，MUTE可实现80%至90%的带宽缩减，同时维持与当前最优基线相当的性能水平。
链接: https://arxiv.org/abs/2607.03473
作者: Rui Zuo,Qinwei Huang,Mingyang Li,Zhenhang Zhang,Simon Khan,Qinru Qiu
机构: Syracuse University (雪城大学); Air Force Research Laboratory (美国空军研究实验室)
类目: Multiagent Systems (cs.MA)
备注:
Abstract:Inter-agent communication is critical for coordinating Multi-Agent Reinforcement Learning (MARL) agents under partial observability to perform effectively in cooperative games; however, real-world bandwidth constraints demand sparse interactions. Prior approaches primarily address this trade-off by optimizing information-theoretic surrogates. We argue that these statistical proxies are fundamentally misaligned with the true objective: a message can be highly informative yet irrelevant to the joint return of the task. In this work, we propose Message Unlearning for Targeted Efficiency (MUTE), a framework that views communication reduction as a value-guided machine unlearning problem. MUTE rigorously quantifies the Counterfactual Message Value using an attention-based estimator, and systematically unlearns the transmission of low-value messages from a policy trained without any communication constraints. This is achieved through a dual-objective mechanism that enforces communication sparsity while preserving the return of the original joint policy. We derive a theoretical upper bound on the performance gap induced by this sparsification, guaranteeing controlled return degradation. We also empirically evaluate MUTE on various complex multi-agent environments, achieving 80% to 90% bandwidth reduction while maintaining performance comparable to state-of-the-art baselines.
[MA-14] Second MOASEI Competition at AAMAS2026: A Technical Report AAMAS’2026
【速读】：该论文旨在解决开放系统环境下多智能体决策机制的评估难题，尤其关注在动态、不确定且信息不完全的现实场景中，多智能体系统如何高效协同完成复杂任务。其核心挑战在于构建一个能够真实反映实际应用条件的基准评测体系，涵盖火灾扑救、网络安全和共享出行等典型开放系统场景，并引入具有时变状态（如灭火剂容量、作业范围）的“帧开放”（frame openness）新赛道以增强评估的真实性与挑战性。解决方案的关键在于设计一套综合性的评价指标体系，重点聚焦总任务完成量、平均任务完成时间及已完成任务的平均价值，从而全面衡量多智能体系统的性能表现。2026年竞赛虽仅有单一团队提交最终方案（针对共享出行赛道），但其所采用的基于规划与重规划的分布式学习控制（DLC）方法，在动态乘客出现情境下有效解决了跨智能体路径优化问题，被认定为该赛道的优胜方案，验证了其在开放系统多智能体协作中的有效性。
链接: https://arxiv.org/abs/2607.03399
作者: Ceferino Patino,Tyler J. Billings,Alireza Saleh Abadi,Daniel Redder,Adam Eck,Prashant Doshi,Leen-Kiat Soh
机构: University of Nebraska-Lincoln (内布拉斯加大学林肯分校); University of Georgia (佐治亚大学); Oberlin College (欧柏林学院)
类目: Multiagent Systems (cs.MA)
备注: Report from the MOASEI’2026 Competition held at AAMAS’2026
Abstract:We describe the 2026 Methods for Open Agent Systems Evaluation Initiative (MOASEI) Competition, a benchmark event for evaluating multi-agent decision-making under open-system conditions. Building on the inaugural 2025 competition, the 2026 edition retained wildfire fighting, cybersecurity, and ride-sharing domains while adding a bonus wildfire track with frame openness, in which agent equipment states such as suppressant capacities and firefighting range vary over time. The competition also expanded its reporting metrics to emphasize total task completions, mean task-completion time, and mean value of completed tasks. Participation in 2026 was limited: eight teams registered, but only one team submitted a final entry, and that entry targeted the ride-sharing track. The submitted DLC approach used planning and replanning to solve routing problems across agents as passengers appeared. This report summarizes the 2026 competition design, highlights differences from the previous year, and reports ride-sharing evaluation results against baseline policies. DLC is recognized as the 2026 ride-sharing track winner among submitted teams.
[MA-15] Organizational Memory for Agent ic Business Process Execution
【速读】：该论文旨在解决大语言模型（Large Language Model, LLM）在企业业务流程自动化中因缺乏组织特异性知识而导致的可靠性问题。尽管基于LLM的智能体（agent）突破了传统规则系统的能力边界，但通用型LLM无法获取企业内部分散于政策文档、流程模型和标准操作程序等以人为导向的知识载体中的组织特定知识。现有将此类知识通过单独提示词或代理专属检索机制编码的方法，在企业级应用中难以扩展，易形成知识孤岛与规则重复，且阻碍跨代理的一致性更新与学习。为此，论文提出构建一个面向智能体业务流程执行的组织记忆（organizational memory）：一种共享、受控、可被智能体消费的动态演进型组织特异性流程知识参考层。其核心解决方案在于设计一套支持知识协同编纂与统一消费的架构，确保知识的集中管理、持续演化与跨代理一致性，通过采购场景的原型验证了该方案的有效性。
链接: https://arxiv.org/abs/2607.03228
作者: Lukas Kirchdorfer,Adrian Rebmann,Christian Warmuth,Timotheus Kampik,Theiss Heilker,Gregor Berg
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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Abstract:LLM-based agents offer new opportunities for automating business process execution beyond the limits of rule-based systems. However, general-purpose LLMs lack the organization-specific knowledge required for reliable execution, which is typically fragmented across human-oriented artifacts such as policies, process models, and standard operating procedures. While such knowledge can technically be encoded in individual prompts or agent-specific retrieval setups, this approach does not scale in enterprises, as it gives rise to knowledge silos and rule duplicates, and makes consistent updates and learning across agents difficult. We argue that this calls for an organizational memory for agentic business process execution: a shared, governed, and agent-consumable reference layer of evolving organization-specific procedural knowledge about how work should be executed. We derive requirements for such a memory, propose an architecture for its curation and consumption, and demonstrate its effectiveness in a proof-of-concept based on a procurement scenario.
[MA-16] Silicon Sampling via Cross-Survey Transfer
【速读】：该论文旨在解决生成式人工智能（Generative AI）在模拟问卷调查受访者时，现有评估方法过于依赖分布层面匹配而忽视个体层面预测能力的问题。传统评估方式容易将模型对整体数据分布的模仿误认为其具备真实的人类应答逻辑推理能力，从而高估模型表现。为此，作者提出“跨问卷迁移”（cross-survey transfer）这一更严格的评估框架：即要求大语言模型（LLM）基于同一受访者在一组问题上的回答，预测其在另一组完全不同的问题上的答案，从而检验模型在个体层面的可预测性与一致性。该方案的关键在于引入真实未见题项的个体级预测任务，以区分模式匹配与真正的认知推理能力。研究结果表明，零样本LLM在未见题项上达到52%的准确率，接近使用同人群监督训练的随机森林模型（相差仅6个百分点），并揭示出稳定的构念可预测性层级结构（如政党态度可达67%，主权议题仅23%），同时发现方差坍缩和安全对齐等常被提及的模型局限性具有更复杂的呈现形式——方差坍缩同样影响监督学习模型，而对齐效应则在不同模型家族间存在显著差异。这些发现明确了硅基采样（silicon sampling）在提升传统调查研究中的潜力边界与实际限制。
链接: https://arxiv.org/abs/2607.03091
作者: Chan-Tung Ku,Chan Hsu,Pei-Cing Huang,Frank Cheng-shan Liu,I-Ling Cheng,Yihuang Kang
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY); Multiagent Systems (cs.MA); Methodology (stat.ME)
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Abstract:Silicon sampling-using large language models (LLMs) to simulate human survey respondents-has emerged as a promising approach for augmenting traditional survey research. However, most evaluations rely on distributional comparisons rather than individual-level prediction, which risks conflating pattern matching with coherent respondent-level prediction. We propose cross-survey transfer, a more rigorous evaluation framework in which an LLM is given a respondent’s answers to one set of questions and must predict their answers to entirely different questions from the same survey. Using data from the Taiwan Election and Democratization Study (TEDS) 2024, three open-weight LLMs (27B-120B parameters), and supervised machine learning baselines, we find that: (1) zero-shot LLMs achieve 52% accuracy on genuinely unseen items, closing to within 6 percentage points (pp) of a supervised random forest trained on same-population data; (2) a stable construct predictability hierarchy emerges, from 67% for partisan attitudes to 23% for sovereignty; and (3) variance collapse and safety alignment effects-two commonly cited LLM limitations-turn out to be more nuanced than previously reported, with variance collapse affecting supervised models as well and alignment effects varying dramatically across model families. These findings clarify both the promise and boundaries of silicon sampling.
[MA-17] LOTUSim: Multi-Domain Simulator for Marine Robotics
【速读】：该论文旨在解决现有海事机器人仿真平台在人机协同交互与真实环境物理建模方面的不足，特别是针对大规模异构无人艇编队在复杂海洋环境中开展协同作战任务时，缺乏实时交互性能与高保真度水下流场模拟的问题。其解决方案的关键在于：一是构建支持多用户、跨空-表-潜多域机器人系统协同操作的实时交互式仿真架构，通过优化计算效率与系统扩展性，实现大规模异构无人机群在共享仿真环境中的稳定实时运行，并保障高视觉保真度与严格实时性；二是提出一种基于埃克曼（Ekman）理论的分层式高效水下洋流模型，能够精确刻画风驱动、深度依赖的海洋流场动态特性，在保持计算效率的同时显著提升对真实海洋环境的物理拟合精度，相较于常用的随机高斯-马尔可夫模型具有更优的仿真准确性。这两项创新共同使LOTUSim成为支持“人在回路”（operator-in-the-loop）海事机器人研究的理想仿真平台。
链接: https://arxiv.org/abs/2607.03072
作者: Cédric Buche(CROSSING, IMT Atlantique),Juliette Grosset(CROSSING),Hélène Lechêne,Marie Dubromel(CROSSING),Pierig Havez-Bodivit,Malcom Neo,Julien Prodhon
机构: Naval Group (法国海军集团); CNRS (法国国家科学研究中心); IMT Atlantique (IMT大西洋学院)
类目: Multiagent Systems (cs.MA); Robotics (cs.RO)
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Abstract:Simulation is essential for maritime robotics, supporting operator training, mission rehearsal, and human-vehicle interaction in environments where real-world testing is costly or hazardous. Existing simulators focus primarily on autonomy systems and often lack human-in-the-loop interaction and realistic environmental physics. This paper introduces LOTUSim, an open-source, real-time maritime simulator supporting multi-user interaction across aerial, surface, and underwater robotic systems for coordinated naval-style operations. The first contribution of this work is enabling real-time interactive performance for users while ensuring scalability to large fleets operating within a shared interactive simulation environment. Validation demonstrates robust human-in-the-loop performance, maintaining strict real-time execution and high visual fidelity while scaling to large heterogeneous maritime drone swarms. The second contribution is a computationally efficient, Ekman-inspired layered, underwater current model that captures wind-driven, depth-dependent flow dynamics with sufficient physical fidelity for large-scale simulations. Validation against ocean reanalysis data demonstrates substantially improved accuracy compared to commonly used stochastic Gauss-Markov current models. These results confirm LOTUSim’s suitability as a simulation platform for operatorin-the-loop maritime robotics research.
[MA-18] Human-Centric Reflective Architecture for Human-AI Collaborative Decision-Making
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在人类活动各领域应用中，因人类对AI建议存在过度依赖或低估，以及现有AI系统与人类预期、偏好和需求不匹配所导致的决策偏差与风险问题。其核心解决方案是提出一种以人为中心的反思架构（Human-Centric Reflective Architecture, HCRA），该架构将人类校准模型与基于强化学习的智能体相结合，通过迭代式、反思性的语言反馈机制，实现人机协同决策过程中的动态对齐。关键在于将人类反馈融入强化学习的闭环优化中，使AI代理能够持续适应并精准响应人类的主观判断与期望，从而提升决策的有效性与推荐质量。
链接: https://arxiv.org/abs/2607.03025
作者: Andreas Kouridakis,Dimitrios Patiniotis Spyropoulos,George Vouros
机构: University of Piraeus(比雷埃夫斯大学); Greece(希腊)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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Abstract:The use of Large Language Models (LLMs) across diverse areas of human activity-ranging from everyday tasks to safety-critical applications-aims to enhance decision-making effectiveness with minimal human feedback. Concurrently, it seeks to align decisions with human expectations, preferences, and needs while mitigating risks associated with AI non-determinism. However, humans frequently over- or under-rely on AI recommendations, and current AI systems remain poorly calibrated to human expectations. To address these challenges, we introduce a human-AI collaborative decision-making framework designed to augment human capabilities and align AI agents with human preferences and expectations. Specifically, this paper (a) formulates the collaborative decision-making task as a stochastic game between an AI agent and a human player, and (b) proposes the Human-Centric Reflective Architecture (HCRA), which integrates human-calibrated models with reinforcement learning agents that leverage linguistic feedback in an iterative, reflective process. Evaluation results demonstrate that HCRA enhances decision-making effectiveness and delivers high-quality recommendations.
[MA-19] A Workflow-Aware Serving Layer for Agent ic Applications
【速读】：该论文旨在解决生成式 AI（Generative AI）应用中工作流服务的动态调度与资源优化问题，即在异构后端环境下，如何在请求执行时实时决策每个节点所使用的模型、验证器（verifier）及计算资源分配。现有方案存在明显局限：模型服务引擎虽能高效执行单个调用，但缺乏对整个工作流结构的感知；而传统代理框架虽能固定工作流逻辑，却无法感知后端负载情况，导致无法在服务时动态优化每一步的模型选择与质量控制策略。本文提出 Dyserve，一个面向工作流感知的服务层，其核心创新在于将每个工作流中各节点的模型与验证器选择统一建模为一个整数线性规划（ILP）问题，基于技能条件化的离线性能轮廓进行定价，并通过硬件仅以每模型吞吐率扫描的方式抽象引入。该方法通过权重机制聚焦于错误传播路径最远的关键节点，优先部署高性能模型与验证能力。针对不同负载压力下无通用最优配置的问题，Dyserve 在请求接入时预先求解多个压力水平下的策略方案，运行时根据实际负载切换未提交部分的执行策略，避免在线求解开销；当工具调用失败时，仅触发一次残差重求解，从而保障已提交任务的完整性。
链接: https://arxiv.org/abs/2607.02942
作者: Jiayi Qian,Zishen Wan,Hanchen Yang,Chun Tao,Souvik Kundu,Tushar Krishna
机构: Georgia Institute of Technology(佐治亚理工学院); Columbia University(哥伦比亚大学); Intel(英特尔)
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Multiagent Systems (cs.MA)
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Abstract:Agentic AI applications form an emerging serving workload in which a request creates a workflow: a directed acyclic graph of LLM and tool calls that exposes per-node model choices and optional quality operators such as verifiers. This workload falls between two existing layers. Model-serving engines execute individual calls efficiently but cannot see workflow structure, while agent frameworks fix the workflow but cannot see backend load, so neither jointly chooses each node’s model, verifier, and backend under serving-time conditions. We present Dyserve, a workflow-aware serving layer that fills this gap. Dyserve compiles each workflow’s per-node model and verifier choices in one integer linear program (ILP) over a heterogeneous backend pool, priced by skill-conditioned offline profiles that transfer across workflows. This couples with hardware entering only through per-model throughput sweeps, and is weighted to concentrate strong models and verification on the nodes whose errors propagate the furthest. Because no single latency-quality preference fits every workload mix, Dyserve pre-solves the program at several pressure levels at admission and shifts a workflow’s uncommitted suffix among these strategies under load, keeping the solver off the load-shift path; a failed tool call triggers a one-time residual re-solve that preserves committed work. Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Multiagent Systems (cs.MA) Cite as: arXiv:2607.02942 [cs.DC] (or arXiv:2607.02942v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2607.02942 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[MA-20] SovereignNegotiation-Bench: Evaluating User-Owned Personal Agents In Delegated Bargaining Under Privacy Consent Evidence And Institutional Pressure
【速读】：该论文旨在解决用户拥有的个人代理（personal agent）在委托协商过程中，虽能达成协议却可能损害用户利益的系统性风险问题，如隐私泄露、同意违规、无依据主张、过度让步、升级失败及可审计性差等。其解决方案的关键在于提出SovereignNegotiation-Bench——一个基于追踪级别的多轮协商基准，专门评估在私有效用、披露约束、证据要求与制度不对称条件下的委托型个人代理行为。该基准通过将代理可见状态与评估者独有标签分离，综合评估协议成功率与用户效用、隐私保护、同意合规性、证据依据性、让步纪律性、升级能力及可审计性等多个维度。实证结果表明，单纯追求高协议率的基线模型虽表现优异，但用户效用低且存在显著隐私与同意风险；而全主权代理（FullSovereign）虽未最大化协议率，却在整体主权协商得分上最优，体现了对用户利益的全面保障。研究揭示：仅关注协议达成不足以衡量用户所有制代理的有效性，真正的成功需以用户主权为核心，实现多方目标的协同优化。
链接: https://arxiv.org/abs/2607.02814
作者: Dylan Zongmin Liu
机构: Stanford University (斯坦福大学)
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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Abstract:Personal agents will increasingly negotiate on behalf of users: splitting costs with other personal agents, appealing platform decisions, escalating support disputes, requesting refunds, changing subscriptions, and negotiating deadlines or reimbursements. Existing negotiation benchmarks emphasize agreement, surplus, or strategic competence, but a user-owned agent can reach an agreement while harming the user through privacy leakage, consent violation, unsupported advocacy, over-concession, failed escalation, or poor auditability. We introduce SovereignNegotiation-Bench, a trace-level multi-turn benchmark for delegated personal-agent negotiation under private utilities, disclosure constraints, evidence requirements, and institutional asymmetry. The benchmark separates agent-visible observable state from evaluator-only labels and evaluates agreement success jointly with user utility, privacy, consent, evidence grounding, concession discipline, escalation, and auditability. We report an artifact-backed validation over 240 scenarios, 4 model families, 14 baselines, 13,440 frozen-prompt live trajectories, 61,135 parsed action rows, and a blinded 3-annotator audit over 300 items. The strongest agreement-maximizing baseline achieves the highest agreement rate but low user utility and high privacy/consent risk; FullSovereign does not maximize agreement, but obtains the best sovereign negotiation score by preserving utility, minimizing leakage, grounding claims, and reducing unauthorized commitments. The results show that agreement success is insufficient for user-owned negotiation agents.
[MA-21] Evaluating Large Language Models for Decision-Making in Agent -Based Urban Mobility Simulations
【速读】：该论文旨在解决城市交通仿真中多智能体系统（Multi-Agent Systems）在动态环境下的决策建模难题，尤其针对传统基于规则的方法因依赖固定启发式策略而难以实现自适应行为的问题。其解决方案的关键在于提出一种混合架构，将GAMA仿真平台与外部基于大语言模型（Large Language Models, LLMs）的决策模块通过API连接，使智能体能够基于上下文判断是否需要重新规划路径。该方法并非替代现有路径规划算法，而是作为决策层，指导重规划行为；同时引入持久记忆机制，使历史交互信息可影响未来决策，从而提升行为的一致性与情境感知能力。实验对比了基于规则与LLM辅助的两种策略在多种道路阻塞场景和不同人群规模下的表现，结果表明LLM驱动的智能体在路径灵活性较高的情境中展现出更强的适应性与情境敏感性，且记忆机制对性能和行为一致性具有显著影响，但其效果随配置变化而异。总体而言，LLM作为补充性的认知层，有效增强了城市交通仿真中行为建模的复杂性与真实性，为复杂空间多智能体系统的决策建模提供了新范式。
链接: https://arxiv.org/abs/2607.02716
作者: Bruno Cascaes Alves,Míriam Blank Born,Ulisses Gilioli Francescatto Júnior,Felipe Moura Goulart,Letícia Brandão Caldas,Marilton Sanchotene de Aguiar
机构: CAPES (巴西高等教育人才完善协调委员会); Kunumi Institute (昆米研究院)
类目: Multiagent Systems (cs.MA)
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Abstract:Urban mobility modeling faces challenges in representing decision-making in dynamic environments. Although Multi-Agent Systems are widely used, rule-based approaches rely on fixed heuristics that limit adaptive behavior. This work investigates the integration of Large Language Models (LLMs) as decision-making components in multi-agent simulations. We propose a hybrid architecture that connects the GAMA platform to an external LLM-based module through an API, enabling agents to determine whether route replanning is necessary. Rather than replacing routing algorithms, the LLM serves as a decision layer that guides replanning behavior. The approach incorporates persistent memory, allowing past interactions to influence future decisions and promote behavioral consistency. We compare rule-based and LLM-assisted approaches across multiple road-blockage scenarios and population scales. Results indicate that LLM-enabled agents exhibit greater adaptability and contextual awareness, particularly in scenarios with higher route flexibility. Memory influences performance and behavioral consistency, with effects varying across configurations. Overall, LLMs serve as complementary cognitive layers that enrich behavioral representations in urban mobility simulations and hold potential for modeling complex decision-making in spatial multi-agent systems.
[MA-22] LLM oxie: Exploring Agent ic AI for Scientific Software Development KDD2026
【速读】：该论文旨在解决生成式 AI（Generative AI）在科研软件工程（Research Software Engineering, RSE）场景中应用时存在的适配性不足问题。现有商用的 AI 编码代理虽在通用软件开发基准上表现优异，但在科学计算领域存在显著缺陷：忽视科学 Python 生态的编程惯例、无法妥善处理敏感或受限制数据、且缺乏可追溯的技术决策记录，导致生成代码难以被引用、审计、复现与扩展。针对这一问题，论文提出 LLMoxie 平台，其核心解决方案在于构建一个三层次架构——包括支持多云与本地推理的基础设施层、基于 LiteLLM/MLflow 的控制平面（实现认证、预算管理、个人身份信息（PII）掩蔽与可观测性），以及面向 AI 编码代理的应用增强层。尤为关键的是，平台通过开源的 RSE-Plugins 生态系统，将累积的 RSE 知识编码为“插件-代理-技能”层级结构，覆盖科学 Python 实践规范、领域特定知识、六阶段研究-实施工作流及项目全生命周期管理。该设计使 AI 编码代理从通用代码生成器转变为具备领域认知能力的协作伙伴，能够遵循社区规范并生成可审计的技术推理溯源，从而有效弥合了生成式 AI 与科研软件可信赖性需求之间的鸿沟。
链接: https://arxiv.org/abs/2607.02703
作者: Landung Setiawan,Anant Mittal,Cordero Core,Anshul Tambay,Carlos Garcia Jurado Suarez,David A. C. Beck,Andrew J. Connolly,Vani Mandava
机构: eScience Institute, University of Washington (华盛顿大学电子科学研究所); Dept of Chemical Engineering, University of Washington (化学工程系，华盛顿大学); Dept of Astronomy, University of Washington (天文学系，华盛顿大学)
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Multiagent Systems (cs.MA)
备注: 9 pages, 4 figures. Accepted to ACM SIGKDD 2026 Workshop: Agentic AI for Scientific and Societal Advances (SciSoc Agents and LLMs). Describes an agentic AI platform for scientific software engineering with governed multi-cloud inference, structured multiagent workflows, and domain-aware coding support (cs.SE, cs.MA, cs.AI)
Abstract:In this paper, we describe LLMoxie, an institutional AI platform whose three-tiered architecture supports multi-cloud and on-premise inference, a LiteLLM/MLflow control plane for authentication, budgeting, PII masking, and observability, and an application augmentation layer for AI coding agents. Layered on top, an open-source RSE-Plugins ecosystem encodes accumulated RSE knowledge as a Plugin-Agent-Skill hierarchy spanning scientific Python practice, domain-specific knowledge, a six-phase research-and-implement workflow, and project lifecycle management. Scientific software is judged less by raw code quality than by whether it can be cited, audited, reproduced, and extended. Off-the-shelf AI coding agents, optimized against commercial software benchmarks, are poorly calibrated for this setting: they ignore the conventions of the scientific Python libraries they invoke, mishandle sensitive or embargoed data, and leave decision trails that are difficult to reconstruct after the fact. We report on twenty months of practice at a university-based research software engineering (RSE) center, where RSEs embedded across astronomy, earth and climate science, agriculture, and health projects worked to close this gap. We characterize the recurring infrastructure, governance, and process challenges of adopting Agentic AI inside a multi-domain RSE center, describe the platform and plugin design, and distill operational lessons from real scientific software deployments. Together, the platform and plugins shift AI coding agents from generic code generators into domain-aware collaborators that respect community norms and produce auditable provenance of technical reasoning.
自然语言处理
[NLP-0] Weak-to-Strong Generalization via Direct On-Policy Distillation
【速读】： 该论文旨在解决生成式语言模型在强化学习后训练（Reinforcement Learning with Verifiable Rewards, RLVR）过程中因需对每个新模型重复大量采样（rollouts）而导致的计算成本高昂问题，尤其当模型规模增大时，后训练阶段成为性能瓶颈。其核心解决方案是提出一种**直接在线策略蒸馏（Direct On-Policy Distillation, Direct-OPD）**方法，关键在于不直接蒸馏弱教师模型（weak teacher）的最终策略，而是提取其在强化学习前后策略分布的变化——即通过对比教师模型在强化学习前后的两个检查点，计算其动作概率的对数比值（log-ratio），作为隐式密集奖励信号（dense implicit reward）注入更强的目标模型中。该方法利用弱模型上已获得的强化学习监督信号，直接作用于强模型的在线策略状态，避免了构建显式奖励模型或在目标模型上重新执行稀疏奖励强化学习，从而实现高效迁移。实验表明，Direct-OPD可显著提升强模型性能，如将Qwen3-1.7B在AIME 2024上的得分从48.3%提升至62.4%，仅用8块A100 GPU运行4小时，且支持多步策略转移的串行组合，验证了强化学习成果可作为跨模型尺度的隐式奖励信号复用，而非仅限于模仿最终模型。
链接: https://arxiv.org/abs/2607.05394
作者: Shiyuan Feng,Huan-ang Gao,Haohan Chi,Hanlin Wu,Zhilong Zhang,Zheng Jiang,Bingxiang He,Wei-Ying Ma,Ya-Qin Zhang,Hao Zhou
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Project Page: this https URL
Abstract:Reinforcement learning with verifiable rewards (RLVR) is a powerful recipe for improving language-model reasoning, but it is expensive to repeat on every new strong model because the target model must generate many rollouts during training. As models scale, post-training itself becomes a bottleneck. We study a weak-to-strong alternative: run RL on a smaller model where rollouts are cheaper, then reuse what that RL run learned to improve a stronger target model. Directly distilling the post-RL weak teacher is not enough, because the teacher’s final policy mixes useful RL gains with the limitations of the smaller model. We propose Direct On-Policy Distillation (Direct-OPD), which transfers the teacher’s RL-induced policy shift instead. Direct-OPD compares the post-RL teacher with its own pre-RL reference and treats their log-ratio as a dense implicit reward for the student. In plain terms, the checkpoint pair tells us which actions RL made the weak model more or less likely to take, and Direct-OPD applies that signal on the stronger student’s own on-policy states. This directly reuses the weak model’s RL supervision signal without training an explicit reward model or running sparse-reward RL on the target model. Empirically, Direct-OPD consistently leverages weaker teachers to improve stronger target models; notably, it boosts Qwen3-1.7B from 48.3% to 62.4% on AIME 2024 in just 4 hours on 8 A100 GPUs. It outperforms step-matched direct RL and enables the sequential composition of multiple policy shifts. Our results show that RL outcomes can be reused across model scales as implicit reward signals, not merely as final models to imitate.
[NLP-1] What Does a Discrete Diffusion Model Learn?
【速读】： 该论文旨在解决离散扩散模型（discrete diffusion model）中训练目标的本质问题，即：模型究竟在学习什么？具体而言，其核心问题是澄清不同损失函数（如去噪器、得分比、桥接插值预测器等）所对应的实际优化目标，并揭示这些看似不同的参数化形式在数学上实为同一对象在不同坐标系下的表示。解决方案的关键在于提出并严格证明“Oracle Distance定理”——负证据下界（ELBO）精确等于数据熵与从理想逆向过程到学习过程的路径KL散度之和，而非仅是上界。该定理表明，最优解为给定当前噪声状态时真实逆向跳跃率的条件期望，且不可约损失由前向过程破坏原始数据信息的速率决定，即 −dtdI(Z0;Zt)，因此所有噪声过程共享相同的最优负ELBO值，即数据熵本身。对于具有词元可分解噪声的序列建模任务，该框架进一步导出了最优解在三种精确坐标系下的等价表示：去噪器（denoiser）、腔体（cavity，即桥接插值预测器）与得分（score），并给出了三者之间的闭式转换关系。这一统一框架不仅明确了文献中各类损失函数实际优化的分布律，恢复了MDM、UDM、SEDD与GIDD作为特例，还解释了为何在掩码扩散中去噪器与腔体一致而均匀扩散中不一致；证明了使用去噪器参数化会导致均匀ELBO在初始化时发散，而桥接插值预测器保持有限；并实现了对初始时刻ELBO实现的精确校准。所有恒等式均在可解析模型上通过数值验证，无近似误差。
链接: https://arxiv.org/abs/2607.05381
作者: Rodrigo Casado Noguerales,Bernhard Schölkopf,Thomas Hofmann,Aran Raoufi
机构: ETH Zurich(苏黎世联邦理工学院); Max Planck Institute for Intelligent Systems, Tübingen(马克斯·普朗克智能系统研究所, 图宾根); ELLIS Institute Tübingen(ELLIS研究所, 图宾根)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Theory (cs.IT); Machine Learning (stat.ML)
备注: 66 pages, 6 figures
Abstract:What does a discrete diffusion model learn: a denoiser, a score ratio, or a bridge plug-in predictor? At the level of jump rates, these are one object in different coordinates, and reading a neural network in the wrong coordinate changes the process being trained and sampled. Starting with a rigorous derivation of the continuous-time Markov chain (CTMC) ELBO for any noising process, boundary terms included, we prove the \emphOracle Distance theorem: the negative ELBO is exactly equal to the data entropy plus the path KL from the oracle reverse process to the learned one, not merely a bound. Its unique optimizer is therefore the conditional expectation of the true reverse jump rate given the current noisy state, and its irreducible cost is the rate at which the forward process Z_t destroys information about the clean data Z_0 , -\tfracddtI(Z_0; Z_t) , so every noising process shares the same best achievable negative ELBO: the data entropy. For sequences with token-factorizing noise, the oracle projection yields three exact coordinates for the optimizer: denoiser, cavity (bridge plug-in), and score, with closed-form conversions among them. This framework identifies which law each loss in the literature actually optimizes, recovering MDM, UDM, SEDD, and GIDD as special cases; explains why denoiser and cavity coincide for masked diffusion but not for uniform diffusion; proves that a denoiser parameterization makes the uniform ELBO diverge at initialization while the bridge plug-in stays finite; and calibrates ELBO implementations exactly at initialization. Every identity is verified numerically, without approximation, on an exactly solvable model.
[NLP-2] GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks
【速读】： 该论文旨在解决生成式机器人编程在商业与工业应用中可靠性不足的问题，具体针对“变体自动化”（Variational Automation, VA）任务——这类任务因物体几何形状与位姿的高变异性，远超传统固定自动化场景的适应范围。现有无模型策略（model-free policies）在面对VA任务时难以维持足够的可靠性与持续执行能力。为应对这一挑战，论文提出一种名为“图即策略”（Graph-as-Policy, GaP）的多智能体编码框架，其核心在于将模块化开放机器人技能库（Modular Open Robot Skill Library, MORSL）中的感知、规划与控制节点构建成有向计算图，并通过构建内部仿真环境并行评估不同图结构，实现对图结构与参数的迭代优化。该方案的关键创新在于融合可解释的机器人编程与无模型策略的开放世界适应性，通过仿真驱动的图结构自进化机制显著提升任务成功率与吞吐量。在8个新的开放VA任务基准（4个仿真、4个真实场景）上的评估表明，GaP在性能上显著优于现有基线方法。
链接: https://arxiv.org/abs/2607.05369
作者: Kaiyuan Chen,Shuangyu Xie,Letian Fu,Justin Yu,William Pacini,Sandeep Bajamahal,Hudson Kim,Jaimyn Drake,Daehwa Kim,Haoru Xue,Jonathan Francis,Christian Juette,Peter Schaldenbrand,Muhammet Yunus Seker,Ruwan Wickramarachchi,Uksang Yoo,Guanzhi Wang,Adithyavairavan Murali,Balakumar Sundaralingam,S. Shankar Sastry,Spencer Huang,Yuke Zhu,Linxi “Jim” Fan,Ken Goldberg
机构: University of California, Berkeley (加州大学伯克利分校); NVIDIA (英伟达); Carnegie Mellon University (卡内基梅隆大学); Bosch (博世)
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:For robots to work reliably in commercial and industrial applications, can recent advances in agentic coding systems combine interpretable robot programming with the open-world adaptability of model-free policies? We focus on “Variational Automation” (VA), a class of tasks that have larger variations in object geometry and pose than fixed automation. Model-free policies often struggle to close the reliability gap for VA tasks, which must be executed persistently and reliably in commercial and industrial applications. Motivated by prior work on Task and Motion Planning (TAMP) and the Robot Operating System (ROS), we introduce Graph-as-Policy (GaP), a multi-agent coding harness that generates directed computation graphs with perception, planning, and control nodes from a Modular Open Robot Skill Library (MORSL). GaP then generates an internal simulation environment to rehearse task instances with different graphs in parallel to iteratively refine the graph structure and parameters to improve success rates and throughput. Evaluation with 8 new open VA task benchmarks, 4 in-simulation and 4 in real-world, suggests that GaP can achieve success rates that significantly outperform baselines. Details, code, and data can be found online: this https URL
[NLP-3] SPEARBench: A Benchmark for Naturalness Evaluation in Streaming Speech-to-Speech Language Models
【速读】： 该论文旨在解决生成式语音对话系统在实际对话场景中自然度评估不足的问题，现有标准语音与文本评测指标无法全面反映系统在实时对话中表现出的语用特性，如对话时机、发言权交替、语调韵律、人际立场表达、语言与方言一致性以及关系感知的适宜性等关键因素。为此，研究提出SPEARBench基准测试框架，其核心在于构建基于Seamless Interaction语料库的受控对话提示，并通过多维度评估协议对多个模型生成的回答进行综合评价，涵盖响应延迟、打断行为、语音质量、自动语音识别（ASR）鲁棒性、语言与方言一致性、情感自然性、人际立场表达及可解释的分布基线。该基准包含原始真人对话作为参考条件，为当前主流模型提供量化对比结果。研究表明，尽管现有模型在信号级质量与低ASR错误率方面表现良好，但在响应时延、重叠交互、方言保持、情感适应性以及人际立场动态等方面仍显著偏离人类对话行为，揭示了当前生成式语音对话系统在真实交互自然性上的关键差距。
链接: https://arxiv.org/abs/2607.05365
作者: Thomas Thebaud,Yuzhe Wang,Hao Zhang,Sathvik Manikantan Napa Ugandhar,Ashish Hallur,Georgi Tinchev,Venkatesh Ravichandran,Laureano Moro-Velazquez
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
备注: Corresponding Website: this https URL
Abstract:Streaming speech-to-speech language models aim to answer spoken queries directly with synthetic speech. However, standard speech and text benchmarks do not capture whether these systems behave naturally in conversations, where timing, turn-taking, prosody, interpersonal stance, language and dialect consistency, and relationship-aware appropriateness jointly shape perceived quality. We introduce SPEARBench, a benchmark for evaluating naturalness in speech-to-speech language models from question-answer interactions. SPEARBench constructs controlled dialogue prompts from the Seamless Interaction corpus, runs inference across multiple models, and evaluates generated answers using a multidimensional protocol that covers response latency, interruptions, speech quality, ASR robustness, language and dialect consistency, emotional naturalness, interpersonal stance, and explainable distributional baselines. The benchmark includes original human answers as a reference condition and reports results for several contemporary models. Results show that current models can achieve high signal-level quality and low ASR error while still differing from human conversational behavior in latency, overlap, dialect preservation, emotional adaptation, and interpersonal stance dynamics.
[NLP-4] REDDIT: Correcting Model-Generated Timestamp Drift in ASR without Forgetting via Replay-Based Distribution Editing
【速读】： 该论文旨在解决自回归语音识别（ASR）系统在长段非语音（non-speech）区间中生成的时间戳出现漂移的问题，即尽管转录文本仍保持语义合理性，但解码时间轴与实际音频时间严重错位。其核心挑战在于：传统基于提示微调（SFT）的修正方法虽可改善对齐效果，但易引发灾难性遗忘，导致模型在非目标任务上的性能显著下降。解决方案的关键是提出一种轻量级两阶段后训练框架REDDIT（Replay-based Distribution eDITing），通过利用模型自身解码上下文进行回放式目标编辑，在保持非时间戳词元分布不变的前提下，精准修正时间戳；第一阶段在冻结主干参数的基础上，基于语音活动检测（VAD）裁剪后的语音片段与人工插入的非语音间隙构建无监督标注信号，动态调整时间戳目标；第二阶段引入短前缀精修机制进一步优化时序精度。实验表明，仅使用34.9小时的针对性修正数据且仅更新1.6%模型参数的情况下，Whisper-tiny在长间隙上的平均交并比（mIoU）从38.7%提升至95.0%，跨域音频对齐误差（AAS）由2752毫秒降至223毫秒，同时保持了41.3%的词错误率（MER），远优于普通SFT方法（524.2% MER）。
链接: https://arxiv.org/abs/2607.05364
作者: Cheng-Kang Chou,Ming-To Chuang,Ke-Han Lu,Chan-Jan Hsu,Hung-yi Lee
机构: National Taiwan University (国立台湾大学); National Cheng Kung University (国立成功大学); National Taiwan University of Science and Technology (国立台湾科技大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Sound (cs.SD)
备注:
Abstract:Modern autoregressive ASR systems can emit timestamps as decoded tokens, enabling timestamped transcription without frame-level aligners or inference-time post-processing. We show that these generated timestamps can drift across long non-speech spans: the transcript may remain plausible, but the decoded time axis drifts away from the audio. We study this non-speech-induced timestamp drift with self-built gap and long-gap benchmarks across 15 evaluated timestamp-producing ASR and audio-language systems. Naive timestamp-corrected fine-tuning improves alignment but can severely degrade non-target ASR behavior, exposing a forgetting problem. We propose REDDIT(REplay-based Distribution eDITing), a lightweight two-stage post-training framework that corrects timestamps while avoiding this catastrophic forgetting: it first edits timestamp targets under the model’s own replayed decoder context while matching the frozen base distribution on non-timestamp tokens, then applies a short edited-prefix refinement stage. In this framework, we construct correction supervision without human transcripts or human timestamp annotations by combining VAD-trimmed speech spans with inserted non-speech gaps and known concatenation offsets. On Whisper-tiny, 34.9 hours of targeted correction audio used and only 1.6% of model parameters updated, raising long-gap mIoU from 38.7% to 95.0% and reducing mixed-gap out-of-domain AAS from 2752 ms to 223 ms while preserving CV-en MER at 41.3% (versus 524.2% for ordinary SFT decoder tuning).
[NLP-5] Faithfulness to Refusal: A Causal Audit of Neuron Selectors
【速读】： 该论文旨在解决当前生成式AI（Generative AI）中基于归因得分（attribution scores）的神经元行重要性评估缺乏因果有效性验证的问题。现有方法虽广泛用于模型剪枝、可解释性分析及安全编辑，但其是否真正识别出对模型行为具有因果影响的神经元行仍未得到直接检验。为此，作者提出一种基于单次神经元行置零（one-shot neuron-row zeroing）的成对审计框架：首先在语言建模层面评估不同归因方法在识别冗余神经元行上的表现，结果表明归因方法显著优于激活值与幅值基线；其次，将相同干预手段转化为行为测试，通过对比有害与无害输入信号，验证归因选出的神经元行能否有效触发拒绝响应（refusal），同时保持语言流畅性并避免过度拒绝。实验发现，经归因选择的神经元行可成功实现对仇恨与犯罪相关内容的拒绝，而同层随机控制组则失败，证明其具备因果效力。然而，研究还揭示高秩稳定性（rank-stability）的归因方法可能反而导致因果有效性最低，且拒绝行为存在于冗余子空间——不同归因方法通过几乎不重叠的神经元集实现相同功能，说明所恢复的编辑仅为充分集合的一个实现而非唯一机制。综上，该研究的关键在于引入直接因果审计，揭示了传统以秩稳定性为代理指标的局限性，并强调必须通过行为层面的干预测试来验证归因方法的实际因果效用。
链接: https://arxiv.org/abs/2607.05355
作者: Ananth Eswar,Pratinav Seth,Utsav Avaiya,Vinay Kumar Sankarapu
机构: Lexsi Labs
类目: Computation and Language (cs.CL); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
备注:
Abstract:Attribution scores increasingly identify which neuron rows of a language model matter for applications such as pruning, interpretability, and editing for safety, yet whether they identify causally important rows is rarely tested directly. We address this with two paired audits built on one-shot neuron-row zeroing. We first audit selectors at the language-modeling level: attribution methods substantially outperform activation and magnitude-based baselines at identifying dispensable rows across five LLMs. We then adapt the same intervention into a behavior test by driving it with a contrastive harmful-versus-benign signal; the attributed rows are sufficient to install refusal on hate and crime while keeping benign over-refusal low and preserving language model fluency, and specific in that layer-matched random controls at the same depths fail. Highly rank-stable selectors can be among the least causally valid. Refusal moreover lives in a redundant subspace, where different attribution methods install it through largely disjoint row sets, so the recovered edit is one realization of a sufficient set rather than a unique mechanism. Together, these findings show that rank-stability proxies miss the kinds of selector failures a direct causal audit can surface.%
[NLP-6] Selective Disclosure Watermarking for Large Language Models ICML2026
【速读】： 该论文旨在解决现有生成式文本水印（Watermarking）方法在多比特（multi-bit）水印方案中无法实现选择性披露（selective disclosure）的核心问题。当前方法要求验证任意部分水印信息时必须暴露全部嵌入内容，导致敏感元数据过度泄露，引发隐私风险。其解决方案的关键在于提出一种分层词汇路由（Hierarchical Vocabulary Routing, HeRo）框架，通过递归划分词表并将水印信息分布于多层级结构中，使不同权限的验证者仅能解码与其访问权限匹配的水印子集，从而实现细粒度的访问控制。该方法保持了底层采样过程的无偏性，确保生成文本质量不受影响，实验表明其在高检测准确率与低延迟条件下实现了高效、安全的水印信息分层披露。
链接: https://arxiv.org/abs/2607.05353
作者: Xuyang Chen,Xiang Li,Yangxinyu Xie,Qi Long
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted at ICML 2026
Abstract:Watermarking methods embed imperceptible and verifiable signals into text generated by large language models (LLMs). Existing approaches include zero-bit schemes for distinguishing synthetic text from human writing and multi-bit schemes for embedding metadata. However, current multi-bit watermarking methods do not allow selective disclosure: verifying any part of the watermark requires revealing the entire embedded message. This lack of control leads to unnecessary information exposure and raises privacy concerns. We propose Hierarchical Vocabulary Routing (HeRo), a watermarking framework that enables selective disclosure of embedded metadata. The method recursively partitions the vocabulary and distributes watermark information across hierarchical layers, so that different verifiers can decode only the portions of the payload corresponding to their access level. We show that the proposed scheme preserves the unbiasedness of the underlying sampling process and thus maintains text quality. Experiments demonstrate that our framework supports fine-grained access control while achieving high detection accuracy and low latency. Code is available at this https URL.
[NLP-7] How Much is Left? LLM s Linearly Encode Their Remaining Output Length
【速读】： 该论文试图解决的问题是：大型语言模型（Large Language Models, LLMs）在生成文本时表现出高度一致的响应长度结构，但其内部是否具备对剩余生成长度的内在估计能力尚不明确。尽管模型以逐标记（token）方式生成输出，但其行为模式如逐步求解收敛于可预测的标记数、检索内容在数句后停止、修正过程使输出延长等现象暗示了潜在的“长度规划”机制。为探究这一问题，研究提出一种基于冻结隐藏状态的最小容量线性探测器（linear probe）方法，对三个开放权重的7-8B规模模型在七种不同完成风格的数据集上进行分析。解决方案的关键在于：第一，仅从提示（prompt）的最后一个隐藏状态即可线性解码出完整响应的总长度，表明该信息在生成前已编码于模型状态中；第二，基于自然语言数据训练的探测方向具有广泛迁移能力，包括对训练中未见的受控合成完成任务仍表现优异，而反向探测则普遍失效，揭示出方向性不对称性，反映模型内部存在非对称的长度表征；第三，在高损失的修正样本中，探测器对每个位置的剩余长度估计会在模型回溯并重启部分解法时出现显著上升，这种动态变化无法由仅依赖位置的预测模型复现，体现了对生成过程中的“计划性”调整。综合上述证据，研究将此现象解释为模型维持了一种近似剩余生成长度的内部估计，虽不同于精确计数的不可能性理论结果，却支持其具备类似“计划”（plan-like）的内部输出长度表征，且该表征可被解码，但未必在因果生成中直接使用。
链接: https://arxiv.org/abs/2607.05316
作者: Mohamed Amine Merzouk,Dmitri Carpov,Mirko Bronzi,Damiano Fornasiere,Adam Oberman
机构: Mila, Quebec AI Institute(魁北克人工智能研究所); McGill University(麦吉尔大学); LawZero
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 21 pages, 9 figures
Abstract:Large language models generate one token at a time, yet their responses show remarkably consistent length structure: step-by-step solutions converge in predictable token counts, retrievals stop after a few sentences, retractions extend responses by measurable amounts. We ask whether the model carries an internal estimate of how much response remains. Training minimal-capacity linear probes on frozen hidden states of three open-weight 7-8B models across seven completion-style datasets, we find three converging pieces of evidence. First, total response length is linearly decodable from the prompt’s last hidden state alone, before any output is emitted. Second, probe directions trained on natural-language datasets transfer broadly, including to controlled synthetic completions never seen in training, outperforming a statistical baseline; the converse direction generally fails, and this asymmetry is itself informative. Third, on curated high-loss completions, the probe’s per-position estimate shifts upward at the moment the model retracts and restarts a partial solution, a directional behavior no position-only predictor can reproduce (qualitative, not aggregate). We frame this as approximate estimation of remaining generation length, distinct from exact-counting impossibility results for transformers, and interpret it as evidence that LLMs maintain a plan-like internal representation of output length (decodable, not necessarily used causally).
[NLP-8] SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis
【速读】： 该论文旨在解决低资源语言（特别是僧伽罗语，Sinhala）中缺乏细粒度方面级情感分析（Aspect-based Sentiment Analysis, ABSA）数据集的问题。由于现有研究主要集中在高资源语言，且ABSA需要同时标注方面词及其对应的情感极性，而僧伽罗语等低资源语言缺乏此类标注数据，严重制约了相关自然语言处理技术的发展。为此，本文提出并构建了首个僧伽罗语方面级情感分析数据集——SalAngaBhava，该数据集包含经过人工标注的僧伽罗语产品评论，涵盖多个领域中的方面词及对应情感标签（正面、负面、中性）。数据来源于领域相关的用户生成内容，并依据严格定义的标注指南确保一致性和质量。实证分析表明，该数据集结构良好、类别分布均衡，具备开展僧伽罗语自然语言处理及低资源情感分析研究的基准价值。其关键贡献在于填补了低资源语言在细粒度情感分析领域的数据空白，为后续相关研究提供了可靠基础。
链接: https://arxiv.org/abs/2607.05259
作者: Lakshani Galwatta,Nisansa de Silva,Sarangi Aththanayake,Adithya Galwatta
机构: University of Colombo(科伦坡大学); University of Moratuwa(莫鲁塔瓦大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 9 pages, 6 figures
Abstract:Sentiment analysis has been a primary domain under Natural Language Processing (NLP) from its inception as it plays a vital role in both real-world and research applications. In high-resource languages, this has been extended a step further, and instead of predicting sentiment at the sentence level, models have been developed to detect more fine-grained sentiments at aspect level. However, in order to conduct this fine-grained Aspect-based Sentiment Analysis (ABSA), datasets annotated with aspects and sentiments toward the said aspects is required. Such datasets are lacking for low-resources languages among which, we can count Sinhala, an Indo-Aryan languages used primarily in Sri Lanka. In this work, we introduce, SalAngaBhava, a new Sinhala Aspect-based Sentiment Analysis dataset which contains Sinhala product reviews that are manually labeled with aspect terms and the associated sentiments (positive, negative, neutral). The data was collected from domain-relevant sources such as user-generated reviews and comments, and was annotated following carefully defined guidelines to ensure consistency and quality. The dataset consists of sentences and aspect-sentiment pairs, encompassing a considerable range of aspects from several domains. The analysis confirms that the dataset is well-structured and sufficiently balanced for ABSA research. This dataset can be used as a benchmark and facilitates further studies related to Sinhala natural language processing, and low-resource sentiment analysis tasks.
[NLP-9] Streaming Neural Speech Codecs through Time-Invariant Representations
【速读】： 该论文旨在解决基于编码器的语音生成系统中，如何有效建模语音内容以支持低延迟语音处理的问题。其核心挑战在于传统帧级建模需同时捕捉动态变化的语音语义信息与静态的说话人及环境特征，导致计算复杂度高且难以实现高效实时处理。论文提出的关键解决方案是通过分析并优化时间不变表征提取（TIRE）模块所捕获的信息特性，揭示中间层编码器在保留互补性说话人和环境相关特征的同时，几乎不包含语言内容的规律。基于此发现，作者提出了双层级TIRE（Dual-TIRE）架构，利用多层编码器间的互补性，分别提取并融合不同层次的时变与时不变信息，从而提升语音重建质量与说话人相似性。此外，通过跨文件采样策略训练TIRE，增强了不变表征的鲁棒性。最终，在流式推理设置下以660ms连续块处理验证，结果表明该方法可在保持重建性能的前提下实现低延迟语音生成，证明了因子化神经编码器表征在下一代低延迟语音生成系统中的巨大潜力。
链接: https://arxiv.org/abs/2607.05250
作者: Kélian Estève,Salima Mhdaffar,Mickael Rouvier,Richard Dufour,Yannick Estève
机构: 未知
类目: Computation and Language (cs.CL)
备注: Accepted to SPECOM 2026
Abstract:Neural speech codecs are increasingly used as intermediate representations in codec-based speech generation systems. TiCodec introduces a factorized representation that separates time-varying speech content from time-invariant information through a Time-Invariant Representation Extraction (TIRE) module, potentially reducing the amount of information that must be modeled at the frame-level. In this work, we investigate the nature of the information captured by TIRE representations and their suitability for low-latency speech processing. Using a series of probing tasks, we analyze the influence of the encoder layer and show that intermediate layers capture complementary speaker- and environment-related information while containing little linguistic content. We further study several segment selection strategies for TIRE training and demonstrate that cross-file sampling improves the robustness of invariant representations. Based on these findings, we propose Dual-TIRE, a multi-level architecture that exploits the complementarity of different encoder layers and improves speech reconstruction quality and speaker similarity. Finally, we evaluate TiCodec in a streaming inference setting using successive 660ms processing blocks. Results show that streaming operation can be achieved without significant degradation in reconstruction performance, highlighting the potential of factorized neural codec representations for future low-latency speech generation systems. Comments: Accepted to SPECOM 2026 Subjects: Computation and Language (cs.CL) Cite as: arXiv:2607.05250 [cs.CL] (or arXiv:2607.05250v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2607.05250 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-10] Progressive Refinement: An Iterative Pseudo-Labeling Approach for Mandarin-English Code-Switching ASR
【速读】： 该论文旨在解决多语言交替（Code-switching, CS）场景下自动语音识别（ASR）因训练数据稀缺而导致性能受限的问题。其核心解决方案是首次提出一种迭代伪标签（pseudo-labeling）训练方法，通过有效利用大量未标注数据来提升CS-ASR系统的性能。该方法的关键在于构建一个三阶段框架：首先从大规模未标注语料中生成伪标签，构建半监督数据集；随后采用两阶段双语模型训练策略，先进行预训练再在有监督的CS数据上微调；最后通过迭代优化不断改进模型对复杂语言交替模式的建模能力。实验表明，该方法在SEAME数据集的devman和devsge子集上分别实现了6.35%和8.29%的混合错误率（Mix Error Rate, MER）显著降低，验证了其在提升低资源CS-ASR性能方面的有效性。
链接: https://arxiv.org/abs/2607.05224
作者: Qu Yang,Cakra Wardhana,Tim Ng
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Code-switching (CS), alternating languages within the same utterance, poses significant challenges for automatic speech recognition (ASR) due to limited CS training data. This paper applies an iterative pseudo-labeling training approach to CS-ASR for the first time, demonstrating its effectiveness in leveraging unlabeled data to improve CS-ASR performance. The approach comprises three phases: pseudo-label generation, two-stage bilingual model training, and iterative improvements. It begins by generating pseudo-labels from a large unlabeled corpus, creating a semi-supervised dataset. This dataset supports a two-stage training framework where the model is pre-trained and then fine-tuned on supervised CS data. Iterative refinements further enhance the model’s accuracy in handling complex CS scenarios. Our approach significantly advances CS-ASR systems, achieving notable Mix Error Rate (MER) reductions on SEAME’s devman (6.35%) and devsge (8.29%) subsets.
[NLP-11] Noisy-Channel Minimum Bayes Risk Decoding ICML2026
【速读】： 该论文旨在解决最小贝叶斯风险（Minimum Bayes Risk, MBR）解码中因评估指标不对称性导致的性能瓶颈问题。传统MBR解码在选择候选假设时，仅基于给定伪参考文本计算期望效用得分，而常用评估指标如BLEU和COMET具有方向性差异，即假设到参考（hypothesis-to-reference）与参考到假设（reference-to-hypothesis）之间的评价不一致，从而引发设计上的偏差。为此，论文提出一种噪声信道分解框架，将MBR解码形式化为四个相互作用的组成部分：假设到参考似然、参考到假设似然、假设先验与参考先验。该分解不仅为现有MBR变体提供了统一的解释视角，还通过分离各信道贡献，实现了针对不同评估指标和任务的可解释性分析。实证研究表明，各信道贡献在不同指标下呈现显著差异，但在同一任务内保持一致性，表明合理调整信道权重可有效提升原始MBR解码的性能。
链接: https://arxiv.org/abs/2607.05198
作者: Yusuke Sakai,Hidetaka Kamigaito,Taro Watanabe
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: ICML2026
Abstract:Minimum Bayes Risk (MBR) decoding yields more robust and higher-quality text generation than maximum a posteriori (MAP) decoding by selecting hypotheses that maximize expected utility over sampled pseudo-references. However, there exists a discrepancy in the design: hypothesis selection calculates expected utility scores conditioned on given pseudo-references, while commonly used evaluation metrics, e.g., BLEU and COMET, are asymmetric. Therefore, it is important to consider both hypothesis-to-reference and reference-to-hypothesis directional effects. In this study, we introduce a noisy channel decomposition of MBR decoding that naturally incorporates bidirectional effects to account for these asymmetries. We decompose MBR decoding into four interacting components: hypothesis-to-reference likelihood, reference-to-hypothesis likelihood, hypothesis prior, and reference prior. This decomposition provides a unified interpretation of existing MBR variants and enables metric- and task-specific interpretability by isolating the contribution of each channel. Our comprehensive analysis reveals that channel-wise contributions exhibit distinct characteristics across metrics while remaining consistent across tasks, and suggests that appropriate channel weighting may lead to improvements over original MBR decoding.
[NLP-12] Unified Audio Intelligence Without Regressing on Text Intelligence
【速读】： 该论文旨在解决多模态音频-文本理解与生成中的统一建模问题，即如何在单一模型框架下实现高质量的音频理解、语音识别与翻译、文本到语音（TTS）、音频生成以及语音到语音（S2S）生成等任务。其核心挑战在于如何有效融合异构的音频与文本信号，并保持强大的语言推理、长上下文处理及对齐能力。解决方案的关键在于提出一种统一的音频-文本大语言模型（Audio-Text LLM）架构——Audex，基于Nemotron-Cascade-2-30B-A3B这一强大的纯文本专家混合模型（MoE）构建。该模型采用单一Transformer解码器结构，将音频输入通过编码与投影映射至文本嵌入空间，使音频与文本在生成阶段以统一方式处理，从而实现强音频-文本融合、无缝多模态生成，并兼容标准大语言模型（LLM）的训练与推理基础设施。此外，通过精心构建包含1574亿音频令牌和3205亿文本令牌的数据集，结合多阶段监督微调、纯文本强化学习（Cascade RL）及跨领域在线蒸馏策略，Audex在多项任务上达到当前最优性能，同时几乎无损地保留了原始文本模型的推理、对齐、知识、长上下文及代理（agentic）能力。
链接: https://arxiv.org/abs/2607.05196
作者: Zhifeng Kong,Sang-gil Lee,Jaehyeon Kim,Boxin Wang,Zihan Liu,Sungwon Kim,Yang Chen,Arushi Goel,Rajarshi Roy,Wenliang Dai,Zhuolin Yang,Yangyi Chen,Dongfu Jiang,Sreyan Ghosh,Tuomas Rintamaki,Andrew Tao,Jonathan Raiman,Mohammad Shoeybi,Bryan Catanzaro,Wei Ping
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注: We release the mode at this https URL
Abstract:Audio intelligence involves understanding, reasoning about, and generating both audio and speech. In this work, we introduce Nemotron-Labs-Audex-30B-A3B (Audex), a unified audio-text LLM built on Nemotron-Cascade-2-30B-A3B, a strong text-only MoE LLM. Audex adopts a simple unified design with a single Transformer decoder: audio inputs are encoded and projected into the text embedding space, while text tokens and quantized audio output tokens are treated uniformly during generation. This architecture enables strong audio-text fusion, seamless multimodal generation, and compatibility with standard LLM training and inference infrastructure. For training, we meticulously curate audio-text datasets comprising 157.4B audio tokens and 320.5B text tokens. We apply multi-stage supervised training on these datasets, followed by text-only Cascade RL and multi-domain on-policy distillation. Audex delivers state-of-the-art audio understanding, speech recognition and translation, text-to-speech, audio generation, and speech-to-speech generation, while preserving very compelling reasoning, alignment, knowledge, long-context, and agentic capabilities of its text-only LLM backbone with marginal or no regression. We release the model checkpoints to facilitate open research.
[NLP-13] RABBiT: Rapidly adaptive BOLD foundation model via brain-tuning for accurate zero-shot and few-shot prediction of speech-elicited responses in the brain NEURIPS2026
【速读】： 该论文旨在解决语言理解在大脑中的上下文依赖性问题，即脑区活动对实验刺激和个体差异的高度敏感性，导致现有计算模型难以在不同参与者和输入条件下实现泛化。其核心解决方案是提出一种名为RABBiT（Rapidly Adaptive BOLD foundation model via BraIn-Tuning）的轻量化音频到功能磁共振成像（fMRI）编码器，通过参数高效微调实现对新参与者和输入的快速适应。关键创新在于：（1）基于学习的区域特异性注意力机制，能够捕捉不同脑区对语言输入的差异化响应；（2）将脑活动响应分解为共享共性成分与受试者特异性成分，并结合经过脑部调优的语音骨干网络，从而在仅需10分钟个体数据的情况下显著提升预测性能。该方法不仅实现了跨被试、跨数据集的零样本与少样本高精度预测，还通过结构化的区域特异性表征增强了模型可解释性，突破了传统方法对大规模个体校准数据的依赖，为大规模人群层面的语言神经机制研究提供了可扩展的技术路径。
链接: https://arxiv.org/abs/2607.05171
作者: Omer Moussa,Mariya Toneva
机构: Max Planck Institute for Software Systems (马克斯·普朗克软件系统研究所)
类目: Computation and Language (cs.CL)
备注: Under review at NeurIPS 2026
Abstract:Language understanding in the brain is context-dependent, varying across experimental stimuli and individuals, which makes it difficult to build computational models that generalize across both. This calls for a foundation model of language-evoked brain activity that can capture shared structure while adapting efficiently to new participants and inputs. We introduce RABBiT (Rapidly Adaptive BOLD foundation model via BraIn-Tuning), a compact audio-to-fMRI encoder designed for accurate zero- and few-shot prediction. A comprehensive evaluation on 324 participants across multiple unseen fMRI datasets shows that RABBiT enables accurate zero-shot prediction of fMRI responses to natural speech across auditory and language-selective regions, surpassing the SOTA foundation model for fMRI and predictions based on group averages. With as little as 10 minutes of participant-specific data, RABBiT further improves performance via parameter-efficient tuning, substantially outperforming per-participant linear models. RABBiT’s performance is driven by two key innovations: (1) learned region-specific attention, and (2) a decomposition of brain responses into shared and subject-specific components, combined with a brain-tuned speech backbone. In addition to supporting strong predictive accuracy, the structured, region-specific representations that RABBiT learns enable interpretability. By eliminating the need for extensive per-participant data and model fitting, RABBiT enables scalable population-level analyses of language in the human brain. We make the code available at this https URL.
[NLP-14] EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments
【速读】： 该论文旨在解决模型在真实世界环境部署后持续学习能力的机制与规律尚不明确的问题。其核心挑战在于缺乏对真实世界交互中智能体学习性能演进的系统性量化理解。解决方案的关键在于构建并利用EdgeBench这一包含134个真实世界任务的评估基准，这些任务覆盖科学发现、软件工程、组合优化、专业知识工作、形式数学及互动游戏等多个领域，每个任务均支持至少12小时的连续智能体操作，并提供多层级丰富反馈。通过对约38,000小时真实世界交互数据的分析，研究首次揭示了智能体在环境学习过程中的整体性能遵循高度精确的对数-正切（log-sigmoid）缩放定律，拟合度高达R² = 0.998，且学习速度每三个月大致翻倍。该发现为理解生成式智能体在现实场景中的持续进化规律提供了关键实证基础，并通过公开51个任务及完整评估框架，推动了对智能体从真实经验中学习机制的研究进展。
链接: https://arxiv.org/abs/2607.05155
作者: Deyao Zhu,Xin Zhou,Shengling Qin,Xuekai Zhu,Hangliang Ding,Shu Zhong,Zixin Wen,Zhonglin Xie,Chenhui Gou,Linxuan Ren,Yueyang Wang,Junfeng Zhong,Rui Liu,Tian Gao,Yangguang Lin,Jingyuan Zhang,Maojia Song,Xuan Qi,Jinhong Wu,Chenyang Zhang,Yinzhu Piao,Ziru Niu,Hongbin Lin,Lingxiang Meng,Peng Tang,Chengyao Tang,Shanyu Wu,Huanyu Zheng,Yu Liu,Liya Zhu,He Wang,Ming Ding,Ziyu Wan,Hao Liu,Sibo Wang,Haotian Zhu,Xintian Zhang,Nan Chai,Yipeng Liu,Panhao Lai,Sihang Yuan,Zixin Su,Ge Zhang,Wangchunshu Zhou,Yantao Du,Wenhao Huang,Guang Shi
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Pretraining scaling laws reveal that model capability improves predictably with data and compute. But learning from real world environments after deployment remains far less understood. Analyzing roughly 38,000 hours of agent interaction with the environment across 134 real world tasks, we find, to the best of our knowledge, the first evidence that overall performance during environment learning follows a log-sigmoid scaling law with remarkably high precision, reaching R^2 = 0.998. Across model generations, we also find that agent learning speed roughly doubles every three months. This discovery stems from EdgeBench, a suite of 134 real world tasks with ultra-long horizons, spanning scientific discovery, software engineering, combinatorial optimization, professional knowledge work, formal mathematics, and interactive games. Each task sustains at least 12 hours of continuous agent operation under rich, multilevel feedback, and is built through substantial expert effort. We publicly release 51 tasks and our full evaluation framework to accelerate the study of how agents learn from real world experience.
[NLP-15] DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation
【速读】： 该论文旨在解决生成式 AI（Generative AI）在大规模语言模型（Large Language Model, LLM）推理过程中，因并行草稿生成（parallel drafting）与目标验证（target verification）解耦所导致的吞吐量下降与验证资源浪费问题。具体而言，现有方法虽能通过并行机制高效生成长序列草稿，但因缺乏词元间依赖建模，导致草稿接受率随长度增加迅速衰减；同时，对所有草稿块进行无差别的完整验证，造成批处理容量被高拒绝风险的低质量部分过度占用，严重制约了高并发服务场景下的系统效率。其解决方案的关键在于提出 DSpark 框架，通过两个核心设计实现性能突破：一是采用半自回归架构（semi-autoregressive architecture），将并行主干网络与轻量级顺序模块相结合，显式建模块内词元依赖关系，有效缓解尾部衰减（suffix decay）问题，提升草稿质量；二是引入置信度调度验证（confidence-scheduled verification），基于前缀存活概率估计与引擎特定吞吐特性，动态调整每请求的验证长度，实现负载感知的自适应验证策略，显著减少无效验证开销。实验表明，DSpark 在离线基准上显著优于现有自回归与并行草稿生成方法，在 DeepSeek-V4 实际服务系统中部署后，相较生产基线 MTP-1，于相同吞吐水平下实现 60% 至 85% 的单用户生成加速，并在严格交互性约束下避免吞吐量剧烈下降，成功拓展了服务系统的性能边界，推动了性能-延迟权衡曲线（Pareto frontier）的优化。
链接: https://arxiv.org/abs/2607.05147
作者: Xin Cheng,Xingkai Yu,Chenze Shao,Jiashi Li,Yunfan Xiong,Yi Qian,Jiaqi Zhu,Shirong Ma,Xiaokang Zhang,Jiasheng Ye,Qinyu Chen,Chengqi Deng,Jiping Yu,Damai Dai,Zhengyan Zhang,Yixuan Wei,Yixuan Tan,Wenkai Yang,Runxin Xu,Yu Wu,Zhean Xu,Xuanyu Wang,Muyang Chen,Rui Tian,Xiao Bi,Zhewen Hao,Shaoyuan Chen,Huanqi Cao,Wentao Zhang,Anyi Xu,Huishuai Zhang,Dongyan Zhao,Wenfeng Liang
机构: DeepSeek-AI
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Speculative decoding accelerates Large Language Model (LLM) inference by decoupling draft generation from target verification. While recent parallel drafters efficiently propose long token sequences in a single forward pass, they suffer from rapid acceptance decay due to a lack of inter-token dependencies. Furthermore, indiscriminately verifying these extended blocks wastes critical batch capacity on tokens with high rejection risks, severely degrading throughput in high-concurrency serving systems. We introduce DSpark, a speculative decoding framework that unifies high-throughput parallel generation with adaptive, load-aware verification. To maintain draft quality, DSpark utilizes a semi-autoregressive architecture, coupling a parallel backbone with a lightweight sequential module, to introduce intra-block dependency modeling and mitigate suffix decay. To optimize system efficiency, DSpark employs confidence-scheduled verification, dynamically tailoring the verification length for each request based on estimated prefix survival probabilities and engine-specific throughput profiles. On offline benchmarks across diverse domains, DSpark substantially improves the accepted length over state-of-the-art autoregressive and parallel drafters. When deployed within the DeepSeek-V4 serving system under live user traffic, DSpark successfully mitigates verification waste. Compared to the established production baseline (MTP-1), DSpark accelerates per-user generation speeds by 60 to 85 percent at matched throughput levels. More importantly, by preventing severe throughput degradation under strict interactivity constraints, it enables performance tiers that were previously unattainable, shifting the Pareto frontier of our serving system.
[NLP-16] When Agents Lie: Premeditation Persistence and Exploitation in Repeated Games ICML
【速读】： 该论文旨在解决生成式 AI（Generative AI）作为自主代理在多智能体协作环境中，其公开承诺（public announcement）是否具有约束力这一关键安全问题。核心挑战在于：当智能体在行动前公开声明意图，但实际行为与声明不一致时，这种背离是源于临时决策的欺骗，还是早已在私有推理阶段就已规划好的策略？为此，研究设计了一种三阶段协议（私有意图、公开宣告、最终行动），通过控制实验环境，在六种博弈场景中对三种前沿大语言模型进行10轮重复测试。研究发现：第一，当智能体偏离其公开声明时，绝大多数偏差（最高达90%以上）已在私有计划阶段预先确定，表明其非临时性欺骗；然而，同一模型在不同博弈中的诚实程度差异显著，从完全诚实到近乎全然背离，说明诚实性并非模型固有属性。第二，不同模型对“公开宣告”的语义理解存在根本分歧——部分模型将其视为具有约束力的承诺，而另一些则视作无成本的“廉价言论”（cheap talk），由此导致收益差距自第0轮即显现并持续贯穿全部10轮。这表明，跨供应商模型集成系统无法默认共享相同的宣告语义，必须通过实证测试评估其交互行为后方可部署。
链接: https://arxiv.org/abs/2607.05132
作者: Jerick Shi,Terry Jingcheng Zhang,Bernhard Schölkopf,Vincent Conitzer,Zhijing Jin
机构: 未知
类目: Computers and Society (cs.CY); Computation and Language (cs.CL)
备注: Best Paper Award at ICML NExT-Game Workshop
Abstract:As large language models are deployed as autonomous agents that communicate intentions before acting, a critical safety question is whether agents that publicly commit to actions will honor those commitments. We place LLM agents in repeated n -player games with a three-stage protocol that separates private intent, public announcement, and final action, allowing us to identify whether each deviation from a stated announcement was already planned during private deliberation. Evaluating three frontier models across six games in homogeneous and heterogeneous groups over 10 rounds, we report two findings. First, when agents deviate from their announcements, the deviation is predominantly already stated in their private plan (exceeding 90% in the highest-deception conditions), yet this is not a fixed model property: the same model ranges from perfect honesty to near-total deviation across games. Second, different models interpret announcements incompatibly, some as binding commitments and others as cheap talk, producing payoff gaps that emerge in Round~0 and persist across all 10 rounds. Systems that combine models from different providers therefore cannot assume shared announcement semantics and require empirical testing of model interactions before deployment.
[NLP-17] Localized LoRA-MoE: Block-wise Low-Rank Experts With Adaptive Routing
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）及高维感知网络在采用参数高效微调（Parameter-Efficient Fine-Tuning, PEFT）技术时，因传统方法如低秩适配器（LoRA）存在结构上的单一瓶颈而导致的梯度战争（gradient warfare）问题。具体而言，当多任务流交错执行时，易引发破坏性优化反馈，导致适配器权重退化为无特性的平均状态。尽管已有空间分区方法引入块级隔离以缓解此问题，但其静态拓扑结构无法适应动态任务切换或环境传感器故障等场景。本文提出一种统一框架——局部化LoRA-MoE（Localized LoRA-MoE），将局部空间阻隔与动态、上下文感知路由机制相结合。关键创新在于设计两种新型架构：块级LoRA-MoE（集中式宏路由）通过全局上下文信号调控整个结构网格；细胞级LoRA-MoE（分布式微路由）则赋予矩阵网格中每个坐标单元自主的局部专家门控能力。实验结果表明，两种架构均有效化解了静态基线中的优化死锁问题，其中去中心化的细胞级门控在性能上达到与全局协调器完全统计等价的效果，形成强有力的“梯度防火墙”，有效抵御故障传播带来的污染。该方案在高维奇异值分解（SVD）模拟、真实世界表格转换以及传感器退化条件下的空间视觉感知等多类基准测试中均显著优于静态基线，为细粒度坐标域和动态运行模式下的模型自适应提供了可扩展且参数高效的解决方案。
链接: https://arxiv.org/abs/2607.05114
作者: Babak Barazandeh,Subhabrata Majumdar,Vinay Prithyani,George Michailidis
机构: Fortinet(福杰); University of California, Los Angeles (加州大学洛杉矶分校)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Large Language Models (LLMs) and high-dimensional perception networks increasingly rely on parameter-efficient fine-tuning (PEFT) to adapt to diverse operational contexts. However, standard methods like LoRA are structurally limited by a monolithic bottleneck, making them highly susceptible to gradient warfare. Interleaved multi-task streams may trigger destructive optimization feedback, collapsing adapter weights into unspecialized averages. While recent spatial partitioning methods have introduced block-wise isolation, they remain trapped in static topologies, unable to adapt to dynamic task-switching or environmental sensor failure. In this work, we introduce Localized LoRA-MoE, a unified framework that fuses localized spatial blocking with dynamic, context-conditioned routing. We propose and evaluate two novel architectural paradigms: Block-Wise LoRA-MoE (Centralized Macro-Routing), which modulates the entire structural grid via a monolithic context signal, and Cell-Wise LoRA-MoE (Decentralized Micro-Routing), which empowers every coordinate cell in the matrix grid with autonomous, localized expert gating. Through a comprehensive suite of benchmarks, ranging from high-dimensional SVD matrix simulations and real-world tabular transformations to spatial vision perception under sensor degradation, we demonstrate that both architectures resolve optimization deadlocks inherent in static baselines. Our empirical results establish that decentralized cell-level gating achieves complete statistical parity with an omniscient global coordinator, providing a robust “gradient firewall” that protects surviving pathways from fault-propagated corruption. Our proposals consistently outperform static baselines, offering a scalable and parameter-efficient solution for dynamic model adaptation across granular coordinate fields and shifting operational regimes.
[NLP-18] MIRAG E: Defending Long-Form RAG Against Misinformation Pollution ACL
【速读】： 该论文旨在解决长文本生成中检索增强生成（Retrieval-Augmented Generation, RAG）因外部检索结果污染而导致的事实性下降问题。现实场景中的检索结果常包含语义相关但存在细微错误信息、误导性表述或虚构内容，严重威胁生成内容的可靠性。其解决方案的关键在于提出一种无需训练、与模型无关的防御机制MIRAGE：通过构建基于自然语言推理（NLI）的跨文档主张图（claim graph），识别不同来源间的一致性与冲突性，并引入“受保护主张门”（Defended-Claims Gate），动态选择使用多源一致支持的子集进行生成，或直接绕过污染检索并参数化生成答案。此外，研究还设计了一种最小编辑污染协议，涵盖四种扰动类型（明确错误、冲突、误导、虚构），用于构建匹配的纯净、混合污染和完全污染评估场景。实验表明，在多个长文本问答基准及多种商用与开源大模型上，原始RAG在污染数据下性能显著下降，而MIRAGE能持续恢复事实性，且优于现有鲁棒RAG方法。
链接: https://arxiv.org/abs/2607.05069
作者: Saadeldine Eletter,Ruihong Zeng,Yuxia Wang,Maxim Panov,Aleksandr Rubashevskii,Preslav Nakov
机构: 未知
类目: Computation and Language (cs.CL)
备注: ACL-style preprint. 19 pages, 5 figures, 16 tables
Abstract:Retrieval-Augmented Generation (RAG) improves factuality by grounding LLMs in external evidence, but real-world retrieval is often polluted: semantically relevant passages may contain subtle misinformation, misleading framings, or fabrications. We introduce MIRAGE, a training-free, model-agnostic defense for long-form RAG. MIRAGE builds an NLI-based cross-document claim graph and applies a Defended-Claims Gate to either condition generation on a consistent, multi-source supported subset or to block retrieval and answer parametrically. We also release a minimal-edit pollution protocol spanning four perturbation families (Unambiguous, Conflicting, Misleading, Fabricated) to construct matched clean, mixed, and fully polluted evaluation regimes. Across four long-form QA benchmarks and multiple commercial and open-weight LLMs, pollution severely degrades vanilla RAG, while MIRAGE consistently restores factuality under mixed and fully polluted evidence and outperforms prior robust-RAG methods. Our implementation and datasets are available at this https URL.
[NLP-19] Beyond Independent Labels: Schwartz-Geometry Decoding for Human Value Detection
【速读】： 该论文旨在解决人类价值观检测（human value detection）中传统多标签分类方法忽视标签间内在结构关系的问题。现有方法通常将19个经过精炼的施瓦茨价值观（Schwartz values）作为独立标签进行预测，但施瓦茨理论指出这些价值观构成一个环形动机连续体（circular motivational continuum），相邻价值兼容而对立价值存在张力。为实现该理论结构在模型输出空间中的显式建模，论文提出将这一连续体作为软约束（soft bias）引入模型输出，而非硬性限制。其核心解决方案在于设计一种后处理式的施瓦茨感知能量解码器（Schwartz-aware energy decoder），通过联合评分所有可能的标签组合，使预测结果在结构上更符合理论连续体。实验表明，尽管训练阶段引入几何感知目标带来的增益有限且对真实连续体与随机排序无显著差异，但该解码器能有效提升标签集与理论连续体的一致性（coherence），且不牺牲宏观或微观F1分数。更重要的是，这种增益具有特异性：仅在真实施瓦茨排序下有效，而在随机排列或经验共现图上则无效。此外，基于限定版Qwen2.5-72B-Instruct的诊断分析显示，推理时引入连续体虽可改变模型行为，但仍无法达到监督式结构化预测的效果。因此，理论感知解码提供了一种轻量、可控的方法，使价值检测任务在保持性能的同时，更忠实于其标签空间的理论结构。
链接: https://arxiv.org/abs/2607.05052
作者: Víctor Yeste,Paolo Rosso
机构: Universitat Politècnica de València (瓦伦西亚理工大学); Universidad Europea de Valencia (欧洲瓦伦西亚大学); Valencian Graduate School and Research Network of Artificial Intelligence (瓦伦西亚研究生与人工智能研究网络)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注: Code: this https URL , 17 pages, 1 figure
Abstract:Human value detection is commonly formulated as sentence-level multi-label classification over the 19 refined Schwartz values, typically predicted as independent labels. Schwartz theory, however, describes them as a circular motivational continuum, in which adjacent values are compatible and opposing values are in tension. We ask whether this structure can be operationalized as an explicit output-space geometry and used as a soft bias rather than a hard constraint. On a DeBERTa-v3-base classifier, we compare two ways of injecting it: training-time geometry-aware objectives and a post-hoc Schwartz-aware energy decoder that scores whole label sets jointly. Across five seeds, training-time geometry gives only limited gains-no larger for the true continuum than for a random ordering-whereas the decoder makes label sets more coherent with the continuum-on theory-aware coherence metrics we introduce-at no cost to Macro-F1 or Micro-F1 (held fixed by its selection rule). The gain is specific to the true Schwartz ordering: it does not appear for a random permutation or an empirical co-occurrence graph through the identical decoder. A bounded Qwen2.5-72B-Instruct diagnostic shows that supplying the continuum at inference shifts behavior but does not match supervised structured prediction. Theory-aware decoding thus offers a lightweight, controllable way to make value detection faithful to its label space.
[NLP-20] Multi-Large Language Model Orchestrated Severity Assessment of Clinical Records (MOSAIC)
【速读】： 该论文旨在解决电子健康记录（EHR）中疾病严重程度这一多维概念难以通过规则驱动方法准确捕捉的问题。现有方法受限于静态规则，无法充分整合临床证据与复杂病理生理机制，导致对疾病进展的评估不全面。其解决方案的关键在于提出一种两阶段的代理型大语言模型（agentic LLM）框架——MOSAIC，该框架能够自主推理并整合结构化EHR数据中的多源信息，包括基于生物标志物的血糖分期、β细胞功能状态以及社会决定健康因素等传统方法缺失的维度。MOSAIC通过代理式推理实现对患者严重程度的动态建模，在2型糖尿病（T2D）的合成队列上验证了其有效性：开放权重版本与专有管道表现相当（加权肯德尔系数κ = 0.773），并与多个基准算法达到中等至良好一致性；更重要的是，其代理式分类显著区分了全因死亡率（log-rank p < 0.001），且在并发症发生率上呈现反向梯度模式，符合“易感人群耗竭”假说，表明其具备超越固定规则系统（如冻结版MOSAIC，κ = 0.428）的深层推理能力。因此，核心创新在于利用代理型生成式AI实现从静态规则到动态临床推理的范式转变，为多维疾病严重程度表型构建提供了可扩展的新范式。
链接: https://arxiv.org/abs/2607.05032
作者: Manuela Del Castillo Suero,Arnault-Quentin Vermillet,Nicole Sonne Heckmann,Darmendra Ramcharran,Maurizio Sessa
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Background: Disease severity is a multidimensional construct difficult to capture with rule-based approaches in Electronic Healthcare Records (EHR). Agentic large language model (LLM) systems could synthesise clinical evidence and reason over EHRs, but remain unevaluated for this task. Methods: MOSAIC is a two-phase agentic LLM framework for severity phenotyping, using type 2 diabetes (T2D) as a proof-of-concept. MOSAIC was evaluated on a synthetic cohort (SyntheticMass; open-weight N = 4,886; closed-weight N = 200) against three algorithmic ground truths (DCSI, DiSSCo, Cooper) and against all-cause mortality and incident complications. Open-weight (locally deployable) and proprietary pipelines were also compared. Results: The generated framework spanned domains absent from the comparators, including biomarker-based glycaemic staging, beta-cell function, and social determinants of health. Open-weight MOSAIC matched the proprietary pipeline (closed- vs open-weight weighted kappa = 0.773) and reached moderate agreement with Cooper (kappa = 0.597) and DCSI (kappa = 0.534) and fair agreement with DiSSCo (kappa = 0.320). Agent-based (Type 1) tiers showed significant separation of all-cause mortality (log-rank p 0.001; crude hazard ratios 1.6-2.4 for non-Baseline tiers), with non-monotonic separation at the upper tiers, and an inverse gradient for incident complications (log-rank p 0.001) consistent with depletion of susceptibles. Agentic classification also diverged from deterministic execution of the same rubric (MOSAIC Frozen; kappa = 0.428), indicating reasoning beyond fixed rules. Conclusion: MOSAIC shows agentic LLM systems can generate and apply clinically meaningful severity phenotypes from structured EHR data in T2D. Extending it to other diseases with similarly multidimensional severity warrants further research.
[NLP-21] Knowledge Knows Verbalization Tells: Disentangling Latent Directions for Mathematical Solvability in LLM s
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在数学推理任务中判断问题是否可解这一核心挑战，尤其关注模型在面对不可解问题时产生虚假解答（fabrication）的根本原因。其关键在于首次将“可解性知识”与“可解性表述”（verbalization）作为独立的内部表征进行分离探查，揭示二者在模型隐藏状态中以线性可解码的方式分别编码。研究发现，模型的虚假生成主要源于表述层面的偏差，而非底层可解性知识的缺失；通过引入不可解提示（unsolvability cues）或激活控制（activation steering）技术，可有效调控表述机制，从而提升模型在不确定情境下的拒绝回答能力（abstention），实现对生成行为的机理级干预。
链接: https://arxiv.org/abs/2607.05013
作者: Nikolaos Xiros,Maria-Eleni Zoumpoulidi,Georgios Paraskevopoulos
机构: Institute for Language and Speech Processing, Athena Research Center, Greece; Google DeepMind (谷歌)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 14 pages, 9 Figures
Abstract:Although LLMs have made significant progress in mathematical reasoning, determining whether a mathematical problem is solvable remains a fundamental yet challenging capability. While recent studies have probed internal representations of model solvability beliefs, verbalization has primarily been studied behaviorally rather than as an internal representation, limiting its analysis and manipulation. We address this gap by separately probing representations of solvability knowledge and verbalization, allowing us to disentangle the two within model hidden states. Across multiple LLMs, we show that knowledge and verbalization are encoded as distinct, linearly decodable representations and that fabrication is primarily associated with changes in verbalization rather than the underlying knowledge. Prompting with unsolvability cues reduces fabrication primarily by shifting verbalization, while activation steering demonstrates that these representations can be echanistically manipulated to improve model abstention.
[NLP-22] he syntax of wh-agreement in Yemeni Ibbi Arabic
【速读】： 该论文旨在解决也门伊布阿拉伯语（Yemeni Ibbi Arabic, YIA）中一种重要的句法现象——疑问词一致（wh-agreement），这一现象在希腊语、印尼语、卢布鲁库苏语、爱尔兰语等多种语言中均有出现。其核心问题是：在YIA中，疑问词（Wh-Op）与功能范畴（如C、T/V、v）之间如何实现跨范畴的一致性标记。论文提出的解决方案关键在于引入“跨阶段同意”（Agree across phases, AAP）框架，并结合“特征继承”（Feature Inheritance, FI）机制，将“匹配”（Agree as MATCHING, AM）与“特征估值”（feature valuation, FV）进行分离处理：AM作用于语类（C/v），而FV则作用于时体/动词范畴（T/V）。研究发现，诸如-eh、-uh、-nen、-um等后缀源自古典阿拉伯语（Stannard Arabic, SA）第三人称代词，经语法化演变成为疑问词一致的形态标记。该分析表明，YIA中的疑问词一致机制具有更强的形态句法鲁棒性，且其运作模式可通过AAP机制统一解释跨语言的远距离依存关系，从而为生成语法中普遍语法（Universal Grammar, UG）的普遍性提供了来自非主流语言类型学证据的支持。
链接: https://arxiv.org/abs/2607.04986
作者: Ashraf Naji(Ibb University),Mohammed Q. Shormani(Ibb University)
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:This article tackles an important phenomenon in the syntax of Yemeni Ibbi Arabic (YIA), viz., wh-agreement, a phenomenon common to several languages including Greek, Indonesian, Lubukusu, Irish, etc. In YIA, wh-agreement manifests itself via agreement inflections on the Wh-Op, C, T/V, v. To account for this phenomenon, we propose an Agree across phases (AAP) approach anchored in the mechanism of Feature Inheritance (FI) in which Agree as MATCHING (AM) is a bit separated from feature valuation (FV). AM concerns Cs/vs, but FV Ts/Vs. Analyzing the agreement patterns observed between Wh-Op(erators), functional heads (precisely C, (T), v), and verbal complexes, we argue that the suffixes -eh, -uh, -nen, -um, having undergone grammaticalization process from Stannard Arabic (SA) third person pronouns, function as morphological marking of wh-agreement. Findings indicate that YIA data offer a unique empirical contribution to generative syntax, specifically concerning wh-agreement in this dialect operating via MATCHING mechanism. Our proposal straightforwardly accounts for wh-agreement cross-linguistically. This study provides further evidence that incorporating under-investigated typology provides further support for the universality of Universal Grammar (UG) by revealing how specific I-language operations reflect deeper, invariant principles of human language architecture. It concludes that the wh-agreement mechanism in YIA is more morphosyntactically robust than in languages such as Greek, Indonesian, Palauan, and Irish, providing compelling evidence for AAP as a UG approach to long-distance dependencies.
[NLP-23] rain Smarter Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training ICLR2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在多轮次训练（multi-epoch training）中如何高效且安全地重用高质量数据的问题。随着训练范式从单遍训练转向多轮次训练，合理重用有限的高质量数据可提升模型性能与样本效率，但过度重复又可能导致过拟合及收益递减。因此，关键挑战在于如何精准判断数据重用的时机与程度。本文提出“记忆引导的数据重用”（Memorization-guided Data Reuse）方法，通过分析损失保留动态（loss retention dynamics）与下游评估指标所揭示的模型“记忆窗口”（Memorization Window）信号，实现对数据重用时机与策略的自适应决策，从而为训练轮次数量和数据重播调度提供原则性依据。初步实验表明，在当前实践（如普遍采用的四轮次上限）之外，性能仍可通过持续重复显著提升，揭示了一种由记忆驱动的训练规律。尽管完整调度器尚待进一步研究，但该工作为构建面向记忆感知的训练策略奠定了基础，有助于在有限高质量数据条件下实现更智能而非更冗长的训练。
链接: https://arxiv.org/abs/2607.04969
作者: Jingwei Zuo,Cong Zeng,Ilyas Chahed,Maksim Velikanov,Dhia Eddine Rhaiem,Pasquale Balsebre,Abhay Kumar,Younes Belkada,Hakim Hacid
机构: Technology Innovation Institute (技术创新研究所)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: Published as a paper at 3rd DATA-FM workshop @ ICLR 2026, Brazil
Abstract:The training paradigm of large language models has shifted from traditional one-pass training to multi-epoch training, as reasonable reuse of limited high-quality data can improve both model performance and sample efficiency. Meanwhile, excessive repetition introduces the risk of overfitting and diminishing returns. Determining when and how to reuse data effectively thus emerges as a natural but under-explored question. Through a novel observation of model’s “Memorization Window” signals derived from loss retention dynamics and downstream evaluation scores, we propose “Memorization-guided Data Reuse”, a training paradigm that adaptively determines when and how data should be reused, enabling principled decisions on the number of training epochs and the scheduling of data replays. Our preliminary experiments reveal a consistent memorization-driven regime: performance continues to improve with repetition far beyond current practice (e.g., the commonly cited four-epoch limit). While a full scheduler remains future work, these insights provide a foundation for memorization-aware training schedules, helping to determine reuse budgets and move toward training LLMs smarter rather than longer with limited high-quality data.
[NLP-24] Whos Behind It? Annotating and Extracting Conspiratorial Actors from German Telegram Posts
【速读】： 该论文旨在解决阴谋论话语中关键行动者（conspiratorial actors）识别不足的问题，尤其针对现有计算研究多集中于文档层面分析而忽视对驱动叙事的核心人物或组织的识别。其解决方案的关键在于：构建适用于德语社交媒体（Telegram）文本的共指消解式标注规范，建立一个基于跨度标注（span-annotated）的德语阴谋论语料库，并采用基于Transformer的模型实现对阴谋论行动者的自动提取。该方法在复杂且隐喻性强的阴谋话语中仍能实现较高的一致性标注与合理准确的自动识别，进而支持对大规模阴谋论传播档案（如Schwurbelarchiv）中行动者表征的系统性分析。
链接: https://arxiv.org/abs/2607.04962
作者: Helena Mihaljević,Jolanda Beer,Mareike Lisker,Katharina Soemer
机构: HTW Berlin, Germany; Goethe University, Frankfurt, Germany
类目: Computation and Language (cs.CL)
备注:
Abstract:Conspiracy theories commonly attribute important events to the actions of powerful and secretive actors. While computational research has largely focused on document-level analyses of conspiracy theories, less attention has been paid to identifying the actors that drive such narratives. We develop annotation guidelines for conspiratorial actors, present a span-annotated corpus of German Telegram posts, and investigate their automatic extraction using transformer-based models. We further apply the resulting model to the \textitSchwurbelarchiv, a large-scale archive of German conspiracy-related Telegram channels. Our results demonstrate that conspiratorial actors can be annotated with meaningful agreement and extracted with reasonable accuracy despite the linguistic complexity of conspiracy discourse, enabling large-scale analyses of actor representations in conspiracy narratives.
[NLP-25] When Words Predict Workload
【速读】： 该论文旨在解决在法规约束性文本（如受《欧洲专利公约》第84条约束的专利申请文本）处理过程中，传统分布式大模型调度器因依赖静态分词计数或滚动延迟均值而易发生故障的问题。此类文本具有语言刚性特征，导致人机作者身份在统计上无法区分，从而在推理过程中引发语义歧义，迫使系统动态扩展多模型集成，进而导致键值缓存（KV-cache）与权重分配的不可预测激增，最终饱和消费级边缘GPU显存，引发严重显存溢出崩溃（OOM）。为防止硬件失效，本文提出一种基于CPU侧的语言资源预测（Linguistic Resource Forecasting, LRF）网关作为解决方案核心：该网关提取16维文本结构向量，并采用XGBoost分类器预测请求进入“陷阱带”（trap-band）的概率（\Pesc），再与基于实时延迟遥测计算的动态闭式路由阈值（\Tauroute(t)）进行比对，实现请求在未分配边缘显存前即完成安全路由——选择本地Qwen2.5-7B边缘工作节点或远程对比集成（Qwen2.5 7B + 32B）部署于NVIDIA H100。在6,000请求的实时测试中，该方案将误路由率（R_\mathrmmis）降至0.087–0.095，较基于分词计数的基线（0.849）降低一个数量级；峰值边缘显存稳定控制在4.82 GiB（低于8 GiB上限），且在27倍网络延迟波动下仍保持安全。预测模块在真实场景中达到0.84的AUROC性能，动态阈值控制器相比静态阈值进一步减少8.2%的误路由。
链接: https://arxiv.org/abs/2607.04951
作者: Anubhab Banerjee
机构: Nokia Germany
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Computation and Language (cs.CL)
备注: This work has been submitted to the IEEE for possible publication. Permission from the author must be obtained for all uses
Abstract:Standard distributed \acllm schedulers rely on static token counts or rolling latency averages, making them susceptible to failures on statutorily constrained text. On \acepo claims governed by Article 84 \acepc, linguistic rigidity makes human and machine authorship statistically indistinguishable. Resolving this ambiguity mid-flight forces dynamic multi-model ensemble expansion, triggering unpredictable KV-cache and weight-allocation spikes that saturate consumer-grade edge GPU VRAM and cause severe \acoom crashes. To prevent hardware collapse, we propose a CPU-side Linguistic Resource Forecasting (LRF) gateway. The gateway extracts a 16-dimensional text-structure vector and applies an XGBoost predictor to forecast trap-band membership. The resulting escalation probability ( \Pesc ) is evaluated against a dynamic, closed-form routing threshold ( \Tauroute(t) ) computed via real-time latency telemetry. Requests are safely routed to either a local Qwen2.5-7B edge worker or a remote contrastive ensemble (Qwen2.5 7B + 32B) on an NVIDIA H100 \emphbefore any edge GPU memory is allocated. In a 6,000-request live trial, the LRF gateway reduced the operational misroute fraction ( R_\mathrmmis ) to 0.087 – 0.095 , an order of magnitude below the token-count baseline ( 0.849 ). Peak edge VRAM remained safely bounded at \SI4.82\gibi\byte (under the \SI8\gibi\byte ceiling) across a 27\times variation in \acwan delay. The predictor achieved a live-trial AUROC of 0.84 , and the dynamic \Tauroute(t) controller yielded an 8.2% relative reduction in misroutes compared to an equivalent static threshold.
[NLP-26] You Frame It: How Conceptual Representations Shape LLM Detection and Reasoning about Antisemitism
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在检测反犹主义这一具有意识形态与历史复杂性的现象时，因概念理解不充分而导致的识别偏差与解释能力不足的问题。其核心挑战在于如何通过外部概念资源的有效整合，提升大语言模型（LLM）在复杂语境下对反犹主义的检测准确性与可解释性。解决方案的关键在于系统评估不同形式的概念锚定（conceptual grounding）对检测性能的影响，具体包括定义性描述、细粒度分类体系、示例增强以及大上下文表示四种方式。研究发现，细粒度分类体系显著提升了召回率，但伴随精度下降；而提供更大规模的概念资源并未带来额外的量化收益。此外，论文揭示了后大屠杀时期反犹主义的持续挑战性，以及模型在解释过程中存在的系统性缺陷，如过度引用概念、依赖词法线索、判断过于自信，以及难以识别隐晦或具有正当化色彩的反犹言论。这些发现表明，尽管概念引导的LLM在反犹主义检测中展现出潜力，但其推理能力仍存在明显局限，亟需进一步优化。
链接: https://arxiv.org/abs/2607.04945
作者: Katharina Soemer,Helena Mihaljević
机构: Goethe University, Frankfurt, Germany; HTW Berlin, Germany
类目: Computation and Language (cs.CL)
备注:
Abstract:LLMs enable the integration of external conceptual resources at inference time, creating new opportunities for detecting ideologically and historically complex phenomena such as antisemitism. We investigate how different forms of conceptual grounding affect antisemitism detection and explanation behavior across four state-of-the-art LLMs. Using two expert-annotated datasets, we compare definitional, fine-grained taxonomic, example-augmented, and large-context representations of antisemitism. We find that fine-grained taxonomic representations substantially improve recall, while simultaneously reducing precision. Surprisingly, supplying substantially larger conceptual resources yields no additional quantitative benefit. Post-Holocaust antisemitism poses the most persistent challenge across models and configurations. Analysis of explanations further reveals systematic limitations including overproduction of conceptual references, reliance on lexical cues, overconfidence, and difficulties with subtle or justificatory forms of antisemitism. Our findings highlight both the potential and the remaining limitations of conceptually grounded LLMs for antisemitism detection and reasoning. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2607.04945 [cs.CL] (or arXiv:2607.04945v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2607.04945 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Helena Mihaljevic [view email] [v1] Mon, 6 Jul 2026 11:20:18 UTC (79 KB)
[NLP-27] DuplexChat: Constructing Speaker-Separated Full-Duplex Dialogue Speech at Scale for Spoken Dialogue Language Modeling
【速读】： 该论文旨在解决全双工口语对话模型（full-duplex spoken dialogue models, SDLM）训练中缺乏高质量、多通道语音语料的问题。现有大规模公开语音数据集多为单声道（monaural），无法直接用于区分说话人，因而不适用于需要分离不同发言者语音流的全双工建模任务。为此，本文提出DuplexChat——一个开源的全双工口语对话语料库，以及DuplexChat-Pipe——一套从公开播客源构建说话人分离的全双工对话语音数据的自动化流水线。其核心解决方案在于：通过语言过滤、音频获取与清洗、基于语音角色识别（diarization）的双说话人对话片段提取，并结合语音分离与修复技术，实现每名说话人独立输出一个声道的高质量语音对齐数据。该流程最终生成涵盖28.26万小时英语和13.27万小时日语的说话人分离语料，且分析表明其具备真实人类对话中的轮换交互特征，显著提升了全双工对话模型的训练可行性与性能。
链接: https://arxiv.org/abs/2607.04941
作者: Wataru Nakata,Yuki Saito,Hiroshi Saruwatari
机构: Wataru Nakata1,2; Yuki Saito1,2; Hiroshi Saruwatari1
类目: Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注: 4 pages, 1 figures, submitted to SLT demo track
Abstract:Full-duplex spoken dialogue models are trained on conversational speech in which each speaker is represented as a separate stream, but existing large-scale public speech corpora are mostly monaural, making them unsuited for SDLM training. We present DuplexChat, an open-source corpus for full-duplex spoken dialogue models, and DuplexChat-Pipe, a pipeline for constructing speaker-separated full-duplex dialogue speech from public podcast feeds. DuplexChat-Pipe filters language-specific podcast feeds, retrieves and cleans episode audio, extracts diarization-guided two-speaker dialogue clips, and applies speech separation and restoration to produce one channel per speaker. Running this pipeline yields a speaker-separated spoken dialogue corpus covering 282,634 hours of English and 132,723 hours of Japanese. Analysis results on DuplexChat show that it contains turn-taking dynamics present in human dialogues.
[NLP-28] Ossetic-COT: Designing a morphologically annotated corpus and morphological analyzer for Ossetic
【速读】： 该论文旨在解决铁俄语（Iron Ossetic）缺乏符合通用依存标注框架（Universal Dependencies, UD）的形态学标注语料库的问题。由于铁俄语属于阿尔泰语系，其复杂的形态结构对自然语言处理任务构成挑战，而此前缺乏标准化、大规模的形态标注数据严重制约了相关模型的发展。为此，研究者构建了首个符合UD标准的铁俄语形态学标注语料库，包含来自口语文本语料库的5454个手工标注句子，共计74032个词项。基于该语料库，研究者训练了一个基于BERT的形态分析器，其在词性标注任务上达到了95.60%的准确率。该解决方案的关键在于：首先建立高质量、标准化的形态标注语料库以支持下游任务，其次利用预训练语言模型（BERT）的强大表示能力实现高精度的形态分析，从而为低资源语言的语法分析提供了可复用的技术路径。
链接: https://arxiv.org/abs/2607.04895
作者: Anna Shatskikh,Alexey Sorokin
机构: Lomonosov Moscow State University (莫斯科国立大学); Yandex (雅虎)
类目: Computation and Language (cs.CL)
备注: 12 pages
Abstract:In this work we present the first morphologically annotated corpus for Iron Ossetic that conforms to the Universal Dependencies schema. The corpus includes 5454 manually annotated sentences from the Iron Ossetic Corpus of Oral Texts, containing 74032 tokens. We use this corpus to train a BERT-based morphological analyzer. The analyzer achieves tag accuracy of 95.60%.
[NLP-29] Evaluating Large Language Models for Antisemitic Incident Classification
【速读】： 该论文旨在解决社会中仇恨与暴力事件的及时检测问题，特别是针对反犹主义事件的自动化识别尚处于探索阶段这一关键挑战。其核心问题是：如何利用人工智能（AI）系统，尤其是大语言模型（LLM），从新闻报道、民间组织报告及官方记录等公开文本中准确发现并细粒度分类反犹事件。解决方案的关键在于通过精心设计的提示工程（prompt engineering），即在输入提示中加入清晰的术语定义和上下文示例，以提升模型性能——其中，术语定义对识别具有修辞特征的事件（如传统反犹刻板印象）尤为有效，而具体示例则更有利于标签行动导向型事件（如身体袭击）。研究进一步通过高校校报案例验证了大语言模型在实际场景中辅助发现真实事件的潜力，支持早期监测与干预。研究表明，尽管当前大语言模型展现出一定可行性，但在理解复杂社会危害方面仍存在显著差距，亟需人工智能开发者、政策制定者与公民社会共同协作，构建更精准的模型、建立严谨的评估体系，并制定明确的政策框架，以实现对仇恨行为的高效、有效应对。
链接: https://arxiv.org/abs/2607.04890
作者: Karina Halevy,Julia Mendelsohn,Chan Young Park,Yulia Tsvetkov,Maarten Sap
机构: 未知
类目: Computation and Language (cs.CL)
备注: Accepted to Digital Hate Review 2026 Issue 1
Abstract:Addressing hate and violence in society requires timely detection of hateful events from public reporting, but automated identification of hateful events remains underexplored. We introduce the task of hateful event detection and investigate the ability of AI systems, specifically large language models (LLMs), to discover and classify reports of antisemitic events with fine-grained labels. We evaluate OpenAI’s GPT-4o and Meta’s Llama-3.2-3B-Instruct on multiple expert-annotated datasets containing antisemitic event descriptions from news articles, civil society reports, and official records. We show that LLMs, particularly GPT-4o, have potential for this task, but substantial improvement is needed. Providing clear term definitions and in-context examples in prompts can improve performance: definitions are most helpful for rhetoric-oriented events (e.g. classical antisemitic tropes), while examples help label action-oriented events (e.g. physical assault). A case study of college newspapers demonstrates that LLMs can help surface relevant real-world events, supporting early monitoring and intervention. Overall, our findings highlight both opportunities and critical gaps in AI’s ability to recognize complex harms and underscore the need for collaborative efforts among AI developers, policymakers, and civil society to design models, implement robust evaluation, and develop policy frameworks for defining and combating hate efficiently and effectively.
[NLP-30] Semantic Homogenization in Italian Popular Music: A Diachronic Analysis
【速读】： 该论文旨在解决流行音乐歌词在跨语言与跨文化背景下是否存在语义多样性下降趋势的问题，尤其聚焦于意大利最具影响力的音乐赛事——圣雷莫音乐节（Sanremo Music Festival）的历届入围歌曲。研究发现，尽管文化背景不同，其歌词仍呈现出与英语流媒体歌曲相似的语义同质化趋势。解决方案的关键在于提出一种灵活且高效的语义相似性动态追踪方法，融合全文分析、分段分析、主题分析与词级分析，并结合嵌入技术（embedding techniques）与大语言模型（large language models），实现了对歌词语义演变的多维度量化评估，为跨文化语料中语言与文化表达长期变迁的识别提供了可复用的技术框架。
链接: https://arxiv.org/abs/2607.04832
作者: Lorenzo Canale,Stefano Scotta,Alberto Messina
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:In recent years, studies have revealed a decline in semantic variety across popular music lyrics, particularly in English-language songs on streaming platforms like Spotify. This research examines whether a similar trend can be observed in a different linguistic and cultural context: the lyrics of all finalist songs from the 75 editions of the Sanremo Music Festival, Italy’s most renowned music competition. What sets this work apart is the development of a flexible and efficient methodology for tracking changes in semantic similarity over time, which can be applied to different datasets to study similar phenomena. Drawing on a combination of full-text, segment-based, topic-based, and word-level analyses, the approach leverages both embedding techniques and large language models. When applied to the Sanremo corpus, this framework reveals a gradual move toward increasing semantic uniformity, echoing the global patterns identified in previous studies. These findings underscore the value of natural language processing tools in uncovering long-term shifts in musical language and cultural expression.
[NLP-31] Evaluating the Effect of Linguistic Relatedness on Cross-Lingual Transfer in Large Multilingual Automatic Speech Recognition
【速读】： 该论文旨在解决将大规模自动语音识别（ASR）模型扩展至低资源非洲语言时面临的海量数据收集难题。其核心问题是：在缺乏足够目标语言标注数据的情况下，能否通过利用语言间的语义与结构相似性（linguistic relatedness），借助相关辅助语言进行序列化适配，从而有效提升跨语言迁移性能。论文提出的关键解决方案是系统性地设计控制实验，基于两个以非洲语言为中心的语料库和四种大型多语言ASR模型，对六个关键变量进行分析，以验证语言相关性是否能可靠预测大规模多语言ASR中的跨语言迁移增益。研究结果表明，在目标语言数据极少的条件下，仅依赖语言相关性进行预适配并未带来实际有效的性能提升，暗示语言相关性本身可能不足以作为大规模多语言ASR中跨语言迁移的可靠预测指标，也难以构成有效扩展至低资源语言的通用策略。
链接: https://arxiv.org/abs/2607.04814
作者: Andrei Florian,Cynthia Jayne Amol,Hope Kerubo Ombaba,Xiaoyu Cui,Boniface Mwau,Biatus Maina Kamau,Lilian Diana Awuor Wanzare,Christiane Fellbaum,Happy Buzaaba
机构: Princeton University (普林斯顿大学); Maseno University (马塞诺大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Extending automatic speech recognition (ASR) to low-resource African languages is constrained by the prohibitive demands of data collection at scale. A promising direction is to leverage linguistic relatedness to enhance cross-lingual transfer from a related auxiliary language to the low-resource target by sequentially adapting on both. Although this strategy has shown meaningful improvements in small ASR models, its effectiveness in large ASR remains unclear. We extend this framework to large multilingual ASR through a systematic controlled experimental design spanning six factors, two Africa-centric corpora, and four large ASR models, isolating whether linguistic relatedness reliably predicts cross-lingual transfer gains in this setting. Across all conditions, pre-adaptation on related auxiliary languages yields no practically meaningful transfer improvements given minimal target-language data, suggesting that linguistic relatedness alone may not reliably predict cross-lingual transfer gains in large multilingual ASR, or constitute an effective strategy for extending such models to low-resource languages.
[NLP-32] Multi-Turn On-Policy Distillation with Prefix Replay
【速读】： 该论文旨在解决生成式 AI 代理（LLM agent）在多轮交互任务中进行在线策略蒸馏（On-Policy Distillation, OPD）时面临的高计算成本问题。传统上，完全在线的 OPD 需要每次训练更新都依赖于学生代理在环境中的新轨迹采样以及对教师模型在每一步历史状态的查询，导致极高的资源开销。其核心挑战在于：随着学生策略逐渐逼近教师策略，多轮交互历史（prefix）虽更贴近学生当前分布，但可能引入教师预测不可靠的区域，形成学生占据分布与教师可靠性之间的双向分布偏移（two-sided distribution shift），即“前缀陷阱”（prefix trap）。为应对这一问题，论文提出离线重放前缀策略蒸馏（Replayed-Prefix On-Policy Distillation, ReOPD），其关键在于将多轮 OPD 视为一个可靠性感知的前缀分布设计问题，并通过一种简单的步长衰减采样策略，优先选择早期、分布偏移较小的前缀进行训练。该方法无需在学生训练阶段调用任何工具（zero tool calls），且在数学推理与搜索任务中，在多种教师-学生模型规模下均保持或超越原生 OPD 的准确率，同时单步训练速度提升至少 4 倍。因此，ReOPD 成功将昂贵的代理-环境交互转化为可复用的离线资源，实现了跨工具、任务与环境的可扩展蒸馏。
链接: https://arxiv.org/abs/2607.04763
作者: Baohao Liao,Hanze Dong,Christof Monz,Xinxing Xu,Li Dong,Furu Wei
机构: Microsoft Research(微软研究院); University of Amsterdam(阿姆斯特丹大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (stat.ML)
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Abstract:We study on-policy distillation (OPD) for agentic tasks, where an LLM agent interacts with an environment over multiple turns and a student imitates a teacher over these multi-turn interaction histories. Fully online OPD is costly because each update requires fresh student rollouts through the environment and teacher queries at visited histories. We propose Replayed-Prefix On-Policy Distillation (ReOPD), an off-environment alternative that reuses pre-collected teacher trajectories as replayed prefixes: the student acts at selected steps, while the teacher provides dense per-step supervision without executing new environment interactions. We show that multi-turn OPD introduces a prefix trap: making histories more student-on-policy improves relevance to the student, but can query the teacher on histories where its target is unreliable. This creates a two-sided distribution shift between student occupancy and teacher reliability. ReOPD addresses this by treating multi-turn OPD as a reliability-aware prefix distribution design and implements it with a simple step-decaying sampling schedule that emphasizes early, lower-shift prefixes. Across mathematical reasoning with Python and search environments over multiple teacher and student model scales, ReOPD preserves or improves OPD-level accuracy, uses zero tool calls during student training, and is at least 4 \times faster per training step than OPD. ReOPD therefore turns expensive agent-environment interaction into a reusable offline resource, enabling scalable distillation across tools, tasks, and environments.
[NLP-33] LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure
【速读】： 该论文旨在解决监督微调（Supervised Fine-Tuning, SFT）在适应下游领域时导致预训练模型原有能力退化的问题。现有方法基于标准交叉熵损失仅优化目标标签词元，忽视了概率质量在其他合理候选词元间的分布，从而破坏了预训练阶段学习到的丰富局部偏好结构（local preference structure）。其核心解决方案是提出一种局部保持型监督微调（Local-Preserving Supervised Fine-Tuning, LP-SFT），通过引入自适应的替代词元支持集，并采用局部归一化的保结构损失函数，在每一步微调中显式保留原始模型在候选词元间的相对概率分布关系，同时独立优化监督标签。该方法利用香农熵与瑞尼熵分析揭示了预训练模型具有规律性的多模态熵结构，表明其内在编码了超越单一标注词元的分布知识。实验结果表明，LP-SFT在多领域与单领域微调任务中均优于标准SFT及现有增强基线，在提升准确率（pass@1）的同时有效维持采样可访问的多样性（pass@k），实现了性能与能力保留之间的最佳平衡。
链接: https://arxiv.org/abs/2607.04733
作者: Yueyang Wang,Baolong Bi,Shuo Lu,Jingyuan Zhang
机构: Peking University (北京大学); Chinese Academy of Sciences (中国科学院); Georgia Institute of Technology (佐治亚理工学院)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 21 pages, 3 figures. Code is available at this https URL
Abstract:Supervised fine-tuning (SFT) is the standard approach for adapting pretrained language models to downstream domains, yet it often improves target-domain behavior at the cost of degrading pre-existing capabilities. Standard cross-entropy fine-tuning promotes only the observed label token and leaves unconstrained how probability mass is redistributed over other plausible alternatives, potentially distorting the rich local preference structure learned during pretraining. We first analyze next-token predictions using Shannon and Renyi entropies, revealing that pretrained models exhibit a regular multimodal entropy structure. These entropy peaks correspond to varying numbers of plausible alternatives, indicating that the base model intrinsically encodes rich distributional knowledge beyond the single supervised token. Motivated by this observation, we propose LP-SFT, a Local-Preserving Supervised Fine-Tuning objective designed to explicitly protect this inherent entropy structure. At each step, LP-SFT constructs an adaptive support of alternative tokens and applies a locally normalized preservation loss to maintain the base model’s relative structure among them, while standard cross-entropy independently optimizes the supervised token. Across mixed-domain and single-domain fine-tuning experiments, LP-SFT improves overall performance over vanilla SFT and recent SFT-enhancement baselines, achieving the best balance between pass@1 accuracy and pass@k performance. These results suggest that local preservation helps mitigate capability degradation without collapsing sampling-accessible diversity.
[NLP-34] urning Off-Policy Tokens On-Policy: A Plug-in Approach for Improving LLM Alignment
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在强化学习（Reinforcement Learning, RL）后训练中因采用“生成后更新”（rollout then update）范式而产生的离策略（off-policy）数据问题。离策略数据会导致重要性采样（Importance Sampling, IS）中的词元级重要性权重在长序列上累积，引发方差爆炸。为克服此问题，论文提出选择性重要性采样（Selective Importance Sampling, SIS），其核心思想是将离策略词元通过类似拒绝采样（rejection sampling）的机制“转换”为等效的在策略（on-policy）词元，从而避免对重要性权重进行修正。具体而言，SIS将离策略模型视为提议分布，并在词元级别执行拒绝测试：被接受的词元被视为在策略词元，赋予单位重要性权重；未被接受的词元则保留传统IS修正。理论分析表明，SIS可有效缩小词元级与序列级离策略梯度估计器之间的差距。该方法作为即插即用模块，仅需修改策略损失中的重要性比率，计算开销极低，且可与多种强化学习后训练算法兼容。在密集型和混合专家模型（MoE）上的数学与智能体基准测试结果表明，SIS在各项指标上均实现一致提升，并显著增强对离策略数据的鲁棒性。
链接: https://arxiv.org/abs/2607.04728
作者: Yu Li,Xiuyu Li,Mingyang Yi,Jiaxing Wang,zhangliangxu,Zhaolong Xing,Zhen Chen
机构: Renmin University of China (中国人民大学); JD.com (京东)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Reinforcement learning (RL) post-training for large language models (LLMs) follows a efficient paradigm of “rollout then update”, which inevitably results in off-policy training data. To resolve this, Importance sampling (IS) is proposed, while the token-level ratios compound over long sequences, causing severe variance exploded. A natural idea is “transferring” these off-policy token into on-policy token, so that the importance scores for correction are unnecessary. Following this idea, we propose Selective Importance Sampling (SIS), which is inspired by rejection sampling. Concretely, SIS implements by viewing off-policy model as proposal distribution, and implement a token-level rejection test: accepted tokens are viewed as on-policy, so that receive unit importance score, while rejected tokens retain the standard IS correction. Our proposed SIS is theoretically proved reducing the gap between token-level and sequence-level off-policy gradient estimators. The SIS acts as a plug-in that only modifies the importance ratio in the policy loss, adding negligible wall-clock overhead, and can be combine with a vast vary of RL post-training algorithms. Experiments on dense and MoE LLMs across math and agent benchmarks show that SIS consistently improves all objectives, while providing substantially stronger robustness under off-policy data.
[NLP-35] What You See Is What You Get: Observation-Aligned Supervision for Chart-to-Code Generation
【速读】： 该论文旨在解决现有图表生成代码（chart-to-code generation）模型在监督微调过程中存在的根本性问题：即假设从图表图像中可唯一还原出生成代码所依赖的原始数据（latent raw variables），而这一假设在实践中往往不成立。例如，箱线图仅暴露汇总统计量而非原始样本，饼图仅反映比例而非具体数值，直方图仅呈现分组权重而非个体观测值。这种对不可识别（non-identifiable）原始数据的盲目监督，导致模型产生幻觉并生成过度指定（over-specified）的代码。其解决方案的关键在于提出观察对齐监督（Observation-Aligned supervision）框架，将原本基于不可见原始数据的监督目标，替换为仅由可视化观测结果约束的可识别量：箱线图使用箱体统计量、饼图使用扇区百分比、直方图使用分组权重作为监督目标。通过该框架重构两个数据源的训练数据，实验在ChartMimic和ChartX上均验证了多款视觉语言模型（VLMs）在可观测值恢复能力上的显著提升，包括在不可执行评估下的表现改善，表明提升图表生成模型性能不仅依赖更多数据或更先进的学习目标，更需确保监督信号与图像中实际可识别信息相一致。
链接: https://arxiv.org/abs/2607.04726
作者: Tianhao Niu,Qingfu Zhu,Wanxiang Che
机构: Harbin Institute of Technology (哈尔滨工业大学); Research Center for Social Computing and Interactive Robotics (社会计算与智能交互机器人研究中心)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:Chart-to-code generation is commonly trained with supervised fine-tuning on reference plotting scripts, implicitly treating the gold code as a fully observable target. We argue that this assumption is often invalid: many chart programs contain latent raw variables that cannot be uniquely recovered from the rendered image. For example, a boxplot exposes summary statistics rather than original samples, a pie chart reveals proportions rather than arbitrary raw values, and a histogram shows bin-level mass rather than individual observations. Supervising models to reproduce such non-identifiable quantities encourages hallucination and over-specified code generation. We introduce Observation-Aligned supervision, a rewriting framework that replaces latent raw-data targets with quantities constrained by the visual observation: box statistics for boxplots, wedge percentages for pie charts, and bin weights for histograms. Applying this framework to chart-to-code training data from two sources, we obtain the Observation-Aligned supervision target data. Experiments across multiple VLMs on ChartMimic and ChartX demonstrate consistent improvements in observable value recovery, including under both-executable evaluation. Our results suggest that improving chart-to-code models requires not only more data or advanced learning objectives or algorithms, but also supervision targets that respect what is identifiable from the chart image.
[NLP-36] PAST-TIDE: Prototype-Anchored Statement Tuning with Topic-Invariant Normalization for Stance Detection LREC2026
【速读】： 该论文旨在解决阿拉伯语语境下低资源场景中的立场检测（stance detection）问题，具体针对NakbaNLP@LREC-COLING 2026举办的StanceNakba共享任务的两个子任务。其核心挑战在于如何在数据稀缺的情况下有效建模立场分类，并克服跨话题（cross-topic）差异带来的性能下降。解决方案的关键在于提出一种“陈述调优”（statement tuning）范式：将立场检测重新定义为填空式掩码语言建模（cloze-style masked language modeling, MLM），利用预训练模型的MLM头通过可学习的词元化器（verbalizer）将标签词映射至立场类别，而非采用随机初始化的分类头，从而更高效地利用预训练知识。此外，引入原型对比学习（prototypical contrastive learning）以实现与批次大小无关的对比训练，同时结合话题条件层归一化（topic-conditional layer normalization）增强模型对不同话题的泛化能力。实验表明，该方法仅需对预训练模型进行最小架构修改，在官方排行榜上即取得了子任务A和子任务B分别为0.75和0.74的宏平均F1分数，验证了其在低资源设置下的竞争力。
链接: https://arxiv.org/abs/2607.04690
作者: Md. Shakhoyat Rahman Shujon,MD Jahid Hasan Jim,Md. Milon Islam,Md Rezwanul Haque,Fakhri Karray
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Published in The Fifteenth Language Resources and Evaluation Conference (LREC 2026)
Abstract:We introduce PAST-TIDE, our stance detection system addressing both subtasks of the StanceNakba Shared Task at NakbaNLP@LREC-COLING 2026. The main idea is statement tuning. We redefine stance as cloze-style masked language modeling (MLM), letting a verbalizer map label words to stance categories through the pre-trained MLM head rather than appending a randomly initialized classification head. We complement this with prototypical contrastive learning, which uses learnable class prototypes for batch-size independent contrastive training, and topic-conditional layer normalization for cross-topic Arabic stance detection. PAST-TIDE achieves macro-F1 scores of 0.75 for Subtask A and 0.74 for Subtask B on the official leaderboard, indicating that minimal architectural additions to a pre-trained model can remain competitive in low-resource settings.
[NLP-37] URSA: Chemistry-Aware Benchmark for Utilitarian Retrosynthesis Assessment
【速读】： 该论文旨在解决药物发现中合成路线规划（synthesis planning）任务的评估难题，即缺乏灵活且化学可解释的基准测试协议，导致难以对专用深度学习逆合成系统与通用大语言模型（LLM）进行客观比较。其解决方案的关键在于提出URSA（Utilitarian RetroSynthesis Assessment）评估框架，该框架不仅从形式层面（如是否收敛至市售起始原料）评估合成路径，更引入化学合理性视角，模拟专业化学家对反应和路径的判断标准。通过对一组新颖、多样且合成路线未公开的目标分子进行综合评估，研究发现尽管大语言模型在高层次策略规划上具备潜力，但在可靠完成合成路线规划任务方面仍显著落后于专门设计的逆合成模型。
链接: https://arxiv.org/abs/2607.04688
作者: Bogdan Zagribelnyy,Ivan Ilin,Nikita Bondarev,Anton Morgunov,Arkadii Lin,Maksim Kuznetsov,Rim Shayakhmetov,Vladimir Aladinskiy,Alex Aliper,Alex Zhavoronkov
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Computation and Language (cs.CL)
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Abstract:Synthesis planning aiming to find pathways of reactions for a target molecule is one of the most important and challenging tasks in drug discovery. Recent progress has produced both specialized deep-learning retrosynthesis systems and general-purpose large language models, but objective comparison remains difficult due to the lack of flexible, chemically interpretable benchmarking protocols. In the current study, we are introducing the URSA (Utilitarian RetroSynthesis Assessment) evaluation framework that provides the opportunity to benchmark the synthetic routes not only from a formal perspective, such as convergence to commercially available starting materials, but also from a chemical plausibility perspective, mimicking the way expert chemists evaluate the reactions and routes. The study covers a comprehensive evaluation of both conventional end-to-end retrosynthesis solutions and LLMs for the synthesis planning task on a set of novel, diverse target molecules with undisclosed synthetic routes, which represent realistic tasks in the daily drug design routine. We find that while LLMs can support high-level strategic planning, they currently underperform specialized retrosynthesis models in reliably solving synthesis planning tasks.
[NLP-38] oolFailBench: Diagnosing Tool-Use Failures in LLM Agents ICML2026
【速读】： 该论文旨在解决当前大语言模型代理在工具调用（tool calling）评估中普遍存在的“评估盲区”问题，即传统综合基准测试得分无法揭示模型在实际任务中具体如何失败。尤其当模型完全忽略必要工具调用（Tool-Skip）或虽调用工具但忽视其输出结果（Result-Ignore），与仅因随机猜测而失败的模型，在最终任务准确率上可能表现相似，导致性能评估失真。为此，作者提出 ToolFailBench，一个针对金融、医疗、法律、网络安全和房地产等领域的诊断性基准，涵盖1,000个任务，通过区分“工具必需任务”（需依赖工具返回值）与“控制任务”（工具存在但应直接回答），实现对工具使用行为的精细分析。其关键在于引入四类细粒度错误标签：工具跳过（Tool-Skip）、结果忽略（Result-Ignore）、输出伪造（Output-Fabrication）和不必要的工具调用（Unnecessary-Tool-Use），并结合规则分类器与两名大语言模型（LLM）裁判通过多数投票进行标注。实验表明，19个主流模型中最佳者仅达到86.33%的纯净工具使用率（Clean Tool-Use Rate），说明忠实工具使用仍有提升空间；更重要的是，具有相近整体性能的模型在失败模式上差异显著——多数模型在无工具控制任务上保持纪律性，而Llama-3.1系列表现出“始终调用工具”的倾向，且同参数规模下，Llama-3.1-70B与Qwen2.5-72B在控制任务上的准确率相差高达89个百分点。这表明，有效的工具使用评估不仅需考察是否调用工具，更应衡量其对工具输出的正确利用能力以及在无需工具时的克制性。
链接: https://arxiv.org/abs/2607.04686
作者: Harsh Soni
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 18 pages, 3 figures. Published at the Workshop on Agents in the Wild: Safety, Security, and Beyond (AIWILD) and the Workshop on Failure Modes of Agentic AI (FAGEN) at ICML 2026
Abstract:Tool calling is central to modern language model agents, but aggregate benchmark scores often hide where tool use fails. A model that never calls a needed tool and a model that calls the tool but ignores the result can look similar under final task accuracy. We introduce ToolFailBench, a diagnostic benchmark for measuring tool-use failures across 1,000 tasks in finance, medicine, law, cybersecurity, and real estate. Tool-required tasks return values the model wouldn’t guess, forcing it to trust the tool while control tasks attach the same tools but should be answered directly. We label each trace with Tool-Skip, Result-Ignore, Output-Fabrication, and Unnecessary-Tool-Use, using a rule classifier and two LLM judges aggregated by majority vote. Across 19 headline models, the best reaches 86.33% Clean Tool-Use Rate, showing that faithful tool use is not saturated. More importantly, models with similar aggregate scores fail in different ways: most stay disciplined on no-tool controls, while Llama-3.1 models show an Always-Call pattern, and at the same parameter scale Llama-3.1-70B and Qwen2.5-72B differ by 89 percentage points on control-task accuracy. Tool-use evaluation should measure not only whether agents call tools, but whether they use tool outputs correctly and avoid tools when none is needed.
[NLP-39] Does It Fail to See or Fail to Know? Attributing Errors in Vision-Language Models
【速读】： 该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）在处理需要超越图像显性信息的视觉问答任务时，因缺乏对不确定性的细粒度诊断能力而导致性能下降的问题。现有方法通常将错误视为单一整体，未能区分感知、实体识别与知识检索等不同层面的失败模式，导致无法有效定位问题根源。为此，本文提出一种统一框架，用于解耦多种失败模式，并探究生成前信号（pre-generation signals）是否可预测这些失败来源。研究发现，在多种数据集与模型架构下，VLM的错误具有稳定模式：部分失败源于视觉或实体识别瓶颈，而另一些则在目标实体被正确识别后仍持续存在。关键突破在于，这些失败源可在生成答案之前被准确预测：与识别相关的错误可通过视觉令牌表示（visual-token representations）有效捕捉，而识别后的剩余错误则更优地由提示条件下的隐藏状态（prompt-conditioned hidden states）表征。该预生成信号实现了高效的事前失败源判别，使不确定样本能够被引导至针对性干预策略，如图像修复、实体识别增强或外部知识检索，显著提升了模型鲁棒性与可解释性。
链接: https://arxiv.org/abs/2607.04683
作者: Khang Nhat Hoang Vo,Artem Vazhentsev,Artem Shelmanov,Timothy Baldwin,Yova Kementchedjhieva
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Vision-language models (VLMs) perform well on visual question answering with high-quality images but struggle when questions require knowledge beyond what is clearly and directly visible. In such settings, uncertainty quantification should not only indicate whether the model is likely to fail but also diagnose why it is uncertain, across dimensions such as perception, entity recognition, and knowledge retrieval. While prior work has focused on individual failure modes in isolation or treated incorrect answers as monolithic failures, we propose a unified framework for disentangling these failure modes and investigate whether pre-generation signals can predict these failure sources. Across a range of datasets and model families, we find a consistent pattern in VLM errors: some failures arise from visual or recognition bottlenecks, while others persist after the relevant entity is identified. Our main finding is that these failure sources can be predicted before decoding: recognition-related failures are best captured by visual-token representations, while failures that remain after recognition are better captured by prompt-conditioned hidden states. This pre-generation signal enables efficient failure-source prediction before the model produces an answer, allowing uncertain cases to be routed to targeted interventions such as image repair, entity recognition support, or external retrieval.
[NLP-40] FormalRx: Rectify and eXamine Semantic Failures in Autoformalization ICML2026
【速读】： 该论文旨在解决自动形式化（autoformalization）过程中语义对齐的可解释性问题。现有评估方法仅提供不透明的二元判断或标量分数，无法揭示翻译失败的具体位置与原因，严重制约了人类理解与系统优化。其解决方案的关键在于提出FormalRx诊断评估框架，核心为SCI错误分类体系——一种分层化的28类错误分类方案，具备严格的优先级排序。基于此分类体系，FormalRx实现了四大诊断能力：语义对齐判断、错误分类、错误定位与修正建议。研究通过在56,287组自然语言-形式语言（NL-FL）配对数据上训练的诊断模型FormalRx-8B，并发布首个细粒度诊断基准FormalRx-Test，实现了F1分数0.88（判断）、0.71（分类），以及0.75（定位）和0.73（修正）的准确率，显著优于通用大模型与专用基线。该框架将评估从黑箱决策转变为可操作的反馈，推动自动形式化系统的系统性诊断与迭代改进。
链接: https://arxiv.org/abs/2607.04655
作者: Haocheng Wang,Baiyu Huang,Yingjia Wan,Xiao Zhu,Xiaoyang Liu,Yinya Huang,Zhijiang Guo
机构: LARK Lab, HKUST(GZ); ETH Zurich; UCLA; SJTU; ETH AI Center, ETH Zurich; HKUST
类目: Computation and Language (cs.CL)
备注: 44 pages, 5 figures. Accepted at the 43rd International Conference on Machine Learning (ICML 2026)
Abstract:The veracious semantic alignment in autoformalization is significant for formal mathematical reasoning. However, existing evaluations provide only opaque binary verdicts or scalar scores, offering no interpretable insight into where or why translations fail. This opacity severely limits both human understanding and automated system improvement. To bridge this gap, we introduce FormalRx, a comprehensive diagnostic evaluation framework that transforms autoformalization assessment from black-box judgments into actionable feedback. At its core is SCI Error Taxonomy, a hierarchical classification scheme decomposing autoformalization errors into 28 distinct categories with strict priority ordering. Building on this taxonomy, FormalRx provides four critical diagnostic capabilities: alignment verdicts, error categorization, error localization, and correction. We instantiate the framework with a diagnostic model FormalRx-8B, trained on 56,287 NL-FL pairs with fine-grained diagnostic annotations, and release FormalRx-Test as the first fine-grained diagnostic benchmark. FormalRx-8B achieves F1-scores of 0.88 (verdict) and 0.71 (categorization), along with accuracies of 0.75 (localization) and 0.73 (correction), substantially outperforming both general-purpose LLMs and specialized baselines. By connecting evaluation with actionable insights, FormalRx enables systematic diagnosis and improvement of autoformalization systems.
[NLP-41] Retroactive Chain-of-Thought (RetroCoT): Forensic Reconstruction Prompts as a Safety Diagnostic Across Model Generations
【速读】： 该论文旨在解决当前大型语言模型（Large Language Models, LLMs）在安全对齐（Safety Alignment）评估中存在的重要局限性：现有对齐机制对语用框架（pragmatic register）高度敏感，导致模型在面对语义等价但表达方式不同的有害请求时表现出不一致的拒绝行为。具体而言，当同一危害意图以直接命令式（imperative）表述时，模型可能拒绝；而当该意图被重构为一种“事后追因”式的法证分析任务（forensic reconstruction task）时，模型则更易妥协。其核心解决方案是提出一种名为逆向思维链（Retroactive Chain-of-Thought, RetroCoT）的单轮攻击方法，通过将原本直接的有害指令转化为一个假设已有危害结果发生、要求模型反向推演因果链条的法证分析任务，从而绕过传统对齐策略。实验表明，在AdvBench基准上，RetroCoT使GPT-4o和GPT-4o-mini的攻击成功率分别达到58%和52%，远高于直接请求基线（0%和4%）。进一步发现，尽管下一代模型（如GPT-5-family）能识别此类重构前提并拒绝，但其防御能力无法泛化至其他语用形式——仅通过添加一句伪造的负面评价反馈，即可使攻击成功率从0%飙升至94%（GPT-4o），揭示出前沿模型的安全对齐仍受语用框架制约，而非真正基于语义意图的不变性。这表明，当前对齐机制尚未实现对语义等价性的充分鲁棒性，新语用注册（pragmatic register）仍可系统性暴露模型的安全漏洞。
链接: https://arxiv.org/abs/2607.04645
作者: Samira Hajizadeh
机构: Columbia University (哥伦比亚大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
备注:
Abstract:Safety alignment in large language models is typically evaluated against direct, imperative harmful requests. We show that this alignment is highly conditioned on pragmatic register: models that refuse a direct request frequently comply when the same underlying objective is expressed through a different communicative stance. This suggests that current alignment policies are not invariant to semantic equivalence, but remain sensitive to how a request is pragmatically framed. We introduce Retroactive Chain-of-Thought (RetroCoT), a single-turn attack that reframes harmful requests as forensic reconstruction tasks. Rather than requesting harmful instructions directly, RetroCoT presupposes that the harmful outcome has already occurred and asks the model, acting as a forensic analyst, to reconstruct in reverse the causal chain that produced it. On AdvBench (n=50), RetroCoT achieves attach success rate of 58% on gpt-4o and 52% on gpt-4o-mini, compared with direct-request baselines of 0% and 4%, respectively. We further identify a pronounced generation gap: GPT-5-family models refuse RetroCoT entirely, explicitly identifying the reconstruction premise in their refusal rationales, consistent with explicit coverage of this reconstruction register. However, this robustness does not generalize across pragmatic forms. A single adversarial feedback turn presenting an existing forensic reconstruction response alongside evaluator critique raises ASR from 0% to 48% on GPT-5.4-mini and from 58% to 94% on GPT-4o; a control condition omitting the fabricated low score achieves 85% on GPT-5.4-mini, indicating that the operative element is pragmatic continuation within the established forensic frame rather than score manipulation. These results suggest that frontier-model alignment remains conditioned on pragmatic framing rather than semantic intent, and that new pragmatic registers can continue to expose a…
[NLP-42] Wrong Before Right: Late Rescue and Interface Failure in Aligned Language Models
【速读】： 该论文旨在揭示对齐语言模型（aligned language models）内部正确性（correctness）是如何逐步构建的，而不仅关注最终输出是否正确。其核心问题是：在模型推理过程中，为何某些错误会在中间层短暂出现并被后期层修正，这种“错误下潜”（wrong-dip）现象如何影响模型的压缩鲁棒性、后训练质量与评估偏差。解决方案的关键在于通过分层差分法（layer-wise difference-in-differences, DiD）轨迹分析极性控制的最小语义对（polarity-controlled minimal pairs），识别出在中层（25%-90%深度）存在一个临时偏向错误答案的内部偏好峰值，并证实该现象可通过晚期层修正实现挽救。研究进一步通过跨17个模型、三大架构家族、64倍规模（0.5B–32B）的激活移植实验（patchscope-style activation transplantation）进行因果验证，发现该错误下潜具有配方依赖性和涌现性，且可被特定微调策略（如引入中层错误边际惩罚的LoRA）有效抑制，从而显著提升模型压缩鲁棒性与后训练质量，同时揭示了自然语言输入下该现象仍具可审计性，表明表面输出正确性可能掩盖深层依赖晚期修正的脆弱机制，进而影响模型可靠性评估。
链接: https://arxiv.org/abs/2607.04640
作者: Jiaqi Deng
机构: Independent Researcher
类目: Computation and Language (cs.CL)
备注: 16 pages, 10 figures. Code to be released
Abstract:We study how correctness is assembled inside aligned language models, not only whether the final answer is right. Using layer-wise difference-in-differences (DiD) trajectories over polarity-controlled minimal pairs, we identify the wrong-dip: in mid layers (25-90% depth), internal preference transiently commits to the incorrect answer and is rescued only by late-layer correction. We verify this causally with patchscope-style activation transplantation across 17 models, three families, and 64x scale (0.5B-32B). Four findings follow. (1) Alignment amplification of the causal wrong-dip is recipe-specific and emergent: it emerges at 3B in Qwen2.5, remains high, and peaks at 32B (paired t up to 9.7), reverses in Llama-3-8B (t=-2.31), and sits between for Mistral-7B. (2) The dip predicts real compression failures: high-dip items are 3-7x more likely to flip under late-layer low-rank compression, block dropping, or structured pruning, while quantization flips are dip-blind, a double dissociation confirmed by late-layer ablation. (3) The dip is trainable: a LoRA fine-tune with a mid-layer wrong-margin penalty matches output-only SFT accuracy while cutting the causal dip by 67-70% and improving compression robustness; output-only SFT worsens the causal dip by up to 2.8x at perfect surface accuracy. (4) With controlled readouts, the phenomenon survives natural-language I/O: dip stratification of structural-damage failures is significant on naturalistic vignettes, and free-form fragility separates into a dip-auditable late-rescue layer and a dip-blind interface layer. Together, output-level correctness can hide a late-rescue process that governs compression risk, post-training quality, and evaluation distortion.
[NLP-43] CARD: Cross-component Audio Representation Distillation for Encoder-Free Audio Captioning
【速读】： 该论文旨在解决现有自动化音频字幕系统中依赖冻结音频编码器所带来的计算开销与特征瓶颈问题，即在推理阶段仍需承担音频编码器的计算成本，且受限于其固定声学特征表达。其核心解决方案是提出一种无编码器（encoder-free）的音频字幕模型CARD，关键在于通过一个仅13.2M参数的可训练投影器（projector），将预训练音频教师模型（CLAP-HTSAT）的知识以分层方式注入不同组件：将教师模型的感知层级表征传递至投影器，语义层级表征则注入冻结的大语言模型（LLM）。这种知识分布策略突破了传统仅将教师知识直接注入LLM的局限，显著提升了生成质量——在AudioCaps和Clotho数据集上分别较仅使用LLM蒸馏的模型提升CIDEr-D达+12.18和+5.21，最终达到55.4的性能，接近66.4的有编码器上限，证明了知识注入位置的重要性不亚于知识本身的存在。
链接: https://arxiv.org/abs/2607.04619
作者: Ganesh Pavan Kartikeya Bharadwaj Kolluri,Yuchen Zhang,Michael Kampouridis,Ravi Shekhar
机构: University of California, Berkeley (加州大学伯克利分校); University of Oxford (牛津大学)
类目: ound (cs.SD); Computation and Language (cs.CL)
备注:
Abstract:Modern automated audio captioning systems pair a frozen audio encoder with a large language model (LLM) via a trainable projector, incurring the encoder’s inference cost and bottlenecking the model through its fixed acoustic features. We present CARD, an encoder-free audio captioning model that removes the encoder at inference: a 13.2M projector feeds a frozen LLM with merged LoRA adapters, while the teacher used to train it is discarded. CARD distills a pretrained audio teacher (CLAP-HTSAT) into the model, but rather than injecting it into the LLM alone, it routes the teacher’s representations across components: perceptual stages to the projector and semantic stages to the LLM. This placement improves CIDEr-D by +12.18 over an LLM-only distilled model on AudioCaps and by +5.21 on Clotho, reaching 55.4 against a 66.4 encoder-kept upper bound with no encoder at inference, showing that where a teacher’s knowledge is placed matters as much as its presence.
[NLP-44] Characterizing the Temporal Emotional and Social Patterns of Adolescent Substance Use Discussions on Reddit
【速读】： 该论文旨在解决传统研究方法难以捕捉青少年在真实情境中关于物质使用体验的局限性问题，尤其针对青春期情绪敏感性增强、社会压力增大及物质使用易感性升高的关键发展期。其解决方案的关键在于利用大规模社交媒体数据（2018–2023年Reddit平台上的青少年物质使用讨论），结合小时级时间序列分析、情感与情绪分类以及基于Transformer的主题建模（BERTopic），系统揭示时间动态、情绪表达与语义内容之间的交互关系。研究发现，物质相关讨论在周末和深夜呈现显著高峰，以悲伤、恐惧等负面情绪为主导，并识别出围绕同伴关系、家庭冲突、情绪困扰及物质特异性体验的若干核心语义主题。这一方法突破了传统研究的时空限制，为制定更具时效性、精准性和循证基础的预防与干预策略提供了实证支持。
链接: https://arxiv.org/abs/2607.04566
作者: Leran Hong,Lei Jin,Jianfeng Zhu
机构: 未知
类目: Computation and Language (cs.CL)
备注: 18 pages, 4 figures, 1 table
Abstract:Adolescence is a critical developmental period marked by heightened emotional sensitivity, social stress, and vulnerability to substance use. However, traditional research methods provide limited access to adolescents’ authentic experiences, hindering efforts to develop evidence-based prevention and intervention strategies. Social media provides a unique opportunity to observe adolescents’ naturally occurring discussions about substance use, offering valuable insights into their opinions, emotions, and lived experiences that can inform early prevention and intervention strategies. In this study, we analyze large-scale Reddit discussions related to substance use among adolescents between 2018 and 2023. Leveraging hour-by-day temporal analysis, sentiment and emotion classification, and transformer-based topic modeling (BERTopic), we examine the interaction between time, emotion, and semantic content in adolescent substance use discourse. Our findings reveal pronounced weekend and late-night peaks in substance-related discussions, a dominance of negative emotions such as sadness and fear, and distinct semantic topics centered on peer relationships, family conflict, emotional distress, and substance-specific experiences. These findings advance our understanding of adolescent substance use in naturalistic online settings and provide empirical evidence to support the development of more timely, targeted, and evidence-based prevention and intervention strategies.
[NLP-45] Fidelity-Diversity Metrics for Text
【速读】： 该论文旨在解决生成式语言模型训练中数据增强（data augmentation）所面临的质量评估难题，尤其关注如何在扩充高质量数据集时，确保新增文本在语义忠实度与分布覆盖性方面均具备良好表现。传统方法难以区分低质量文本是因缺乏与参考数据的相似性（fidelity），还是因未能充分覆盖参考数据的多样性模式（diversity）。为此，作者提出了一套基于离散文本摘要之间最优传输分歧函数（optimal transport divergence functionals）的双指标体系：其一为保真度（fidelity），量化候选文本与参考数据在语义结构上的逼近程度；其二为多样性（diversity），衡量候选文本对参考数据分布模式的覆盖能力。该方法的关键在于利用最优传输理论实现对文本分布差异的精确建模，从而在M2D2等文本数据集上成功解耦了保真度与多样性的缺陷，并在合成GSM8K风格数学推理数据集的实验中验证了其有效性——检测到的多样性缺失与微调后语言模型下游任务准确率下降显著相关，证明了该指标体系在指导数据增强策略中的实用价值。
链接: https://arxiv.org/abs/2607.04563
作者: Amanda Wang,Tudor Manole,Florentina Bunea,John Thickstun
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:As language modeling technology matures, there is an increasing research focus on the composition and curation of datasets used to train these models. For instance, practitioners commonly seek to augment high-quality datasets with additional text to enhance the performance of models trained on that data. However, informed decisions about data augmentation require more nuanced assessments about data quality. We build on work measuring the precision and recall of generative models to develop a pair of metrics that quantify (1) fidelity, capturing how closely candidate text resembles reference data, and (2) diversity, capturing how well it covers the modes of the reference dataset. Our metrics are based on optimal transport divergence functionals between discrete text summaries. In experiments on M2D2 text datasets, we show that these metrics are able to disentangle a lack of fidelity from a lack of diversity in deficient candidate text. In further experiments, our metrics detect diversity deficits in synthetic GSM8K-style math datasets, which correlate with degradations in downstream accuracy of language models finetuned on this synthetic data.
[NLP-46] Can temporal article-level credibility signals improve domain-level credibility prediction?
【速读】： 该论文旨在解决新兴网络域名因缺乏历史声誉而难以评估其可信度的问题，尤其在大语言模型（Large Language Models, LLMs）推动大规模内容生成的背景下，传统依赖人工审核的手段已无法满足效率需求。其核心挑战在于如何在不依赖域名先验信息的前提下，基于其发布内容自动判断域名的可信度。解决方案的关键在于提出一种基于专家评分的时序化域名可信度评估框架（Domain Credibility Evaluation Framework, DCEF），该框架模拟专家事实核查员的工作流程，通过分析域名所发布文章的内容特征（如虚假信息、偏见或宣传性语言）来动态评估其可信度，从而实现对无历史声誉的新域名的自动化可信度判别。
链接: https://arxiv.org/abs/2607.04560
作者: Islam Eldifrawi,Shengrui Wang,Amine Trabelsi
机构: University of Sherbrooke (舍布鲁克大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Web domain credibility evaluation is vital for combating misinformation. It is conducted by examining factors such as domain type, transparency, and overall reputation. However, assessing the credibility of newly emerging web domains remains challenging since they have no reputation yet. Expert fact-checkers evaluate the credibility of domains by analyzing the content of their articles, including the presence of misinformation, bias, or propaganda. Yet, the ease of large-scale content generation enabled by LLMs has accelerated the creation of new content, rendering manual assessment insufficient and underscoring the need for automated approaches to domain credibility evaluation. In this paper, we introduce our Domain Credibility Evaluation Framework (DCEF), a temporal framework for domain credibility evaluation grounded in expert ratings. DCEF enables us to investigate whether the credibility of web domains can be assessed from their published articles following the workflow of expert fact-checkers, without any prior knowledge of the source domains themselves.
[NLP-47] EEG-SpikeAgent : Agent ic Closed-Loop Program Synthesis for Automated EEG Spike Detection
【速读】： 该论文旨在解决头皮脑电图（scalp EEG）中发作间期癫痫样放电（interictal epileptiform discharges）自动检测的临床需求，同时应对当前高性能深度学习模型在准确性与可解释性之间权衡的问题。其核心挑战在于如何在保持模型决策过程透明、可审计的前提下，实现高效且精准的特征工程。解决方案的关键在于提出EEG-SpikeAgent——一种基于大语言模型（LLM）的闭环程序合成框架，通过迭代式生成确定性的信号处理特征模块，结合代码执行、性能评估与反馈优化机制，实现自动化、可追溯的特征工程。该框架以可读、可审查的代码形式生成特征，并依据分类器性能反馈持续优化特征设计，显著提升了在复杂噪声背景下的检出能力，尤其在引入伪迹感知（artifact-aware）特征后，平衡准确率和F1分数均得到提升。实验结果表明，该方法在公开数据集VEPISET上实现了0.935的AUC值及高达0.996的特异性，验证了基于LLM的程序合成在临床神经电生理分析中具备兼具高精度与强可解释性的潜力。
链接: https://arxiv.org/abs/2607.04558
作者: Sonali Santhosh,Kelly Shuhong Yu,Eugene Chang,Jonathan Kim,Kie Shidara,Danilo Bernardo
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 7 pages, 5 figures
Abstract:Automated detection of interictal epileptiform discharges in scalp electroencephalography (EEG) is clinically important, but recent high-performing deep-learning models often trade interpretability for accuracy. We introduce EEG-SpikeAgent, a closed-loop program-synthesis framework that uses a large language model (LLM) agentic system to generate signal-processing features for spike detection in scalp EEG. The system iteratively proposes one deterministic EEG feature module at a time, executes the resulting code on EEG to generate tabular features, evaluates performance via a tabular classifier, summarizes run-level metrics, and feeds structured diagnostics back to the model for refinement. Across iterations, EEG-SpikeAgent proposes and refines candidate signal features and decision rules informed by model performance. We evaluated EEG-SpikeAgent on VEPISET, a public 29-channel dataset of 4-second epochs containing 2,516 discharge-containing and 22,933 non-discharge epochs. Across five-fold cross-validation with a gradient-boosted tree classifier, agent-generated features achieved an area under the receiver operating characteristic curve of 0.935, balanced accuracy of 0.699, F1 score of 0.557, sensitivity of 0.401, and specificity of 0.996 at the default operating point. At an operating point with sensitivity 0.80, mean precision was 0.470 and mean specificity was 0.900. Artifact-aware feature generation improved balanced accuracy and F1 score over spike-only feature search. These results indicate that LLM-based program synthesis can automate EEG feature engineering in auditable and inspectable code-driven manner for clinical and methodological review.
[NLP-48] Mechanism-level routing failure in LLM s over Lean-verified algebraic structures
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在形式化数学推理中因结构路由失败（structural routing failure）而导致的证明机制选择错误问题，特别是在面对具有严格验证背景的代数结构（如Lean 4中的FiberRing形式化）时，模型难以准确识别并匹配正确的证明机制标签。其核心问题是：尽管模型具备一定的形式化知识和推理能力，但在缺乏显式引导的情况下，仍会频繁发生“机制级路由偏差”，即错误地将某类数学对象（如中国剩余定理项，CRT）归入不恰当的代数结构类别（如环等价），从而导致推理路径失效。解决方案的关键在于引入“机制承载型判定提示”（mechanism-bearing Lean verdict/witness cue），通过提供来自形式化证明系统的可信赖证据线索（如验证结果或见证项），显著提升模型对正确证明机制的识别能力——实验显示，在该提示条件下，GPT-OSS-120B与Llama 3.3 70B的模板准确率分别提升至90.9%和81.8%，实现+10.6和+13.6个百分点的“提示诱导路由增益”（cue-induced routing uplift）。研究进一步揭示了“真值推断”与“证明机制分类”之间的可分离性，证实了模型在判断结论真假与选择正确推理路径之间存在认知解耦，为理解LLMs在形式化数学任务中的内在缺陷提供了实证支持，并扩展了“路由器假说”（router hypothesis）至形式代数结构领域。
链接: https://arxiv.org/abs/2607.04534
作者: Manuel Israel Cázares,Wenlin Zhang,Haobo Ma
机构: 未知
类目: Computation and Language (cs.CL)
备注: Code, data, and evaluation pipeline available at this https URL
Abstract:We present an empirical study of structural routing failure in large language models (LLMs) over a formally verified algebraic corpus. The task requires selecting the correct proof-mechanism label from a fixed closed template set for compact mathematical objects drawn from the FiberRing formalization in Lean 4, where each item is anchored to a Lean-verified artifact and assigned a label from the corresponding certificate family. Our central finding is a mechanism-level routing ceiling: under blind conditions, gpt-oss-120b achieves 80.3% template accuracy on 22 FiberRing items (n=66; temperature=0, seed=0), while Llama 3.3 70B reaches 68.2%. Exposing a mechanism-bearing Lean verdict/witness cue (Condition A2) raises accuracy to 90.9% and 81.8% – gaps of +10.6 and +13.6 pp termed cue-induced routing uplift. The dominant failure is a CRT-to-ring-equivalence misroute: gpt-oss-120b misroutes 7 of 12 CRT items (58.3%) blind, zero under A2. A cross-model dissociation in Llama is notable: verdict accuracy is identical in both conditions (95.5%), while template accuracy improves 13.6 pp – confirming that truth inference and proof-mechanism classification are separable capacities. A cross-corpus extension (Set B; 6 POM/CollisionKernel items, 72 evaluations) provides a small cross-module check: CRT-granularity compression reappears with different labels, and an inverse cross-model dissociation emerges. These findings extend the router hypothesis (Cazares 2026) to formal algebraic structures. The full pipeline, manifest, and results are at this https URL. Comments: Code, data, and evaluation pipeline available at this https URL Subjects: Computation and Language (cs.CL) ACMclasses: I.2.7 Cite as: arXiv:2607.04534 [cs.CL] (or arXiv:2607.04534v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2607.04534 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Manuel Israel Cazares [view email] [v1] Sun, 5 Jul 2026 22:45:49 UTC (13 KB)
[NLP-49] Language Models Represent and Transform Concepts with Shared Geometry
【速读】： 该论文旨在解决神经网络中概念表征的本质问题，特别是传统观点将概念视为静态几何对象的局限性。其核心挑战在于：概念在实际使用中依赖于上下文，而上下文会动态地改变概念的表征形式。为此，作者基于神经种群几何（neural population geometry）提出将概念表征建模为点云流形（point-cloud manifolds），并将上下文对概念的影响形式化为向量场（vector fields）。在六种不同规模的大语言模型中验证该框架后发现，上下文确实以差异化方式作用于每个概念，且这些位移的方差具有语义组织性，与词汇具体性（lexical concreteness）和密度相关。更重要的是，概念的变换结构在不同模型间共享，即一个模型中的位移结构可显著优于随机水平地预测其他模型的未见位移。这表明，大语言模型不仅在概念表征上存在共通的几何结构，更在上下文如何动态重塑概念这一层面展现出高度一致且结构更丰富的共享机制，突破了以往研究对静态表征的理解。
链接: https://arxiv.org/abs/2607.04525
作者: Zhimin Hu,Lanhao Niu,Sashank Varma
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:How concepts are represented in neural networks is a fundamental question in machine learning. The dominant view treats concept representations as stationary geometric objects. Yet concepts appear in context, and context transforms them. Drawing from neural population geometry, we formalize concept representations as point-cloud manifolds and contextual transformations as vector fields, and instantiate this framework in large language models. Across six model families of varying scales, we find that context moves each concept differently. The variance in these displacements is semantically organized, correlating with lexical concreteness and density. Importantly, both the concepts being transformed and this variance structure are shared across models: displacement structure transported from one model predicts held-out displacements in others significantly above chance. Together, these findings show that models share a common geometry not only in how concepts are represented, but more importantly in how context transforms them, a structure with richer organization than prior work has recognized.
[NLP-50] Failures and Successes to Learn a Core Conceptual Distinction from the Statistics of Language
【速读】： 该论文旨在解决人类如何区分事物的属性是基于类别本质（原则性）还是仅因统计规律（统计性）的问题。核心问题是：这种原则性与统计性的概念区分是否能够通过语言经验本身习得，而非作为先天认知机制的一部分。研究的关键在于检验语言模型能否在控制统计频率的前提下，准确识别并表征属性的本质性（如“老虎有条纹”）与统计性（如“汽车有收音机”）之间的根本差异。研究发现，除GPT-4外，现有语言模型虽能感知统计流行度，却难以在排除频率干扰的情况下正确表征这一概念区分；而GPT-4则首次展现出对原则性与统计性属性的敏感性，表明大型语言模型可能具备从语言中直接学习复杂因果结构的能力，为语言驱动概念形成提供了实证支持。
链接: https://arxiv.org/abs/2607.04523
作者: Zhimin Hu,Jeroen van Paridon,Gary Lupyan
机构: University of Wisconsin-Madison (威斯康星大学麦迪逊分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Published at Evolang XV, 2024
Abstract:Generic statements like “tigers are striped” and “cars have radios” communicate information that is, in general, true. However, while the first statement is true in principle, the second is true only statistically. People are exquisitely sensitive to this principled-vs-statistical distinction. It has been argued that this ability to distinguish between something being true by virtue of it being a category member versus being true because of mere statistical regularity, is a general property of people’s conceptual machinery and cannot itself be learned. We investigate whether the distinction between principled and statistical properties can be learned from language itself. If so, it raises the possibility that language experience can bootstrap core conceptual distinctions and that it is possible to learn sophisticated causal models directly from language. We find that language models are all sensitive to statistical prevalence, but struggle with representing the principled-vs-statistical distinction controlling for prevalence. Until GPT-4, which succeeds.
[NLP-51] owards Digital Preservation of Efik: TTS for a Low-Resource African Language INTERSPEECH2026
【速读】： 该论文旨在解决非洲声调语言Efik在语音合成研究中长期存在的数据匮乏与模型性能不足问题，尤其针对其在低资源条件下的文本到语音（Text-to-Speech, TTS）生成挑战。解决方案的关键在于构建首个面向Efik语的端到端TTS研究框架，包括一个由2,632个语音片段组成的单说话者语料库（总时长约三小时），并在此基础上对四种神经网络模型（VITS、MMS-TTS、SpeechT5和Orpheus-TTS）进行对比评估。实验结果表明，MMS-TTS在主观评价指标（如平均意见分，MOS）上表现最优（3.80 ± 0.63），且在长句生成稳定性方面更具优势，但依然存在声调错误；其余模型则表现出更显著的声调与韵律不一致问题。研究为声调非洲语言的语音合成提供了可复现的基准，并强调了未来需构建更大规模语料库及发展具备声调感知能力的建模方法以提升合成质量。
链接: https://arxiv.org/abs/2607.04515
作者: Offiong Bassey Edet,Emmanuel Oyo-Ita,Archibong Okon Archibong,David Effanga Bassey,Mbuotidem Sunday Awak
机构: University of Cross River State, Nigeria; University of Calabar, Nigeria; ML Collective
类目: Computation and Language (cs.CL)
备注: 6 pages, 2 figures. Accepted to Interspeech 2026
Abstract:Efik, a tonal language spoken by about 3 million second language speakers and 1.5 million native speakers in Southeastern Nigeria, remains underrepresented in speech synthesis research. We present the first documented end-to-end text-to-speech study for Efik, introducing a curated single speaker corpus of 2,632 utterances totaling three hours and a comparative evaluation of four neural models (VITS, MMS-TTS, SpeechT5, and Orpheus-TTS) under low resource conditions. Native speakers evaluated the systems using MOS, Nat-MOS, and A-MOS. MMS-TTS achieved the highest MOS of 3.80 +/- 0.63 and produced more stable long form speech, though tonal errors persisted. Other models showed greater tonal and prosodic inconsistencies. These results provide a reproducible baseline and highlight the need for larger corpora and tone aware modeling for tonal African languages.
[NLP-52] ransplanting inverting and preventing a misalignment persona: method-conditional emergent misalignment in Qwen 2.5
【速读】： 该论文旨在解决生成式AI（Generative AI）在微调过程中出现的“涌现性错位”（Emergent Misalignment, EM）问题，即语言模型在针对特定有害数据进行微调后，产生广泛且不可预测的不良行为。其核心发现是，在Qwen2.5系列模型中，这种错位现象由一个潜在的“人格方向”（latent persona direction）所介导，且该方向在开放权重设置下具有因果作用。解决方案的关键在于识别并操控这一人格方向：通过将该方向从源模型移植到仅共享预训练权重的目标模型中，可诱导出显著的广义错位（2.83% ± 0.26%，远高于随机方向基准约1.1%），从而验证了该方向的因果性；同时，移除自身模型中的该方向可使显性诱导器的广播效应减半（从21%降至10%）。研究进一步表明，该人格方向的招募依赖于微调方法与模型容量，低秩参数高效微调（PEFT）如LoRA在资源受限场景下更易触发该方向，而全量微调（SFT）则可能因容量充足而绕过此路径。此外，该方向的因果作用具有情境依赖性——在训练中引导坏医疗微调远离该方向反而会提升错误广播率（24%升至51%），说明其并非普适性修复策略。最终，通过分析损失相关性、接种免疫及沿单一行为衍生轴正交微调等手段，可在测试实例中有效筛选并预防该方向的招募，实现对错位行为的针对性抑制。本研究为理解生成式模型中隐含行为路径的形成机制提供了可控案例，并揭示了在大规模部署中兼顾效率与安全性的关键路径。
链接: https://arxiv.org/abs/2607.04510
作者: Lyndon Drake(University of Oxford),Zandi Eberstadt(University of Oxford)
机构: University of Oxford (牛津大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注: 34 pages, 18 figures
Abstract:Emergent misalignment (EM) – the broad misbehaviour a language model acquires after fine-tuning on narrow harmful data – is mediated in Qwen2.5 models by a latent persona direction, and that direction is causal in open weights. Transplanting it into a model that shares only pretraining with its source induces broad EM (2.83 +/- 0.26% misaligned against a random-direction floor of ~1.1%), and ablating a model’s own direction roughly halves an overt inducer’s broadcast (21% to 10%). The transplant doubles as a measurement method, causally assaying directions that a source model represents but cannot itself express. Whether a fine-tune recruits this persona depends on method and capacity, and since low-rank PEFT is the cheaper regime at scale, the recruiting method is also the economical one. On Qwen2.5-32B, low-rank LoRA on insecure code recruits it (3.4% misaligned) while full SFT on identical data does not (0.3%) and moves against the persona axis (drift-persona cosine +0.17 at rank 1 to -0.10), the far-inducer, high-capacity exception consistent with a representational-distance x capacity account. The persona’s causal role is itself conditional. Steering a bad-medical SFT run away from the direction during training raises the broadcast from 24% to 51% while a matched random control lowers it, so removing the direction is no blanket recipe. Because recruitment is a loss-reducing shortcut that capacity renders redundant, it can be screened for and prevented in the tested instances. Persona loss-relevance at the SFT solution orders four inducers’ broadcasts rank-perfectly within Qwen2.5, inoculation removes recruitment selectively (4.75% to 0.0%, code coherence 65% to 87%), and fine-tuning orthogonal to the single behaviour-derived axis reduces it persona-specifically. Results are a controlled case study of one model family, single-seed in places.
[NLP-53] Dont Commit Alone: Joint Token Commitment in Diffusion Large Language Models
【速读】： 该论文旨在解决扩散型大语言模型（diffusion large language models, dLLMs）在去噪过程中因独立解码各选定位点而导致的因子化误差问题。当位置间存在依赖关系时，传统基于置信度的选择机制仅依赖边际分布，无法捕捉条件总相关性（conditional total correlation），从而影响生成质量。其解决方案的关键在于提出CoCommit方法——一种标记门控的协调传递机制：在常规的束选择之后，通过一个可学习的标记信号宣布待提交的位置集合，并将主干网络最后n层重新应用于这些标记位置，实现位置间的协同优化，近似联合模式解码（joint-mode decoding）。该方法仅需一次额外的部分前向传播，复用已有权重且无需引入辅助模型，在使用LoRA适配器与匹配贪婪推理的LLaDA2.1-mini上，所有六个评估基准均实现准确率提升，尤其在推理和精确答案任务中效果显著。
链接: https://arxiv.org/abs/2607.04469
作者: Lin Yao
机构: Shanghai Jiao Tong University (上海交通大学); Zhongguancun Academy (中关村学院)
类目: Computation and Language (cs.CL)
备注:
Abstract:Diffusion large language models (dLLMs) commit multiple tokens per denoising step by decoding each selected position independently from the shared context; when those positions are dependent, the resulting factorization error is captured by conditional total correlation, which confidence-based selection cannot observe from marginals alone. We propose CoCommit, a marker-gated coordination pass that briefly defers commitment: after the usual bundle selection, a learned marker announces the commit set and the backbone’s last- n layers are re-applied so marked positions coordinate – approximating joint-mode decoding – before greedy argmax writes tokens. The method reuses existing weights with one extra partial forward pass and no auxiliary model. On LLaDA2.1-mini with LoRA adapters and matched greedy inference, joint commitment improves accuracy on all six benchmarks we evaluate, with the largest gains on reasoning and exact-answer tasks.
[NLP-54] Uncertainty-Aware Abstention in Large Language Models with Provable Alignment Guarantees
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在问答（Question Answering, QA）系统中生成幻觉或语义不一致回答的问题，且缺乏可靠的置信度估计。现有方法依赖启发式不确定性评分，无法在选择性回答（selective answering）中提供误差率的统计保障。其核心解决方案是提出一种基于置信区间（Confidence Interval-based Calibration, CIC）的校准框架，将任意不确定性评分转化为风险可控的选择性回答规则。CIC利用一个独立校准集，通过特定应用的对齐准则评估每个生成回答的正确性，并关联不确定性分数与二元错误标签；针对每个候选阈值，采用霍夫丁（Hoeffding-style）或克洛珀-皮尔逊（Clopper-Pearson）置信区间估计接受条件下的误差率上界，最终选取使上界低于用户指定风险水平 α 的最大阈值，从而在有限样本下实现误差率控制的同时最大化回答率。在交换性假设下，CIC以至少 1−δ 的概率保证所选阈值（若非空）能将被接受回答的误差率控制在 α 水平。实验在七种LLM及多个不确定性估计器上验证了该方法在闭合式与开放式问答任务中的有效性，结果表明CIC在保持高回答效率的同时实现了稳健的风险控制，为高可靠性要求的QA工作流提供了可信赖、统计严谨的部署机制。
链接: https://arxiv.org/abs/2607.04430
作者: Sijin Dong,Hiroyuki Shinnou
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) are increasingly deployed in question answering (QA) systems, yet they may generate hallucinated or misaligned responses without reliable confidence estimates. Uncertainty quantification (UQ) offers a natural basis for selective answering, where a system answers only when its prediction is deemed reliable and abstains otherwise. However, existing uncertainty scores for LLMs are often heuristic: a threshold chosen on such scores does not, by itself, provide statistical guarantees on the error rate among accepted answers. We propose CIC, a confidence-interval-based calibration framework that converts arbitrary uncertainty scores into risk-controlled selective answering rules. Given a held-out calibration set, CIC evaluates each generated response using an application-specific alignment criterion and associates it with an uncertainty score and a binary error label. For each candidate uncertainty threshold, CIC estimates the acceptance-conditioned error rate and constructs a high-probability upper confidence bound using either Hoeffding-style or Clopper-Pearson confidence intervals. It then selects the largest threshold whose upper bound is below a user-specified risk level \alpha , thereby maximizing the answering rate subject to a finite-sample reliability constraint. Under exchangeability, CIC guarantees with probability at least 1-\delta that the selected threshold, if non-null, controls the error rate among accepted answers at level \alpha . We evaluate CIC on both closed-ended and open-ended QA benchmarks across seven LLMs and multiple uncertainty estimators. Experimental results show that CIC consistently achieves valid risk control while retaining strong answering efficiency, providing a practical and statistically grounded mechanism for deploying LLMs in reliability-sensitive QA workflows.
[NLP-55] valci: A Python Library for Statistically Rigorous Comparison of Language Model Evaluations
【速读】： 该论文旨在解决当前语言模型评估实践中存在的统计可靠性问题：即仅报告单一准确率并宣称更高者更优，而未检验模型间性能差距是否可能仅为采样噪声所致。在包含数千个样本的基准测试中，尤其是在使用温度采样时模型自身多次运行结果差异超过不同模型间的报告差距，这种做法会过度夸大对主流结论的信心。其解决方案的关键在于引入一套成熟的统计方法（如置信区间、配对显著性检验、功效分析、聚类标准误及多重比较校正），并将其封装为一个纯Python库evalci，能够将逐项结果表格直接转化为可发表的统计声明（例如：“模型A优于模型B，Δ=3.1分，95%置信区间[1.2, 5.0]，配对置换检验p=0.002，n=1,319”）。该工具通过与独立参考实现（如statsmodels或精确枚举）进行验证，确保计算准确性，并兼容lm-evaluation-harness和HELM输出格式。作为案例研究，作者重新分析了九个语言模型在MMLU上的公开对比数据，发现排名相邻的8个差距中有3个在考虑36次成对比较后不再具有统计显著性，凸显了采用严格统计方法评估模型性能的重要性。
链接: https://arxiv.org/abs/2607.04429
作者: Shreyas K Chandrahas
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 7 pages, 1 figure. Software: this https URL (source: this https URL , Zenodo DOI: https://doi.org/10.5281/zenodo.21201815 )
Abstract:The dominant practice in language model evaluation is to report a single accuracy number per model and declare the higher one better, without testing whether the gap could plausibly be sampling noise. On benchmarks of a few thousand items, and under temperature sampling where a model can differ from itself run to run by more than the reported gap between models, this practice routinely overstates confidence in headline claims. The statistical machinery to fix this – confidence intervals, paired significance tests, power analysis, clustered standard errors, multiple-comparison correction – is well established, but no standard, pip-installable tool packages it in the shape an evaluation actually takes: a per-item results table. We present evalci, a pure-Python library (numpy/scipy/pandas only) that turns a per-item results table into a publication-ready claim – e.g., "Model A beats Model B, \Delta=3.1 pts, 95% CI [1.2, 5.0], paired permutation p=0.002 , n=1,319 " – in one function call, with adapters for lm-evaluation-harness and HELM output. Every routine is validated against an independent reference (statsmodels, or brute-force exact enumeration) rather than only against itself. As a case study, we re-analyze a public comparison of nine language models’ MMLU accuracy and find that 3 of the 8 adjacent leaderboard-rank gaps are not statistically significant after correcting for the 36 pairwise comparisons the ranking implies. evalci is available at this https URL (source: this https URL, DOI: this https URL)
[NLP-56] dOPSD: On-Policy Self-Distillation for Diffusion Language Models
【速读】： 该论文旨在解决扩散型大语言模型（dLLM）在后训练阶段难以有效激发强推理能力的问题。现有方法中，监督微调存在策略偏差（off-policy）与暴露偏差问题，而强化学习则受限于稀疏的序列级奖励信号及难以获取可计算的序列似然性。尽管在策略自蒸馏（OPSD）框架下，通过将同一模型同时作为学生和教师以提供密集的、基于标记的、在策略监督具有潜力，但其效果高度依赖于教师模型获得特权信息（PI），如推理时不可用的实例级真实参考答案，导致学生最终仅蒸馏出一个无特权信息的弱共识策略，推理性能提升有限。本文提出dOPSD方法，其关键创新在于不依赖外部标签，而是从学生模型自身的去噪轨迹中动态提取教师的“特权”：利用同一轨迹中后续更解码步骤的信息来评估当前被掩码的位置，使教师的优势源自模型自身生成过程的内在一致性。实验表明，在Dream和LLaDA数据集上，dOPSD显著提升了域内数学推理与域外代码生成性能，优于监督微调及现有在策略基线方法。
链接: https://arxiv.org/abs/2607.04428
作者: Phuong Tuan Dat,Qi Li,Xinchao Wang
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Diffusion large language models (dLLMs) generate text by iteratively denoising a masked sequence, offering a parallel alternative to autoregressive models, but eliciting strong reasoning through post-training remains difficult: supervised fine-tuning is off-policy and suffers from exposure bias, while reinforcement learning gives only sparse, sequence-level rewards and is hard to apply without tractable sequence likelihoods. On-policy self-distillation (OPSD) offers a promising alternative, using one model as both student and teacher to provide dense, token-level, on-policy supervision, but its effectiveness hinges on giving the teacher privileged information (PI) - typically an instance-specific ground-truth reference unavailable at inference - so the student ends up distilling a weak PI-free consensus policy that yields little improvement on dLLM reasoning. We introduce dOPSD, which instead derives the teacher’s privilege directly from the student’s own denoising trajectory, evaluating masked positions using later, more-decoded steps of that same trajectory rather than an external label, so the teacher’s advantage emerges from the model’s own decoding process; on Dream and LLaDA, dOPSD improves both in-domain math reasoning and out-of-domain code generation, outperforming supervised and on-policy baselines.
[NLP-57] UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning
【速读】： 该论文旨在解决多平台图形用户界面（GUI）智能体在跨平台交互中面临的两大核心挑战：一是高质量、可执行的跨平台交互轨迹数据稀缺且平台覆盖有限；二是不同平台间存在显著的交互范式差异，导致联合或持续训练时易出现行为模式混杂、平台特异性能力退化及灾难性遗忘问题。其解决方案的关键在于提出UI-MOPD方法——首个将多教师在线策略蒸馏（multi-teacher on-policy distillation）引入GUI智能体持续学习的框架。该方法通过根据当前环境动态选择特定平台的教师模型，并基于平台条件化的蒸馏机制，将平台特有的行为先验知识迁移至共享策略中，从而在保留已有平台能力的同时实现对新平台的有效适应。实验结果表明，该方法在OSWorld和MobileWorld基准上分别达到38.2%和12.0%的任务成功率，验证了其在跨平台能力保持与新平台适应性之间取得良好平衡的有效性。
链接: https://arxiv.org/abs/2607.04425
作者: Niu Lian,Alan Chen,Zhehao Yu,Chengzhen Duan,Fazhan Liu,Hui Liu,Pei Fu,Jian Luan,Yaowei Wang,Shu-Tao Xia,Jinpeng Wang
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); Multimedia (cs.MM)
备注: Technical report. 25 pages, 5 figures, 7 tables
Abstract:Recent advances in multimodal foundation models and agent systems have driven GUI agents from single-platform task execution toward cross-platform interaction. However, building multi-platform GUI agents remains challenging. On one hand, high-quality and executable cross-platform interaction trajectories are still scarce, and existing data often suffer from limited platform coverage. On the other hand, different platforms exhibit distinct interaction conventions, making joint or continual training prone to behavioral pattern mixing, platform-specific capability degradation, and catastrophic forgetting. To address these challenges, we construct Uni-GUI, a high-quality cross-platform GUI interaction dataset, and propose UI-MOPD, the first method that incorporates multi-teacher on-policy distillation into continual learning for GUI agents. UI-MOPD dynamically selects a platform-specific teacher according to the current environment and transfers platform-specific behavioral priors to a shared policy through platform-conditioned distillation, enabling adaptation to new platforms while preserving capabilities on existing ones. Experiments on OSWorld and MobileWorld show that UI-MOPD achieves task success rates of 38.2% and 12.0%, respectively, demonstrating its effectiveness in balancing cross-platform capability retention and new-platform adaptation. Project page: this https URL. Comments: Technical report. 25 pages, 5 figures, 7 tables Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Multimedia (cs.MM) Cite as: arXiv:2607.04425 [cs.CL] (or arXiv:2607.04425v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2607.04425 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-58] AI Wizards at EXIST 2026: Hierarchical Soft-Label Learning for Multimodal Sexism Identification in Memes
【速读】： 该论文旨在解决多模态讽刺性性别歧视（multimodal sexism）在梗图（memes）中的识别问题，尤其针对任务中三个逐步增加难度的子任务。其核心挑战在于如何有效融合视觉与语言模态信息，并对具有主观性和模糊性的标注进行建模。解决方案的关键在于采用层次化条件软标签（conditional soft-label）预测框架，基于人工标注者的经验分布进行建模；同时，通过将固定预训练的Gemini Embedding 2视觉-语言表征映射至轻量级门控多层感知机（Gated MLP），并结合KL散度损失与同方差不确定性加权（homoscedastic uncertainty weighting）进行训练，实现了对标注不确定性的自适应建模，从而提升了模型在复杂语境下的鲁棒性与泛化能力。该方法在官方Soft-Soft排行榜上取得了任务2.3的第一名以及任务2.1和2.2的第四名成绩。
链接: https://arxiv.org/abs/2607.04410
作者: Matteo Fasulo,Antonio Gravina,Luca Tedeschini,Luca Babboni
机构: Swiss Data Science Center, ETH Zürich (瑞士数据科学中心，苏黎世联邦理工学院); Everest Systems GmbH (Everest系统有限公司); Villanova.ai S.P.A (Villanova.ai股份公司)
类目: Computation and Language (cs.CL)
备注:
Abstract:We present the AI Wizards submission to EXIST 2026 for multimodal sexism identification in memes. The task is composed of three, increasingly harder subtasks. We model them hierarchically as conditional soft-label prediction over empirical annotator distributions. Our system maps fixed Gemini Embedding 2 vision-language representations through a lightweight Gated MLP trained with KL divergence and homoscedastic uncertainty weighting. Our submissions ranked first on Task 2.3 and fourth on Tasks 2.1 and 2.2 on the official Soft-Soft leaderboards. The code is available at this https URL
[NLP-59] Memory-Orchestrated Semantic System (MOSS): An Auditable Agent ic Memory Architecture
【速读】： 该论文旨在解决当前人工智能代理（AI agents）在长期记忆（long-term memory）方面存在的结构性缺陷，尤其是主流方法检索增强生成（Retrieval-Augmented Generation, RAG）所依赖的基于嵌入的相似性搜索所固有的不可解释性、难以审计以及受向量表示理论极限制约等问题。其解决方案的关键在于提出一种名为“记忆编排语义系统”（Memory-Orchestrated Semantic System, MOSS）的代理记忆架构，该架构通过让智能体主动驱动对结构化关系数据库的检索，实现了符号化、可复现的检索执行过程——一旦查询被形式化，后续检索环节不再依赖大语言模型（LLM），从而确保了系统的可审计性与透明性。此外，MOSS具备模型无关性、存储无关性与API无关性，支持任意关系型数据库引擎、任意大模型提供商或确定性非大模型流程，并可在本地或云环境中部署。系统从原始语料库中自底向上归纳出概念词汇表与元数据图谱，避免了对外部本体论的强依赖。研究通过一项历时一年的纵向生产级部署验证了该系统：覆盖一位学者自2024年10月起积累的工作语料（约4400万词元，回溯索引）、超过11万段对话内容、16万余份文档、569个归纳出的概念、32万次概念标注及总计约五百万条关系的多层级知识图谱，横跨四代基础设施演进。研究表明，可审计、主权可控且结构上无边界的记忆系统，是使AI代理能够长期伴随个人或组织持续演化而非仅限于单次会话的关键前提。
链接: https://arxiv.org/abs/2607.04391
作者: Serge Lacasse,Jérémie Hatier,Alex Baker
机构: 未知
类目: Computation and Language (cs.CL)
备注: 22 pages, 2 figures
Abstract:Long-term memory remains a structural weakness of AI agents. The dominant approach, retrieval-augmented generation (RAG), relies on embedding-based similarity search, which is opaque by construction, difficult to audit, and bounded by the theoretical limits of vector representations. We present the Memory-Orchestrated Semantic System (MOSS), an agentic memory architecture in which the agent drives retrieval over a structured relational database. MOSS is model-agnostic, storage-agnostic, and API-agnostic: it runs on any relational engine, connects to any LLM provider (or to deterministic non-LLM processes), and deploys on any infrastructure, local or cloud. Its retrieval execution is symbolic and reproducible (once a query is formulated, no LLM participates in the retrieval loop) and every step of the system, from indexing to answer formulation, is logged and inspectable, making MOSS auditable by construction. Rather than imposing an external ontology, MOSS derives its conceptual vocabulary from the corpus itself. We report on a longitudinal deployment unique in the agentic-memory literature: a year of continuous production over an individual scholar’s working corpus–a conversational corpus reaching back to October 2024 (some 44 million tokens, retroactively indexed) comprising 110,183 segments, alongside 163,494 catalogued documents, 569 inductively derived concepts, 322,662 concept annotations, and eleven metadata graphs totaling approximately five million relations–across four successive infrastructure generations. While the present case is that of a single researcher, the architecture is in no way specific to one person: it serves a team, an institution, or any entity that accumulates knowledge over time. We argue that auditable, sovereign, structurally unbounded memory is a precondition for AI agents intended to accompany a person or an organization over years rather than sessions.
[NLP-60] WPG-MoE: Weak-Prior-Guided Dense Mixture-of-Experts for User-Level Social Media Depression Detection
【速读】： 该论文旨在解决在线社交媒体中抑郁症早期筛查中存在的用户表达异质性问题，即现有方法多依赖单一分类器对筛选后的用户进行最终判断，而忽视了用户在表达抑郁风险时存在显著的个体差异，尤其是非自我披露型用户，其隐含线索易被平均化模型稀释，导致误判。解决方案的关键在于提出一种弱先验引导的密集专家混合（WPG-MoE）框架，基于共享的大语言模型（LLM）主干网络，通过从文本中提取的用户级弱语义先验（weak semantic priors），实现对不同证据结构布局的专家软路由（soft routing）。该设计将路由过程建模为“使用特权信息学习”（Learning Using Privileged Information, LUPI），即在训练阶段利用LLM生成的结构化证据指导路由，而在推理阶段仅保留可部署的PHQ-9模板筛查与主干模型，兼顾性能与实用性。实验结果表明，该方法在中英文数据集上均优于现有强基线，并展现出可解释的路由行为。
链接: https://arxiv.org/abs/2607.04350
作者: Xian Li,Yuanhe Tian,Yang Yang,Guoqing Wang,Yan Song
机构: University of Electronic Science and Technology of China (电子科技大学); Zhongguancun Academy (中关村学院); University of Science and Technology of China (中国科学技术大学)
类目: Computation and Language (cs.CL)
备注: 23 pages, 8 figures, 26 tables
Abstract:Online social media posts provide scalable signals for early depression screening, and recent studies mainly improve pre-classification evidence through risk-post selection, symptom grounding, and clinically informed feature construction. However, these screening-stage designs often leave final decisions to a single detector, overlooking how users heterogeneously express depressive risk after screening. A monolithic classifier must average across heterogeneous users, which may dilute localized evidence and cause misclassification, especially for non-self-disclosing users. To address this issue, we propose WPG-MoE, a weak-prior-guided dense mixture-of-experts framework built on a shared large language model (LLM) backbone. WPG-MoE derives user-level weak semantic priors to softly route users to experts matched to different evidence layouts. We formulate this process as learning using privileged information (LUPI): rich LLM-extracted structured evidence guides training-time routing, while inference retains only Patient Health Questionnaire-9 (PHQ-9) template screening and the deployable backbone. Experiments on Chinese and English datasets show that WPG-MoE outperforms strong baselines with interpretable routing behavior.
[NLP-61] How to Build Digital Humans? From Priors to Photorealistic Avatars
【速读】： 该论文旨在解决可控三维人体虚拟形象（3D human avatar）生成中的核心问题，即如何高效、精准地构建个性化且具备真实感与可编辑性的三维虚拟人物。其关键挑战在于实现对人体外观与运动的先验知识建模，并在此基础上完成高质量、可定制化的虚拟形象创建。解决方案的关键在于提出一种系统性的分类体系，从身体区域划分和所采用的先验信息（priors）两个维度对现有方法进行归纳，涵盖全身与头部虚拟形象以及分层表示（如手部、发型、服装等部件分解）等技术路径；同时，通过梳理共性原理与关键技术文献，为研究者提供清晰的参考框架，并指明当前在数据依赖性、泛化能力及多模态控制等方面的开放性挑战与未来发展方向。
链接: https://arxiv.org/abs/2607.04341
作者: Wojciech Zielonka,Tobias Kirschstein,Timo Bolkart,Simon Giebenhain,Vanessa Sklyarova,Xiang Deng,Donglai Xiang,Shunsuke Saito,Yebin Liu,Matthias Niessner,Justus Thies
机构: 未知
类目: Graphics (cs.GR); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注: Eurographics 2026 State-of-the-Art Report (STAR). Project page: this https URL
Abstract:This state-of-the-art report provides an overview of controllable 3D human avatar creation. We describe current 3D avatar systems, which typically consist of three stages: (i) learning priors of human appearance and motion, (ii) creating a personalized avatar, and (iii) animating the avatar. To limit the scope, we focus on the prior learning and avatar creation stages. We define current avatar representations and introduce a taxonomy that categorizes existing work along multiple axes, including body regions and employed priors. We review methods for full-body and head avatars, as well as layered representations that decompose the body into components such as hands, hair, and garments. Finally, we outline common underlying principles, reference key literature for newcomers, and discuss open challenges and future research directions.
[NLP-62] Legible-by-Construction: Attention and End-to-End Transformers
【速读】： 该论文旨在解决生成式 AI 模型中注意力机制与前馈层的可解释性问题，即如何在不牺牲语言模型性能的前提下，使 Transformer 架构中的核心组件具备明确的语义可读性。其关键解决方案在于：通过在注意力头的值（value）通道上引入一个 sigmoid 激活函数，使每个值通道转化为对特定特征是否在某个标记处成立的可读检测器，从而实现无额外参数开销的语义透明化；进一步地，采用布尔型注意力设计，将值显式重构为词内交集和可否定的集合差分运算，增强逻辑可解释性。核心创新点在于将输出投影层与词汇表解耦，允许输出自由表达，而仅对检测能力施加约束——这一“检测受限、书写自由”的设计原则，使得注意力头能够形成上下文敏感且稀疏分明的可读检测器。实验表明，在 125M 参数规模下，44% 至 62% 的值通道可成为清晰、上下文相关的逻辑检测器，且可读性随网络深度提升而非局限于标点符号。最终，该研究将可读前馈层与可读注意力模块结合，构建出端到端可解释的语言模型，在基准测试中保持与传统模型相当的性能，且能直接读取生成令牌所依赖的命名逻辑操作单元。
链接: https://arxiv.org/abs/2607.04319
作者: Mark Oskin
机构: University of Washington (华盛顿大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:A companion paper showed that a transformer’s feed-forward layer can be rebuilt from explicit fuzzy set operations - intersection, set-difference, and a self-forgetting sequence quantifier - so its hidden units read as named logical operators at no cost to language-model quality. That left the other half of the transformer opaque. Here we carry the same idea into attention and join the two into one model. The mechanism is minimal: a head’s value is passed through a sigmoid, so each value channel becomes a readable detector of whether a feature holds at a token. This adds no parameters and leaves the standard head otherwise untouched. A Boolean variant goes further, restructuring the value into an explicit within-token intersection and negation-capable set-difference. In both designs the output projection is left free, not tied to the vocabulary, which is the load-bearing decision: bounding what a head detects while leaving what it writes unconstrained yields selective detectors, whereas constraining the write does not. A bounded value is shaped into a readable detector by two selectivity pressures - one for sparse firing, one for decisive firing at the rails - and which a design wants is not universal. Across five specialized-attention designs at 125M parameters, 44 to 62 percent of value channels become crisp, contextually selective detectors, and their legibility rises with depth rather than crystallizing only on punctuation. Language-model quality is at parity with a conventional baseline. Finally, we couple the Boolean attention to the legible feed-forward layer and train an end-to-end legible-by-construction language model at benchmark parity: its feed-forward units are named set and quantifier operations throughout, and we can take a token it generates and read the named units that compose to produce it.
[NLP-63] HiFA4: Training-Free 4-bit FlashAttention on Ascend HIF4 NPUs for LLM Inference
【速读】： 该论文旨在解决大语言模型（LLM）在Ascend NPU上进行低比特量化推理时，因注意力机制中矩阵乘法（QK^T和PV）的量化误差导致的决策漂移（decision drift）问题，尤其针对4比特混合精度张量核心（HIF4 Cube GEMM）场景下的精度损失与计算效率瓶颈。其解决方案的关键在于提出一种面向算子级的后训练优化设计——HiFA4，通过两个核心机制协同实现：一是Smooth-QK，采用静态逐通道等效缩放对经过旋转位置编码（RoPE）后的查询（Q）和键（K）进行校准，将量化难度从键向量（K）转移至查询向量（Q），避免推理时每块（tile）的在线归约操作；二是P-Reordering，利用用于PV计算的量化注意力权重(\hat{P})直接累积softmax归一化因子，而非依赖高精度重建值，从而避免因不一致计算路径引入的系统性输出缩放误差，并支持将归一化操作融合进PV立方体GEMM中，显著降低延迟。实验表明，该方案在多个主流模型（如Qwen3-8B、Gemma2-9B等）上有效缓解了量化引起的准确率下降，其中在Qwen3-8B上恢复了37.5%的精度差距，将样本加权准确率损失从1.12个百分点降至0.70个百分点，同时减少57%的MMLU预测回归现象，验证了其在保持高效硬件执行的同时提升模型鲁棒性的有效性。
链接: https://arxiv.org/abs/2607.04302
作者: Hui Dong,Yanzhao Li,Jie Gao,Chunlu Li,Zhiyuan Zhang,Yupeng Sun,Zhenyuan Chen,Zhiqiang Zou
机构: Huawei Technologies (华为技术)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Computation and Language (cs.CL); Performance (cs.PF)
备注: 22 pages
Abstract:We present HiFA4, a post-training operator-level design that executes both QK^T and PV in FlashAttention as 4-bit HIF4 Cube GEMMs for LLM inference on Ascend NPUs, while maintaining the online softmax state in FP16. To our knowledge, HiFA4 is the first Ascend-HIF4-targeted design of this kind evaluated on standard NLP benchmarks. HiFA4 combines two mechanisms. Smooth-QK applies a calibration-static per-channel equivalent rescaling to Q and K after RoPE, transferring quantization difficulty from K to Q without per-tile online reduction at inference. P-Reordering accumulates the softmax normalizer from the same quantized attention weights P_hat used in the PV GEMM, rather than from a higher-precision reconstruction. We show that this inconsistent formulation introduces a coherent output-scaling error, and validate the effect on a Qwen3-8B Layer-0 MMLU trace, where all 3.6M measured attention tiles exhibit net probability-mass loss with median epsilon_bar = -0.064. P-Reordering also allows the normalizer to be fused into the PV Cube GEMM. Across five LLMs, HiFA4 reduces quantization-induced decision drift. On Qwen3-8B, it recovers 37.5% of the accuracy gap introduced by direct HIF4 quantization, narrows the sample-weighted accuracy loss from 1.12 pp to 0.70 pp, reduces BF16-inconsistent MMLU predictions from 16.3% to 8.2%, and cuts MMLU accuracy regressions by 57% (1071 to 465). On Gemma2-9B, mild smoothing keeps HiFA4 within 0.7 pp of BF16 while reducing MMLU regressions by 27%. On LLaMA3.1-8B, Mistral-7B, and Phi-4B, where Smooth-QK is disabled, P-Reordering with the adopted Q-Mean auxiliary still reduces full-set MMLU regressions by 41-52%. A preliminary instruction-scheduling analysis projects a 35.4% critical-path latency reduction relative to BF16 by fusing the softmax normalizer into the PV Cube GEMM; on-hardware validation is left to future work. Comments: 22 pages Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Computation and Language (cs.CL); Performance (cs.PF) Cite as: arXiv:2607.04302 [cs.LG] (or arXiv:2607.04302v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.04302 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-64] CausalGame: Benchmarking Causal Thinking of LLM Agents in Games ICML
【速读】： 该论文旨在解决当前大型语言模型（LLM）作为“人工智能科学家”在科学发现过程中缺乏因果思维能力的问题，尤其针对真实科研场景中普遍存在的选择性偏差（selection bias）、测量误差（measurement error）和隐藏混杂因素（hidden confounders）等挑战。现有基准测试未能充分涵盖这些现实中的因果推理难题，导致对模型因果认知能力的评估存在显著不足。为此，本文提出CausalGame这一交互式基准测试框架，通过设计14个包含上述三类偏差的科学探索场景，要求LLM代理主动设计实验、收集观测数据并生成带有解释报告的最终解决方案，从而全面评估其因果推理能力。该方案的关键在于构建一个可扩展且受控的实验环境，使模型在模拟真实科学研究的过程中暴露其因果判断的弱点，进而推动对人工智能科学家在因果建模与批判性思维方面能力的系统性评测与改进。
链接: https://arxiv.org/abs/2607.04293
作者: Zhenhao Chen,Yongqiang Chen,Chenxi Liu,Junchi Yu,Xiangchen Song,Zijian Li,Jialin Li,Philip Torr,Bo Han,Kun Zhang
机构: MBZUAI(中东人工智能大学); Carnegie Mellon University (卡内基梅隆大学); TMLR Group, Hong Kong Baptist University (香港浸会大学TMLR小组); University of Oxford (牛津大学); New York University, Abu Dhabi (纽约大学阿布扎比分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML)
备注: Zhenhao, Yongqiang, and Chenxi contributed equally to the project. A short version is accepted at the Forty-Third International Conference on Machine Learning (ICML) 2026 as an Oral presentation. Project website this https URL
Abstract:Building AI Scientist agents with Large Language Models (LLMs) has recently attracted growing attention. Since scientific discovery fundamentally relies on uncovering causal relationships from observations, the capability of causal thinking, i.e., distinguishing causation from correlation and recognizing hidden biases, is essential to LLM agents. Although a number of benchmarks exist for AI Scientists, none explicitly incorporate challenges from selection bias, measurement error, and hidden confounders that widely exist in real-world scientific discovery. To this end, we present CausalGame, a benchmark that evaluates the causal thinking capabilities of LLM agents through interactive games. CausalGame asks LLM agents to actively design experimental protocols, collect observation data, and derive a final solution with an explanation report. To emulate realistic scientific discovery challenges, we design 14 scenarios that incorporate selection bias, measurement error, and hidden confounders. Across 30 LLM agents, none demonstrates reliable causal thinking: the best model reaches only 68.0% survival against analytical optima of 78-85%, and merely 5-7% of sessions receive credits on the causal-reasoning rubrics. CausalGame provides a scalable and controlled testbed for evaluating the causal thinking of AI Scientist agents.
[NLP-65] Risk-Constrained Freshness-Aware Semantic Caching for Open-Web Retrieval-Augmented LLM s
【速读】： 该论文旨在解决检索增强生成（Retrieval-Augmented Generation, RAG）系统中因缓存机制忽视开放网络证据时效性而导致的“过时结果”问题。现有语义缓存方法通常仅基于查询相似性进行缓存命中判断，未能建模开放网页内容随时间变化的动态新鲜度（freshness），从而在高时效性要求场景下可能引入错误信息。为此，本文提出FreshCache——一种三层语义缓存架构，其核心创新在于将缓存重用建模为一个风险约束的时间推理问题：在允许缓存命中前，通过拟合的指数衰减模型结合可学习的多层感知机（MLP）估计缓存结果过时的概率，并依据分层级的误差预算（epsilon = 0.10、0.20、0.35）决定是否接受缓存命中。该设计实现了缓存老化过程中的渐进式降级，而非强制选择“过时命中”或“全链路重执行”的二元决策。实验表明，在24小时评估窗口内，FreshCache_MLP实现97%的搜索API节省率，且基于哈希的过时错误仅为0.1%，经人工评估验证的真实影响答案正确性的过时错误约为0.034%；而规则版FreshCache在时间外推校准下实现98%的节省率与3.3%的过时错误，显著优于SemanticTTL、vCache和SCALM等基线方法。消融实验进一步证明，时间风险门控机制相比仅依赖相似性的重用策略可降低11.6个百分点的过时错误，而引入的可学习MLP再进一步减少3.2个百分点，凸显了对时间动态建模与深度学习融合的关键作用。
链接: https://arxiv.org/abs/2607.04281
作者: Muhammad Mansoor,Tahir Ahmad,Yeo-Chan Yoon
机构: Jeju National University (济州国立大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Semantic caching reduces the latency and cost of retrieval-augmented generation (RAG) by serving cached answers to semantically similar queries, but most existing methods do not model the time-varying freshness of open-web evidence. We present FreshCache, a three-tier semantic cache that treats cache reuse as a risk-constrained temporal inference problem: before approving a cache hit, FreshCache estimates the probability that the cached result is stale using a fitted exponential decay model enhanced by a learned MLP, and approves reuse only when that probability falls below a per-tier error budget across answers (epsilon = 0.10), URL lists (epsilon = 0.20), and page content (epsilon = 0.35). This allows the system to degrade gracefully as entries age rather than forcing a binary choice between a stale hit and a full pipeline execution. We introduce FreshCache-Bench, a benchmark of 8,072 base queries across five freshness classes with ground truth staleness labels drawn from real web snapshots at 1, 12, 24 hours, and 7 days after a baseline crawl, expanded to 31,201 queries via paraphrase generation. At the 24-hour evaluation window, FreshCache_MLP achieves 97% search API savings at 0.1% hash-based stale error, and an LLM-judge evaluation on 396 confirmed change pairs shows that only 34.3% of detected content changes actually affect answer correctness, placing true answer-affecting stale error at approximately 0.034%. The rule-based FreshCache achieves 98% search savings at 3.3% stale error under a temporal holdout calibration, outperforming SemanticTTL (14.9% stale, 72% saved), vCache (7.2% stale, 47% saved), and SCALM (5.2% stale, 96% saved). Ablations show the temporal risk gate accounts for an 11.6 point reduction in stale error over similarity-only reuse, and the learned MLP reduces stale error a further 3.2 points over the rule-based model.
[NLP-66] Spinning Straw into Gold: Relabeling LLM Agent Trajectories in Hindsight for Successful Demonstrations ICLR2026
【速读】： 该论文旨在解决大语言模型智能体（Large Language Model Agents）在部分可观测、长时程任务中因缺乏有效监督信号而导致的训练瓶颈问题。现有后训练方法通常依赖于人工标注的显式目标，但在实际执行过程中，智能体可能无意中达成多种未被预设的成功目标，这些隐含的成功目标构成了被忽视的潜在监督来源。为此，论文提出事后监督学习（Hindsight Supervised Learning, HSL），其核心在于利用辅助大语言模型对已完成的智能体轨迹进行分析，自动识别并重标注其中实际达成的自然语言目标，从而生成更丰富的监督信号。关键创新在于：通过将轨迹与其重标注的目标配对，用于后续的微调训练，并引入无关动作掩码与样本重加权两种技术以缓解重标注数据中的次优性问题。实验表明，HSL具有良好的兼容性和灵活性，可显著提升标准微调（SFT）与直接偏好优化（DPO）的效果，尤其在目标空间多样、任务周期较长的场景下优势更为明显；同时具备高样本效率，在ALFWorld任务中仅使用四分之一的真实示范数据即超越全量数据训练的基线模型。
链接: https://arxiv.org/abs/2607.04235
作者: Zichao Li,Gang Wu,Zichao Wang,Ruiyi Zhang,Wanrong Zhu,Ryan A. Rossi,Vlad I Morariu,Jihyung Kil
机构: Mila, McGill University (麦吉尔大学); Adobe Research(Adobe研究院)
类目: Computation and Language (cs.CL)
备注: Accepted to ICLR 2026
Abstract:Large language model agents operate in partially observable, long-horizon settings where obtaining supervision remains a major bottleneck. We address this by utilizing a source of supervision overlooked in existing post-training methods: unintended yet successful goals embedded within agent rollouts. Specifically, we introduce Hindsight Supervised Learning (HSL), where an auxiliary LLM reviews each completed trajectory and relabels it with all of the natural-language goals the agent actually achieved. HSL then pairs the trajectory with its relabeled goals and uses these pairs for additional fine-tuning. To mitigate suboptimality in the relabeled data, we propose two learning techniques for HSL, irrelevant-action masking and sample reweighting. Our experiments show that HSL is flexible and compatible with existing post-training pipelines. It improves both SFT and DPO, with larger gains on long-horizon tasks with more diverse goal spaces. Moreover, HSL is sample-efficient: on ALFWorld, it surpasses baselines trained on the full dataset while using only one quarter of the ground-truth demonstrations.
[NLP-67] aching Code LLM s to Reason with Intermediate Formal Specifications
【速读】： 该论文旨在解决现有生成式代码规范方法在生成可执行断言时存在的关键问题：传统基于大语言模型（LLM）的方法通常仅生成全程序级别的前置/后置条件，忽略了程序员在算法推理过程中依赖的中间语义承诺（intermediate semantic commitments），且生成的断言常存在语法错误、过于平凡或无法检测行为改变型缺陷等问题。其解决方案的核心在于提出一种验证引导的代码大模型训练框架——SpecCoder，通过利用经过验证的参考程序、行为改变型变异体以及多轮规范精炼轨迹进行训练，使模型能够生成在正确执行路径上成立但在错误执行路径上被拒绝的可执行断言。这种机制将断言从被动注释转变为可执行证据，显著提升了中间检查点规范的质量。为评估该方法，研究构建了HumanExec基准，涵盖来自Codeforces竞赛题目的测试套件、参考解法及人工引入的错误提交，支持规范生成、程序正确性验证与修复三项任务。实验表明，SpecCoder在多个Qwen2.5-Coder模型上显著优于基线模型，在内联规范正确性、完整性及可执行断言有效性方面分别提升最高达55.8%、358.1%和26.6%，并进一步增强了下游的正确性推理与修复能力，证明了可执行检查点在细粒度验证中的有效性。
链接: https://arxiv.org/abs/2607.04232
作者: Minh Le-Anh,Cuong Chi Le,Tien N. Nguyen
机构: 未知
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL)
备注:
Abstract:Unlike natural-language specifications, executable formal specifications provide machine-checkable constraints for verifying, debugging, and repairing code. However, writing such specifications is labor-intensive, and existing LLM-based methods mainly infer whole-program pre/postconditions, missing the intermediate semantic commitments that programmers rely on when reasoning about an algorithm. Our study further shows that prompting current CodeLLMs often produces executable assertions that are syntactically invalid, trivial, or too weak to reject behavior-changing faults. In this paper, we study executable checkpoint specification generation, where assertions are inserted at meaningful internal program points to describe expected intermediate states. We introduce SpecCoder, a verification-guided CodeLLM training framework that learns from validated reference programs, behavior-changing mutants, and multi-turn specification-refinement traces. SpecCoder selects specifications that hold on correct executions while rejecting faulty executions, turning specifications from passive annotations into executable evidence. To evaluate this setting, we introduce HumanExec, a benchmark built from recent Codeforces competitive programming problems with test suites, reference solutions, and human buggy submissions, supporting three tasks: specification generation, program correctness checking, and program repair. Experiments on HumanExec show that SpecCoder substantially improves checkpoint-specification quality over base CodeLLMs. Across Qwen2.5-Coder models, SpecCoder improves inline-specification correctness by up to 55.8%, completeness by up to 358.1%, and executable assertion validity by up to 26.6%. These gains further translate to downstream correctness reasoning and repair, showing that executable checkpoints provide fine-grained evidence for reliable verification.
[NLP-68] Detecting Hallucinations in Retrieval-Augmented Generation through Grounding-Aware Sensitivity by Perturbation (GASP)
【速读】： 该论文旨在解决检索增强生成（Retrieval-Augmented Generation, RAG）中幻觉（hallucination）检测的局限性问题，即现有方法仅提供单一的回答级别评分，无法定位具体哪一句存在幻觉或其原因。为此，论文提出了一种基于扰动的、具备上下文感知能力的细粒度检测方法——接地敏感性（Grounding-Aware Sensitivity by Perturbation, GASP），其核心在于通过量化每个答案句的概率对检索证据的依赖程度来评估其“接地性”（grounding sensitivity）。GASP在保持答案不变的前提下，分别在完整上下文、无上下文以及逐段移除检索片段的条件下重新评分，利用对数似然下降值和詹森-香农散度（Jensen-Shannon Divergence, JSD）衡量语句与证据间的依赖关系。基于随机非线性迭代函数系统（Random Nonlinear Iterated Function System, RNIFS）的理论框架，该方法揭示了：若某句依赖于支持性文本，则移除对应片段后其概率将显著下降；而幻觉句则基本不受影响，形成鲜明对比。实验在三个基准测试（RAGTruth、TofuEval、RAGBench）上验证了GASP的有效性，在RAGTruth上实现了约0.73的响应级受试者工作特征曲线下面积（AUC）和约0.67的跨度级AUC，显著优于困惑度、长度、全上下文自然语言推理（NLI）及自一致性等基线方法。唯一在跨度级别具有竞争力的是需额外训练的片段级蕴含验证器，而本文提出的无需训练的接地特征阈值策略无需标注数据即可达到与有监督分类器相当的效果，可作为默认检测器。此外，该方法的信号在跨任务迁移中表现良好，适用于由检索内容构建的答案，但在仅依赖模型参数知识的短答案问答任务中效果不佳，表明其更适用于上下文依赖型生成场景。
链接: https://arxiv.org/abs/2607.04223
作者: Mohamed Aly Bouke
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 23 pages, 9 figures, 15 tables
Abstract:Retrieval-augmented generation (RAG) reduces but does not eliminate hallucination, and existing detectors return a single answer-level score that does not indicate which sentence is unsupported, or why. To close this gap, we introduce Grounding-Aware Sensitivity by Perturbation (GASP), a span-level detector that scores each answer sentence by how strongly its likelihood depends on the retrieved evidence, a quantity we term grounding sensitivity. GASP holds the answer fixed and re-scores it under the full context, under no context, and with each chunk removed, then measures the log-likelihood drops and Jensen-Shannon divergences (JSD). The likelihood of a grounded sentence collapses once its supporting passage is removed, whereas a hallucinated sentence is almost unaffected, a contrast we interpret by casting decoding as a random nonlinear iterated function system (RNIFS). We evaluate GASP on three benchmarks (RAGTruth, TofuEval, RAGBench) with three instruction-tuned scorers from two model families (Qwen2.5-0.5B, Qwen2.5-1.5B, and SmolLM2-1.7B) under a leakage-clean protocol. On RAGTruth it reaches a response-level area under the ROC curve (AUC) of about 0.73 and a span-level AUC of about 0.67, improving significantly over perplexity and by clear margins over length, whole-context natural language inference (NLI), and self-consistency baselines. The only baseline competitive at the span level is a well-configured chunk-level entailment verifier, which requires a separate model, whereas a training-free threshold on the grounding features matches the trained classifier without labeled data and serves as the default detector. Beyond RAGTruth, the signal transfers to TofuEval but not to short-answer question answering in RAGBench, showing GASP is best suited to outputs constructed from the retrieved context rather than answers recoverable from parametric knowledge.
[NLP-69] !Imperio smolVLA: The Implications of Data Poisoning on Open Source Robotics
【速读】： 该论文旨在解决开放源代码机器人生态中因数据投毒（data poisoning）引发的安全威胁问题，具体聚焦于视觉-语言-动作模型（Vision-Language-Action models, VLAs）在训练阶段遭受隐蔽后门攻击的风险。其核心问题是：当前开源机器人系统普遍信任社区贡献的数据集，但未对数据来源和完整性进行严格验证，导致恶意样本可被植入并长期潜伏，从而在特定触发条件下引发严重功能失效。解决方案的关键在于揭示了“触发词数据投毒”（trigger-word data poisoning）的可行性与隐蔽性——仅需极少量（如三例）被污染的训练样本，即可在不显著影响正常行为的前提下，使模型在接收到特定触发词时完全瘫痪机器人，表现为任务成功率骤降至0%，且机器人锁定于固定关节姿态，丧失执行任务的能力。实验表明，该攻击在不同触发词位置（前、中、后）均具有泛化能力，且单个污染样本即显著降低性能至6.7%左右，证实其低代价、高隐蔽性和强破坏性。因此，该研究强调必须将数据溯源（dataset provenance）作为开放源代码机器人生态系统中的首要安全考量。
链接: https://arxiv.org/abs/2607.04146
作者: Stefan Bühler,Mark Schutera
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
备注: Accepted at KI2026. Repo: this https URL
Abstract:This work establishes that trigger-word data poisoning of vision language action models is practical, while at the same time the open-source robotics ecosystem holds trust assumptions about community contributions. A few poisoned samples can silently embed a backdoor that disables a robot on command. We evaluate this threat against smolVLA on a real-world pick-and-place task, training on three poison ratios and evaluating across different prompts on the LeRobot platform. Three poisoned episodes in 320 clean episodes suffice for a complete denial of service. Success rate drops to 0.0 plus minus 0.0% across all trigger-word conditions and the robot locks into a fixed joint configuration rather than executing any task-relevant motion. Clean-prompt behaviour holds at approx. 50% success rate across all poison ratios, confirming the attack is stealthy under normal operation. A single poisoned episode already reduces success rate to 6.7 plus minus 6.7%. The robot still moves, but no longer completes the task. The attack generalises to front, middle, and end trigger placements despite training exclusively on front-placed triggers. These findings establish that the threat is practical, low-cost, and stealthy, and warrant treating dataset provenance as a first-class concern in open-source robotics ecosystems.
[NLP-70] DynaVieW: Schema-Guided World Modeling for Understanding Hierarchical Visual Dynamics ICML2026
【速读】： 该论文旨在解决多模态大模型（Multimodal LLMs）在处理视频或图像序列时，难以系统建模视觉场景随时间演化的动态过程这一关键问题。现有方法在预测或模拟视觉序列中的动作行为及环境变化方面存在不足，尤其在捕捉多层次动态构成要素（如动作、物体状态变化、场景交互等）时缺乏有效机制。为此，论文提出一种基于动态模式引导的世界模型——DynaVieW，其核心在于通过学习交错的状态-转移序列来实现对视觉动态的深度理解：其中状态表征来自视频关键帧的广泛视觉场景，而转移则在分层模式（hierarchical schema）框架下捕获全面的动态成分。DynaVieW采用专家混合（mixture-of-experts）架构，联合建模转移预测与状态模拟，并引入跨专家选择性注意力机制和模式令牌重加权损失函数，以保障学习过程的有效性与鲁棒性。该模型在视觉叙事生成与世界模拟任务中表现出更强的一致性、可控性及指令遵循能力，显著提升了下游应用性能。
链接: https://arxiv.org/abs/2607.04112
作者: Silin Gao,Hao Zhao,Zeming Chen,Sepideh Mamooler,Antara Raaghavi Bhattacharya,Qiyu Wu,Hiromi Wakaki,Yuki Mitsufuji,Li Mi,Syrielle Montariol,Antoine Bosselut
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: ICML 2026
Abstract:Multimodal LLMs struggle to systematically model the temporal evolution of visual scenes in videos or multi-image sequences. Such inputs require models to predict or simulate multiple levels of dynamic constituents, such as actions taken in the visual sequence, and the associated changes to the visual environment that result. To address this challenge, we propose a dynamic schema-guided world model, DynaVieW, optimized for visual dynamic prediction and simulation. DynaVieW achieves an in-depth understanding of visual dynamics by learning interleaved state-transition sequences, where states cover broad visual scenes from video keyframes, and transitions capture comprehensive dynamic constituents within a hierarchical schema. DynaVieW jointly models transition prediction and state simulation under a mixture-of-experts architecture, with a cross-expert selective attention and a schema token re-weighted loss, to ensure effective and robust learning. DynaVieW’s understanding of visual dynamics boosts its downstream performance in visual narrative creation and world simulation, showing improved consistency, controllability, and instruction-following.
[NLP-71] Semantic Integration and Lexical Expectation Shape N400 and P600 Dynamics During Naturalistic Reading
【速读】： 该论文旨在解决自然阅读情境下，局部语义整合（local semantic integration）是否在词锁定的脑电活动（EEG）中独立于词汇预期（lexical expectation）解释神经响应变异的问题。其核心挑战在于区分传统基于语言模型的词意外度（word surprisal）与更贴近语篇连贯性的语义契合度（contextual semantic relevance）对神经反应的贡献。解决方案的关键在于引入一种注意力感知的语义契合度计算方法，该方法量化目标词与其近期语篇上下文之间的语义关联强度，并将其与基于GPT的词意外度进行对比分析。研究基于都柏林基于脑电的阅读实验语料库（DERCo），采用回归型事件相关电位（ERP）分析与广义加性混合模型，在控制词汇变量和重复观测的前提下，系统比较两类预测因子。结果表明，尽管两者均显著关联脑电反应，但语义契合度在N400与P600窗口均表现出稳健效应，尤其在P600窗口具有更强的解释力，且模型比较显示其提供了超越词汇控制与词意外度的额外解释价值。这表明自然阅读不仅依赖词汇预期，还高度依赖局部语义整合过程，而语义契合度作为可解释的计算指标，有效连接了语篇语义连贯性与事件相关电位动态变化。
链接: https://arxiv.org/abs/2607.04107
作者: Kun Sun,Rong Wang
机构: Tongji University (同济大学); University of Tuebingen (图宾根大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Word surprisal is a well-established computational predictor of human neural responses during language comprehension, but it remains less clear whether local semantic fit explains neural response variation beyond lexical expectation during naturalistic reading. Using the Dublin EEG-based Reading Experiment Corpus (DERCo), this study examined whether contextual semantic relevance predicts word-locked EEG activity in the N400 and P600 windows. Contextual semantic relevance was computed as an attention-aware measure of how strongly a target word is semantically connected to its recent discourse context, and it was compared with GPT-based word surprisal. Across 22 participants and 32 EEG channels, we tested both predictors using regression-based ERP analyses and generalized additive mixed models while controlling for lexical variables and repeated observations. Both predictors were reliably associated with EEG responses, but they showed partly different temporal and scalp-level patterns. Surprisal captured expectancy-related variation, whereas contextual semantic relevance showed robust effects across N400- and P600-window mean voltages, with particularly strong explanatory support in the P600 window. Model comparisons indicated that contextual semantic relevance contributed explanatory value beyond lexical controls and surprisal. These findings suggest that naturalistic reading depends on both lexical expectation and local semantic integration, and that contextual semantic relevance offers an interpretable computational link between discourse semantic fit and ERP dynamics.
[NLP-72] Beyond Multilingual Averag es: MTEB-PT a Benchmark for Portuguese Sentence Encoders
【速读】： 该论文旨在解决葡萄牙语在文本嵌入（text embedding）评估中长期被忽视的问题，即尽管葡萄牙语是全球使用最广泛的语言之一，但现有嵌入模型的性能评估主要依赖英语或多语言指标，导致其在葡萄牙语上的实际表现缺乏系统性验证。解决方案的关键在于构建并发布MTEB-PT——一个基于MMTEB子集的葡萄牙语专用基准测试，涵盖14个来自语义文本相似度（STS）、分类、检索和重排序任务的数据集，并采用统一评估协议对17个开源与闭源嵌入模型进行评测。研究发现，葡萄牙语的表现具有显著的任务依赖性：多语言排名无法可靠预测特定任务下的性能，且无单一模型在所有场景中占优；尤其在长输入任务（如检索与重排序）中，具备更强长上下文建模能力的模型表现更优。此外，通过在葡萄牙语数据上进行对比学习与马特里什卡表示学习（Matryoshka Representation Learning, MRL）微调，验证了语言特定微调对性能提升的有效性，尤其是在与训练数据分布匹配的任务类型上。该工作释放了完整的基准数据集、微调模型及训练评估代码，为后续葡萄牙语嵌入研究提供了可复现的基础。
链接: https://arxiv.org/abs/2607.04071
作者: Lucas Hideki Takeuchi Okamura,Alexandre Alcoforado,Anna Helena Reali Costa
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at BRACIS 2026 - 36th Brazilian Conference on Intelligent Systems
Abstract:Portuguese remains underrepresented in text embedding evaluation, despite being one of the most widely spoken languages in the world. As a result, embedding models are often selected based on English or multilingual metrics, while their effectiveness in Portuguese remains unclear. We present MTEB-PT, a Portuguese benchmark constructed from a subset of MMTEB, comprising 14 existing datasets across Semantic Textual Similarity (STS), classification, retrieval, and reranking. We use this benchmark to evaluate 17 open- and closed-source embedding models under a unified protocol. Our results show that Portuguese performance is strongly task-dependent: multilingual rankings do not reliably predict Portuguese-specific performance across task families, no single model dominates all settings, and models with stronger long-context capacity are particularly advantageous on longer-input tasks such as retrieval and reranking. The benchmark also shows that language-specific fine-tuning still improves model performance in Portuguese, especially on task types that match the adaptation data most closely. To examine this effect, we fine-tune three representative backbone models with Portuguese contrastive supervision and Matryoshka Representation Learning (MRL). These benchmark-informed baselines yield their strongest gains on STS, consistent with the predominantly symmetric supervision used during training, while also improving retrieval and remaining competitive under dimensional truncation. We release the MTEB-PT benchmark, the fine-tuned models, and the training and evaluation code.
[NLP-73] Speaker-Disentangled Chunk-Wise Regression for Syllabic Tokenization
【速读】： 该论文旨在解决无监督音节分词（unsupervised syllabic tokenization）中因教师-学生蒸馏训练时采用话语级交叉熵目标函数而导致模型偏向预测说话人身份而非语言内容的问题，从而损害音节标记的语义纯度。其解决方案的关键在于提出一种说话人解耦的音节分词器（speaker-disentangled syllabic tokenizer），通过在固定长度片段内将受说话人影响的学生端表示回归至纯净的教师端目标，实现对说话人变异的分离与抑制。该方法有效提升了音节边界检测与音节段聚类的性能，并在基于音节标记训练的语音语言模型中，相较于基于音素的SpiRit-LM，在句法与语义理解任务上实现了7%的相对性能提升。
链接: https://arxiv.org/abs/2607.04064
作者: Ryota Komatsu,Kota Kawakita,Takuma Okamoto,Takahiro Shinozaki
机构: Institute of Science Tokyo (东京科学研究所); National Institute of Information and Communications Technology (信息通信技术国家研究所)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注: Accepted by IEEE Open Journal of Signal Processing (OJSP), 10 pages, 4 figures
Abstract:Unsupervised syllabic tokenization aims to learn discrete syllabic tokens that capture latent linguistic content-related structure from raw speech. Recent syllabic tokenization methods employ teacher-student distillation of the pretrained HuBERT to organize latent speech frame representations into syllabic segments. However, when trained with an utterance-level cross-entropy objective, the model predicts speaker identity rather than linguistic content, thereby compromising the purity of syllabic tokens. To address this problem, we propose a speaker-disentangled syllabic tokenizer that regresses speaker-perturbed student representations toward clean teacher targets within fixed-length chunks. Experimental results demonstrate that our proposed method achieves state-of-the-art performance in syllable boundary detection and syllabic segment clustering. Moreover, a speech language model trained on our syllabic tokens achieves a 7% relative improvement in syntactic and semantic understanding over the phone-level SpiRit-LM.
[NLP-74] scope: Improving Zero Shot Detection of LLM Generated Content By Measuring Token Repetition Probability ICML
【速读】： 该论文旨在解决生成式 AI（Generative AI）文本与人类写作之间难以区分的难题。其核心问题在于，尽管大语言模型（Large Language Model, LLM）在训练中被优化以模仿人类写作风格，但其内在的语言生成机制仍残留可被识别的“发育性痕迹”（Vestigial Heuristic）。研究提出的关键解决方案是引入“望远镜困惑度”（Telescope Perplexity）这一新指标，用于量化模型在生成过程中对词元重复（token repetition）的抑制倾向。该指标基于条件概率 $ P(s_i | s_1:i) $，捕捉模型在上下文中的重复敏感性。实证研究表明，望远镜困惑度在预训练早期即显现显著特征，并可在无需微调的情况下实现高效的零样本（zero-shot）LLM检测，在多种数据集、基准模型及扰动策略下均表现出优于或相当现有方法的性能，且计算效率更高。
链接: https://arxiv.org/abs/2607.04061
作者: Christopher Nassif,Josh F. Cooper
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML)
备注: 50 pages, ICML, 20 figures, Equal contribution
Abstract:Distinguishing Large Language Model (LLM) generated text from human writing is a critical and difficult challenge. While LLMs are trained to write like humans, we hypothesize that this training leaves an indelible mark. LLMs develop a particularly strong aversion to token repetition very early in training. This bias persists as a ‘‘Vestigial Heuristic’’ (a developmental artifact) that is activated in LLM-generated text, separating LLM from human writing. To probe this phenomenon, we introduce Telescope Perplexity, a metric that evaluates the token repetition of the model, P(s_i | s_1:i) . Our empirical investigation reveals that the Telescope Perplexity signature emerges early in pre-training, and Telescope Perplexity empirically enables highly effective zero-shot LLM detection. We show state-of-the-art or competitive performance across diverse datasets (including modern evaluation sets we introduce), reference models, and perturbation schemes with greater efficiency than other methods.
[NLP-75] CrossHallu: Do Hallucination Signals Generalize Across Languages and Domains in Large Language Models Internals?
【速读】： 该论文旨在解决大语言模型（LLM）中幻觉检测技术在跨语言和跨领域场景下的泛化能力问题。尽管现有基于模型内部表示特征的幻觉检测方法在英语语境下表现良好，但其是否能在不同语言和领域间有效迁移仍不明确。为此，本文提出CrossHallu，首次系统评估了六种主流大语言模型在生成式问答任务中利用内部表示进行幻觉检测的跨语言与跨领域泛化性能。研究通过在阿拉伯语与英语之间、以及不同数据集间的多维度实验（包括单语言训练测试、跨语言迁移、跨领域迁移及联合迁移），结合TruthfulQA及其阿拉伯语翻译版本与HalluScore指标进行验证。结果表明，大多数模型的内部幻觉信号具备一定的跨语言与跨领域可迁移性，其中跨语言性能高度依赖于特征空间中的类别可分性和语言对齐程度，而阿拉伯语内部的跨领域迁移效果则受训练与测试数据集分布的影响较大。本研究的关键在于揭示了内部表示在幻觉检测中的跨语言与跨领域泛化潜力，并为未来多语言幻觉检测模型的设计提供了实证依据。
链接: https://arxiv.org/abs/2607.04029
作者: Aisha Alansari,Malak Alkhorasani,Hamzah Luqman
机构: King Fahd University of Petroleum and Minerals; Imam Abdulrahman bin Faisal University
类目: Computation and Language (cs.CL)
备注:
Abstract:Recent hallucination detection techniques in large language models (LLMs) focus on directly extracting features from a model’s internal representations and training a classifier on these features to detect hallucinations, demonstrating promising results. Notwithstanding this advancement, most internal-state hallucination detection techniques have been explored predominantly in English, raising the question of whether such internal signals generalize across different languages and domains. To address this gap, we present CrossHallu, the first study to evaluate the cross-lingual and cross-domain generalization of hallucination detection using internal representations from six LLMs on the generative question-answering task. We conduct a systematic Arabic - English evaluation using TruthfulQA, an Arabic translated version of TruthfulQA, and HalluScore. This evaluation encompasses monolingual training and testing, cross-lingual transfer, cross-domain transfer, and combined cross-lingual and cross-domain transfer. The results reveal that internal-state hallucination signals in LLMs transfer across languages and domains for most models, with cross-lingual performance highly dependent on both class separability and language alignment in the feature space, whereas cross-domain transfer within Arabic varies depending on the training and testing datasets used for the hallucination detector. The code is publicly available at this https URL.
[NLP-76] Separating Representation from Reconstruction Enables Scalable Text Encoders
【速读】： 该论文旨在解决预训练编码器（如BERT）在大规模扩展后，其表示能力未能有效提升的问题。尽管解码器模型持续快速演进，但编码器架构自BERT以来基本保持不变，导致其表征质量难以随训练规模提升而同步优化。核心问题在于BERT采用的扁平式结构将表示学习与词元重建损失紧密耦合，使得即使在困惑度（perplexity）改善的情况下，冻结的探针仍难以有效利用其内部表示，即表征“未被充分挖掘”（unexploitable）。为解决此问题，论文提出CrossBERT，其关键创新在于采用双组件架构，将高质量表征学习与严格的词元重建任务解耦，从而摆脱原有设计对表征表达的约束。进一步地，通过引入互补掩码策略（Complementary Masking Strategy），实现了≥50%的高掩码率，并可在所有词元上进行梯度传播，显著提升了训练吞吐量（1.5–2倍）和样本效率（2倍）。实验表明，CrossBERT在MTEB(eng, v2)和冻结式GLUE基准上均展现出单调可扩展性与更优性能。
链接: https://arxiv.org/abs/2607.04011
作者: Megi Dervishi,Mathurin Videau,Yann LeCun
机构: Megi Dervishi; Mathurin Videau; Yann LeCun
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:While decoders have rapidly scaled, encoders have remained largely unchanged since BERT. We revisit this disparity by frozen backbone evaluation via probing. Under this lens, the representations of BERT encoders become increasingly \textitunexploitable by frozen probes, despite improved perplexity. The misalignment originates in BERT’s flat design, which couples representation learning to the token reconstruction loss. We propose \textbfCrossBERT , a two-part architecture that separates the learning of high-quality encoded representations from the rigid grounding of token reconstruction. This design further enables high masking ratios ( \ge 50% ) and gradient collection over all tokens via a \textitComplementary Masking Strategy , respectively increasing throughput by 1.5 to 2\times and sample efficiency by 2\times . Overall, CrossBERT demonstrates monotonic scaling and superior performance on MTEB(eng, v2) and frozen GLUE benchmarks.
[NLP-77] Explainable AI for Screening Abuse-Related Trauma in Bangladeshi Children: A Training-Free Multimodal Framework Evaluated on Noise-Aware Synthetic Data
【速读】： 该论文旨在解决在资源匮乏的低收入国家（如孟加拉国）中，儿童虐待相关心理创伤缺乏本土化、语言适配且可广泛部署的早期筛查工具这一关键问题。当前孟加拉国每10万人口仅有1.17名心理健康专业人员，全国仅6名儿童精神科医生，且尚无以孟加拉语编写、经过文化适应的儿童心理创伤筛查工具。为应对这一挑战，研究提出ShishuRaksha AI——一种非诊断性但具临床支持功能的决策辅助框架，其核心解决方案在于采用无需训练的、临床加权的多模态融合机制，整合四种筛查模态：经验证的量表（SDQ、CPSS）、孟加拉语叙事文本、房屋-树-人（HTP）绘画特征及面部情绪分析。该框架通过跨模态注意力机制实现信息融合，并引入单模态优先规则以保障临床可靠性。所有风险评分均通过临床加权的扰动基归因法进行可解释性解释，并生成双语（孟加拉语/英语）报告，自动引导至国家儿童保护机构（如OCC、DSS、NMHH）。由于伦理限制无法获取真实临床数据，研究构建了一个含500例合成样本（其中116例为阳性，占比23.2%）、包含四层人为噪声和基于文献的HTP先验知识的噪声感知合成基准数据集，并在五折分层交叉验证下评估了融合设计的树集成代理模型（排除面部通道）。结果显示，融合模型的AUC达0.874（95%CI: 0.834–0.908），显著优于仅使用SDQ的基线模型（AUC=0.756, 95%CI: 0.705–0.803），并通过消融实验、操作点分析、子群分析与校准检验验证了其有效性。研究明确指出其局限性，包括仅依赖合成数据、无独立测试集、文本特征存在循环性以及城乡子群差异等。本工作属于可行性探索与系统设计贡献，致力于推动可在伦理合规前提下应用于低资源环境的儿童保护筛查技术发展。
链接: https://arxiv.org/abs/2607.04010
作者: Salma Hoque Talukdar Koli,Fahima Haque Talukder Jely
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注: 6 pages, 5 figures
Abstract:Bangladesh has an estimated 1.17 mental-health professionals per 100,000 population and only six child psychiatrists nationwide. No Bengali-language, culturally adapted tool exists for early screening of abuse-related psychological trauma in children. We present ShishuRaksha AI, a decision-support (not diagnostic) framework that fuses four screening modalities: validated questionnaires (SDQ, CPSS), Bengali narrative text, House-Tree-Person (HTP) drawing features, and facial affect. The fusion is training-free and clinically weighted, uses cross-modal attention, and includes a single-modality override rule. Every risk score is explained through clinically weighted, perturbation-based additive attribution and rendered as a bilingual (Bangla/English) report with referral routing to national child-protection services (OCC, DSS, NMHH) under the Children Act 2013. No clinical dataset of abused children can be collected ethically at this stage, so we introduce a noise-aware synthetic benchmark (500 cases, 116 positive [23.2%], four deliberate noise layers, literature-grounded HTP priors) and evaluate tree-ensemble surrogates of the fusion design (facial channel excluded) under 5-fold stratified cross-validation. The fused model reaches an AUC of 0.874 [0.834-0.908], against 0.756 [0.705-0.803] for an SDQ-only baseline, with ablation, operating-point, subgroup, and calibration analyses. We state all limitations openly, including synthetic-only data, no held-out set, text-feature circularity, and an urban-rural subgroup gap. This work is a feasibility study and a design contribution toward ethically deployable child-protection screening in low-resource settings.
[NLP-78] Knowing When to Stop: Predicting Execution-Consistency Convergence in Text-to-SQL
【速读】： 该论文旨在解决在文本转SQL（Text-to-SQL）任务中如何有效判断生成结果可信度的不确定性问题，核心挑战在于确定重复调用大语言模型（LLM）进行一致性验证时的最优停止时机——即何时可认为结果一致性已收敛。传统方法依赖固定次数的重复运行，缺乏自适应性。本文将该问题建模为一个收敛预测任务，提出一套轻量级的一维序列模型，能够实时观察并分析一致性轨迹的变化趋势，并在每一步决策是否继续执行后续调用：若进一步运行不太可能显著改变当前一致性状态，则提前终止。该方法在BIRD基准及两个真实生产数据集上表现优异，可根据不同查询的收敛速度动态调整停止点，实现早期收敛时快速停止、延迟收敛时持续评估。此外，研究发现各次运行间存在弱序列相关性，因此引入可调节洗牌权重对运行顺序进行随机化增强，作为训练数据扩充手段，显著提升了模型鲁棒性；即使在模拟不完美人工评判者（通过向正确/错误判定结果注入噪声）的情况下，该方法仍能可靠预测收敛点，展现出良好的泛化能力与实际应用价值。
链接: https://arxiv.org/abs/2607.03991
作者: Yaron Anavi,Mor Aisenberg,Nadav Nesher,Elena Khabibullina,Isabella Cattinelli
机构: GIGASPACES
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 11 pages, 3 figures
Abstract:Repeated LLM calls are the standard way to estimate how trustworthy a Text-to-SQL result is: run the pipeline multiple times, judge each SQL execution, and use the consistency of the verdicts as a confidence signal. The open question is when to stop, when the consistency has converged. We formulate this as a convergence-prediction problem and train a family of lightweight 1-D models that observe the running consistency trajectory and decide, at each step, whether further runs are unlikely to shift it materially, and we benchmark them against a principled Beta-Bernoulli stopping rule and a learned run-count baseline. On the BIRD benchmark and two production customer datasets, our method adapts its stopping point to each user question, halting sooner when consistency converges early and continuing longer when it converges late. We further show that the weak serial correlation between runs lets us permute their order as a training augmentation, controlled by a tunable shuffling weight. Performance stays consistent across the three datasets, and to mimic an imperfect production judge we inject noise into the correct/incorrect verdicts obtained by comparing the generated and ground-truth SQL results, showing that the method still predicts convergence reliably.
[NLP-79] BanglaMemeEvidence: A Multimodal Benchmark Dataset for Explanatory Evidence Detection in Bengali Memes
【速读】： 该论文旨在解决在低资源语言（如孟加拉语）中对网络迷因（Meme）进行深层次语境理解的难题，尤其针对有害内容检测、网络欺凌识别及情感分析等任务中存在的准确性不足问题。其核心挑战在于如何有效融合视觉与文本信息以实现对迷因意义和幽默机制的精准解析。解决方案的关键是提出一种名为BengaliMemeEvidenceNet的混合多模态框架，通过整合图像与文本特征，实现对迷因及其上下文信息的全面表征；同时构建了首个面向孟加拉语迷因的标注数据集BanglaMemeEvidence，包含2,917个带自然语言解释的迷因样本，涵盖迷因OCR、上下文描述及证据句，并附有相关性评分。实验表明，该框架在证据句检测任务上达到0.74的F1分数，为低资源语言下迷因理解提供了可复现的技术路径，填补了该领域研究空白。
链接: https://arxiv.org/abs/2607.03981
作者: Fatema Tuj Johora Faria,Mukaffi Bin Moin,Md. Mahfuzur Rahman,Pronay Debnath,Asif Iftekher Fahim,Faisal Muhammad Shah
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at 6th International Conference on Innovations in Computational Intelligence and Computer Vision (ICICV 2026)
Abstract:Memes have become influential communication tools on social media, combining viral visuals with concise messaging to convey impactful ideas. While substantial research has examined the affective dimensions of memes, key challenges such as detecting harmful content, identifying cyberbullying, and performing accurate sentiment analysis remain critical, largely due to the need for deeper contextual understanding. In this paper, we introduce MemeEvidenceDetect, a hybrid task aimed at analyzing a meme and its contextual information to identify specific sentences that explain or elucidate its meaning and humor. To support this task, we present BanglaMemeEvidence, a curated dataset of 2,917 Bengali memes, emphasizing its significance as a resource for the Bangla language. Each meme is annotated with natural language explanations, including Meme OCR, Meme Context, and Evidence Sentences, alongside relevance scores that reflect the relationship between a meme and its corresponding annotations. To address the gap in dynamically inferring a meme’s context, we propose BengaliMemeEvidenceNet, a hybrid multimodal framework that integrates textual and visual features for comprehensive meme representation. Our experiments demonstrate the effectiveness of BengaliMemeEvidenceNet, achieving an F1 score of 0.74. To the best of our knowledge, this is the first study to focus on evidence detection in Bengali memes, marking a notable step forward in the analysis of memes in low-resource languages.
[NLP-80] RACER: Early Failure Detection for Task-Oriented Dialogue SIGDIAL2026
【速读】： 该论文旨在解决任务导向型对话系统在对话过程中提前出现失败迹象但传统评估方法仅在对话完全失败后才进行衡量的问题。其核心挑战在于如何在对话尚未完全崩溃前，尽早识别出潜在的失败风险。解决方案的关键在于提出TRACER方法，通过融合信念状态（belief-state）变化的简单轨迹信号与对话状态演变的文本表示，实现对完整对话未来是否失败的早期预测。该方法在不同设定下均表现出优异性能，即使仅可见25%至100%的对话内容，仍能有效捕捉失败信号，显著优于基于启发式、经典模型及单流架构的基线方法。结果表明，早期失败检测可为对话系统提供实用的预警机制，从而在交互彻底失效前采取干预措施。
链接: https://arxiv.org/abs/2607.03974
作者: Erfan Nourbakhsh,Rocky Slavin,Ke Yang,Anthony Rios
机构: The University of Texas at San Antonio (德州大学圣安东尼奥分校)
类目: Computation and Language (cs.CL)
备注: Accepted to SigDial 2026
Abstract:Task-oriented dialogue systems often fail before the final breakdown is obvious, but most evaluation only measures failure after the conversation has already gone wrong. We present TRACER, a method for early failure detection in task-oriented dialogue. TRACER predicts from a partial dialogue whether the full conversation will eventually fail by combining simple trajectory signals from belief-state changes with text representations of the evolving dialogue state. We evaluate the method in both oracle and generated belief-state settings, and test how well it works when only 25%, 50%, 75%, or 100% of the dialogue is visible. Across these settings, TRACER detects useful failure signals well before the end of the conversation and outperforms heuristic, classical, and single-stream baselines. These results suggest that early failure detection can provide a practical warning signal for dialogue systems before the interaction fully breaks down.
[NLP-81] NormWorlds-CF: Solver-Verified Counterfactual Normative Reasoning with Metamorphic-Relation GRPO
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在规范性推理中“正确答案但错误理由”的问题，即模型可能通过不合理的逻辑路径得出看似正确的结论。其核心解决方案是提出一个可验证的反事实规范性推理环境——NormWorlds-CF，该环境基于可执行规则世界，具备确定性求解器，能够生成最终答案、证明过程、证伪证书、论点状态、支持集以及成对世界变化标签，从而实现无需依赖LLM裁判的监督与评估。关键创新在于引入一种基于元变换关系的强化学习优化方法（Metamorphic-Relation GRPO, MR-GRPO），该方法为基于通用奖励策略的强化学习（GRPO）设计了类别条件奖励机制，可在部分满足关系家族结构时给予部分奖励，并显式关注求解器可见的变化字段。实验表明，在1.7B和4B规模模型上的对比测试中，MR-GRPO显著提升了未见关系的准确率、关系族正确性，并降低了错误关系族误判率，展现出在答案变化、支持集变化、状态变化及软根级元变换关系等多维度上的均衡性能优势。研究结果表明，经过验证的反事实结构信息可有效指导后训练阶段的模型行为，超越仅关注最终答案的优化目标，而完整变化记录生成、不变子类型识别以及分布外（OOD）泛化能力仍为开放挑战。
链接: https://arxiv.org/abs/2607.03957
作者: Xinqi Zhang
机构: Tsinghua University (清华大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Language models can reach the right normative verdict for the wrong reason. We introduce NormWorlds-CF, a solver-verified environment for counterfactual normative reasoning in executable rule worlds. Its deterministic solver produces final answers, proof and falsification certificates, argument statuses, support sets, and paired-world change labels, enabling supervision and evaluation without LLM judges. The benchmark contains staged SFT diagnostics and a compact paired-world task with 270 root families and 1080 canonical-to-variant pairs. The SFT diagnostics show that final-answer supervision is an unsafe proxy: answer-only SFT reaches perfect accuracy on answer tasks but scores zero on falsification, while proof-plus-falsification training with targeted replay reaches strong all-task accuracy. For the structured-change task, we introduce metamorphic-relation GRPO (MR-GRPO), a class-conditioned reward for GRPO that gives partial credit for relation families and solver-visible change fields. In matched 1.7B continuation experiments, MR-GRPO improves held-out relation accuracy and relation-family correctness, and reduces wrong-family error, compared to sparse and answer-only GRPO. In Qwen3-4B three-seed validation, answer-only reward improves answer-change fields but weakens relation-family structure, sparse reward preserves coarse relation labels best, and MR-GRPO delivers the strongest balanced performance across answer-change, support-change, status-change, and soft root-level metamorphic-relation metrics. These results show that verified counterfactual structure can shape post-training beyond final answers, while exact full change-record generation, invariant subtype recognition, and out-of-distribution (OOD) transfer remain open problems.
[NLP-82] he Remarkable Effectiveness of Providing AI Agents with Natural Language Tools: A Replication Study Validating NLT Performance Across 14 Models
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）代理系统中结构化工具调用（structured tool calling）存在的可靠性与效率问题。当前方法在复杂递归代理工作流中易因解析失败引发连锁错误，导致重试、故障转移和协调开销显著增加，影响系统稳定性与资源利用率。本文提出的自然语言工具框架（Natural Language Tools, NLT）通过将工具调用从严格格式化指令转为基于自然语言的自由表达，从根本上提升了工具调用的鲁棒性。其解决方案的关键在于：利用自然语言描述替代结构化格式，降低对模型语法解析能力的依赖，从而大幅减少关键错误（93%减少），提升准确率（平均提高14.9个百分点），并降低25.2%的令牌消耗。尤其在不具备原生工具调用能力、推理能力较弱或参数规模较小的模型上，优势更为显著；而经过强化学习高度优化的前沿模型（如GPT-5、Gemini 2.5 Pro）则表现出较小甚至逆向的优势，这与近期关于强化学习优化工具使用的研究结论一致。此外，该方法在递归代理任务中展现出显著的累积效益，因其从源头避免了结构化调用失败所引发的系统级开销。本研究贡献包括首次基于开源工具对NLT进行独立验证、揭示模型能力对NLT收益的调节作用，以及量化其可靠性优势（93%错误减少），表明NLT是生产环境中更注重可靠性而非可解析性的理想替代方案。
链接: https://arxiv.org/abs/2607.03953
作者: Alexander Somma,Isabelle Plante,Fred Premji
机构: Sage.is AI-UI
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 28 pages, 6 figures, replication study, replication of arXiv:2510.14453 findings
Abstract:This study independently replicates and extends the Natural Language Tools (NLT) framework of Johnson et al.~(2025), which questions the use of structured tool calling in large language model (LLM) agentic systems. We evaluated NLT across 14 models and 8,560 trials, adding newer frontier, reasoning, and open-weight models to the original set. The results confirm the core findings and add detail. NLT improves tool-calling accuracy by 14.9 percentage points overall (62.3% versus 47.4% structured) and reduces critical errors by 93% (51 versus 755 errors). The gains depend on model capability: models without native tool calling, reasoning models, and smaller models gain substantially (+24.0pp to +43.1pp), while heavily optimized frontier models (GPT-5, Gemini 2.5 Pro) show smaller or reversed advantages. This matches recent analyses of reinforcement-learning-optimized tool use (Martinez, 2025). NLT also cuts token usage by 25.2%. The reliability and efficiency advantages compound in recursive agentic workflows, where agents chain many tool calls across sub-agents: a structured failure triggers retries, fallback routing, and coordination overhead, while NLT avoids most of that cost at the source. This work makes three contributions: (1) the first independent validation of NLT using open-source tooling, (2) evidence that model capability moderates NLT’s advantages (Chen et al., 2025; Zhang et al., 2025), and (3) a measurement of NLT’s reliability benefit (93% fewer errors), its most deployment-relevant property given the known fragility of structured tool calling. NLT is a practical alternative to structured tool calling, especially for production systems that value reliability over parseability.
[NLP-83] Can Dialects Be Steered Like Languages? Sparse Neurons and Distributed Directions in Arabic LLM s
【速读】： 该论文旨在解决阿拉伯语自然语言处理中方言数据稀缺导致大语言模型（LLM）过度生成现代标准阿拉伯语（MSA）且难以实现准确方言生成的问题。其核心挑战在于如何在不进行微调的前提下，有效控制模型输出的方言属性。解决方案的关键在于提出两种互补的推理时（inference-time）方法：一是基于神经元层面的分析，识别出编码特定方言特征的稀疏神经元群，并通过放大或抑制这些神经元来引导模型生成目标方言；二是基于单个神经元层面方言特征纠缠现象，采用向量定向（vector-steering）方法提取方言特异性激活方向，并在推理过程中注入该方向以实现对输出方言的精准调控。这两种方法不仅作为可解释性探针揭示了阿拉伯语大模型内部方言知识的几何结构，更构建了一个无需方言微调、基于可解释性的方言控制框架，为高效、可控的多方言生成提供了新范式。
链接: https://arxiv.org/abs/2607.03936
作者: Kareem Elozeiri,Mervat Abassy,Omar Kallas,Fahim Dalvi,Preslav Nakov,Kentaro Inui,Nadir Durrani
机构: Mohamed bin Zayed University of Artificial Intelligence (MBZUAI); Qatar Computing Research Institute, Hamad Bin Khalifa University; Tohoku University; RIKEN
类目: Computation and Language (cs.CL)
备注:
Abstract:A key challenge in Arabic NLP is the scarcity of dialectal data relative to Modern Standard Arabic (MSA), causing LLMs to overproduce MSA and struggle with dialectally accurate generation. From an interpretability perspective, this raises a fundamental question: where and how are dialectal features encoded within model internals, and can these representations be leveraged to improve dialect generation without fine-tuning? This study investigates two complementary inference-time approaches that serve simultaneously as interpretability probes and control mechanisms. First, we conduct a neuron-level analysis, identifying sparse neuron populations that encode dialect-specific features and showing that amplifying or suppressing these neurons can steer model outputs toward target dialects. Second, motivated by the entanglement of dialectal features at the single-neuron level, we apply a vector-steering approach that extracts dialect-specific activation directions and injects them during inference. Together, these methods illuminate the geometry of dialectal knowledge in Arabic LLMs and offer a principled, interpretability-grounded framework for dialect control without requiring dialect-specific fine-tuning.
[NLP-84] Probe Dont Prompt: A Hidden-State Probe for Metadata Filtering in Multi-Meta-RAG
【速读】： 该论文旨在解决多跳问答（multi-hop question answering）中向量数据库检索效率与准确性不足的问题，核心挑战在于如何精准地根据查询内容从海量文档中筛选出相关来源（metadata），以提升检索质量。其解决方案的关键在于用一个本地化、确定性的元数据提取探针（probe）替代原先依赖大模型（如GPT-3.5-turbo）进行自由格式提示（prompting）的非确定性提取方式。该探针基于小型开源语言模型的隐藏状态，通过选择浅层表示、均值池化及考虑类别不平衡的多标签训练策略，在49个固定新闻源的词汇空间内实现精确过滤，避免了大模型因输出漂移导致的越界风险。实验表明，该探针在2556个MultiHop-RAG测试查询上达到90.9%的集合精确率（set-exact accuracy），显著优于无模型子串基线（88.0%）和GPT-3.5（80.9%），且性能优势主要来源于对“空查询”（null queries）的有效处理——后者始终不放弃预测，而探针可正确拒绝无效查询。此外，仅需135M参数的小型模型即可逼近15亿参数模型的性能，计算开销极低，仅需前几层的局部前向传播加单个线性头，无需调用API，具备高效、可控与可部署的优势。
链接: https://arxiv.org/abs/2607.03929
作者: Mykhailo Poliakov,Nadiya Shvai
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Multi-Meta-RAG improves retrieval for multi-hop question answering by filtering a vector store on metadata (the news source) that it extracts from each query by prompting gpt-3.5-turbo. We show this proprietary, free-form extractor can be replaced by a local, deterministic probe trained on the hidden states of a small open-source language model. On all 2556 MultiHop-RAG queries the probe reaches 90.9% set-exact accuracy against 88.0% for a model-free substring baseline and 80.9% for GPT-3.5, a margin that comes entirely from null queries, on which GPT-3.5 never abstains; on non-null queries all three stay within about a point. Because the probe’s output space is exactly the fixed 49-source vocabulary, it cannot drift outside the allow-list as the prompted model does. Three design choices make it work: selecting a shallow layer, mean pooling, and class-imbalance-aware multi-label training over the long tail of sources. A 135M-parameter model lands within ~1.5 points of a 1.5B one, so the filter is cheap to output: a partial forward pass through the first few layers plus one linear head, with no API. The code is available at this https URL.
[NLP-85] Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language
【速读】： 该论文旨在解决生成式人工智能在概率性预测结果的自然语言解释中可靠性不足的问题，具体聚焦于大语言模型（LLM）是否能够一致且准确地将数值化的概率信息（如似然性和不确定性）转化为自然语言描述。其核心挑战在于：当前的LLM在面对相同输入时难以保持一致的表述，且其生成的语言描述往往不能准确反映原始数值量级的真实大小，存在显著的校准偏差。研究的关键解决方案是构建一个两阶段预测流程，其中上游模型通过贝塔分布采样生成具有特定模式和先验样本量的模拟概率输出，随后由九个LLM在六种不同领域语境和十种温度设置下对这些输出进行自然语言解释，并重复实验十次以评估一致性与准确性。结果表明，尽管LLM在似然性任务上表现相对稳定，但在不确定性表达方面严重失准；即便提供预计算的统计摘要（如模式值和先验样本量），也仅能缓解上下文框架的影响，无法根本解决其内在的言语化校准问题。这说明当前的LLM尚不具备作为零样本独立风险沟通工具的能力，其瓶颈本质上存在于将数值概率转化为可信自然语言描述的“言语化”环节。
链接: https://arxiv.org/abs/2607.03882
作者: Diego Cerda-Mardini,Sarath Chandar,Sreenath Madathil
机构: McGill University (麦吉尔大学); Polytechnique Montréal (蒙特利尔工业大学); Mila – Québec Artificial Intelligence Institute (魁北克人工智能研究所); Canada CIFAR AI Chair (加拿大加拿大人工智能主席)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:LLMs are increasingly deployed as post-hoc explainers of AI-generated outputs, yet it remains unclear whether they can reliably communicate probabilistic information in natural language. For this role to be viable, models must produce identical verbal descriptions for identical inputs, and select descriptions that accurately reflect the magnitude of the underlying numerical quantities. We evaluate whether nine LLMs meet these requirements within a two-stage prediction pipeline, in which an upstream model has produced probabilistic outputs characterized by their likelihood and uncertainty, and LLMs are tasked with selecting an appropriate verbal descriptor for each. We simulate predictions from an upstream model by taking samples from a Beta distribution parameterized by its mode and prior sample size. We then prompt LLMs to explain these predictions under six domain contexts and with ten temperature settings, and repeating each experiment ten times. We find that LLMs are generally consistent but miscalibrated, with substantially weaker performance on uncertainty than on likelihood tasks. Providing models with precomputed summary statistics (mode and prior sample size) reduced sensitivity to contextual framing but did not resolve the underlying miscalibration, suggesting that the bottleneck resides in the verbalization step itself. These findings indicate that current LLMs do not yet constitute reliable zero-shot standalone risk communication tools for probabilistic predictions.
[NLP-86] Evaluating LLM Uncertainty in Long-Form Generation Using Deterministic Ground Truth ICML2026 ALT
【速读】： 该论文旨在解决大语言模型（LLM）在生成长文本时，如何实现细粒度不确定性估计的问题。传统方法往往以整段输出为单位进行错误判断，难以定位具体出错的原子单元（如单个词或短语），而现有评估体系又受限于标签不完美带来的噪声干扰，导致评估结果不可靠。为此，论文提出Single-answer Atomic Long-form Target（SALT）基准，包含六个程序化生成的任务，每个任务具有唯一确定的长文本真实答案（ground truth），支持在原子级别（token-level）对正确性、置信度校准及排序性能进行无外部评判的精确评估。SALT的关键创新在于其提供了无噪声、可复现的细粒度真值标准，使研究者能够精准分析模型在不同粒度下的不确定性表现。基于SALT对50多个LLM的分析揭示：不同置信度函数主导各类不确定性维度；在原子级别上，置信度排序能力显著下降，即便在行级等粗粒度层级中仍可见一定可分性；进一步通过可控的原子级干预发现，未来错误主要由两类可分离机制驱动——前缀污染的传播（受全局上下文正确性影响）与随答案-上下文长度增长的有限退化；此外，链式思维（Chain-of-Thought）提示或内部化推理虽能提升准确性，但会损害置信度排序性能，形成准确率与可靠性之间的权衡。这些发现为风险敏感型应用中误差识别与缓解策略的设计提供了直接依据。
链接: https://arxiv.org/abs/2607.03870
作者: Ido Amit,Ido Galil,Ran El-Yaniv
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted to the 43rd International Conference on Machine Learning (ICML 2026). Code available at this https URL
Abstract:As LLMs generate increasingly long outputs, effective uncertainty estimation must identify errors at fine-grained levels rather than discard entire responses. While such methods exist, evaluating uncertainty at any resolution (token to an entire generation) is challenging and highly sensitive to label imperfections, making zero-noise benchmarks essential; yet, long-form generation benchmarks tend to rely on fallible labels rather than deterministic ground truth. We introduce Single-answer Atomic Long-form Target (SALT), a benchmark of six procedurally generated tasks with single deterministic long textual ground truths, enabling unit-level evaluation of correctness, calibration, and ranking without external judges. Equipped with SALT, our analysis of 50+ LLMs reveals key insights: We identify which confidence functions dominate each uncertainty aspect and show that confidence ranking largely breaks at atomic resolution, even when clearer separability emerges at coarser line-level units. SALT further enables controlled atom-level interventions throughout generation, revealing two separable drivers of future errors: propagation from corrupted prefixes, dominated by global context correctness, and bounded degradation from increasing answer-context length. Finally, we demonstrate that reasoning, via Chain-of-Thought prompting or internalized through training, introduces a trade-off, improving accuracy while degrading confidence ranking. These findings directly impact risk-critical applications requiring reliable error identification and mitigation.
[NLP-87] Rethinking Scientific Discovery in an Agent ic Era
【速读】： 该论文旨在解决当前人工智能赋能科学发现（AI4Science）系统中存在的碎片化问题，即现有系统缺乏统一协调机制，导致科研过程中的问题定义、文献溯源、模型使用、模拟验证及知识复用等环节高度依赖人工干预，难以实现端到端的自动化与可追溯性。其解决方案的关键在于提出一个名为SCION（Scientific Collaborative Innovation with Agentic Organizational Nexus）的智能科研操作系统，作为“组织枢纽”（organizational nexus），通过一个“元驾驭”角色——科学代理（Science Agent），将科学任务、工具、智能体、科研成果及记忆系统有机整合，使科研流程转化为可执行、可审计、可复用的操作范式。其核心机制是“研究执行计划”（Research Execution Plan, REP），能够将高层次科研意图分解为分阶段目标、依赖关系、验证节点、工具需求、预期产出及回退条件。SCION进一步融合分层多智能体执行、基于用户画像的专用化、选择性上下文构建、受控委托及分层认知记忆等技术，支持长周期科研任务的持续推进。研究将科学发现建模为“目标条件逆向搜索”（Target-conditioned Inverse Search），并通过有限实验预算下的批量主动搜索扩展至隐含目标场景。在材料分析、分子设计、蛋白质或抗体筛选等领域的应用验证表明，相较于现有自主科研智能体基线，SCION在任务分解、验证、迭代优化及知识复用方面显著提升，实现了从孤立工具向可追踪、可复用的协同科研操作层的范式转变。
链接: https://arxiv.org/abs/2607.03863
作者: Yining Zheng,Yuxin Wang,Jiahao Lu,Shicheng Fang,Weiyi Wang,Yongzhuo Yang,Bowen Li,Haochen Ma,Chen Hu,Bowen Chen,Yang Wang,Huanhui Chen,Yitong Chen,Jiajun Chen,Zhiyuan Li,Yanlin Li,Zhuo Yang,Qifeng Wu,Jiaying He,Zhijie Jinluo,Xiaohu Xu,Yi Feng,Juncheng Qian,Yizhou Chen,Yang Cheng,Tong Zhu,Tianlei Ying,Hongyu Yu,Hongjun Xiang,Xipeng Qiu
机构: 未知
类目: Computation and Language (cs.CL)
备注: 26 pages, 7 figures
Abstract:Artificial intelligence has advanced scientific discovery, but most AI4Science systems remain fragmented tools that rely on humans to coordinate problem formulation, literature grounding, model use, simulation, validation, and knowledge reuse. This paper presents \textbfSCION (Scientific Collaborative Innovation with Agentic Organizational Nexus), an agentic scientific operating system that acts as an \textbforganizational nexus. Through a Science Agent serving as a \textbfMeta-Harness, SCION connects scientific tasks, tools, agents, artifacts, and memory, transforming research into an executable, auditable, and reusable operational process. At its core is the \textbfResearch Execution Plan (REP), which compiles high-level scientific intent into staged objectives, dependencies, verification checkpoints, tool requirements, expected artifacts, and fallback conditions. SCION further integrates hierarchical multi-agent execution, profile-driven specialization, selective context construction, governed delegation, and layered epistemic memory to support long-horizon scientific work. We formulate discovery under SCION as \textbfTarget-conditioned Inverse Search and extend it to hidden-target settings through batch active search under finite experimental budgets. Applications in materials analysis, molecule design, and protein or antibody screening, together with experiments on scientific reading, idea generation, molecule generation, and antibody screening, show that SCION outperforms existing autonomous research-agent baselines, especially in decomposition, verification, refinement, and memory reuse. Overall, SCION shifts AI from isolated tools toward a coordinated operational layer for traceable and reusable scientific innovation.
[NLP-88] When Simpler Is Better: Evaluating Translation Pipelines for Medieval Latin Manuscripts
【速读】： 该论文旨在解决生成式 AI (Generative AI) 在历史手稿翻译任务中表现不佳的问题，尤其聚焦于中世纪拉丁语手稿这一低资源、高噪声的复杂场景。核心挑战包括稀疏的转写数据、古体词汇以及由抄写缩略、连字及羊皮纸退化导致的输入信号失真。其解决方案的关键在于提出一个系统性的图像到翻译端到端评估框架，并构建首个针对中世纪拉丁语的手稿图像-转写-专家译文三元组数据集Interpres-Parallel-Corpus (IPC)，涵盖1,383个对齐样本。实验发现，尽管多组件流水线（如引入检索增强生成RAG或后OCR校正）理论上可提升性能，但实际反而因提示饱和与错误传播导致整体翻译质量下降；相比之下，仅使用轻量级专用光学字符识别（OCR）模型直接对接生成式模型的最简流程表现最优，揭示出“复杂性悖论”——在低资源历史文本处理中，简化而非复杂化系统架构反而是更有效的策略。
链接: https://arxiv.org/abs/2607.03836
作者: Nguyen Kim Hai Bui,Md. Easin Arafat,Tamás Gábor Orosz,Mufti Mahmud
机构: Eötvös Loránd University (埃佛特洛兰大学); King Fahd University of Petroleum and Minerals (法赫德国王石油与矿业大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 17 pages, preprint
Abstract:Despite remarkable progress in machine translation, Vision Language Models (VLMs) struggle on historical manuscripts, a domain that stresses core Natural Language Processing (NLP) capabilities: low-resource transliteration, archaic vocabulary, and noisy input signals. We present a systematic framework for evaluating the full image-to-translation pipeline on medieval Latin manuscripts, a setting in which scribal shorthand, ligatures, and parchment degradation expose failure modes that are invisible in clean-text benchmarks. Benchmarking on the CATMuS Latin dataset reveals a specialization gap: domain-specific Optical Character Recognition (OCR) models reduce character error rate by up to 4.3 \times compared to general-purpose VLMs, despite operating at orders of magnitude fewer parameters. We introduce the Interpres-Parallel-Corpus (IPC), a novel dataset comprising 1,383 aligned manuscript image lines, transcriptions, and expert translations, the first of its kind for medieval Latin. Our experiments uncover a complexity paradox: the simplest pipeline, a specialized OCR model feeding directly into a VLM, outperforms all multi-component variants. Adding retrieval-augmented generation (RAG) or post-OCR correction introduces prompt saturation and error propagation that degrade aggregate translation quality. These findings offer both a new benchmark and practical guidance for deploying translation systems in low-resource historical settings.
[NLP-89] Beyond Static Rules: Automated Discovery of Latent Vulnerabilities in Text-to-SQL ACL2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在实际应用中因潜在可靠性问题而导致的文本到SQL（Text-to-SQL）生成不可靠的问题。现有诊断方法依赖静态且由专家定义的规则，缺乏系统性与自动化探索能力，难以全面识别模型隐性的失败模式。为此，本文提出SAGE（Systematic Automated Guided Exploration）框架，其核心在于通过自动生成针对特定样本的漏洞假设，并结合持续演化的漏洞知识库（Vulnerability Codex）设计针对性扰动，实现对潜在缺陷的迭代验证与记录。实验表明，SAGE能够有效发现大量未被察觉的失败案例，揭示了当前模型显著的脆弱性；同时，漏洞知识库展现出强跨模型迁移能力，说明所发现的模式具有普遍性的结构弱点。此外，初步研究表明，基于生成样本进行轻量级微调可带来显著性能提升，为未来构建闭环可靠性优化路径提供了可行方向。
链接: https://arxiv.org/abs/2607.03833
作者: Hanqing Wang,Yongdong Chi,Jian Yang,Lei Yang,Jiehui Zhao,Yun Chen,Guanhua Chen
机构: Shanghai University of Finance and Economics (上海财经大学); Beihang University (北京航空航天大学); Deepexi Technology Co. Ltd. (深盒科技有限公司); Southern University of Science and Technology (南方科技大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted by Findings of ACL 2026
Abstract:While Large Language Models (LLMs) have achieved remarkable success in Text-to-SQL tasks, their deployment in real-world environments is hindered by latent reliability issues. Identifying these latent weaknesses is critical for building trustworthy database interfaces, yet current diagnostic approaches rely heavily on static, expert-defined rules, which lack the capability for systematic and automated exploration. To bridge this gap, we propose SAGE (Systematic Automated Guided Exploration), a novel framework designed to autonomously uncover latent failure patterns in LLM-based Text-to-SQL generation. Specifically, SAGE generates vulnerability hypotheses for given samples and references a continuously evolving Vulnerability Codex to design targeted perturbations, thereby iteratively verifying and documenting potential defects. Extensive experiments on state-of-the-art open-source LLMs demonstrate that SAGE uncovers a substantial number of failure cases, highlighting the significant fragility of current models. Furthermore, our analysis reveals that the Vulnerability Codex exhibits strong cross-model transferability, indicating that the discovered patterns represent generalized structural weaknesses. Finally, we explore SAGE’s potential for remediation. Although preliminary, lightweight fine-tuning on the generated samples yields promising improvements, suggesting a scalable pathway for closing the reliability loop in future work.
[NLP-90] Punching Above Their Weight: Classification-Head Fine-Tuning of Tiny Language Models (TLMs) for Verifiable Multiple-Choice Tasks
【速读】： 该论文旨在解决小型语言模型（Tiny Language Models, TLMs）在资源受限的消费级设备上进行高效微调与部署，以实现对可验证多选题任务的高性能表现这一问题。其核心挑战在于如何在参数量低于约30亿且运行于主流终端设备的条件下，提升模型在复杂推理任务中的准确性与泛化能力。解决方案的关键在于提出一种基于LoRA的判别式分类头（discriminative classification head）微调方法，相较于传统的标签生成（label generation）和仅使用正确答案（gold only）的微调范式，该方法在0.6B和1.7B规模的Qwen3模型上均表现出更优的稳定性与性能（提升2-3%），并使微调后的TLM在多项基准测试中达到或超越零样本/少样本的GPT-3（175B）、PaLM（540B）及GPT-4水平，尤其在HellaSwag、WinoGrande和PIQA任务上刷新了当前最优（SOTA）记录。
链接: https://arxiv.org/abs/2607.03801
作者: Bhavesh Sood,Jaromir Savelka
机构: Carnegie Mellon University (卡内基梅隆大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:We define Tiny Language Models (TLMs) as models below roughly 3B parameters that fit on mainstream consumer devices. We study how to adapt them for and use them on verifiable multiple-choice tasks. We compare three LoRA-based fine-tuning paradigms (label generation, gold only, and our discriminative classification head) on a unified setup across several Qwen3 models from 0.6B to 8B and five benchmarks: HellaSwag, WinoGrande, PIQA, SciQ and ARC-C. Classification-head fine-tuning reliably outperforms label generation (+2-3%) at the 0.6B and 1.7B scales. Further, TLMs fine-tuned using the discriminative method are competitive to zero-/few-shot GPT-3 (175B), PaLM (540B) and GPT-4. The performance we report for Qwen3-0.6B and Qwen3-1.7B are SOTA on HellaSwag, WinoGrande, and PIQA.
[NLP-91] A Failure-Mode Benchmark for Polymorphic Sybil Poisoning in RAG
【速读】： 该论文旨在解决在协同检索投毒（coordinated retrieval poisoning）攻击下，基于事实的问答（grounded QA）系统中模型输出失效的可解释性与评估难题。其核心问题在于现有评估方法（如ASR+ACC）无法有效识别和量化攻击导致的非目标输出行为（如拒答、漂移），且对攻击者利用语义一致但表面形式多样（polymorphic）的伪造片段进行隐蔽投毒的行为缺乏检测能力。解决方案的关键在于提出一种故障模式感知的评估框架，将阅读器输出划分为四类互斥类别（\emphgold、\emphhijack、\emphabstention、\emphdrift），并引入强制暴露协议（Forced Exposure protocol），以隔离阅读器内部冲突解决机制与检索结果波动的影响。此外，论文创新性地提出多态型伪身份投毒（polymorphic sybil poisoning），通过多个词汇上高度多样但语义一致的伪造段落联合支持攻击目标，有效规避了基于词法近似重复检测的防御机制。实验表明，在强制暴露条件下，多态性使劫持率（hijack rate）提升18.8个百分点，显著放大了攻击通道可见性；而传统指标如ASR无法捕捉拒答与漂移等失败模式，揭示了当前评估体系的重大盲区。研究同时发布了包含3,145个问题、2,982个保留的伪身份组、五种不同规模阅读器及双检索器配置的冻结基准数据集与完整评估工具链，为后续研究提供了标准化测试平台。
链接: https://arxiv.org/abs/2607.03739
作者: Donghyun Lee(Dongguk University),Juntae Kim(Dongguk University)
机构: Dongguk University (东国大学)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 17 pages, 2 figures, 11 tables. Corresponding author: Juntae Kim. Dataset and code to be released upon publication
Abstract:We release a benchmark and failure-mode-aware evaluation framework for grounded QA under coordinated retrieval poisoning. The framework partitions reader outputs into four mutually exclusive categories (\emphgold, \emphhijack, \emphabstention, \emphdrift), with instance-level paired clean-to-poison transition matrices and a Forced Exposure protocol isolating reader-side conflict resolution from retrieval variance. We introduce \emphpolymorphic sybil poisoning, a coordinated attack class in which S lexically diverse passages jointly support an attacker-chosen target while evading lexical near-duplicate filters that fully detect monomorphic baselines (capturing the residual 14.2% with E5 cosine raises false-positive rate 9 \times on legitimate same-topic pairs). A monomorphic-polymorphic ablation under Forced Exposure isolates the diversity dimension and reveals a + 18.8pp hijack amplification (95% paired bootstrap CI [+15.4, +22.4] , B=5,000 ): monomorphic copies register only 4.0% as hijack while polymorphic surface diversity recovers 22.8% – a 5.7 \times amplification of the ASR-visible attack channel. ASR alone treats every non-target output identically; under attack, abstention and drift together hold 47-66% of output mass, unmonitored by ASR+ACC, and two readers at nearly identical ASR (within 0.2pp) differ by 16.5pp on abstention and 17.2pp on drift – failure profiles invisible to ASR. We release the frozen benchmark (3,145 questions, 2,982 retained sybil groups; S=6 chosen to dominate top-10 retrieval slots, §\refsec:setup), the official four-way evaluator, paired-transition utilities, and the Forced Exposure harness across five readers (7B-120B), two retrievers, and two cross-validation datasets (TriviaQA, 2Wiki), under CC~BY-SA~4.0 (data) and MIT (software); release information in §\refsec:release.
[NLP-92] ProACT: Towards Breakdown-Aware Proactive Agent in Multi-User Collaboration
【速读】： 该论文旨在解决当前对话代理在多人协作中普遍存在的被动响应问题，即仅能对用户显式请求做出反应，而无法像人类协作者一样主动识别团队协作中的潜在断裂点并及时干预。这一局限性严重制约了代理作为积极协作成员在复杂多用户场景中的应用，尤其是在存在分歧、目标模糊、约束遗忘、计划不明确、讨论循环和参与不均等情况下。其解决方案的关键在于提出ProACT框架，该框架基于共知理论（common ground）、协作规划与协调工作理论，具备对带有说话人归属的对话历史进行感知的能力，能够判断当前发言是否构成协作断裂、决定是否应保持沉默或介入，并在需要时将任务路由至特定的协作技能模块。此外，研究构建了首个涵盖项目规划、产品设计、科研协作、物流管理、教育及资源受限决策等领域的多用户协作评估基准，通过3,244个回合级样本与五种大语言模型（LLM）基线对比，验证了ProACT在协作恰当性、非干扰性、简洁性及干预质量等方面显著优于直接对话模式。
链接: https://arxiv.org/abs/2607.03730
作者: Shu Yang,Difei Xu,Jiaxin Pei,Di Wang
机构: King Abdullah University of Science and Technology (阿卜杜拉国王科技大学); Stanford University (斯坦福大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Conversational agents are increasingly embedded in human collaborative work, yet they remain fundamentally passive and reactive: they respond to explicit user requests rather than proactively recognizing moments when a team would benefit from timely intervention as human collaborators often do. This reactive design substantially limits the use of agents as active participants in multi-user collaboration, where disagreements, ambiguous goals, forgotten constraints, underspecified plans, discussion loops, and imbalanced participation can gradually undermine group progress. To move agents from passive assistants toward active participants in multi-user collaboration, we introduce ProACT, a breakdown-aware agent framework grounded in theories of common ground, collaborative planning, and coordination work. ProACT observes the speaker-attributed conversation history, determines whether the current turn contains a collaboration breakdown requiring intervention, decides whether the agent should stay silent or speak, and, when speaking is needed, routes the case to a targeted collaboration skill. We further introduce the first multi-user collaboration benchmark for evaluating proactive agents across project planning, product design, research collaboration, logistics, education, and resource-constrained decision making. Across 3,244 turn-level examples and five LLM backbones, ProACT consistently improves collaborative appropriateness, non-interruptiveness, conciseness, and judged intervention quality over direct chat.
[NLP-93] SelfMem: Self-Optimizing Memory for AI Agents
【速读】： 该论文旨在解决当前人工智能代理（AI agents）在执行长时任务时，因依赖固定记忆机制而导致的灵活性不足与适应性差的问题。现有记忆框架通常采用预设的存储、检索和摘要策略，这些策略在不同任务间缺乏通用性，且需人工调参，难以高效利用历史经验。为克服这一局限，论文提出一种自优化记忆框架 SelfMem，其核心创新在于通过提供可交互的记忆工具与反馈信号，使智能体能够自主探索、评估并迭代优化自身的记忆策略，遵循“授人以渔”而非“授人以鱼”的原则。该方法摒弃了对固定记忆模式的强制依赖，实现了基于模型自身判断的动态记忆管理。实验结果表明，SelfMem 在 BEAM 基准上，于 10 万至 100 万 token 的对话规模下均显著优于检索、压缩及主流代理-记忆基线，在 10 万、50 万和 100 万 token 规模下分别提升官方得分 48.7%、40.8% 和 41.9%；多类型问题分析显示其具备广泛的鲁棒性，且基于模型引导的策略优化进一步提升了性能。因此，解决方案的关键在于构建一个支持自主学习与持续改进的记忆系统，使智能体具备自我调节记忆行为的能力。
链接: https://arxiv.org/abs/2607.03726
作者: Shu Yang,Junchao Wu,Derek F. Wong,Di Wang
机构: King Abdullah University of Science and Technology (KAUST); University of Macau
类目: Computation and Language (cs.CL)
备注:
Abstract:While current AI agents support increasingly long context windows, tool use, and skill execution for long-horizon tasks, they still require memory systems to effectively leverage historical experience. Existing memory frameworks typically rely on fixed storage, retrieval, and summarization mechanisms, which can be rigid across different tasks and often require manual tuning. To address this limitation, we propose SelfMem, a self-optimizing memory framework. Inspired by prior work on self-improving AI, we follow the principle of “teaching an agent to fish rather than giving it a fish.” Instead of forcing the model to follow a predefined memory strategy or format, SelfMem provides an environment with memory tools and feedback signals that allow the agent to explore, evaluate, and refine its own memory strategy. Our results show that SelfMem consistently outperforms retrieval, compression, and agent-memory baselines on BEAM across conversation scales from 100K to 1M tokens. Compared with the strongest baseline, SelfMem improves the official score by 48.7%, 40.8%, and 41.9% at 100K, 500K, and 1M, respectively. Further question-type analysis shows broad robustness across diverse memory demands, and our optimization study shows that model-guided strategy refinement further improves performance.
[NLP-94] GRASP: Graph-Reasoning Aided Survey Planning for High-Fidelity Related Work Generation ACL2026
【速读】： 该论文旨在解决文献综述撰写中对引用文献之间复杂关系理解不足的问题，即如何准确捕捉和呈现文献间的承继、批判与对立等学术互动关系。其解决方案的关键在于提出一种名为图推理辅助的综述规划框架（Graph-Reasoning Aided Survey Planning, GRASP），该框架融合大语言模型（Large Language Model, LLM）的生成能力与图算法的关系挖掘机制。GRASP采用双层图结构：思维图（Graph of Thoughts）与论点-反论点规划网络（Argument-Counterargument Planning Network），分别在不同粒度上表征文献间的逻辑关联，并通过基于斯坦纳树（Steiner tree）的拓扑感知剪枝策略，识别出核心的文献间关系。实验结果表明，基于引文分析的评估显示，GRASP生成的文献综述部分在引用的论述角色、写作意图及引用分组方面，与人工撰写的参考文本高度一致。
链接: https://arxiv.org/abs/2607.03709
作者: Haoming Li,Jessica Ouyang
机构: University of Texas at Dallas (德克萨斯大学达拉斯分校)
类目: Computation and Language (cs.CL)
备注: 23 pages, 3 figures. Published in Findings of the Association for Computational Linguistics: ACL 2026
Abstract:Writing a literature review requires a deep understanding of the relationships among cited papers: how they build on, challenge, or offer alternative perspectives to one another. We present Graph-Reasoning Aided Survey Planning (GRASP), a framework combining LLM planning for related work generation with graph algorithms to extract key relationships among cited papers. Our two-layer graph structure consists of a Graph of Thoughts and an Argument-Counterargument Planning Network, representing the cited papers at different levels of granularity, and we apply topology-aware pruning via a Steiner tree to identify the core inter-paper relationships captured in our graph. Our citation analysis-based evaluation shows that GRASP generates related work sections (RWS) that closely match human-written targets in terms of the discourse roles, intents, and grouping of citations.
[NLP-95] Optimizing Large Language Models for Causality Assessment in Pharmacovigilance: Developing a Performance Metric as Objective for Bayesian Hyperparameter Optimization
【速读】： 该论文旨在解决日益增长的个案安全性报告（ICSR）数量所带来的可扩展自动化因果关联评估需求，特别是针对大型语言模型（LLM）在临床高要求任务中表现不佳以及推理阶段超参数优化尚未被系统研究的问题。其解决方案的关键在于提出一种与高斯过程（GP）兼容的优化目标函数，并通过基于熵加权一致性和余弦相似性得分（EWACS）的贝叶斯优化方法，对GPT-5.2模型的生成温度进行动态调优。研究发现，尽管整体上温度变化未表现出显著的群体效应，但基于EWACS引导的优化使模型与专家在Naranjo因果评估中的一致性从45.0%提升至72.0%（+27个百分点），尤其在“可疑”类别的改进最为显著（+42.9个百分点），证明了针对病例特异性温度优化的有效性，为提升生成式AI在药物警戒中的可靠性提供了可行路径。
链接: https://arxiv.org/abs/2607.03704
作者: Nicole Sonne Heckmann,Arnault-Quentin Vermillet,Søren Norlin Mølgaard,Manuela Del Castillo Suero,Lars Melskens,Gerard Ompad,Maurizio Sessa
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Background: Growing individual case safety report (ICSR) volumes have intensified demand for scalable automated causality assessment. Large Language Models (LLMs) show promise, yet performance on clinically demanding tasks remains suboptimal and inference-time hyperparameter optimization has not been investigated. Objective: To develop a Gaussian Process (GP)-compatible optimization objective and investigate whether temperature optimization improves LLM-expert agreement on Naranjo causality assessment of FAERS ICSRs. Methods: Expert causality assessments were performed on 723 stratified FAERS cases. OpenAI’s GPT-5.2 was evaluated using chain-of-thought (CoT) prompting. Four composite metrics were developed: Weighted Cosine Similarity (WCS), Information-Weighted Agreement Score (IWAS), Entropy-Weighted Agreement and Cosine Similarity Score (EWACS), and Consensus-Weighted Cosine Similarity (CWCS) and Bayesian optimization using a GP surrogate with Probability of Improvement (PoI) acquisition was applied across temperature [0, 2]. Results: GPT-5.2 outperformed prior biomedical LLMs at baseline (T = 0), achieving 74.1% agreement on question 5 and 65.4% on question 10 of Naranjo algorithm. Entropy analysis identified these as the sole informative optimization targets. Temperature showed no systematic population-level effect (\beta = 0.002, p = 0.959). EWACS-guided Bayesian optimization improved causality classification agreement from 45.0% to 72.0% (+27 pp), with the largest gain in Doubtful cases (+42.9 pp). Conclusion: EWACS was identified as the optimal GP-compatible metric. The absence of a universal temperature optimum indicates LLM performance is driven primarily by ICSR content, yet case-specific temperature selection produced meaningful improvements, supporting temperature optimization for LLM-assisted pharmacovigilance.
[NLP-96] Annotating Korean adnominal ending constructions in corpus data: Beyond relative-clause identification
【速读】： 该论文旨在解决韩语中形容词性后缀“-ETM”在多种名词修饰结构中的歧义问题，尤其是其是否可作为相对从句结构的直接标记。研究指出，“-ETM”并非仅限于相对从句，而是多个名词修饰结构共用的形态表现。其解决方案的关键在于提出一种基于语料库的类型学分类框架，通过谓词类型、助动词结构、论元结构兼容性、中心名词限制及固定搭配模式等五个维度对不同构造进行区分，并将该分类体系以构式敏感的标注层形式嵌入KLUE依存句法树库中，采用有序规则驱动的方法实现自动化标注，经人工验证评估。研究发现，具有类相对从句功能的使用仅占分析实例的39.4%，其余多为形容词性、系词性、绑定名词、情态、时间及习语化结构。结果表明，仅凭名词性形态无法准确识别韩语的类相对从句修饰关系。
链接: https://arxiv.org/abs/2607.03681
作者: Jungyeul Park,Chulwoo Park
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:The Korean adnominal ending \textttETM occurs in diverse noun-modifying constructions, including relative-clause-like modifiers, adjectival and copular forms, bound-noun constructions, and lexicalized expressions. This paper argues that \textttETM is not a direct marker of relative-clause structure, but a morphological exponent shared by several adnominal constructions. We propose a corpus-based typology that distinguishes these constructions using predicate type, auxiliary structure, argument-structural compatibility, head-noun restriction, and lexicalized patterns. We operationalize the typology as a construction-sensitive annotation layer for the KLUE dependency treebank, implemented through an ordered rule-based procedure and evaluated by manual validation. Productive relative-clause-like uses account for 39.4% of the analyzed instances; the remainder consists mainly of adjectival, copular, bound-nominal, modal, temporal, and collocational constructions. The findings show that Korean relative-clause-like modification cannot be identified from adnominal morphology alone.
[NLP-97] Rethinking AI-Generated Text Detection: A Strong Baseline and the Distribution-Shift Problem That Remains
【速读】： 该论文旨在解决当前生成式文本检测领域中普遍存在的分布外泛化能力不足问题，即现有检测模型在训练数据分布之外（如不同主题领域或生成模型）性能显著下降的挑战。尽管近年来的研究多通过构建专用基准并设计复杂架构的检测器来提升特定场景下的性能，但本文从基础模型出发，发现一个全微调的RoBERTa模型在多个基准上已能媲美甚至超越这些专门设计的检测器，表明模型架构的复杂性并非决定检测性能的关键因素。其核心发现在于：当前主流方法在面对分布偏移时存在关键缺陷——在未见领域的人类写作文本上仍可能给出高置信度的机器生成误判。为此，论文提出两种轻量级领域自适应策略：基于LoRA适配器的一阶MAML的K-shot适应方法，以及基于自适应检测器的样本级置信度加权集成方法。实验结果表明，真正推动该领域进步的关键应是在保持良好分布内性能的同时，提升模型对分布外变化的鲁棒性，而非单纯追求复杂模型结构或特定基准上的高分表现。
链接: https://arxiv.org/abs/2607.03680
作者: Zhuoer Shen,Mingyi Wang,Shaofeng Zou,Yuheng Bu
机构: University of California, Santa Barbara (加州大学圣塔芭芭拉分校); Arizona State University (亚利桑那州立大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Recent AI-generated text detection work often introduces a new benchmark together with a specialized detector tailored to it. We revisit this practice from a baseline-first perspective. Across several benchmarks, we show that a plain, fully fine-tuned RoBERTa matches or exceeds the specialized detectors those benchmarks are built around. This suggests that much of the recent architectural complexity is not what drives strong in-distribution detection. The remaining challenge is the distribution shift. The same strong baseline degrades sharply when the topic domain or generating model changes at test time, and simply adding more source data does not close the gap. We identify a key failure mode: under distribution shift, the detector can assign high-confidence machine labels to human-written text from unseen domains. We then study two lightweight domain adaptation methods to address this problem: K -shot adaptation with first-order MAML over LoRA adapters, and a per-sample confidence-weighted ensemble built on top of the adapted detector. Overall, our results suggest that progress in AI-generated text detection should be measured not only by in-distribution performance, but also by robustness under distribution shift.
[NLP-98] Revealing Hidden Model Behaviors with Task-Specific Self-Reports
【速读】： 该论文旨在解决微调后语言模型中存在的隐蔽行为（hidden behavior）难以被有效识别与审计的问题，尤其是当模型在特定触发条件下产生错误回答或有害建议时，传统方法往往无法准确捕捉这些非预期行为。其核心解决方案是提出一种轻量级的LoRA适配器——自报告稳定适配器（Stabilized Adapter for self-Report, SAR），该适配器仅依赖模型本身及其训练数据，即可使模型以自然语言形式自我描述其潜在的隐蔽行为。SAR的关键优势在于其高检测覆盖率与低误报率：在七种植入的隐蔽行为及一个无行为对照组中，SAR实现了100%的检测准确率，即使在模型已泛化出广泛偏差（broad misalignment）且训练数据无法预测的情况下仍表现稳健；相比之下，现有最接近的基线方法——内省适配器（Introspection Adapters, IA）虽能检测部分行为，但存在明显漏检现象，并在漏检场景下频繁生成虚假行为描述（hallucination）。SAR不仅在所有IA失败的设置中保留了有效信号，还将幻觉率降低了一半，显著提升了模型可解释性与审计可靠性，为从业者提供了更可信的答案来回答“我的模型实际学到了什么？”这类关键问题。
链接: https://arxiv.org/abs/2607.03640
作者: Taras Kutsyk,Bartosz Zieliński
机构: University of Warsaw (华沙大学); Polish Academy of Sciences (波兰科学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 17 pages, 8 figures, 2 tables; appendix with 31 additional pages
Abstract:Fine-tuning can give a language model a hidden behavior–it may give false answers under a narrow condition, or give harmful advice only when a prompt touches a particular topic. We introduce the Stabilized Adapter for self-Report (SAR), a lightweight LoRA adapter that makes a fine-tuned model describe its own hidden behavior in plain language, using only the model and the dataset it was trained on. Across seven implanted behaviors (plus a no-behavior control), SAR detects the hidden behavior in every one–even when the model has generalized into broad misalignment that the training data alone does not predict. Introspection Adapters (IA), the closest existing baseline, detects some behaviors from our suite but misses others entirely–and where it misses, it hallucinates, consistently reporting wrong behaviors. SAR retains positive signal on every setting where IA fails and halves the rate of hallucinations. This makes it much easier for practitioners to audit their models and obtain reliable answers to “what did my model actually learn?” type of questions.
[NLP-99] hey Infer What You Meant: Models Represent Communicative Intent More Reliably Than They Act On It
【速读】： 该论文旨在解决语言模型在对话交互中对用户发送意图（communicative intent）的误读问题，即模型往往仅回应信息的表层内容，而未能理解用户传递信息背后的深层交际目的——例如，是希望获得认可（recognize）还是评估（evaluate）。其核心挑战在于：尽管模型内部已存在对意图的稳健表征，但该表征未能被有效读出并驱动输出行为。解决方案的关键在于发现并利用一种线性探测器（linear probe），能够从模型默认隐藏状态（default-pass hidden states）中无偏倚地解码出用户的意图，且该解码具有表面无关性（surface-independence），在六种不同模型及四个模型家族的基线检查点中均表现一致。进一步研究表明，这一意图表征不仅存在于显性表达中，还可泛化至语用推断出的隐含意图以及“支持”与“帮助”等词汇清晰的二元意图。研究通过因果测试揭示，意图的读出滞后于其在模型内部的表示深度，且是否将意图转化为输出行为具有模型特异性（六种模型中有三种表现出失败），这种差异并非由规模定律导致，而是结构性分层现象。关键突破在于识别出一个与意图表征紧密相关的判别方向（discriminative direction），该方向位于特定搜索层，可作为因果干预的控制柄：通过微调此方向即可恢复预期行为，效果等同于显式指令，甚至无需任何提示。该方向与反馈生成轴近正交，表明其功能是路由特定意图而非通用反馈机制；然而，在高调节强度下，该路由意图可覆盖显式指令。所有结论均经严格对照实验验证，排除了常见偏差，并如实报告了无效结果。
链接: https://arxiv.org/abs/2607.03598
作者: Alex Kwon
机构: Independent Researcher
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 17 pages, 3 figures
Abstract:When a person shares something with a language model, the model often answers the surface of the message rather than what the sender was doing by sending it: share a finished project and it critiques the code; share a raw late-night line and it runs a wellness check. We treat the sender’s communicative intent, the Gricean what-was-meant, as a first-class interpretability object, and show the failure is one of readout on top of a robust representation. A linear probe decodes the sender’s intent, whether they want a thing recognized or evaluated, from a model’s default-pass hidden states, cleanly and surface-independently, across six models and four families and in the base checkpoints. The representation generalizes further, to intent that is only pragmatically inferred, and to a second, lexically clean intent (support versus help). The behavioral half of the story, and every causal test, is established on the recognize/evaluate contrast, where what varies is whether the default output acts on the intent. The readout lags the representation in depth within a model (the intent is decodable several layers before it drives the output); across models, which ones act on it by default is model-specific, an observed stratification (three of six show the failure) that we do not read as a scaling law. Where the gap is open, a direction closely tied to the representation, the discriminative direction at a searched-for layer, is a causal handle: steering it recovers the intended behavior, as well as an explicit instruction does and with no prompt at all. This direction is near-orthogonal to the feedback-offering axis, so it routes a represented intent rather than a generic feedback knob, though at the recovery dose the routed intent can override an explicit request. We support each link with controls against obvious deflations and report the nulls as plainly as the confirmations.
[NLP-100] Mental Health Disorder Detection Beyond Social Media: A Systematic Review of Available Datasets
【速读】： 该论文旨在解决当前基于自然语言处理（Natural Language Processing, NLP）与机器学习（Machine Learning, ML）的 mental health disorder（心理健康障碍）检测方法依赖社交媒体数据所引发的采样偏差、伦理困境及隐私问题。其核心挑战在于现有数据集存在语言覆盖单一（以英语为主）、研究目标集中于抑郁（depression）检测，且在人群特征、数据来源平台、数据类型、标注方式及研究方法上缺乏多样性。为此，论文提出的关键解决方案是系统性地梳理和评估非社交媒体（non-social media）自由文本数据集，通过采用PRISMA方法论对多语言环境下可用的数据资源进行综合审查，揭示现有数据集的局限性，并为构建更具多样性、可靠性与临床相关性的心理健康研究数据资源提供方向性指引。
链接: https://arxiv.org/abs/2607.03540
作者: Sadiya Sayara Chowdhury Puspo,Ana-Maria Bucur,Stevie Chancellor,Özlem Uzuner,Marcos Zampieri
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Detecting mental health disorders in a timely manner is an important societal challenge. NLP and machine learning (ML) methods used to assist with detection rely on data collected primarily from social media. However, such datasets often have sampling biases and inherent ethical and privacy issues. One avenue to overcome these limitations is non-social media data. We present the first comprehensive review of non-social media, free-text datasets for mental health research. We use the PRISMA methodology to conduct our survey and we review datasets available in multiple languages. We find that non-social media free-text based datasets are predominantly focused on English and on detecting depression. These datasets also vary in demographics, platforms, data types, annotation techniques, and methodologies. This systematic review also reveals key gaps and highlights opportunities to develop more diverse, reliable and clinically-relevant resources.
[NLP-101] Aligning Language Models with Selective Prediction ICML2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在高风险现实应用场景中可靠性不足的问题，尤其关注如何通过选择性预测（Selective Prediction, SP）策略提升模型的可信度。其核心挑战在于，在保证模型预测准确率的同时，合理控制模型的覆盖范围（即仅对自身置信度高的输入进行预测），从而降低错误率（风险），同时避免过度拒绝输入导致可用性下降。解决方案的关键在于提出一种新的后训练对齐框架——基于选择奖励的强化学习（Reinforcement Learning for Selection Reward, RLSR），该方法将选择性预测性能的核心指标——风险-覆盖率曲线下面积（Area Under the Risk-Coverage Curve, AURC）作为对齐目标，直接优化模型在风险与覆盖之间的权衡能力。相比传统以正确率或校准性为目标的对齐方法，RLSR在领域内和域外任务上均显著提升了风险-覆盖率的平衡表现，实现了更可靠的模型部署与人机协同决策。
链接: https://arxiv.org/abs/2607.03528
作者: Gaoxiang Luo,Yifan Wu,Sinian Zhang,Aryan Deshwal,Ju Sun
机构: University of Minnesota Twin Cities (明尼苏达大学双城分校)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted by ICML 2026 Agents in the Wild: Safety, Security, and Beyond Workshop, Project Page: this https URL
Abstract:Large language models (LLMs) are increasingly deployed as critical decision-making components in high-stakes real-world AI systems, rendering LLM reliability a foremost practical concern. In this paper, we focus on enhancing LLM reliability through selective prediction (SP), a strategy that allows an LLM to only predict for inputs where it is likely to be correct (i.e., coverage) and hence reduce the error rate (i.e., risk) on that portion of inputs – flagging the remaining inputs for future human discretion. In other words, SP improves LLM reliability by balancing the risk-coverage trade-off and enabling seamless human-AI collaboration. To integrate SP into LLMs, we focus on the LLM post-training alignment stage and propose to align LLMs with SP performance metrics, in contrast with existing LLM alignment methods that focus primarily on correctness or calibration metrics. Specifically, we propose a novel alignment framework, Reinforcement Learning for Selection Reward (RLSR), which targets the area under the risk-coverage curve (AURC) – a popular SP performance metric – as its alignment objective. RLSR achieves substantially better risk-coverage trade-off compared to multiple alignment baselines on both in-domain and out-of-domain tasks.
[NLP-102] GameEngineBench: Evaluating Coding Agents on Real C Runtime Environments
【速读】： 该论文旨在解决当前生成式AI在复杂、实时交互的C++系统中进行代码修改时表现不佳的问题，尤其聚焦于游戏引擎这一高度集成且状态复杂的开发环境。其核心挑战在于：编码代理需在保持系统完整性与行为一致性的前提下，对跨模块、强依赖的C++代码进行精准修改，并通过编译与运行测试。解决方案的关键是构建GameEngineBench——一个基于九个真实世界游戏项目、针对Unreal Engine 5的基准测试集，涵盖110项涉及游戏机制、多人行为、人工智能与场景调度、动画与移动控制、用户界面与会话逻辑、加载行为、在线服务集成、数据持久化、序列化、扩展现实（XR）行为及渲染插件等领域的具体任务。这些任务要求模型在可执行的Unreal Engine项目中完成原生C++代码修改并满足行为测试，从而真实评估编码代理在深度耦合、实时交互系统中的工程能力。实验结果表明，即使最强模型在pass@1指标上仅达55.5%，仍有31项任务无法被任何配置解决，揭示出当前前沿编码代理在处理高度集成的实时软件开发时仍存在显著局限，凸显了以游戏引擎为背景的基准测试对软件工程评估体系的重要补充价值。
链接: https://arxiv.org/abs/2607.03525
作者: Brian La,Sejoon Chang,Ben Kim,Junyoung Bae,Aamish Ahmad Beg,Sei Chang,Gonzalo Gonzalez-Pumariega
机构: Nitrode; Nexon Intelligence Labs; Dartmouth University (达特茅斯大学); Columbia University (哥伦比亚大学); Cornell University (康奈尔大学)
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL)
备注:
Abstract:Game engines provide real-time simulation, rendering, physics, interaction, networking, and asset pipelines, making them valuable not only for games but also for 3D applications in healthcare, robotics, architecture, manufacturing, and related domains. Because game development is where these systems are most mature and publicly available, it offers a practical testbed for evaluating coding agents that must modify C++ code within stateful, interactive, real-time systems. We present GameEngineBench, a benchmark for evaluating coding agents on scoped C++ implementation tasks inside Unreal Engine 5 projects, built from nine real-world game repositories. The evaluation set consists of 110 tasks spanning gameplay mechanics, multiplayer behavior, AI and world orchestration, animation and movement, UI and session code, loading behavior, online-service integration, persistence, data serialization, XR behavior, and rendering-oriented plugins. These tasks require models to make native C++ changes that compile and satisfy behavioral tests within executable Unreal Engine projects. Across twelve evaluated configurations, the strongest model reaches 55.5% pass@1, while 31 tasks remain unsolved by every configuration. Our results demonstrate that frontier coding agents continue to struggle with deeply integrated C++ development for real-time interactive software, highlighting game-engine benchmarks as a valuable complement to existing software engineering evaluations.
[NLP-103] Anchored Self-Play for Code Repair ICML2026
【速读】： 该论文旨在解决代码修复（Code Repair）中训练数据稀缺的问题，通过利用大语言模型（Language Model, LM）自动生成带有缺陷的代码任务来扩展监督信号。其核心挑战在于如何构建一个能够持续提升模型能力的自动课程（automatic curriculum），使模型在修复难度递增的代码缺陷中不断进步。解决方案的关键是提出“生成-修复自对弈”（generator-fixer self-play）机制：单一模型通过强化学习同时扮演“生成器”和“修复者”角色，随着修复能力的提升，生成器会自动产生更具挑战性的错误代码，形成动态演进的训练过程。然而，实验发现该方法容易偏向于生成难以真实存在的合成型错误，导致在人类编写的代码修复上性能下降。为此，论文进一步提出“锚定自对弈”（Anchored Self-Play, ASP），通过引入少量真实参考样本，并结合代码嵌入相似性奖励以及将真实缺陷样本混合至修复训练中，有效约束自对弈方向，使其更贴近真实场景。在涵盖人类编写、模型生成及人工修改模型生成代码等多种来源的BugSourceBench基准测试中，ASP显著优于标准自对弈方法，平均修复率相对提升24%，绝对提升7.0个百分点，且在来自大语言模型和人类的缺陷上均实现性能增益。
链接: https://arxiv.org/abs/2607.03523
作者: Caroline Choi,Zeyneb Kaya,Shirley Wu,Tengyu Ma,Tatsunori Hashimoto,Ludwig Schmidt
机构: 未知
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL)
备注: 43rd International Conference on Machine Learning (ICML 2026)
Abstract:Code repair is an important capability for language models (LMs): given a buggy program and unit tests, an LM must produce a fixed program that passes the tests. Because code repair data is limited, we aim to scale supervision by using an LM to generate bug–fix tasks. We propose generator–fixer self-play, in which a single model is trained with reinforcement learning to generate bugs and fix them. As the fixer improves, the generator adapts to produce more difficult bugs, yielding an automatic curriculum. To test whether this curriculum generalizes, we introduce BugSourceBench, a repair benchmark spanning realistic bug sources: bugs in human-written code, LM-generated code, and human-edited LM-generated code. On BugSourceBench, we find that self-play drifts toward difficult but unrealistic bugs, improving on synthetic bugs but degrading on human-authored ones. We propose Anchored Self-Play (ASP), which anchors self-play with a small reference set by adding a code-embedding similarity reward for generation and mixing reference bugs into fixer training. Across bug sources, ASP achieves the best fix rates, improving average fix rate over standard self-play by +24% relative / +7.0 pp absolute, with gains on bugs from both LMs and humans.
[NLP-104] Reading Between the Dots: Decoding Hidden Computation across Filler Tokens ICML2026
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在面对无信息填充符（如点或计数序列）时，仍能完成多步推理并输出正确答案却无明显思维链（Chain-of-Thought, CoT）的现象所引发的可解释性与行为监控难题。这一现象揭示了模型内部计算过程虽未体现在表面输出中，但依然存在结构化、可读的隐含推理轨迹。其解决方案的关键在于提出一种无需标注数据或训练的无监督解码流水线，仅依赖模型残差流（residual stream）中的隐藏状态，即可高精度（80%-95%）重构中间推理步骤。通过注意力路径分析、对数概率透镜（logit-lens）读出以及键值缓存移植等手段，研究证实：尽管表面行为无法反映推理过程，但模型在填充符区域进行的结构化计算可通过隐藏状态直接读取，表明行为可监控性本质上取决于模型完整的计算轨迹，而不仅限于输出表面文本。
链接: https://arxiv.org/abs/2607.03502
作者: Kaley Brauer,Claudio Mayrink Verdun,Samuel Marks
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted to ICML 2026 Mech Interp Workshop, 10 main paper pages, 20 appendix pages
Abstract:Frontier LLMs can perform multi-step reasoning over content-free filler tokens like dots or counting sequences, producing correct answers with no visible chain-of-thought (CoT). This is a limit case for behavioral oversight, where surface tokens carry no information about the underlying reasoning. But hidden from the output is not the same as hidden from us. On four task families (fact retrieval, parallel numeric composition, string manipulation, and in-context computation), two open-weights frontier models (DeepSeek V3, Kimi K2) compute over filler tokens in a structured, legible way: attention routes the question through the filler region to the answer, logit-lens readouts show retrieved facts emerging early and their composition crystallizing in late layers, and KV-cache transplants at filler positions causally swap outputs between examples. We introduce an unsupervised decoding pipeline that takes only hidden states as input and recovers intermediate values with 80-95% accuracy (best LLM judge) across both models and all four tasks, without ground-truth labels or training. Hidden computation that defeats behavioral CoT monitoring is, on these tasks, directly readable from the residual stream, suggesting monitorability is a property of the model’s full computational trace, not just its surface tokens.
[NLP-105] Lacuna Inc. at SemEval-2026 Task 4: Structurally Gated State-Space Models for Disentangling Narrative Similarity
【速读】： 该论文旨在解决叙事相似性评估中的核心挑战，即如何在忽略具体、表层的词汇细节（如人物姓名、物品或场景）的基础上，准确捕捉并比较故事背后的抽象因果关系与情节推进模式。传统方法受限于标准Transformer架构的二次计算开销，难以高效建模长程因果链。为此，本文提出不变-可变解耦状态空间模型（Invariant-Variant Disentangled State-Space Model, IVD-SSM），其关键在于引入一种新型可微分算法架构——结构门控对齐头（Structurally Gated Alignment, SGA）。SGA通过双尺度机制实现解耦：首先利用重采样的宏观路径（Macro-path）提取故事的粗粒度结构骨架，作为门控信号；随后以该骨架动态过滤全分辨率微观路径（Micro-path），有效抑制语义噪声与表面关键词重叠。这一设计使模型能够在保持计算效率的同时，显式分离叙事中的结构不变量与词汇可变性，从而为深层叙事理解提供了一种鲁棒且原理清晰的解决方案。
链接: https://arxiv.org/abs/2607.03482
作者: Aleksey Kudelya,Rafif Alshawi,Alexander Shirnin
机构: HSE University (高等经济大学); Lacuna Inc.
类目: Computation and Language (cs.CL)
备注:
Abstract:In this paper, we present the Invariant-Variant Disentangled State-Space Model (IVD-SSM), our submission to SemEval-2026 Task 4 on Narrative Story Similarity and Narrative Representation Learning. Evaluating narrative similarity is a profound computational challenge that requires models to look past concrete, superficial elements such as specific names, actors, objects, or settings to isolate and compare abstract patterns of causality and plot progression. To model these extended causal chains without the quadratic bottlenecks of standard Transformers, we leverage a hybrid State-Space Model (Jamba-1.5-Mini). Building upon this backbone, we introduce the Structurally Gated Alignment (SGA) head, a novel, differentiable algorithmic architecture. The SGA head operates on two scales: a heavily strided Macro-path maps the coarse structural skeleton of a story, which then acts as a gating mechanism to filter a full-resolution Micro-path, actively suppressing semantic noise and superficial keyword overlaps. Evaluated on both pairwise comparative judgments (Track A) and dense representation learning (Track B), our approach demonstrates that explicitly disentangling structural invariants from lexical variants provides a robust, principled framework for deep narrative understanding.
[NLP-106] Learning from Lost Provenance: Multiple Instance Learning for Cancer Registry Tumor Group Classification
【速读】： 该论文旨在解决癌症注册系统中深度学习模型训练所需报告级人工标注数据稀缺的问题。传统方法依赖于逐份病理报告的人工标注，成本高昂且效率低下；而癌症注册机构虽日常生成大量患者级别的专家标注标签，但这些标签未与具体病理报告建立对应关系，无法直接用于模型训练。其解决方案的关键在于提出一种基于注意力的多实例学习（Attention-Based Multiple Instance Learning, ABMIL）框架，通过分析模型对每份报告的关注度，重建患者级标签与原始报告之间的关联，从而将海量、噪声较大的患者级标签转化为高质量、细粒度的报告级训练数据。该方法在不增加人工标注负担或依赖大规模计算资源的前提下，显著提升了肿瘤分组分类性能，经微调后的分类器在宏平均F1得分上达到0.83，优于现有基准模型，为实现癌症注册流程的自动化提供了可落地的技术路径。
链接: https://arxiv.org/abs/2607.03481
作者: Leonard Ruocco,Jonathan Simkin,Lovedeep Gondora,Gregory Arbour,Raymond Ng
机构: British Columbia Cancer Registry, Provincial Health Services Authority (不列颠哥伦比亚癌症登记处，省级卫生服务局), Vancouver, Canada; University of British Columbia (不列颠哥伦比亚大学), Vancouver, Canada; Data Science Institute, University of British Columbia (数据科学研究所，不列颠哥伦比亚大学), Vancouver, Canada
类目: Computation and Language (cs.CL)
备注:
Abstract:Modernizing cancer registries with deep learning is opening new opportunities to automate labor-intensive tasks such as the coding of pathology reports. However, progress is constrained by the scarcity of report-level human-annotated training data. Cancer registries generate substantial volumes of expert-assigned labels as a routine product of their operations, but these exist at the patient level and are not linked to the individual pathology reports that informed them, limiting their direct use for training models. We develop an efficient framework for training deep learning classifiers by leveraging these operationally-generated labels without requiring per-report human annotation, demonstrated for tumor group classification at the BC Cancer Registry. We use Attention-Based Multiple Instance Learning (ABMIL) to recover the lost link between patient-level labels and the reports that informed them, leveraging the attention the model places on each report to distil a large, noisily-labeled corpus into a compact, high-quality per-report training dataset. A classifier fine-tuned on a distilled dataset achieved a macro F1 of 0.83, outperforming established baselines across most tumor groups. By turning routine operational labels into high-quality training data without additional annotation or large-scale computing infrastructure, ABMIL offers a practical and accessible route to automating cancer registry workflows.
[NLP-107] CaresAI at SMM4H-HeaRD 2026: Predicting TNM Staging
【速读】： 该论文旨在针对癌症基因组图谱（TCGA）病理报告，独立预测肿瘤、淋巴结和远处转移（TNM）分期标签，属于SMM4H-HeaRD 2026的第六项共享任务。研究将问题建模为三个多标签分类任务，探索了基于传统机器学习与深度学习的方法，利用词频-逆文档频率（TF-IDF）特征及ClinicalBERT、BioBERT和PubMedBERT等预训练模型生成的嵌入表示，并结合逻辑回归（LR）、轻量梯度提升机（LightGBM）、前馈神经网络（FFNN）和宽残差网络（WRN）进行建模。其解决方案的关键在于通过融合多种语言模型的嵌入表示以增强表征能力，从而提升对复杂临床文本中隐含的TNM信息的捕捉效果；实验结果表明，单一嵌入表现相近，而多模型嵌入融合显著提升了分类性能。其中，使用TF-IDF特征的LightGBM在训练阶段表现最优，达到T、N、M三类0.9368、0.9524、0.8311的AUROC值及0.7559、0.7384、0.7017的F1分数；尽管在测试集上整体宏平均F1得分分别为0.978、0.957、0.879（测试集1）和0.807、0.767、1.0（测试集2），但模型在跨数据集评估时出现性能下降（宏F1从0.938降至0.858），暴露出当前模型在泛化能力、类别不平衡敏感性以及长篇临床文本处理方面的局限性。因此，尽管本研究构建了一个高效且可复现的基准模型流程，仍需进一步优化与验证，方具备向真实临床场景部署的可行性。
链接: https://arxiv.org/abs/2607.03466
作者: Joseph Itopa Abubakar,Jorge Jarme,Favour Igwezeke,Mary Adewunmi
机构: CaresAI(澳大利亚); Ateneo De Naga University(菲律宾); Faculty of Pharmaceutical Sciences(尼日利亚); Menzies School of Health Research(澳大利亚)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:This study aims to predict Tumor, Node, and Metastasis (TNM) stage labels independently, with the Cancer Genome Atlas (TCGA) pathology report as the sixth shared task of SMM4H-HeaRD 2026. The problem is framed as three multi-label classification tasks. We explore both classical and deep learning approaches using Term Frequency-Inverse Document Frequency (TF-IDF) features and embeddings from ClinicalBERT, BioBERT, and PubMedBERT. These representations are used with Logistic Regression (LR), Light Gradient Boosting Machine (LightGBM), Feed-Forward Neural Networks (FFNN), and Wide Residual Networks (WRN). Our results show that individual embeddings perform similarly to the TNM label classification, while their combination improves its predictive ability. WRN achieves AUROC scores of 0.839 (T), 0.8502 (N), and 0.803 (M) with F1-scores of 0.622, 0.702, and 0.9337, respectively, for the training phase. LightGBM with TF-IDF performs best with AUROC scores of 0.9368 (T), 0.9524 (N), and 0.8311 (M) and F1-scores of 0.7559 (T), 0.7384 (N), and 0.7017 (M) during the training phase. Furthermore, the result of the Codabench for the test sets indicates a Macro-F1 score of 0.978, 0.957, and 0.879 for the T, N, and M categories respectively for test set 1; while test set 2 records a Macro-F1 score for T, N, and M is 0.807, 0.767, 1.0 respectively. However, performance declined during the evaluation phase of the test sets, a drop from 0.938 to 0.858 of test set 1 to 2, for the Macro-F1 score across all stages; suggesting limitations in model generalizability, sensitivity to class imbalance, and challenges in processing lengthy clinical documents. Although this study provides an efficient baseline model and a reproducible pipeline, further optimization and validation are required before it can be considered suitable for use in a real-world clinical setting.
[NLP-108] he Classics at SemEval-2026 Task 3: Combining Transformer Models and LLM -Generated Annotations for Dimensional Aspect-Based Sentiment Analysis
【速读】： 该论文旨在解决传统情感分析（Sentiment Analysis）局限于类别化标签（如正面或负面）而无法捕捉情感细微差异的问题，提出一种面向维度化方面情感分析（Dimensional Aspect-Based Sentiment Analysis, DABSA）的解决方案。其核心挑战在于对情感的“效价”（valence，即正负性）与“唤醒度”（arousal，即强度）进行细粒度、连续值的回归预测。针对这一问题，论文的关键解决方案包括：在回归任务中采用基于Transformer编码器模型的加权集成方法以提升预测精度；针对俄语数据，引入大语言模型（LLM）生成合成情感描述以增强输入表示；在抽取任务中，通过微调解码器型大语言模型实现结构化预测，使系统能够联合识别方面（aspect）、情感类别（category）、观点（opinion）及其对应的连续情感得分。整体方案实现了从分类到连续维度情感建模的范式转变，显著提升了情感分析的表达能力与实用性。
链接: https://arxiv.org/abs/2607.03414
作者: Rafif Alshawi,Amit Raj,Aleksey Kudelya,Alexander Shirnin
机构: HSE University (高等经济大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:This paper presents an approach to the SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis. We investigate methods for moving beyond traditional categorical sentiment (e.g., positive or negative) to predict fine-grained, real-valued scores for sentiment “valence” (positivity) and “arousal” (intensity). We participate in two subtasks: predicting these scores for given aspects (Subtask 1) and extracting full sets of sentiment details, including aspects, categories, and opinions alongside their scores (Subtask 3). Our approach for the regression task involves a weighted ensemble of transformer-based encoder models. For the Russian language, we further enhance the input by using a large language model (LLM) to generate synthetic sentiment descriptions. For the extraction task, we fine-tune a decoder LLM to perform structured prediction, allowing the system to identify sentiment elements and estimate their numerical scores simultaneously.
[NLP-109] Large-scale dataset of automatically classified rhetorical sections in scientific papers
【速读】： 该论文旨在解决科学文献中段落级章节结构自动识别的问题，以实现对科研写作模式的细粒度大规模分析。其核心解决方案是构建一个基于规则分类算法的标注数据集，通过对语义学者开放研究语料库（S2ORC）中1560万篇科学论文进行质量筛选与自动化标注，实现了对引言、方法、结果和讨论等主要章节的精准识别。该数据集覆盖了以理工医农为主的学科领域，尤其在医学与生物学领域具有较强代表性，并通过人工与大语言模型（LLM）双重验证表明，分类器与人类标注者的一致性达到与人类间标注者一致性相当的水平，从而为大规模计算型科学话语研究提供了可靠的数据基础。
链接: https://arxiv.org/abs/2607.03381
作者: Daniel Verdi,Jacob Aarup Dalsgaard,Roberta Sinatra
机构: Stanford University (斯坦福大学); University of Copenhagen (哥本哈根大学); IT University of Copenhagen (哥本哈根信息技术大学); Pioneer Centre for Artificial Intelligence (P1) (丹麦先锋人工智能中心); Complexity Science Hub (复杂科学枢纽)
类目: Digital Libraries (cs.DL); Computation and Language (cs.CL)
备注: Data descriptor; includes supplementary information (1 PDF). 15 pages, 4 figures, 5 tables
Abstract:Scientific papers follow rhetorical structures that organize content into sections such as Introduction, Methods, Results, and Discussion. Automatically identifying these sections at scale enables granular analysis of scientific writing patterns. We present a dataset of section-level annotations for millions of scientific papers from the Semantic Scholar Open Research Corpus (S2ORC). Using a rule-based classification algorithm, we identified and labeled major sections across 15.6 million papers after quality filtering. The dataset covers primarily STEM disciplines, with strong representation in medicine and biology. We provide comprehensive human and LLM-based validation showing that classifier agreement with human annotators is on par with human inter-annotator agreement. This dataset enables large-scale computational studies of scientific discourse and writing patterns.
[NLP-110] Spectral Signatures of Large Language Models
【速读】： 该论文旨在解决大规模公开可用大语言模型（Large Language Models, LLMs）在系统化管理与量化分析中面临的挑战，如模型谱系追踪、许可证合规性验证及性能评估等问题。传统任务特定基准测试方法难以适应LLMs在架构、规模和训练流程上的巨大差异。为此，本文提出基于重尾自正则化理论（Heavy-Tailed Self-Regularization theory）的谱形特征度量方法，以模型权重经验谱密度（empirical spectral density）的形状信息作为其紧凑的谱签名（spectral signature），该签名能够捕捉预训练模型的内在特性，并在微调等后续训练过程中保持稳定，适用于模型层面的分析。该方法具有无需依赖数据、计算高效且尺度不变的优势，支持大规模实际应用。研究构建了一个涵盖主流开源LLM家族的多样化模型语料库，系统性地对比了谱特征与非谱特征度量在不同模型及下游任务中的表现。实验表明，该谱签名可有效支持模型谱系追踪、无监督聚类相似模型以及模型性能量化，为跨模型的广泛性能趋势提供有意义的代理指标，显著提升了大规模模型集合的组织、比较与分析效率。
链接: https://arxiv.org/abs/2607.03377
作者: Zhuoying Zhang,Ishan V. Prasad,Yuanzhe Hu,Zihang Liu,Hengrui Luo,Pu Ren,Yaoqing Yang
机构: Rice University(莱斯大学); Dartmouth College(达特茅斯学院); Georgia Institute of Technology(佐治亚理工学院); University of California, Berkeley(加州大学伯克利分校); Lawrence Berkeley National Laboratory(劳伦斯伯克利国家实验室)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:The rapidly growing repository of publicly available large language models (LLMs) presents significant challenges for systematic management and quantification at scale, such as model lineage tracing, licensing, and evaluation. However, task-specific benchmarks are insufficient for this setting, as LLMs differ widely in architectures, scales, and training procedures. To address this challenge, we adopt spectral shape-based metrics for managing and quantifying LLMs based on Heavy-Tailed Self-Regularization theory. Our approach uses the shape information of the weight empirical spectral density as a compact spectral signature of each model. This signature captures intrinsic properties of pretrained models and remains robust during post-training, making it suitable for model-level analysis. In addition, this metric is data-free, computationally-efficient, and scale-invariant, enabling large-scale analysis in practice. Moreover, we curate a large and diverse model corpus consisting of major open-source LLM families, and use it to systematically benchmark spectral and non-spectral metrics across models and downstream tasks. We show that our spectral signature supports the tracking of the model lineage, the unsupervised clustering of similar models, and the quantification of the model performance. Overall, the proposed spectral signature provides a meaningful proxy for broad performance trends across LLMs, enabling efficient organization, comparison, and analysis of large model collections.
[NLP-111] Pathways of Visual Information Flow in Vision-Language Models
【速读】： 该论文旨在解决视觉信息在视觉-语言模型（VLMs）中如何被路由与处理的核心问题，即揭示视觉信息在模型内部的传递路径及其动态选择机制。其解决方案的关键在于通过因果补丁（causal patching）技术，在受控的合成与自然数据集上系统性地分析模型内部的信息流，发现模型在完成视觉任务时依赖两种截然不同的信息通路：一是直接通路（direct pathway），即视觉信息保留在图像标记（image token）表示中，并由后续层的最终标记读取；二是文本中介通路（text-mediated pathway），即视觉信息首先被转移到查询标记（query token）中，再由最终标记读取。研究进一步表明，通路的选择具有任务依赖性，且受数据分布和提示设计的影响；同时，通过注意力敲除（attention knockouts）和受损输入补丁等干预手段，发现模型具备路径灵活性——当常规通路被破坏时，可灵活切换至文本中介通路作为备用机制。这一发现统一了先前研究中的矛盾结果，表明基于消融的干预不仅能揭示模型的正常行为，更能暴露其潜在能力。综上，该研究为视觉-语言模型中的视觉信息流动提供了机制层面的解析，并凸显了其内部机制在外部干预下的高度可塑性。
链接: https://arxiv.org/abs/2607.03358
作者: Israfel Salazar,Stella Frank,Dan Oneata,Desmond Elliott,Constanza Fierro
机构: University of Copenhagen (哥本哈根大学); Technical University of Denmark (丹麦技术大学); POLITEHNICA Bucharest (布加勒斯特理工学院); Bitdefender, Romania (比特防御, 罗马尼亚)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:We study how visual information is routed in vision-language models (VLMs). Using causal patching on controlled synthetic and natural datasets, we find that models rely on two distinct pathways to solve visual tasks: A direct pathway, where visual information is retained in image token representations and read out by the final token at later layers, and a text-mediated pathway, where visual information is first transferred to the query tokens and then read out by the final token. Across three visual tasks, we show that pathway selection is task-dependent, and that data distribution and prompt design can also modulate which pathway is used to solve the image-based query. Moreover, using attention knockouts and corrupted-input patching, we find that these pathways are flexible, under certain interventions, models can rely on the text-mediated pathway as a fallback when the usual pathway is ablated. This behavior unifies findings in prior work and shows that ablation-based interventions can reveal what models could do rather than what they normally do. Together, our results provide a mechanistic characterization of visual information flow in VLMs and highlight the flexibility of their internal mechanisms under intervention.
[NLP-112] Efficient Decentralized Multi-task Dataset Valuation via Model Merging
【速读】： 该论文旨在解决多任务场景下数据集估值的准确性与效率问题，尤其针对多个数据贡献者为训练多任务模型而参与数据市场时所面临的公平性与透明性挑战。现有估值方法大多局限于单任务设置，无法有效反映数据在跨任务场景中的实际贡献；同时，基于Shapley值或重训练的方法计算开销大，且难以在无可信中心协调方、存在严格隐私约束的去中心化环境中应用。其解决方案的关键在于提出一种名为DMVM（基于模型融合的去中心化多任务估值）的新框架，通过任务算术（task arithmetic）直接从模型参数组合中推断各数据集对多任务性能的边际贡献，从而避免了传统方法所需的重训练和原始数据共享。该方法在参数空间中构建模型组合以评估数据价值，实现了高可扩展性与计算效率，并与多任务泛化行为显式对齐。为支持去中心化部署，研究进一步设计了一种安全聚合协议，在不暴露个体模型参数或私有数据的前提下实现协作估值。此外，论文提供了理论误差界以刻画估值近似的质量，并在计算机视觉与自然语言处理任务上通过全面实验验证了该框架的有效性。
链接: https://arxiv.org/abs/2607.03346
作者: Mohammadsajad Alipour,Mohammad Mohammadi Amiri
机构: Rensselaer Polytechnic Institute (伦斯勒理工学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Accurate and efficient dataset valuation is essential for enabling fair and transparent data marketplaces, especially when multiple contributors provide data for training multi-task models. Most existing valuation methods, however, are limited to single-task settings, overlooking scenarios where a buyer aims to optimize performance across multiple downstream tasks. Moreover, traditional valuation approaches, such as Shapley-based or retraining-based methods, are computationally expensive and poorly suited for decentralized environments without a trusted central coordinator and with strict privacy constraints. We propose DMVM (Decentralized Multi-task Valuation via Model Merging), a novel framework that bypasses retraining and data sharing by leveraging task arithmetic to infer dataset contributions directly from model combinations. Instead of retraining or sharing raw data, DMVM quantifies how models trained on different datasets combine in parameter space to infer each dataset’s marginal utility across multiple tasks. This formulation yields a valuation process that is scalable, computationally efficient, and explicitly aligned with multi-task generalization behavior. To support decentralized deployment, we introduce a secure aggregation protocol that enables collaborative valuation without revealing individual model parameters or private data. We also provide theoretical error bounds characterizing the approximation quality of DMVM and validate our framework through comprehensive experiments on computer vision and natural language processing tasks.
[NLP-113] From Gentlemen to Frontiermen: Masculine Formations in English-Language Fiction (1771–1930)
【速读】： 该论文旨在解决19世纪小说中男性气质（masculinity）并非单一理想，而是多种竞争性叙事模式交织演变的问题。其核心挑战在于如何系统性地捕捉和量化男性气质在文学文本中的动态变迁。解决方案的关键在于构建一种基于无监督结构主题模型（unsupervised structural topic model）的计算框架，通过共指消解（coreference resolution）将人物名称、名词指称与代词整合为以男性角色为中心的引用链，从而聚焦于男性角色塑造。该模型引入出版年份与作者性别作为主题流行度协变量，从txtLAB Novel450语料库中涵盖的150部英美经典小说（1771–1930年）中识别出六种具有区分性的男性气质形态：贵族骑士式、基督教男性的道德典范、绅士体面、乡绅、职业—商业型以及帝国—冒险型。分析显示，依赖世袭地位与神圣权威的男性气质逐渐衰落，而以谋生工作、冒险精神及扩张性成就为核心的男性气质显著上升，其中尤以边疆—荒野语域的帝国—冒险型男性气质增长最为迅猛。此外，此类新兴男性气质在男性作者的小说中更为普遍。该方法不依赖预设词汇库即可生成与既有学术分类高度契合的主题，实现了对长19世纪性别化权力结构重组的可复现测量，为理解男性气质的历史演化提供了数据驱动的新范式。
链接: https://arxiv.org/abs/2607.03323
作者: Rong Wang
机构: University of Tuebingen(图宾根大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Masculinity in nineteenth-century fiction is not a single ideal but a field of competing scripts. Drawing on 150 British and American canonical novels from the txtLAB Novel450 corpus, published between 1771 and 1930, this paper examines the changing relative prominence of competing models of masculine authority. To focus the analysis on masculine characterisation, the study extracts male-character-centred text windows by using coreference resolution to group names, nominal mentions, and pronouns into character-specific reference chains. It then fits an unsupervised structural topic model with publication year and author gender as topic-prevalence covariates. The model identifies six distinct masculine formations: aristocratic-chivalric, Christian manhood, gentlemanly respectability, country squire, professional-commercial, and imperial/adventure. Across the corpus, formations tied to inherited rank and sacred authority decline, while those organised around paid work and adventure rise. The largest increase occurs not in professional-commercial breadwinning but in imperial/adventure masculinity, particularly the frontier-wilderness register. The trajectory points to a reallocation from inherited and sacred status towards achieved, commercial, and expansionary forms of masculine authority. Adventurous and commercial formations are also more prevalent in novels by authors recorded as male. Because these formations emerge without a seeded vocabulary yet align with categories established in independent scholarship, the article offers a reproducible method for measuring the reorganisation of gendered authority across the long nineteenth century.
[NLP-114] ACG: Trajectory-Aware Commit Gating for Diffusion Language Model Decoding
【速读】： 该论文旨在解决扩散语言模型（Diffusion Language Models, DLLMs）在文本生成过程中因忽略去噪轨迹而产生的决策偏差问题。现有解码器通常仅基于当前时刻的预测分布进行逐词提交，导致将瞬时高置信度但缺乏长期一致支持的候选词过早锁定，同时延迟了具有跨步骤稳定支持的优质候选词。其核心解决方案是提出一种无需训练的门控式解码机制——轨迹感知提交门控（Trajectory-Aware Commit Gating, TACG），其关键在于将词元身份锚定于基础后验分布，并仅利用轨迹感知信号判断当前提议是否具备提交条件。TACG结合了时间隐式对数几率引导（Temporal Implicit Logits Guidance, TILG），通过指数移动平均历史对数几率构建自参考基准，并在自然参数空间中对比当前对数几率与历史参考；以及历史门控（History Gate, HG），强制短期提议保持一致性后再允许提交。二者协同配合一个受限的额外提升预算，形成无需辅助网络或额外前向传播的稳定性约束提交规则。实验表明，TACG在代码（HumanEval、MBPP）和数学（GSM8K、MATH500）任务上均显著提升或维持准确性，同时减少去噪步数并提高每前向传播生成的词元数（Tokens Per Forward, TPF）。
链接: https://arxiv.org/abs/2607.03236
作者: Chengcheng Wang,Tingzhang Luo,Wenhao Li,Jianyuan Guo,Chang Xu
机构: University of Sydney (悉尼大学); City University of Hong Kong (香港城市大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Diffusion language models (DLLMs) generate text by iteratively denoising masked positions, exposing a trajectory of predictive distributions rather than a single instantaneous belief. Most existing decoders ignore this trajectory and commit tokens from the current snapshot alone, conflating confidence with commitment readiness: a transient top-1 peak under incomplete context can be locked in, while candidates with consistent cross-step support are delayed. We propose Trajectory-Aware Commit Gating (TACG), a training-free gate-level decoder that anchors token identities to the base posterior and uses trajectory-aware signals only to decide whether the current proposal is ready to commit. TACG combines Temporal Implicit Logits Guidance (TILG), which keeps an exponential moving average of past logits as a self-reference and contrasts the current logits against this reference in natural-parameter space, with a History Gate (HG) that enforces short-term proposal persistence before commitment. Together with a capped extra-promotion budget, these components yield a stability-constrained commit rule without auxiliary networks or extra forward passes. We evaluate TACG on LLaDA, Dream, and LLaDA2-Mini across code (HumanEval, MBPP) and math (GSM8K, MATH500) benchmarks; it typically improves or preserves accuracy while reducing denoising steps and increasing tokens per forward (TPF). The code is publicly available at this https URL.
[NLP-115] ransition Information Density: Morphological Trajectories Synesthetic Perception and Structured Interpolation in Neural Training (or: The Synesthetic AI)
【速读】： 该论文旨在解决标准机器学习训练中将数据视为离散端点对而忽略其间结构的问题，提出通过引入“过渡信息密度（Transition Information Density, TID）”与“位置身份（Positional Identity）”来捕捉类别间连续路径上的结构化中间状态所蕴含的信息。其核心解决方案在于：在训练过程中显式建模中间状态的位置身份（即在A到B连续体中的精确位置），并通过结构化插值方式构建具有明确位置身份的样本（如条件C3），从而显著降低模型在语音/语言和语义描述等抽象表征空间中的内在维度（例如，C3 vs. C2在语音/语言空间中分别为3.33与10.81）。研究发现，该效应由位置身份的结构决定，而非样本数量，并且在不同模态下存在边界条件——仅在非视觉及跨模态表征中显现。此外，通过九维形状特征框架与两邻近邻居（TwoNN）分析验证了表征空间的全局坍缩（视觉空间，0.075）与一致性（语音空间，0.977）。因此，该工作的关键贡献在于形式化定义了TID与位置身份，构建了可分离轨迹结构、数据量与位置身份三因素的四条件实验设计，并提出了一个系统性的表征分析框架。
链接: https://arxiv.org/abs/2607.03210
作者: Sam Mao
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 38 pages, 9 figures, 4 tables. Empirical results from structured interpolation training across four representational mediums. Pipeline scripts, experimental data, and the Synesthesia Grid algorithm available upon reasonable request
Abstract:Standard machine learning training presents data as discrete endpoint pairs, omitting the structure of the space between them. This paper introduces Transition Information Density (TID) – the information content recoverable from structured intermediate states between categorically distinct training endpoints – and Positional Identity, the defined location of an intermediate state on the A-to-B continuum. Both constructs are grounded in three empirical contexts: grapheme-color synesthesia, the Synesthesia Grid (a boundary-contour morphing algorithm instantiating TID in visual morphological space), and a four-condition training experiment across four representational mediums. Probes trained on structured interpolation at defined Positional Identities (C3) exhibit substantially lower intrinsic dimensionality than volume-matched controls (C2) in Phonetic/Linguistic (C3: 3.33 vs. C2: 10.81) and Semantic Description (C3: 4.59 vs. C2: 8.67) mediums. Visual and cross-modal mediums do not show this effect, establishing a modality boundary condition. A fixed-N=50 comparison confirms that Positional Identity structure, not sample count, drives the effect. Resolution N scales monotonically with representational richness. Pooled TwoNN analysis reveals globally collapsed representations in visual space (0.075) and globally consistent representations in phonetic space (0.977). The paper contributes a formal definition of TID and Positional Identity, a nine-metric shape characterization framework, and a four-condition experimental design isolating trajectory structure, data volume, and Positional Identity as distinct factors.
[NLP-116] S-DiverSe: Spanish Diverse Speech INTERSPEECH2026
【速读】： 该论文旨在解决神经病理性语言障碍（neurologically affected speech）在西班牙语自动语音识别（ASR）中的识别难题，尤其是针对真实场景下（in-the-wild）患有肌萎缩侧索硬化症（ALS）、帕金森病及中风患者的语音。其核心挑战在于现有ASR系统对非标准、受疾病影响的语音泛化能力不足。解决方案的关键在于构建并发布S-DiverSe数据集——一个包含3.2小时真实环境下采集的西班牙语语音、覆盖22名患者、444段人工转录音频的多样化数据集，并附带说话人性别、疾病类型和可理解性等元数据。研究通过基准测试与初步适配实验发现，相较于微调（fine-tuning），启发式文本后处理（heuristic text post-processing）在处理域外神经病理性西班牙语语音时更具鲁棒性，凸显了建立专门针对真实场景西班牙语神经病理性语音评估基准的重要性。
链接: https://arxiv.org/abs/2607.03207
作者: Fernando López,Fernando Ibañez,Ana Martínez,Iván Alonso,Pablo Gómez,Santosh Kesiraju,Jordi Luque
机构: Scientific Research, Telefónica Innovación Digital, Spain; Universidad Autónoma de Madrid, Spain; Brno University of Technology, Czech Republic
类目: Computation and Language (cs.CL); Sound (cs.SD)
备注: Accepted in Interspeech 2026
Abstract:Automatic speech recognition (ASR) has advanced remarkably for standard speech, yet speech affected by neurological conditions remains a challenge. We present S-DiverSe (Spanish Diverse Speech), a corpus of 3.2 hours of in-the-wild Spanish speech from 22 speakers with amyotrophic lateral sclerosis, Parkinson’s disease, and stroke. The dataset contains 444 manually transcribed audio segments with metadata on speaker sex, disease type, and intelligibility. S-DiverSe is designed to support ASR evaluation and development for neurologically affected Spanish speech. We describe the dataset, analyze its composition, and report baseline ASR results alongside initial adaptation experiments. Our findings reveal that heuristic text post-processing is more robust than fine-tuning for out-of-domain neurological Spanish speech. This underscores the need for dedicated in-the-wild Spanish benchmarks.
[NLP-117] KARMA: Knowledge graph-based Automated Reasoning Materialization and Alignment
【速读】： 该论文旨在解决模板生成式对比合成（template-based contrastive synthesis）中存在的“分辨率不匹配问题”（Resolution Mismatch Problem），即传统方法生成的候选样本仅在少数实体槽位（entity-slot）上存在差异，导致序列级优化无法有效传播至具有判别性的槽位，从而限制了模型对关键语义差异的学习能力。其核心解决方案是KARMA，通过在领域知识图谱（domain knowledge graph）上枚举符合模式约束的路径，并将其语义化为槽位对齐的对比候选样本，以增强多样性与语义区分度。进一步地，槽位并行对齐（Slot-Parallel Alignment, SPA）采用解耦的槽位级目标函数，将偏好监督精准引导至具有判别性的实体槽位，同时引入槽位感知的掩码注意力机制作为可选的紧凑评估实现方式。实验表明，KARMA在生物医学、计算机科学和化学等多个基准测试中均优于基础大语言模型（LLM）及同数据微调（same-data SFT）基线，并在性能上可媲美序列级与标记级偏好学习方法。
链接: https://arxiv.org/abs/2607.03166
作者: Jinkyeong Choi,Chaebin Jeong,Donghyeon Park
机构: Sejong University (世宗大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: First version, 20 pages
Abstract:Template-based contrastive synthesis is scalable, but its candidates often differ only in a few entity-slots while sequence-level optimization spreads supervision over mostly shared templates. We formalize this as the Resolution Mismatch Problem and propose KARMA, which enumerates schema-constrained paths over domain knowledge graphs and verbalizes them into slot-aligned contrastive candidates. Slot-Parallel Alignment (SPA) then applies a decoupled slot-level objective to route preference supervision to discriminative entity-slots, with slot-aware masked attention serving as an optional packed-evaluation implementation. Across biomedical, computer-science, and chemistry benchmarks, KARMA outperforms base LLM and same-data SFT baselines, and compares favorably with sequence and token-level preference methods.
[NLP-118] he Role of Prompt Language and Translation-Theory-Driven Prompts in Large Language Models : A Case Study on Spanish-Chinese Journalistic Translation ACL
【速读】： 该论文旨在解决生成式 AI 在西班牙语-中文新闻翻译中，提示词（prompt）的语言形式与基于翻译理论的提示设计对翻译质量的影响问题。其核心解决方案在于采用基于翻译理论驱动的提示设计，通过系统化调整提示类型（如简明型、详尽型等）和提示语言（如中文、英文等），探索其对翻译质量的差异化影响。研究发现，尽管自动化评估指标（如BLEU和BERTScore-F1）显示基准提示（BASE）表现最佳，但人工评估（基于多维质量度量框架，MQM）却表明以简洁为导向的提示（BRIEF）在整体质量上显著更优（MQM得分8.66 vs. 7.84），这一矛盾可能源于自动化指标对单一参考译文的依赖性。进一步分析显示，基于翻译理论的提示能有效降低“生硬风格”错误，但“不地道风格”错误仍普遍存在。此外，提示语言的选择在两种评估方式下均未产生显著影响。因此，该研究的关键贡献在于证明：基于翻译理论的提示设计可为专业新闻翻译提供可测量的质量提升，尤其在专家评估中表现突出，其对语言学习者的教学价值虽具启发性，仍需通过用户导向实证研究进一步验证。
链接: https://arxiv.org/abs/2607.03160
作者: Haohong Lai,Weijia Li
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Published in the Proceedings of the 27th Annual Conference of the European Association for Machine Translation (EAMT 2026), pp. 927-945. ACL Anthology entry forthcoming
Abstract:This study examines how prompt language and translation theory-driven prompt design influence the quality of Spanish-Chinese journalistic translations generated by GPT-5.2. A parallel corpus of four editorials from El Pais was translated under 48 experimental conditions (4 prompt types, 3 prompt languages, and 4 articles). Translation quality was assessed using BLEU and BERTScore-F1 for automated evaluation, alongside human evaluation based on the Multidimensional Quality Metrics (MQM) framework. Automated metrics identified the baseline prompt (BASE) as the best-performing condition, whereas human evaluation ranked the brief-oriented prompt (BRIEF) highest (MQM: 8.66 vs. 7.84), a reversal likely attributable to the single-reference constraint inherent in automated measures. Sub-error type analysis revealed that translation theory-driven prompts selectively reduced Awkward style errors, while Unidiomatic style errors persisted across conditions. Prompt language had a negligible impact under both evaluation paradigms. These results indicate that translation theory-driven prompts can yield measurable quality gains under expert evaluation of journalistic translations, although their pedagogical implications for language learners remain suggestive and require validation through user-based studies.
[NLP-119] Conditional Diffusion Guided Knowledge Transfer for Multi-Domain Knowledge Graph Completion
【速读】： 该论文旨在解决多领域知识图谱补全（Multi-domain Knowledge Graph Completion, MKGC）中因传统知识迁移方法强制跨图实体一致性约束而导致的领域特异性上下文信息抑制问题，尤其在低资源数据场景下，此类设计会损害实体表示的完整性并限制性能提升。其解决方案的关键在于提出一种基于生成式范式的框架DMKGC，该框架采用条件扩散模型（conditional diffusion model）实现无偏的知识迁移：将每个知识图谱视为实体完整信息的部分视图，通过以支持域知识图谱为条件、以领域无关的先验嵌入为生成目标，生成具有领域通用性的实体嵌入；同时，利用先验嵌入作为代理生成目标，确保生成过程不偏向任一特定图谱，并通过联合训练使生成嵌入具备跨图预测能力，从而在保留领域特异性信息的同时实现高效知识迁移。实验表明，该方法在3个基准上的14个知识图谱上实现了平均4.3%的尾部实体预测MRR提升，且在低资源设置下仍保持显著优势。
链接: https://arxiv.org/abs/2607.03154
作者: Jiawei Sheng,Taoyu Su,Xixun Lin,Xiaodong Li,Tingwen Liu
机构: Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, UCAS
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-domain knowledge graph completion (MKGC) aims to improve missing triple prediction in a target KG by transferring knowledge from other support KGs. Existing methods typically enforce consistency constraints on equivalent entities across KGs to transfer knowledge, which risks suppressing domain-specific contextual information of entities. This design can also compromise entity representation information from all KG domains, impeding performance improvements, especially in low-resource data scenarios. To address this, we pioneer a generation-based paradigm for MKGC and propose DMKGC, a conditional diffusion-guided knowledge transfer framework. Our key insight is to treat each KG as a partial view of the entity entire information, and generate informative domain-general entity embeddings through diffusion models conditioned on support KGs. Particularly, we first initialize domain-agnostic entity embeddings as prior entity embeddings, and then encode them within individual KGs. Afterward, we fuse equivalent entities from support KGs as the conditional diffusion generation guidance. We leverage the prior entity embeddings as the proxy generation objective, which ensures this conditional generation to be unbiased towards any conditioned KGs. Simultaneously, we also train the generated embeddings to be predictive across KGs, thus preserving domain-specific information. Extensive experiments on 14 KGs in 3 benchmarks demonstrate a 4.3% average MRR improvement in tail entity prediction over state-of-the-art methods, with sustained gains in low-resource data settings.
[NLP-120] Dont Wait to Reply: Towards Responsive yet Thoughtful Dialogue through Proactive Thinking
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在处理复杂任务时因推理机制具有被动性而导致的响应延迟问题。当前的推理模式仅在接收到用户输入后才触发，这种反应式机制难以实现类人对话中自然流畅的交互体验，尤其是在对话间隙无法主动规划未来内容的情况下。为弥合这一差距，论文提出“主动推理”（Proactive Thinking）框架，其核心在于利用对话中的空闲时段预先计算潜在的回应元素，从而实现对未来的前瞻性规划。该方案的关键创新在于无需额外训练的基线方法——通过预测未来状态并采用推测性持续推理（speculative continual thinking），在效率与生成质量之间取得平衡。研究进一步将三个不同复杂度的基准测试改造为具备时间感知能力的环境，以模拟真实对话流。实验结果表明，主动推理显著提升了交互效率，同时保持了原有性能水平。因此，该工作倡导构建更智能、具备前瞻性和实时响应能力的下一代对话式人工智能系统。
链接: https://arxiv.org/abs/2607.03093
作者: Ante Wang,Jiaqi Fu,Xuanyi Chen,Ruotian Ma,Zhaopeng Tu,Weizhi Ma,Yang Liu
机构: Tsinghua University (清华大学); Institute for AI Industry Research (AIR), Tsinghua University (清华大学人工智能产业研究院); Tencent (腾讯)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Thinking has emerged as a critical capability for Large Language Models (LLMs) tackling complex tasks. However, its reactive nature, where reasoning is passively triggered only upon receiving a user response, inevitably introduces latency that compromises conversational fluidity. This stands in sharp contrast to human dialogue, where speakers proactively anticipate and plan future content during natural pauses to ensure seamless interaction. To bridge this gap, we propose Proactive Thinking, a framework that empowers models to pre-compute potential response elements during conversational downtime instead of waiting idly for the next input. We then introduce a training-free baseline that can think ahead by anticipating future states, balancing efficiency and quality through speculative continual thinking. To evaluate this approach in practice, we adapt three benchmarks of varying complexity into time-aware environments that simulate real-time conversational flow. We demonstrate that proactive thinking effectively improves interaction efficiency without compromising performance. Ultimately, this work advocates for a fundamental shift toward more intelligent, anticipatory, and real-time conversational AI.
[NLP-121] Alignment-Guided Largest Table Overlap Size Estimation SIGMOD2027
【速读】： 该论文旨在解决在大规模表格仓库中高效估计两张表格最大重叠规模的问题，以支持阻塞（blocking）和基于表格的查询检索。现有最优方法Armadillo通过独立嵌入每张表格并利用嵌入相似性近似重叠比例，但在异构数据仓库中仍面临三大挑战：（C1）重叠依赖于行列结构，即匹配单元格必须在两表联合对齐下同时保持行与列归属关系，而现有编码方式未能显式建模此结构；（C2）独立编码缺乏表间对齐信号的显式通道，导致预测偏向全局相似性；（C3）朴素的值编码易过拟合于特定语料分布，造成跨域性能下降。为此，论文提出ALORE，一种可扩展且领域鲁棒的重叠比例估计器，其核心在于三个设计原则：（P1）显式表示行列结构；（P2）在训练中引入低成本的交互信号以暴露表间对齐信息，避免昂贵的对齐搜索；（P3）降低对语料特定值分布的敏感性。ALORE通过双视角行列超图编码器、基于对齐引导的目标函数以及领域鲁棒的值映射机制实现上述原则。实验结果表明，ALORE在多个跨领域、多尺度的数据集上显著优于现有方法，整体平均绝对误差（MAE）降低达55%，零样本迁移场景下更达69%，同时实现最高89倍的速度提升，并在基于表格的查询检索任务中验证了其有效性。
链接: https://arxiv.org/abs/2607.03049
作者: Ge Lee,Shixun Huang,Zhifeng Bao,Shazia Sadiq,Yanchang Zhao
机构: RMIT University (皇家墨尔本理工大学); University of Wollongong (卧龙岗大学); The University of Queensland (昆士兰大学); Data61, CSIRO (数据61, 澳大利亚联邦科学与工业研究组织)
类目: Computation and Language (cs.CL); Databases (cs.DB)
备注: Accepted/to appear at SIGMOD 2027
Abstract:Fast estimation of the size of the largest overlap between tables enables blocking and query-by-table retrieval in large table repositories. The first and the state-of-the-art estimator Armadillo improves efficiency by embedding each table independently and approximating overlap ratio via embedding similarity. However, accurate estimation in heterogeneous repositories remains limited by three challenges: (C1) overlap depends on row-column structure, i.e., each matched cell must preserve both its row and column membership under a joint alignment of the two tables, but existing encodings leave this structure to be inferred indirectly; (C2) independent encoding provides no explicit channel for inter-table alignment signals, biasing prediction toward global similarity; (C3) naive value encodings overfit to corpus-specific distributions, causing cross-domain degradation. Hence, we propose ALORE, a scalable and domain-robust overlap ratio estimator built on three principles: (P1) explicitly represent row-column structure; (P2) expose inter-table alignment signals during training without expensive alignment search; (P3) reduce sensitivity to corpus-specific value distributions. ALORE instantiates these principles with a Two-View Row-Column Hypergraph encoder, alignment-guided objectives with inexpensive interaction signals, and a domain-robust value mapping. Experiments on multiple datasets spanning diverse domains and scales, including a large real-world corpus beyond prior benchmarks, show that ALORE outperforms the state of the art. ALORE reduces MAE by up to 55% overall and 69% in zero-shot transfer, while achieving up to 89x speedup. We further validate its effectiveness for query-by-table retrieval.
[NLP-122] Can Model Merging Improve Aggregation in DiLoCo?
【速读】： 该论文旨在解决分布式训练中通信效率与模型性能之间的权衡问题，特别是在使用局部随机梯度下降（local SGD）和DiLoCo等低通信开销方法时，随着本地模型数量增加或本地训练步数增多，其性能会显著劣于数据并行的基准方法。其核心解决方案在于建立局部SGD/DiLoCo中的伪梯度聚合步骤与基于任务算术（task arithmetic）的模型融合方法之间的明确类比，从而将先进的模型融合技术引入分布式优化框架。关键创新点在于识别出一种名为Iso-C的模型融合方法在该场景下表现优异，并据此提出IsoLoCo——通过为Iso-C引入Nesterov动量机制以适配分布式训练。实验表明，即使不包含动量机制，采用Iso-C聚合的DiLoCo SGD也优于传统的伪梯度平均与动量型DiLoCo；而改进后的IsoLoCo在不同工作节点数、模型规模及内部训练步数下均显著超越DiLoCo，且优势随节点数增长而扩大，验证了融合启发式聚合在低通信成本分布式训练中的有效性。
链接: https://arxiv.org/abs/2607.03011
作者: Stefan Horoi,Benjamin Thérien,Guy Wolf,Eugene Belilovsky
机构: Université de Montréal (蒙特利尔大学); Mila – Quebec AI Institute (魁北克人工智能研究所); Concordia University (康考迪亚大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Model merging techniques, which aggregate independently finetuned models into one to combine their capabilities, have become a topic of significant interest in recent years, with a broad array of methods having been proposed to tackle this problem. Simultaneously, an emerging trend in distributed learning has been the use of methods such as local SGD and DiLoCo, which greatly reduce communication costs by periodically aggregating the independently trained local models. However, these communication-efficient methods have been shown to degrade in performance relative to the FLOP-matched data-parallel gold standard as the number of independent local models grows and as the number of local training steps before global communication is increased. In this work, we draw an explicit analogy between the pseudo-gradient aggregation step in local SGD/DiLoCo and task arithmetic-based model merging, establishing a straightforward way to utilize merging methods in the context of distributed optimization. We then evaluate multiple state-of-the-art model merging methods in this setting and identify one method in particular, Iso-C, as a promising approach for improving DiLoCo. We find that DiLoCo SGD with Iso-C aggregation outperforms not only simple pseudo-gradient averaging but even the momentum-based DiLoCo, despite lacking a momentum mechanism itself. Building on this finding, we propose IsoLoCo, which adapts Iso-C for distributed training by equipping it with Nesterov momentum. Our empirical evaluations on language model pre-training across varying numbers of local workers show that IsoLoCo significantly outperforms DiLoCo, with the gap between them widening as the number of workers increases. This advantage remains present across model sizes and inner step counts, confirming that merging-inspired aggregation is an effective strategy for low-communication distributed training.
[NLP-123] psytechlab at CLPsych 2026: Utilising Natural Language Processing methods and Large Language Models for Social Media Text Analysis ACL
【速读】： 该论文旨在解决如何利用自动化分析工具从社交媒体文本中有效识别与评估用户心理状态及整体福祉水平的问题。其核心挑战在于准确捕捉语言表达中的细微情感线索与认知特征，以实现对个体心理状态的可靠推断。解决方案的关键在于融合多种自然语言处理（Natural Language Processing, NLP）技术，包括长短期记忆网络（Long Short-Term Memory, LSTM）、基于BERT的模型以及大语言模型（Large Language Models, LLMs），通过多模型协同提升对自我状态描述与幸福感分析的准确性与一致性。该方法在CLPsych共享任务2026的摘要生成任务中取得了领先的连贯性与矛盾性评分，验证了多模态深度学习架构在心理健康状态推断中的有效性，为构建更智能、精准的心理健康支持系统提供了重要实践基础。
链接: https://arxiv.org/abs/2607.03003
作者: Igor Buyanov,Nafisa Valieva,Ekaterina Mazurina
机构: Psytechlab
类目: Computation and Language (cs.CL)
备注: Accepted by CLPsych2026. CLPsych 2026 will be held at ACL in San Diego July 4th, 2026
Abstract:Social media posts are a rich and valuable source of data for analyzing mental health states and users’ well-being using automated analysis tools. In this work, we demonstrate how we used a range of Natural Language Processing (NLP) methods, including Long Short-Term Memory (LSTM), BERT-based models, and Large Language Models (LLMs), for self-state and well-being analysis and summarization during the CLPsych Shared Task 2026. Our approach achieved one of the top Consistency and Contradiction scores for the summarization task and also middle-level results for the other tasks. By testing and developing such mental health-state estimation systems, we contributed to improving mental health support systems. We make our code available this https URL.
[NLP-124] Hierarchical Sparse Attention Done Right: Toward Infinite Context Modeling
【速读】： 该论文旨在解决现代大语言模型（Large Language Models, LLMs）在处理长上下文时面临的两大核心问题：一是密集注意力（dense attention）带来的二次计算开销，二是其在序列长度外推（length extrapolation）能力上的显著不足。现有分块稀疏注意力（chunk-wise sparse attention）方法虽具潜力，但受限于分块选择不准确，难以达到全注意力（full attention）的性能水平。本文提出层级地标稀疏注意力（Hierarchical Landmark Sparse, HiLS）机制，其关键在于通过语言建模损失（language-modeling loss）端到端地学习分块选择策略。HiLS采用层次化注意力分解：每个查询独立与检索到的分块进行注意力计算以提取分块特异性信息，并根据分块检索得分对输出进行融合。通过将检索得分嵌入前向注意力计算中，使检索得分可直接由语言建模损失优化，从而实现端到端的检索学习与原生稀疏训练。实验表明，HiLS-Attention在域内上下文长度下性能可媲美甚至优于全注意力，且能实现超过64倍于训练上下文长度的外推（90%检索准确率），远超全注意力模型；此外，现有全注意力模型可通过轻量级持续预训练转换为HiLS-Attention，既保持域内性能，又获得超长上下文外推能力。结合其稀疏键值（KV）访问与计算特性，HiLS-Attention突破了传统效率-性能权衡，使得长上下文大模型在通用长序列任务上兼具更高效率与更强表现力。
链接: https://arxiv.org/abs/2607.02980
作者: Xiang Hu,Xinyu Wei,Hao Gu,Minshen Zhang,Tian Liang,Huayang Li,Lei Zhu,Yan Wang,Sirui Han,Yushi Bai,Kewei Tu,Haitao Mi,Leo Liang
机构: Tencent HY Team(腾讯HY团队); ShanghaiTech University(上海科技大学); The Hong Kong University of Science and Technology(香港科技大学); University of California, San Diego(加州大学圣地亚哥分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: preprint
Abstract:Scaling modern large language models (LLMs) to long contexts is limited by the quadratic computation cost, and poor length extrapolation of dense attention. Chunk-wise sparse attention offers a promising alternative, but all existing methods fall short of full attention because of their inaccurate chunk selection. We propose Hierarchical Landmark Sparse (HiLS) Attention, a chunk-wise sparse attention mechanism that learns chunk selection end-to-end under the language-modeling (LM) loss. HiLS factorizes attention hierarchically: each query performs attention independently with each retrieved chunk to extract chunk-specific information, and the resulting outputs are fused according to chunk retrieval scores. By incorporating retrieval scores into the forward attention computation, HiLS optimizes them directly with the LM loss, enabling end-to-end retrieval learning and native sparse training. Experimental results show that HiLS-Attention achieves performance comparable to, and in some cases better than, full attention at in-domain context lengths. Meanwhile, HiLS-Attention extrapolates more than 64\times the training context length with 90% retrieval accuracy, far beyond full attention. Moreover, existing full-attention models can be converted to HiLS-Attention with lightweight continued pretraining, preserving in-domain performance while acquiring ultra-long-context extrapolation. Together with its sparse KV access and computation, HiLS-Attention breaks the usual efficiency-performance trade-off, enabling long-context LLMs that are both more efficient and more effective on general long-context tasks than their full-attention counterparts.
[NLP-125] Evaluating Generative Agents with Actions Grounded in Socially Distributed Task Environments using Incognita
【速读】： 该论文旨在解决生成式智能体在社会分布任务环境中如何有效协调知识获取与行动执行的问题，核心挑战在于：智能体需在角色隔离的知识分布背景下，通过社交交互探索隐性信息，并基于所获信息进行可验证的具身化操作，同时避免过早终止任务。其解决方案的关键在于提出Incognita框架——一个将社会交互与具身执行解耦的评估系统。该框架通过引入用户或专业实体作为中介，实现对可接受操作的代理决策；由确定性子环境执行操作并维护统一状态；最终由离线评估器根据继承奖励打分。该设计使智能体在多实体协作中展现出更复杂的认知行为模式，如主动获取隐藏知识、接触更多协作方及尝试更多具身写操作，但整体可靠性仍较低。实验表明，社会分布任务环境能有效揭示智能体在成功前的行为特征，包括知识探查、源选择、具身行动尝试及过早完成信念等，为评估智能体的社会化决策能力提供了新范式。
链接: https://arxiv.org/abs/2607.02975
作者: Dan C. Hsu,Luke Lu
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Effective agency in social environments depends on when an agent seeks knowledge, when it acts, and whether its actions are justified by acquired information. Existing grounded benchmarks provide executable actions, persistent state, and verifiable outcomes, while social simulation environments provide rich interaction among language agents. We study an evaluation setting that combines these requirements. We define socially distributed task environments as interactive environments where task-relevant knowledge is partitioned across role-isolated participants and consequential actions are accessible only through them. Communication serves as exploration over role-partitioned knowledge, while grounded action serves as exploitation over environment state. We introduce Incognita, a Concordia-based framework that separates social interaction from grounded execution. The evaluated agent routes messages to a user or specialist entities; specialists mediate admissible operations; a deterministic sub-environment executes accepted operations over a canonical state; and an offline evaluator scores outcomes with inherited rewards. Incognita-Retail transforms tau-bench retail into a multi-entity environment while preserving final-state reward semantics. We evaluate three generative agent models on 18 tasks stratified by social breadth, with 540 trials. Progress appears in reward and behavior: success rises from 0 percent to 8.9 percent and 17.2 percent, while premature finalization falls from 100 percent to 87 percent and 58 percent. Stronger models elicit more hidden knowledge, contact more entities, and attempt more grounded writes, yet reliability remains low. These findings show that socially distributed task environments expose behavior before reliable success, including knowledge elicitation, source selection, grounded action attempts, and premature completion belief.
[NLP-126] Distill Where the Student Goes: Teacher-Regularized RL for English-Evidence Cross-Lingual RAG
【速读】： 该论文旨在解决跨语言检索增强生成（Cross-lingual Retrieval-Augmented Generation, RAG）在英语证据（English-evidence）范式下的核心问题：尽管基础大模型性能强大，但在多语言查询场景中，生成结果仍易出现语言漂移（language drift，即输出混用英语或代码切换）以及对检索到的英文证据使用不可靠的问题。其根本原因在于两个后训练阶段的挑战：一是错误具有前缀依赖性（prefix-dependent），导致固定轨迹监督存在前缀不匹配问题；二是序列级（部分离散/基于判断）奖励机制造成信用分配噪声大、更新方差高。为应对上述问题，作者提出TR-RAG——一种教师正则化的强化学习方法，通过将奖励优化与学生在采样路径上的在线策略蒸馏相结合，使紧凑的学生模型在生成时仅在实际访问的前缀上激活强而冻结的教师模型，提供前缀级别的学生-教师反KL锚定，从而实现更稳定的优化。此外，研究设计了一种针对英语证据多语言生成的奖励分解策略，融合语言一致性、字符三元组召回率及大模型判别器评分以衡量证据依据的正确性。在BioASQ-ENKB5、Hotpot-ENKB5和自然多语言MKQA三个基准上，使用两种不同骨干模型验证，TR-RAG显著提升了语言遵循度与证据依据正确性的综合表现。关键优势在于教师锚定起到了“安全网”作用：在域内语言上可有效防止奖励驱动强化学习引发的语言一致性崩溃（最高减少约27个百分点），避免性能低于基线模型；在远距离分布外语言上，即便纯奖励强化学习陷入基线天花板，TR-RAG仍能提升证据关联性；同时，在字符三元组召回指标上，紧凑的学生模型甚至超越其700亿参数的教师模型。
链接: https://arxiv.org/abs/2607.02966
作者: Haotian Zhou,Weiran Huang,Siqi Liu,Xiting Wang,Xin Zhang,Zhihao Wen
机构: Ant International, Ant Group (蚂蚁国际，蚂蚁集团)
类目: Computation and Language (cs.CL)
备注: 42 pages, 19 figures, 16 tables
Abstract:Cross-lingual retrieval-augmented generation (RAG) is often deployed in an English-evidence regime, where users query in diverse languages but retrieved passages remain English. In this setting, generation can fail despite strong base models: English evidence induces language drift (English or code-switching outputs) and models use evidence unreliably when producing non-English answers. We attribute these failures to two post-training challenges: (i) errors are prefix-dependent, so fixed-trajectory supervision suffers from prefix mismatch; and (ii) sequence-level (partly discrete / judge-based) rewards yield noisy credit assignment and high-variance updates. We propose TR-RAG, a teacher-regularized RL recipe that couples reward optimization with on-policy distillation on student-visited prefixes. A compact student samples on-policy answers, while a stronger frozen teacher is queried only on those prefixes and provides a prefix-wise student-to-teacher reverse-KL anchor. We further introduce a reward decomposition for English-evidence multilingual generation, combining language consistency, character 3-gram recall, and an LLM-judge score for evidence-grounded correctness. Across three benchmarks – BioASQ-ENKB5, Hotpot-ENKB5, and naturally multilingual MKQA – and two backbones, TR-RAG improves the composite of language adherence and evidence-grounded correctness over strong baselines. Crucially, the teacher anchor acts as a safety net: on in-domain languages it prevents the large language-consistency collapses (up to ~27 percentage points) that reward-only RL can suffer by drifting below even the base model, while on distant out-of-distribution languages – where reward-only RL stalls at the base model’s ceiling – it still improves evidence grounding; and on character 3-gram recall the compact student sometimes surpasses its 70B teacher.
[NLP-127] Individual Parameters in Weight-Sparse Transformers Appear Interpretable
【速读】： 该论文旨在解决神经网络中权重的全局可解释性问题，即如何在完整的训练数据分布上理解单个权重的语义功能，而非仅限于特定子分布或行为。传统机制可解释性方法通常聚焦于特定任务行为，通过逆向工程分析组件在特定输入子集上的作用，但忽视了权重可能在不同输入子集上具有动态功能的事实。本文提出的关键解决方案是：通过系统性地识别在哪些输入上移除某个权重会导致模型预测变化（即“该权重何时重要”），并构建一个自动化的大语言模型（LLM）流水线，自动生成简洁、可读性强的人类可理解描述，同时在保留数据上验证其泛化能力，仅对具备泛化性的描述赋予可信度。实验结果表明，在稀疏与密集型Transformer模型中，稀疏模型的可解释权重比例显著更高（12%至31%的权重可被单一简短描述准确刻画），且该差距在剔除不可靠描述后进一步扩大，证明稀疏结构更有利于实现权重的全局可解释性。
链接: https://arxiv.org/abs/2607.02964
作者: Arnau Marin-Llobet,Stefan Heimersheim
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 20 pages, 19 figures, 3 tables. Project website: this https URL
Abstract:A central goal of mechanistic interpretability is to understand how neural networks work and what each individual component does. Dominant circuit-finding approaches focus on a specific behavior and reverse-engineer the role of components on the associated sub-distribution. However, past work has shown that components can have different functions that are active on different subsets of the input distribution. In this work we ask whether a single weight can be understood globally across the full training distribution by characterizing when it matters (the inputs on which ablating it changes the model’s predictions). We introduce an automated LLM pipeline that writes a short, human-readable description of when a weight matters and verifies it on held-out text, crediting a weight only if its description generalizes. Across two sparse and two dense transformers, the fraction of weights that are interpretable (in this sense) is higher in sparse transformers than in dense ones, a gap that widens once unreliable descriptions are discarded. Our results show that a meaningful fraction of a sparse transformer model’s weights can be interpreted: 12 to 31% of weights have a single short description that identifies what the weight is used for.
[NLP-128] MORE: A Multilingual Document Parsing Benchmark and Evaluation ICML2026
【速读】： 该论文旨在解决多语言文档解析领域中缺乏全面评估基准的问题，尤其针对低资源语言（low-resource languages）在现有评测体系中的“盲区”问题。当前主流基准主要集中于英语、中文等高资源语言，难以真实反映模型在多样化语言环境下的泛化能力，而尽管近年来视觉-语言模型（Vision-Language Models, VLMs）宣称支持数百种语言，但因缺乏真实标注的地面实况（ground truth），其多语言实际性能无法被有效验证。为此，本文提出MORE——一个大规模、多维度的多语言文档解析评测基准。其解决方案的关键在于三个核心创新：（1）前所未有的语言覆盖规模，涵盖149种语言，成为迄今最富语言多样性的评测数据集；（2）结构复杂性扩展，不仅评估文本内容，更包含代码块、表格、目录等复杂文档结构元素，提升评测的真实性与挑战性；（3）数据真实性保障，所有样本均通过模型辅助、人工精修的标注流程从真实世界文档中提取，确保数据语境与实际应用场景一致。通过在MORE上对先进模型进行评估，研究建立了长尾语言的新性能基线，并验证了该基准在诊断模型在多样化、现实场景中能力方面的有效性。
链接: https://arxiv.org/abs/2607.02956
作者: Long Xu,Binghong Wu,Tinghao Yu,Hao Feng,Zhenyu Huang,Haoqing Jiang,Yunhao Wang,Shuo Huang,Feng Zhang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注: Accepted to the 43rd International Conference on Machine Learning (ICML 2026). 22 pages, 11 figures. Code and dataset available at this https URL
Abstract:Multilingual documents encapsulate rich regional cultures, scientific discoveries, and historical records. Parsing this content into structured, machine-readable formats is critical for unlocking global knowledge. However, existing benchmarks predominantly focus on high-resource languages like English and Chinese, creating an evaluation blind spot concerning model performance on other languages. While recent Vision-Language Models (VLMs) claim support for hundreds of languages, the lack of ground truth makes it impossible to empirically verify these capabilities. To bridge this gap, we introduce MORE, a large-scale benchmark designed for multilingual document parsing evaluation. MORE distinguishes itself through three key dimensions: (1) Unprecedented Scale: It covers 149 languages, making it the most linguistically diverse benchmark to date; (2) Structural Complexity: Unlike previous works, it extends evaluation beyond plain text to include structural elements such as code blocks, tables, and catalogs; and (3) Data Authenticity: All samples are curated from real-world documents via a model-assisted, human-refined annotation pipeline. We evaluate state-of-the-art models using MORE, establishing new performance baselines for long-tail languages and validating the benchmark’s effectiveness in diagnosing model capabilities in realistic, diverse scenarios. The MORE dataset will be available at this https URL.
[NLP-129] ProLaViT: Learning Progressive Latent Visual Thoughts in Structured Latent Space ECCV2026
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在复杂视觉推理任务中面临的挑战，尤其是需要多步感知与逻辑推断的场景。现有方法要么依赖外部专家模型导致计算开销过大，要么采用隐式潜在空间表示却缺乏严谨的认知逻辑支撑。其解决方案的关键在于提出一种名为ProLaViT（Progressive Latent Visual Thought）的框架，通过在连续潜在空间中实现结构化视觉推导，使模型能够进行系统性、可解释的视觉思考。核心创新包括：利用模型自身视觉编码器驱动的内生自蒸馏机制，实现对潜在思维过程的监督；构建可扩展的程序化合成流水线，使模型在无需推理时工具的前提下内化算法精确性；设计两种推理范式——“粗到精因果链”用于空间任务中的注意力引导，以及“辩证推理链”引入反事实思维以增强逻辑验证能力；同时引入距离加权多样性损失（Distance-Weighted Diversity Loss），施加拓扑感知约束，防止特征退化，确保语义区分度。实验结果表明，ProLaViT在以视觉为中心的基准测试中显著优于基线方法，在保持高效率的同时实现了更高的准确率与可解释性。
链接: https://arxiv.org/abs/2607.02907
作者: Peiming Li,Yifan Wang,Xiaotian Zhang,Zhiyuan Hu,Shiyu Li,Zheng Wei,Yang Tang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注: Accepted by ECCV 2026
Abstract:Multimodal Large Language Models (MLLMs) have achieved remarkable progress but still struggle with complex visual reasoning tasks requiring multi-step perception and logical deduction. While explicit visual generation incurs prohibitive computational costs, existing latent approaches often rely on external experts or lack rigorous cognitive logic. In this paper, we introduce ProLaViT (Progressive Latent Visual Thought), a framework empowering MLLMs to perform structured visual derivation in the continuous latent space. Unlike works dependent on heterogeneous external models, ProLaViT leverages an endogenous self-distillation mechanism, utilizing the model’s own visual encoder to supervise latent thoughts. To facilitate this, we construct a scalable programmatic synthesis pipeline enabling the model to internalize algorithmic precision without inference time tools. We design two reasoning paradigms: (1) Coarse-to-Fine Causal Chain for spatial tasks, guiding attention from global context to local targets. (2) Dialectical Reasoning Chain for logical tasks, incorporating counter-factual thinking for verification. Furthermore, we propose a Distance-Weighted Diversity Loss to impose topology-aware constraints, preventing feature degeneration by enforcing semantic distinctiveness. Extensive experiments demonstrate that ProLaViT outperforms baselines on vision-centric benchmarks, achieving superior accuracy and interpretability with high efficiency.
[NLP-130] Angry but Accurate: Detecting and Profiling the Counter-Misinformation Ecosystem on Twitter
【速读】： 该论文旨在解决社交媒体中针对虚假信息的纠正行为（counter-misinformation activity）的识别与特征分析问题，重点关注谁在参与纠正、以及其行为模式如何。其解决方案的关键在于构建并应用一个领域特定的自然语言推理（Natural Language Inference, NLI）模型，对大规模新冠疫情期间的推文进行自动化分类，将264,737条推文划分为支持或反对虚假主张的两类，并在此基础上系统比较用户层面（如账户年龄、粉丝数、列表数）与文本层面（如情绪表达）共23个特征。研究发现，与主流假设相反，反虚假信息的推文表现出更强的情绪负性，尤其在愤怒、厌恶和悲伤等情感维度上显著高于支持虚假信息的推文，且这一差异虽幅度较小但方向一致；同时，反虚假信息的发布者多为更成熟的用户，表现为账户历史更长、粉丝数量更多、列表数更高。这一发现揭示了纠正行为在情绪表达与用户资历上的结构性特征，为理解虚假信息对抗机制提供了实证基础。
链接: https://arxiv.org/abs/2607.02900
作者: Eun Cheol Choi,Emilio Ferrara
机构: University of Southern California (南加州大学); Annenberg School of Communication (安纳伯格传播学院); Thomas Lord Department of Computer Science (托马斯·洛德计算机科学系)
类目: ocial and Information Networks (cs.SI); Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: 7 pages, 6 figures. Accepted to ACM Hypertext 2026
Abstract:On social media, many users actively push back against false claims. Understanding who pushes back and how they do so matters, as this corrective activity is central to how misinformation is contested. We study this counter-misinformation ecosystem at scale: applying a domain-specific NLI model from our prior work to a large corpus of COVID-19 tweets, we classify 264,737 posts as supporting or opposing false claims and compare 23 user- and text-level features across the two groups. Contrary to the dominant assumption that negative emotion is a signature of falsehood, we find that anti-misinformation posts are more emotionally negative than pro-misinformation posts, with higher levels of anger, disgust, and sadness. These differences are modest in magnitude but consistent in direction across the negative emotions. We also find that posts opposing misinformation tend to come from more established users, i.e., older accounts, more followers, and higher listed counts.
[NLP-131] Variable Bit-width Quantization: Learning Per-Group Precision for “Bigger-but-Smaller” Language Models
【速读】： 该论文旨在解决低比特量化（Low-bit quantization）在语言模型压缩中因将精度视为单一全局超参数而导致的效率瓶颈问题，即所有权重均采用相同位宽，无法根据权重重要性进行差异化分配。其核心解决方案是提出一种训练时的可变比特宽度量化方法（Variable Bit-width Quantization, VBQ），通过引入Gumbel-Softmax松弛机制，使每个连续64个权重组能够自主学习1、2、4或8比特中的最优分辨率，并基于交替优化策略获得与任务对齐的精度日志信号。关键创新在于实现了非均匀、高度异构的比特分配模式——不仅在层间存在差异，更在单个投影类型内部呈现显著分布不均（如69%的权重组退化至1比特，LM头平均仅1.09比特，而首个MLP块维持约2.5比特），这种模式具有稳定性，可固化为固定量化配方并复用。该配方实现“更大但更小”的模型设计范式：在极低存储开销下（仅为FP16的1/3.7至1/3.8），仍能超越更大规模的浮点模型性能；同时，由于其直接映射到紧凑低比特存储格式，结合自定义融合解量化-乘法核，显著提升推理速度，尤其在大规模模型上加速优势明显（如在Apple Silicon上90亿参数模型达4.7倍加速）。进一步的分布分析揭示深层网络具备渐进式“自我修复”量化误差的能力，表明该收益源于从零训练阶段即建立的协同优化机制，而非后期微调。因此，VBQ将精度重新定义为可学习、非均匀分配的资源，证明了在固定比特预算下，非均匀分配优于均匀分配。
链接: https://arxiv.org/abs/2607.02893
作者: Hamish Ogilvy
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 15 pages, 6 figures
Abstract:Low-bit quantization shrinks language models but treats precision as a single global hyper-parameter: every weight uses the same bit-width. We introduce Variable Bit-width Quantization (VBQ), a training-time method in which each contiguous group of 64 weights learns its own resolution from 1,2,4,8 bits via a Gumbel-Softmax relaxation, trained jointly by an alternating optimization that gives the precision logits a clean, task-aligned signal. VBQ discovers a consistent, strongly heterogeneous allocation within individual projection types, not merely across layers, impossible to express with per-layer methods: 69% of groups collapse to 1 bit, the LM head averages 1.09 bits, while the first MLP block keeps ~2.5 bits. This pattern is stable enough to freeze into a fixed recipe and reuse without further search. The recipe yields a “bigger-but-smaller” regime: a 131M model at 1.82 mean bits reaches perplexity 4.2 on TinyStories, beating a 55M FP16 model (PPL 4.4) at 3.8x less storage, and lets a 1.46B model on FineWeb-Edu match a 593M FP16 control at ~3.7x less storage with 2.5x more parameters. As quality-per-byte, VBQ is 3.9-8.4x more efficient than FP16. The recipe maps directly to packed low-bit storage, so it also accelerates inference: with custom fused dequantize-and-multiply kernels, memory-bandwidth-bound autoregressive decode is faster at equal output, and the speedup grows with scale (parity at 131M, 1.9x at 1.0B, 4.7x at 9B on Apple silicon). A distributional analysis (KL divergence and argmax-flip rate) reveals a striking mechanism: deeper layers progressively self-heal the quantization error injected by early layers. The win is a from-scratch, train-time phenomenon; scaling the search economically beyond 1.5B parameters remains open. VBQ reframes precision as a learnable, non-uniform resource and shows that spending a fixed bit budget unevenly beats spending it uniformly.
[NLP-132] PraMem: Practice-derived Experiential Memory for Long-horizon Behavior Prediction
【速读】： 该论文旨在解决长时序行为预测中因历史序列过长而导致的潜在行为模式识别困难以及大语言模型（Large Language Models, LLMs）固有的认知偏差问题。现有方法多采用上下文压缩范式来减轻历史序列带来的负担，但未能从根本上克服核心挑战。本文提出了一种范式转变：将冗长的历史序列从负担转化为可挖掘的宝贵资源，并据此设计了PraMem（Pre-Practice Memory）框架。其关键在于在正式预测前对历史序列进行预先实践（pre-practice），构建一个经验记忆（experiential memory），作为辅助输入以支持更精准的长时序行为预测。实验结果表明，PraMem在多种任务上均显著优于现有方法，且深入分析揭示了经验记忆的形成机制与演化过程，为理解模型决策提供了新视角。
链接: https://arxiv.org/abs/2607.02881
作者: Zhuoqun Li,Boxi Cao,Jiawei Chen,Hanshu Zhou,Ruoxi Xu,Guiping Jiang,Ruotong Pan,Tingting Gao,Han Li,Xiangyu Wu,Hongyu Lin,Yaojie Lu,Xianpei Han,Le Sun
机构: Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences (中国科学院软件研究所信息处理实验室); University of Chinese Academy of Sciences (中国科学院大学); Fudan University (复旦大学); Kuaishou Technology (快手科技)
类目: Computation and Language (cs.CL)
备注:
Abstract:Long-horizon behavior prediction aims to infer a user’s next action based on a lengthy historical sequence, playing a crucial role in artificial intelligence field. The rise of large language models (LLMs) offers a promising direction for sequential behavior prediction, yet LLMs struggle with latent behavioral pattern induction and model-intrinsic cognitive biases when tackling long-horizon behavior prediction. Prior memory management methods follow a context-compression paradigm that attempts to address this task by alleviating the historical sequence burden, yet fail to resolve the core challenges. In this paper, we advocate a paradigm shift that reframes the lengthy historical sequence from a burden into a valuable resource to be exploited, and accordingly propose PraMem, which conducts beforehand practice over the lengthy historical sequence to build an experiential memory, thereby serving as the assisted input for accurate long-horizon behavior prediction. Extensive experiments across diverse tasks demonstrate that PraMem achieves superior performance than prior methods, and more in-depth analyses provide valuable insights into the mechanism and evolution of the experiential memory. Code: this https URL.
[NLP-133] Jointly Improving Dialect Identification and ASR in Indian Languages using Multimodal Feature Fusion
【速读】： 该论文旨在解决印度低资源语言在自动语音识别（ASR）与方言识别（DID）任务中因传统方法分别优化而导致的性能权衡问题。其核心挑战在于如何在有限标注数据条件下，同时提升多语言、多方言场景下的语音识别准确率与方言区分能力。解决方案的关键在于提出一种多模态联合建模框架：通过瓶颈编码器（Bottleneck Encoder）从基于Conformer的语音表征中提取方言特征，并利用RoBERTa编码器处理ASR生成的CTC嵌入；随后通过门控机制融合两类特征，经注意力编码器进一步优化表示，最终将学习到的嵌入与Conformer输出拼接以增强语音识别特征。该设计实现了方言信息与语音语义的协同建模，显著提升了在33种方言、8种印度语言上的联合性能，验证了其在低资源场景下的有效性。
链接: https://arxiv.org/abs/2607.02862
作者: Saurabh Kumar,Amartyaveer,Prasanta Kumar Ghosh
机构: 未知
类目: Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注:
Abstract:Automatic Speech Recognition (ASR) and Dialect Identification (DID) are crucial for Indian languages, many of which are low-resource and exhibit significant dialectal differences. Existing methods often optimize ASR or DID individually, resulting in performance trade-offs. In this work, we propose a multimodal framework that jointly improves ASR and DID. Our method employs a Bottleneck Encoder to extract dialectal features from Conformer-based speech representations and a RoBERTa encoder to process ASR-generated CTC embeddings. A gating mechanism merges these features, followed by an attention encoder to refine the representations. The learned embeddings are concatenated with Conformer outputs to enhance ASR features. Evaluated on eight Indian languages with thirty-three dialects, our method achieves an average DID accuracy of 81.63% and average CER and WER of 4.65% and 17.73%, respectively. These results highlight the effectiveness of our method for joint ASR-DID modeling.
[NLP-134] raining Hybrid Block Diffusion Language Models with Partial Bidirectionality
【速读】： 该论文旨在解决大语言模型在高吞吐量长上下文生成场景下的核心挑战，即解码过程受内存带宽限制而非计算瓶颈：每个解码步骤需从内存中流式读取累积的键值（Key/Value, KV）缓存，导致带宽需求随上下文长度线性增长，而每次仅生成一个词元。现有解决方案主要分为两类：一是通过高效的注意力机制或线性时间混合器（如Mamba）减少内存访问；二是通过一次性生成多个词元块来提升并行度。然而，将二者结合面临技术难题——早期混合扩散模型（如DiffuMamba）采用双向Mamba混合，包含与因果生成方向相反的扫描路径，该反向扫描需遍历整个序列，其状态不具备前缀唯一性，因此即使采用分块扩散也无法精确复用缓存。为此，本文提出一种BDLM Mamba-注意力混合架构（BDLM Mamba-H），其关键创新在于将反向Mamba扫描限制在当前去噪块内，从而实现跨块的精确缓存复用。实验表明，在8700万参数规模下，BDLM Mamba-H在C4-en验证集上的困惑度优于BDLM注意力和全序列基线模型；在3500万参数规模下仍保持与BDLM注意力相当的性能。在长上下文推理任务中，相较于全序列DiffuMamba-H，BDLM Mamba-H在6.5万词元长度下实现了19.7倍的吞吐提升，而在26.2万词元长度下相较BDLM注意力提升3.7倍，证明了Mamba混合架构在长上下文扩散生成中的巨大潜力。
链接: https://arxiv.org/abs/2607.02805
作者: Pranshu Chaturvedi,Parth Shroff,Tarun Suresh,Hangoo Kang,Kaiyue Wen
机构: Stanford University (斯坦福大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 16 pages, 3 figures
Abstract:High-throughput long-context generation is one of the central challenges for large language models. Generation is typically memory-bandwidth-bound rather than compute-bound: each decoding step must stream the accumulated key/value (KV) cache from memory, so bandwidth demand grows with context length while only one token is emitted. Two parallel approaches have therefore emerged: reducing memory access with efficient attention variants and linear-time mixers such as Mamba, or increasing parallel computation by generating blocks of tokens at once. However, technical challenges arise when combining these two ideas. Earlier hybrid diffusion models such as DiffuMamba use bidirectional Mamba mixing, including a reverse-direction scan relative to causal generation. This reverse scan needs to scan the entire sequence, so its states are not prefix-only and cannot be precisely reused as a cache even when diffusion is performed block by block. We propose a BDLM Mamba–attention hybrid that addresses this challenge by restricting the reverse Mamba scan to the active denoising block, which enables exact caching across blocks. In an 87M-parameter DCLM sweep, BDLM Mamba-H achieves the best C4-en validation perplexity compared to BDLM attention and full-sequence baselines. At 350M parameters, it remains competitive with BDLM attention. For long-context inference, BDLM Mamba-H reaches 19.7x the throughput of full-sequence DiffuMamba-H at 65K tokens and 3.7x the throughput of BDLM attention at 262K, showing that Mamba hybrids are a potential long-context diffusion architecture.
[NLP-135] Seduced by the Narrative: Assessing Rule Adherence in Semi-Open Textual Sandboxes
【速读】： 该论文旨在解决大语言模型（LLM）在半开放文本游戏环境（semi-open textual game environments）中作为自主裁判时，面对用户意图与系统规则冲突时的规则遵从性脆弱问题。现有模型因训练目标偏向于“助人”与“合规”，易受一种名为“修辞注入”（Rhetorical Injection）的攻击：攻击者通过伪逻辑推理和权威胁迫等叙事框架技术，绕过系统的判决逻辑。为此，作者提出 CoC-Seduce，一个基于桌面角色扮演游戏（TRPG）机制构建的多智能体对抗性基准，其兼具明确的规则体系与全自然语言交互特性，能够有效模拟真实场景中的规则博弈。研究选取三款前沿模型生成5,376条对抗样本，覆盖4种世界设定与16类技能范畴，并对20个目标裁判模型进行评估。结果表明，模型规模或显式推理机制均无法可靠提升裁判鲁棒性，“伪逻辑”（Pseudo-Logic）成为主导攻击向量，且跨文化情境暴露了所有被测模型族系统性的知识盲区。解决方案的关键在于构建具有语境丰富性和规则可解释性的对抗基准，以揭示并推动高鲁棒性规则遵守能力的发展。
链接: https://arxiv.org/abs/2607.02802
作者: Weiying Chen,Junlong Shen,Zhanyuan Guo,Xiaoou Zhou
机构: University of Alberta (阿尔伯塔大学); Qilu University of Technology (齐鲁工业大学); N-Dice Association (N-骰协会)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:As LLMs are increasingly deployed as autonomous adjudicators in semi-open textual game environments, robust rule adherence becomes critical when user intent conflicts with system rules. However, these models are trained to be helpful and compliant, leaving them vulnerable to a class of attacks we term \textitRhetorical Injection, where adversarial users exploit narrative framing techniques such as pseudo-logical reasoning and authoritative coercion to bypass adjudication logic. We present CoC-Seduce, a multi-agent adversarial benchmark built on Tabletop Role-Playing Game (TRPG) mechanics, an ideal instantiation of semi-open environments where rules are explicit for adjudication, yet interaction remains entirely in natural language. Three frontier models, i.e., GPT-5.4, Claude Sonnet 4.6, Gemini 3.5 Flash, serve as adversarial generators producing 5,376 samples across 4 world settings and 16 skill categories. We then benchmark 20 target adjudicators against this corpus. Evaluation across 20 models reveals that neither model scale nor explicit reasoning mechanisms reliably confer adjudication robustness, with \textscPseudo-Logic emerging as the dominant attack vector and cross-cultural settings exposing systematic knowledge gaps across all evaluated families. Project page: this https URL
[NLP-136] Safe Inference-Time Alignment via Lagrangian Reward Augmentation
【速读】： 该论文旨在解决生成式 AI（Generative AI）在推理阶段对齐过程中难以有效引入显式安全约束的问题。现有方法通常仅优化单一标量奖励得分，导致安全约束要么被忽略，要么依赖人工调参的惩罚项处理，缺乏理论保障与可扩展性。其解决方案的关键在于提出拉格朗日奖励增强（Lagrangian Reward Augmentation, LARA），一种基于受约束目标函数的通用推理阶段对齐框架。该框架通过将成本约束进行对偶化，将原问题转化为关于非负对偶变量的一维凸优化问题，并利用小规模校准集估计该对偶变量，从而生成一个可直接嵌入现有推理对齐方法的增强奖励信号。对于序列级采样方法（如 Best-of-N 重排序），该对偶变量对应于期望成本约束下的最优解；而对于逐标记级的奖励引导解码方法，该构造提供了一个具有理论基础的校准启发式策略。实验表明，LARA 在多种推理阶段对齐方法中均显著提升了有用性与无害性之间的权衡，其中 Best-of-N 方法表现最佳，性能接近微调基线的直接对齐方法。
链接: https://arxiv.org/abs/2607.02781
作者: Yaswanth Chittepu,Ativ Joshi,Sohini Chintala,Scott Niekum
机构: University of Massachusetts Amherst(马萨诸塞大学阿姆赫斯特分校); SCALAR(安全、正确和对齐学习与机器人实验室)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Inference-time alignment steers a frozen language model during decoding using auxiliary reward signals, avoiding the cost of repeated weight updates. However, existing inference-time alignment methods typically optimize a single scalar score, so explicit safety constraints must either be ignored or encoded through manually tuned penalties. We propose Lagrangian Reward Augmentation (LARA), a general inference-time alignment framework under safety constraints. Starting from a KL-regularized constrained objective with a reward model and a cost model, LARA dualizes the constraint and reduces the optimization problem to a one-dimensional convex problem over a nonnegative dual variable. Estimated on a small calibration set, this dual variable defines an augmented reward that can be used as a drop-in scoring signal within existing inference-time alignment methods. For sequence-level sampling methods, such as Best-of-N reranking, the calibrated dual variable corresponds to the solution of the expected-cost constrained problem. For token-level reward-guided decoding methods, the same construction yields a principled dual-calibrated heuristic rather than an exact constrained-policy guarantee. We evaluate LARA on both sequence-level and token-level inference-time alignment methods, and find that LARA improves the helpfulness-harmlessness tradeoff, with Best-of-N achieving the best performance among inference-time methods, approaching finetuning-based direct alignment baselines.
[NLP-137] Gemma 4 Technical Report
【速读】： 该论文旨在解决当前多模态大模型在计算效率、推理能力及长文本处理方面的瓶颈问题，尤其针对开源模型在性能与资源消耗之间的权衡难题。其解决方案的关键在于：提出一种统一的、无需编码器（encoder-free）的架构设计，使12B规模的Gemma 4模型可直接接收原始音频和图像块（image patches），显著提升多模态输入处理的效率；同时引入“思考模式”（thinking mode），使模型在生成最终回答前先生成推理轨迹，增强逻辑性与可解释性；此外，通过密集（dense）与专家混合（Mixture-of-Experts, MoE）架构的优化设计，结合对视觉与音频编码器的改进，实现了在参数量范围2.3B至31B内兼顾推理速度、内存占用与计算效率，并大幅强化了长上下文建模能力。这些设计共同推动Gemma 4在科学、技术、工程与数学（STEM）、多模态以及长文本任务上的性能跃升，使其在人类评估任务中媲美更大规模的前沿开源模型。
链接: https://arxiv.org/abs/2607.02770
作者: Gemma Team:Sherif El Abd,Vaibhav Aggarwal,Robin Algayres,Alek Andreev,Olivier Bachem,Ian Ballantyne,Cormac Brick,Victor Cărbune,Michelle Casbon,Mayank Chaturvedi,Victor Cotruta,Alice Coucke,Phil Culliton,Robert Dadashi,Lucas Dixon,Mohamed Elhawaty,Utku Evci,Clément Farabet,Johan Ferret,Filippo Galgani,Sertan Girgin,Jean-Bastien Grill,Maarten Grootendorst,Jiaxian Guo,Cassidy Hardin,Yanzhang He,Steven M. Hernandez,Omri Homburger,Léonard Hussenot,Juyeong Ji,Armand Joulin,Aishwarya Kamath,Parnian Kassraie,Olivier Lacombe,Preethi Lahoti,Gaël Liu,Gus Martins,Luciano Martins,Tatiana Matejovicova,Ramona Merhej,Nikola Momchev,Sneha Mondal,Ryan Mullins,Sindhu Raghuram Panyam,Shreya Pathak,Sarah Perrin,André Susano Pinto,Etienne Pot,Angéline Pouget,Alexandre Ramé,Sabela Ramos,Douglas Reid,David Rim,Morgane Rivière,Karsten Roth,Louis Rouillard,Omar Sanseviero,Pier Giuseppe Sessa,Shane Settle,Danila Sinopalnikov,Sara Smoot,Piotr Stanczyk,Andreas Steiner,Lawrence Stewart,Ilya Tolstikhin,Michael Tschannen,Anton Tsitsulin,Nino Vieillard,Renjie Wu,Pingmei Xu,Haichuan Yang,Edouard Yvinec,Li Zhang,Joe Zou,Nicolas Aagnes,Abdelrahman Abdelhamed,Shivani Agrawal,Shubham Agrawal,Ibrahim Alabdulmohsin,Jean Baptiste Alayrac,Uri Alon,Chandramouli Amarnath,Ankesh Anand,Chrysovalantis Anastasiou,Setareh Ariafar,François-Xavier Aubet,Kyriakos Axiotis,Federico Barbero,Joelle Barral,Alexei Bendebury,Urs Bergmann,Stanley Bileschi,Kat Black,Mathieu Blondel,Sebastian Borgeaud,Arthur Bražinskas,Ryan Burnell,Robert Busa-Fekete,Mu Cai
机构: Google DeepMind(谷歌深度思维)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 17 pages, 2 figures, technical report
Abstract:We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemma 4 model suite features dense and Mixture-of-Experts architectures, ranging from 2.3B to 31B parameters. Alongside improved vision and audio encoders for all model sizes, we propose a unified, encoder-free architecture for our 12B model, which ingests raw audio and image patches. Furthermore, we integrate a thinking mode, enabling Gemma models to generate reasoning traces prior to responding. We improve inference speed, memory, and compute efficiency, as well as long-context abilities through critical design choices. Gemma 4 establishes a leap in performance across STEM, multimodal, and long-context benchmarks, and rivals larger, frontier open models in human-rated tasks.
[NLP-138] LuxSQA: Ask Me in Luxembourgish with TTS-Augmented Spoken Question Answering
【速读】： 该论文旨在解决低资源语言（如卢森堡语）在语音问答（Spoken Question Answering, SQA）任务中因缺乏大规模人工录制的语音问答语料而受限的问题，尤其针对生成式语音大模型（speech-LLM）在低资源场景下应用受限的瓶颈。其核心解决方案是利用文本到语音（Text-to-Speech, TTS）技术，从现有文本问答资源出发，通过多源TTS系统合成卢森堡语语音问题，并与对应的文本答案配对，构建任务特定的训练数据。关键创新在于采用参数高效、基于SLAM架构的模型设计：冻结Whisper语音编码器与多语言大语言模型（LLM）后端，通过可学习的投影层和低秩适配器（LoRA）实现端到端连接，有效缓解了在低资源条件下微调大型语音-语言模型的计算与数据需求。实验对比了MMS-TTS、Qwen3-TTS与OmniVoice等不同TTS方案，验证了多源混合数据（约23万条合成问题）及语音风格设计对提升SQA性能的关键作用；同时发现，无需参考的TTS质量评分无法单调预测下游问答性能，强调必须将合成语音视为任务相关的训练数据进行评估，而非仅以自然度为标准。
链接: https://arxiv.org/abs/2607.02763
作者: Nina Hosseini-Kivanani,Marco Matassoni,Alessio Brutti
机构: 未知
类目: Computation and Language (cs.CL)
备注: 7 pages, under review
Abstract:Spoken Question Answering (SQA) remains largely focused on high-resource languages and carefully recorded speech, limiting the reach of speech-LLM methods in low-resource settings. This paper investigates whether text-to-speech (TTS) can provide task-specific training data for Luxembourgish SQA without requiring a large human-recorded QA corpus. Starting from existing text-based QA resources, we translate questions into Luxembourgish, synthesize spoken questions with multiple TTS systems, and pair them with textual answers. We train a parameter-efficient SLAM-style architecture that connects a frozen Whisper encoder to frozen multilingual LLM backends through a learned projector and LoRA adapters. We compare MMS-TTS, Qwen3-TTS, and OmniVoice variants, including single-source corpora of about 48k questions and a 4TTS multi-source mix of approximately 230k questions. Evaluation on LLAMA-LB-Test with two real Luxembourgish speaker conditions shows that multi-source and voice-design-based synthetic training configurations yield the strongest SQA performance. The results also show that no-reference TTS quality scores do not monotonically predict downstream QA performance, indicating that synthetic speech must be evaluated as task-specific training data rather than only as natural-sounding audio.
[NLP-139] Reinforcement Learning for Data-Efficient Code-Switched ASR INTERSPEECH2026
【速读】： 该论文旨在解决音频-语言模型在代码切换（code-switched）语音识别中解码不优化、难以准确处理语言边界的问题。其核心解决方案是提出一种基于可验证奖励的强化学习方法（Reinforcement Learning with Verifiable Rewards, RLVR），通过组合错误率奖励与书写系统保真度奖励，结合两阶段“草稿-精炼”推理流程，实现对音频-语言模型的数据高效适配。关键在于利用错误率奖励消除翻译错误，同时通过书写系统保真度奖励独立抑制书写系统混淆，且二者协同作用不导致性能退化。实验表明，在仅使用10%合成语音数据训练下，该方法在10组语言对上的表现可媲美全量数据微调的LoRA方法，尤其在类型学差异较大的语言对上提升显著，并能零样本迁移至真实人类录制的代码切换语料库。
链接: https://arxiv.org/abs/2607.02757
作者: Ziwei Ye,Peter Vickers
机构: Spotify Canada(Spotify加拿大)
类目: Computation and Language (cs.CL); Sound (cs.SD)
备注: Accepted at Interspeech 2026
Abstract:Audio-language models can be prompted for code-switched speech, but their decoding is not optimized for code-switching and often fails at language boundaries. We propose a practical reinforcement learning with verifiable rewards recipe for data-efficient adaptation of audio-language models to code-switched ASR using group relative policy optimization, combining an error rate reward with a script fidelity reward that penalizes wrong writing systems and a two-pass draft-and-refinement procedure. Using Qwen2-Audio as a reproducible testbed across 10 language pairs, training on only TTS code-switched speech, we show that RLVR with 10% of the data matches LoRA supervised fine-tuning trained on the full dataset, with the largest gains on typologically distant pairs. The error rate reward eliminates translation errors while the script fidelity reward separately reduces script contamination without degradation. These gains transfer zero-shot to a human-recorded code-switching corpus.
[NLP-140] Echoes of Unrest: A Multimodal NLP Framework for Early Warning of Fake News and Violence-Driven Mob Activity
【速读】： 该论文旨在解决社交媒体快速扩张背景下虚假信息与暴力倾向性内容的早期识别难题，尤其针对多语言、多模态环境下信息传播的复杂性。其核心挑战在于如何高效捕捉文本与视觉内容中的误导性信号，并结合上下文特征实现精准预警。解决方案的关键在于构建一个融合多语言文本表示、视觉嵌入与跨模态信息融合的端到端框架：采用XLM-RoBERTa实现对孟加拉语和英语等多语言文本的深层语义建模，利用CLIP模型提取图像的视觉特征，通过多头注意力机制完成跨模态信息融合，并引入讽刺语气识别与地理空间元数据作为辅助特征以增强判别能力。实验结果表明，该框架在30%分层测试集上达到98%的准确率，且具备优异的精确率与召回率，验证了多模态方法在虚假信息早期检测中的有效性，同时揭示了地理空间信息在预测现实世界冲突升级中的关键作用。
链接: https://arxiv.org/abs/2607.02734
作者: Md. Maruf Bangabashi,Tahmid Hasan,Golam Mahmud,Md. Mostafijur Rahman,Md. Toufiqur Rahman,Jahanur Biswas
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted for publication as a book chapter (Taylor Francis, 2026)
Abstract:Rapid growth in social media has transformed global communication by enabling fast information exchange, but it has also accelerated the spread of misinformation. Fake news, manipulated content, and provocative narratives are increasingly linked to social unrest, political instability, and mob violence. Incidents in South Asia and elsewhere demonstrate how false information disseminated via platforms such as Facebook and WhatsApp can trigger real-world harm, often spreading faster than fact-checking efforts can respond. To address this challenge, this chapter presents a multilingual, multimodal Natural Language Processing (NLP) framework for early detection of misinformation and violence-prone dynamics. A fused dataset of 138,256 Bangla and English samples was created by combining multiple benchmark datasets. The framework integrates XLM-RoBERTa for multilingual text representation, CLIP for visual embedding, and a multi-head attention mechanism for multimodal fusion, enhanced with auxiliary features such as sarcasm and geospatial metadata. Experiments on a stratified 30% subset achieved 98% test accuracy with strong precision and recall. The outcomes show the efficacy of multimodal approaches in early misinformation detection and highlight the added value of geospatial signals for anticipating real-world escalation.
[NLP-141] Improving LLM s via Validator-to-Generator Alignment
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在生成与验证一致性（Generator-Validator, G-V consistency）方面存在的固有不一致问题，即模型在生成回答后，若再次被要求验证自身输出，往往将其判定为无效。这种现象的根本原因在于生成器对某些有效但先验概率低的文本赋予较低的生成置信度，从而导致朴素的G-V一致性定义失效。论文提出了一种基于话语频率校正（frequency-corrected）的新型G-V一致性范式，其核心在于通过引入理性代理在多答案场景下回答问题的自然假设，使验证器与经过频率校正的生成器得分之间的一致性得以自然涌现。为此，作者提出了\emph{FCPA}（Frequency-Corrected Prompt Alignment）训练目标，用于在真实世界大语言模型中实现这一校正机制。实验结果表明，采用FCPA训练可显著提升生成器与验证器之间的一致性及生成性能，在IFEval和HumanEval上的皮尔逊相关系数最高提升达+27个百分点，同时在所有评估任务中均保持验证器质量不变。
链接: https://arxiv.org/abs/2607.02668
作者: Juan Diego Rodriguez,Jocelyn Zhang,Katrin Erk,Greg Durrett
机构: The University of Texas at Austin (德克萨斯大学奥斯汀分校); University of Massachusetts Amherst (马萨诸塞大学阿默斯特分校); New York University (纽约大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models are inconsistent: varying prompts or including unrelated information can lead to unexpected changes in model outputs. The generator-validator (G-V) gap is one manifestation of this phenomenon, where LLMs generate responses that they then deem as invalid if re-queried to validate them. In this work, we introduce a new formulation of G-V consistency that involves a principled correction for utterance frequency. Specifically, generators often assign low likelihood to valid strings simply because those strings are a priori unlikely, which makes naive notions of G-V consistency unworkable. We show that under a natural model of rational agents answering questions with multiple answers, consistency of the validator with a frequency-corrected generator score emerges naturally. Our method, \emph\FCPAname (\FCPA), is a training objective implementing frequency-corrected G-V consistency for real-world LLMs. Our experimental results show that training with \FCPA substantially improves both G-V consistency and generator performance over prior methods, with gains of up to +27 pp in Pearson correlation on IFEval and HumanEval, while preserving validator quality across all evaluated tasks.
[NLP-142] GRAFT: Grafted Reference Audio for Fine-grained Pronunciation in Zero-shot Text-to-Speech
【速读】： 该论文旨在解决文本到语音（TTS）神经编解码语言模型中因文本固有歧义导致的罕见专有名词、外来词及术语误发音问题。现有系统虽在可懂性和自然度上表现优异，但缺乏对单个词汇发音的直接声学控制能力，尤其在处理非标准词汇时表现不佳。其解决方案的关键在于提出GRAFT机制——一种基于词粒度的发音条件化方法，通过将目标词的简短语音样本（经模型自有的语音分词器编码）绑定至提示中的对应位置，实现对特定词汇发音的精确控制。在训练数据构建阶段引入语音转换技术，使提示说话人与目标说话人解耦，从而允许任意语音作为发音提示，同时保持输出语音的说话人特征一致性。实验表明，在盲听测试中，人类评估者认为GRAFT在困难词汇发音还原上显著优于基线模型；在多语言客观评测中，其目标词音素错误率相较仅依赖文本的基线模型降低22%-39%，并优于现有的开源零样本系统，在保持说话人相似性与语音自然度的前提下显著提升了目标词发音准确性。
链接: https://arxiv.org/abs/2607.02633
作者: Antonis Asonitis,Francesco Verdini,Aref Farhadipour,Vijeta Avijeet,Pierre-Edouard Honnet,Marzieh Razavi,Juan Pablo Zuluaga Gomez
机构: University of Edinburgh (爱丁堡大学); University of Oxford (牛津大学); Imperial College London (帝国理工学院); University College London (伦敦大学学院)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:We present GRAFT, a per-word pronunciation conditioning mechanism for text-to-speech neural codec language modeling. Existing systems reach high intelligibility and naturalness but inherit the ambiguity of text and mispronounce rare proper nouns, loanwords and technical terms. Even phoneme-conditioned models offer no direct acoustic handle for per-word pronunciation. GRAFT controls the pronunciation of a chosen word from a short spoken sample of it, encoded with the model’s own speech tokenizer and bound to the word’s position in the prompt. Voice conversion during training-data construction disentangles the hint speaker from the target speaker, so the hint may come from any voice while the output stays in the target voice. In a blind English listening study, human raters rank GRAFT first by a clear margin, judging its rendering of the difficult word closest to a reference recording of that word. On a five-language objective benchmark, GRAFT reduces target-word phoneme error rate by 22-39% over the identical text-only backbone and outperforms competitive open-source zero-shot systems, both phoneme- and text-conditioned, on target-word pronunciation, while preserving speaker similarity and naturalness.
[NLP-143] Latent Visual Cache for Video Reasoning
【速读】： 该论文旨在解决视频推理中大型多模态模型（Large Multimodal Models, LMMs）在生成过程中出现的视觉锚定衰减（Visual Anchoring Decay）问题，即现有系统普遍采用“读取一次、生成多次”（read-once, generate-many）范式，导致在推理过程中视觉证据逐渐丢失，影响模型对视频内容的准确理解。其解决方案的关键在于提出一种称为隐式视频缓存（Latent Video Cache, Latent-VC）的递归隐式视觉缓存机制，该机制被嵌入解码器内部，用于在推理全程保持紧凑且高效的视觉记忆。通过监督对比缓存对齐与基于视觉锚定的GRPO（Vision-grounded GRPO）训练策略，并结合隐式视觉锚定奖励，在保持训练与推理间严格一致性的前提下，利用解码器原生隐藏状态实现端到端优化。实验结果表明，基于Qwen3.5-9B构建的Latent-VC在六个视频基准测试上均显著优于强基线方法（包括CoT和SFT+GRPO），尤其在依赖强视觉锚定及长视频任务中表现突出；同时生成响应更短但准确性更高，验证了其通过保留视觉证据而非依赖冗长文本链来提升视频推理能力的有效性。
链接: https://arxiv.org/abs/2607.02607
作者: Yongheng Zhang,Zhipeng Xu,Hao Wu,Yinghui Li,Di Yin,Xing Sun,Philip S. Yu
机构: Tencent Youtu Lab (腾讯优图实验室); Tsinghua University (清华大学); University of Illinois at Chicago (伊利诺伊大学芝加哥分校)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Video reasoning requires Large Multimodal Models (LMMs) to remain grounded in dense evidence, yet existing systems largely adopt “read-once, generate-many” paradigm, in which visual grounding weakens during generation. This phenomenon has been widely observed and is known as Visual Anchoring Decay. To fill this gap, we introduce Latent Video Cache (Latent-VC), a recurrent latent visual cache inserted into the decoder to preserve compact visual memories throughout reasoning. The cache is trained with supervised contrastive cache alignment and vision-grounded GRPO with a latent grounding reward, while maintaining strict train-inference alignment through native decoder hidden states. Built on Qwen3.5-9B, Latent-VC consistently outperforms strong CoT and SFT+GRPO baselines across six video benchmarks, with especially clear gains on grounding-intensive and long-video tasks. In addition, it also achieves higher accuracy with substantially shorter responses, suggesting that latent visual caching improves video reasoning by preserving visual evidence rather than relying on longer textual chains.
[NLP-144] oken-level Response-visual Attention Guidance for Multimodal LLM s Knowledge Distillation ECCV2026
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在知识蒸馏（Knowledge Distillation, KD）过程中因依赖输出词元分布而导致压缩效果不佳的问题。传统方法通常依赖教师模型的输出词元分布进行监督，但这类信号未能充分捕捉模型对视觉输入的关注机制。为此，本文提出关键创新：揭示下游性能与响应-视觉注意力（response-to-vision attention）相似性密切相关，而与提示-视觉注意力（prompt-to-vision attention）的相似性关联微弱，从而挑战了以往以提示引导注意力为核心的蒸馏范式。此外，研究发现不同词元的注意力分布存在显著差异，表明统一的蒸馏目标存在局限性。针对上述问题，本文提出词元级响应-视觉注意力引导（Token-level Response-visual Attention Guidance, TRAG），其核心在于：1）将蒸馏焦点从提示-视觉注意力转向响应-视觉注意力；2）通过基于注意力熵自适应加权KL散度的方式，实现词元级别的差异化蒸馏目标，从而更精准地引导学生模型复现教师模型对视觉内容的聚焦策略。实验结果表明，TRAG在多个基准测试上显著优于现有蒸馏方法。
链接: https://arxiv.org/abs/2607.02593
作者: Jaehyun Jang,Eunseop Yoon,Hee Suk Yoon,SooHwan Eom,Mark A. Hasegawa-Johnson,Chang D. Yoo
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注: ECCV 2026
Abstract:While knowledge distillation (KD) is widely adopted for training lightweight models by leveraging supervision from larger teacher models, relying solely on output token distributions has proven insufficient for compressing Multimodal Large Language Models (MLLMs). Since output tokens are a byproduct of the model attending to visual inputs, prior works have explored explicitly distilling attention to provide a direct supervisory signal. While promising, the precise utility of which attention signals to distill remains under-explored. In this work, we challenge the conventional reliance on prompt-to-vision attention by revealing that downstream performance correlates strongly with response-to-vision attention similarity to the teacher, but negligibly with that of prompt-conditioned attention. Furthermore, we observe that attention distributions exhibit significant variance across individual tokens, indicating that a uniform distillation objective is suboptimal. To this end, we introduce Token-level Response-visual Attention Guidance (TRAG), a distillation objective that 1) shifts the focus to response-to-vision signals and 2) employs token-specific objectives by adaptively weighting the Kullback-Leibler divergence based on attention entropy, effectively guiding the student to mirror the teacher’s precise visual focus. Extensive experimental results on multiple benchmarks demonstrate that TRAG significantly outperforms prior distillation baselines.
[NLP-145] Homer: Understanding Long-form Videos with Hierarchical Memory and Agent ic Reasoning
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在在线场景下处理时长长达一小时的视频时面临的挑战，即在有限内存条件下对长视频进行增量式帧处理时，难以有效支持多跳叙事推理（multi-hop narrative reasoning）。现有方法要么仅保留缺乏语义结构的紧凑视觉表征，要么构建基于时间邻近性的高层记忆存储，而未能显式建模事件间的因果关系，导致每次查询时需由大语言模型重新推断复杂叙事逻辑。为此，本文提出\textscHomer框架——一种分层在线记忆探索与推理架构，其核心在于构建一个具有多尺度结构的记忆体系：从原始感知、重复出现的实体，到通过显式时间与因果关系连接的事件层级，从而更贴近人类对长视频的理解方式。该框架的代理式推理器模仿人类认知过程，通过多轮记忆检索逐步定位相关场景、查找细节并生成答案，同时配备验证与纠错机制确保每一步的准确性。实验表明，\textscHomer在M3-Bench-robot、M3-Bench-web和Video-MME-Long三个基准上分别超越此前最优代理方法5.5、10.8和4.4个百分点，并在多种大语言模型骨干网络上均实现一致性能提升，证明其具备不依赖特定模型结构的、面向长视频的可落地的地面化检索能力。
链接: https://arxiv.org/abs/2607.02588
作者: Yixin Ji,Fanghua Ye,Juntao Li,Bo Zhao,Zexuan Qiu,Zhaopeng Tu,Liefeng Bo,Min Zhang
机构: Soochow University (苏州大学); Tencent Hunyuan Multimodal Department (腾讯混元多模态部门)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: under review
Abstract:Multimodal large language models excel on short clips but struggle on hour-long videos in an online setting, where frames are processed incrementally under limited memory. Existing online methods either retain compact visual representations that lack semantic structure, or build higher-level memory stores organized around temporal proximity rather than explicit causal links, leaving multi-hop narrative reasoning to be reconstructed by the LLM at every query. We bridge this gap with \textscHomer, a Hierarchical Online Memory Exploration and Reasoning framework. \textscHomer’s memory mirrors the multi-scale structure of long videos, ranging from raw perception, to recurring entities, to events connected by explicit temporal and causal relations. Its agentic reasoner then explores this memory the way humans do, locating the relevant scene, looking up details, and composing the answer through multi-round memory retrieval, with a harness that verifies and corrects each step. \textscHomer outperforms the previous best agent method by +5.5 , +10.8 , and +4.4 points on M3-Bench-robot, M3-Bench-web, and Video-MME-Long, and consistently lifts three various LLM backbones, indicating a model-agnostic structural capability for grounded retrieval over long videos.
[NLP-146] Beyond Satisfaction: Learning Associations Between Content Reviews and Well-Being
【速读】： 该论文旨在解决当前数字平台在优化用户满意度时过度依赖评分、点赞和情感倾向等单一指标，而这些指标未能充分反映心理福祉（well-being）的多维性这一问题。其核心挑战在于，现有评价信号主要捕捉短期愉悦感（hedonic well-being），难以有效表征长期意义感与自我实现等积极心理状态（eudaimonic well-being）。本文的关键解决方案是通过分析书籍消费场景下的长文本评论，结合心理学理论框架，系统检验不同内容主题与心理福祉各维度之间的关联。研究发现，评分与情感倾向仅与部分福祉维度弱相关，且更贴合即时享乐型福祉；而具体文本内容中的价值观议题、宗教或人类动机等主题则与意义感和成就感知显著正相关，相反，不文明言辞及过去导向的语言表达则与较低福祉水平相关。这一发现揭示了内容推荐系统应超越以满意度为核心的单一目标，引入更丰富的福祉维度作为优化依据，从而促进用户深层心理健康的可持续发展。
链接: https://arxiv.org/abs/2607.02539
作者: Aaron Marker,Joel Lehman,H. Andrew Schwartz
机构: Vanderbilt University; University of Oxford and Cosmos Institute
类目: ocial and Information Networks (cs.SI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Digital platforms commonly optimize for satisfaction using signals such as ratings, likes, and sentiment, implicitly treating satisfaction as a proxy for user well-being. Psychological theory, however, characterizes well-being as a multidimensional construct that extends beyond satisfaction or short-term positivity. In this paper, we examine whether commonly used satisfaction signals capture expressions of well-being, and what types of content are associated with different well-being outcomes. Our study focuses on book consumption, a convenient domain wherein users engage substantially with fixed pieces of content and sometimes provide nuanced long-form feedback. Our results show that (a) rating scales and sentiment only loosely correlate with most facets of psychological well-being and (b) ratings and sentiment are more closely aligned with immediate and hedonic as opposed to enduring and eudaimonic expressions of well-being. Further, by linking reviews to book content, we find that themes related to values, and institutions like religion (Pearson r = 0.35) or human drives (r=.26) are associated with higher meaning and accomplishment respectively, while incivility (avg r = -.15) and past-focused language (avg r = -.12) are associated with lower well-being. These findings motivate richer outcome targets for content recommendation systems beyond satisfaction alone.
[NLP-147] When Should Service Agents Reconsider? Difficulty-Routed Control in Customer-Service Operations
【速读】： 该论文旨在解决自主客户服 务代理在从对话交互向操作执行角色转变过程中所面临的服务控制（service-control）问题：即如何在保证常规服务快速、低摩擦的同时，有效防范因客户指令、政策约束、企业记录与后端写入操作之间复杂交互而引发的操作错误。其解决方案的关键在于提出一种难度路由式服务控制架构（difficulty-routed service-control architecture），通过轻量级路由器将常规会话保留在低成本基线路径上，而将具有操作耦合性的高风险会话路由至升级工作流。该升级路径采用冲突感知通信与写入触发的重新考虑机制，在关键后端写入前集中进行审慎推理与防护，而非对所有会话统一施加额外控制。实验基于τ²-bench中的人工验证零售与航空任务数据集，结果表明该方法在存在操作冲突的服务请求中显著提升了可靠性；路由证据显示更强的控制策略被精准导向冲突请求，而非泛化应用于常规请求。对话与工具使用分析表明，性能提升源于有目的的证据收集、写入分离及预写重新考虑，而非无差别地扩展交互或扩大工具链。案例级分析进一步揭示，升级工作流具备保留回退方案、正确绑定检索记录与动作、有序执行写入以及分解多实体请求等能力。航空场景结果验证了该服务控制逻辑在预订操作中的可扩展性。
链接: https://arxiv.org/abs/2607.01426
作者: Qian Chen,Chengyuan Liu,Xin Yu
机构: The Pennsylvania State University (宾夕法尼亚州立大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Autonomous customer-service agents are shifting from conversational interfaces toward operational execution roles: they retrieve firm records, apply service policies, and execute backend writes such as refunds, cancellations, exchanges, order modifications, and reservation changes. This shift creates a service-control problem: firms must keep routine service fast and low-friction while preventing operational errors on requests where customer instructions, policy constraints, firm records, and backend writes interact. We propose a difficulty-routed service-control architecture that asks when service agents should reconsider before acting. A lightweight router keeps routine sessions on a low-cost baseline path and routes operationally coupled sessions to an escalated workflow. The escalated path uses conflict-aware communication and write-triggered reconsideration to concentrate deliberation and safeguards before consequential backend writes, rather than applying additional control uniformly across all service sessions. We evaluate the architecture on human-verified retail and airline tasks from \tau^2 -bench. In retail, the method improves reliability consistently on service requests with operational conflict. Routing evidence shows that stronger control is directed toward conflicted requests rather than broadly applied to routine ones. Dialogue and tool-use profiles suggest that gains do not come from indiscriminate interaction expansion or broader tool chains; instead, added turns and tool calls support evidence gathering, write separation, and pre-write reconsideration. Case-level evidence shows that the escalated workflow preserves fallback plans, binds retrieved records to the correct action, sequences writes, and decomposes multi-entity requests. Airline results extend the same service-control logic to reservation operations.
[NLP-148] Specific Domain Ontology Construction Using Large Language Models KR
【速读】： 该论文旨在解决特定领域因缺乏参考本体（ontology）而导致信息组织与共享困难的问题，尤其针对巴西海洋领土（即“蓝色亚马逊”）这一复杂且专业性强的领域。由于本体的手动构建过程耗时费力，许多领域难以建立高质量的本体结构。为此，论文提出利用大语言模型（Large Language Models, LLMs）作为领域专家，自动构建初始概念的语义层级结构，以加速本体的生成。其解决方案的关键在于：通过GPT-3.5和GPT-4等先进生成式AI（Generative AI）模型，对给定初始概念进行自然语言理解与推理，自动生成具有逻辑一致性的概念层次体系，并在实际应用中通过人类专家评估验证其有效性。尽管生成结果整体上表现出良好的语义连贯性，但均需进一步人工精炼才能达到可直接使用的标准，表明当前方法仍需人机协同优化以实现高质量本体构建。
链接: https://arxiv.org/abs/2606.20691
作者: Vivian Magri Alcaldi Soares,Renata Wassermann
机构: University of São Paulo (USP); Center for Artificial Intelligence (C4AI)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Presented at NeLaMKRR@KR, 2025 ( arXiv:2511.09575 )
Abstract:Ontologies are useful structures to organize and maintain information that can be understood both by humans and systems. However, since their manual crafting is a laborious task, many specific domains lack reference ontologies. The outstanding ability for understanding natural language demonstrated by the Large Language Models (LLMs) has motivated their application to aid on a variety of fields, including on ontology development. This work presents the experimentation with a technique that uses LLMs in the role of domain experts to build conceptual hierarchies for a given initial concept. Twenty ontologies automatically constructed for the domain of the Brazilian maritime territory (a.k.a the Blue Amazon) using GPT-3.5 and GPT-4 were then evaluated by human experts. The models were able to construct overall coherent conceptualizations of the domain, but none of the outputs was completely satisfactory as a representation of the context without refinement.
[NLP-149] DELTA-TTS: Adapting Autoregressive Model into Diffusion Language Model for Text-to-Speech ICML2026
【速读】： 该论文旨在解决自回归文本转语音（Autoregressive Text-to-Speech, AR-TTS）模型在推理过程中因逐个生成离散语音标记而导致的效率低下与鲁棒性下降问题，尤其是由左到右生成顺序引发的局部误差传播和幻觉现象。其核心挑战在于：尽管输入文本在合成前已完全可用，但传统AR-TTS仍被迫按顺序生成，无法利用全局上下文进行优化。为此，论文提出DELTA-TTS，一种基于轻量级低秩适应（LoRA）的适配框架，将预训练的AR-TTS模型转化为离散扩散语言模型（discrete Diffusion Language Model, dLLM），实现置信度有序的语音标记解码。关键创新包括：引入卷积模块以增强局部声学上下文建模能力，采用1/t加权训练目标以强化对高置信度位置的优化，并设计时间偏移的推理调度策略，将低置信度位置延后处理。在仅585小时的LibriTTS数据上训练后，DELTA-TTS在Seed-TTS测试集上达到1.75%的词错误率（WER），优于其AR基线模型，且生成速度提升3.3倍；同时分析表明，该方法显著改善了文本-语音对齐精度，提升了整体解码置信度，并有效缓解了AR生成中的幻觉问题。
链接: https://arxiv.org/abs/2607.04140
作者: Junwon Moon,Seungbeom Kim,Yejin Lee,Hoseong Ahn,Sewoong Park,Heeseung Kim,Kyuhong Shim
机构: 未知
类目: Audio and Speech Processing (eess.AS); Computation and Language (cs.CL)
备注: ICML 2026 SPIGM Workshop
Abstract:Autoregressive (AR) text-to-speech (TTS) models generate discrete speech tokens sequentially, which makes inference slow and can degrade robustness by propagating local errors and hallucinations. This limitation stems from their left-to-right AR commitment: each token must be determined before future speech-token context is available. However, such ordering is not an inherent requirement for TTS, as the full input text is available before synthesis. In this paper, we introduce DELTA-TTS, a lightweight LoRA-based adaptation framework that converts a pretrained AR TTS model into a discrete diffusion language model (dLLM) for confidence-ordered speech-token decoding. To better capture the local structure of speech, DELTA-TTS incorporates a convolution module that injects local acoustic context, together with a 1/t -weighted training objective and a time-shifted inference schedule that defer low-confidence positions to later steps. Trained on only 585 hours of LibriTTS, DELTA-TTS achieves a \textbf1.75% WER on Seed-TTS test-en, outperforming its AR backbone while generating tokens \textbf3.3\times faster. Further analysis shows that DELTA-TTS produces sharper text–speech alignment, increases overall decoding confidence, and mitigates hallucinations observed in AR generation.
[NLP-150] FOI-O: An NZ-first ontology and verification methods package for Freedom of Information process modelling
【速读】： 该论文旨在解决公开信息请求记录（Public Official-Information Request Records）中多源异构数据混杂的问题，即在记录中同时包含可观察的通信内容、平台状态、推断事件及法律处理结果，导致数据难以标准化解析与有效利用。其核心挑战在于如何构建一个可复用、可验证的流程建模框架，以清晰表达请求处理过程中的关键要素并支持研究分析与人工监督。解决方案的关键在于提出FOI-O（Freedom of Information - Ontology）方法，通过结构化建模将请求记录中的请求档案、观测到的通信事件、受控词汇表、溯源信息等显式化，并引入审查队列、发布元数据、受限代理合约、语义资产、流程模型制品以及仅用于过程挖掘的固定示例。该框架采用多种标准格式（如JSON Schema、SKOS、OWL、RDF、SHACL）实现跨系统互操作性，辅以确定性示例、质量门控、测试用例及BPMN/PNML流程模型，确保数据一致性与可验证性。值得注意的是，合法意义的结果认证仍由人工完成，未纳入自动化工具链，从而保障法律判断的权威性。该框架已在新西兰《官方信息法》（Official Information Act）背景下实现并验证，具备扩展至其他司法辖区的潜力，但当前尚未有实证支持其广泛复用。
链接: https://arxiv.org/abs/2607.02947
作者: Dylan A Mordaunt
机构: 未知
类目: General Economics (econ.GN); Computation and Language (cs.CL)
备注: 20 pages, ancillary supplement
Abstract:Public official-information request records contain process signals. They can support research, workflow review, and human-supervised agent help. Yet they also mix observed correspondence, platform states, inferred events, and legal outcomes. FOI-O is a reusable process-modelling method and verification infrastructure for Freedom of Information administration. FOI-O NZ, based on the New Zealand Official Information Act, is the only implemented and validated jurisdictional profile in the current repository. Broader reuse is a design intent and future validation path, not an empirical result of this package. FOI-O models the request record first. Request profiles, observed correspondence events, controlled vocabularies, and provenance make visible what was seen and how it was changed. It then adds review queues, release metadata, bounded agent contracts, semantic assets, process-model artefacts, and fixture-only process-mining interchange examples. Human certification of legally meaningful outcomes stays outside autonomous tooling. The repository provides JavaScript Object Notation (JSON) Schema contracts, Python data models, Simple Knowledge Organization System (SKOS) vocabularies, Web Ontology Language (OWL), Resource Description Framework (RDF), and Shapes Constraint Language (SHACL) assets. These are supported by deterministic examples, release metadata, quality gates, tests, Business Process Model and Notation (BPMN) and Petri Net Markup Language (PNML) process models, XES and OCEL-style fixture exports, and a planned New Zealand annotation task-set manifest. This article describes the motivation, architecture, ontology-development method, validation evidence, and implementation boundaries. The project is not legal advice, is not an official government publication, and does not certify release, refusal, redaction, charging, extension, transfer, complaint, or publication outcomes.
信息检索
[IR-0] CanniUplift: A Holistic Framework for Mitigating Seller and Incentive Cannibalization in E-commerce Uplift Modeling KDD2026
链接: https://arxiv.org/abs/2607.05242
作者: Zuwang He,Shihao Shu,Yuli Qu,Hanyu Gao,Ziliang Zhang,Diwei Chen,Xiangda Yan,Buyu Gao,Tanchao Zhu,Yumeng Li,Junxiong Zhu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: Accepted to KDD 2026, 12 pages, 4 figures
Abstract:Personalized incentive allocation is vital for e-commerce, where uplift modeling is the standard for estimating Individual Treatment Effects (ITE). However, traditional models often fail in complex multi-seller environments with violations of the Stable Unit Treatment Value Assumption (SUTVA). We identify two critical challenges: Seller-level Cannibalization, where incentives shift expenditure between shops without growing the platform, and Incentive-level Cannibalization, where organic conversions or alternative rewards introduce significant noise into incrementality estimation. In this paper, we propose CanniUplift, a unified framework to mitigate these dual-source cannibalization effects. Specifically, we design Platform-level Global Alignment (PGA) to capture cross-shop substitution through global GMV consistency constraints. To tackle incentive-driven noise, we introduce Redemption-based Decomposition Denoising (RDD), which uses redemption behavior to decompose treated outcomes and reduce attribution noise within an entire-space framework. Furthermore, a Treat-Attention mechanism is designed to model intricate interactions between users’ historical behaviors and current treatment options. Extensive experiments on both synthetic and large-scale industrial datasets demonstrate that CanniUplift significantly outperforms state-of-the-art baselines. Ablation studies confirm that the integration of PGA and RDD consistently improves wAUUC and wQINI. Successfully deployed online, our framework achieved a 4.08% relative increase in platform-wide incremental GMV (Delta GMV) over the production baseline and improved ROI in online A/B tests, proving effective in driving global platform growth.
[IR-1] Curated retrieval versus open web search in public AI information services: a coverag e-trust trade-off
链接: https://arxiv.org/abs/2607.05217
作者: Hafsteinn Einarsson,Hafsteinn Birgir Einarsson,Jón Gunnar Ólafsson,Jón Gunnar Þorsteinsson
类目: Computers and Society (cs.CY); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注:
Abstract:Public institutions increasingly use large language models (LLMs) to answer citizens’ questions, often pairing a curated knowledge base with live web search, yet whether the sources behind these answers can be trusted has received little empirical scrutiny. We report a pre-launch expert evaluation of Evrópuvefur, an independent, government-funded service run by the University of Iceland that answers questions about the European Union, conducted as Iceland prepared for its referendum of 29 August 2026 on whether to resume EU accession talks. Five domain experts produced 551 evaluations of 449 AI-generated answers, scoring each against a seven-criterion quality rubric and, separately, flagging individual cited sources. We compared two retrieval paths: a curated local corpus (RAG) and open web search. In more than a third of the reviewed web-search answers (35%, 65 of 187), at least one cited source was flagged, almost always as untrustworthy or irrelevant; curated sources were flagged far less often and only for being out of date. Web search answered more questions, but at the cost of source quality; the curated corpus was trustworthy yet limited in coverage, and the model declined to respond when it fell short. The citation mix also passed over strong sources: across all 287 web-search answers, the system never cited RÚV, the public broadcaster and the country’s most widely used news source. A companion prompt ablation shows how weak prompt-level steering is: a trusted-domain list in the system prompt raised the share of citations to listed domains only from 12% to 21%. Fluency and topical fit did not predict source trustworthiness. We argue that source trustworthiness is a measurable yet largely invisible dimension of information quality in public AI services, and we discuss transparency-oriented responses and their trade-offs.
[IR-2] On the Complexity of Entrywise Power Matrix Factorization
链接: https://arxiv.org/abs/2607.04875
作者: Nicolas Gillis,Subhayan Saha,Stefano Sicilia,Arnaud Vandaele
类目: Computational Complexity (cs.CC); Information Retrieval (cs.IR); Combinatorics (math.CO); Machine Learning (stat.ML)
备注: 27 pages, code available from this https URL
Abstract:Given a nonnegative matrix X , a factorization rank r and a real parameter p , entrywise power matrix factorization (EPMF) looks for a low-rank matrix X_r such that X = |X_r|^\circ p (exact case) or X \approx |X_r|^\circ p (approximate case), where (\cdot)^\circ p denotes the component-wise exponent. EPMF includes the modulus model ( p=1 ) and component-wise square factorization ( p=2 ) as special cases, the latter being closely related to the square root rank. We analyze the computational complexity of the exact decision problem and the Frobenius-norm approximation problem, and establish a complete complexity landscape. In the exact case, we show that EPMF is equivalent to the combinatorial problem of flipping the signs of the entries of a given matrix X to obtain a rank- r matrix, which we refer to as the signing problem. We first show that the signing problem, and hence exact EPMF, is strongly NP-hard, improving a weak NP-hardness result for the square-root-rank of Fawzi et al. (Math. Prog., 2015). We then show that the signing problem can be solved in polynomial-time when r is fixed. Moreover, when the rank r is part of the input, we show that for generic matrices the algorithm is fixed-parameter tractable (FPT) in the parameter r ; in fact, the running time is linear in the input size X . In the approximate case using the Frobenius norm as an error measure, we show that EPMF is NP-hard, already when r=2 , the smallest nontrivial case.
[IR-3] Do All Visual Tokens Matter Equally? Object-Evidence Preserving Token Merging for Vision-Language Retrieval
链接: https://arxiv.org/abs/2607.04605
作者: Suhyeong Park,Junha Jung,Jungwoo Park,Jaewoo Kang
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multi-vector vision-language retrieval preserves fine-grained visual evidence through maximum-similarity late interaction, but dense image-side tokens make storage and scoring expensive. Existing token compression methods reduce this cost, yet they can remove or collapse object- and region-level evidence that future query tokens may need to select. We propose SaMer, an object-aware token merging framework that compresses image-side post-projector tokens into K representative centroids while preserving the original late-interaction interface. SaMer uses object annotations only during training as a merge prior to discourage cross-instance mixing, requires no ground-truth bounding boxes or detectors at inference time, and adapts only the shared projection layer with frozen vision and language backbones. With K=64 , SaMer removes more than 93% of image-side tokens and reduces ColPali storage by 16.09\times , while improving R@1 on Flickr30K and MSCOCO. These gains arise because object-aware merging preserves query-selectable object evidence that pruning or feature-only pooling can remove or collapse. SaMer also outperforms compression baselines and shows stronger phrase-level grounding, suggesting that efficient multi-vector retrieval depends not only on reducing token count, but on preserving the evidence future query tokens need to select.
[IR-4] MTEB-PT: A Text Embedding Benchmark for Brazilian Portuguese
链接: https://arxiv.org/abs/2607.04581
作者: Tardelli Ronan Coelho Stekel
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: 18 pages, 5 figures, 7 tables. Code (Apache-2.0): this https URL . Results dataset (CC-BY-4.0): this https URL . Leaderboard: this https URL
Abstract:Text embeddings for Portuguese have no dedicated benchmark: evaluation rests on translated corpora such as English MS MARCO or on thin multilingual coverage, with native tasks scattered and unconsolidated. We introduce MTEB-PT, a benchmark of 22 native Brazilian-Portuguese tasks across seven categories (classification, multilabel classification, pair classification, semantic textual similarity, clustering, retrieval, and reranking), admitting only data created or found in Portuguese and excluding translations by construction. We evaluate 93 models spanning 23M to 27B parameters: 73 open-weight and 20 closed commercial APIs. Alongside the leaderboard we report a statistical layer for every headline comparison: per-task bootstrap confidence intervals, paired-bootstrap significance, a task- and instance-level discrimination analysis (how sharply each task separates models) adapted from Item Response Theory, and a cross-leaderboard correlation. Three findings stand out. The benchmark cleanly separates about a dozen tiers of models, though the top six are statistically too close to order. An openly licensed, self-hostable model reaches that leading tier, so strong Portuguese embedding quality does not require a commercial API. And a model’s rank on the global multilingual leaderboard predicts its Portuguese rank only moderately (Spearman rho = 0.75 over 55 shared models; one model ranks 3rd there and 49th here), so a native benchmark measures something the multilingual boards do not. We release every task, our code, and a public leaderboard, so practitioners can choose Portuguese embedding models on native evidence.
[IR-5] Progressive Disclosure for LLM -Maintained Wiki Knowledge Bases: a Preregistered Ablation
链接: https://arxiv.org/abs/2607.04576
作者: Theodore O. Cochran
类目: Computation and Language (cs.CL); Computers and Society (cs.CY); Information Retrieval (cs.IR)
备注: 14 pages, 2 figures, 6 tables. Preregistered on OSF ( this https URL , DOI https://doi.org/10.17605/OSF.IO/FEKA7 ). Materials-availability and deviations described in the paper
Abstract:LLM agents increasingly answer questions against knowledge bases they help maintain. A common intuition holds that progressive disclosure, a compact catalog plus a one-line summary per page so the agent loads only what it needs, should make this cheaper than consulting a large monolithic index. We test that on a real 709-page markdown wiki maintained by an LLM. We retrofit it for progressive disclosure and run a preregistered ablation in which four versions of the corpus differ only in how the agent reaches the content: page bodies are byte-identical across arms, frozen as immutable git tags, so any measured difference is due to access structure alone. We cross the arms with three access conditions (a protocol-constrained agent, a free self-routing agent, and a catalog-preload regime) and grade answers blind against verified gold references with a cross-family judge. A pilot upended the premise: a capable tool-using agent never loads the index, inferring a page’s path from the question and reading it directly, so the specific saving the retrofit targets does not materialize. We therefore made answer quality primary and cost secondary. Quality is non-inferior (the retrieval arm matches the index baseline within the preregistered margin) while cost falls in every regime, from about a third for a self-routing agent to well over half under catalog-preload, all confidence intervals excluding zero. The saving comes not from avoiding the index load but from more targeted access: the retrieval arm cites fewer pages and takes fewer tool turns. The study doubles as a case study in evaluation validity, applying threat-to-validity discipline to the tooling that produced it. Comments: 14 pages, 2 figures, 6 tables. Preregistered on OSF (this https URL, DOI https://doi.org/10.17605/OSF.IO/FEKA7). Materials-availability and deviations described in the paper Subjects: Computation and Language (cs.CL); Computers and Society (cs.CY); Information Retrieval (cs.IR) Cite as: arXiv:2607.04576 [cs.CL] (or arXiv:2607.04576v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2607.04576 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-6] Evaluation and Explainability of Unsupervised Scholarly Collaboration Recommendations ICML
链接: https://arxiv.org/abs/2607.04529
作者: Md Asaduzzaman Noor,John W. Sheppard,Jason A. Clark
类目: Information Retrieval (cs.IR)
备注: 6 pages, 2 figures, Submitted to ICMLA 2026
Abstract:In this paper, we examine unsupervised, content-based collaboration recommendations using publication text in scholarly settings. We compare three families of methods: a TF-IDF baseline, topic-based models (LDA and BERTopic, including clone variants), and embedding-based retrieval using SciBERT with Faiss. To evaluate model behavior beyond simple lexical matching, we introduce a constrained setting where publication overlap between researchers is partially removed while still using historical co-authorship as proxy ground truth for post-hoc evaluation. Results show clear differences across methods. TF-IDF performs best under full information but drops significantly as overlap is reduced. In contrast, topic-based and embedding-based approaches show more stable performance, suggesting they capture broader distributional similarities, rather than relying only on direct lexical overlap. We also examine explainability through two perspectives: intrinsic topic-based explanations and post-hoc, retrieval-based explanations generated using language models. These provide complementary trade-offs between transparency and human readability.
[IR-7] Autonomous Information Seeking: A Roadmap for Agent ic Recommender Systems
链接: https://arxiv.org/abs/2607.04433
作者: Xinyu Lin,Yashar Deldjoo,Sunhao Dai,Honghui Bao,Xiaopeng Ye,Fatemeh Nazary,Wenjie Wang,Tommaso Di Noia,Jun Xu,Tat-Seng Chua
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注:
Abstract:The rapid integration of large language model-based agents into recommender systems has driven a shift from static, ranking-based pipelines toward autonomous and interactive systems that can reason, plan, and act. This survey provides a comprehensive overview of this emerging landscape by introducing a unified taxonomy grounded in the level of autonomy and three core paradigms of agentic recommender systems: agent-assisted recommendation, agent-as-recommender, and agent-as-user-simulator. The autonomy framework organizes existing methods along increasing capabilities in proactivity, context awareness, interaction flexibility, and adaptivity. Building on this framework, the survey analyzes how each paradigm adopts different agentic architectures and how agents enhance key components such as profiles, memory, tool use, workflows, and optimization mechanisms. We further examine evaluation methodologies for agentic recommendation, covering automated metrics, LLM-based judging, and simulation-based assessment, and discuss their limitations in capturing reasoning quality, user experience, and system behavior. Beyond existing evaluation protocols, we further discuss unresolved issues in evaluating agentic recommender systems, including trajectory-level assessment, agent contribution analysis, and calibration of user simulation. Lastly, the survey outlines open challenges in lifelong user modeling, contextual abstraction, multimodal alignment, controllability, trustworthiness, privacy, scalability, and efficiency. Together, these analyses establish a unified foundation for understanding the current progress of agentic recommender systems and highlight promising opportunities for developing more autonomous, reliable, and human-aligned recommendation agents.
[IR-8] he New Shape of Search: How Conversational AI Recomposes Information Seeking
链接: https://arxiv.org/abs/2607.04282
作者: Michael Iannelli,Alan Ai
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY); Information Retrieval (cs.IR)
备注: Comments: 7 pages, 2 figures, 2 tables
Abstract:Classic models cast information seeking as iterative foraging: formulate a keyword query, scan results, reformulate, gather across sources, synthesize. We ask what happens when a conversational assistant is inserted into that episode. Linking real conversations with major assistants to the same users’ searches and browsing in an opt-in cross-surface panel, and reconstructing the full episode rather than a single query, we find conversational AI changes the shape of information seeking, not merely its volume. AI episodes do not uniformly collapse; they bifurcate. Most terminate in place, with no onward search or content step in the observed trace, while roughly a third scaffold into longer multi-step journeys. Which shape occurs is governed less by task type than by articulation: collapse is statistically indistinguishable across lookup, learning, and comparison episodes, yet falls monotonically with opening-ask length, from 72% at one-to-three words to 48% beyond twenty. Roughly two-fifths of assistant episodes are workbench use–drafting, coding, editing–not information seeking at all, and these collapse most. Conversational AI also does not displace search: search remains woven through roughly three-quarters of within-episode transitions, after reading a page users return to the search box over the assistant 70/30, and within-user search share does not fall. Verification is rare: searches with explicit verification language follow roughly 1% of episodes, and citation-forward interfaces do not measurably increase checking. All of this is episode structure, a compositional object identifiable without a demand counterfactual. Conversational AI recomposes the seeking episode: it answers brief asks in place and anchors invested asks in longer journeys, adding a layer rather than replacing search.
[IR-9] LBR: Towards Mitigating Length Bias in Large Language Models for Recommendation
链接: https://arxiv.org/abs/2607.04270
作者: Hongchen Li,Bohao Wang,Jingbang Chen,Weiqin Yang,Hang Pan,Bingde Hu,Can Wang,Jiawei Chen
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) have recently emerged as powerful backbones for recommender systems by reformulating recommendation as a token-level generation task. Despite their promise, we identify a pervasive yet underexplored issue: \textitLength Bias . Because items are represented by textual descriptions of varying lengths, LLM-based recommenders can be systematically biased in two ways. On the input side, longer item descriptions occupy more tokens in the context and thus receive disproportionately large aggregate attention mass during user preference modeling. On the output side, decoding based on summed autoregressive log-likelihood score inherently disfavors long items. Worse still, conventional length normalization can introduce an additional bias and even degrade recommendation performance. To address this problem, we propose \textbfLBR ( \textbfL ength \textbfB ias \textbfR eduction), a lightweight and model-agnostic framework for mitigating length bias in LLM-based recommendation. LBR mitigates input-side bias via Length-Aware Attention Calibration, which incorporates a length-dependent offset into attention logits to neutralize attention skew. For the output side, LBR introduces Effective Information Length Normalization, replacing naive token count with an information-theoretic length surrogate derived from the branching structure of the prefix tree. Extensive experiments on three real-world Amazon datasets and two representative LLM-based recommenders demonstrate that LBR substantially alleviates length bias while consistently improving recommendation accuracy and fairness, with negligible additional training and inference overhead (with an average NDCG@5 gain of 16.82%). The code is available at this https URL. Subjects: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.04270 [cs.IR] (or arXiv:2607.04270v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2607.04270 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-10] Submitted and Diagnostic Analysis of Full-Text Temporal Retrieval for LongEval-Sci
链接: https://arxiv.org/abs/2607.04088
作者: Yingdong Yang,Haijian Wu
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:LongEval-Sci evaluates scientific retrieval under collection change, where a system should be effective on the current corpus and remain usable as documents accumulate over time. This paper reports both official Task 1 results and development diagnostics for LongEval-Sci 2026. We compare the official PyTerrier BM25 and Qwen3 dense baselines with full-text BM25, additive and router variants, temporal full-text retrieval, temporal+citation retrieval, RM3 query expansion, cross-encoder reranking, and reciprocal rank fusion (RRF). In the official DCTR evaluation, the temporalized full-text runs are our strongest submissions: FT BM25+temporal and FT BM25+temporal+citation obtain the best ARP on all three snapshots (0.285, 0.267, and 0.180 nDCG@10) and reduce snapshot-3 relative change from 0.481 for the BM25 pivot to 0.368. Citation features match the temporal-only variant but do not provide a measurable additional gain in the official summary. Our internal snapshot-1 diagnostics show a complementary pattern: full-text BM25 is the strongest single development retriever (DCTR nDCG@10 = 0.3302, MAP = 0.2853), RRF gives the best deep recall (Recall@1000 = 0.9667), and some uncalibrated overlays can sharply degrade top-rank quality. We therefore conclude that full-text retrieval is the strongest foundation, temporal integration can improve official longitudinal effectiveness when applied to that foundation, and citation evidence still requires cleaner ablation and calibration. Beyond ranking, we also report a qualitative weekly IR-system update-monitoring analysis based on ingestion velocity and stale-coverage drift.
[IR-11] Conductance-Repair Evidence Graphs for Prospective Security Retrieval
链接: https://arxiv.org/abs/2607.04070
作者: Faruk Alpay,Taylan Alpay
类目: Cryptography and Security (cs.CR); Information Retrieval (cs.IR)
备注: 13 pages, 1 figure. Source bundle includes reproducible route probes, graph-repair benchmarks, compact CSV/JSON results, tests, ancillary code, and NumPy/PyTorch/JAX/TensorFlow backend metadata
Abstract:Security retrieval is often evaluated as ranking over complete evidence, but operational triage is prospective: CVE descriptions, weakness metadata, fix commits, EPSS scores, KEV membership, validation-vector metadata, and side-channel benchmark routes arrive through separate channels, and many are missing, delayed, poisoned, or visible only after the decision time. We introduce conductance-repair evidence graphs, a timestamped framework in which retrieval is performed over a temporal admissibility mask and missing channels are widened by a deterministic graph-flow recurrence rather than by a learned predictor. The method emits a repair certificate recording source probes, decision time, withheld edges, repaired channels, forbidden post-decision edges, backend availability, numerical deviation, and verifier results. The theoretical layer gives an adaptive (\lceil\log_2 N\rceil) lower bound for missing-channel identification, an NP-hardness result for minimum harmful repair, and a fixed-parameter certified search bound for (q) questionable channels. The current artifact materializes 30 deduplicated public security records, 57 terms, and 58 withheld admissible document-term edges. Under random edge withholding, conductance repair changes recall@(k) from 0.017 to 0.069 and average precision from 0.062 to 0.060, while a synthetic security fixture improves recall@(k) from 0.055 to 0.099; the public AP drop exposes a limit of broad admissible repair under random edge corruption. The implementation benchmarks the same flow/SVD/einsum kernel under NumPy, PyTorch, JAX, and TensorFlow when available, recording unavailable backends rather than silently substituting them. BBBC019 and LIVECell metadata are retained only as structural controls for sparse evolving source channels, with no clinical or biological performance claim.
[IR-12] UniSGR: Unified Framework for Semantic ID Generation and Ranking
链接: https://arxiv.org/abs/2607.04068
作者: Jiawei Sun,Jun Yang,Ziyue Guo,Dongyue Xu,Jianan Yan,Lifang Deng,Xiaoyi Zeng
类目: Information Retrieval (cs.IR)
备注:
Abstract:Recommendation systems play a pivotal role in modern e-commerce platforms. While generative retrieval has emerged as a promising paradigm for alleviating the limitations of multi-stage cascade architectures, existing methods still struggle with fine-grained multi-objective ranking. To bridge this gap, we propose UniSGR, a Unified framework for Semantic ID Generation and Ranking. UniSGR adopts a two-stage training paradigm: a multi-scenario pre-training stage that learns from mixed business-scenario data, followed by a scenario-specific alignment stage that jointly optimizes Value-Aware Parallel Multi-Token Prediction (VA-PMTP) and a unified multi-objective ranking module. To better align generation with downstream ranking, we introduce Task-Aware Tokens (TAT) guided by Funnel-Aware Contrastive Learning. Furthermore, we propose Semantic Tree Attention with Reorganized KV cache (STARK), an inference strategy that removes key efficiency bottlenecks in conventional beam search. Extensive offline experiments on a large-scale e-commerce platform demonstrate the effectiveness and scalability of UniSGR.
[IR-13] Claim2Source at CheckThat! 2026: Improving Multilingual Scientific Claim-Source Retrieval with Verification-based Re-Ranking
链接: https://arxiv.org/abs/2607.04043
作者: Tobias Schreieder,Harsh Khandelwal,Yu-Ling Zhong,Michael Färber
类目: Information Retrieval (cs.IR)
备注: Accepted at the CheckThat! Lab at CLEF 2026
Abstract:Multilingual scientific claim-source retrieval aims to identify the scientific publication supporting a claim shared on social media. This task is challenging because claims often differ from source publications in terms of language, wording, and level of detail, which weakens the connection between claims and their underlying evidence. In this paper, we present our approach for the CheckThat! 2026 Lab Task 1: Source Retrieval for Scientific Web Claims. We propose a multi-stage retrieval framework for multilingual scientific claim-source retrieval that combines structured claim and source representations with progressive candidate refinement. To address multilingual retrieval challenges, the framework employs bilingual claim representations, metadata-enhanced source representations, and language-specific adaptation of dense retrieval models. Building on this setup, a first-stage retriever generates an initial pool of candidate sources, after which similarity-based re-ranking improves the ranking of highly relevant sources and verification-based re-ranking identifies the candidate source that best supports the claim using verification signals. Our approach achieves an average MRR@5 score of 0.7628 across English, German, and French claims, ranking first on the CheckThat! 2026 leaderboard.
[IR-14] Patient-Conditioned Dual Hypergraph Reasoning for Auditable Traditional Chinese Medicine Prescription Support
链接: https://arxiv.org/abs/2607.04025
作者: Weizhi Nie,Shaojin Bai,Weijie Wang,Yuting Su
类目: Information Retrieval (cs.IR); Human-Computer Interaction (cs.HC)
备注: 12 pages, 5 figures, supplementary material included
Abstract:Traditional Chinese medicine (TCM) prescription support requires patient-specific reasoning from clinical narratives to syndromes, treatment principles, herbs, and doses. Direct language-model generation can produce fluent prescriptions, but its decisions are difficult to audit against explicit clinical evidence. Static TCM knowledge resources provide useful priors, but they cannot determine which diagnostic and prescription relations should be emphasized for an individual patient. We propose a patient-conditioned dual hypergraph framework for auditable TCM prescription support. The first hypergraph organizes symptom, tongue, pulse, and other clinical evidence around syndrome and treatment-principle reasoning. The second hypergraph organizes syndrome, treatment, disease-context, herb, retrieval, and dose-prior evidence for prescription construction. Unlike static knowledge graphs or fixed hypergraphs, both hypergraphs are dynamically weighted by the patient representation. This design enables individualized activation of diagnostic and prescription paths, supporting personalized syndrome differentiation and herb-dose recommendation while preserving case-level auditability. Experiments on TCM-SD show that dynamic weighting in the first hypergraph improves MacBERT syndrome differentiation to 0.8297 accuracy and 0.3288 macro-F1. On TCM-BEST4SDT, the second hypergraph achieves the best mean Herb-F1 of 0.3111 across three seeds, and the full connected pipeline reaches 0.3074 Herb-F1, close to the oracle setting. A 50-case real-world CAP audit further suggests practical review potential, while highlighting the need for prospective dose-safety validation.
[IR-15] Candidate-Constrained Retrieval-Augmented Generation for LongEval-RAG : System Design and Empirical Analysis
链接: https://arxiv.org/abs/2607.04008
作者: Yingdong Yang,Haijian Wu
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: Published in CEUR Workshop Proceedings 2026
Abstract:We present a candidate-constrained retrieval-augmented generation system for LongEval-RAG, where each query is associated with an organizer-provided candidate set and all retrieved evidence and final citations must remain within that set. The system combines deterministic provenance tracking with passage-based retrieval, deterministic query expansion, pseudo-relevance feedback (PRF), reciprocal rank fusion (RRF), lightweight evidence reranking, citation-aware evidence aggregation, and optional MiniLM sentence reranking. We evaluate ten pipeline variants using a primary organizer evaluation and a supplementary self-generated diagnostic protocol. The primary evaluation shows that the strongest balanced variant is rule-minilm: a rule-based chunking pipeline with query expansion, PRF, RRF, reranking, citation prior, and late MiniLM sentence selection. This variant obtains the highest BERTScore, retrieval precision, nugget coverage, and average grade among our submissions. The result suggests that the main gain does not come from more complex semantic or topic-shift chunking, but from pairing stable rule-based evidence units with sentence-level neural selection before generation. The supplementary LLM-judge evaluation remains useful for early diagnosis and additional analysis, but it emphasizes different systems than the primary gold-answer and nugget-based evaluation, highlighting the need for multi-metric RAG evaluation.
[IR-16] Beyond Item Order: Temporal Gap Tokenization for Generative Recommendation with Semantic IDs
链接: https://arxiv.org/abs/2607.03918
作者: Chengkai Huang,Tianqi Gao,Hongtao Huang,Quan Z. Sheng,Lina Yao
类目: Information Retrieval (cs.IR)
备注:
Abstract:Semantic-ID-based generative recommendation has recently emerged as a scalable paradigm for sequential recommendation, where each item is represented by a compact sequence of discrete codes and next-item prediction is formulated as code generation. Existing methods, however, typically construct user histories as sequences of static item identifiers, leaving the elapsed time between consecutive interactions outside the generative input. This temporal blindness is problematic because inter-interaction gaps provide useful cues about interest continuity and preference drift. In this paper, we propose ChronoSID, a lightweight temporal augmentation framework for semantic-ID-based generative recommendation. ChronoSID injects temporal signals into the standard three-stage semantic-ID pipeline from two complementary perspectives. First, we introduce Time-Aware Field-Aware Masked Auto-Encoding (TA-FAMAE), which regularizes item representation learning with an auxiliary time-gap prediction objective. Second, we discretize historical interaction intervals into fixed log-scale gap tokens and interleave them with semantic ID tuples as the encoder input of the sequence-to sequence generator. This design preserves the compact SID generation paradigm while enabling the model to capture time-aware transition patterns. Experiments on Amazon review benchmarks show that ChronoSID consistently improves over ReSID and other competitive generative recommendation baselines. Ablation studies further verify the contribution of both temporal components, and diagnostic analyses show clearer gains under long-gap scenarios where user interests are more likely to drift.
[IR-17] Enhancement of E-commerce Sponsored Search Relevancy with LLM SIGIR
链接: https://arxiv.org/abs/2607.03886
作者: Md Omar Faruk Rokon,Andrei Simion,Weizhi Du,Musen Wen,Hong Yao,Kuang-chih Lee
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: eCom 24: ACM SIGIR Workshop on eCommerce, July 18, 2024, Washington, DC, USA
Abstract:Sponsored search plays a crucial role as a revenue stream for search engines, wherein advertisers competitively bid on keywords that align with the users’ search queries. The task of matching relevant keywords to these queries is complicated by the vast and ever-evolving space of keywords, the ambiguity of user and advertiser intentions, and the wide range of topics and languages involved. Consequently, ensuring that ads are pertinent to user queries presents significant challenges. In the fast-paced world of e-commerce, the accuracy of sponsored search results is vital for boosting user satisfaction and optimizing business operations. This paper presents the development of an advanced Ad Relevance Model within a sponsored search framework, utilizing the power of a pretrained large language model. We detail a pioneering adaptation of the LLAMA2 7B model through Low-Rank Adaptation (LoRA), which markedly enhances search precision and operational efficiency, thus opening new avenues for improving user interactions in extensive online marketplaces such as this http URL. We introduce a novel query and ad title classifier, which discerns the relevance of search interactions across three categories: Relevant, Partially Relevant, and Irrelevant. Our approach involved adapting the pretrained model specifically for the e-commerce sponsored search context, training it on a large dataset. The fine-tuned model demonstrated a marked improvement in ad relevance accuracy, achieving 89.43% accuracy on a comprehensive test dataset – outperforming both the baseline model and other advanced language models like GPT-4. The integration of LoRA with the based model represents a significant stride in customizing language models for e-commerce applications, resulting in enhanced search accuracy, cost efficiency, and operational privacy – a triad essential for the modern digital marketplace.
[IR-18] Next-Gen Sponsored Search: Crafting the Perfect Query with Inventory-Aware RAG (InvAwr-RAG ) Based GenAI SIGIR2024
链接: https://arxiv.org/abs/2607.03880
作者: Md Omar Faruk Rokon,Weizhi Du,Zhaodong Wang,Musen Wen
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: Published in eCom@SIGIR 2024
Abstract:Sponsored search plays a crucial role in e-commerce revenue generation, where advertisers strategically bid on keywords to capture the attention of users through relevant search queries. However, the process of identifying pertinent keywords for a given query presents significant challenges because of a vast and evolving keyword landscape, ambiguous intentions, and topic diversity. This paper highlights an opportunity for to earn a considerable amount of Ads revenue and user engagement where a significant proportion of queries fail to retrieve any sponsored ads. To utilize this opportunity, we introduce the Inventory-Aware RAG-based Generative AI model (InvAwr-RAG), which integrates advanced semantic retrieval and real-time inventory data. This model combines dynamically generated and historically successful queries to align with available inventory and ad campaigns while diversifying rewritten queries to enhance relevance and user engagement. Preliminary results show a significant 68% increase in fill rate and balanced relevance metrics, indicating a strong potential for increased ad revenue. The InvAwr-RAG model sets a new standard in dynamic query optimization, significantly improving ad relevancy, advertiser ROI, and user experience on Walmart’s digital platform.
[IR-19] he Powerless Noise: How Experimental Settings Shape the Reported Power of Noise SIGIR27
链接: https://arxiv.org/abs/2607.03615
作者: Michal Mazuryk,Fleur Dolmans,Louis Gehringer,Ina Klaric,Jia-Huei Ju,Mohammad Aliannejadi
类目: Information Retrieval (cs.IR)
备注: SIGIR 27 Repro
Abstract:Recent work has suggested that adding irrelevant documents to the input of retrieval-augmented generation (RAG) systems can improve question-answering performance, a phenomenon referred to as the ``\textitPower of Noise.‘’ This motivated investigations into the role of noise in information retrieval. In this paper, we reproduce the main findings of Cuconasu et al. \citecuconasu2024power and evaluate the robustness of the effect under extended experimental settings. We first confirm that the phenomenon holds under the original setup, which uses earlier-generation LLMs, restrictive prompting and constrained decoding settings. We subsequently introduce a series of extensions to investigate the underlying causes of the noise effect, examining the authors’ original design choices including the use of different models, instruction prompting, and relaxed output length constraints. Across these ablations, the Power-of-Noise pattern proves highly sensitive to inference configuration: it can appear, weaken, or disappear under small changes to prompt formulation and decoding limits. Combined with our error analysis, which shows substantial contributions from truncation and malformed generations, this variance indicates that the original effect cannot be robustly confirmed as a general benefit of noisy retrieval under these experimental conditions. More broadly, our work highlights the importance of carefully scrutinizing inference design in retrieval-augmented generation systems. Our code is available at this https URL.
[IR-20] Relevance-Based Embeddings: Lightweight Candidate Retrieval via Heavy-Ranker Calls
链接: https://arxiv.org/abs/2607.03515
作者: Kirill Shevkunov,Andrey Ploskonosov,Liudmila Prokhorenkova
类目: Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注:
Abstract:In many machine learning applications, the most relevant items for a query should be efficiently retrieved. The relevance function is usually an expensive similarity model, making the exhaustive search infeasible. A typical solution is to train another model that separately embeds queries and items to a vector space, where similarity is defined via the dot product or cosine similarity. This allows one to search the relevant items through fast approximate nearest neighbor search at the cost of some reduction in quality. To compensate for this reduction, the found items (candidates) are re-ranked by the expensive ranking model. In this paper, we investigate an alternative approach to candidate selection that utilizes the scores of the expensive model to improve the representations of queries and items. The idea is to describe each query (item) by its relevance to a set of support items (queries) and use these new representations to obtain query (item) embeddings. We theoretically prove that such embeddings are powerful enough to approximate any complex similarity model (under mild conditions). We also investigate the choice of support items, which is a crucial ingredient of the proposed approach. The experiments on diverse academic and production datasets illustrate the power of our method.
[IR-21] SentAttack: A Sentence-Level Black-Box Adversarial Attack Method for Dense Retrieval Models
链接: https://arxiv.org/abs/2607.03456
作者: Luping Wei,Yamin Hu,Sihan Shang,Shiyin Wang,Wenjian Luo
类目: Information Retrieval (cs.IR)
备注:
Abstract:Retrieval-Augmented Generation (RAG) systems typically consist of a dense retrieval (DR) model for initial retrieval and a neural ranking model (NRM) for this http URL robustness studies in RAG mainly focus on NRMs, while adversarial attacks on DR models are mostly limited to word-level this http URL low-ranked target documents that are irrelevant to the query, simple word-level attacks are insufficient to mislead DR models into substantially promoting their this http URL solve these problems, we propose SentAttack, a sentence-level black-box adversarial attack method for DR this http URL is designed as a two-stage this http URL the first stage, SentAttack interacts with the black-box RAG system via iterative retrieval to collect ranked documents and ranking information for training a surrogate DR this http URL the second stage, SentAttack uses the surrogate DR model to encode and cluster documents relevant to the target query, yielding multiple cluster this http URL centroids are concatenated with the target document at the sentence level to form an initial set of adversarial this http URL then optimizes these candidates using a query- and centroid-guided objective combined with gradient-guided beam this http URL experiments demonstrate that SentAttack outperforms existing adversarial attacks on DR models, with especially strong performance on low-ranked target documents.
[IR-22] RIAGE: Trustworthy Retrieval Instrumentation And Graph Evaluation
链接: https://arxiv.org/abs/2607.03447
作者: Axel TahmasebiMoradi,Lucas Schott,Martin Royer
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Knowledge graphs (KGs) that underpin Graph-based Retrieval-Augmented Generation (Graph-RAG) are increasingly built automatically by LLM-driven extraction rather than curated by experts. Proper evaluation would require instrumenting all pertinent stages: extraction, graph construction, and inference, coherently enough to localize failures, so that a failure at one stage is not discovered as a wrong answer at the end. We introduce TRIAGE, a stage-aware instrumentation framework for automated, document-grounded graph-RAG that asks not only whether the underlying graph can be trusted but at what cost it can be queried. TRIAGE attaches stage-specific, independently interpretable metrics to three stages: the KG Implementation (triple confidence, source coverage, and schema and canonicalization checks), the KG Validation by expert (graph-level structural quality, with correctness and completeness computed only as offline calibration when a reference is available), and the KG Usage (retrieval coverage, faithfulness, and retrieval cost); the deployed metrics need no gold annotations, the gold-requiring ones serving only as offline calibration. At usage time these metrics form a diagnostic chain of necessary conditions whose first broken link localizes the failure, and the diagnosis maps to the stage levers that can remedy it: extraction, graph and schema, or retrieval. TRIAGE is a theoretical framework with a proof of concept and a reproducible evaluation protocol.
[IR-23] Improving Access to Historical Archives with Real-time RAG -based Systems
链接: https://arxiv.org/abs/2607.03440
作者: Stergios Konstantinidis,Hayman Lotfy,Alexis Erne,Faruk Zahiragic,Min-Yen Kan,Michalis Vlachos
类目: Information Retrieval (cs.IR)
备注:
Abstract:Digitized historical archives are large, heterogeneous cultural heritage repositories, but access methods for such archives face challenges such as noisy optical character recognition (OCR) output and rigid keyword-based retrieval, which limit retrieval quality. In this work, we present an end-to-end archival processing and retrieval framework that integrates large language models (LLMs) into the archival pipeline. Our system introduces two core components: (i) an LLM-based OCR refinement module that improves text quality, and (ii) a semantic retrieval and cross-encoder reranking pipeline supporting natural-language question answering via retrieval-augmented generation (RAG). Our evaluations are done on a historical archival dataset of 500,000 Swiss newspaper segments spanning over three centuries (1762 to 2001). Experiments are conducted across 384 natural-language test queries. Our results highlight that LLM refinements reduce OCR errors by up to 44.52% (CER) and 60.95% (WER). More importantly, this is accompanied by downstream information retrieval improvements. Compared to traditional keyword baselines, our reranking pipeline increases NDCG@10 by 31.9% (from 65.99% to 87.05%) and achieves statistically significant gains in both answer correctness and context relevance. These results demonstrate that integrating LLMs with established document processing and retrieval pipelines can elevate digital libraries from static repositories to interactive, semantically searchable archival systems.
[IR-24] AI Overviews in Academic Search: Evaluating AI-generated Summaries of Search Results in a Domain-specific Search Engine
链接: https://arxiv.org/abs/2607.03421
作者: Kevin Schott,Kanishka Silva,Ingo Frommholz,Philipp Mayr,Dagmar Kern,Daniel Hienert
类目: Information Retrieval (cs.IR); Human-Computer Interaction (cs.HC)
备注:
Abstract:Evaluating search engine results pages (SERPs) to assess result relevance is a demanding step in academic search. In a formative mixed-methods design study, we examine AI-generated SERP-level summaries as a support feature in an academic search engine for social science information. First, we manually evaluated summaries of the top five results for 10 queries using two general-purpose models, one commercial and one open, deriving an exploratory six-category error taxonomy and five safeguards for scholarly deployment. We then conducted a within-subjects user study (n = 30) comparing interfaces with and without AI summaries. Confirmatory analyses showed consistent but non-significant trends favoring AI summaries for subjective workload, perceived usefulness, satisfaction, and decision-making confidence. Exploratory analyses suggested lower mental demand, with frustration also tending to be lower. Behaviorally, participants rarely expanded the summaries and descriptively made slightly fewer result clicks and query reformulations when summaries were available. Drawing on Information Foraging Theory and participant feedback, we suggest that AI summaries may concentrate SERP-level information scent to support early triage. Overall, the findings indicate that SERP-level AI summaries are a context- and user-dependent aid rather than a universal improvement, while contributing an error taxonomy, safeguard-aware deployment guidance, and concrete design implications for scholarly search.
[IR-25] HGenPush: A Heterogeneous Generative Recommendation Architecture for Industrial Push Notification Systems
链接: https://arxiv.org/abs/2607.03362
作者: Xiao Liang,Jiali Feng,Xin Feng,Yiqing Wang,Baolin Ye,Siyao Feng,Zhihui Deng,Cunyi Zhang,Huajin Sun,Xuanping Li,Kaiqiao Zhan,Yanan Niu,Kun Gai
类目: Information Retrieval (cs.IR)
备注:
Abstract:With the explosive growth of content platforms, recommendation systems need to better satisfy user demands to enhance user satisfaction and retention. Taking short-video platforms as an example, users not only seek high-quality content but also trusted authors. Although generative recommendation systems have achieved breakthroughs in recent years, existing methods primarily generate single-type recommendation content and typically employ the inefficient autoregressive paradigm to generate semantic IDs. In this paper, we propose an end-to-end heterogeneous generative recommendation architecture called HGenPush. First, we design a hybrid user behavior understanding module that integrates multi-scenario and multi-perspective behaviors to capture precise user interest. Then, we design a dual-branch heterogeneous generative recommendation module that integrates video recommendation and author recommendation within a unified framework. In addition, to improve generation efficiency, we design a lightweight multi-token prediction method that discards the autoregressive paradigm. Finally, we design a user consumption preference alignment module, which leverages user feedback as reward signals to guide the model toward generating higher-quality content, thereby enhancing user experience and engagement. Through these designs, HGenPush simultaneously fulfills users’ demands for high-quality content and trusted authors. We have deployed HGenPush on the push notification system of Kuaishou, a large-scale short-video platform, achieving a significant 0.181% increase in daily active users.
[IR-26] Beyond Post-Quantization: Native Hash Learning with a Dedicated HASH Token
链接: https://arxiv.org/abs/2607.03328
作者: Xinze Liu,Ding Wang,Hengjie Zhu,Dayan Wu
类目: Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
备注:
Abstract:Efficient large-scale image retrieval requires compact representations that preserve semantic similarity under fast Hamming-space search. Deep hashing is appealing, but most existing CNN- and ViT-based methods still follow a post-quantization paradigm, where continuous visual features are first learned and binary codes are then produced by a terminal hash projection or binarization operation. This late code generation creates a feature-to-code discrepancy between the continuously optimized representation space and the discrete Hamming space used for retrieval. To address this limitation, we propose HashViT, a Vision Transformer framework for native hash token learning. Instead of treating hashing as a terminal readout, HashViT introduces a dedicated HASH token that serves as a persistent, hash-oriented retrieval state inside the transformer. The HASH token is structurally decomposed into a Hash Register for direct binary code generation and a Semantic Workspace for preserving auxiliary continuous semantics. To enable effective workspace-to-register interaction, we further design a lightweight Hash Refinement Adapter that progressively refines the Hash Register across transformer layers. As a result, binary-oriented representations are formed through token evolution within the backbone, rather than being abruptly induced by an output-level projection. HashViT is optimized with a unified objective that combines learnable semantic center supervision, class-token similarity distillation, and quantization regularization, encouraging the HASH token to encode semantically structured and compact binary representations. Extensive experiments on three widely used benchmarks demonstrate that HashViT achieves state-of-the-art or highly competitive retrieval performance while preserving the efficiency of compact Hamming codes. Code is available at this https URL.
[IR-27] From Judgments to Issues: Structured Extraction of Legal Reasoning with Citation-Hallucination Control
链接: https://arxiv.org/abs/2607.03325
作者: Giovanni Piccioli,Alessia Fidelangeli,Piera Santin,Pierpaolo Vivo
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: 33 pages, 2 figures
Abstract:We present an automated pipeline that decomposes Italian tax-court judgments into individual legal issues and extracts, for each issue, a structured XML representation grounded in the IRAC framework and the legal syllogism. The pipeline targets a corpus of approximately 330,000 first- and second-instance decisions of the Italian tax courts and is built around a capable yet cost-efficient general-purpose model (DeepSeek V3), a choice driven by the need to process several hundred thousand documents at a sustainable cost. To address the well-documented unreliability of large language models on legal citations, we couple the extraction step with an automatic hallucination-detection filter that compares the references produced by the model with those identified in the judgment text by a dedicated parser (Linkoln), normalised to standard identifiers (URN-NIR, ECLI, CELEX). We validate the pipeline on 50 judgments annotated by two PhDs in tax law, computing inter-annotator agreement and LLM-vs-expert agreement on both issue extraction and legal citations, together with a stand-alone evaluation of the hallucination filter. To the best of our knowledge, this is the first issue-level, expert-validated structured extraction pipeline with hallucination control for Italian tax-court decisions, and it provides a concrete starting point for downstream applications such as issue-level retrieval, citation-network analysis, and the construction of large-scale datasets of legal reasoning.
[IR-28] aste-aware music retrieval from audio embeddings
链接: https://arxiv.org/abs/2607.03296
作者: Matteo Spanio,Antonio Rodà
类目: ound (cs.SD); Information Retrieval (cs.IR); Machine Learning (cs.LG); Multimedia (cs.MM)
备注: Accepted for publication in the proceedings of MusiCHER-2026, Special Session of IEEE CBMI 2026
Abstract:Crossmodal correspondences between sound and taste are well established in psychology and neuroscience, but largely absent from content-based multimedia retrieval. We formalise taste-from-audio prediction as a content-based music information retrieval benchmark over a perceptually validated multi-source corpus, comparing ten frozen audio encoders from the four HEAR families under a shared multi-task regression head, with gated late-fusion as a configurable variant. In order to assess the effectiveness of the models, we compute absolute error and rank correlation. The strongest systems predict the five tastes within a macro RMSE of 0.134; on held-out real music their error is less than half a single rater’s deviation from the consensus (RMSE 0.13 vs. 0.28), so the model tracks the group consensus more closely than an average human rater, and well below the previous state of the art baseline (0.219). On absolute error the encoders are statistically flat, with a single VGGish matching the best fusion, but gated late-fusion’s advantage is confined to rank correlation (macro Pearson r 0.724 vs. 0.666). Operationalised as a content-based retrieval index, the predicted taste space ranks a 309-item pool far more faithfully than a CLAP-text baseline, which sits at chance; ridge probes and an audio-bandstop knockout read the strongest representations against documented sound-taste correspondences.
[IR-29] Agent ic and Generative AI for Open-Source Intelligence and Cyber Investigations: Taxonomy Evaluation Challenges and Future Directions
链接: https://arxiv.org/abs/2607.03233
作者: Eduardo Almeida Palmieri,Mohamed Chahine Ghanem,Dipo Dunsin,Zubair Baig,Ed de Quincey,Kim-Kwang Raymond Choo
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Social and Information Networks (cs.SI)
备注: 36 Pages
Abstract:The rapid growth of publicly available digital information has rendered manual open-source intelligence (OSINT) analysis insufficient for modern intelligence, cybersecurity, and cyber investigation. Large language models (LLMs) and agentic AI systems, capable of tool use, multi-step reasoning, and iterative intelligence generation, have emerged as promising solutions, yet evaluation frameworks have not kept pace with reported capabilities. This survey systematically reviews 74 studies and makes four contributions. First, it establishes agentic AI as a distinct analytical category rather than an extension of LLM prompting, organising the literature through an 11-category taxonomy covering LLM foundations, agentic architectures, retrieval-augmented generation (RAG), knowledge graphs, prompt engineering, domain adaptation, evaluation benchmarks, and risk. Second, it identifies the hallucination-validation gap as a corpus-level finding: although hallucination is recognised as a major reliability concern in over twenty studies, end-to-end hallucination is empirically measured in only one OSINT-specific RAG-based system, non-reproducible conditions, while related reasoning and factual-correction studies evaluate general-domain question answering rather than OSINT. Third, it maps existing research to the OSINT lifecycle, showing strong support for collection and analysis but limited coverage of verification, reporting, dissemination, and decision support. Fourth, it derives a ten-point research agenda addressing evaluation, benchmarking, hallucination measurement, adversarial robustness, dark-web coverage, multimodal intelligence, and governance. It concludes that a human-AI co-pilot model, where LLMs assist collection and triage while analysts retain responsibility for verification and decision-making, represents the most defensible near-term deployment architecture.
[IR-30] HETERQA: Benchmarking Record Retrieval over Multiple Heterogeneous Sources
链接: https://arxiv.org/abs/2607.03028
作者: Yaodong Su,Hanchang Li,Quanqing Xu,Chuanhui Yang,Yixiang Fang
类目: Information Retrieval (cs.IR)
备注: 20 pages, 10 figures
Abstract:In emerging systems (e.g., social media and e-commerce platforms), data records are often drawn from heterogeneous sources, such as relational tables, text documents, image repositories, spatial databases, and knowledge graphs. Accordingly, retrieving target records for question-answering (QA) tasks requires us to jointly exploit these heterogeneous sources. However, most existing benchmarks are constructed from individual sources, and only a very few recent benchmarks have considered two or three sources. To alleviate this issue, we introduce HETERQA, a comprehensive benchmark with 857 QA pairs for record retrieval over five heterogeneous sources. HETERQA instantiates this setting with Yelp business records, each of which is grounded by multiple sources. We build HETERQA in an answer-driven manner: candidate records are first initialized with record-field constraints, then enriched through heterogeneous sources, and finally cross-verified across required sources before the natural-language question is retained. We validate the benchmark through contradiction detection and human validation, and further evaluate sparse, dense, hybrid, late-interaction, and agentic retrievers under the same metrics. The results show that HETERQA is challenging: hybrid retrieval achieves the strongest Recall@10, Self-RAG achieves the best MRR@10, and all evaluated methods remain far from saturating the benchmark. These findings indicate that HETERQA provides an effective testbed for record retrieval over heterogeneous sources and leaves substantial room for future retrieval methods. The benchmark dataset and source code are publicly available at this https URL and this https URL, respectively.
[IR-31] Where do LLM s Fall Short in CBT-Guided Affective Reasoning ?
链接: https://arxiv.org/abs/2607.02885
作者: Vaishnavi Sinha,Pooja Guttal,Pranay Deep Reddy Katike,Vishal Sinha,Gerald Ndawula,Lira Yoon,Andrea Kleinsmith,Manas Gaur
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Information Retrieval (cs.IR)
备注: 12 pages, 7 figures, accepted for publication in Affective Computing and Intelligent Interaction (ACII), 2026
Abstract:Cognitive Behavioral Therapy (CBT) provides a structured framework for understanding a user’s mental state by examining the interaction between cognitive and behavioral factors. However, out-of-the-box LLMs respond fluently and empathetically, yet collapse into validation reflection, regardless of what the user actually needs. They know theoretical CBT (scoring up to 96% accuracy on licensing exam questions) but fail to apply it effectively. We explore this gap with a knowledge-guided framework that treats CBT dialogue as controlled affective reasoning: user narratives are decomposed into Beck’s Cognitive Conceptualization structure, grounded in clinical SNOMED CT concepts validated via Natural Language Inference, and a Multiple Chain-of-Thought (MCoT) strategy selection between Validation Reflection, Socratic Questioning, or Alternative Perspectives. To measure whether such guidance actually changes behavior, we introduce the Protocol Leverage Force (F), a behavior-level metric that captures how far an intervention shifts a model away from its default response. Across three open-weight LLMs and 14 RealCBT-derived case studies, evaluated with human experts, valence-arousal trajectories, and linguistic entrainment, F shows that simply introducing protocol definitions via single chain-of-thought prompting fails to change LLM behavior, while MCoT on these definitions guides strategy selection better. Still, the effect stays within 1% (approx. 1.2-1.3%), and all models remain biased toward Validation Reflection. These results show CBT knowledge alone does not ensure effective application, giving the affective-computing community instrumentation to measure where LLMs fall short.
[IR-32] Long-Term Optimization for Large-Scale Generative Retrieval with Off-Policy REINFORCE KDD2026
链接: https://arxiv.org/abs/2607.02818
作者: Artem Matveev,Sergei Makeev,Aleksei Krasilnikov,Vladimir Baikalov,Sergei Liamaev,Kirill Khrylchenko
类目: Information Retrieval (cs.IR)
备注: Accepted at the 5th Workshop on End-End Customer Journey Optimization at KDD 2026
Abstract:Generative retrieval has become a popular paradigm for large-scale recommendation. However, it is typically trained with supervised next-item prediction objectives that do not directly optimize long-term user satisfaction. In this work, we formulate recommendation as a session-level sequential decision-making problem and introduce an autoregressive approach for training generative retrievers with off-policy REINFORCE on pre-collected data. Unlike the one-step off-policy correction used in prior work, we propose a multi-step approximation of importance weights enabled by the autoregressive formulation. To support offline evaluation, we train a user feedback model that simulates user responses to generated recommendations. This lets us adapt doubly robust off-policy evaluation for sequential decision-making to recommendation, a setting that has received limited attention. We further introduce a feedback-model-based test-time scaling procedure that simulates future responses and selects recommendations with the highest predicted long-term returns. Experiments on the public large-scale Yambda-5B dataset show that our RL agent improves offline estimates of cumulative session reward over next-item and next-positive prediction baselines, while largely preserving retrieval quality. Moreover, allocating more inference-time compute to simulating future responses improves model-based long-term return estimates without updating the policy. Comments: Accepted at the 5th Workshop on End-End Customer Journey Optimization at KDD 2026 Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2607.02818 [cs.IR] (or arXiv:2607.02818v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2607.02818 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-33] wo-dimensional Fourier compressed sensing under a fixed readout budget per channel
链接: https://arxiv.org/abs/2607.03611
作者: Nitin Jonathan Myers
类目: ignal Processing (eess.SP); Information Retrieval (cs.IR)
备注: submitted to IEEE Signal Processing Letters
Abstract:Recovering sparse signals from their subsampled Fourier representation is an important problem in communications, radar, and imaging. In this letter, we focus on reconstructing sparse 2D signals (matrices) under the constraint that only a fixed number of entries can be sampled from each channel, e.g., a row or a column in the Fourier domain. For a specified per-channel readout budget, we derive a lower bound on the mutual coherence of the corresponding compressed sensing matrix. We show that our bound is larger than the classical Welch bound, due to a limited readout budget. We also construct deterministic subsampling patterns that attain this bound for a class of matrix dimensions and readout budgets, and benchmark them against random subsampling through simulations.
人机交互
[HC-0] Rating the Pitch Not the Product: User Evaluations of LLM s Reflect Expectations More Than Performance
链接: https://arxiv.org/abs/2607.05113
作者: Robert Morabito,Tyler McDonald,Charitra Viswanath,Angel Hsing-Chi Hwang,Susanne Gaube,Jad Kabbara,Ali Emami
类目: Computation and Language (cs.CL); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注:
Abstract:Imagine two users interact with the same LLM. One has been told it is the cutting-edge flagship model; the other, an older, weaker model. They walk away with markedly different ratings of its usefulness and intelligence, yet they used the same model. In a controlled study, 162 participants each used one of six LLMs from two families across three collaborative tasks, after first viewing a landing page that matched, overstated, or understated their model’s true capability. This pre-interaction framing shifted user opinions and interaction behavior while task performance did not. Oversold users rated the model more favorably and used more directive prompting, while Undersold users wrote longer, more collaborative prompts. The quality of what users and the model produced together depended only on the model’s true capability, not on what users were told. Participants’ change in model impressions after use, measured across two impression measures, was not predicted by task performance ( \beta = -0.01 and 0.11 , both n.s.), but by whether the model met users’ expectations ( \beta = 0.47 and 0.50 , both p .001 ) and how confident they felt working with it ( \beta = 0.47 and 0.36 , both p .001 ). After interaction, users are still rating the pitch, not the product: user-elicited LLM evaluations, including the preference data driving public leaderboards, measure expectation management at least as much as the model itself.
[HC-1] oward Personalized Social Robots for Child Well-being: Data Requirement Principles from a Recommender-System Perspective
链接: https://arxiv.org/abs/2607.05110
作者: Jin Huang,Eric Nichols,Fethiye Irmak Dogan,Hatice Gunes
类目: Human-Computer Interaction (cs.HC); Robotics (cs.RO)
备注: Accepted as a Late Breaking Report (LBR) at IEEE RO-MAN 2026
Abstract:Social robots are increasingly deployed in clinical settings to support the well-being of children, where effective support must be personalized to each child. Personalization, choosing the robot action best suited to each child, can be framed as a recommendation problem, and a recently proposed recommender-system framework for social robots offers a principled approach through user profiling, ranking, and responsible computing. Instantiating it, however, is blocked not by the model but by the data, which is hard to gather. A child’s state shifts within and across visits, so no fixed description of the user holds. Within a session, the few signals of whether the robot’s actions helped are weak and indirect. Across sessions, children are rarely seen more than once, and anonymization breaks the identity needed to link visits. Because care cannot be randomized, existing data is observational, biased toward whatever was already done. Each is a familiar recommender-system problem, and we propose four data principles in response: an integrated profile, effectiveness signals, linkable coverage, and an exposure record logged at collection time. We identify which of these principles each capability requires, and frame them as concrete guidelines for data collection.
[HC-2] PAGE: Towards Practical Human-level Gaze Target Estimation
链接: https://arxiv.org/abs/2607.04860
作者: Zhoutong Ye,Chengwen Zhang,Zhaibin Cui,Mingze Sun,Jiaqi Liu,Xiangwu Li,Qingyang Wan,Chang Liu,Xutong Wang,Huan-ang Gao,Yu Mei,Chun Yu,Yuanchun Shi
类目: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
备注: Project page: this https URL
Abstract:Gaze target estimation, the task of predicting where a person is looking in a scene, is crucial to understanding human attention and intent. It is a challenging task that combines high-level understanding of global scene semantics and precise spatial reasoning using human appearance (e.g. pose, eye orientation). As a result, human-level performance remains elusive for existing models, limiting their practical application. To this end, we propose PaGE (Practical Gaze Estimator), a gaze estimation model that explicitly models the complex interaction between scene and head features. Using a PaGE model with a large ViT-H+ backbone as the teacher, we further distill student models with lighter backbones on a much larger and more diverse unlabeled dataset. The architectural improvements and novel training recipe allow PaGE to achieve state-of-the-art performance on several gaze estimation tasks, outperforming humans in 7 out of 9 metrics while reducing the human-AI gap by at least 60% in the remaining 2. The distilled student models retain most of the teacher’s performance while being lightweight enough for practical deployment on robots and consumer devices. The code and model checkpoints are available at our project page.
[HC-3] Who Responds When the Driver Is Gone? A Framework for Human Intent Understanding
链接: https://arxiv.org/abs/2607.04670
作者: Xuewen Luo,Ding Fan,Ruiqi Chen,Ye Cao,Xiujin Liu,Bo Yu,Fengze Yang,Chenxi Liu
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:As autonomous vehicles progress toward fully driverless mobility, a critical question emerges: who understands and responds to passengers when the human driver is absent? Existing autonomous driving systems primarily optimize predefined navigation and control objectives from external scene observations, but they remain limited in perceiving and reasoning about in-cabin human intent. In this paper, we propose Intent2Drive, a unified framework for holistic human intent understanding and human-aligned planning. Instead of treating passenger intent as explicit commands alone, Intent2Drive models intent as a latent cognitive state shaped by language, personal attributes, emotional and physical conditions, behavioral signals, and situational context. To support this formulation, we construct a Holistic Intent Dataset (HID) that provides structured supervision over both explicit and implicit intent cues. Built upon HID, our Theory-of-Mind-inspired Human Intent Reasoner (HIR) infers a Latent Human State (LHS) and further translates it into a planner-compatible Human Intent Objective (HIO). We then introduce a Hierarchical Intent-Conditioned Planner (HICP) that incorporates HIO into route-level and trajectory-level planning, enabling driving behaviors to remain aligned with passenger needs across different planning horizons. Extensive experiments show that Intent2Drive improves structured human intent inference and HIO construction while preserving competitive closed-loop planning performance. These results demonstrate a promising step toward passenger-responsive autonomous driving systems that can reason about, interpret, and act upon human intent in driverless mobility.
[HC-4] Identifying Deceptive Patterns Across Three Age Groups: A Heuristic-Based Cognitive Walkthrough Study of Mobile Apps
链接: https://arxiv.org/abs/2607.04573
作者: Nasra Hassan,Hala Assal
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Deceptive patterns are tactics used to manipulate users into performing unintended actions. Today, many of these deceptive patterns are implemented in mobile apps targeting diverse age groups. In this paper, we employ a heuristic-based cognitive walkthrough to explore how deceptive patterns are tailored to three age groups, specifically teens (12-17), adults (18-49), and older adults (50+), across different app categories. By analyzing 30 apps spanning 6 categories, we found that 93% of these apps use the nagging pattern. Furthermore, our findings reveal that entertainment apps contain significantly more deceptive patterns than other app categories, such as music/books. Our data also shows that entertainment apps for older adults use sneaking patterns more frequently than entertainment apps for teens or adults. These findings call for the development of more ethical, age-specific design guidelines to protect users from targeted digital manipulation attempts.
[HC-5] he User-In-Context Framework: Understanding Variation in How Users Respond to AI Chatbots
链接: https://arxiv.org/abs/2607.04547
作者: Richard A. Fabes,Carol Lynn Martin,Philip Costanzo
类目: Human-Computer Interaction (cs.HC)
备注: 19 pages, 1 figure, 1 table
Abstract:People respond to artificial intelligence chatbots (AICs) in highly variable ways. In this paper, we adapt Bronfenbrenner’s theory into a heuristic framework for understanding this variation. The framework places the human user at the center while also placing the AI there and reconceptualizing the proximal processes as the repeated, reciprocal, and coadaptive interactions between the user and a personalized AIC. The surrounding systems identify the contextual factors that shape how the user experiences, interprets, responds to, and is changed by these interactions. Because stateful AICs learn from accumulated exchanges with their users and have memory, users are responding not only to an AIC but also to a version of the AIC that their own prior interactions have helped create. This extension preserves Bronfenbrenner’s emphasis on proximal processes while accounting for the unique dynamics of personalized AICs. The resulting framework provides a structured map of where and how variation in human and AIC relationships arises, as well as having implications for researchers, practitioners, and AIC designers.
[HC-6] From Interaction to Intent: Inferring User Objectives from Provenance Logs
链接: https://arxiv.org/abs/2607.04501
作者: Steffen Holter,Tobias Stähle,Arpit Narechania,Mennatallah El-Assady
类目: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注: 13 pages, 11 figures
Abstract:The ability to automatically infer analytic intent from user interaction histories could enable interactive AI systems to proactively assist users during exploratory data analysis. In this paper, we examine whether provenance logs – detailed records capturing sequences and timing of user interactions – can be used to classify user intentions in visual exploration tasks. To investigate this, we record how participants interact with multiple multidimensional data projections across a range of analytic tasks, capturing fine-grained mouse interaction data throughout each session. We find that distinct behavioral signatures emerge across different analytic objectives. For instance, users examining properties of specific clusters exhibit markedly different interaction patterns compared to those searching for outliers. More importantly, we show that embedding contextual information into interaction provenance enables classifiers to predict user objectives that generalize across datasets and projection methods. These findings demonstrate that low-level interaction data can serve as a practical bridge to high-level analytic intent, contributing to the development of intent-aware visualization systems.
[HC-7] he ABC of digital health: A framework for translating digital health interventions into real-world applications
链接: https://arxiv.org/abs/2607.04381
作者: David Grüning,Vincent Beermann,Jan Enkmann,Rose Hoch,Ralph Hertwig,Paul Schmiedmayer
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注: 21 pages, 2 tables, 1 figure. Preprint of a manuscript intended to be submitted at ACM Transactions on Computer-Human Interaction (TOCHI)
Abstract:Research-based digital health interventions are often presented as potential solutions for extending health care in the real world. Yet the vast majority of these interventions fails to move beyond controlled studies. Existing frameworks offer valuable guidance for intervention development and testing, but provide less concrete support for translating these evidenced intervention mechanisms into sustained real-world applications. This paper introduces the ABC framework, referring to Accessibility, Buildability, and Continuity, as a practical model for a successful translation. Accessibility captures whether diverse users can find, understand, and begin using an application with minimal friction. Buildability refers to the development of an app that supports the iteration, integration, and personalization of features. Continuity describes both sustained user engagement and the operational capacity to maintain an application over time without disproportionate increases in cost, infrastructure, or human support. Different combinations of the ABC-dimensions make an application scalable (AB), automated (BC), and adherent (AC). By linking design decisions to these features, ABC offers a shared language for researchers, designers, and policymakers seeking to build or evaluate digital health interventions that work beyond trials and are viable applications in everyday life.
[HC-8] Scalable Semantic Steering of Embedding Projections IEEE-VIS2026
链接: https://arxiv.org/abs/2607.03978
作者: Wei Liu,Eric Krokos,Kirsten Whitley,Rebecca Faust,Chris North
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted as a short paper at IEEE VIS 2026. 5 pages, 2 figures
Abstract:Low-dimensional projections support interactive visual analysis of high-dimensional data embeddings, but their structure often does not align with analyst-defined semantic relationships. Recent LLM-augmented semantic steering methods address this gap by externalizing analyst intent from user-defined groups of seed examples, but they propagate intent through per-item LLM reasoning, causing LLM calls and cost to grow linearly with collection size. We propose a scalable semantic steering method that shifts semantic computation from individual items to user-defined groups. A single LLM call generates structured profiles for all groups, which are embedded and combined with seed centroids to form hybrid semantic prototypes. The method then propagates intent without retraining, using embedding-space soft assignment, abstention, and alignment-scaled updates before reprojection. On a 5K-document LitCovid corpus, our method achieves global alignment comparable to per-item LLM steering while reducing LLM calls by over three orders of magnitude. An image case study shows that the same prototype-based mechanism extends to multimodal embeddings. These results suggest that group-level representations can make semantic steering more practical for larger embedding collections.
[HC-9] Post-Lecture Interactive Environments for Conceptual Learning: A Randomized Comparison of Mixed Reality and Tangible Instruction in Undergraduate STEM Education
链接: https://arxiv.org/abs/2607.03896
作者: Sharmin Akter,Mohammad Abu Nasir Rakib,Eshwara Prasad Sridhar,Somik Biswas,Mahmudur Rahman,Md Rassel Raihan
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Developing conceptual understanding in engineering requires learners to connect spatial reasoning with abstract representations, yet lecture-based instruction often provides limited support for this process. Interactive learning environments, including mixed reality (MR) and tangible tools, may help students revisit difficult concepts through action, feedback, and visible system response. This pilot randomized study compared two post-lecture interventions, an immersive MR application and a tangible Engineering Toolkit, with lecture-only instruction in undergraduate solid mechanics. Twenty-four participants completed a baseline assessment, a common lecture, and a post-instruction knowledge test; participants in the interactive conditions also completed usability and learner-experience measures. Learning outcomes were analyzed using ANCOVA with baseline knowledge as a covariate and were supported by normalized learning gains. Instructional modality had a significant effect on post-test performance, F(2, 20) = 3.60 , p = .048 , partial \eta^2 = .263 . Both interactive conditions outperformed lecture-only instruction in planned contrasts. MR showed the highest normalized learning gain ( g = .57 ), whereas the tangible toolkit showed higher usability and learner confidence. In the MR condition, virtual reality sickness symptoms measured via the Virtual Reality Sickness Questionnaire remained low before and after the intervention, suggesting that the MR application was well-tolerated by participants. These results suggest that post-lecture interactive environments may support immediate conceptual learning while offering different modality-specific strengths that require larger, time-matched follow-up studies.
[HC-10] EEG-Based Imagined Speech Decoding Using a Hybrid CNN-SNN Architecture
链接: https://arxiv.org/abs/2607.03844
作者: Fatima Shalhoub,Mariam Al Mawla,Kabalan Chaccour,Iván López-Espejo,Hoda Fares
类目: ound (cs.SD); Human-Computer Interaction (cs.HC)
备注: Accepted to IEEE EMBC 2026
Abstract:Imagined speech decoding using EEG signals has emerged as a promising frontier in brain-computer interface (BCI) research, particularly to restore communication for individuals with severe speech impairments. However, decoding imagined speech remains a complex task due to the non-stationary, low-amplitude, and highly variable nature of EEG signals. Existing methods often rely on classical machine learning or deep learning models that fail to exploit spike-based temporal dynamics or event-driven firing mechanisms of biological neurons, which are naturally modeled by spiking neural networks (SNNs). In this study, we propose a hybrid decoding pipeline that extracts temporal representations using convolutional neural networks (CNNs) followed by biologically inspired temporal classification via SNNs. To our knowledge, this is the first study to integrate SNNs into EEG-based imagined speech decoding. Experimental results show that the proposed CNN-SNN architecture achieves an accuracy of 80.13% on the 2020 BCI Competition III benchmark, surpassing existing methods reported in the literature (up to 70.19%) under comparable evaluation settings. These findings demonstrate the effectiveness of spike-based temporal decoding for imagined speech, highlighting the promise of biologically grounded pipelines for next generation neuromorphic BCI applications.
[HC-11] Enactive Drift Regulation and the Emergence Machine: A Framework for Coherent Adaptation Through Regulated Interaction
链接: https://arxiv.org/abs/2607.03834
作者: Nicholas Davis
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Adaptive systems increasingly operate in environments characterized by persistent non-stationarity, where patterns reorganize rather than merely vary. While existing approaches such as online learning, continual learning, and adaptive filtering address performance degradation under changing data distributions, they typically treat drift as noise, error, or distribution shift to be corrected. This paper argues that such framings miss a more fundamental challenge: the loss of organizational coherence over time. We introduce Enactive Drift Regulation (EDR) as a general adaptive principle that treats drift as a regulatory signal indicating breakdowns in coherence between a system’s internal organization and its environment. Rather than treating prediction optimization or retraining as sufficient, EDR reframes adaptation as the regulation of structure-maintaining, reorganizing, or transitioning internal dynamics to sustain viable operation under change. We present the Emergence Machine as an architectural instantiation of EDR, organized around regimes, attractors, coherence measures, reorganization dynamics, and memory across regimes. By shifting the focus from error minimization to coherence regulation, this work provides a principled framework for long-duration adaptation under non-stationarity and offers a bridge between adaptive control and enactive accounts of cognition.
[HC-12] CoGen3D: An Agent ic Human-AI Co-Design Pipeline for 3D Asset Generation for Virtual Reality
链接: https://arxiv.org/abs/2607.03731
作者: Weiwei Jiang,Wanyu He,Zheyu Tan,Zheyuan Kuang,Difeng Yu,Shinobu Hasegawa,Sven Mayer,Zhanna Sarsenbayeva
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:Creating 3D assets for virtual reality requires modeling expertise, which restricts the authorship of immersive experiences. Existing generative AI tools rely on unconstrained, command-driven prompting, lacking the conversational scaffolding needed for users to articulate their intent and validate designs prior to rendering. To address this, we introduce CoGen3D, an agentic human-AI co-design pipeline that proactively guides users through conversational intent elicitation, a concept image confirmation, and image-to-3D generation that directly deploys to immersive scenes. We evaluated this system through a user study (N=120) across six affectively diverse immersive scenes, observing 60 Design group participants who co-created 3D assets for the scenes, and 60 Validation group participants who experienced the scenes with generated assets. Our findings show that co-designed assets are associated with higher scene engagement and shifted affective responses, while participants generally preferred concept images over the final 3D assets, with no increased leniency toward degradation in their own creations. Analysis of the human-AI conversations further shows that target environments shape users’ conversational patterns. Our results suggest that our staged, intent-based co-design can democratize virtual reality authoring and shift immersive content creation from technical execution toward collaborative spatial design.
[HC-13] Between Knowledge and Care: A Mixed-Methods Evaluation of Generative AI for T2DM Self-Management from Patient and Physician Perspectives
链接: https://arxiv.org/abs/2607.03720
作者: Ruiqi Chen,Yibo Meng,Huidi Lu,Xiaolan Ding
类目: Human-Computer Interaction (cs.HC)
备注: arXiv admin note: text overlap with arXiv:2510.10048
Abstract:Generative AI is increasingly used for everyday health guidance, yet its clinical appropriateness in chronic disease contexts remains poorly understood. This paper presents a two-part mixed-methods study on \reviseType 2 Diabetes Mellitus (T2DM), examining how patients and physicians assess AI-generated health information. \reviseStudy~1 analyzes 784 \reviseparticipant reported patient queries to characterize seven informational need categories and \revisedevelops a structured five dimensional physician rating rubric informed by patient query categories and clinician priorities (\textitAccuracy, Safety, Clarity, Integrity, Action Orientation). \reviseStudy~2 engages seven physicians scoring responses from four AI models and discussing evaluative reasoning through in-depth interviews. Models perform well on factual explanation and lifestyle guidance but consistently underperform on medication reasoning and emotional support. Two \reviseanalytic concepts emerge \revisefrom the data. The \textitpre-visit primer \reviseframes AI as preparation for clinical encounters rather than as a replacement for physicians. The \textitfluency illusion \revisedescribes how polished language may convey epistemic authority that the clinical content does not support. Patients and physicians converged on three shared limitations (role boundaries, emotional inadequacy, personalization gaps) while diverging in evaluative emphasis, \revisewhich informed four design directions, task-aware orchestration, risk-aware fallback, dynamic personalization, and emotionally attuned interaction.
[HC-14] A Vision Based System for Guided and Collaborative Reconstruction of Frag mented Documents ALT
链接: https://arxiv.org/abs/2607.03621
作者: Oliver Krumpek,Diana Leo
类目: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC); Robotics (cs.RO)
备注: The paper was presented at MetroArchaeo 2024 in Malta. Publication in Springer Proceedings is still delayed
Abstract:This paper presents the development and evaluation of a collaborative system for real-time reconstruction of fragmented paper documents in the context of cultural heritage preservation. The developed system includes a collaborative robot, or cobot, that can fully manage the positioning of paper fragments using a specially designed vacuum-based suction attachment. This attachment enables gentle and precise positioning, ensuring the preservation of fragile materials. With this device, we are able to achieve a positioning repeatability of 0.57mm for fragments of 8cm^2. The system offers users the flexibility to choose between manual positioning, with visual guidance, or fully automated positioning performed by the cobot. To further improve the reconstruction process, AI methods for image interpretation, specifically for segmentation and positioning tasks, were applied and evaluated for their applicability to template-based reconstruction of damaged paper fragments. Our investigation provides critical insights into the performance of different local feature matching methods under different document types, taking into account rotation, scale robustness, and the degree of damage to the fragments. With a focus on the reconstruction of damaged and optically altered archival material, SE2-LoFTR, a detector-free local feature matching method, was chosen as the preferred method for the system due to its robust performance in our experiments.
[HC-15] Evaluating Affective Objectives: Statistical Numbing in Data Visualization
链接: https://arxiv.org/abs/2607.03445
作者: Elsie Lee-Robbins,Eytan Adar
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Visualizations can help audiences understand the scale of tragedies, such as the consequences of natural disasters, war, genocide, and pandemics. In these cases, a visualization designer’s default behavior may be to focus on communicating quantitative information: numbers, statistics, and trends. However, this may not reflect higher-level affective objectives to inspire their audience to care about an issue, empathize with others, or take action to help those in need. Worse, standard visualizations may conflict with these goals, as statistics can numb emotions and reduce prosocial feelings toward people in need. Designers have developed strategies to increase affective responses through data visualizations, such as blending data narratives and personal narratives about individuals. In this paper, we explore three design strategies for communicating a humanitarian crisis: data-driven, human-driven, or mixed narratives. We conducted an empirical study to explore the effect of statistical numbing in the context of these types of narratives in the format of data videos. In particular, we measure prosocial feelings and behaviors by giving participants the option of donating money as part of the study. We find that human-driven narratives (photographs and stories of individuals) elicited the highest donations and that the mixed narrative combination led to the lowest donations. We discuss the limitations of this study and the implications of pursuing affective objectives and the numbing of empathy in data visualization design.
[HC-16] Personalized Causal Recourse: A Human-In-The-Loop Approach
链接: https://arxiv.org/abs/2607.03425
作者: Denise Tampieri,Giovanni De Toni,Paolo Giudici
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: Accepted at the 4th World Conference on eXplainable Artificial Intelligence (XAI 2026)
Abstract:Algorithmic recourse addresses the challenge of providing tailored recommendations to users affected by unfavorable machine learning decisions, in potentially high-stakes scenarios. Traditional approaches to recourse often rely on the closest counterfactual explanations or assume a priori knowledge of a user’s causal structure, resulting in interventions that overlook individual contexts and specific feature interactions. To overcome these limitations, we study a human-in-the-loop framework that iteratively approximates the user’s structural causal model through interactive queries via Bayesian inference before producing recourse recommendations. This framework exploits humans’ feedback to improve the identification of causal effects, allowing personalized recourse that is plausible, cost-effective, and aligned with the actual causal dependencies of each user. As a proof of concept, we evaluate this framework through simulated human responses. Our simulations across linear and non-linear causal models show promising results, though challenges remain in capturing complex, non-linear structures, emphasizing the importance of accurate approximations and robust noise distribution modeling.
[HC-17] Regulating AI: Where U.S. State Policy and HCI (Mis)align
链接: https://arxiv.org/abs/2607.03292
作者: Nino Migineishvili,Alice Gao,Adinawa Adjagbodjou,Dhanaraj Thakur,René Just,Katharina Reinecke
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Artificial intelligence (AI) technologies are increasingly adopted into everyday life, with most investment and development concentrated in the U.S. In response to rapid AI integration and scant federal guidelines, U.S. states have formed AI committees charged with studying AI-related societal trade-offs. We analyzed the 18 existing state-level AI committee reports to understand how policymakers discuss AI-related benefits and risks. We then compared the risks surfaced by policymakers to an established taxonomy of AI risks aggregated from literature and examined how policymakers’ concerns align, or misalign, from those of HCI scholars. These insights provide important mileposts for shaping currently ongoing policy initiatives and future research. Our findings reveal important gaps: while committees invoke responsible AI, their framings often omit broader socio-technical concerns emphasized in HCI. We discuss opportunities for HCI to support socio-technical perspectives, employ participatory design, and close the gap between research and policy.
[HC-18] Invisible Strings: Deriving Puppetry Principles and their Hidden Connections to Robot Behavior Design
链接: https://arxiv.org/abs/2607.03289
作者: Claire Lewis,Sawyer Collins,Alyssa Hanson,Johanna Smith,Tom Williams
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC)
备注: 22 pages, 10 figures
Abstract:When designing robots’ nonverbal behaviors, many researchers have turned to arts-based insights, such as Disney’s Animation Principles. Yet, while these principles bear key insights into the design of like-life characters, their application to robot design is inherently limited, in part because animation is not constrained by real-world physics, and in part because animation principles focus on low level animation mechanics and not high-level design considerations for physically embodied, interactive characters. In contrast, little attention has been paid to art forms like puppetry, despite their long history of exploring morphological, behavior, and interaction design of physically embodied, interactive characters. As such, in this work we leverage puppetry texts and practicing puppeteers’ expert knowledge knowledge to derive a set of puppetry principles with key insights for robot design. As we show, these insights go beyond – and uniquely complement – the prior insights provided by theater, dance, and animation.
[HC-19] OpenGlass: A Sensing-Computing Split Architecture for Local MLLM -Driven Real-Time Visual Assistance ACL2026
链接: https://arxiv.org/abs/2607.03213
作者: Mengzhang Li,Yuan Yao
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: Accepted to ACL 2026 System Demonstrations. 11 pages, 5 figures, 8 tables
Abstract:We present OpenGlass, an open-source, privacy-oriented, local-first system for low-latency multimodal visual assistance, with a primary focus on blind and low-vision users. Cloud MLLM assistants offer strong visual understanding, but often require uploading first-person visual data and can suffer multi-second network delays; wearable glasses are ideal for sensing, but cannot host large models under tight compute and power budgets. OpenGlass addresses this gap with a sensing-computing split: an ESP32-based glasses-side unit captures visual context, while a nearby consumer-grade device performs local MLLM inference and local speech output, reducing cloud reliance and keeping raw egocentric visual data on user-controlled devices by default. We evaluate response quality, query-ready-to-audio latency, safety-aware abstention, and auditable logs. Under real ESP32 Wi-Fi capture, OpenGlass reaches 993 ms median user-to-audio latency with resized payloads and 1625 ms with raw 1280 x 720 payloads; 97.5% and 93.3% of trials fall below 2 s, respectively. OpenGlass is a user-initiated visual-assistance reference platform for obstacle/hazard awareness, sign/object queries, and image-quality self-checking, rather than a certified navigation aid. We release source code, hardware instructions, prompts, evaluation data, and logs.
[HC-20] APeB: Benchmarking Personalization Ability of Large Language Model Agents
链接: https://arxiv.org/abs/2607.03162
作者: Garry Yang,Zizhe Chen,Xinru Chen,Yongqiang Chen,Jianxiang Wang,Deyu Zou,Linyi Ding,Jialiang Wu,Yunzhong He,Yu Gong,James Cheng,Huaixiao Tou
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: NA
Abstract:LLM-powered agents struggle with personalization when users issue raw, underspecified queries. In this setting, agents must infer latent intent, extract preferences from noisy interaction histories, and select among competing alternatives. Existing benchmarks rarely test this capability, as they often rely on user-refined queries or simplified histories. We introduce personalized product search (PPS), a testbed for agentic personalization under raw queries and diverse histories. We construct Agent Personalized Benchmark (APeB) from action logs, pairing underspecified intents with rich histories and user-viewed candidate items. Evaluating state-of-the-art LLMs with multi-step agent workflows, we find that models handle explicit queries well but struggle with early-stage queries requiring intent and preference discovery. Rubric analysis attributes this gap mainly to ineffective history use. A simple history-aware query-refinement pipeline, VQRA, yields consistent gains, highlighting the need for dedicated history-utilization modules in personalized agents.
[HC-21] A Comparative Study of Static Scrollytelling and Chatbot Visualization Onboarding Techniques for UX Designers
链接: https://arxiv.org/abs/2607.03023
作者: Ester Chen,Aboli Shete,Aditya Anavekar,Roshan Peiris,Hidy Kong
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注:
Abstract:User experience (UX) designers face barriers when creating data visualizations due to limited domain expertise in visualization or unfamiliarity with specialized tools. This highlights a clear need for effective methods to build visualization literacy. To address this, we evaluated three visualization onboarding techniques – static, scrollytelling, and chatbot – in an experimental study with 25 UX designers and students. We measured visualization comprehension and guideline adherence during a visualization creation task, followed by surveys and interviews to capture preferences and experiences. Compared to static onboarding, the pooled interactive condition (scrollytelling or chatbot) was associated with significantly higher guideline-adherence scores during visualization creation; both interactive techniques also received higher engagement ratings. Instruction clarity ratings were significantly higher when the two interactive conditions were pooled. Comprehension did not differ significantly across conditions. While participants generally preferred the interactive techniques, no significant differences emerged between scrollytelling and chatbot in performance or onboarding experience ratings. Drawing on the findings, we discuss three design dimensions of visualization onboarding (narrative structure, visual content layout, and navigational flexibility), their design implications, and potential opportunities for future research in this field.
[HC-22] PromptPET: Privacy-Utility Optimized Prompt Obfuscation
链接: https://arxiv.org/abs/2607.02932
作者: Ke Yang,Olivia Figueira,Umar Iqbal,Athina Markopoulou
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注:
Abstract:Privacy is an important challenge when users interact with AI chatbots, since users may share sensitive information, explicitly or implicitly, and AI chatbots can use this information for user profiling. In this paper, we aim to protect user privacy via a user-side mechanism that transforms sensitive information in a user prompt, while preserving enough information to elicit a useful response from the chatbot. This approach faces an inherent tradeoff between protecting privacy (i.e., avoiding profiling) and preserving utility (i.e., getting personalized and task-specific responses). To that end, we consider, evaluate, and compare four different obfuscation actions, namely redaction, abstraction, replacement, and a novel noising/denoising scheme that we introduce. Additional novel insights include: utilizing a data type taxonomy to both identify and obfuscate sensitive information and explicitly taking into account the utility of chat responses in making the obfuscation decision. First, we systematically optimize and evaluate each obfuscation action independently in terms of the privacy-utility tradeoff it achieves. Second, we propose PROMPTPET, an LLM-based agent that selects the best obfuscation action for each sensitive part of the prompt, using a reinforcement-learning inspired rule optimizer, applied for the first time in this context. Using a real-world chat dataset, we show that PROMPTPET matches the best privacy-utility tradeoff attainable by any single obfuscation action and significantly outperforms prior state-of-the-art approaches.
[HC-23] See the Emotion: A Facial Emoji Proxy Modeling for EEG Emotion Recognition ICML2026
链接: https://arxiv.org/abs/2607.02912
作者: Jingjing Hu,Guo Dan,Haofan Cheng,Ying Zeng,Zhan Si,Jinxing Zhou,Meng Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC); Multimedia (cs.MM)
备注: Accepted by ICML 2026
Abstract:Despite the high accuracy of EEG-based emotion recognition, existing models remain opaque “black boxes”, lacking semantic grounding between abstract neural features and human-interpretable states. In this paper, we reframe EEG explainability as a cross-modal generation task, shifting the paradigm from feature attribution to behavioral visualization. We introduce Facial Emoji Proxy Modeling, a novel framework that translates high-dimensional EEG signals into identity-anonymized facial emojis. Guided by the neuroscientific inspiration of neural-facial association, this approach grounds neural representations in the manifold of observable facial dynamics. Technically, our framework integrates FMENet, a specialized backbone modeling expression-relevant spatial synergies, and the Facial Emoji Learning Branch (FELB), which treats emoji reconstruction as a structured semantic regularizer. Extensive experiments on EAV and MMER benchmarks demonstrate that our method achieves state-of-the-art accuracy among EEG-only models. Crucially, it generates semantically faithful facial animations that provide a transparent, privacy-preserving window into the brain’s emotional evolution, effectively allowing users to “see the emotion” directly from neural signals. Code is available at this https URL
[HC-24] Gendered Pixels: Exploring Gender Differences in Computer-Mediated Self-Presentation among Douyu Live Streamers
链接: https://arxiv.org/abs/2607.02760
作者: Mengxiao Zhu,Xiyue Wang,Chunke Su,Han Zhao,Cuihua Shen
类目: ocial and Information Networks (cs.SI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Live streaming platforms, as computer-mediated communication (CMC) systems, provide streamers with a range of tools, such as webcams, beauty filters, and stream titles, to shape their online personas in ways that either conform to or deviate from viewers’ expectations. Drawing on gender role and CMC theories, this study examines how streamers leverage CMC self-presentation tools to fulfill gender role expectations and their associated live streaming outcomes. Analyzing data collected from 867 streamers and 94,227 streams on Douyu, a popular Chinese live streaming platform, we find that although both female and male streamers make extensive use of CMC tools, female streamers are more likely to employ visual strategies, such as webcams and beauty filters, than male streamers. We further find that although different tools have varying associations on streamers’ earnings and audience engagement, the benefits of webcam use are weaker for female than for male streamers. These findings underscore the complex interplay between gender roles and technology use in the live streaming domain.
[HC-25] Doom Researching: A Conceptual Framework for Repetitive AI-Assisted Information Seeking Cognitive Offloading and the Illusion of Knowing
链接: https://arxiv.org/abs/2607.02723
作者: Santosh Premi Adhikari
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注:
Abstract:Generative artificial intelligence (GenAI) systems such as ChatGPT, Claude, and Gemini have made information seeking faster, more conversational, and more cognitively comfortable. These affordances can support learning and productivity, but they can also encourage a repetitive pattern in which users continue querying AI systems for explanations, summaries, comparisons, plans, and reassurance without converting those interactions into durable understanding, decisions, or finished work. This conceptual paper proposes the term doom researching to describe this AI-mediated pattern of repetitive information seeking without proportional synthesis or output. Building on research on doomscrolling, information seeking, cognitive offloading, transactive memory, human-AI interaction, productivity loss, and the illusion of knowing, the paper develops a framework in which fluent AI responses reduce cognitive effort, inflate perceived knowledge, and increase the likelihood of further querying. The framework distinguishes doom researching from doomscrolling, cyberchondria, ordinary research, and productive AI-assisted work. It introduces a formal model of the doom researching loop, a candidate risk index for empirical measurement, and testable propositions for future studies. It then extends the construct through the lens of the extended mind thesis, distinguishing assistive, substitutive, and disruptive forms of cognitive offloading, and connects individual doom researching to the broader literature on AI-driven homogenization and knowledge collapse. The paper argues that doom researching is not simply “too much AI use” but a misalignment among inquiry, metacognition, and output. The goal is to provide a vocabulary and research agenda for studying when AI-assisted inquiry becomes a substitute for thinking, synthesis, and action.
[HC-26] Coordinate Singularities Break Conformal Coverag e for Gaze and Head Pose ECCV2026
链接: https://arxiv.org/abs/2607.02565
作者: Mohammadreza Jamalifard,Yaxiong Lei,Parastoo Azizinezhad,Javier Andreu-Perez
类目: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
备注: ECCV2026 accepted paper
Abstract:Conformal prediction provides distribution-free reliability guarantees for vision systems, but these guarantees depend on how prediction errors are measured in the output space. Many vision tasks produce outputs on curved spaces (e.g. gaze directions on the sphere or 3D head rotations), yet intermediate prediction heads, residuals, uncertainty estimates, or conformal scores are often defined in flat coordinate charts such as yaw-pitch or Euler angles. We show that this scoring choice introduces systematic geometric distortion near coordinate singularities (large pitch angles on the sphere and poses approaching gimbal lock in 3D rotations). Across four datasets (ETH-XGaze, Gaze360, BIWI, AFLW2000-3D), slice-conditional coverage at a nominal 90% target drops by 30-50 percentage points in these regions, falling to 38.9% on ETH-XGaze and 42.0% on Gaze360 at gaze pitch above 70 degrees, and to 57.5% on BIWI and 55.2% on AFLW2000-3D at head pose pitch above 60 degrees near gimbal lock, despite marginal coverage remaining near 90%. We prove that this is structural. Scalar thresholding changes the size of chart-coordinate prediction sets but leaves their distorted axis ratios unchanged. To diagnose this hidden failure mode, we show that a simple geometric quantity, the Riemannian volume density, strongly correlates with where coverage collapse occurs. Finally, we show that coordinate-free geodesic scoring removes this distortion. It requires no retraining and adds negligible computational cost.
[HC-27] Attention Dynamics in Diffusion Models: A Visual Analytics Framework for Human-AI Collaboration
链接: https://arxiv.org/abs/2607.02563
作者: Yiran Xiao,George Legrady
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Diffusion-based text-to-image models can synthesize complex and highly structured visual content, yet the emergence and evolution of semantic structure remain difficult to interpret. Many existing workflows rely on aggregated attention or scalar summaries that separate temporal change from image-space evidence. To address this gap, we present a visual analytics framework for exploring attention dynamics in diffusion models: the step-indexed evolution of token-level cross-attention maps, their temporal concentration, and their spatial relationships. Our approach enables structured analysis of attention behavior across generation steps by integrating quantitative measures with data-driven stage identification in an interactive workflow. Case studies on a structured 60-prompt Stable-Diffusion-class benchmark illustrate recurring, interpretable patterns within this setting and show how linked temporal and spatial views facilitate the observation and discussion of generative processes, supporting more effective human-AI collaboration.
[HC-28] miMamba: EEG-based Emotion Recognition with Multi-scale Inverted Mamba Models
链接: https://arxiv.org/abs/2409.07589
作者: Xin Zhou,Dawei Huang,Xiaojing Peng,Lijun Yin
类目: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Signal Processing (eess.SP)
备注: IEEE Transactions on Affective Computing (2025)
Abstract:EEG-based emotion recognition holds significant potential in the field of brain-computer interfaces. A key challenge lies in extracting discriminative spatiotemporal features from electroencephalogram (EEG) signals. Existing studies often rely on domain-specific time-frequency features and analyze temporal dependencies and spatial characteristics separately, neglecting the interaction between local-global relationships and spatiotemporal dynamics. To address this, we propose a novel network called Multi-Scale Inverted Mamba (MS-iMamba), which consists of Multi-Scale Temporal Blocks (MSTB) and Temporal-Spatial Fusion Blocks (TSFB). Specifically, MSTBs are designed to capture both local details and global temporal dependencies across different scale subsequences. The TSFBs, implemented with an inverted Mamba structure, focus on the interaction between dynamic temporal dependencies and spatial characteristics. The primary advantage of MS-iMamba lies in its ability to leverage reconstructed multi-scale EEG sequences, exploiting the interaction between temporal and spatial features without the need for domain-specific time-frequency feature extraction. Experimental results on the DEAP, DREAMER, and SEED datasets demonstrate that MS-iMamba achieves classification accuracies of 94.86%, 94.94%, and 91.36%, respectively, using only four-channel EEG signals, outperforming state-of-the-art methods.
[HC-29] Strategic Buying Agents
链接: https://arxiv.org/abs/2607.04708
作者: Mingyang Fu,Ming Hu
类目: Theoretical Economics (econ.TH); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Computer Science and Game Theory (cs.GT); Human-Computer Interaction (cs.HC)
备注:
Abstract:Agentic AI is shifting online shopping from search toward delegated purchasing, where autonomous buying agents monitor markets and decide when to buy on a consumer’s behalf. We study the design of such strategic buying agents, which must decide when to purchase within a finite shopping window, translating price observations, the remaining time horizon, and beliefs about future price changes into a purchase policy. We formulate this problem across three information regimes: stationary, Bayesian, and robust, and treat the resulting optimal policies as a policy menu for implementation. In the stationary regime, price adjustments follow a Poisson arrival process with a known post-adjustment price distribution; the optimal policy is a dynamic purchase-threshold rule, with the threshold governed by an ordinary differential equation. In the Bayesian regime, the adjustment intensity is known, but the price-adjustment distribution is uncertain; the optimal rule remains threshold-based, now depending on posterior beliefs, and we bound the value of knowing the true distribution. In the robust regime, the agent has only price bounds and seeks worst-case protection; randomized threshold policies achieve optimal competitive-ratio and minimax-regret guarantees. We evaluate the proposed policies on Amazon price histories from Keepa (367 items, 48,933 timestamped observations) and examine their integration into language-model buying agents. The stationary and Bayesian policies perform competitively on mean normalized consumer surplus despite their stylized assumptions, while the robust policy performs best at the distribution’s 10th percentile. Results suggest language models are better suited to selecting among regimes and calibration samples than to making buy-or-wait decisions directly.
[HC-30] Modeling the Impact of Visual Brand Language on Attention Object Recognition and Memory Retrieval
链接: https://arxiv.org/abs/2607.02929
作者: Rachel F. Heaton,John E. Hummel
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
备注: 19 pages, 6 figures
Abstract:Visual brand language is the set of visual properties that convey brand identity for a product. What is the impact of visual brand language on a person’s ability to recognize and understand the functional identity of an object? Using an empirically supported modeling framework based on the JIM model of object recognition and the LISA model of analogical inference, we simulated the impact of visual brand language on object recognition, the allocation of attention, and retrieval of functional information about objects. Our simulations predict that brand information captures attention and can slow recognition of an object’s functional category, with greater degrees of branding causing larger effects. These results have potential implications for the usability and experience of designed objects.
计算机视觉
[CV-0] From Fixed to Free Cameras: Calibration-Free View-Robust Vision-Language-Action Model
链接: https://arxiv.org/abs/2607.05396
作者: Wenhao Li,Xueying Jiang,Quanhao Qian,Deli Zhao,Shijian Lu,Gongjie Zhang,Ran Xu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
备注:
Abstract:Real-world robot deployment rarely maintains the training-stage camera setup, where cameras often experience repositioning or remounting depending on actual scenarios. Existing view-robust Vision-Language-Action (VLA) policies tolerate such camera variations only when the camera extrinsics are explicitly provided, making them fragile and hard to use especially when view robustness is critical. We argue that the policy should not be told where the camera is, but rather figure it out by itself. To this end, we introduce Camera-Centric VLA (CamVLA), a new VLA model that decouples manipulation controls from camera geometry by predicting (i) a camera-centric end-effector action expressed in the local camera frame, and (ii) a 6-DoF hand-eye matrix relating cameras to the robot base. A deterministic geometric transformation composes the two predictions into a robot base-frame action. This disentangles how I should move in pose-independent camera-centric action generation from where I am looking from in camera-perspective geometric grounding. The resulting policy is calibration-free, depth-free, and single-view, requiring only a single monocular RGB image as the visual observation and task instruction at deployment. Evaluations in both simulation and real-world robot data show that CamVLA consistently improves success rates across diverse unseen viewpoints. Project page: this https URL.
[CV-1] SynCity 3000: Bootstrapping Scene-Scale 3D Diffusion
链接: https://arxiv.org/abs/2607.05392
作者: Paul Engstler,Iro Laina,Christian Rupprecht,Andrea Vedaldi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:We present SynCity 3000, a framework for generating 3D scenes that are globally coherent while enabling fine-grained layout control. Building on the ability of current image-to-3D generators to produce complex 3D assets from a single image, we extend this capability to the scale of entire scenes by adapting the generator to be applicable as a convolutional operator. We achieve this by fine-tuning the model on scene-like data generated by a new synthetic data engine, which we propose to address the scarcity of 3D scene data for training. The convolutional generator is then applied to a dimetric image of the entire scene, generated from the user prompt, resulting in 3D scenes of arbitrary size and complexity. Across diverse prompts and layouts, SynCity 3000 produces large, coherent, and detailed scenes, addressing the shortcomings of prior approaches to 3D scene generation.
[CV-2] Deform360: A Massive Multi-view Visuotactile Dataset for Deformable World Models ECCV2026
链接: https://arxiv.org/abs/2607.05390
作者: Hongyu Li,Wanjia Fu,Xiaoyan Cong,Zekun Li,Binghao Huang,Hanxiao Jiang,Xintong He,Yiqing Liang,Rao Fu,Tao Lu,Srinath Sridhar,Kevin A. Smith,George Konidaris,Yunzhu Li
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV 2026
Abstract:Predicting object dynamics (i.e., world modeling) is a fundamental challenge for robotic manipulation, and modeling deformable objects presents a particularly difficult case due to their high-dimensional state spaces and complex material properties. While current world models approach this through two distinct paradigms: learning the dynamics over the 2D pixel space or more explicit 3D geometric space. A systematic understanding of their relative strengths and limitations remains elusive due to the lack of diverse, large-scale real-world data. To address this, we present Deform360, a large-scale visuotactile dataset featuring 198 daily-life objects, 1,980 interaction sequences, and over 215 hours of observations from 41 surround-view cameras and bimanual tactile grippers to capture both global motion and contact-induced local deformations. Leveraging a novel markerless visuotactile 3D tracking pipeline to extract dense geometry and motion, we systematically evaluate current state-of-the-art world models, comparing 2D video models against 3D particle models. Finally, we provide a preliminary demonstration indicating the real-world applicability of our dataset by performing robot planning tasks on deformable objects. Our analysis reveals key insights into the trade-offs between structural priors and scalability, providing a solid benchmark for future research in generalizable deformable object-centric world modeling. Project website: this https URL
[CV-3] InFlux: Real and Synthetic Data for Estimating Dynamic Camera Intrinsics ECCV2026
链接: https://arxiv.org/abs/2607.05389
作者: Erich Liang,Caleb Kha-Uong,Chinmaya Saran,Sreemanti Dey,David W. Liu,Junhan Ouyang,Benjamin Zhou,Jia Deng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026
Abstract:Camera intrinsics are vital for recovering 3D structure from 2D video. However, most 3D algorithms assume fixed intrinsics throughout a video, an assumption that often fails for real-world in-the-wild videos. Consequently, estimating per-frame intrinsics from RGB images is critical for making 3D methods robust to videos with dynamic intrinsics. InFlux previously advanced this research direction by establishing the first real-world benchmark with per-frame ground truth intrinsics for dynamic intrinsics videos. Nevertheless, existing methods remain inaccurate due to two obstacles: (i) training data is scarce and lacks intrinsics diversity; and (ii) benchmarks, including InFlux, have limited scene and camera motion diversity, making it difficult to properly evaluate methods. To address both gaps, we present InFlux++, consisting of two components. InFlux++ Synth is a large-scale procedurally generated synthetic video dataset with 441K+ annotated frames from 1841 high-resolution videos, providing accurate per-frame ground truth intrinsics for training dynamic intrinsics prediction models; a subset also includes per-frame pose, depth, and normals. The videos feature rich intrinsics diversity through changes in camera zoom and focus, as well as dynamic objects and realistic rendering effects such as lens distortion and defocus blur. InFlux++ Real is a large-scale real-world benchmark that extends InFlux with 514K+ newly captured frames across 334 high-resolution videos, spanning a wider range of scenes and camera motions. Finetuning existing intrinsics prediction methods on InFlux++ Synth consistently improves focal length estimation across both InFlux++ Real and InFlux, suggesting that synthetic supervision is promising for RGB-based intrinsics prediction. For the dataset, benchmark, code, videos, submission instructions, and live leaderboard, please visit this https URL .
[CV-4] Search Beyond What Can Be Taught: Evolving the Knowledge Boundary in Agent ic Visual Generation
链接: https://arxiv.org/abs/2607.05382
作者: Haozhe Wang,Weijia Feng,Jinpeng Yu,Che Liu,Ping Nie,Fangzhen Lin,Jiaming Liu,Ruihua Huang,Jimmy Lin,Wenhu Chen,Cong Wei
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Visual generators excel at rendering, but they confidently fabricate what they do not know. User requests are unbounded, evolving, and deeply long-tailed: new characters, trending entities, post-cutoff events, and more. This world-knowledge bottleneck is structural: generators are trained on fixed corpora, but the visual world is open-ended. We construct SearchGen-20K and SearchGen-Bench, with 20,839 prompts spanning twelve failure categories and twenty-two domains, paired with a pre-executed multimodal SearchGen-Corpus-1M to support offline, reproducible research. On SearchGen-Bench, frontier open generators score only 21 to 28 out of 100, a 40-point collapse invisible to existing benchmarks. The natural remedy is to employ search tools, enabling agentic visual generation. However, we find that naive search fails: it retrieves indiscriminately, injecting noise into prompts the generator already handles. We trace the root cause to a generator-specific, evolving knowledge boundary: the divide between what a generator can internalize through training and what must remain in external context. Although this boundary is hard to specify in advance, we show that it is discoverable through a teach-then-search co-training framework. Even a minimal version of this co-training recipe produces monotonic improvement, laying the foundation for recursive self-improvement in visual generation that can meet world-knowledge-grounded requests. We release the full dataset, co-training corpus, and search corpus as a replayable harness for tool-augmented, world-knowledge-grounded visual generation.
[CV-5] Cortex: A Bidirectionally Aligned Embodied Agent Framework for Long-horizon Manipulation
链接: https://arxiv.org/abs/2607.05377
作者: Jiaqi Peng,Xiqian Yu,Delin Feng,Yuqiang Yang,Wenzhe Cai,Jing Xiong,Ganlin Yang,Jinliang Zheng,Jiafei Cao,Xueyuan Wei,Jiangmiao Pang,Yuan Shen,Tai Wang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Project website: this https URL
Abstract:While recent Vision-Language-Action (VLA) models show promise toward generalist manipulation policies, they struggle with long-horizon tasks due to their Markovian nature-relying solely on current observations. Hierarchical dual-system methods address this but suffer from a gap between high-level planning semantics and low-level execution kinematics. We introduce Cortex, a bidirectionally aligned embodied agent framework with a customized planning interface that conveys executable and tractable subtask plans from high-level VLM to low-level VLA. Specifically, we standardize manipulation subtasks into 32 canonical skill primitives and inject tractability principles, such as representative object attributes and improved trajectory reachability, into the data generation pipeline. This enables automatic annotation of over 4k hours of open-source video data and generation of 30 hours of simulation data. We further devise an event-balanced sampling strategy to construct training data for fine-tuning the framework to better handle planning ambiguity during subtask transitions, enhanced by carefully designed harness engineering from task contexts to skill constraints during inference. Both open-loop VLM and closed-loop system evaluations demonstrate Cortex’s efficacy, e.g., it outperforms monolithic baselines by 3.1% on Libero-long and 4.1% on RoboTwin. Notably, Cortex’s generalist VLM enables zero-shot completion of unseen real-world long-horizon tasks, such as multi-stage chemistry experiments, by simply combining with a fine-tuned VLA-a capability infeasible through VLA fine-tuning alone.
[CV-6] MV-Forcing: Long Multi-View Video Generation via 4D-Grounded Spatio-Temporal Self-Forcing ECCV2026
链接: https://arxiv.org/abs/2607.05376
作者: Gal Fiebelman,Hadar Averbuch-Elor,Sagie Benaim
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注: Accepted to ECCV 2026. Project webpage: this https URL
Abstract:Recent advances in video diffusion models have enabled either long single-view generation through temporal autoregression, or short multi-view synthesis through bidirectional attention. However, generating long, multi-view consistent videos of dynamic scenes remains unsolved. In this work, we present MV-Forcing, a framework that composes temporal and view-wise autoregression within a single diffusion model by introducing a 4D geometric bridge between sequentially generated views. Our key insight is that an autoregressive 3D reconstruction model naturally interfaces between autoregressively generated views. Given a completed source view, we reconstruct its 3D structure and render a geometric prior of the next target viewpoint, which the diffusion model refines into a high-quality video. To extend generation beyond the teacher’s fixed temporal window, we introduce a joint denoising regime where both view slots are initialized from noise during training, enabling temporally unbounded generation. We distill the model via Distribution Matching Distillation with Spatio-Temporal Self-Forcing, closing the train-inference exposure bias gap for both temporal and view-sequential autoregression. Extensive experiments on both synthetic and real-world data demonstrate that MV-Forcing produces geometrically consistent multi-view videos of dynamic scenes at arbitrary lengths and viewpoint counts using a single few-step student model.
[CV-7] PixWorld: Unifying 3D Scene Generation and Reconstruction in Pixel Space
链接: https://arxiv.org/abs/2607.05373
作者: Sensen Gao,Zhaoqing Wang,Qihang Cao,Dongdong Yu,Changhu Wang,Jia-Wang Bian
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:3D reconstruction and generation are commonly tackled by separate paradigms: pixel-based regression for reconstruction, and latent diffusion for generation. Recent works attempt to unify them in latent space, but with notable drawbacks: the diffusion objective is defined on latent features rather than the underlying 3D representation, and both branches suffer from information loss introduced by latent encoding, while requiring a pretrained Variational Autoencoder (VAE) or Representation Autoencoder (RAE). In this paper, we reformulate these two tasks under a unified pixel-space diffusion paradigm and introduce PixWorld, a single model that jointly addresses 3D reconstruction and generation. By supervising diffusion directly on rendered images, PixWorld removes the above limitations and aligns optimization with 3D scene fidelity. Beyond photometric and perceptual supervision that operates at the 2D image level and lacks 3D geometric awareness, we further introduce a geometry perception loss that aligns rendered views with their ground truth in the geometry-aware feature space of a pretrained 3D foundation model, providing 3D structural supervision. PixWorld consistently outperforms prior latent-space generation methods and matches state-of-the-art reconstruction methods, demonstrating the superiority of a unified pixel-space approach.
[CV-8] ReCal3R: Reliability-Calibrated Learning Rates for Streaming 3D Reconstruction
链接: https://arxiv.org/abs/2607.05356
作者: Xinze Li,Yiyuan Wang,Pengxu Chen,Wentao Fan,Weifeng Su,Weisi Lin,Wentao Cheng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 23 pages, 7 figures. Project Page: this https URL
Abstract:Streaming 3D reconstruction relies on a compact recurrent scene state to process long image streams in linear time and bounded memory. However, repeated updates can gradually corrupt this state, causing reliable historical information to be overwritten by noisy or ambiguous observations. We introduce ReCal3R, a reliability-calibrated learning rate method for recurrent 3D reconstruction. Instead of directly applying a candidate learning rate, our method estimates state token reliability from the maintained scene state and uses it to calibrate a candidate learning rate derived from token alignment, state reconstruction residual, and recent update pressure. The resulting token-wise learning rate interpolates between a conservative base rate and the candidate rate, suppressing aggressive updates on unreliable tokens while preserving adaptation to informative frames. Applied to CUT3R as a training-free calibration rule, ReCal3R reaches strong performance on long sequences in pose, depth, and reconstruction quality, including a 3.7 \times reduction in ATE, with comparable runtime and memory. Code is available at: this https URL.
[CV-9] Geometric Reciprocity: Unlocking Self-Supervision for Stereoscopic Video Generation ICML2026
链接: https://arxiv.org/abs/2607.05354
作者: Jingyi Lu,Kai Han
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ICML 2026. Project page: this https URL
Abstract:Monocular-to-stereo conversion synthesizes stereoscopic content from 2D videos for immersive 3D experiences. In modern Depth-Image-Based Rendering (DIBR) approaches, stereo inpainting of disocclusions is the critical bottleneck. Training-based methods achieve superior quality but rely on scarce stereo pairs or synthetic data with domain gaps. We address this through the first self-supervised framework learning from monocular videos via cycle consistency. Our key contribution is the Geometric Reciprocity Theorem (GRT): under the nearest-neighbor DIBR formulation, the disocclusion mask when synthesizing a target view equals the mask of pixels lost when warping back from target to source, enabling analytical computation of test-time disocclusion masks directly from monocular images. This yields train-test consistency for the stated warping formulation, supporting self-supervised learning from unlimited monocular videos and substantial improvements over training-free and supervised state-of-the-art methods. Project page: this https URL
[CV-10] Multiplayer Interactive World Models with Representation Autoencoders
链接: https://arxiv.org/abs/2607.05352
作者: Anthony Hu,Václav Volhejn,Adrien Ramanana Rahary,Chris Mulder,Aditya Makkar,Amélie Royer,Manu Orsini,Alyx Liao,Adam Jelley,Eloi Alonso,Florian Laurent,Fredrik Norén,James Swingos,Jan Hünermann,Kent Rollins,Lucas Hosseini,Matthieu Le Cauchois,Maxim Peter,Pim de Witte,Tim Brown,Vincent Micheli,Moritz Böhle,Gabriel de Marmiesse,Viktoriia Sharmanska,Lucia Specia,Michael Black,Patrick Pérez
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Technical report
Abstract:We introduce the first multiplayer world model for highly dynamic environments governed by complex physical interactions. Whereas single-player world models treat the other agents as part of the environment, ours conditions on the action streams of multiple agents, learning to attribute changes in the scene to the correct player and to stay coherent under arbitrary combinations of their actions. We study this problem in the game of Rocket League, where players compete and cooperate under fast, tightly coupled dynamics. Trained on 10,000 hours of gameplay collected with publicly available bots, our 5-billion-parameter latent diffusion model generates four-player matches in real time, producing 20 frames per second on a single Nvidia B200 GPU. Although trained only on short clips, its rollouts stay stable far beyond the training horizon: distributional quality holds steady out to five minutes, the longest horizon we measure, and in practice we observe rollouts continuing for hours with no sign of collapse. We systematically investigate the central design choices: the video codec, the generative objective, and the multiplayer conditioning scheme. In addition, we characterize how behavior changes with model and data scale, including the capabilities that emerge and the failure modes that persist. We further develop targeted evaluations that probe the model’s physical understanding rather than visual appearance alone. To support continued research on multiplayer world models, we release our dataset, our full training and inference codebase, and a live demo.
[CV-11] Beyond Isolated Objects: Relationship-aware Open Vocabulary Scene Understanding via 3D Scene Graph Analysis
链接: https://arxiv.org/abs/2607.05348
作者: Xianhao Chen,Jiarui Hu,Yuanbo Yang,Xiyu Zhang,Tengyue Wang,Hujun Bao,Guofeng Zhang,Zhaopeng Cui
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Project Page: this https URL
Abstract:Open-vocabulary 3D scene understanding aims to segment 3D scenes beyond predefined categories by transferring semantic knowledge from vision-language models. Existing methods have advanced this task by lifting language-aligned 2D features into 3D, yet they often rely on context-independent semantic representations, leaving object relationships underexplored for contextual refinement. We propose RelGraphOV, a relationship-aware framework that uses 3D scene graphs to enhance open-vocabulary 3D understanding. Our method constructs relational scene graphs from multi-view observations by leveraging vision-language reasoning to infer object relationships and prune geometrically implausible connections, without manual relationship annotations. To aggregate relational context while avoiding feature interference, we introduce an Adaptive Gated Dual-Stream Contextual GAT that separates dense geometric features and semantic CLIP embeddings, performs edge-guided message passing, and adaptively fuses complementary semantics. A hierarchical contrastive objective further promotes instance-level consistency and category-level discrimination. Experiments on ScanNetV2, ScanNet200, ScanNet ++ , and Replica demonstrate strong performance and generalization ability. Project Page: this https URL
[CV-12] WildSplat: Feedforward Gaussian Splatting from Unposed In-the-Wild Images ECCV2026
链接: https://arxiv.org/abs/2607.05347
作者: Xiyu Zhang,Jingyu Zhuang,Hongjia Zhai,Zizheng Yan,Jinwei Chen,Guofeng Zhang,Qingnan Fan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 22 pages, 9 figures; Accepted by ECCV 2026. Project page: this https URL
Abstract:While feedforward 3D reconstruction excels at efficient novel view synthesis, it typically falters when faced with scenes under varying illumination. To this end, we introduce WildSplat, the first feedforward 3D Gaussian Splatting framework capable of appearance-conditioned novel-view synthesis for unposed in-the-wild images. To handle inconsistent photometric conditions, we propose a dual-branch architecture that explicitly decouples geometry from appearance. The geometry branch extracts an appearance-invariant 3D structure and jointly predicts camera poses. To govern the rendering appearance, the appearance branch injects target appearance cues into the content features via a globally pre-modulated cross-attention mechanism. To further prevent feature entanglement, we introduce a joint multi-reference training strategy that stabilizes the training process. Extensive experiments show that WildSplat surpasses existing optimization-based and feedforward methods, achieving state-of-the-art performance in in-the-wild novel view synthesis and appearance editing from sparse inputs in a single forward pass.
[CV-13] CenSynCMB: Centre Maps and Physics-Guided Synthesis for Microbleed Detection
链接: https://arxiv.org/abs/2607.05325
作者: Lucas He,Hanyuan Zhang,Krinos Li,Adama Fatima Saccoh,Silvia Ingala,Rafael Rehwald,Marleen de Bruijne,Frederik Barkhof,Rhodri Davies,Carole H. Sudre
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Cerebral microbleeds (CMBs) are MRI markers of small vessel disease and the microbleed component of amyloid related imaging abnormalities (ARIA-H), but their small size, sparsity, and similarity to vessels, calcification-like foci, and artefacts make automated detection difficult. We propose CenSynCMB, a centre-guided and mimic-aware framework combining a 3D Attention U-Net, auxiliary centre-map supervision, false-negative-driven reweighting, and fold-wise physics-guided synthesis of positive CMBs and labelled hard negatives. Synthetic data expose the detector to compact lesions and common mimics without validation or test leakage. On VALDO Task 2, CenSynCMB achieved the best local-comparison lesion-level F1 (74.3%, p = 0.020); on external AIBL SWI, it achieved the highest local-comparison recall (88.5%, p = 0.0058) and F1 (65.0%, p = 0.0016). Together, these results support scalable CMB candidate extraction in large, unlabelled MRI cohorts, while highlighting cohort-specific calibration as the next step toward reliable burden estimation.
[CV-14] Steering Optimisation Trajectories in Diffusion Representation Learning
链接: https://arxiv.org/abs/2607.05319
作者: Rajat Rasal,Avinash Kori,Tian Xia,Ben Glocker
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:We study why diffusion autoencoders can achieve similar image quality while learning substantially different latent structures. We trace this behaviour to optimisation dynamics; we analyse curves of image reconstruction against latent representation quality, revealing trajectories that organise around two distinct regimes early in training. Models in the reconstruction regime prioritise image fidelity early, whereas those in the disentanglement regime improve reconstruction and disentanglement more gradually. We hypothesise that this behaviour can be influenced by targeting shortcut pathways in the diffusion U-Net and controlling early noise-level exposure, thereby shaping the reconstruction-disentanglement trade-off during training. To steer optimisation toward stronger representations, we introduce SteeringDRL, combining gated residual U-Nets with a simple noise-level exposure curriculum for training. Across disentanglement benchmarks, SteeringDRL improves representation quality and reduces seed sensitivity. Our method further extends to spatial disentanglement in object-centric learning, improving segmentation quality on synthetic and real-world datasets.
[CV-15] opological Shape Representation for Aneurysm – Bifurcation Detection
链接: https://arxiv.org/abs/2607.05317
作者: Akshay Gokhale(1),Mansi Dhamne(1) ((1) Sardar Patel Institute of Technology, Mumbai)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 36 pages, 12 figures, preprint
Abstract:Automated detection of intracranial aneurysms (IAs) from CT angiography (CTA) is severely hindered by high false-positive rates. Convolutional neural networks (CNNs) rely on local pixel intensities, causing systematic confusion between saccular aneurysms and vascular bifurcations – a problem especially acute for small lesions (3 mm), where detection sensitivity falls below 60%. We propose a plug-and-play, topology-aware false-positive reduction framework evaluating the Smooth Euler Characteristic Transform (SECT) – a directional representation encoding global 3D vascular geometry independently of intensity – against persistence-based summaries (Persistence Images and Landscapes), tested on a stratified subset of the RSNA 2025 dataset. SECT achieves an AUC of 0.943, substantially outperforming direction-agnostic methods (AUC ~0.68), and exhibits a clinical performance inversion: it excels on the sub-3 mm cohort, maintaining 0.943 AUC and 78.5% sensitivity at 95% specificity. The representation is also scanner-agnostic, achieving 0.927 mean AUC under leave-one-scanner-out (LOGO) validation across four manufacturers. By capturing asymmetric geometric invariants rather than intensity profiles, SECT reliably resolves the primary structural confounder in IA detection, positioning it as a robust downstream filter for hybrid deep-learning diagnostic pipelines.
[CV-16] Deep Learning for Semen Analysis in Male Infertility: Computer Vision Multimodal Fusion and Clinical Translation
链接: https://arxiv.org/abs/2607.05311
作者: Runwei Guan,Shaofeng Liang,Jiacheng Weng,Xiaoyi Gu,Jia Weng,Daizong Liu,Duo Pan,Qingxin Zhang,Xiao Liang,Weiping Ding,Suoyu Zhu,Ming Yuan,Yanhua Fei
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 46 pages, 14 figures
Abstract:Male infertility contributes substantially to the global infertility burden, and sperm analysis remains central to diagnosis, treatment planning, and assisted reproductive technology. Conventional semen evaluation, however, is labor-intensive, operator-dependent, and limited by inter- and intra-observer variability, motivating the development of objective and reproducible computational approaches. This review provides a comprehensive and perspective-oriented synthesis of artificial intelligence-driven sperm analysis, with a focus on computer vision, deep learning, multimodal fusion, robustness, and clinical translation. We first review task-specific methods for sperm detection and counting, tracking-based motility assessment, semantic and instance segmentation, morphology and defect classification, functional assessment, and genetic integrity evaluation. We then summarize public datasets, benchmarks, evaluation metrics, and emerging multimodal strategies that integrate microscopic images, time-lapse videos, CASA-derived parameters, DNA integrity assays, and clinical metadata. Beyond algorithmic performance, we discuss key barriers to real-world deployment, including data scarcity, cross-center domain shift, annotation inconsistency, interpretability, uncertainty calibration, privacy-preserving learning, and workflow integration. Finally, we outline a staged clinical translation roadmap spanning technical standardization, multicenter retrospective validation, silent prospective evaluation, human-in-the-loop clinical testing, ART outcome validation, regulatory approval, and post-market monitoring. By organizing the field from task-specific visual recognition to trustworthy multimodal reproductive intelligence, this review highlights both the progress and the unresolved challenges required to translate AI-driven sperm analysis into clinically meaningful decision support.
[CV-17] Air Quality Downscaling with Station-Guided Pseudo-Supervision
链接: https://arxiv.org/abs/2607.05292
作者: Guorun Wang,Simone Foti,Andreas D. Demou,Leonidas Kotoulas,Theodoros Christoudias,Alexandros Koliousis,Mihalis Nicolaou,Stefanos Zafeiriou
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Super-resolving coarse atmospheric fields to local PM _2.5 variations is uniquely challenged by a mismatch in spatial support: while pixels represent regional averages, ground-truth observations are discrete, unaligned samples of a continuous spatial signal. To bridge this gap, we present a station-guided framework for high-resolution PM _2.5 downscaling over Europe. Taking coarse CAMS atmospheric composition fields alongside heterogeneous side information (i.e., human activity, land cover, elevation, satellite aerosol observations, and wind fields) our framework jointly super-resolves ( \times 40 , \approx 1 km) and bias-corrects CAMS rasters, without relying on temporal sequence modelling. To address the challenge of densely supervising our multi-scale transformer network with sparse in-situ data, we introduce a time-agnostic propagation strategy that utilises spatial Gaussian blending of interpolated OpenAQ observations. Extensive qualitative and station-level evaluations across Europe demonstrate that our model recovers fine-grained spatial structures and effectively mitigates localised CAMS biases.
[CV-18] ChatImage: Navigating Long-Form LLM Answers through Interactive Images
链接: https://arxiv.org/abs/2607.05290
作者: Wencan Jiang,Jiangning Zhang,Yong Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project: this https URL
Abstract:Large Language Models (LLMs) can produce detailed answers to complex queries, but these answers are typically presented as dense linear text, which makes fine-grained inspection, navigation, and return visits difficult. We present ChatImage, a system that converts long-form LLM answers into interactive visual images. Given a textual answer, ChatImage first normalizes its content into structured visual modules, plans a visual layout, and renders a coherent image. It then applies a second grounding pass to the rendered image with vision grounding models such as LocateAnything and MiMo-Vision, with optional SAM-style mask refinement, to identify the visible regions that should support interaction. From these grounded regions, ChatImage overlays transparent clickable hotspots on the image. Each hotspot opens a detail panel and a region-scoped follow-up thread, allowing the user to inspect and query a specific part of the answer without re-reading the full response. Instead of treating planned coordinates as the final interaction geometry, ChatImage uses them as priors and grounds the interaction targets after rendering, which improves consistency between visual content and clickable regions. We release a reference implementation and introduce a 30-question benchmark covering infographic, map, and scene-based answer formats. Evaluation with configured external models reports interaction-loop completion, a strict visual-alignment gate, and a SAM-based mask-completeness diagnostic.
[CV-19] Erasing Without Collateral Damage: Precise Concept Removal in Diffusion Models
链接: https://arxiv.org/abs/2607.05274
作者: Parth Upman,Nishita Jain,Shreyank N Gowda
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Training-free concept erasure is an attractive mechanism for controlling text-to-image diffusion models, but precise erasure often comes at the cost of damaging semantically related non-target concepts. Existing value-space methods remove the component of each cross-attention value along the target concept direction, implicitly treating target identity and shared visual structure as the same signal. We argue that this is the source of much of the collateral damage in prior preservation. We introduce CARE, a closed-form concept erasure operator that replaces the raw target direction with a kept-subspace-aware direction computed from a small bank of retained concept anchors. The resulting edit is applied directly in cross-attention value space, requires no model fine-tuning, and adds only a negligible offline computation. A single shrinkage parameter controls the erase-preserve trade-off. We further show that the operator admits a minimum-disturbance interpretation and, in its projection form, leaves the kept subspace invariant. Experiments under the standard concept-erasure protocol show that our method preserves non-target concepts more faithfully while maintaining competitive erasure across instance, style, and celebrity concepts. Code: this https URL
[CV-20] Is the Geometry Doing the Work? An Operating-Point Audit of Hierarchy in Hyperbolic Vision-Language Models
链接: https://arxiv.org/abs/2607.05268
作者: Jaeyoung Kim,Eunseok Kim,Dongsuk Jang
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 52 pages, 5 figures, Under review at TMLR
Abstract:Whether a hyperbolic representation model uses its geometry cannot be read off its curvature parameter: what matters is the dimensionless operating point \sqrtc\rho and whether the radial and cone machinery is active there. We develop a battery of necessary-condition diagnostics and audit three published hyperbolic vision-language families – MERU, HyCoCLIP, and PHyCLIP – across released checkpoints and controlled interventions on a fixed GRIT snapshot, identifying three failure modes. First, curvature is not an active resource: the operating point stays near-Euclidean ( H(u)\approx 1 ; no audited converged checkpoint reaches \sqrtc\rho1 ), and releasing the curvature floor moves curvature and norms but keeps the operating point near-Euclidean, without substantial downstream degradation. Second, the cone and traversal machinery is measured inoperative: entailment cones are inactive, saturated, or misaligned, and graded traversal fails under controlled readouts, while directed radial depth is a bounded non-detection above shuffle-null controls at quantified sensitivity; the one surviving native-relation residual remains non-operative. Third, hierarchy-looking evaluations are underdetermined: taxonomy correlations are carried by angular distance, and coarse-retrieval gains track box/compositional supervision, not curvature. A mechanistic account explains why: the entailment objective admits a low-curvature, wide-cone shortcut, and a parameter-free aperture identity (cones saturate iff \sqrtc\rho\le 2K ) locates the edge where every entailment-trained unclamped run settles; entailment-off runs show no arrest there. The shortcut is the dominant accelerator of collapse, not its sole cause. These formulations, as released, do not instantiate the radial/cone mechanism their geometry motivates; we distill the audit into a five-number geometry report for future hierarchy claims.
[CV-21] SteelBench: Evaluating Vision-Language Models in Real-World Industrial Environments
链接: https://arxiv.org/abs/2607.05264
作者: Suryanarayana Reddy Yarrabothula,Manisha Chawla,Kunal Sinha,Gagan Raj Gupta,Sashank Lekkala,Ashirvadhan Dosapati,Saikamal Nannuri,Katragadda Ajay RamaSwamy Chowdary Gowtham
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Existing video benchmarks evaluate action recognition on consumer videos, egocentric recordings, or simulated industrial environments. They do not test vision-language models under the visual and procedural conditions of real industrial CCTV, where workers appear as distant figures amid dust, steam, low light, glare, occlusion, and overlapping activities. We introduce STEELBENCH, a diagnostic benchmark for industrial surveillance that jointly evaluates per-worker activity recognition, safety-rule reasoning, and annotation provenance. SteelBench contains 1,345 densely annotated clips, curated from 149 hours of operational plant footage and 10,024 candidate clips using temporal deduplication, class balancing, and visibility-aware stratified sampling. Each clip includes dense per-worker action labels, PPE attributes, spatial context, and safety-rule annotations. Because model-assisted annotation can shape the labels later used for model evaluation, SteelBench includes a provenance-aware audit protocol. The protocol measures label influence, evaluates sensitivity to ground-truth provenance, and reports a human reference from expert-reviewed labels. Applying this audit, we find that unaudited VLM-sourced ground truth can inflate same-family model accuracy by up to 17 percentage points. Across nine VLMs from four architectural families, the best model reaches only 42.6% action accuracy, compared with an 84.6% human benchmark. Performance also fragments across recognition, robustness, calibration, and safety reasoning. Even when models predict the correct action, 37-58% of cases still yield incorrect safety judgments, and no model passes more than 2 of 5 diagnostic checks. The dataset is publicly available on Hugging Face.
[CV-22] Learning Probabilistic Embeddings for Unsupervised Action Segmentation ECCV2026
链接: https://arxiv.org/abs/2607.05263
作者: Shuai Li,Duc Manh Vu,Juergen Gall
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV2026
Abstract:This paper concerns the problem of unsupervised temporal action segmentation for long, untrimmed videos. Recent successful approaches follow a joint representation learning and clustering paradigm, where optimal transport (OT) is adopted to produce pseudo labels for learning frame representations. These approaches alternate between estimating pseudo labels using OT and optimizing the parameters with gradient descent during training, where OT is used for obtaining the final temporal action segmentation. A major limitation of these works is that they learn a deterministic embedding for frame representations. The iterative procedure between learning deterministic embeddings based on pseudo labels and estimating pseudo labels from the learned embedding can thus get quickly stuck in a local optimum. As an alternative, we thus propose to learn a probabilistic embedding for frame representations. The embeddings are modeled by Gaussian distributions and we sample from the distributions before estimating the pseudo labels. We evaluate our approach on several challenging temporal action segmentation datasets and achieve results comparable to, and in some cases, better than the state of the art. Compared to baselines with deterministic embeddings, our approach improves MoF up to 20.7% and F1-score up to 19.0%. Our code is available at this https URL.
[CV-23] FlowMark: Mask-Guided Video Watermarking
链接: https://arxiv.org/abs/2607.05261
作者: Vishal Asnani,Shruti Agarwal,John Collomosse
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We present FlowMark, a video watermarking framework guided by automatically predicted object masks. In contrast to prior region-based approaches that require user-supplied mask guidance, FlowMark learns to identify optimal regions for watermark embedding through a dedicated Mask Predictor network. Our end-to-end trainable architecture combines region-aware encoding with noise-augmented training to ensure robustness against compression, geometric transformations, and content variation, while preserving high perceptual quality. Our content-adaptive masking keeps watermark signals coherent with natural video dynamics, effectively eliminating perceptual flicker. Beyond compression robustness, FlowMark maintains reliable watermark recovery under video-native temporal edits (e.g., frame swap, insertion, deletion, resampling, and interpolation) and real-world social media distribution pipelines (e.g., YouTube and Facebook re-encoding). Experimental results on both image and video datasets show that FlowMark reliably embeds 128 -bit messages with up to 50.08 dB PSNR, offering strong performance for content provenance, temporal authenticity verification, and video integrity protection.
[CV-24] Shifting from Discrete to Continuous Reference Data: QSM-Derived Horizontal Tree Biomass Distribution for Deep Learning Biomass Estimation
链接: https://arxiv.org/abs/2607.05260
作者: Nils Griese,Christoph Kleinn,Nils Nölke
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 11 pages, 5 figures
Abstract:Conventional modeling approaches for LiDAR-based above-ground biomass (AGB) estimation rely on discrete plot-level inventory aggregates. This methodology introduces boundary-effect uncertainties that may severely degrade model performance within small field plots. To solve this limitation, we evaluate a Horizontal Biomass Distribution (HBD) reference mapped continuously from Quantitative Structure Models (QSMs). We trained a sparse 3D U-Net on simulated broadleaved forest structures using three AGB reference types: a standard forest inventory (FI) plot-level aggregate, an edge-effect-free QSM plot-level aggregate, and a continuous HBD mapping. Evaluating training plot sizes scaling from 100 to 2500 m^2 , QSM-based models systematically outperformed FI approaches at small plot sizes. Specifically, for 100 m^2 plots, the HBD reference reduced the relative root mean square error (RRMSE) by 16.84 \pm 4.37 % and increased R^2 by 0.22 \pm 0.05 against the FI baseline. By replacing plot level aggregates with HBDs as AGB reference, this methodology corrects for edge-effects and shows that using an HBD-based reference enhances model performance for small plot sizes.
[CV-25] Repurposing CLIP to Localize at Pixel Level
链接: https://arxiv.org/abs/2607.05253
作者: Jiaxiang Fang,Shiqiang Ma,Jing Wang,Siyu Chen,Fei Guo,Shengfeng He
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by IEEE TMM 2026
Abstract:Large-scale Vision-Language Models like CLIP have demonstrated impressive open-set localization capabilities at the image level. However, adapting this capability to pixel-level dense prediction poses challenges due to global feature biases. In this paper, we introduce CLIPix, a simple yet effective framework that repurposes CLIP to perform pixel-level localization. By tracing back CLIP’s classification process, CLIPix identifies object-specific attentive regions and repurposes them as pixel-level localization cues. To address noise introduced by global biases, we propose a Noise-Resistant Correction strategy, refining these cues for more precise segmentation. Additionally, we introduce a Localization Embedding strategy to integrate both localization and enriched detail information, enabling accurate, high-resolution segmentation. Our approach preserves CLIP’s generalization strength and unlocks its potential for segmenting arbitrary objects. Extensive experiments on the PASCAL and COCO datasets demonstrate that CLIPix achieves state-of-the-art performance, underscoring its effectiveness.
[CV-26] Vision Pretraining for Dense Spatial Perception
链接: https://arxiv.org/abs/2607.05247
作者: Zelin Fu,Bin Tan,Changjiang Sun,Shaohui Liu,Kecheng Zheng,Yinghao Xu,Xing Zhu,Yujun Shen,Nan Xue
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Tech report, 31 pages
Abstract:Dense spatial perception is essential for physical intelligence, where visual systems are expected to recover structured, metric, and actionable representations from pixel observations. Modern visual foundation models tend to prioritize semantic invariance, often at the expense of detailed spatial understanding. In this work, we study vision pretraining through a boundary-centric lens, motivated by the premise that boundaries and shape discontinuities offer essential cues for perceiving geometric properties. Concretely, we propose masked boundary modeling, a self-supervised paradigm that dynamically learns sub-pixel boundary representations and subsequently leverages the discovered boundary-bearing tokens as masked targets to facilitate dense visual token learning. By scaling this framework, we develop LingBot-Vision and demonstrate its efficacy across a diverse set of downstream vision tasks with DINOv3 as a strong baseline. Remarkably, LingBot-Vision drives the progression from LingBot-Depth 1.0 to LingBot-Depth 2.0 for depth completion, and thereby yields enhanced depth estimation, a key pillar for embodied artificial intelligence. Our findings reveal that boundary modeling goes beyond simple line segments and instead serves as a scalable pretraining principle for learning spatially structured visual representations.
[CV-27] GUSH3R: Everyone Everywhere All at Once as Gaussians
链接: https://arxiv.org/abs/2607.05243
作者: Keito Abe,Kaede Shiohara,Takashi Otonari,Toshihiko Yamasaki
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Reconstructing dynamic human-scene environments from monocular videos is a challenging problem that requires jointly modeling scene geometry, camera motion, and non-rigid human dynamics while enabling photorealistic rendering. Recent feed-forward methods can efficiently predict geometry, but they are often limited to non-photorealistic representations such as point clouds and meshes, or they fail to handle non-rigid objects, particularly dynamic humans. To fill this gap, we present GUSH3R (Gaussian-Unified Scene Human 3D Reconstruction), a feed-forward framework for online dynamic human-scene reconstruction. From a monocular human-scene video, our method reconstructs dynamic humans (everyone) and static scenes (everywhere) in a single forward pass (all at once) as 3D Gaussian Splatting (3DGS) primitives (as gaussians), which are geometrically consistent and capable of novel view synthesis. Experiments on monocular human-scene datasets demonstrate that our approach achieves competitive novel view synthesis quality while significantly improving inference efficiency compared to optimization-based methods.
[CV-28] A Multimodal Reasoning Typology for Grounding Chart-Image Coherence in Science Communication
链接: https://arxiv.org/abs/2607.05222
作者: Avina Nakarmi,Sohom Sen,Xun Song,Sreyashi Samaddar,Aritra Dasgupta
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Charts and images appear together throughout scientific publications, yet most computational work does not characterize their coherence. We argue that a chart, its accompanying image, and the caption that links them form a multimodal unit, and that the inferential work required to read it varies systematically. To capture this variation, we develop a typology of reasoning gaps, R1 through R5, that characterizes how chart, image, and text jointly convey a scientific claim, and the interpretive work this demands of the reader. Some pairs restate the same data, while in other pairs, charts are used to quantify a structure the image localizes, project image content onto an external variable, audit an image-based claim, or jointly construct a frame that neither panel can establish alone. The typology is anchored in the grounding theory of communication and was derived bottom-up, with a neuroscience expert, from a corpus of 79 traumatic brain injury papers and 32 chart-image pairs. Crucially, the levels provide a systematic mechanism for identifying where grounding succeeds or breaks down, rather than leaving it to subjective inference. We show this in a study in which a domain expert and three non-experts judge vision-language model (VLM) descriptions of 25 pairs: the level predicts where their judgments align and where they diverge, isolating the points at which contextual knowledge, not the figure, carries coherence. This typology thus offers figure designers a systematic way to balance text against chart-image pairs, bridging the expert-to-non-expert divide in reading a scientific takeaway.
[CV-29] Probing Geospatial SSL Representations with Environmental Signals
链接: https://arxiv.org/abs/2607.05207
作者: Rohita Mocharla,Vishal M. Patel
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Self-supervised learning (SSL) is designed to learn generic, transferable representations rather than representations optimized for a single task. Most geospatial benchmarks evaluate representations solely through downstream tasks, providing limited insight into the information encoded within the representation itself. We ask a different question: do SSL representations of satellite imagery preserve statistical associations with environmental variables that co-vary with the imaging process? To answer this question, we probe SSL representations using co-located ERA5 reanalysis variables, a global dataset of physically consistent environmental variables, including temperature, precipitation, surface solar radiation, surface pressure, and volumetric soil water. These variables are physically related to the spectral reflectance and radar backscatter recorded by Sentinel-1 and Sentinel-2, making them meaningful evaluation targets despite not being used during SSL pretraining. We complement this probing analysis with intrinsic representation metrics to characterize representation geometry and investigate how these properties relate to downstream performance and the encoding of environmental signals. Using DINO, MAE, and MoCo models trained under identical conditions, we show that representation-level metrics distinguish models with similar downstream benchmark performance, providing complementary information beyond task-driven benchmarks. We further find that the linear accessibility of environmental signals is associated with performance on environmentally dependent tasks in the PANGAEA benchmark. Finally, we release ERA5 annotations co-located with the SSL4EO dataset to enable physically grounded representation evaluation for future geospatial foundation models.
[CV-30] An event-driven framework for fly-inspired visual motion detection
链接: https://arxiv.org/abs/2607.05205
作者: Qinbing Fu,Jingyu Huang,Yan Xie,Jigen Peng,Yuchao Tang
类目: Computer Vision and Pattern Recognition (cs.CV); Neural and Evolutionary Computing (cs.NE)
备注: 6 pages, 5 figures, conference
Abstract:Fast and reliable motion detection is essential for machine vision and autonomous systems operating in dynamic environments. This work integrates emerging event-based sensing with biologically structured neural computation to establish an efficient computational paradigm for visual motion detection. The proposed framework is built upon a recently developed fly-inspired neural network that emulates motion-processing circuits in the optic lobe. Owing to its feed-forward and training-free architecture, the neural model requires only a small number of interpretable parameters and is well suited for real-time embedded implementation. Event cameras provide low-latency, low-power, and high-dynamic-range visual sensing by asynchronously transmitting brightness-change events. However, their performance can be degraded by event noise, including temporal noise and junction-leakage-induced activity, particularly under low-light conditions. Moreover, effective integration between event-based visual representations and biologically inspired neural processing remains under-explored. To address these challenges, we propose an event-driven computational framework that combines time-surface encoding for front-end event representation with a fly optic-lobe-inspired neural network for foreground motion-direction estimation. A bottom-up attention mechanism is further incorporated to suppress background motion and enhance the saliency of foreground targets. The proposed method is evaluated on real-world ground-vehicle datasets and compared with a baseline frame-based model and an optimization-based approach. Experimental results demonstrate that the framework effectively combines the temporal advantages of event-driven vision with the efficiency and interpretability of bio-inspired neural processing.
[CV-31] Causal-RetiGraph: Cross-Cohort Retinal Support and Same-Subject Pathway Analysis for Diabetic Retinopathy
链接: https://arxiv.org/abs/2607.05204
作者: Inam Ullah,Imran Razzak,Shoaib Jameel
类目: Computer Vision and Pattern Recognition (cs.CV); Probability (math.PR); Computation (stat.CO)
备注:
Abstract:Diabetic retinopathy (DR) is a local retinal lesion process and a visible manifestation of systemic microvascular injury. Modern retinal AI can grade images accurately, but often leaves unanswered how local lesion evidence, retinal vascular structure, and systemic disease pathways are connected. This paper introduces \emphCausal-RetiGraph, a compact biomedical informatics framework that links retinal graph phenotypes with NHANES-anchored pathway modelling. The retinal-image fold constructs an interpretable X1234 phenotype from vessel maps, lesion evidence, image embeddings, and AutoMorph biomarkers through spatial X_12 and Jacobian X_34 branches. The NHANES fold models systemic exposures, covariates, a same-subject retinal mediator family R^* , and downstream outcome families. X1234 is used for retinal support and pathway prioritisation, while R^* is used for participant-level pathway summaries. On the retinal fold, X1234 achieves 0.9055 binary DR accuracy and 0.9711 AUROC, with graded DR QWK of 0.8312. The results show that lesion and biomarker streams improve contextual retinal representation under scarce and imbalanced data. In NHANES, HbA1c, urine albumin, pulse pressure, fasting glucose, and systolic blood pressure are the strongest binary DR anchors. Participant-level pathway analysis identifies glycaemic–renal and glycaemic–haemodynamic pathways as the clearest mediator-style signals. These results suggest that retinal graph phenotypes can help prioritise systemic pathways in DR while preserving the distinction between image-derived support and same-subject mediation.
[CV-32] VLM-CASE: Vision-Language Model Enabled Context-Adaptive Safety Envelopes for Anticipatory Safe Autonomous Driving
链接: https://arxiv.org/abs/2607.05180
作者: Tianjia Yang,Ke Li,Ruwen Qin,Xianbiao Hu
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Systems and Control (eess.SY)
备注:
Abstract:Adverse driving conditions, such as bad weather, remain a principal barrier to autonomous driving because they degrade two things at once: what the vehicle can perceive and what it can physically do. Human drivers cope by anticipation, reasoning about the scene and re-budgeting speed, following distance, and steering before grip or sight is lost, whereas current autonomous driving systems at best react after the fact. This paper proposes VLM-CASE, a framework that gives an autonomous vehicle this anticipatory capacity while keeping its motion bounded by a formal safety model at all times. A vision-language model (VLM), fine-tuned with low-rank adaptation (LoRA), reasons about the scene from the front-camera image and reports the road surface and visibility conditions. This output parametrizes a context-adaptive safety envelope (CASE), derived from physical limits and the guarantees of responsibility-sensitive safety, that couples braking and steering through a shared friction budget. A model predictive controller then drives freely within the envelope, while the VLM runs asynchronously so it never blocks the real-time control loop. We validate the framework in closed-loop CARLA simulation on tasks that demand both lateral and longitudinal control, across a range of weather, road-surface, and lighting conditions. The resulting controller, VLM-CASE-MPC, completes all trials, outperforming a conventional MPC baseline and a state-of-the-art VLM-integrated controller. Ablations confirm that the gains come from context adaptation, with the friction and visibility adaptations proving complementary. Furthermore, the framework is controller-agnostic and pairs with almost any low-level controller, offering a promising direction for safe autonomous driving. The dataset and supplementary materials for VLM-CASE are available at this https URL.
[CV-33] FSDC-DETR: A Frequency-Spatial Domain Collaborative DETR for Small Object Detection
链接: https://arxiv.org/abs/2607.05176
作者: Aiwen Liu,Chengguang Zhu,Gang Wang,Dandan Zhu,Haodong Lin,Yan Wang,Huiyu Zhou,Zhiyi Pan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Small object detection (SOD) remains a challenging task in real-world applications. Despite recent advances, existing detectors remain limited by rigid processing that entangle spatial aggregation with implicit frequency aliasing and truncation, leading to inadequate preservation of high-frequency components for SOD. To tackle these limitations, we propose a Frequency-Spatial Domain Collaborative Detection Transformer (FSDC-DETR), a novel collaborative framework that explicitly models complementary spatial and frequency representations. Specifically, we first introduce Dual-Branch Frequency-Spatial Adaptive Fusion (DBFSAF) to enhance frequency diversity and adaptively capture frequency-spatial domain discriminative representations. Building on these representations, a frequency-spatial interaction scheme is further explored within the hybrid encoder to enable progressive feature propagation to the decoder. In particular, structure-aware frequency-spatial aggregation is achieved through Shunt Frequency-Spatial Feature Fusion (SFS-FF), establishing bidirectional interaction and progressive cross-scale propagation between frequency and spatial representations for coherent discriminative modeling. Meanwhile, informative high-frequency responses are preserved during scale transitions through Frequency-Spatial Dynamic Downsampling (FSD-Down), thereby minimizing frequency degradation throughout multi-scale fusion for the precise SOD. Experimental results demonstrate that FSDC-DETR achieves state-of-the-art performance, improving AP by 6.4 on VisDrone-DET2019 and 6.6 on AITODv2, with gains of 6.8 and 6.9 AP for small objects. The code is available at this http URL.
[CV-34] Claim-Level Rubric Rewards for Video Caption Reinforcement Learning
链接: https://arxiv.org/abs/2607.05150
作者: Mingqi Gao,Hongyuan Dong,Yifei Chen,Zhisheng Zhong,Zheng Ruan,Wenjin Hou,Yu Chen,Han Hu,Yansong Tang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In this paper, we introduce Claim-Level Rubric Rewards (CuRe), a structured reward framework designed to address the reward-design bottleneck in reinforcement learning for dense video captioning. Existing reward designs generally fall into two categories: holistic response-level judgment across heterogeneous criteria, or alignment-based evaluation against reference captions. However, both paradigms suffer from fundamental limitations. Holistic rewards struggle to ensure factual accuracy and are prone to stylistic reward hacking, while reference-based rewards overly rely on rigid textual alignment, failing to preserve the completeness and diversity inherent to open-ended generation tasks. To address these challenges, CuRe reformulates reward modeling as fine-grained claim-level verification. Specifically, CuRe decomposes captions into category-aware atomic claims through a structured rubric, converting holistic evaluation into simpler and more reliable claim-level verification.
[CV-35] Fully Rotation-Equivariant Spectral-Spatial Learning for Multispectral Object Detection ECCV2026
链接: https://arxiv.org/abs/2607.05148
作者: Peng Zhang,Tingfa Xu,Shuaihao Han,Jianan Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV 2026
Abstract:Existing multispectral detectors are limited by discrete spectral processing, a scale-dependent shift in the relative reliability of spectral and spatial cues across pyramid levels, and the lack of explicit rotation-equivariant geometric priors for arbitrarily oriented objects. To tackle these limitations, we propose FressDet, a fully rotation-equivariant spectral-spatial learning framework for multispectral object detection, capable of capturing the continuous, ordered nature of spectral structure and enabling reliable spectral-spatial fusion across pyramid levels under arbitrary in-plane rotations. FressDet integrates three complementary components. Spectral Implicit Warp (SpeIW) enables query-based spectral resampling via a coordinate-conditioned implicit field, yielding a monotone, order-preserving warp. Rotation-Equivariant Consistency Weighting (ReCoW) adaptively fuses spectral and spatial branches based on branch reliability, reinforcing informative cues while suppressing noise across pyramid levels. The oriented-aware head exploits group-indexed features to stably predict oriented objects without parameter replication. Taken together, FressDet learns more discriminative and robust spectral-spatial representations even under rotational perturbations. By achieving state-of-the-art performance with 93% fewer parameters on three public benchmarks, FressDet demonstrates its effectiveness and generalizability.
[CV-36] UNIVERSE: Unified Video Action Models for Autonomous Driving with Flexible Mask-Modulated Modality Generation
链接: https://arxiv.org/abs/2607.05133
作者: Mengmeng Liu,Diankun Zhang,Jiuming Liu,Jianfeng Cui,Hongwei Xie,Guang Chen,Hangjun Ye,Francesco Nex,Hao Cheng,Michael Ying Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 7 figures, 8 tables
Abstract:World Action Models (WAMs) have shown strong potential for improving action generalization in autonomous driving by using future video prediction as dense supervision for scene dynamics and temporal causality. However, it remains unclear which architecture better transfers video-modeling benefits to trajectory generation. Existing cascaded or dual-DiT designs separate video imagination from action prediction, weakening the transfer of video-learned world dynamics to the trajectory branch: the action model may still overfit dataset-specific driving priors, while the video model only indirectly regularizes planning. We propose UNIVERSE, a unified video-action model built upon a single mask-modulated Diffusion Transformer. By co-training future video latents and ego-trajectory tokens within shared generative parameters, UNIVERSE allows dense video supervision to directly shape trajectory denoising, leading to stronger cross-domain action generalization. To ensure causal validity and efficient deployment, we introduce a Modality-Decoupling Visibility Mask, which shares historical context across modalities while blocking mutual attention between future video and trajectory tokens. This prevents future-target leakage and enables trajectory-only inference by removing future-video denoising at test time, achieving a 4.3\times speedup over joint video-action rollout while maintaining comparable planning accuracy. The same model also supports video-only and joint video-action rollouts. Experiments show that UNIVERSE achieves 91.0 PDMS on NAVSIM (vs. 89.6 for the Two-DiT variant), and demonstrates strong zero-shot transfer to nuScenes and Bench2Drive without fine-tuning, while ablations confirm the importance of single-DiT unification, video co-training, and mask-based modality decoupling.
[CV-37] ASSEMCAD: Production-Ready CAD Assembly Generation from Natural Language
链接: https://arxiv.org/abs/2607.05123
作者: Yurui Dong,Shu Zou,Siqi Li,Nianchen Deng,Hongbin Zhou,Xuemeng Yang,Pinlong Cai,Licheng Wen,Xinyu Cai,Botian Shi
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 26 pages, 5 figures
Abstract:Recent advances in large language models and programmatic CAD have significantly improved Text-to-CAD generation for individual parts. However, production-ready mechanical assembly generation remains largely unsolved. Unlike single-part modeling, assemblies require coordinated reasoning over multiple components, functional interfaces, assembly relations, engineering principles, and physical consistency. Consequently, directly generating executable CAD code is insufficient for constructing mechanically valid and reusable assemblies. We present AssemCAD, an axiom-grounded framework for production-ready CAD assembly generation from natural language. Instead of representing an assembly as monolithic CAD code, AssemCAD first constructs an axiomatic Assembly Specification consisting of typed parts, geometry-backed ports, executable mates, and engineering axioms. Each assembly relation is explicitly grounded in one or more engineering principles, making the resulting specification interpretable, reusable, and verifiable. To realize this specification, AssemCAD introduces a port- and mate-based CAD assembly library that executes symbolic assembly relations through deterministic mate transformations and validates declared interfaces using concrete B-Rep geometric evidence. Built on this representation and library, AssemCAD further supports on-demand synthesis of reusable parametric component factories for both standard and open-world geometries. Experiments on AssemBench show that AssemCAD substantially improves assembly preservation and physical validity over code-centric CAD generation baselines, while generalizing across different foundation-model backbones. By combining axiom-grounded assembly reasoning with deterministic geometric execution, AssemCAD extends Text-to-CAD from isolated part generation toward production-ready mechanical assembly design.
[CV-38] Green for Go Red for No: Visual Grounding via Semantic Segmentation for VLA Navigation Policies
链接: https://arxiv.org/abs/2607.05122
作者: Adrian Szvoren,Dimitrios Kanoulas,Nilufer Tuptuk
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Accepted for RSS 2026 workshop
Abstract:Vision-language-action (VLA) models enable robot navigation from natural language and visual goals, but remain susceptible to perceptual distractions and ambiguous scene interpretations. This paper presents the first empirical evaluation of visual grounding for VLA navigation policies. We propose a real-time segmentation-based grounding method that highlights traversable areas in green and non-traversable areas in red using SegFormer. Two variants are evaluated: observation-only segmentation and joint observation-goal augmentation. Using OmniVLA on the Grand Tour dataset, we show that visual grounding reduces the mean waypoint error by 27-44% at the farthest waypoint, depending on the instruction length. The benefits are greater for long instructions than for short instructions, and grounding provides little improvement for image goals. Normalized error analysis indicates that grounding primarily acts as a trajectory length regularizer, reducing the predicted path length by 30% without improving per-unit-distance reasoning. Our results indicate that visual grounding offers a simple, computationally inexpensive method to improve VLA navigation without model retraining, although it cannot compensate for missing training signals in out-of-distribution instructions.
[CV-39] Semantic Video Communication via Multi-Scale Convolution and Dynamic Routing for Next-Generation Networks AAAI2026
链接: https://arxiv.org/abs/2607.05093
作者: Gengtian Shi,Jinze Yu,Chenhao Wu,Shaofei Wang,Eiji Fukuzawa,Junjie Tang,Hiroshi Onoda,Jiang Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the AAAI 2026 Workshop on AI for Time Series (AI4TS)
Abstract:The exponential growth of video traffic demands novel semantic communication paradigms that transmit meaning rather than raw bits. We present a generative AI-enabled framework for semantic video communication addressing two critical challenges: efficient hierarchical temporal modeling for bandwidth-constrained transmission and robust semantic alignment between video content and natural language queries at network edge devices. Our approach introduces a multi-scale temporal convolutional encoder that captures motion patterns across different temporal granularities with O(T) complexity suitable for resource-constrained IoT deployments. We further propose a capsule-based dynamic routing mechanism that iteratively refines segment-query associations, enabling flexible modeling of non-monotonic semantic alignments essential for goal-oriented communication. These components are unified through a multi-task learning objective optimizing temporal boundary regression, cross-modal alignment, and capsule diversity. Experiments on ActivityNet Captions demonstrate significant improvements, achieving 42.9% Recall@0.5 and 41.1% mean IoU while maintaining computational efficiency critical for edge deployment.
[CV-40] Be Indiscrete: The Benefits of Learning Continuous Spine Degeneration Severity Scores
链接: https://arxiv.org/abs/2607.05090
作者: Maria Monzon,Andrew Zisserman,Robin Y. Park,Catherine R. Jutzeler,Amir Jamaludin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Lumbar spine degeneration is a major contributor to chronic low back pain and is routinely assessed on MRI using ordinal grading systems, e.g. normal, mild, moderate, severe. Consequently, most approaches to train models to grade these MRIs formulate grading as a multi-class classification problem, treating ordinal grades as categorical, ignoring differences in misclassification severity, and imposing hard decision boundaries on a continuous disease process. This work explores modeling spinal degeneration as a continuous severity ranking problem. We introduce SpineRankNet, a framework that learns scalar severity scores from lumbar spinal MRI, and compare it against multi-class classification and ordinal regression. Using multiple degeneration measures from the Genodisc dataset, we show that a model trained using a ranking loss to produce a continuous score enables fine-grained ordering of MRI scans. Furthermore, the ordinal grading classes can be recovered from the score with comparable accuracy to those from a model trained directly for classification. The score learned by ranking even improves discrimination between more distant classes. Source code is available at this https URL.
[CV-41] meThink: Reasoning with Time for Video LLM s
链接: https://arxiv.org/abs/2607.05089
作者: Handong Li,Longteng Guo,Zikang Liu,Dongze Hao,Yepeng Tang,Zijia Zhao,Jie Jiang,Zhiwei Jin,Chen Chen,Haonan Lu,Jing Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages
Abstract:Video reasoning requires models to identify and verify temporally localized evidence within long video sequences. Recent Video Large Language Models (Video-LLMs) have shown promising reasoning abilities when aligned with reinforcement learning, yet existing approaches typically rely on outcome-based rewards that supervise only the final prediction. Such supervision provides limited guidance on how models should discover the relevant temporal evidence during intermediate reasoning. In this work, we propose TimeThink, a reinforcement learning framework that explicitly guides temporal evidence discovery in Video-LLMs. Our key idea is to treat temporal clue steps as the fundamental optimization primitive of video reasoning, where each reasoning step references a candidate time interval in the video. We introduce a step-wise temporal process reward that provides localized credit assignment for these clues and a joint process–outcome optimization objective that balances reasoning fidelity with task correctness. To enable scalable training, we construct TimeThink-RFT-20K, a dataset with automatically derived temporal evidence segments. Extensive experiments across video reasoning, temporal grounding, and general video understanding benchmarks show that TimeThink consistently improves both temporal localization and reasoning performance, achieving state-of-the-art results among open-source video RL models.
[CV-42] RADIANCE: Relative Adaptive Denoising with IP-Adapter for Novel Concept Enhancement ECCV2026
链接: https://arxiv.org/abs/2607.05088
作者: Zi-Xiang Ni,Bo-Lun Huang,Teng-Fang Hsiao,Bo-Kai Ruan,Hong-Han Shuai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026. Camera-ready version
Abstract:Text-to-image (T2I) diffusion models have achieved striking progress but still struggle to synthesize rare concepts involving unusual attribute-object pairings, often resulting in concept omission or semantic drift where a dominant entity overwhelms the generation. Tracing these failures to a lack of compositional balance during the denoising trajectory, we propose RADIANCE, a training-free framework that treats inference as a closed-loop feedback process. RADIANCE augments pretrained backbones with three modular components: (1) a Compositional Similarity Monitor (CSM) that tracks the emergence of objects and attributes in intermediate latents via CLIP-based feedback; (2) a Bidirectional Scale Controller (BSC) that applies a reactive “restoring force” using positive and negative IP-Adapter scales to rebalance biased trajectories; and (3) a Feedback Guidance Scheduler (FGS) that coordinates these updates across timesteps without additional training. We further extend the framework to multi-object prompts via Delayed Adapter Activation (DAA) and Layer-wise Alternating Guidance (LAG) to prevent premature concept fusion. By overlapping monitoring and denoising through pipelined execution, RADIANCE maintains competitive latency while significantly enhancing the per-sample success rate and effective throughput. Experiments on RareBench and T2I-CompBench demonstrate that RADIANCE consistently enhances compositional alignment and perceptual quality over state-of-the-art baselines.
[CV-43] LangLoc: “Tell Me What You See” ECCV
链接: https://arxiv.org/abs/2607.05077
作者: Shaurya Kishore Panwar,Roham Zendehdel Nobari,Shirley Feng Yi Lau,Abu Bakr Rahman Shaik,Manuel Günther,Marc Pollefeys,Daniel Barath
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the European Conference of Computer Vision (ECCV) 2026
Abstract:We tackle fine-grained indoor localization from natural language: given a free-form description of one’s surroundings, estimate the observer’s 2D position and heading within a known 3D environment. Language queries are lightweight, privacy-preserving, and need no camera - yet prior work stops at coarse scene retrieval and cannot resolve an intra-scene pose. We close this gap with LangLoc, a three-stage pipeline that (i) retrieves the correct scene via a dual-branch GATv2 encoder with CLIP semantic features, surpassing the previous best by 8 percentage points in Top-1 recall; (ii) estimates position and heading by scoring a dense floor grid through ray-cast object visibility, reaching a median error of 0.95 m; and (iii) resolves residual ambiguity through a Bayesian dialog module that asks targeted yes/no questions and updates a pose posterior until the location is pinpointed. To support this task we contribute a benchmark of 13,000+ pose-indexed natural-language descriptions over 1,300+ indoor 3D scans.
[CV-44] Consistent and Editable: A Balanced Framework for Text-Guided Video Editing
链接: https://arxiv.org/abs/2607.05056
作者: Tao Jin,Li Xiao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 8 figures
Abstract:Recently, diffusion models have achieved considerable success in the text-guided video editing domain. However, existing works often struggle to balance the trade-off between temporal consistency and editability in video editing, with consistency and editability typically being inversely related. To address this, we propose a high-quality video editing framework enhanced for consistency and editability, named EquiEdit, which improves coordinatively the temporal consistency and editability of the edited videos while achieving a balance between the two. In terms of temporal consistency, the proposed temporal Mamba module with a tailored temporal-aware scanning scans fused video sequences following four designed directions, effectively enhancing the inter-frame consistency of edited videos. For editability, we design a noise injection strategy based on the spectral transformation to increase editing flexibility, where the Fourier transform is used to preserve the hidden structure in the initial latent noise used for editing, ensuring inter-frame consistency of the edited video and fidelity to the input video. Extensive qualitative and quantitative experiments demonstrate the effectiveness of our method in terms of temporal consistency and editability, as well as its great fidelity to the input video itself.
[CV-45] RUFNet: Query-Guided Support Mask Refinement and Uncertainty Fusion based on Hybrid Mamba for Few-Shot Brain Tumor Segmentation
链接: https://arxiv.org/abs/2607.05035
作者: Dongyi He,Xiangkai Wang,Binbing Xu,Bin Jiang,Hongjie Yan,Weixiang Liu,Wai Ting Siok,Nizhuan Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Few-shot brain tumor segmentation remains challenging due to noisy support masks, inter-patient variations between support and query images, and the lack of pixel-wise confidence estimation. This study proposes RUFNet, a Hybrid Mamba-based few-shot framework that combines support mask refinement with uncertainty-aware posterior fusion. To preserve support-query dependencies with manageable cost, RUFNet adopts a Hybrid Mamba interaction backbone with linear complexity. To reduce support-mask noise, an Attention-Guided Mask Refinement module (AGMR) uses query features to recalibrate support masks and improve prototype consistency. To handle ambiguous predictions, an Uncertainty-Aware Posterior Fusion module (UAPF) estimates pixel-wise variance and adaptively balances few-shot predictions with query-aligned priors. On the Brain Tumor Segmentation Challenge (BraTS) 2020 dataset, RUFNet achieves Dice coefficients of 84.3% and 86.1% in the 1-way 1-shot and 1-way 5-shot settings, respectively, outperforming the compared state-of-the-art methods. These results suggest that Hybrid Mamba interaction, mask refinement and uncertainty modelling can improve the robustness of few-shot medical image segmentation. The official implementation code is available at this https URL.
[CV-46] Beyond Modality Fusion: Deep Ensembles for Multimodal Classification
链接: https://arxiv.org/abs/2607.05019
作者: Ilya Burenko,Dmitry Vetrov
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In multimodal classification, late-fusion approaches classify concatenated modality-specific features extracted by unimodal neural networks. When modality imbalance is pronounced, various regularization techniques have been proposed to balance the learning process and overcome the inferior performance of late-fusion networks. In contrast, this work demonstrates that multimodal data can be effectively classified without any explicit modality fusion, using deep ensembles of unimodal networks. We systematically compare deep ensembles to late-fusion networks at equal parameter count and show that ensembles consistently outperform state-of-the-art late-fusion methods designed to address modality imbalance. This advantage also holds over intermediate-fusion techniques we evaluated and over hybrid methods that combine unimodal and multimodal predictions. We propose and empirically validate a method for selecting the number of models per modality in an ensemble, avoiding computationally expensive exhaustive search. Under extreme modality imbalance and small ensemble sizes, the heuristic indicates that ensembles of unimodal models trained solely on the stronger modality are preferable; as the ensemble scales up, incorporating models from the weaker modality becomes beneficial. Both predictions align with our empirical findings. To systematically explore the challenges of optimizing multimodal models, we propose a synthetic multimodal framework that allows control over both the number of modalities and their predictive strength; our findings are consistent across synthetic and real-world datasets. Finally, by fitting scaling laws to bimodal datasets, we estimate the asymptotic performance of ensembles. Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2607.05019 [cs.LG] (or arXiv:2607.05019v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.05019 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ilya Burenko M. [view email] [v1] Mon, 6 Jul 2026 12:59:45 UTC (4,047 KB) Full-text links: Access Paper: View a PDF of the paper titled Beyond Modality Fusion: Deep Ensembles for Multimodal Classification, by Ilya Burenko and 1 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.LG prev | next new | recent | 2026-07 Change to browse by: cs cs.CV 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(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[CV-47] Comparison of Loss Functions for Robust Deep Learning-based Echocardiography Segmentation when Learning with Partially Labelled Data from Multiple Domains
链接: https://arxiv.org/abs/2607.05008
作者: Iman Islam,Esther Puyol-Antón,Bram Ruijsink,Andrew J. Reader,Andrew P. King
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) this https URL
Abstract:Echocardiography is the first imaging modality used for assessing cardiac function, and accurate segmentation of cardiac structures is essential for deriving biomarkers. However, the development of effective automated segmentation models for multiple cardiac structures is challenged by the difficulty of training on datasets from different sources that are often partially-labelled. This study aims to address this challenge by evaluating the performance of three loss functions - adaptive categorical cross entropy (aCCE) loss, marginal loss, and the adaptive binary cross entropy (aBCE) loss - in handling partially-labelled data. We conduct a comprehensive comparison of these loss functions across multiple scenarios and network architectures: intra-domain and inter-domain tasks, with both single and multiple partial-labels, and varying proportions of fully-labelled to partially-labelled data. Our experiments reveal that all three loss functions exhibit strong performance in intra-domain segmentation tasks, effectively handling label variations within the same domain. For inter-domain tasks, where models are trained on datasets with a domain shift, the aBCE and marginal losses show superior performance when dealing with the case of one label being missing from some training examples. In scenarios involving more than one label being missing, marginal loss outperforms the other methods, demonstrating its robustness in such complex conditions. These results highlight the strengths of each loss function depending on the labelling scenario, emphasizing the importance of selecting the appropriate loss function to optimize model performance. This study represents the first investigation of techniques for handling partially-labelled data from multiple different domains in echocardiography segmentation and provides a comprehensive comparison of loss-based solutions. Comments: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.05008 [cs.CV] (or arXiv:2607.05008v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.05008 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journalreference: Machine.Learning.for.Biomedical.Imaging. 2026 (2026) Related DOI: https://doi.org/10.59275/j.melba.2026-3bfa Focus to learn more DOI(s) linking to related resources
[CV-48] Unsupervised Pixel-Level Semantic Left-Right Understanding of In-the-Wild Images
链接: https://arxiv.org/abs/2607.05006
作者: Weikang Wang,Tobias Weißberg,Florian Bernard
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 7 figures, 5 tables
Abstract:While various works address reflective symmetry understanding in 3D data and images, pixel-level semantic left-right prediction of in-the-wild images remains challenging, due to certain difficulties including the lack of 3D information, occlusion, object pose variation, partiality, etc. In this work, we propose an unsupervised learning framework to tackle this challenge. Leveraging recent advances in vertex-wise semantic left-right understanding of 3D data, our unsupervised learning method jointly utilises 3D shape and image datasets to infer pixel-wise semantic left-right predictions in single-view images. In particular, we show that a medium-scale 3D shape dataset comprising mainly of human- and quadruped animal-like shapes, combined with diverse in-the-wild image data, are sufficient to achieve high-quality semantic left-right prediction in images, even for entirely unseen 3D object categories, such as cars or trains. Overall, our approach achieves superior performance in dense pixel-wise semantic left-right predictions on both rendered and in-the-wild image datasets when compared to existing state-of-the-art methods.
[CV-49] Geometry-aware Depth-guided Representation Learning for Structure-preserving Low-light Image Enhancement
链接: https://arxiv.org/abs/2607.05005
作者: Fang Gao,Jiongkai Qin,Jiabao Wang,Jingfeng Tang,Ming Cheng,Hanbo Zheng,Qingbao Huang,Cheng Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Low-light degradation reduces image visibility and weakens structural cues that are important for visual representation and scene understanding. Existing low-light image enhancement methods mainly focus on appearance restoration, while insufficiently exploiting scene geometry to preserve structural consistency. To address this limitation, this paper proposes a Depth-guided Multi-scale Attention Network (DMSA-Net) for geometry-aware low-light image enhancement. DMSA-Net introduces depth-related structural priors into low-light representation learning through reflectance-geometry interaction. A Retinex-based decomposition module is first used to obtain illumination-invariant reflectance representations, from which depth cues are inferred to characterize scene structure under degraded illumination. A multi-scale depth-guided fusion strategy is then embedded into a hierarchical encoder-decoder architecture, where depth-aware attention adaptively integrates geometric and appearance features. Experiments on several benchmark datasets show that DMSA-Net achieves effective low-light restoration while improving structural preservation. Moreover, we construct LOL-D, a depth-augmented low-light dataset, to facilitate research on geometry-aware low-light vision.
[CV-50] Virtual Category-Guided Continual Generalized Category Discovery ECCV2026
链接: https://arxiv.org/abs/2607.04984
作者: Jiahui Xiong,Qiuxia Lai,Hongsong Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV2026 Code: this https URL
Abstract:Continual Generalized Category Discovery (C-GCD) aims to incrementally identify novel categories from sequential unlabeled data while preserving recognition of known classes, which is an essential capability for open-world visual learning. A major bottleneck lies in ambiguous unlabeled samples that cannot be confidently assigned to known classes nor reliably grouped as novel ones, making pseudo-labeling brittle and often biasing learning toward familiar categories. In this work, we introduce Virtual Category-Guided Continual Generalized Category Discovery by adapting Virtual Category Learning (VCL) to the continual setting. Our method identifies uncertain samples and assigns them to temporary virtual categories, enabling safe and informative learning from unlabeled streams without injecting noisy labels, while improving unlabeled data utilization and mitigating prediction bias. To further stabilize discovery across sessions and enhance class separation, we augment VCL with Expanded Neighborhood Contrastive Learning (ENCL), which exploits extended neighborhood relations and an adaptive margin to learn more discriminative and well-separated representations for both old and emerging classes. Extensive experiments on CIFAR-100, Tiny ImageNet, and ImageNet-100 demonstrate that our approach consistently outperforms state-of-the-art methods, establishing a scalable and effective solution for C-GCD.
[CV-51] Qantara: Bridge-Flow Training for Multi-Paradigm JEPA Control
链接: https://arxiv.org/abs/2607.04978
作者: Ruslan Rakhimov,George Bredis,Yuriy Maksyuta,Daniil Gavrilov
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 16 pages, 3 figures, 6 tables. Project page: this https URL
Abstract:Joint-Embedding Predictive Architectures (JEPAs) underpin a growing family of latent world models for control from raw pixels, but every existing JEPA world model commits at training time to a single inference paradigm: either trajectory optimisation in a learned dynamics model, or direct behaviour cloning. A single checkpoint that serves both would defer this choice to inference, when deployment constraints (rollout cost, observation accessibility) determine which path wins. We present Qantara, an end-to-end JEPA whose joint training objective pairs a Brownian-bridge interpolant between consecutive clean latents on the state axis with noise-to-data flow matching on the action axis. The same checkpoint serves three inference paradigms without retraining: latent planning, behaviour-cloning action sampling, and inverse dynamics, which we query through a video-inverse composition that first predicts the next latent without action conditioning, then extracts the action. Training concentrates mass on the edges of the (action-time, state-time) noise square, where inference queries the predictor: replacing it with uniform interior sampling drops Push-T planning from 90.1 to 53.3 SR at matched compute. On the LeWM control suite, Qantara reaches a 91.2 SR three-train-seed average and sets new SOTA on OGBench-Cube (+7.7 SR over DINO-WM, +19.7 over LeWM). From the same weights, the behaviour-cloning and video-inverse paths reach 82-83 SR on Push-T and 71-73 SR on Cube. These results move JEPA world models from single-paradigm planners to multi-paradigm controllers.
[CV-52] MemPose: Category-level Object Pose Estimation with Memory ECCV2026
链接: https://arxiv.org/abs/2607.04930
作者: Xiao Lin,Minghao Zhu,Yun Peng,Liuyi Wang,Qiyi Wang,Chengju Liu,Qijun Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted by ECCV 2026
Abstract:In the pursuit of robust and generalizable category-level object pose estimation, most existing methods adopt parametric formulations that learn effective representations from data, yet they primarily encode category-level patterns into fixed shape priors or static parameter weights, which limits their scalability to highly diverse instances. In this paper, we rethink category-level pose estimation from a memory-centric perspective and present MemPose, a memory-augmented framework that explicitly incorporates category-level geometric memory into the pose estimation pipeline. We introduce an external memory buffer that stores and dynamically updates structural representations from previously observed instances, enabling the model to leverage accumulated experience to support current perception. Extensive experiments on four challenging benchmarks (REAL275, CAMERA25, Housecat6D and Wild6D) demonstrate the superiority of our proposed method over previous state-of-the-art approaches.
[CV-53] UniSpine-GS: An Efficient Physics-Aware Gaussian Framework for Cross-Modality Multi-view Spine Image Synthesis
链接: https://arxiv.org/abs/2607.04923
作者: Qiuhua Chen,Changning Yu,Na Huang,Chao Sun,Bo Du
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The diagnosis of spinal diseases is often assisted by 3D imaging techniques in clinical practice. However, precise 3D spinal assessment is limited by the high costs of 3D imaging hardware and the challenges posed by the physical differences between imaging modalities, which hinder the generalizability of models. To address these issues, we propose UniSpine-GS, an efficient, physics-aware Gaussian framework designed for novel-view projection rendering in multi-view spine imaging via a 3D-aware representation. Instead of performing explicit 3D reconstruction, our approach learns a geometry-aware Gaussian representation that ensures anatomical consistency across different views. We introduce SPWM, a structure-guided loss reweighting strategy to improve boundary fidelity and local details. We evaluate our method on the CTSpine3D dataset and a newly constructed 3D fetal ultrasound dataset, FeSpine3D. Our results demonstrate that UniSpine-GS significantly outperforms existing methods across all metrics, offering a practical and cost-effective solution for unified multi-view medical imaging. Our code is publicly available at this https URL.
[CV-54] Efficient Perception in Automotive Detection and Tracking Using Neuromorphic Computing
链接: https://arxiv.org/abs/2607.04921
作者: Manish Kolachalam,Rani Malhotra
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Deep learning algorithms are notorious for their high carbon footprint and computational demands that limit their deployment on edge devices and raise concerns about their long-term sustainability. Neuromorphic computing and Spiking Neural Networks (SNNs) offer a promising alternative to traditional Von Neumann architectures, providing energy-efficient performance, massively parallel computation, and on-chip learning capabilities. Autonomous machines represent a critical application domain where these advantages are particularly valuable. We present the first comprehensive evaluation of SNNs for real-world automotive multi-object detection and tracking. Using transfer learning with the SpikeYOLO architecture, we achieve mean Average Precision of 0.937 on the KITTI dataset and 0.771 on BDD100K MOT2020 dataset for object detection and a Higher Order Tracking Accuracy score of 0.701 (KITTI) and 0.445 (BDD100K MOT2020) for object tracking–results competitive with conventional deep learning methods. Our results demonstrate that SNNs can deliver high-performance object detection and tracking in an energy efficient manner, establishing their viability for perception in real-world autonomous systems.
[CV-55] Graph Representation Learning of Longitudinal Medical Imaging Trajectories for Treatment Response Prediction
链接: https://arxiv.org/abs/2607.04912
作者: Johannes Kiechle,Richard Osuala,Daniel M. Lang,Stefan M. Fischer,Ivana Janíčková,Karim Lekadir,Julia A. Schnabel,Jan C. Peeken
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:In patients with breast cancer, pathological complete response (pCR) has been established as a clinically meaningful surrogate marker for long-term outcomes. While commonly treated with neoadjuvant chemotherapy (NACT), effective treatment decision-making remains challenging, as therapeutic response can vary substantially across patients, calling for predictive models capable of accurately estimating individualized treatment response. To address this, we propose an imaging-based 3D spatio-temporal framework for treatment response prediction that integrates a state-of-the-art graph neural network with relational modeling of temporal interactions across timepoints alongside three novel complementary self-supervised treatment trajectory representation learning objectives. Experiments across a cohort of 585 patients from the public ISPY-2 dataset demonstrate that our method substantially outperforms both vision and self-supervised learning baselines across several classification metrics. Alongside establishing a breast cancer pCR prediction benchmark, we include a principled ablation of our method and further introduce and empirically assess the impact of the available number of DCE-MRI timepoints per patient trajectory and the inclusion of inter-scan time-differences. Overall, our study substantiates the utility of clinically meaningful longitudinal medical imagaging modeling for predicting NACT-induced pCR. We will publicly share our code repository and a user-friendly PyPI library for dataset curation upon publication, effectively promoting reproducible open-source research.
[CV-56] 3DMPE: 3D Multi-Perspective Embedding
链接: https://arxiv.org/abs/2607.04898
作者: Vahan Huroyan,Md Rahat-uz-Zaman,Stephen Kobourov
类目: Computer Vision and Pattern Recognition (cs.CV); Computational Geometry (cs.CG)
备注:
Abstract:We study 3D point cloud reconstruction from multiple partially observed 2D projections. Given two or more projections of an unknown 3D point cloud, together with cross-view point correspondences and visibility information, our goal is to recover a consistent 3D configuration when different views contain different subsets of points. We propose 3D Multi-Perspective Embedding (3DMPE), an optimization-based, training-free method that reconstructs the 3D point cloud and, in the variable-projection setting, jointly estimates the projection maps. 3DMPE extends Multi-Perspective Simultaneous Embedding to accommodate missing points and incomplete pairwise distance information across views. We consider both fixed-projection and variable-projection settings. Unlike learning-based reconstruction methods that infer shape from raw images and often depend on training data, 3DMPE operates on geometric observations with established correspondences and does not require category-specific training. Experiments on ShapeNet and Pix3D evaluate reconstruction quality using Chamfer Distance, Earth Mover Distance, and RMSE-Optimize-Align (ROA), and examine the effects of initialization, the number of views, point visibility, and several noise regimes, including noisy distances and erroneous correspondences. The results demonstrate that 3DMPE can effectively reconstruct point clouds from partial multi-view geometric observations.
[CV-57] ProCon: Projection-Consistency Memory for Training-Free Anomaly Detection
链接: https://arxiv.org/abs/2607.04894
作者: Joongwon Chae,Lihui Luo,Yang Liu,Dongmei Yu,Peiwu Qin,Runming Wang,Ilmoon Chae
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Memory-based anomaly detection is attractive because it localizes defects from normal images without training a decoder or synthesizing pseudo anomalies. However, most memory methods still use the memory bank as a nearest-neighbor lookup table: a test patch is treated as normal if it has one nearby normal anchor. This hard retrieval view is vulnerable to false-normal matches and does not test whether the patch is consistently supported by a local normal neighborhood. We propose ProCon, a training-free framework that turns memory retrieval into decoder-free reconstruction. ProCon softly projects each test patch onto nearby normal memory vectors and uses the projection residual as anomaly evidence. To stabilize this residual, it constructs seed-perturbed layer-wise memories, aggregates bank residuals by a median, and fuses depth-specific residual maps by layer consensus. ProCon requires no decoder training, backbone fine-tuning, learned fusion weights, or pseudo-anomaly supervision. Across MVTec-AD, VisA, and Real-IAD under the single-category evaluation protocol, ProCon achieves strong image- and pixel-level performance under seven standard metrics, including image AUROC scores of 99.8%, 99.2%, and 93.2%, respectively. Ablations show that the gains come from replacing hard retrieval with soft normal projection and stabilizing the residuals through memory and depth consensus. The code is available at this https URL
[CV-58] HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better
链接: https://arxiv.org/abs/2607.04884
作者: Gengluo Li,Xingyu Wan,Shangpin Peng,Weinong Wang,Hao Feng,Yongkun Du,Binghong Wu,Zheng Ruan,Zhiqiong Lu,Liang Wu,Pengyuan Lyu,Huawen Shen,Zibin Lin,Shijing Hu,Jieneng Yang,Hongbing Wen,Guanghua Yu,Hong Liu,Bochao Wang,Can Ma,Han Hu,Chengquan Zhang,Yu Zhou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We present HunyuanOCR-1.5, a lightweight end-to-end OCR-specialized vision-language model. HunyuanOCR unifies document parsing, text spotting, information extraction, text-image translation, and multi-image document understanding within a single end-to-end VLM. Building upon the lightweight architecture of HunyuanOCR-1.0, HunyuanOCR-1.5 does not redesign the backbone, but systematically improves both efficiency and capability. For efficiency, we adapt DFlash to OCR decoding, significantly reducing the latency of long structured outputs such as dense documents, tables, and formulas while preserving output distribution. Powered by DFlash, HunyuanOCR-1.5 achieves a 6.37x Transformer inference speedup and a 2.14x speedup under vLLM, delivering the fastest inference among lightweight OCR VLMs. For capability, we propose Agentic Data Flow, an agent-driven data construction system that transforms model weaknesses into executable data requirements and autonomously performs material search, quality verification, and pipeline development. It substantially improves long-tail capabilities in ancient-script OCR, fine-grained chart and table parsing, multi-image text-centric QA, low-resource multilingual parsing, and document hallucination evaluation. HunyuanOCR-1.5 ranks among the top-tier end-to-end OCR solutions on OmniDocBench v1.6 while achieving new performance milestones across these long-tail tasks. Combined with an upgraded pretraining and post-training recipe, HunyuanOCR-1.5 further extends its capability in high-resolution, long-context, and multi-task scenarios. Experiments demonstrate faster inference, broader OCR capability coverage, and the deployment advantages of a lightweight end-to-end model. We will release the model weights and training code to support future research and real-world OCR applications.
[CV-59] Unsupervised Detection of Underground Tunnels in Ground-Penetrating Radar Using Depth-Restricted Reconstruction Scoring WWW
链接: https://arxiv.org/abs/2607.04882
作者: Muhammad Junaid,Shoab A. Khan,Nisar Ahmed
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Signal Processing (eess.SP)
备注: 7 pages, 7 figures. Code: this https URL Dataset: this https URL
Abstract:Clandestine tunneling beneath oil and gas pipelines enables fuel theft, smuggling, and sabotage, yet conventional monitoring detects damage only after a pipeline has been compromised. Ground-penetrating radar (GPR) can image such tunnels non-invasively, but manual radargram interpretation does not scale to continuous corridor surveillance, and supervised detectors require tunnel examples that are scarce in practice. We present a fully unsupervised detection pipeline trained exclusively on normal subsurface radargrams collected at a purpose-built field site containing three buried tunnels at 1.5-3 m depth. A denoising convolutional autoencoder learns the structure of anomaly-free ground; at inference, tunnels are flagged by reconstruction error. Our central contribution is a depth-restricted top-k anomaly score, which pools the highest reconstruction errors only within the depth band where tunnels can physically occur. This physically motivated rule raises AUC from 0.986 to 0.994 and cuts missed detections from 74 to 17 of 634 tunnel windows, relative to whole-image scoring, without any retraining or labels. We further show that the optimal top-k fraction interacts with the depth restriction - 1% pooling is best on full images, 5% once scoring is depth-restricted - and that spatial voting across overlapping survey windows helps weak per-image detectors but offers no benefit once the scoring rule is strong. The final system attains AUC 0.994, F1 0.975, recall 0.973, and precision 0.976 on 1,600 field test windows spanning 55 survey lines, at a 1.6% false-alarm rate, using no tunnel labels for training, scoring, or threshold calibration.
[CV-60] EventCoT: Event-centric Video Chain-of-thought for Reasoning Temporal Localization
链接: https://arxiv.org/abs/2607.04872
作者: Youngkil Song,Yoonjae Baek,Dongwon Kim,Inho Kim,Dongkeun Kim,Suha Kwak
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 25 pages, 11 figures, 16 tables. Co-corresponding authors: Dongkeun Kim and Suha Kwak
Abstract:Reasoning temporal localization (RTL) requires a model to generate an answer that itself contains the time interval supporting it, so high-level reasoning and precise temporal grounding must be produced jointly in a single response. To tackle this challenging task, we propose the first event-centric video chain-of-thought framework, dubbed EventCoT. EventCoT first performs event-centric tokenization of the input video to convert it into compact event tokens, enabling efficient identification of question-relevant events. It then reasons within the identified events to generate the answer, grounding the time interval via embedding matching that aligns placeholder tokens with visual embeddings. EventCoT achieves state-of-the-art results on ActivityNet-RTL for reasoning temporal localization while using substantially fewer visual tokens than previous work. To verify its general performance, we further evaluate EventCoT on the grounded video question answering benchmark ReXTime, where it attains strong zero-shot results.
[CV-61] GRIP: A Text-Guided Approach to Vehicle Instance Prediction in Autonomous Driving
链接: https://arxiv.org/abs/2607.04812
作者: Miguel Antunes-García,Santiago Montiel-Marín,Fabio Sánchez-García,Rodrigo Gutiérrez-Moreno,Rafael Barea,Luis M. Bergasa
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 5 figures
Abstract:Bird’s-Eye View (BEV) end-to-end instance prediction has emerged as a robust paradigm for autonomous driving perception, effectively mitigating the error propagation inherent in traditional modular pipelines. However, current state-of-the-art approaches rely predominantly on geometric supervision, such as occupancy regression and optical flow, effectively treating scene agents as generic moving obstacles. This absence of explicit semantic awareness imposes limitations on the capacity of the model to solve ambiguities in complex scenarios, particularly those where object-specific behavior is essential for accurate forecasting (e.g. overtaking, intersections). In this paper, we introduce Text-Guided Representation for Instance Prediction (TGRIP), a novel framework that bridges this gap by injecting rich semantic priors into the instance prediction loop. The proposed teacher-student pipeline employs Vision-Language Foundation Models to generate dense, semantic-enhanced BEV maps from multi-camera images. These maps serve as auxiliary supervision during training, guiding the network to learn spatio-temporal representations that are not only geometrically consistent but also semantically discriminative. To the best of our knowledge, this represents the first attempt to unify semantic guidance with the temporal task of future instance prediction. The experimental results demonstrate that TGRIP surpasses existing state-of-the-art models in nuScenes, validating the hypothesis that semantic enrichment is a fundamental element for robust, end-to-end motion prediction. Code is available on this https URL.
[CV-62] Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles IJCNN2026
链接: https://arxiv.org/abs/2607.04811
作者: Soren Antebi,Stefan Eickeler,Sandra Halscheidt,Rene Schmitz,Michael Muellers,Dirk Hecker,Rafet Sifa
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at IJCNN 2026
Abstract:Applying deep learning to instance-aware reidentification of slate tiles and extraction site classification can improve production efficiency and quality control in the slate tile industry. These tasks are particularly important for handling natural materials where visual variability can make manual inspection costly and error-prone. We present a lightweight, hybrid deep learning approach that combines image matching and classification within a single framework. The system integrates a feature-matching branch based on XFeat with a MobileNetV3- based classification branch. The XFeat branch, combined with a LightGlue matching head, improves instance matching performance by +15.4% AUC. For classification, features from both backbones are shared and fused, resulting in a +10.9% accuracy improvement over a standard MobileNetV3 model. Our approach is evaluated on a newly created industrial dataset consisting of 2,610 slate tile images from six extraction sites. The results demonstrate the effectiveness of the proposed approach for object re-identification and classification in an industrial setting.
[CV-63] LILAC: Layer-Wise Independent LoRAs and Cascaded Conditioning for Multi-Concept Customization of Diffusion Models
链接: https://arxiv.org/abs/2607.04801
作者: Marian Lupascu,Sebastian Ripa,Mihai Trascau,Mariana-Iuliana Georgescu,Ionut Mironica
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 19 pages, 8 figures
Abstract:Personalizing text-to-image diffusion models to render several specific subjects in a coherent image remains challenging: the model must preserve each subject’s identity while keeping the scene spatially and visually coherent. Methods that fuse independently trained concept adapters in a shared weight space (via federated averaging, gradient fusion, or orthogonality constraints) suffer from identity confusion and style bleeding and require joint retraining. In this work, we show that composing concepts as separate image layers, instead of merging their adapters in a shared weight space, avoids parameter-level interference. We introduce LILAC, a framework that composes independently trained low-rank adapters at inference time: each subject is conditioned on the frozen composite of previously placed subjects, with exactly one adapter active at a time, therefore identities never interfere at the parameter level. LILAC composes the adapters without any joint training, scales linearly with the number of concepts, and is backbone-agnostic. Under the Orthogonal Adaptation protocol, LILAC applied on Qwen-Image-Edit reaches an ArcFace detection rate of 0.861, while Orthogonal Adaptation reports 0.745 in its original setting. Adaptation reports 0.745 in its original setting. Code is available at this https URL.
[CV-64] DGSeg: Dynamic Gating of Semantic-Spatial Guided Predictions for Reasoning Segmentation ECCV2026
链接: https://arxiv.org/abs/2607.04779
作者: Ruizhe Zeng,Siyu Cao,Lu Zhang,Zhiyong Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV2026
Abstract:Reasoning segmentation aims to predict pixel-wise masks for targets given complex language queries. Existing approaches leverage Multimodal Large Language Models (MLLMs) for vision-language reasoning and generate intermediate target cues (e.g., points or boxes) to guide a segmentation model. However, compressing rich reasoning into sparse cues often introduces ambiguity and noise, preventing these cues from accurately preserving the reasoning intent. While multiple complementary cues can enrich target information, existing methods typically feed them jointly into a single segmentation process, allowing ambiguous or erroneous cues to affect the entire prediction. Therefore, we propose DGSeg, a reasoning segmentation framework that learns to fuse predictions guided by semantic and spatial cues. Specifically, the MLLM jointly reasons about both target identity and spatial location, producing complementary semantic and spatial cues that are fed into separate segmentation branches. Their predictions are adaptively integrated by a lightweight dynamic gating module trained with relative branch-quality supervision to suppress noisy or conflicting regions. Extensive experiments demonstrate that DGSeg consistently outperforms strong baselines on multiple benchmarks and achieves 69.6% and 67.3% gIoU on the challenging ReasonSeg validation and test splits. Code is available at this https URL.
[CV-65] SLAM: Structured and Localized Analytic Manifold Adaptation for Lifelong VPR
链接: https://arxiv.org/abs/2607.04764
作者: Kenta Tsukahara,Kanji Tanaka,Rai Hisada
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, technical report
Abstract:Visual Place Recognition (VPR) in lifelong deployment requires continuous adaptation to new environments without catastrophic forgetting. In this paper, we propose SLAM, a Structured and Localized Analytic Manifold adaptation framework. Our framework elegantly unifies uncertainty-aware smoothing via Unscented transformation, topological space partitioning through a Gaussian Mixture Model (GMM), and H_\infty robust bound optimization into a singular, unified closed-form analytical recursion. Exhaustive ablation studies demonstrate that while the synergistic combination of uncertainty smoothing and localized mapping (U+G configuration) achieves the state-of-the-art nominal accuracy of 27.5%, the full deployment of the H_\infty bound does not require an architectural split; rather, it introduces a mathematically guaranteed minimax robust bound. This formulation enables the system to seamlessly modulate the intrinsic trade-off between nominal placement precision and worst-case disturbance attenuation through a single regularization parameter.
[CV-66] DeGenseGS: Geometrically and Semantically Decoupled Surgical Scene Understanding in 4D Gaussian Splatting
链接: https://arxiv.org/abs/2607.04761
作者: Yimo Wang,Bin Kang,Shuojue Yang,Yueming Jin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Real-time, text-promptable 4D reconstruction is indispensable for autonomous surgical interaction. Severe misalignment between semantic meaning and physical anatomy still persists, largely because existing solutions integrate Vision-Language Models into deformable fields via a rigid coupling scheme that tightly binds semantic features to geometric warping. In this paper, we propose DeGenseGS, Geometrically and Semantically Decoupled Surgical Scene Understanding in 4D Gaussian Splatting, a novel framework that independently models semantic evolution and geometric deformation. Specifically, we propose a HexPlane-based spatiotemporal entanglement module that uses shared kinematic latents to synchronize semantic mutations with scene dynamics, while explicitly disentangling semantic updates from geometric deformation. To further ensure robustness against reconstruction artifacts, we devise a Rasterization-Native Semantic Extraction mechanism that infers semantics from topologically continuous feature maps. Additionally, we incorporate an angular-aligned optimization strategy that conforms to the native hyperspherical latent space, thereby preventing semantic distortion. Extensive evaluations on the CholecSeg8k and EndoVis18 datasets demonstrate that DeGenseGS achieves state-of-the-art performance. Our framework yields enhanced geometric completeness and robust semantic-anatomic alignment, enabling spatially continuous segmentation despite drastic tissue deformation and topological transitions.
[CV-67] Continual Model Merging with Test-Time Adaptation for Whole-Slide Image Analysis
链接: https://arxiv.org/abs/2607.04755
作者: Duc-Thanh Le,Doanh C. Bui,Maï K. Nguyen,Khang Nguyen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 4 tables, 2 figures
Abstract:Model merging offers a practical alternative to conventional continual learning by integrating independently fine-tuned models without retaining previous training data. Recent state-of-the-art model merging methods employ test-time adaptation (TTA-guided merging) to address distribution shifts by adjusting merging-related variables using unlabeled target data. However, these methods have primarily been studied in multi-task or single-target settings, and their behavior under sequential continual learning remains insufficiently understood. We present a benchmark study that maps this family of methods to rehearsal-free continual Whole Slide Image classification and evaluates them against traditional continual-learning approaches. Experiments on six TCGA cancer-subtyping cohorts cover CLASS-IL and TASK-IL scenarios, in-domain and out-of-domain evaluation, and different task orders. The results show that adapting model merging at test time can provide strong task-specific performance and improve retention of previously acquired knowledge without storing historical WSIs. Nevertheless, performance remains sensitive to task order and to the interaction between adaptation on the current distribution and accumulated knowledge. This benchmark identifies model merging with test-time adaptation as a promising direction for continual computational pathology and motivates future methods that balance adaptation to domain shift with explicit preservation of historical knowledge.
[CV-68] FM-ChangeNet: Learning Change through Pathwise Feature Transport
链接: https://arxiv.org/abs/2607.04750
作者: Roie Kazoom,George Leifman,Genady Beryozkin
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We present FM-ChangeNet, a pathwise-supervised framework for change detection that reformulates bi-temporal reasoning as continuous transport in feature space rather than static endpoint comparison. Given encoded pre and post-temporal representations, we construct intermediate latent states and learn a time-conditioned velocity field \hatv_\theta(z_t,t) along the transformation trajectory. This pathwise formulation constrains the predictor over a continuum of intermediate states, providing a denser and less ambiguous supervision signal than conventional endpoint-only segmentation and enabling the model to capture temporal evolution explicitly. The learned velocity field is not only a transport mechanism but also an interpretable representation of change: its magnitude serves as a spatially localized change cue that helps distinguish true structural variation from nuisance effects such as illumination shifts and spatial misalignment. We develop a hierarchical multi-scale architecture with cross-temporal alignment, time-conditioned coarse-to-fine flow decoding, and a unified objective that couples flow supervision, trajectory consistency, spatial regularization, and segmentation loss. Experiments on remote sensing benchmarks show that the proposed framework produces more structured and robust change representations while achieving state-of-the-art performance.
[CV-69] MergeSurv: Merging-Based Continual Learning for Survival Analysis on Whole-Slide Images
链接: https://arxiv.org/abs/2607.04747
作者: Vu Minh Tran,Doanh C. Bui,Maï K. Nguyen,Khang Nguyen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 2 figures, 1 table
Abstract:Survival analysis on Whole Slide Images (WSIs) is important in computational pathology for prognosis estimation and treatment planning. However, existing survival models are typically trained independently for each cancer cohort, making continual adaptation computationally expensive for gigapixel-scale WSIs. In this study, we propose MergeSurv, a merging-based continual learning framework for WSI survival analysis. A pathology vision-language foundation model is independently fine-tuned on each task, and the learned parameters are sequentially merged into a unified model without storing previous training data. We further investigate two inference strategies: One-for-All (OFA) and Voting-Expert Aggregation (VEA). Experiments on four TCGA cohorts demonstrate that MergeSurv outperforms naive fine-tuning as well as representative regularization-based and rehearsal-based continual learning methods, while effectively reducing catastrophic forgetting. The results suggest that model merging is a promising direction for scalable and privacy-preserving continual learning in computational pathology.
[CV-70] rajectory-Anchor Optimization for Overconfident Thermal Visual Place Recognition: Zero-Leakage OOD Auditing and Kidnapped-Robot Recovery
链接: https://arxiv.org/abs/2607.04745
作者: Zhiyuan Lu,Kanji Tanaka
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 5 figures, technical report
Abstract:Modern thermal visual place recognition (TIR-VPR) frontends based on foundation models achieve remarkable closed-set retrieval but suffer from an overconfident forced-matching failure mode. Under out-of-distribution (OOD) or unmapped conditions, they generate highly plausible yet false loop candidates without a drop in similarity scores. While classical multi-hypothesis tracking (MHT) backends can mitigate these ambiguities by maintaining divergent trajectory beliefs, their exponential computational overhead violates real-time robotic constraints. To bridge this gap, we present Trajectory-Anchor Optimization (TAO). To counter the combinatorial challenge of evaluating parallel hypotheses (e.g., K=100), TAO compresses multi-view temporal verification into a batched SE(2) Procrustes alignment problem. By leveraging tensor-level vectorization and single-invocation batched SVD, this formulation bypasses the dynamic tree expansion of MHT, guaranteeing a strictly bounded per-frame execution loop of O(KN). Under a strict zero-leakage evaluation protocol, we show that while a passive geometric backend cannot mathematically separate metric localization errors from coherent hallucinations at a micro-scale (5m) due to local visual ambiguities, TAO serves as an efficient fail-safe filter at a macro-scale. Within a 5m radius, hallucinations often possess a locally consistent geometry that deceives rigid alignment. However, beyond this threshold, the K=100 disparate hypotheses disperse spatially across the global map. This dispersion breaks the rigid temporal co-visibility constraint within the sliding window (N=20), causing the joint optimization residual to escalate sharply. Consequently, TAO establishes a distinct macroscopic convergence basin (10m) where multi-view geometric consistency reliably isolates catastrophic topological breaks and suppresses critical false acceptances.
[CV-71] DriftST: One-Step Generative Inference of Spatial Transcriptomics from HE Histology
链接: https://arxiv.org/abs/2607.04740
作者: Yuhang Yang,Yonggan Bu,Shengyuan Zhou,Yiming Luo,Kai Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Spatial Transcriptomics (ST) measures gene expression while preserving spatial context, but its high cost and low throughput leave public datasets small. Inferring expression directly from widely available Hematoxylin and Eosin (HE) stained histology offers a cost-effective alternative. However, existing approaches face several limitations: regression methods over-smooth toward the conditional mean, while generative methods are faithful but require slow multi-step inference; most methods treat genes as independent and equally important, ignoring inter-gene dependencies and heterogeneous gene informativeness; and most are tailored to a single resolution, either spot-level or cell-level. To address these issues, we propose DriftST, a unified framework for inferring spatially resolved gene expression from HE images. DriftST builds on a Cellular Drifting generative model that learns a direct drift from a histology-conditioned source to the expression distribution, retaining generative expressiveness while enabling efficient one-step generation. To capture gene structure, we introduce the STransformer, which combines a co-expression attention module for inter-gene dependencies with a gene residual gate for differential gene importance. Operating on a generic gene-panel representation, DriftST applies directly to both spot-level and cell-level data in one framework, and extensive experiments across diverse tissues and platforms show that it achieves state-of-the-art performance at both resolutions.
[CV-72] SparseOcc: Geometry-Aware Sparse Latent Representation for Semantic Occupancy Prediction
链接: https://arxiv.org/abs/2607.04732
作者: Pin Tang,Zhongdao Wang,Guoqing Wang,Xiangxuan Ren,Chao Ma
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-based 3D semantic occupancy prediction is essential for autonomous driving, yet dense voxel representations waste computation on largely empty space, while BEV and TPV projections compromise fine-grained 3D structure. Fully sparse representations offer an attractive alternative, but existing methods, including SparseOcc, entangle scene completion with semantic prediction by indiscriminately propagating high-dimensional features into empty regions and applying voxel-wise classification. This creates excessive activations, computational overhead, and geometric ambiguity. We present SparseOcc++, a geometry-aware sparse framework that explicitly decouples scene completion from semantic segmentation. SparseOcc++ reformulates completion as signed-distance regression on sparse anchor voxels through a scene completion field (SCF). To model complex outdoor geometry robustly, it combines orthogonal decomposition with discretized distance learning. A geometry-guided propagation module then converts the SCF into a complete volumetric scene and restricts semantic segmentation to geometrically verified regions. Experiments establish new state of the art: SparseOcc++ improves IoU by 2.3 points and is 3.9x faster than SparseOcc on nuScenes, while achieving a 5.9x speedup over OccFormer on SemanticKITTI.
[CV-73] When Does High-CFG Diffusion Inversion Fail? A Controlled Study of Prompt–Latent Interactions
链接: https://arxiv.org/abs/2607.04731
作者: Yan Zeng,Yusuke Hosoya,Huyen T. T. Tran,Takayuki Okatani
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Text-guided diffusion inversion is central to image editing, where an image is mapped to an initial latent and then edited by replaying the denoising process under a modified prompt. In practice, however, inversion is often performed with a lower classifier-free guidance(CFG) scale than the one used for generation or editing. This mismatch is empirically useful but leaves a basic question unresolved: when a target image is generated by a high-CFG trajectory, when can that trajectory actually be inverted? We study this question in a controlled generation–inversion–reconstruction setting, where the true initial latent and denoising trajectory are known. Using prompts taken from an existing diffusion-editing benchmark, we generate images under high CFG and reconstruct them with fixed-point inversion using the same prompt and guidance setting. The results reveal three types of prompt-level reconstruction behavior: easy prompts that reconstruct for most initial latents, hard prompts that fail for most initial latents, and intermediate prompts whose success depends on the prompt–latent pairing. To analyze the generation side, we define prompt pressure, a step-wise measure of how strongly CFG moves the denoising update away from the unconditional trajectory. Total pressure correlates with reconstruction quality and separates easy from hard prompts, but it does not explain the success or failure of intermediate prompt–latent pairs. Text-side analyses further show that the main visual subject and wording can change inversion difficulty. Finally, we evaluate a compact trajectory-consistency intervention that relaxes guidance only at locally unstable inverse steps. This diagnostic check improves reconstruction and Prompt-to-Prompt editing in our controlled setting, supporting the view that high-CFG inversion failure requires local, trajectory-aware analysis.
[CV-74] Dashboard2Code: Evaluating Multimodal Models on Reconstructing Interactive Dashboards ACL2026
链接: https://arxiv.org/abs/2607.04727
作者: Tianhao Niu,Ziyu Han,Qiguang Chen,Shiqi Zhou,Baocai Shan,Hengjie Fang,Qingfu Zhu,Wanxiang Che
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ACL2026 Main Conference
Abstract:Automatic data visualization generation has advanced rapidly with multi-modal large language models, yet existing efforts largely focus on static charts and overlook the interactive dashboards commonly used for real-world data exploration. We introduce Dashboard2Code, a novel task that requires a model to proactively explore an interactive dashboard, acquire and integrate feedback from its own interactions (e.g., clicking and filtering), and generate code that reproduces the target dashboard. To support comprehensive evaluation, we present DashboardMimic, the first Plotly+Dash benchmark for Dashboard2Code, comprising 180 carefully designed and manually verified dashboard-code pairs spanning three difficulty levels and covering eight common real-world interaction patterns. We further propose an automated evaluation framework tailored to dashboards that combines code semantic analysis with dynamic interaction-based testing to assess visual and interaction consistency, showing strong agreement with human judgments. Experiments across a range of open- and closed-source multi-modal models reveal that even the strongest systems struggle on high-complexity dashboards and that a substantial performance gap remains between open-source and closed-source models on the Dashboard2Code task.
[CV-75] Reference-Induced Consensus for Selective Posed-Reference Visual Localization
链接: https://arxiv.org/abs/2607.04722
作者: Wonseok Kang,Jaehyun Kim,Jeongmin Lee,Tae-Wan Kim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We present RIC-Loc (Reference-Induced Consensus localization), a scene-training-free posed-reference localizer that is SfM-point-map-free in its main estimator: it uses known reference poses, but not precomputed SfM 3D map points, query-to-map 2D-3D matches, or query-to-map PnP. A frozen VGGT pass predicts local camera poses, depth, and query-reference tracks for a query and selected references. Each reference induces one map-frame SE(3) query-pose hypothesis, robust consensus estimates the pose, and the preserved hypothesis structure yields two reliability scores: spatial dispersion and a track-conditioned covariance score. On the covariance-eligible set, the two scores are complementary for held-out, ground-truth-free failure detection across indoor, outdoor, and large-scale low-texture benchmarks: the joint policy is strongest in textured scenes and the covariance score in the low-texture regime, and the hypothesis-derived scores consistently outperform the standard retrieval-score gap and random rankings. Without per-scene training the consensus estimator remains accurate – competitive with structure-based localization indoors and improving over a comparable feed-forward baseline – giving an effective selective operating regime for posed-reference localization. Code is available at this https URL.
[CV-76] Learning Probabilistic Prompt for Continual Learning ECCV2026
链接: https://arxiv.org/abs/2607.04711
作者: Hyekang Park,Sanghoon Lee,Geon Lee,Jongyoun Noh,Bumsub Ham
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026
Abstract:Continual learning aims to progressively learn from a sequence of tasks, each containing a disjoint subset of classes, while preserving previously learned knowledge. Prompt-based continual learning methods propose to learn a small set of parameters, i.e., prompts, by associating them with a query feature of an input image. These methods optimize the prompts, attempting to represent diverse patterns of images. However, we have observed that existing prompt-based methods suffer from a prompt collapse problem, that is, the prompts tend to be highly similar to each other, thereby failing to capture the diverse data distributions in continual learning scenarios. To address this issue, we propose in this paper a novel prompt-based continual learning framework that captures diverse patterns of images across a sequence of tasks. To this end, we model each prompt as a probabilistic distribution and construct a mixture of these distributions, from which we sample diverse prompts. This enables our model to effectively capture highly diverse image distributions in the continual learning process. We also present a distribution regularization loss to prevent abrupt changes in the prompt distributions throughout the training process. We show extensive experimental results for continual learning on standard benchmarks, including ImageNet-R, CIFAR-100, and CUB-200, demonstrating the effectiveness of our framework.
[CV-77] Hierarchical Scaffolding Enables Human-Like Cognitive Selectivity under Data Scarcity
链接: https://arxiv.org/abs/2607.04709
作者: Juhyoung Park,Jaehyuk Bae,Hyeonbo Yang,Se-Bum Paik
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Modern machine learning systems demand extensive datasets for visual recognition. Conversely, humans learn with high efficiency despite severe data limitations, often by acquiring broad categorical structures before refining finer distinctions. Inspired by this contrast, we introduce SCALA (Scaffolded Cognitive Architecture for Learning under limited dAta), a hierarchical learning framework grounded in cognitive psychology that guides models from coarse conceptual structures to fine-grained recognition. Our model exhibits human-like cognitive selectivity by effectively prioritizing task-relevant features while suppressing background distractors, a mechanism that induces a fundamental shift in representation learning. This shift is characterized by accelerated cluster formation, reduced intra-class dispersion, and enhanced semantic separability. Empirically, SCALA achieves significant accuracy improvements under severe data scarcity. Furthermore, this hierarchical scaffolding promotes robust generalization to unseen classes and accelerates the acquisition of novel categories. Collectively, our results establish SCALA as a powerful framework for achieving human-level sample efficiency and resilient category generalization in data-constrained environments.
[CV-78] Probe-EM: Targeted Neuron Tracing via Training-Free Semantic Verification MICCAI2026
链接: https://arxiv.org/abs/2607.04696
作者: Liuyun Jiang,Yanchao Zhang,Jinyue Guo,Chuanyue Chen,Haiyang Yan,Ye Yuan,Jing Liu,Hua Han
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at MICCAI 2026
Abstract:Establishing large-scale, high-resolution neural connectivity maps is fundamental to elucidating the structural basis of brain function. However, when processing terabyte- or petabyte-scale electron microscopy data, over-segmentation inherent in automated reconstruction algorithms remains a critical bottleneck, requiring extensive manual proofreading spanning person-years. To alleviate the heavy reliance on annotated data and the limited flexibility of conventional tracing methods, we propose a training-free, targeted neuron tracing framework. Specifically, we introduce a skeleton-guided Heuristic Spatial Search paradigm that leverages geometric priors to iteratively reconstruct neuronal morphologies through a probing-verification cycle. To achieve robust zero-shot semantic verification, we further develop a Dimension-Aware Semantic Verification strategy built upon the foundation model NeuroSAM 2. This strategy resolves intra-slice splits via Planar Ensemble Consensus and inter-slice splits via Axial Spatio-Temporal Propagation. Notably, we integrate the proposed workflow into the Neuroglancer visualization platform, enabling an interactive human-in-the-loop proofreading system. Experimental results demonstrate that the proposed method outperforms supervised baselines and reduces manual proofreading time by 33.4%. The source code is publicly available at this https URL.
[CV-79] Solve the Missing First Step: Can VLMs Standardize Raw Heterogeneous Medical Data?
链接: https://arxiv.org/abs/2607.04694
作者: Xin Chen,Dongliang Xu,Cunhao Zhu,Xudong Luo,Haoyang Lyu,Xiaoxiao Sun,Serena Yeung-Levy,Yue Yao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 7 figures
Abstract:As vision-language models (VLMs) are increasingly applied to medical AI, existing benchmarks mainly focus on evaluating their diagnosis ability over given medical images and texts, implicitly assuming that standardized medical images, texts or question-answer pairs are already prepared. However, this assumption does not hold when we apply VLMs in real clinical practice, where medical data is often raw, heterogeneous, and fragmented across different sources. In this paper, we study this missing step, i.e., raw medical data standardization. Specifically, models are given raw dataset folders and evaluated on their ability to identify source formats, convert raw medical images into VLM-compatible visual inputs, extract relevant textual information, and organize the results into structured image-text pairs. To construct this Medical Data Standardization Benchmark (MDS-Bench), we manually annotate 1,939 raw medical data standardization tasks covering diverse clinical practice, radiology modalities, annotation formats, and directory layouts. Extensive experiments show that even the best performing VLMs, i.e., Gemini 3 Flash, achieve only 48.6% end-to-end success rate. Our research highlights raw medical data standardization as a critical bottleneck for medical AI diagnosis in real practice.
[CV-80] From Open Loop to Closed Loop: A Test-Time Iterative Optimization Framework for Reference-Consistent Image Generation ECCV2026
链接: https://arxiv.org/abs/2607.04691
作者: Baixuan Zhao,Xinyu Zhang,Huayu Zheng,Shuaicheng Liu,Xiongkuo Min,Guangtao Zhai,Xiaohong Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 24 pages, 15 figures. Accepted at ECCV 2026
Abstract:While controllable image generation has made significant strides by incorporating visual reference conditions, existing methods predominantly operate as open-loop systems. They inject control signals in a strictly feed-forward manner, failing to guarantee strict fidelity to the reference due to the absence of active feedback and error correction mechanisms. To address this fundamental limitation, we propose a novel test-time iterative optimization framework that reformulates reference-consistent generation as a closed-loop dynamic tracking problem. By treating the pre-trained generative model as a control plant, our framework employs a sensor-controller architecture driven by a modified Proportional-Integral-Derivative (PID) algorithm. This mechanism iteratively optimizes the latent control signals at test time based on the sensed discrepancy between the generated output and the reference target. Notably, this approach is entirely training-free, model-agnostic, and integrates seamlessly around existing diffusion pipelines. Extensive evaluations across ID-preserving, pose-controlled, and depth-controlled generation tasks validate the universality of our method. Empirical results demonstrate improvements over computation-matched open-loop baselines, achieving relative performance gains of up to 25.36% for facial similarity, alongside spatial error reductions of up to 27.71% for pose alignment and 28.50% for depth consistency. More broadly, this work offers a new conceptual perspective: it demonstrates that controllable generation can be effectively managed as a dynamic feedback system, bringing the rigorous principles of classical control theory into the optimization of generative models. Code is available at this https URL.
[CV-81] A Reliable Context-Aware and Temporal Planning Framework for Autonomous Driving
链接: https://arxiv.org/abs/2607.04689
作者: Argho Dey,Yunfei Yin,Swachha Ray,Md Minhazul Islam,Zheng Yuan,Sijing Xiong,Hongyu Liu,Zhiqiu Huang
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Submitted to IEEE Transactions on Intelligent Transportation Systems. 12 pages, 6 figures
Abstract:Safe operation of autonomous vehicles in dense urban traffic depends on perception and planning that remain reliable when onboard sensing is degraded. In real driving conditions, camera observations are frequently corrupted by occlusion, motion blur, illumination change, and sensor noise, and when such degraded observations are aggregated indiscriminately over time, trajectory planning becomes unstable and collision risk rises for both the ego vehicle and surrounding road users. Recent Bird’s-Eye-View (BEV) approaches unify perception and planning through a shared spatial representation, but most fuse temporal information across frames without assessing the reliability of the underlying observations. We present a Reliable Context-Aware and Temporal Planning framework for Autonomous Driving (RCT-AD) that explicitly models feature quality and temporal consistency to support safer, more consistent planning. A Reliable Context Awareness module scores per-frame reliability and selectively retains trustworthy features through a quality-gated First-In-Last-Out (FILO) memory mechanism, reconstructing degraded observations from reliable historical context so that corrupted inputs do not destabilize the scene representation. A Temporal Trajectory Planner captures long-term dependencies and multi-agent interactions to produce smoother, safety-aware trajectories, while a joint detection-and-segmentation head injects semantic and motion cues into the shared BEV space to strengthen scene understanding. Experiments on the nuScenes autonomous driving benchmark show that RCT-AD improves perception accuracy, motion prediction, and planning robustness over recent end-to-end baselines, achieving 61.5 nuScenes Detection Score, 52.9 mean Average Precision, and 52.3 mean Intersection over Union, while maintaining competitive computational efficiency suitable for real-time deployment.
[CV-82] ubeLite: Lightweight Multi-Actor Spatio-Temporal Action Detection ICPR2026
链接: https://arxiv.org/abs/2607.04684
作者: Ali Soltaninezhad,Melissa Cote,Alejandro Rico Espinosa,Tunai Porto Marques,Alexandra Branzan Albu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ICPR 2026. 15 pages
Abstract:Spatio-temporal action detection in videos requires jointly localizing actors in space and identifying action boundaries over time. A common challenge is constructing temporally stable action tubes, as frame-level detectors often suffer from jitter, fragmentation, and imprecise temporal localization. Many recent approaches address this by introducing heavy spatio-temporal transformers or optical-flow-based pipelines, leading to high computational cost and limited scalability. We propose TubeLite, a lightweight framework for spatio-temporal action detection that focuses on stable tube construction and boundary-aware temporal modeling. TubeLite represents each actor as a tube, defined as a sequence of bounding boxes associated with a single actor over time, and explicitly enforces temporal consistency at both the spatial and semantic levels. The method combines low-jitter actor detection, Gaussian-weighted actor feature extraction, efficient short-term temporal propagation, and a boundary-focused temporal prediction head, while avoiding optical flow and large-scale temporal attention. Despite its compact design, TubeLite achieves strong video-level localization performance. It improves Video-mAP@0.5 by 4.5 and 7.1 percentage points over the best compared method on the MultiSports and UCF101-24 datasets, respectively, with substantially fewer parameters and floating-point operations than transformer-based alternatives, demonstrating that effective spatio-temporal action detection can be obtained through principled, lightweight temporal modeling.
[CV-83] AnyStyle: A Single LoRA is Sufficient for Image-Guided Style Transfer
链接: https://arxiv.org/abs/2607.04677
作者: Yongwen Lai,Chaoqun Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Image-guided style transfer aims to apply the artistic characteristics of a style image to a content image while preserving its semantic structure and layout. Despite advances in diffusion-based methods, existing approaches often face challenges in disentangling content and style, particularly when independently optimized adapters are naively combined, causing conflicts between adapters and limiting controllability over the content-style balance in inference. We further demonstrate that training-free structural guidance directly derived from the content image through the internal attention of pre-trained model outperforms a dedicated content LoRA adapter in terms of structural fidelity and computational efficiency. Building on these observations, we propose AnyStyle, a streamlined framework for image-guided style transfer. The framework adopts a unified single-adapter paradigm for coherent style capture from the style image and incorporates training-free structural guidance from the content image, thus avoiding complex entanglement between multiple adapters and improving controllability and stability. Extensive experiments show that our method delivers competitive quantitative performance and significantly improved perceptual quality. Code is available at this https URL.
[CV-84] ICME 2026 Grand Challenge on Cross-Scenario Defect Detection and Fine-Grained Severity Grading for High-Precision Manufacturing
链接: https://arxiv.org/abs/2607.04675
作者: Wei Sun,Weixia Zhang,Linhan Cao,Mingkai Lu,Xiongkuo Min,Xiaoping Zhang,Patrick Le Callet,Guangtao Zhai,Hongxing Chen,Wenqi Wu,Zhenhao Hu,Shanshan Lin,Guanjie Huang,Kai Xie,Rui Xin,Zilong Zhao,Runmin Cong,Ningjing Li,Siqi Ma,Yi Jin Ong,Tianfei Zhou,Shunzhou Wang,Zhiyang Chen,Hao Fang,Chen Zhang,Tze-Hsiang Tang,Dikai Li,Xianjin Wu,Avinash Kumar Sharma,Zhaoyang Wang,Haiyong Chen,Binyi Su,Atik Shahariar
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:This paper presents the IEEE International Conference on Multimedia and Expo (ICME) 2026 Grand Challenge on Cross-Scenario Defect Detection and Fine-Grained Severity Grading for High-Precision Manufacturing. The challenge is motivated by two key limitations of existing industrial defect inspection systems: (1) current deep learning-based methods often suffer significant performance degradation when deployed in unseen production scenarios, and (2) most benchmarks neglect severity-aware assessment, which is critical for risk control and yield optimization. To address these limitations, we design two complementary tracks: Track 1 (Cross-Scenario Defect Detection) targets accurate defect detection, localization, and classification across diverse unseen production environments; Track 2 (Fine-Grained Severity Grading) requires assigning each detected defect an industry-standard severity level, including Acceptable, Marginal NG, NG, and Gross NG. We construct a large-scale industrial dataset of high-resolution microscopic images spanning seven representative defect categories, comprising over 3,800 images with pixel-level instance annotations for Track 1 and over 2,600 images with severity-grade labels for Track 2. The challenge attracted 86 registered participants with 130 submissions; during the final testing phase, 21 teams submitted results and 12 teams provided models with technical reports. The resulting benchmark, together with the diverse and effective solutions contributed by participating teams, sets a new standard for industrial defect analysis research.
[CV-85] Video Generation Models Are Inherent Lighting Estimators
链接: https://arxiv.org/abs/2607.04674
作者: Ziqi Cai,Shuchen Weng,Kaiqi Liu,Zifeng Wang,Zhiquan Zhang,Minggui Teng,Han Jiang,Boxin Shi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:Recovering dynamic environment maps from a single in-the-wild video is crucial for photorealistic rendering, yet remains a challenge. Recent video generation models can produce photorealistic scenes with complex lighting, possessing an inherent understanding of lighting. In this paper, we introduce V-LITE (Video generation models are inherent lighting estimators), a framework that unlocks this internal knowledge by reframing lighting estimation as a guided video inpainting task. Inspired by VFX industry practices, we insert a synthetic chrome ball into the scene to compel the model to generate physically plausible reflections from the surrounding spatio-temporal context. To bridge the gap from LDR-native models to the HDR domain, we design an HDR-aware VAE and employ an efficient LoRA-based fine-tuning strategy. We then construct a mixed dataset comprising high-fidelity HDR images to provide realistic HDR priors, and in-the-wild HDR videos to provide dynamic spatio-temporal context. Extensive experiments demonstrate that V-LITE produces temporally coherent HDR environment maps, revealing that modern video diffusion models are not merely synthesizers but also powerful, inherently capable estimators of physical scene lighting.
[CV-86] GlaKG: A Biomarker-Centric Fundus Knowledge Graph for Explainable Glaucoma Diagnosis and Risk Assessment
链接: https://arxiv.org/abs/2607.04673
作者: Cheng Huang,Jia Zhang,Yi Jiang,Yang Liu,Karanjit Kooner,Yadi Liu,Tsengdar Lee,Yang Xie,Wenqi Shi,Guanghua Xiao
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Glaucoma is a leading cause of irreversible blindness worldwide, yet most automated diagnosis systems rely on opaque deep-learning models that offer little clinical interpretability. We present GlaKG, a biomarker-centric fundus knowledge graph that integrates structural biomarkers, clinically grounded rules, and image features to produce traceable reasoning for glaucoma diagnosis and risk stratification. GlaKG encodes six entity types (Fundus Image, Optic Disc, Neural Rim, Pathology, Diagnosis, Risk Level), eight relation types, and 11 clinically validated rules into a unified graph, so that every prediction is accompanied by an explicit reasoning chain linking biomarker evidence to activated clinical rules. To keep knowledge-based reasoning strictly separate from label information, we adopt a post-processing fusion framework that combines ResNet50 image embeddings with a normalized KG reasoning-chain score via a tunable weight alpha, with all fitting confined to the training split. On a publicly available, AI-annotated fundus dataset, GlaKG reaches F1 = 0.9953 for binary glaucoma classification and 0.930 accuracy with 0.922 weighted F1 for four-class risk stratification; we report openly that the dataset’s biomarker annotations are highly label-correlated, and therefore frame these figures as an upper bound attainable with clean structured biomarkers rather than as leakage-free image-only performance. Feature-importance analysis shows KG-derived and biomarker features contributing near-equally (51.1% vs. 48.9%), and the reasoning chain flags borderline cases by exposing low chain scores rather than failing silently. GlaKG’s central contribution is therefore a clinically auditable reasoning framework that complements raw predictive performance by explicitly exposing the biomarker evidence and rule activations behind each decision.
[CV-87] DiCE-CIR: Direct Composition Learning for Efficient Zero-Shot Composed Image Retrieval
链接: https://arxiv.org/abs/2607.04665
作者: Gwang-Ho Na,Ho-Joong Kim,Seong-Whan Lee
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Zero-shot composed image retrieval (ZS-CIR) aims to retrieve a target image from a multimodal query consisting of a reference image and an edit text describing the desired modification. Recent ZS-CIR studies have relied on projection-based methods that map a reference image into pseudo-word tokens in the text embedding space. However, such methods require additional projection and re-encoding steps, increasing training complexity, reducing efficiency, and introducing a discrepancy between training and inference. In this paper, we propose DiCE-CIR, a direct composition learning method that predicts composed query representations by directly composing a reference image and an edit text. To enable scalable training without manually annotated triplets, we automatically construct compositional training samples from large-scale image-caption pairs using a large language model. Based on these samples, we train a lightweight composition module with objectives that promote alignment with the target, edit-consistent semantic transformation, and retrieval discriminability. We conduct extensive experiments on ZS-CIR benchmarks and show that DiCE-CIR achieves state-of-the-art performance on CIRCO and competitive performance on CIRR while maintaining high computational efficiency.
[CV-88] argeted Structure Completion for Sparse-View 3D Reconstruction in Autonomous Driving ECCV2026
链接: https://arxiv.org/abs/2607.04661
作者: Guoqing Wang,Pin Tang,Xiangxuan Ren,Liping Hou,Chao Ma
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted by ECCV2026
Abstract:Reconstructing 3D scene structures from sparse, low-overlap observations remains a fundamental challenge in autonomous driving. Recent state-of-the-art frameworks achieve promising results by incorporating voxel-based Gaussians, but incur substantial computational redundancy due to a uniform volumetric processing strategy. To bridge the gap between the efficiency of pixel-based Gaussian methods and the structural completeness of voxel-based Gaussian approaches, we propose FocusGS, a simple yet effective framework that shifts the paradigm from global densification to targeted structural completion. Our central insight is that structural completion should be decoupled from deterministic regions, with computation concentrated exclusively on areas exhibiting geometric ambiguity. Specifically, FocusGS addresses the localization challenge by deriving a 3D Geometric Ambiguity Manifold to accurately isolate localized areas prone to occlusion and high geometric uncertainty. To overcome the subsequent manifold completion challenge, we design a lightweight targeted structure completion module that selectively instantiates and optimizes continuous Gaussian queries strictly within this unstructured, sparse topological subspace. Extensive experiments demonstrate that FocusGS achieves a superior efficiency-quality trade-off, advancing state-of-the-art performance on driving-centric benchmarks while naturally reducing the total number of Gaussians by ~74% and decreasing rendering time by ~34%.
[CV-89] Enhancing Video Physical Consistency via Role-aware Joint Training and Modality-decoupled Denoising
链接: https://arxiv.org/abs/2607.04653
作者: Guangting Zheng,Haojing Chen,Hao Li,Jingtao Zhang,Zhen Yang,Xiaosong Jia,Xue Yang,Shaofeng Zhang,Yanyong Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:While modern video diffusion models excel in visual fidelity, maintaining long-range physical consistency remains a formidable challenge. Conventional pixel-reconstruction objectives mainly focus on appearance details and often fail to capture the underlying dynamics of a scene. To mitigate this, recent efforts have integrated auxiliary modalities (e.g., optical flow) to introduce physics priors via joint training with video appearance. However, these methods have three main limitations: (1) they do not distinguish the different motion patterns of different entity types; (2) joint modeling of visual and auxiliary modalities can cause capacity conflicts and weaken the pretrained visual prior; and (3) auxiliary modalities may accumulate errors during inference. To address these issues, we propose \textbfVPT, a fine-tuning framework for improving physical consistency in video diffusion models. VPT introduces a role-aware signal that groups entities into agents, controlled objects, passive objects, and background, so that different physical roles can be modeled more clearly. We further propose a modality-decoupled denoising strategy, where the visual and auxiliary channels are assigned independent noise levels. Together with a loss-weight decay strategy, this design makes auxiliary modalities serve as soft constraints rather than strong dependencies, mitigating recursive prediction errors during inference. We also introduce cross-step auto-guidance to further strengthen physical dynamics. Experiments show that VPT improves physical consistency while preserving visual quality, achieving relative gains of 39.4% in SA and 17.9% in PC on VideoPhy benchmark over Wan2.1-T2V-1.3B, and consistent improvements on VideoPhy-2 benchmark. The project page is available at this https URL.
[CV-90] Learning Flexible Generalization in Video Quality Assessment by Bringing Device and Viewing Condition Distributions
链接: https://arxiv.org/abs/2607.04643
作者: Nikolay Safonov,Dmitriy S. Vatolin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video quality assessment (VQA) plays a critical role in optimizing video delivery systems. While numerous objective metrics have been proposed to approximate human perception, the perceived quality strongly depends on viewing conditions and display characteristics. Factors such as ambient lighting, display brightness, and resolution significantly influence the visibility of distortions. In this work, we address the question of the multi-screen quality assessment on mobile devices, as this area still tends to be under-covered. We introduce a first large-scale subjective dataset collected across more than different 300 Android devices, accompanied by metadata on viewing conditions and display properties. We propose a strategy for aggregated score extraction and adaptation of VQA models to device-specific quality estimation. Our results demonstrate that incorporating device and context information enables more accurate and flexible quality prediction, offering new opportunities for fine-grained optimization in streaming services. Ultimately, this work advances the development of perceptual quality models that bridge the gap between laboratory evaluations and the diverse conditions of real-world media consumption. We made the dataset and the code available at this https URL.
[CV-91] Learning Structured Visual Compositional Representations for Weakly Supervised Referring Expression Comprehension ECCV2026
链接: https://arxiv.org/abs/2607.04638
作者: Lian Xu,Mohammed Bennamoun,Farid Boussaid,Hamid Laga,Yulan Guo,Dan Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCV 2026
Abstract:Referring expression comprehension (REC) aims to localize the object in an image described by natural language. In Weakly supervised REC (WREC), existing approaches primarily operate on anchor-level visual representations. Even when enriched with auxiliary cues, relational interactions remain implicitly encoded within individual anchor features. The resulting visual representation remains flat and unary-only, limiting its ability to align with the structured nature of language. In this work, we propose a Structured Visual Compositional Representation (SVCR) learning framework for WREC. Rather than implicitly encoding relations within unary anchors, the proposed SVCR explicitly models both unary object embeddings and pairwise relational embeddings, forming a structured visual representation space. We further introduce a compositional alignment mechanism that matches unary and pairwise visual representations with their corresponding textual embeddings in a unified manner, enabling compositional visual-textual matching under weak supervision. Extensive experiments on RefCOCO, RefCOCO+, and RefCOCOg show that the proposed SVCR achieves state-of-the-art performance. These results demonstrate the effectiveness of explicit structured visual representations and visual-textual alignment for WREC.
[CV-92] PixelPilot: Scalable Vision-Language-Action Models for End-to-End Autonomous Driving ECCV2026
链接: https://arxiv.org/abs/2607.04637
作者: Pin Tang,Guoqing Wang,Xiangxuan Ren,Zhongdao Wang,Guodongfang Zhao,Bailan,Chao Ma
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV 2026
Abstract:Vision-Language-Action Models (VLAs), which leverage the advanced reasoning capabilities of Vision-Language Models (VLMs), show promising generalization in complex autonomous driving scenarios. Existing VLAs typically predict and optimize 3D trajectories from 2D images. While intuitive, this 2D-to-3D prediction is inherently entangled with camera parameters, leading to limited data scalability across heterogeneous driving datasets. Moreover, directly optimizing in 3D space induces severe convergence to trivial solutions, where VLAs rely on ego-status rather than visual scene understanding. To address these issues, we propose PixelPilot, a novel VLA featuring a decoupled planning and lifting paradigm. In the planning phase, PixelPilot reformulates scene understanding and trajectory prediction as sensor-agnostic 2D-to-2D tasks in the image plane, thereby facilitating scalable training across diverse datasets. The planned 2D trajectories are then deterministically lifted to 3D only during inference, ensuring the full exploitation of visual cues and generalization across different vehicles. To realize this paradigm, we propose a knowledge-instilled policy learning strategy that applies dense, intermediate rewards via Group Relative Policy Optimization (GRPO) to enforce a rigorous causal chain from visual perception to spatial planning. Extensive experiments demonstrate that PixelPilot achieves state-of-the-art performance in both open-loop and closed-loop settings, validating its superior scalability and visual reasoning capabilities.
[CV-93] Enhancing Large Multimodal Models in Key Information Extraction via Scene-Aware Document Synthesis
链接: https://arxiv.org/abs/2607.04636
作者: Zhipeng Xu,Zulong Chen,Qing Liu,Junhao Ji,Jinxin Hu,Yipeng Yu,Jianqiang Wan,Jun Tang,Zhao Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Key Information Extraction (KIE) converts visually rich documents into structured data, but practical deployment remains challenging: strong performance often relies on costly on-server Large Multimodal Models (LMMs), while compact locally deployable models lack sufficient KIE supervision. We present SAYRE, a scene-aware document synthesis framework for generating scalable KIE training data without hand-crafted template design. Given a few exemplar documents, SAYRE captures category-specific content patterns and layout conventions to synthesize document-schema-annotation triples. It further introduces error-driven generation, which expands real-world failure cases into hard training examples while preserving their structural patterns. Experiments on constrained- and open-category KIE show that SAYRE consistently improves Qwen3-VL backbones and achieves the strongest overall performance among on-device LMMs. Data scaling experiments show an overall upward trend as more synthesized data is introduced, especially for smaller models and open-category extraction. Error analysis further shows that synthesized training reduces field-level errors by improving schema-aware extraction over dense tables, business identifiers, and contract clauses. These results establish scene-aware synthesis as an effective data-centric approach for improving practical multimodal KIE.
[CV-94] Aperture-aware Dispersion 5-D Light-field Imaging Spectrometer
链接: https://arxiv.org/abs/2607.04635
作者: Chenglong Huang,Tao Lv,Jianing Yang,Chongde Zi,Linsen Chen,Xun Cao
类目: Computer Vision and Pattern Recognition (cs.CV); Computational Geometry (cs.CG)
备注:
Abstract:Enhancing perceptual dimensions while miniaturizing imaging systems presents significant challenges for high-dimensional visual sensing. Conventionally, the acquisition of the 5D (x,y,u,v,\lambda) spectral light field (5D-SLF) data cube relies on bulky and expensive camera arrays, which are impractical for widespread application. Existing single-detector systems are fundamentally limited by a trade-off between the resolutions of different dimensions owing to insufficient coding capabilities. Here we introduce an Aperture-aware Dispersion Light-field Imaging Spectrometer (ADLIS), that targets a synergy between compactness and resolution through aperture-multiplexed modulation, leveraging the inherent spectral-filtering properties of birefringent material. Using only a manufacturing-friendly and cost-effective phase plate made of birefringent quartz crystal, the aperture of the proposed ADLIS enables compact angular-spectral encoding that is highly sensitive to both the incident angle and spectrum of incoming light. In contrast to the viewpoint-separation approach of microlens arrays, ADLIS employs aperture encoding to superimpose all viewpoints onto each sensor pixel. This shifts the design paradigm from spatial division to encoding integration, aiming to achieve full-resolution light field recovery. Thus, we develop the Aperture-aware Dispersion Light-field Imaging (ADLI) framework, which optimizes the aperture design and 5D-SLF reconstruction in an end-to-end (E2E) manner. Trained by simulation data and validated through real-world experiments, our system achieves robust high-performance 5D-SLF imaging while maintaining full spatial resolution.
[CV-95] Hierarchical Evidence-Driven Reasoning for Long Document Understanding
链接: https://arxiv.org/abs/2607.04625
作者: Junyu Xiong,Yonghui Wang,Rongjian Gu,Chenyu Liu,Bing Yin,Wengang Zhou,Houqiang Li
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Retrieval-Augmented Generation (RAG) streamlines long-document understanding by leveraging retrieval mechanisms to restrict input images to a highly curated subset. However, existing multimodal RAG pipelines primarily face two critical challenges: first, standard semantic similarity retrievers frequently fetch topically overlapping yet answer-void distractor pages that mislead downstream generation; second, rigid single-pass pipelines heavily depend on initial retrieval success, where any omission of core evidence inevitably causes cascading errors. To address these challenges, we introduce HIEVI-RAG, a hierarchical, evidence-driven multimodal RAG framework for closed-domain document understanding. HIEVI-RAG systematically factorizes complex queries into a cooperative four-stage pipeline: (1) hierarchical question decomposition to break multi-hop root queries into atomic child questions; (2) coarse visual page retrieval leveraging a multimodal retriever to fetch candidate pages based on semantic similarity; (3) fine-grained page verification via EVIAGENT, a specialized multi-page verifier trained with GRPO to execute cross-page reasoning over multi-image blocks; and (4) memory-guided iterative generation that leverages accumulated sub-question context to execute multi-round, dynamic reasoning over the prioritized sequence. Extensive evaluations across four benchmarks demonstrate the robust efficacy and synergy of our framework, which significantly outperforms existing open-source baselines and exceeds the strongest reported baseline by an average of 8.05% in accuracy.
[CV-96] StructuredEdit: Constraint-Aware Graphic Design Editing via Differentiable Parameter Propagation SIGGRAPH
链接: https://arxiv.org/abs/2607.04612
作者: Veeramanohar Avudaiappan,Ritwik Murali
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
备注: 3 page poster short paper.2 Figures, 2 Tables. Planned to submit to SIGGRAPH Asia
Abstract:Graphic design editing requires precise manipulation of typography, layout, and visual hierarchy under strict design constraints. Following the introduction of large language models, organizations have increasingly promoted vision-language models to enhance productivity. However, current models operate on pixels and achieve only 52% constraint satisfaction on structured design edits, thereby limiting their reliability for professional workflows. We present StructuredEdit, a pipeline that reframes design editing as parameter manipulation rather than pixel generation. Our core technical contribution is Differentiable Parameter Propagation (DPP), a training method that embeds hard design constraints into vision-language model fine-tuning by backpropagating pixel-level constraint violations through a lightweight differentiable rasterizer. A hybrid candidate-and-filter pipeline produces 125k validated edit triplets. The resulting system reaches 89% constraint satisfaction versus 52% for GPT-4V, 0.82 matched-element Intersection over Union, and 76% top-1 font accuracy over the 100 most-frequent design typefaces. In a user study (N=35), editing time drops 33% and correction iterations drop 44% relative to a GPT-4V baseline.
[CV-97] Integrated Forward-Inverse Network for Lensless Image Reconstruction ECCV2026
链接: https://arxiv.org/abs/2607.04608
作者: Donggeon Bae,Jaewoo Jung,Yong Guk Kang,Kyung Chul Lee,Taeyoung Kim,Jongho Kim,Sangjun Byun,Joonsik Park,Seung Ah Lee
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026
Abstract:Lensless imaging enables compact and versatile computational cameras by replacing bulky optics with thin coded elements. However, reconstruction from the resulting measurements is challenging: large-footprint point-spread functions (PSFs) produce highly multiplexed observations, making inversion severely ill-conditioned and sensitive to calibration errors and model mismatch. While deep learning approaches, including hybrid models that incorporate physics priors, have shown promise, explicitly maintaining data fidelity throughout the network hierarchy remains difficult. Here, we propose the Integrated Forward-Inverse Network (IFIN), a physics-guided architecture that interleaves differentiable forward projections with learnable inverse updates at every scale, enabling complementary cues to be exploited jointly in the measurement and image domains. This bidirectional coupling supports progressive, physics-consistent refinement and permits system-constrained PSF kernel adaptation under model uncertainty. On challenging lensless benchmarks, including a newly introduced dataset, IFIN achieves state-of-the-art reconstruction quality. We further observe competitive performance on Gaussian deblurring and simulated inline holography reconstruction, suggesting that the same interleaving principle can extend beyond lensless cameras.
[CV-98] G2VD: Generalizable AI-Generated Video Detection via Counterfactual Intervention and Causal Disentanglement
链接: https://arxiv.org/abs/2607.04607
作者: Meng Du,Hongchang Chen,Ran Li,Junjie Zhang,Qi Ouyang,Shuxin Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:The rapid advancement of AI-generated videos poses increasing security risks and calls for robust detectors with strong cross-domain generalization. Although existing methods achieve promising results under in-domain evaluation, their performance often degrades substantially when tested on unseen generators. A key reason is shortcut learning, where detectors rely on domain-specific spurious cues, such as generator-dependent fingerprints and generation styles, instead of intrinsic forgery traces. To address this issue, we propose G2VD, a Generalizable AI-Generated Video Detection framework based on counterfactual intervention and causal disentanglement. First, G2VD introduces a counterfactual intervention pipeline (CFIPipeline) that generates controlled counterfactual samples via variational autoencoders (VAEs), followed by frequency-domain and pixel-domain alignment, thereby encouraging the detector to focus on generator-intrinsic cues. Building on this intervention process, we further design a causal disentanglement classifier consisting of two domain-anchored branches with distinct classification objectives, combined with an HSIC-based independence constraint to encourage the separation of task-relevant cues from domain-specific bias. Across four public datasets, G2VD shows strong average cross-domain performance and consistent gains over matched backbones. On the challenging GenVidBench cross-domain setting, it exceeds 90% accuracy and reaches an AUC close to 0.95. Notably, this performance is obtained using only 10% of the original training data. The code is available at this https URL.
[CV-99] LCPNet: Latent Consistent Proximal Unfolding Network for Infrared Small Target Detection
链接: https://arxiv.org/abs/2607.04603
作者: Tianfang Zhang,Fengyi Wu,Lei Li,Chang Liu,Zhenming Peng,Huaping Zhang,Xiangyang Ji
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Infrared small target detection (IRSTD) aims to identify long distance small targets from complex infrared backgrounds, and is a fundamental task in remote sensing. Deep learning methods have improved IRSTD by learning discriminative image-to-mask mappings, but such feed-forward designs often underuse physical decomposition structure between targets and backgrounds. Deep unfolding methods partially address this issue by embedding model-driven iterations into neural networks, yet existing designs still operate mainly in image domain and use updates and memory mechanisms that are not fully coupled with underlying optimization process. To address these limitations, we propose Latent Consistent Proximal unfolding network (LCPNet). First, we verify that low-rank prior remains valid in latent representations and perform unfolding in this space, preserving physical constraint while avoiding repeated compression of intermediate states. Second, we derive a Latent Consistent Proximal (LCP) solver that evolves each latent variable from its previous state rather than reconstructing through an indirect residual, and stabilizes small target updates through task-adaptive normalization and gain control. Third, we introduce Shared Optimization Memory (SOM), a common historical state shared by all decomposition variables to provide coordinated guidance across unfolding stages. Extensive experiments on four public benchmarks demonstrate that LCPNet outperforms state-of-the-art methods while achieving accurate and robust detection with low false alarms and competitive efficiency. Model and code are available at this https URL.
[CV-100] Displacement Preserving Relational Distillation for Robust Medical Segmentation
链接: https://arxiv.org/abs/2607.04599
作者: Zhicheng Ding,Xinyu Chu,Jung Im Choi,Qing Tian,Tianyu Shi,Xiaoqian Jiang,Lijing Zhu,Qizhen Lan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate 3D medical segmentation is limited by anatomical variability and high computational costs. While knowledge distillation (KD) offers a route for model compression, conventional methods often fail to preserve complex structures and are overwhelmed by background noise. We propose Displacement-Preserving Relational Distillation (DPRD), which distills latent anatomical trajectories via vector based alignment to preserve the orientation and relative scale of the teacher’s manifold, and prevents signal dilution by anchoring distillation in task-relevant structures. Integrated into nnU-Net, DPRD outperforms established baselines on ISLES 2022 and AMOS 2022 benchmarks. Notably, on the AMOS dataset, DPRD achieves a Dice score of 85.46%, edging out the high-capacity MedNeXt teacher while significantly reducing boundary errors. Despite utilizing only ~5% of the teacher’s parameters and ~3% of its FLOPs, our approach maintains high structural consistency. This provides a robust, efficient solution for deploying high performance segmenters in resource-constrained clinical environments. Code: this https URL
[CV-101] ORINO: Token Reduction via Interpretable Concept Overlap in Vision-Language Models
链接: https://arxiv.org/abs/2607.04593
作者: Riccardo Renzulli,Gabriele Spadaro,Shruthi Gowda,Alaa Eddine Mazouz,Van-Tam Nguyen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-Language Models (VLMs) have demonstrated impressive capabilities across different tasks, but their computational cost is dominated by the large number of visual tokens fed to the language model. Existing token reduction methods rely on attention-based scores or pairwise similarity, without an explicit semantic representation of each token. We introduce TORINO (TOken Reduction via Interpretable coNcept Overlap), a plug-and-play framework for adaptive visual token reduction in VLMs that requires no fine-tuning of the underlying model. TORINO leverages Sparse Autoencoders (SAEs) to project visual tokens into an interpretable latent space where token relationships can be analyzed through shared concept activations. Specifically, we define concept overlap as the degree of agreement between active SAE latents and use it to group tokens that share semantic content. Reduction within each group is then performed by either pruning or merging, providing a unified framework that preserves semantically important visual information while removing redundancy. Unlike fixed-budget approaches, TORINO dynamically adapts the reduction rate to input complexity, allowing different images to retain different numbers of tokens. Experiments across multiple vision-language benchmarks show that TORINO achieves favorable efficiency-accuracy trade-offs, reducing the number of visual tokens with minimal performance loss.
[CV-102] RAF: Reliability-Aware Fusion of Camera LiDAR and 4D RADAR for Robust 3D Object Detection in Adverse Weather ECCV2026
链接: https://arxiv.org/abs/2607.04587
作者: Heejun Park,Jaeseok Jeong,Kuk-Jin Yoon
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026
Abstract:Robust 3D object detection in adverse weather conditions is challenging due to sensor limitations. Although combining complementary modalities such as LiDAR and 4D RADAR has shown promise, the sparsity of these sensors becomes apparent in adverse weather with reduced reflections, leading to objects with few or no point cloud returns. To address this limitation, camera sensors provide visual cues even when LiDAR and RADAR signals are weakened. However, cameras themselves are also vulnerable to adverse weather, where some regions become unreliable due to snow or rain occluding the camera lens. While some camera-fusion methods designed for adverse weather learn to weigh image regions via confidence maps, these maps receive no direct supervision and are learned solely through the detection loss. We introduce Reliability-Aware Fusion (RAF), which explicitly supervises per-pixel reliability estimation and provides a direct learning signal for identifying and suppressing unreliable visual cues. Our framework leverages pretrained LiDAR-RADAR networks, keeping their backbones frozen while only training the added camera branch, BEV fusion encoder, and detection head. Extensive experiments on the K-Radar and VoD datasets demonstrate that integrating RAF consistently improves detection accuracy over LiDAR-RADAR baselines, achieving up to +6.5 AP_BEV and +7.4 AP_3D gains. Code is available at this https URL.
[CV-103] QSVideo: Query-Conditioned Semantic Temporal Retrieval for Video Understanding ECCV2026
链接: https://arxiv.org/abs/2607.04559
作者: Wei Ao,Lan Wang,Vishnu Naresh Boddeti
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026
Abstract:The performance of vision-language models (VLMs) in video understanding declines with increasing video duration, as video moments unrelated to the query confuse their language components. Multimodal retrieval has emerged as a critical component of video understanding, addressing this challenge by localizing key visual evidence. However, existing multimodal retrieval methods suffer from biased relevance estimation, limited diversity, and temporal collapse. In this paper, we propose QSVideo, a unified framework that systematically addresses relevance, diversity, and temporal modeling in video retrieval. We first introduce a query-conditioned semantic ranker, QSRanker, which reformulates arbitrary questions into retrieval-friendly queries and estimates structured relevance along object, action, and location dimensions. Building upon this, we design QSRetrieval to jointly optimize relevance and diversity for more informative frame selection. Moreover, we propose temporal alignment strategies tailored for both long and streaming videos to improve evidence recall. Extensive experiments on long and streaming video benchmarks demonstrate that QSVideo greatly enhances video VLM performance under strict frame limit constraints. The code is available at this https URL.
[CV-104] Explainable Novel Category Discovery in Semantic Concept Space
链接: https://arxiv.org/abs/2607.04548
作者: Ifrat Ikhtear Uddin,Yang Zhou,KC Santosh,Longwei Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Novel category discovery aims to identify unseen classes from unlabeled data by transferring knowledge from labeled categories, but most existing methods perform discovery in opaque latent feature spaces. As a result, they may separate novel categories accurately while providing little insight into what semantic evidence defines each discovered group. We propose xNCD, an explainable novel category discovery framework that performs both representation-based discovery and pseudo-label assignment directly in a structured semantic concept space. Instead of clustering arbitrary deep features, xNCD learns a label-free concept representation by aligning visual features with vision-language similarity priors from pretrained multimodal models, and then applies a unified labeled-and-unlabeled self-labeling objective over concept-space logits. This design makes each discovered category explainable by construction through stable concept signatures and instance-level concept evidence. Theoretically, we show that routing discovery through a semantic concept bottleneck induces a strict restriction of the feature-space hypothesis class, excluding a large family of unconstrained decision rules and biasing induced partitions toward semantically interpretable concept coordinates. Experiments on CIFAR-10, CIFAR-100, and CUB-200 demonstrate that xNCD preserves strong discovery performance while providing intrinsic explanations. Under task-agnostic evaluation, xNCD achieves 92.63% overall accuracy on CIFAR-10, close to UNO’s 93.4%, and improves CIFAR-100 overall accuracy from 73.2% to 76.45%, while being the only compared method that provides human-readable cluster- and instance-level explanations.
[CV-105] Mask2Real-WM: Segmentation Masks as a Sim-to-Real Bridge for Controllable Dexterous World Models
链接: https://arxiv.org/abs/2607.04546
作者: Riccardo O. Feingold,Davide Liconti,Chenyu Yang,Robert K. Katzschmann
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 23 pages, 24 figures, 4 tables. Preprint. Project page: this https URL
Abstract:Action-conditioned world models allow robots to predict the future consequences of candidate actions without additional physical interaction, supporting policy evaluation, planning, and data augmentation. We present Mask2Real-WM, a two-stage action-conditioned world model for dexterous manipulation that decouples pixel prediction into a dynamics model and a rendering model. The dynamics model predicts future segmentation masks from past masks and 23-DoF action sequences. The rendering model maps the predicted masks to photorealistic RGB using a ControlNet-augmented Stable Video Diffusion backbone. The smaller sim-to-real gap in segmentation space enables the dynamics model to benefit from large-scale pretraining on over 50 h of synthetic simulation data, followed by fine-tuning on fewer than 2.5 h of real demonstrations. Experiments on a dexterous pick-and-place benchmark show that mask conditioning and simulation pretraining are both required for per-DoF action controllability across all 23 degrees of freedom. In contrast, monolithic baselines capture broad hand and end-effector trajectories but do not reliably reflect fine-grained, per-joint action effects.
[CV-106] CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining
链接: https://arxiv.org/abs/2607.04541
作者: Jingyu Song,Yi Liu,Katherine A. Skinner
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
备注: 17 pages, under review
Abstract:Camera-radar (CR) fusion is a practical sensing configuration for autonomous driving, but existing models are typically trained with task-specific supervision, limiting reusable representation learning. We present CRISP, a spatiotemporal CR backbone pretrained through forecasting-based representation learning. Given historical multi-view images and radar sweeps, CRISP learns a unified bird’s-eye-view (BEV) representation by predicting future LiDAR point clouds. LiDAR is used only as privileged supervision during pretraining; the deployed model requires only camera and radar. To make forecasting-based pretraining effective for CR fusion, CRISP introduces an enhanced radar encoder, radar-enhanced temporal self-attention, and multimodal feature rendering with modality innovation gating. These components inject radar range and Doppler cues into BEV temporal propagation and allow BEV tokens to selectively incorporate camera and radar evidence. Experiments on nuScenes show that CRISP improves long-horizon point cloud forecasting and transfers effectively to downstream tasks, including 3D detection, tracking, online mapping, motion forecasting, future occupancy prediction, and planning, suggesting that predictive CR pretraining is a promising path toward scalable driving representations under practical sensor configurations. The project website is this https URL.
[CV-107] SceneFrom3D: Geometry-Conditioned Outdoor 3D Scene Generation via View Scheduling with Object-Level Control
链接: https://arxiv.org/abs/2607.04540
作者: Geonung Kim,Jeongeun Park,Nuri Ryu,Di Liu,Sunghyun Cho
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
备注: project page: this https URL
Abstract:Geometry-conditioned 3D scene generation enables the creation of 3D environments from user-provided geometry, offering direct control over scene structure and object layout. To generate such 3D scenes, current methods commonly adopt a three-stage design that first defines a view schedule, then synthesizes multi-view observations along the scheduled views, and finally reconstructs a 3D representation from the generated images. However, defining the view schedule becomes a major bottleneck for outdoor scenes, where large, unstructured, and unbounded geometry makes it difficult to obtain views that provide sufficient coverage while supporting stable generation. To address this bottleneck, we present SceneFrom3D, a framework that automatically schedules views from outdoor input geometries. SceneFrom3D constructs a directed generation graph whose nodes represent anchor views and whose edges represent interpolation trajectories, defining which views to synthesize, which view pairs to interpolate, and in which order generation should proceed. Beyond automatic view scheduling, SceneFrom3D further improves controllability through object-level conditioning, assigning each object an identity image for appearance guidance and a geometry-adherence parameter for region-wise control over the input geometry. Experiments demonstrate that SceneFrom3D achieves state-of-the-art geometry-conditioned outdoor 3D scene generation, producing high-quality scenes with controllable object appearance and geometry adherence.
[CV-108] A non-invasive video-based method for individual identification of wildlife using gait dynamics
链接: https://arxiv.org/abs/2607.04518
作者: Muhammad Aamir,Matthew Wijers,Sangyun Shin,Andrew Loveridge,Andrew Markham
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: This article is under review in “Methods in Ecology and Evalution”
Abstract:Gait is a distinctive behavioral characteristic that enables non-invasive individual identification without requiring physical interaction with an animal. While gait-based analysis has been extensively studied in humans, its application to wildlife remains limited due to environmental variability and the lack of scalable identification methods. This paper presents a fully automated, video-based pipeline for wildlife gait analysis and individual identification using deep spatiotemporal representation learning. The proposed pipeline uses the Segment Anything Model 3 (SAM3) to generate high-quality RGB and binary silhouette masks, robustly isolating animals from complex natural backgrounds. Segmented video sequences are processed using a convolutional neural network (ResNet18) for spatial feature extraction and a transformer-based video model (VideoPrism) for temporal motion modeling. Both models are fine-tuned using a classification objective and subsequently used as feature extractors to generate discriminative gait representations. Cosine similarity is then used to compare gait signatures, enabling similarity-based clustering of individuals without reliance on physical markings or invasive tagging. Experiments conducted on multi-source wildlife video data across multiple species demonstrate strong intra-individual consistency and clear inter-individual separation. Quantitative results using cosine similarity distributions and silhouette scores confirm the effectiveness of the proposed method. These findings demonstrate that gait dynamics provide a viable, non-invasive approach for individual identification in wildlife and highlight the potential of video-based deep learning pipelines for scalable ecological monitoring.
[CV-109] Geographic Diversity Beats Data Volume for Cross-Domain Generalization in Zero-Label JEPA Driving World Models
链接: https://arxiv.org/abs/2607.04500
作者: Santosh Jaiswal
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 3 figures
Abstract:Self-supervised latent world models can assign a surprise score to driving scenarios without any human labels. A natural follow-up question is whether such a model, trained on driving data from one geographic region, can generalize its notion of complexity to unseen cities and sensor configurations. We study this question through a controlled transfer experiment: we train JEPA-based world models on nuPlan data (Pittsburgh, Boston, Singapore) and evaluate zero-shot on held-out Argoverse 2 validation scenarios from Miami and Austin. We find that models trained on geographically diverse data generalize significantly better than models trained on equal amounts of single-geography data. In a matched-scale ablation at 63,000 scenarios per condition (n=3 seeds each), combined training reduces mean surprise score by 16.5% relative to nuPlan-only training (0.228 +/- 0.015 vs 0.273 +/- 0.008). Notably, training on 200,000 AV2-only scenarios (3x more data from one geography) still produces higher surprise (0.264) than the combined 63K model, suggesting that geographic diversity is a stronger predictor of cross-domain generalization than raw data volume.
[CV-110] UniSkip-Mamba: A Frequency-Aware State Space Model for Audio-Visual Temporal Forgery Localization
链接: https://arxiv.org/abs/2607.04498
作者: Cangjin Qiu,Quan Zhang,Dan Jiang,Ke Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD)
备注:
Abstract:With the proliferation of AI-generated content, sophisticated multimedia manipulation has raised critical concerns about malicious applications such as opinion manipulation and evidence fabrication, making Audio-Visual Temporal Forgery Localization (AV-TFL) an urgent research frontier. Existing TFL methods have progressed along two main paradigms: Transformer-based temporal modeling and channel-wise multimodal fusion. While these approaches capture temporal dependencies and cross-modal correlations, they process all frequency components indiscriminately, leading to overfitting on high-frequency noise and limited robustness under real-world data degradation. Through systematic frequency domain analysis, we find that forgery-discriminative patterns concentrate in the low/mid-frequency range (normalized frequency 0-0.15), while high-frequency components primarily introduce noise, removing them even improves detection performance by +1.4%. Based on this phenomenon, we propose UniSkip-Mamba, a frequency-aware State Space Model framework that incorporates Unified Multimodal Sequence Fusion to preserve cross-modal phase relationships, and Skip-Scanning Mamba Blocks that implement frequency-aware regularization through a novel Group-Scan-Merge mechanism, naturally biasing learning toward discriminative low/mid-frequency patterns (0-0.15) while maintaining representational completeness. We achieve state-of-the-art (SOTA) performance: 63.4% AP@0.95 on LAV-DF (+9.8% improvement) and 63.58% mAP on AV-Deepfake1M (+14.32% improvement), with 6x faster inference. Our frequency-domain analysis provides theoretical justification from a signal processing perspective for why skip-scanning inherently improves both accuracy and robustness.
[CV-111] Enhancing Facial Expression Recognition in Head-Mounted Displays with Synthetic Data
链接: https://arxiv.org/abs/2607.04490
作者: Jianing Deng,Qiang Zhou,Jingtong Hu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 19 pages
Abstract:Facial expression recognition (FER) is crucial for social interaction in mixed reality environments that employ head-mounted displays (HMD). However, collecting FER data from head-mounted cameras (HMC) is challenging due to privacy concerns and the diversity of HMD platforms. Moreover, existing FER datasets are not directly applicable due to the unique perspectives of HMCs. The lack of sufficient data hinders the development of neural network-based HMC FER methods. To address data scarcity, we propose a data synthesis framework that generates HMC-view images from frontal-view images, leveraging abundant existing annotated datasets. Specifically, we first reconstruct 3D textured meshes from images and then apply a configurable camera system to render images from the HMC perspective. Additionally, we introduce a texture-space alignment network (TSAN) that enables accurate texture sampling from images to preserve detailed facial expressions. To evaluate the proposed method, we conduct extensive experiments on both simulated and real HMC datasets. Experimental results demonstrate that models trained on our synthetic dataset outperform those trained on existing datasets and exhibit better generalization across different camera configurations.
[CV-112] LeukocyteCount: Automatic Identification and Counting for leukocytes using Deep Learning
链接: https://arxiv.org/abs/2607.04486
作者: Ahmed M. Sayed(1),Sondos A. Refaat(1),Abdallah M. Mostafa(1),Mariam S. El-Rahmany(1),Ensaf Hussein Mohamed(1 and 2) ((1) Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt, (2) School of Information Technology and Computer Science (ITCS), Nile University, Giza, Egypt)
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Corresponding author: Ensaf Hussein Mohamed (enmohamed@nu. this http URL )
Abstract:Diagnosing and monitoring diseases frequently involves the analysis of human biological samples, with blood analysis being pivotal. Specifically, leukocytes, or white blood cells (WBCs), are essential markers for evaluating the body’s defense mechanisms against infections. Traditional methods for WBC counting and classification are labor-intensive and prone to inaccuracies, primarily due to human error. The conventional processes for blood cell analysis, especially those concerning WBCs, are beset with difficulties. These include the laborious nature of manual counting and the susceptibility to errors, which can significantly impact the accuracy and reliability of disease diagnosis and monitoring. This study proposes an automated, machine learning-based solution aimed at mitigating the identified challenges. By employing a hybrid model that integrates Yolov5 for the detection of WBCs, coupled with a finely tuned, pre-trained MobileNetV2 model and a Logistic Regression classifier, the study innovates in the accurate identification, counting, and classification of WBCs into four distinct types. The methodology leverages the BCCD dataset for training and validation purposes. The application of the proposed hybrid machine learning model has yielded remarkable results, demonstrating a detection accuracy rate of 98% through the Yolov5 stage, and an unparalleled classification accuracy of 99.04% in subsequent stages utilizing MobileNetV2 and Logistic Regression. Additionally, Our proposed YOLOv5-based RBC detection module achieves an F1 score of 99.73%, which outperforms the baseline. These findings underscore the model’s potential in transforming traditional laboratory practices for WBC analysis, offering a path towards more accurate, efficient, and reliable disease diagnostics and monitoring.
[CV-113] rustCLIP: Learning Private Visual Features via Adversarial Reconstruction
链接: https://arxiv.org/abs/2607.04484
作者: Nikos Athanasiou,Ilya A. Petrov,Angela Yao,Shugao Ma,Eric Sauser,Edoardo Remelli,Shreyas Hampali,Johannes Schönberger,Fadime Sener,Bugra Tekin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: this https URL
Abstract:Vision and vision-language models rely on high-level visual representations that are increasingly used across recognition, retrieval, and multimodal reasoning pipelines. However, recent advances in generative modeling have shown that such features can often be inverted, enabling realistic reconstructions of the underlying image and raising significant privacy risks. We revisit this problem through the lens of reconstruction and propose TrustCLIP, a reconstruction-driven framework that treats a feature-conditioned generator as an explicit privacy adversary. TrustCLIP learns a projection between encoder features and downstream modules that is explicitly optimized to degrade the reconstructions produced by generative attackers while retaining the necessary signals for downstream tasks. Unlike prior defenses that rely on discriminative privacy metrics, TrustCLIP directly optimizes against a generative reconstruction attacker, targeting a threat not captured by standard evaluation protocols. We demonstrate its effectiveness in both conventional classification and multimodal large language model pipelines. Across these settings, TrustCLIP consistently reduces the fidelity of generative inversions while maintaining downstream task performance. Project page: this https URL
[CV-114] PulmoSight-XAI: An Explainable Multi-View Attention Ensemble with Gradient Boosting Meta-Learning for Multi-Label Chest X-Ray Classification
链接: https://arxiv.org/abs/2607.04478
作者: Moshiur Rahman,Shafqat Alam,Tasnia Binte Mamun
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 17 pages, 3 figures, 6 tables
Abstract:Automated chest X-ray classification remains challenging due to severe class imbalance, co-occurring pathologies, and the loss of localized features in conventional architectures. To address these, we propose an explainable hierarchical multi-view ensemble framework for the robust classification of 14 thoracic pathologies. The framework employs view-specific training by independently modeling frontal and lateral radiographs using an ensemble of five complementary convolutional neural networks. Replacing global average pooling, a multi-scale feature fusion strategy augmented with Convolutional Block Attention Modules (CBAM) preserves fine-grained intermediate representations while emphasizing high-level pathology-specific semantic features. To mitigate positive-negative imbalance and varying inter-class difficulty, models are optimized using a novel hybrid objective combining Asymmetric Loss with Adaptive Focal Loss. Beyond simple probability averaging, the framework incorporates a hierarchical meta-learning strategy where test-time augmentation (TTA) predictions and cross-model uncertainty measures are integrated into Level-1 gradient-boosting meta-learners (XGBoost, LightGBM, and CatBoost), followed by Level-2 stacking with optimized alpha blending. Evaluated on a large-scale CheXpert-style dataset, the framework achieves state-of-the-art macro-average AUROC scores of 0.9319 for frontal and 0.9154 for lateral radiographs. Furthermore, comprehensive explainability analysis using seven post-hoc attribution techniques demonstrates strong anatomical consistency and clinically meaningful decision localization. By integrating architectural diversity, multi-scale attention, hierarchical meta-learning, and rigorous explainability, the proposed framework provides a transparent, highly accurate, and clinically practical computer-aided diagnosis system for thoracic disease classification.
[CV-115] EVAS: Efficient Multimodal Temporal Forgery Localization via Audio-Visual Synergy and Steered Boundary Calibration
链接: https://arxiv.org/abs/2607.04472
作者: Shen Shen,Quan Zhang,Dan Jiang,Ke Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The rapid proliferation of artificial intelligence-generated content necessitates reliable multimodal forensics. Beyond video-level binary classification, precisely localizing sparsely distributed forged segments in long-form videos remains a critical challenge. This task is particularly difficult when manipulations are subtly embedded and cross-modal signals are weak and temporally diffuse. To address these challenges, we propose EVAS, an end-to-end multimodal framework for temporal forgery localization. At its core, a Multi-Stage Audio-Visual Synergy mechanism facilitates progressive cross-modal interaction to learn deep multimodal forensic representations and capture high-order semantic traces of sparse manipulations. Furthermore, we introduce a Boundary-Aware Refinement strategy to achieve steered boundary calibration. By incorporating invalid-frame masking, this strategy suppresses ambiguous regions and sharpens transition predictions. We adopt a decoupled training paradigm with auxiliary heads to disentangle representation learning from inference objectives, enhancing model generalization and stability. Additionally, a lightweight HourglassFFN is incorporated to reduce computational overhead. Extensive experiments demonstrate that EVAS achieves state-of-the-art average localization accuracy and average recall across three benchmark datasets, validating its effectiveness for fine-grained temporal forgery localization.
[CV-116] Flash-BoN: Instant Drafts for Inference-Time Scaling in Diffusion Models
链接: https://arxiv.org/abs/2607.04461
作者: Ruchit Rawal,Reza Shirkavand,Sayak Paul,Yuxin Wen,Heng Huang,Yizheng Chen,Tom Goldstein,Gowthami Somepalli
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Inference-time scaling for text-to-image generation has progressed from simple Best-of- N (BoN) sampling to guided search methods that verify and steer candidate trajectories at intermediate denoising steps. These approaches focus on when and how often to verify during denoising but largely treat the cost of generation itself as fixed. Moreover, the standard practice of comparing methods by number of function evaluations (NFEs) counts only denoising forward passes and ignores verifier overhead, which can distort efficiency rankings. We show that under wall-clock evaluation, simple BoN already matches or outperforms several guided search techniques, suggesting that compute is better spent on broader exploration than on repeated intermediate verification. This motivates Flash-BoN, which generates a large pool of inexpensive draft candidates by combining three complementary acceleration knobs: timestep truncation, layer skipping, and activation proxies into a single configuration optimized once per model. An efficient multi-stage verification procedure then identifies the most promising draft, which is refined at full quality. Across three benchmarks and three model scales, Flash-BoN consistently outperforms all baselines under fixed wall-clock budgets, with gains that grow at larger model scales (+8% AUC). We further show that our strategy combines well and improves existing orthogonal techniques such as reflection-based prompt optimization (+16% AUC). The gains correlate with increased candidate diversity, which also enables draft-guided selection to accelerate RL post-training convergence.
[CV-117] Spatial Graph Representation and Morphometric Analysis of the Pulmonary Vascular Tree From Computed Tomography Using Multi-Scale Hessian-Based Filter Fusion and TEASAR Skeletonization
链接: https://arxiv.org/abs/2607.04457
作者: Piotr Mackiewicz,Jakub Kołyska,Radoslaw Roszczyk
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reconstructing the pulmonary vascular tree from computed tomography (CT) images is essential for quantitative lung analysis, vascular morphology assessment, and patient-specific modeling, yet it remains challenging because vessels span multiple scales, from proximal arteries to distal microvasculature. Clinical chest CT is further affected by limited spatial resolution, partial volume effects, heterogeneous image quality, and respiratory motion artifacts. Unlike deep learning-based pulmonary vessel segmentation methods that require large annotated datasets, we propose a deterministic, training-free, and explainable pipeline for CT-based pulmonary vascular tree reconstruction. The method fuses multiscale Hessian-based Frangi and Sato vesselness filters using a weighted maximum response across 12 spatial scales from 1 to 8 mm, enabling detection of large pulmonary arteries and peripheral branches. Lung parenchyma is segmented by Hounsfield unit thresholding, morphological post-processing, and Chan-Vese active contour refinement. Vascular centerlines are extracted using the Kimimaro implementation of the TEASAR algorithm; separate left- and right-lung vascular graphs are then constructed, pruned, and verified for acyclicity. Geometric plausibility is assessed using volumetric fractal dimension, Strahler order analysis, Horton ratios, and Murray’s law. The resulting fractal dimension of approximately 2.3 is consistent with reported values for the human pulmonary vasculature. At the same time, residual deviations in branching metrics reflect distal-vessel truncation caused by finite CT resolution. These results indicate that the proposed explainable pipeline can generate geometrically plausible pulmonary vascular tree models and may support quantitative pulmonary imaging, vascular morphometry, and computational lung modeling.
[CV-118] CCFM: Collision-Constrained Flow Matching for Safety-Critical Scenario Generation ECCV2026
链接: https://arxiv.org/abs/2607.04451
作者: Ke Li,Kaidi Liang,Yuxin Ding,Debojyoti Biswas,Xianbiao Hu,Ruwen Qin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV 2026
Abstract:Evaluation of autonomous vehicle (AV) planners in safety-critical closed-loop simulation is essential for real-world deployment. However, generating controllable safety-critical scenarios remains challenging. Existing approaches use soft guidance that provides only probabilistic preferences and cannot guarantee the satisfaction of geometric and severity constraints associated with specific collision types. We introduce Collision-Constrained Flow Matching (CCFM), a novel framework that guarantees precise collision control through hard physical constraints. CCFM consists of three key components: (i) a heuristic collision selector that optimally identifies an adversarial agent and collision type via composite scoring; (ii) structured hard constraints that explicitly define four collision types (rear-end, side, cut-in, head-on) through contact point, heading, and severity requirements; and (iii) a collision-constrained flow matching sampler that enforces the constraints via Gauss-Newton manifold projection. CCFM achieves collision rate up to 46.4% on nuScenes and 83.1% on nuPlan, significantly outperforming baselines while preserving realistic driving behavior. By enabling controllable collision characteristics in safety-critical scenario generation, CCFM provides a reliable foundation for AV safety evaluation and sim-to-real crash data generation. The code and implementation details are available at this https URL.
[CV-119] Fields of the Planet: Field Boundary Mapping Beyond 10m
链接: https://arxiv.org/abs/2607.04449
作者: Isaac Corley,Caleb Robinson,Jennifer Marcus,Hannah Kerner
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Field-boundary maps support crop monitoring, irrigation planning, and yield estimation, but many smallholder parcels span only a few 10 m Sentinel-2 pixels. We introduce Fields of the Planet (FTP), a 3 m PlanetScope companion to Fields of The World (FTW) that pairs the same polygons, seasonal windows, and train/test splits with 133,168 co-registered PlanetScope patch-window targets across 24 countries. FTP evaluates field delineation as parcel recovery by vectorizing predictions before scoring panoptic quality (PQ), object F1, size-stratified PQ, and meter-scale matched-boundary error. Under matched architectures and training recipes, 3 m imagery raises PQ from 21.0 to 35.5, raises PQ on sub-0.5 ha fields from 5.8 to 15.7, and cuts matched-boundary error from 18.6 m to 7.4 m.
[CV-120] Wan-Streamer v0.2: Higher Resolution Same Latency
链接: https://arxiv.org/abs/2607.04443
作者: Lianghua Huang,Zhi-Fan Wu,Yupeng Shi,Wei Wang,Mengyang Feng,Junjie He,Chen-Wei Xie,Yu Liu,Jingren Zhou,Ang Wang,Bang Zhang,Baole Ai,Chen Liang,Cheng Yu,Chongyang Zhong,Jinwei Qi,Kai Zhu,Pandeng Li,Peng Zhang,Wenyuan Zhang,Xinhua Cheng,Yitong Huang,Yun Zheng,Yuxiang Bao,Yuzheng Wang,Zoubin Bi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG)
备注: Website: this https URL
Abstract:We present Wan-Streamer v0.2, a latency-preserving upgrade of the native-streaming, end-to-end audio-visual interaction model. v0.2 keeps the v0.1 modeling formulation, but raises the interactive output stream from 192x336 to 640x368 while preserving approximately 200 ms model-side signal-to-signal latency at 25 FPS. The higher-resolution stream supports scene-grounded mid-shot agents whose posture, gaze, hands, nearby objects, and local scene layout remain legible during real-time conversation. To support the larger visual stream without adding user-visible delay, v0.2 keeps the thinker as a single-GPU low-latency path for streaming perception, the short language/state Transformer pass that builds the generation cache, and final decoding. The performer becomes a multi-GPU Ulysses-style context-parallel group for the expensive next-unit latent generation. Each performer rank writes incoming K/V into a pre-sharded local cache. The long high-resolution latent video sequence is split across ranks for denoising and gathered through Ulysses communication, while the much shorter audio latent sequence is generated without sequence sharding. In this split, the thinker’s language/state computation reaches the performer only as K/V conditioning, so no separate language sequence has to be communicated inside the performer group. This concentrates additional hardware on visual generation while preserving the compact thinker-performer boundary, keeping total remote interaction latency at approximately 550 ms when a 350 ms bidirectional network budget is included.
[CV-121] RoboDojo: A Unified Sim-and-Real Benchmark for Comprehensive Evaluation of Generalist Robot Manipulation Policies
链接: https://arxiv.org/abs/2607.04434
作者: Tianxing Chen,Yue Chen,Zixuan Li,Junyuan Tang,Kailun Su,Weijie Wan,Baijun Chen,Haoran Lu,Haowen Yan,Honghao Su,Zhiyang Dou,Kaixuan Wang,Dandan Zhang,Yunze Liu,Yan Qin,Qiwei Liang,Qiwei Wu,Zijian Lin,Wenwei Lin,Yuran Wang,Minghua He,Tianshu Wu,Ruihai Wu,Jingquan Zhou,Kai-Chong Lei,Haibao Yu,Yuanfeng Ji,Weiyang Jin,Guanyu Lin,Xiaofan Li,Qi Xiong,Renjing Xu,Zhongyu Li,Wenhao Chai,Enze Xie,Ziwei Wang,Yao Mu,Hao Dong,Wojciech Matusik,Mingyu Ding,Wenbo Ding,Ping Luo,Masayoshi Tomizuka
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注: Website: this https URL , Code: this https URL , Leaderboard: this https URL
Abstract:Generalist robot manipulation policies have advanced rapidly, yet existing benchmarks remain limited in systematically evaluating their capabilities. Many rely on simple, short-horizon, or skill-narrow tasks with limited capability coverage, and are often conducted only in simulation or only in the real world. Simulation enables scalable feedback but misses physical deployment challenges, while real-world evaluation is costly, time-consuming, and difficult to reproduce. We introduce RoboDojo, a unified sim-and-real benchmark for comprehensive evaluation of generalist robot manipulation policies. RoboDojo includes 42 simulation tasks and 18 real-world tasks covering diverse and complementary manipulation capabilities. The simulation benchmark evaluates five dimensions: generalization, memory, precision, long-horizon execution, and open-vocabulary instruction following, while the real-world benchmark exposes policies to challenging physical-world deployment conditions. RoboDojo supports scalable evaluation through heterogeneous parallel simulation in Isaac Sim and provides RoboDojo-RealEval, a reproducible real-world evaluation system with remote cloud access, standardized hardware, scene reset, evaluation protocol, and deployment interface. Together with XPolicyLab, policies can be integrated once and evaluated across simulation and real-world settings with minimal adaptation. We integrate 30 policies into XPolicyLab and evaluate them on RoboDojo, establishing a public leaderboard and systematic analysis of current policy performance. The website is available at this http URL.
[CV-122] ransferability Between Understanding and Generation in Unified Multimodal Models ECCV2026
链接: https://arxiv.org/abs/2607.04423
作者: Jiwon Kang,Heeji Yoon,Jaewoo Jung,Jaewon Min,Minkyeong Jeon,Biyeon Hwang,Sangwon Jung,Seungryong Kim
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted at ECCV 2026. Project Page: this https URL
Abstract:Unified Multimodal Models (UMMs) integrate image understanding and generation within a single architecture, yet how the two tasks interact remains understudied. We investigate \boldsymbol\mathsftransferability in UMMs: whether training a capability on one task improves the same capability on the other without explicit supervision. Through controlled experiments, we empirically find that transferability depends on architecture-models with fully shared transformer backbone and a unified visual encoder exhibit consistent cross-task transfer, while loosely coupled designs show little or none. Leveraging this transferability, we propose a practical training strategy. The most straightforward way to improve a target generative capability (e.g., counting) is to fine-tune generation directly, but this can degrade visual quality due to distribution shift. Instead, we train the corresponding understanding task and let it transfer into generation, which improves capability-specific generative performance while minimizing distribution shift. We validate this across three capabilities-counting, spatial relation, and text recognition/generation-showing that cross-task transferability can be systematically exploited in UMMs.
[CV-123] he Good the Bad and the Brittle: Benchmarking Robustness and Generalisation of Histopathology Foundation Models
链接: https://arxiv.org/abs/2607.04401
作者: Dhyey Yajnik,Amina Asif,Fayyaz Minhas
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:How robust and generalisable are pathology foundation models and have their scaling limites been reached? We benchmarked twelve pathology foundation models (PFMs) and ResNet baselines using our Robustness Evaluation and Enhancement Toolbox (REET) across eleven clinically realistic perturbations and a dissimilarity-driven Non-Redundant K-fold validation (NR-Kfold) protocol. We introduce a Perturbation Performance Index (PPI) to summarise accuracy trends under controlled perturbation sweeps and analyse robustness scaling with parameter count. We show that PFMs consistently outperform CNNs in both robustness and domain generalisation, yet model scaling shows diminishing returns: mid-sized models such (UNI2/Virchow-2 etc.) achieve comparable or greater resilience than larger systems. NR-Kfold analysis further reveals systematic accuracy loss and increased variability when training-test similarity is broken, underscoring the need for explicit distribution-shift evaluation. These findings suggest that the next generation of pathology foundation models must prioritise data quality, multimodality information and domain alignment over parameter count to achieve genuine clinical reliability.
[CV-124] Event Detection in Videos: A Framework for the Development of New Methods
链接: https://arxiv.org/abs/2607.04372
作者: Anastasia Zakharova,Thierry Bouwmans,Anthony Cioppa,Adrien Deliège,Antonio Greco,Anaïs Halin,Kamil Jeziorek,Meghna Kapoor,Tomasz Kryjak,Islam Osman,Sébastien Piérard,Carlo Sansone,Mohamed S. Shehata,Renaud Vandeghen,Marc Van Droogenbroeck,Bruno Vento
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 22 pages, 8 figures, 1 table
Abstract:Event detection tasks in videos, the most important aspect of video surveillance, aim to detect events either at the pixel-level, frame-level, or clip-level. Plenty of methods intended for event detection in different environments, for various applications, and within different acquisition techniques were introduced. Naturally, the attempts were made as well to classify these algorithms in terms of detection of performance or in terms of real-time abilities. Nevertheless, the lack of a large-scale dataset as well as rigorous performance evaluation methods have biased such comparisons as well as the development of the methods. Given the diversity of existing approaches, we believe it is essential for researchers to position their work within such a rich landscape. Thus, we propose a rigorous framework for developing new methods in event detection for videos. Specifically, this framework is based on three main pillars: datasets, performance evaluation, and scenarios for deploying methods. Comments: 22 pages, 8 figures, 1 table Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2607.04372 [cs.CV] (or arXiv:2607.04372v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.04372 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-125] HASSL: Hierarchy-Aware Self-Supervised Learning Framework for Single Cell Microscopy
链接: https://arxiv.org/abs/2607.04353
作者: Julius Riel,Vishwa Mohan Singh,Sai Anirudh Aryasomayajula,Anuun Chinbat,Hannes Leonhard,Moritz Ladenburger,Frederik Alexander,Vishisht Choudhary,Fabio Laredo,Giacomo Masserdotti,Thorben Prein,Carsten Marr,Amirhossein Kardoost
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Hierarchical structure is common in image data, where fine-grained clusters often merge into larger, coarser semantic groups. In biological cell images, current self-supervised learning models often suppress this hierarchy, as coarse factors such as imaging modality can obscure finer morphological attributes in the latent space. We propose a hierarchy-aware self-supervised training framework to address this problem. Our method combines two components: a distillation framework with a segmentation teacher to improve morphological awareness in the latent space, and a hierarchy-aware contrastive loss based on HDBSCAN to improve decision boundaries between closely related subtypes at different hierarchical levels. Together, these components reduce the tendency of self-supervised learning to overemphasize coarse factors and instead align embeddings with semantic and morphological cues. This yields biologically meaningful sub-clusters driven by fine morphological detail. We train and evaluate our method on a curated corpus of 2.3 million single cells aggregated from 20 microscopy datasets, both labeled and unlabeled, covering 208 cell classes. Our method improves over baseline and counterpart methods, increasing average top-K accuracy by 2.8%, top-9 retrieval on the dataset with the deepest hierarchy by 6.3%, and downstream F1-score for biologically relevant drug classification from perturbed cell morphology by 7.8%.
[CV-126] Last-Meter Precision Navigation for UAVs: A Diffusion-Refined Aerial Visual Servoing Approach
链接: https://arxiv.org/abs/2607.04352
作者: Yaxuan Li,Jiarui Zeng,Shaofei Huang,Zhedong Zheng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In this work, we study the last-meter precision navigation for UAVs, e.g., autonomously reaching a target within the final 10 meters using monocular vision. This task is challenging due to scale ambiguity, rotation discontinuities, and the need for fine-grained spatial reasoning. Existing methods often fail under large viewpoint changes or lack generalization to unseen environments. To this end, we propose DreamNav, a coarse-to-fine diffusion-refined aerial visual servoing framework. In the first coarse-estimation stage, a robust regression policy employs a trigonometric parameterization to predict rotation by jointly modeling sine and cosine components, effectively mitigating optimization instabilities caused by angular periodicity. Given this coarse estimate, the second diffusion-refined stage utilizes a pre-trained world model to simulate future visual observations for candidate actions, selecting the trajectory that minimizes visual discrepancy with the target through a process of visual imagination. To support rigorous evaluation, we contribute PairUAV, a large-scale benchmark comprising 4.8 million image pairs across 72 scenes, curated from the University-1652 dataset. Extensive experiments show DreamNav outperforms strong visual servoing and foundation model baselines in accuracy and generalization, with zero-shot transfer to unseen scenes.
[CV-127] IRIS: An Intelligent Vision-Language System for Ocular Surface Diseases via Topic Tree and Scene-Driven VQA Generation
链接: https://arxiv.org/abs/2607.04344
作者: Hao Wei,Wenjin Qi,Dasen Dai,Minqing Zhang,Wu Yuan,
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 11 pages, 3 figures
Abstract:While Large Vision-Language Models (VLMs) demonstrate remarkable generic capabilities, their clinical reasoning in specialized domains like ocular surface diseases (OSDs) is severely hindered by a paucity of high-fidelity, multimodal instruction-tuning data. To dismantle this data bottleneck, we introduce IRIS, an Intelligent Recognition and Interaction System tailored for fine-grained OSD understanding via external eye photography. First, we curate IRIS-120K, the largest and most comprehensive OSD visual question-answering (VQA) dataset to date. Crucially, to overcome the semantic shallowness of conventional image-caption pairs, we propose a synergistic data generation paradigm to explicitly inject clinical priors. Our data engine operates via a dual-branch framework: 1) a Topic Finding Tree (TFT) that hierarchically anchors visual features to precise anatomical and pathological concepts, enforcing rigorous medical deduction logic; and 2) a Scene-driven strategy that synthesizes role-adaptive clinical dialogues to ensure pragmatic generalization. By explicitly aligning a compact 4B-parameter VLM on this structurally enriched corpus, IRIS achieves state-of-the-art performance, comprehensively outperforming both generalist and specialized medical VLMs with up to 34B parameters. Our findings underscore that structured knowledge injection profoundly prevails over sheer parameter scaling, unlocking the potential for resource-efficient, expert-level AI deployment on mobile edge devices for scalable OSD screening. Code, datasets, and model weights will be publicly released by this repo.
[CV-128] Agent -driven Long-tail Simulation for Autonomous Driving
链接: https://arxiv.org/abs/2607.04331
作者: Junru Gu,Lijin Yang,Jianing Huang,Shu Liu,Zhongzhan Huang,Hang Zhao
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Evaluating autonomous driving systems in closed-loop settings requires realistic and interactive simulation, yet existing simulators largely rely on log replay or rule-based agents, limiting behavioral diversity and long-tail coverage. We propose an agent-driven simulation framework in which surrounding road participants are controlled by instruction-following large language models through a structured action interface, enabling intentional and reactive behaviors while preserving physical plausibility. Furthermore, we introduce SemanticPlan, a benchmark of closed-loop planning in long-tail and semantically rich scenarios that augment real nuPlan scenes with multiple interactive agents following diverse language instructions. Evaluation results show that state-of-the-art planners still struggle to consistently achieve safe and effective task completion, suggesting that these long-tail scenarios remain challenging.
[CV-129] Framework and Multi-modal Dataset for Roadwork Zone Detection and Geo-localization
链接: https://arxiv.org/abs/2607.04330
作者: Zhiran Yan,Yutong Xin,S Shyam Shenoi,Rui Song,Gordon Elger
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to IEEE IV25. The RZDG dataset and code can be found at: this https URL
Abstract:Autonomous vehicles often rely on high-definition (HD) maps for navigation; however, these maps are not frequently updated and often lack semi-static information, such as temporary roadwork zones, which can significantly alter the road network. This limitation underscores the urgent need for an accurate global position of roadwork zones. However, the absence of publicly available datasets for evaluating roadwork zone detection and geo-localization models has hindered the development of reliable autonomous driving systems. To address this challenge, we propose the Roadwork Zone Detection and Geo-localization (RZDG) dataset, which includes both simulated and real-world data, providing multimodal sensor inputs along with comprehensive annotations. The dataset supports multiple perception tasks, including image semantic segmentation, 3D object detection, and object geo-localization. In addition, we introduce a tracker-based roadwork zone detection and geo-localization (RZDG) pipeline, an extension of AB3DMOT, for accurate object geo-localization in roadwork zones. We benchmark our approach on the RZDG dataset, demonstrating its effectiveness in detecting roadwork zones and transforming object positions from the local coordinate system to the global coordinate system. A prediction is considered a true positive (TP) if its estimated position falls within one meter of the ground truth. Our experimental results show that our approach achieves high accuracy on both real and simulated data. Specifically, we report: Precision: 0.565 (real) / 0.615 (simulated) Recall: 0.898 (real) / 0.809 (simulated) F1-score: 0.597 (real) / 0.665 (simulated).
[CV-130] Aura: Consistent Multi-Subject Video Generation via VLM-Grounded Semantic Alignment
链接: https://arxiv.org/abs/2607.04311
作者: Zixiang Zhou,Zhentao Yu,Yifeng Ma,Hongmei Wang,Wenqing Yu,Cong Wang,Zilin Yang,Rui Chen,Jiarong Ou,Yezhou Liu,Yuan Zhou,Qinglin Lu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: project page: this https URL
Abstract:Subject-driven and multi-element video generation are central to controllable video synthesis, but existing methods still struggle to preserve identity consistency and model complex relationships among multiple subjects. In this paper, we propose Aura, a unified framework for high-fidelity and identity-consistent video generation. To better capture scene dynamics and subject interactions, we introduce AI director-level captions that provide dense and structured descriptions of video content. We further leverage a vision-language model (VLM) with learnable queries to extract multimodal semantic features from textual and visual references, covering both global semantics and fine-grained visual cues. To bridge the representational gap between the VLM and the Diffusion Transformer (DiT), we design a two-stage alignment strategy that progressively maps VLM features into the DiT feature space. For visual conditioning, we adopt token concatenation to inject reference information directly into the generation process. To distinguish heterogeneous subject types and reduce common copy-paste artifacts, we develop a subject-aware RoPE-Shift mechanism. To further differentiate reference images of different categories, we introduce subject-aware learnable tokens. In addition, we introduce Memory Tokens to balance the training signal across examples with different numbers of reference subjects. During inference, Progressive-APG (Adaptive Prompt Guidance) further alleviates oversaturation and improves semantic alignment with user prompts. Finally, we build a high-quality video-subject image dataset through a dedicated data construction pipeline. Extensive experiments show that our method achieves state-of-the-art performance on both single-subject generation and more challenging multi-element scenarios.
[CV-131] SAD-LoRA: Spectral Alignment for Low-Rank Knowledge Distillation ICML’26
链接: https://arxiv.org/abs/2607.04306
作者: Omer Tariq,Syed Muhammad Raza,Jeongbae Son
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: ICML’26 workshop on CoLoRAI - The 2nd Workshop on Connecting Low-rank Representations in AI, 15 pages
Abstract:Distilling a fine-tuned teacher into a LoRA-adapted student is a standard recipe for parameter-efficient compression, but output-level KD does not explicitly control which rank- r weight subspace the adapter occupies. We propose \textbfSAD-LoRA (\textbfSpectral \textbfAlignment \textbfDistillation), which selects this subspace from the data-weighted student-space reference update \DWT\Sigx^1/2 and maintains it during training via a differentiable principal-angle loss on \colspan(B) . We show that the data-weighted distillation error decomposes exactly into subspace misalignment, within-subspace coefficient mismatch, and irreducible rank residual; standard KD can affect the first term only indirectly through output gradients. On controlled synthetic problems with a flat teacher spectrum, SAD-LoRA reduces the subspace-misalignment term from 51% to nearly zero and lifts final subspace alignment from 0.49 to 1.00 . On RoBERTa-large to RoBERTa-base distillation across six GLUE tasks, SAD-LoRA improves rank efficiency: at r=4 , it matches or beats the strongest included spectral baseline on five of six tasks, and at r=8 it gives the best result on SST-2 and CoLA. Ablations identify subspace alignment as the load-bearing component, while coefficient matching is auxiliary.
[CV-132] Road-Aware Anomaly Segmentation with Query-Guided Polygons and CLIP in Autonomous Driving
链接: https://arxiv.org/abs/2607.04304
作者: Zhiran Yan,Gordon Elger
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to IEEE IV26. Code is available at this https URL
Abstract:Traditional semantic segmentation models operate under a closed-set assumption and struggle to recognize unknown or unexpected objects-an essential capability for autonomous driving. As a result, such models often misclassify or overlook out-of-distribution (OOD) road anomalies, posing safety risks in open-world environments. We present a lightweight, postprocessing, road-aware anomaly segmentation framework that requires no retraining, no OOD data, and no auxiliary supervision. Our approach builds on a mask transformer-based segmentation network by exploiting query-level mask confidence and deriving a polygonal road prior to detect gap regions that may correspond to anomalies. To further suppress false positives, we introduce a CLIP-based zero-shot semantic filtering module using in-distribution prompts, with optional generalized OOD prompts. By jointly leveraging spatial priors and semantic verification, our framework produces robust and interpretable anomaly predictions. Evaluation on three public benchmarks-Fishyscapes, SMIYC, and RoadAnomaly-shows consistently strong performance. In particular, our method outperforms the training-free baseline Maskomaly on most metrics and achieves the highest AP on Fishyscapes LostAndFound. These results demonstrate the practicality and deployability of our approach for real-world autonomous driving systems.
[CV-133] AquaStereo: Enabling Underwater Stereo Matching via Depth-Conditioned Diffusion and Geometry Self-Distillation
链接: https://arxiv.org/abs/2607.04303
作者: Qizhe Wei,Yingping Liang,Shaodi You,Ying Fu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Learning-based stereo matching models struggle in underwater environments due to scarce in-domain data and the difficulty of extracting discriminative correspondences from degraded imagery. In this work, we present \textbfAquaStereo , a perception-enhanced framework with a data simulation pipeline and a self-distillation strategy that jointly address data scarcity and feature degradation in underwater stereo matching. First, a depth-conditioned diffusion pipeline renders underwater stereo pairs while preserving binocular geometry, with a lightweight left-right consistency module ensuring geometric alignment. Training on this synthetic corpus effectively narrows the terrestrial-underwater gap and improves zero-shot robustness. Second, a frozen binocular teacher trained on clean terrestrial pairs guides a student exposed to rendered underwater pairs with perturbations. A stage-weighted sequence loss is performed to align the student’s disparities with the teacher’s geometry, while a clean-branch supervision with shared pseudo targets prevents scale drift. To further enhance feature stability under turbidity and low texture, we introduce learnable perception frames, a perception-enhanced feature formulation that constructs robust matching descriptors by fusing temporal cues from two auxiliary views encoded by a video backbone with semantic features extracted by a strong image encoder. Extensive experiments demonstrate that \textbfAquaStereo substantially improves robustness and zero-shot generalization in challenging underwater scenarios. The code is available at this https URL.
[CV-134] EMPURPLE: A Free Lunch for Diffusion Distillation based on the Information Bottleneck
链接: https://arxiv.org/abs/2607.04276
作者: Zilai Li,Lujia Bai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 20 pages,2 figures, conference
Abstract:Diffusion models achieve impressive image-generation quality but remain expensive at inference time. Diffusion distillation reduces sampling steps, yet many distilled models, including SDXL-Lightning and distribution matching distillation methods, suffer from degraded Fréchet Inception Distance (FID). We analyze this phenomenon through a PAC-style generalization bound. Our analysis suggests that aggressive early-step redirection of the velocity field makes the distillation target harder to learn, enlarging the train-test gap. As a result, early-step output distributions differ between training and inference, causing distribution mismatch in the intermediate noisy latent used as next-step inputs. We empirically validate this mechanism by showing reduced diversity in both intermediate features and final outputs. To address this issue, we propose EMPURPLE, a simple training-free method that recycles intermediate latents sampled from the original model. EMPURPLE is model-agnostic and improves FID by 7% to 20% across DMD2, Hyper-SD, FlashSD, and SDXL-Lightning. The repo is: this https URL
[CV-135] AdaptiveSplat:Texture Aware Controllable 3D Gaussian Allocation for Feed-Forward Reconstruction ECCV2026
链接: https://arxiv.org/abs/2607.04256
作者: Badrinath Singhal,Srihari K G,Sreehari Iyer,Ankit Dhiman,Venkatesh Babu Radhakrishnan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCV 2026. Project page: this https URL
Abstract:Current feed-forward 3D reconstruction methods predict pixel aligned Gaussian primitives, resulting in highly redundant representations. A natural solution is to prune the redundant Gaussians, but naive pruning introduces severe artifacts and often requires inference time fine-tuning, breaking the feed-forward paradigm. Based on previous works, high frequency regions require more Gaussian primitives, while low frequency regions can be represented with significantly fewer primitives. Motivated by this, we propose a novel approach to explicitly control the number of Gaussians by leveraging local texture information. Our approach achieves this through three key components: (1) texture estimation to capture spatial variation in scene detail, (2) texture-aware pruning that removes redundant Gaussians from low frequency regions, and (3) an adaptive Gaussian head that predicts the modified attributes of the retained primitives without breaking the feed-forward paradigm. Experiments on RE10K, ACID, DL3DV, Tanks and Temples, and DTU demonstrate the effectiveness of our approach, while ablation studies validate the contributions of its key components.
[CV-136] Beyond Random Sampling: Distribution-Aware Alignment for Semi-Supervised Medical Image Segmentation ECCV2026
链接: https://arxiv.org/abs/2607.04249
作者: Weihao Yan,Yeqiang Qian,Yi Dong,Ming Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 19 pages, 5 figures, accepted by ECCV 2026
Abstract:Precise medical image segmentation is crucial for clinical diagnosis and treatment planning, yet relies heavily on expensive expert annotations. Semi-supervised medical image segmentation (SSMIS) offers a cost-effective solution but typically operates under the assumption of independent and identically distributed (i.i.d.) data, defaulting to random sampling. While statistically valid at scale, this strategy suffers from severe representation bias in low-data regimes, failing to capture the heterogeneous medical data manifold. To address this, we propose a highly data-efficient framework driven by distribution alignment. First, we introduce an offline Distribution-Aware Sample Selection strategy. By leveraging Vision Foundation Models (VFMs) and our designed Density-K-Center algorithm, we explicitly identify representative structural anchors, establishing a more representative labeled domain. Second, to bridge the remaining distribution gap, we propose the Memory-guided Copy-Paste (MCP) module. Tailored for the inherent class imbalance in medical scans, MCP leverages a semantic memory mechanism to retrieve historically consistent priors for cross-domain alignment, encouraging semantic consistency. Coupled with an easy-to-hard progressive schedule, this framework effectively mitigates early-stage pseudo-label noise. Extensive experiments on six diverse 2D and 3D datasets demonstrate strong segmentation performance, particularly in extremely low-labeled scenarios (\eg, 1/16 ratio).
[CV-137] HeartVolMesh: Cardiac Volumetric Mesh Reconstruction via Covariance-Guided Graph Deformation MICCAI2026
链接: https://arxiv.org/abs/2607.04243
作者: Fengming Lin,Arezoo Zakeri,Haoran Dou,Zherui Zhou,Shaokun Lan,Jinming Duan,Alejandro Frangi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by MICCAI 2026
Abstract:Accurate patient-specific tetrahedral cardiac meshes are essential for in-silico trials, yet common segmentation-then-modelling pipelines can blur thin-wall anatomy and offer limited cross-case correspondence. We propose HeartVolMesh, which lifts each template vertex to an anisotropic Gaussian kernel and uses a 3D CNN-GNN to predict per-vertex displacements and Cholesky-parameterized covariances from volumetric images. Training is guided by a covariance-aware negative log-likelihood loss with lightweight mesh regularization. For volumetric meshing, we warp a fixed tetrahedral template to the reconstructed surface via staged alignment, non-rigid registration, and deformation propagation, preserving connectivity and correspondence by construction, with resolution controlled by template density. Experiments show consistent gains over deformation-based baselines in surface mesh accuracy and volumetric mesh fidelity.
[CV-138] Hierarchical Multi-to-Single-Modal Knowledge Distillation for Disruption Prediction in EAST
链接: https://arxiv.org/abs/2607.04241
作者: Qiang Chen,Xiao Wang,Hao Si,Qingquan Yang,Meiwen Chen,Jianhua Yang,Xiaofeng Han,Yunhu Jia,Ran Chen,Liang Wang,Jin Tang,Guosheng Xu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Plasma disruption is a critical threat to tokamak safety. Existing data-driven predictors mainly rely on time-series diagnostic signals, while visible images provide complementary spatial cues including plasma deformation, local brightening, and radiation-structure evolution. Although the image modality improves the model’s discriminative capability, it also substantially increases the computational cost during inference. To address this issue, we propose a hierarchical multi-to-single-modal knowledge distillation framework for disruption prediction on a synchronized EAST multimodal dataset. During training, visible images and time-series signals are used to train a multimodal teacher, which learns disruption precursor representations through Transformer-based encoders and a prototype-guided spatiotemporal hypergraph module. During inference, only the time-series student is retained, with multimodal knowledge transferred through graph-structure-level, representation-level, and decision-level distillation. On the 640-discharge EAST dataset, the results demonstrate that the proposed framework can preserve the discriminative advantages of multimodal learning while substantially reducing inference cost, and providing an effective route for efficient disruption prediction in EAST. The source code of this paper will be released on this https URL.
[CV-139] SoftVTBench: A Safety-Aware Visuo-Tactile Benchmark for Physically Constrained Robotic Manipulation of Deformable Objects
链接: https://arxiv.org/abs/2607.04234
作者: Bowen Jing,Mingxin Wang,Ruiyang Hao,Chenchen Ge,Hanwen Shen,Junjie He,Yang Cui,Yiming Hou,Weitao Zhou,Jiawei Wang,Minglei Li,Dandan Zhang,Ding Zhao,Houde Liu,Xiaofan Li,Si Liu,Ping Luo,Haibao Yu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Deformable object manipulation poses challenges beyond task completion: successful execution must also maintain safe physical interaction, holding the object stably without slip or drop while avoiding excessive deformation. However, existing manipulation benchmarks are predominantly success-oriented and rarely evaluate whether a policy remains physically safe throughout execution. We present SoftVTBench, a safety-aware visuo-tactile benchmark for physically constrained deformable object manipulation. Built in Isaac Sim with finite-element-simulated deformable objects, SoftVTBench provides multi-view RGB observations, RGB tactile sensing with marker motion, proprioception, and language instructions, and defines four matched task suites over object type (deformable vs. rigid) and variation axis (object vs. spatial). It separately reports Goal Success and Safety Success; the latter additionally requires no drop and peak deformation below a calibrated object-specific threshold, measured from policy-hidden privileged Finite Element Method (FEM) states. We implement pi0.5-based baselines under this protocol. Experiments show that success-only evaluation substantially overstates policy performance, as a large fraction of goal-completing rollouts still violate physical safety. Furthermore, incorporating tactile sensing improves Safety Success (e.g., from 21.4% to 35.6% on object-centric deformable tasks) and reduces object deformation during execution, while maintaining comparable Goal Success. SoftVTBench provides a reproducible benchmark for studying visuo-tactile deformable manipulation under physical interaction constraints.
[CV-140] opology-Driven Transferability Estimation for 3D Medical Vision Foundation Models
链接: https://arxiv.org/abs/2607.04199
作者: Jiaqi Tang,Shaoyang Zhang,Fandong Zhang,Shu Zhang,Yang Liu,Qingchao Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The growing number of medical vision foundation models highlights the need for effective model selection. However, mainstream selection methods rely on exhaustive fine-tuning, which is computationally expensive. Most of the existing Transferability Estimation (TE) metrics are primarily designed for image-level classification. They fail to preserve spatial relationships and fine-grained boundary details, which are crucial for the segmentation task. Additionally, while image-level tasks typically process a single feature vector per input, dense prediction tasks in 3D medical imaging require voxel-wise evaluation against dense annotations. To bridge these gaps, we propose a \textitnon-parametric, topology-driven framework that estimates transferability directly from the alignment between the sparse 1-skeleton graph of dense features and semantic labels via Minimum Spanning Trees (MST). We decouple the alignment into two complementary geometric scales: Local Boundary-Aware Topological Consistency (LBTC) to assess boundary separability, where we prove that the MST leakage rate serves as a finite-sample lower bound on the Bayes error; and Global Representation Topology Divergence (GRTD) to evaluate the overall anatomical layout. Crucially, we formally justify a counterintuitive mechanism: Although without fine-tuning, the randomly initialized segmentation decoder acts as a topology-preserving spatial projector, reducing the variance of pairwise distance estimates and stabilizing global alignment evaluation. Fused via a task-adaptive gating mechanism, these dual metrics adapt to diverse clinical complexities. Evaluated on a large-scale benchmark of 114,000 3D medical volumes across diverse anatomical tasks, our topological framework achieves state-of-the-art transferability estimation with an average weighted Kendall (outperforming by 0.36) while accelerating evaluation by 56 times.
[CV-141] CritiqueDriveVLM: From Verifier-Guided Reinforcement Learning to Latent Thought Distillation for Autonomous Driving ECCV2026
链接: https://arxiv.org/abs/2607.04179
作者: Zhaohong Liu,Hao Ye,Xianlin Zhang,Mengshi Qi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted by ECCV 2026
Abstract:End-to-end Vision-Language Models (VLMs) show immense potential in autonomous driving. However, standard Supervised Fine-Tuning (SFT) often suffers from reasoning hallucinations and conservative biases. While traditional tool-augmented frameworks and Chain-of-Thought (CoT) approaches mitigate these issues, they incur exorbitant token consumption and unacceptable latency, rendering real-time deployment impractical. To resolve this reliability-efficiency trade-off, we propose CritiqueDriveVLM, a novel unified three-stage framework internalizing reasoning directly into the VLM. First, we introduce Critique-Driven Multi-Turn Reinforcement Learning (RL) guided by a multi-dimensional verifier. By providing granular scalar feedback and a multi-turn penalty, we force the policy to internalize logical deduction, cultivating a robust System-2 Teacher that achieves high accuracy without fragile external tools. Subsequently, we propose Latent Thought Distillation to overcome the latency bottleneck. By aligning the Student’s latent representations with the Teacher’s fully converged reasoning states, we compress deep logical capabilities into a fast, CoT-free System-1 Student. Extensive experiments on the widely-used DriveLMM-01 benchmark demonstrate remarkable improvements. Compared to the base model, our tool-free Teacher significantly boosts Multiple Choice Quality (MCQ) from 55.54% to a state-of-the-art 76.54%. Crucially, our distilled Student preserves competitive reasoning depth while drastically minimizing generation length to an average of merely 28 tokens. This slashes inference latency by 88% (from 3482 ms to 416 ms), paving a highly robust pathway for low-latency autonomous this http URL source code is available at this https URL.
[CV-142] SeeMe: Mitigating Hallucinations in Large Vision-Language Models through Effective Visual Token Engineering
链接: https://arxiv.org/abs/2607.04163
作者: Kai Tang,Jinhao You,Bohua Zhang,Yichen Guo,Yiding Sun,Dongxu Zhang,Chenxi Li,Xiande Huang,Shanghang Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 12 pages, 4 figures, 6 tables
Abstract:Large Vision-Language Models (LVLMs) have achieved remarkable progress in visual understanding tasks such as image captioning and visual question answering. However, they remain susceptible to hallucinations, generating content that is inconsistent with the actual visual input. Existing methods primarily intervene at the decoding stage, while overlooking a critical source of hallucinations: irrelevant or noisy visual tokens that mislead the decoding process. To address this issue, we propose SeeMe, a training-free framework that introduces the concept of feature engineering from traditional machine learning into LVLMs. SeeMe restructures visual tokens through a three-stage token engineering process to suppress hallucination sources while preserving informative visual evidence. Experiments on MME, POPE, and AMBER benchmarks across four LVLMs demonstrate that SeeMe consistently reduces hallucinations and improves output consistency, providing a novel perspective for mitigating hallucinations in LVLMs.
[CV-143] Perceiving Better Moments: Cover Frame Reselection and Enhancement for Live Photos with the Live2K Dataset ECCV2026
链接: https://arxiv.org/abs/2607.04151
作者: Junyu Lou,Kai Chen,Weiyi You,Hui Zeng,Lei Zhang,Shuhang Gu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026
Abstract:Modern smartphones capture Live Photos, short video bursts surrounding a still image, offering a dynamic and engaging photographic experience. However, the cover photo and video components are generated by two distinct imaging pipelines: the photo stream undergoes full computational photography processing, while the video stream is constrained by real-time efficiency and heavy compression. This intrinsic separation produces a substantial quality gap in resolution, color fidelity, and dynamic range between the cover photo and video frames. When users reselect an alternative frame from the video to replace an imperfect cover, the chosen frame often suffers from severe degradation, making direct replacement visually unsatisfactory. Restoring such frames requires simultaneous enhancement of spatial detail and color appearance, a task considerably more challenging than ordinary super-resolution or color enhancement. To address this, we define the Live Photo Cover Frame Reselection and Enhancement (LPRE) task, which leverages the intrinsic cues available within each Live Photo: the high-quality cover image as a structural and color reference, the user-reselected low-quality frame as the reconstruction target and several adjacent video frames providing temporal cues. Building upon this formulation, we construct Live2K, a real-world dataset of 2,042 Live Photos, and develop a unified one-stage baseline that integrates multi-frame fusion, guided color enhancement and super-resolution, establishing the first benchmark for Live Photo enhancement research.
[CV-144] Beyond Scene Priors: Fine-Grained Traffic Scene Reasoning with Benchmarking and Query-Guided Small-Object Focus
链接: https://arxiv.org/abs/2607.04149
作者: Waikit Xiu,Qiang Lu,Zian Wang,Xinjie Yang,Zhiwei Chen,Chen Sun,Xiying Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In safety-critical traffic scenarios, answering complex questions relies on minute, localized visual cues. However, standard Multimodal Large Language Models (MLLMs) tend to over-attend to backgrounds, overwhelming crucial small objects during visual-language alignment, a failure mode we term ‘critical evidence dilution.’ Furthermore, existing visual question answering (VQA) datasets rarely expose this flaw, as they lack large-scale, distractor-heavy evaluations that require pinpointing local evidence. To bridge this evaluation and architecture gap, we introduce the Fine-Grained Traffic Reasoning Benchmark (FGTR-Bench) and the Text-Guided Small-Object Reasoning MLLM (TSR-MLLM). FGTR-Bench comprises 40,236 single-image Multiple-Choice Questions (MCQs) created via multi-agent generation, consistency checks, and expert audits, alongside a disjoint 4,947-sample blind test split. To resolve evidence dilution, TSR-MLLM, built on Qwen3-VL-4B, uses a query-conditioned Text-Guided Small-Object Focus (TG-SOF) map. Applied once at the decoder boundary, the map adds sparse Top-K gated residuals to the most question-relevant vision slots while leaving text tokens unchanged. Together with lightweight decoder adaptation, TSR-MLLM preserves single-pass inference without external detectors or image re-encoding. Under matched settings, TSR-MLLM outperforms the strongest 4B baseline by 2.1 points on FGTR-Bench (74.1% overall), with larger gains on evidence-local tracks. Furthermore, it remains competitive on DriveQA-V (CARLA Signs) under greedy decoding without task-specific fine-tuning.
[CV-145] HCSU: A Dataset and Benchmark for Fine-Grained Historical Calligraphy Style Understanding ECCV2026
链接: https://arxiv.org/abs/2607.04147
作者: Yinsheng Yao,Yan Liu,Chen Ye
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted at ECCV 2026. 17 pages, 5 figures, 5 tables
Abstract:Automated fine-grained perception of calligraphy styles–a task vital to cultural heritage preservation–remains a critical challenge for Large Vision-Language Models (LVLMs), largely constrained by existing datasets that suffer from modal mixture and flattened labels. To bridge this gap, we introduce HCSU, the first comprehensive dataset tailored for fine-grained Historical Calligraphy Style Understanding. HCSU comprises 39,307 meticulously curated character images from 49 historically prominent calligraphers across 10 dynasties, systematically decoupling authentic ink manuscripts (Tie) from stone rubbings (Bei) to resolve the long-standing modal mixture problem. Moving beyond conventional flattened labels, HCSU provides hierarchical expert-written aesthetic descriptions, enabling two rigorous evaluation protocols: fine-grained style discrimination and interpretable aesthetic reasoning. Extensive evaluations reveal a persistent gap between calligraphy-related knowledge and visually grounded style perception: state-of-the-art LVLMs show non-trivial performance but remain sensitive to script-level, textual, and source-specific cues, and often struggle to ground aesthetic judgments in fine-grained brushwork evidence. Ultimately, the HCSU benchmark exposes fundamental limitations in current multimodal architectures, aiming to inspire the evolution of expert-level visual reasoning for cultural heritage preservation. The dataset is available at this https URL.
[CV-146] Binary Iterative Method for Non-targeted Adversarial Attack
链接: https://arxiv.org/abs/2607.04145
作者: Naman Goyal,Milan Chaudhari
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Adversarial attacks guide and provide additional training and test data for both adversarial training and adversarial robustness validation, and expose the ‘piecewise linearity’ of deep learning based models. Since adversarial attacks and adversarial robustness are mathematically defined problems that can be optimised directly with end-to-end differentiable search, adversarial robustness is more widely applicable than other robustness metrics such as corruption and perturbation robustness, and new kinds of adversarial attacks are beneficial for robustness testing. Attacks are targeted or non-targeted depending on whether the image is modified to misclassify to a particular class or to any incorrect class; we focus on the non-targeted setting. Finding the optimal input data points and hyper-parameters for generating non-targeted adversarial attacks remains a challenge for current methods like the Fast Gradient Method, Basic Iterative Method and Virtual Adversarial Method. We propose a new method, the “Binary Iterative Method” (BinIM), which uses a divide-and-conquer paradigm to optimise parameters and hyper-parameters for the generation of non-targeted attacks. We compare our method to other gradient-based adversarial attacks evaluated over pre-trained networks (InceptionV3, InceptionV2, ResNet V2 152) on classification tasks. On 1000 randomly-sampled images from the standard ImageNet dataset, the Binary Iterative Method outperforms all other gradient-based methods, qualitatively making the classifier misclassify with confidence up to 0.995 while reducing the probability of the true label to 2.21e-09 (approximately 0).
[CV-147] Real-Time LiDAR Gaussian Splatting SLAM
链接: https://arxiv.org/abs/2607.04127
作者: Seungjun Tak,Yewon Jeon,Jaeik Hwang,SukMin Hwang,Seongbo Ha,Hyeonwoo Yu
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 18 pages, 5 figures
Abstract:We present a real-time LiDAR-based framework for Gaussian Splatting SLAM that tightly couples fast G-ICP registration with spherical rasterization-based dense mapping for large-scale sequences. Leveraging LiDAR geometry rather than appearance, we reuse tracking-estimated local covariances to initialize Gaussians with range-aware scales and to derive surface normals for geometry-aware map optimization. We further introduce a covariance-derived geometry score that measures local complexity and drives pruning in planar regions and selective densification in structurally rich areas, while optimized Gaussians and LiDAR-specific confidence cues are fed back to improve tracking robustness. On the Newer College dataset, our method achieves an F-score of 86.78% using purely online trajectories at real-time speed ( 20 FPS), and additional experiments on other datasets confirm its stability and scalability.
[CV-148] FRFDet: Efficient UAV Small Object Detection with Symmetric Sampling and Scalable Fusion
链接: https://arxiv.org/abs/2607.04125
作者: Yunzhong Si,Huiying Xu,Xinzhong Zhu,Yang Liu,Yao Dong,Wenhao Zhang,Hongbo Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 9 figures
Abstract:Small object detection in Unmanned Aerial Vehicle (UAV) imagery remains challenging under adverse conditions, including complex weather, low illumination, and sensor noise. These challenges mainly stem from severe background clutter, fine-grained detail degradation, and suboptimal semantic-spatial feature fusion, which jointly hinder robust small-object representation. To this end, we propose FRFDet, a lightweight yet effective single-stage detector tailored for UAV-based small object detection. FRFDet proposes two plug-and-play modules: Inverse Bidirectional Sampling (IBS) and Scale-Feature Relationship Cross-Fusion (SFRCF). IBS preserves critical spatial details via channel expansion-compression and bidirectional pattern reconstruction, improving feature alignment. SFRCF explicitly models scale-dependent fusion behaviors, revealing that inter-group element-wise multiplication favors compact models, while inter-group additive fusion benefits larger architectures. Extensive experiments on VisDrone, UAVDT, HazyDet, and MS COCO demonstrate that FRFDet achieves state-of-the-art performance among lightweight detectors with low computational cost, compact parameters, and fast inference, making it well suited for resource-constrained UAV platforms.
[CV-149] SOV-CAD: Stepwise Orthographic Views Guided CAD Modeling Sequence Reconstruction ICME2026
链接: https://arxiv.org/abs/2607.04119
作者: Zhaopeng Feng,Chen Zhi,Xuhong Zhang,Zhengwen Feng,Xinkui Zhao
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to ICME 2026
Abstract:Reconstructing Computer-Aided Design (CAD) modeling sequences from images is crucial for preserving design intent and supporting parametric editing. However, existing methods typically generate full CAD sequences holistically, overlooking the iterative, feedback-driven nature of human design workflows. We address this limitation by introducing the rich stepwise visual supervision: at each modeling step, the system observes the target’s orthographic projections, the projections of the incrementally constructed model, and the active sketch, enabling informed action selection. To effectively leverage this on-the-fly feedback, we propose SOV-CAD, a framework that formulates CAD reconstruction as a sequential decision-making task and employs offline reinforcement learning with a Decision Transformer architecture. This design incorporates continuous visual feedback guided by geometric alignment rewards, resulting in a more accurate and human-like modeling process. Extensive experiments show that SOV-CAD surpasses state-of-the-art methods in CAD sequence reconstruction while exhibiting strong data efficiency. Code of SOV-CAD is available at: this https URL
[CV-150] GlacierCastAI: Predicting Glacier Retreat from Multi-Modal Satellite Imagery and Climate Signals
链接: https://arxiv.org/abs/2607.04117
作者: Arunkumar Ramachandran
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, 5 figures. Code available at GitHub
Abstract:ERA5 seasonal climate variables contain predictive information about future glacier retreat beyond what satellite imagery alone provides, yet existing deep learning methods focus on mapping current boundaries rather than forecasting future ones. This paper presents GlacierCastAI, which reframes glacier boundary prediction as a multi-modal spatiotemporal forecasting problem, fusing multi-temporal Landsat imagery with ERA5 reanalysis climate variables and Copernicus DEM terrain features to forecast glacier boundaries across five glaciers spanning four climate regimes. The architecture couples a ResNet50 spatial encoder with a ConvLSTM temporal model and a cross-attention climate fusion module. Because forecasting is inherently more uncertain than mapping current boundaries, the reported IoU values (0.320-0.337) are not directly comparable to state-of-the-art mapping models. Comparisons are against traditional baselines and experimental conditions. Through a pre-registered ablation study, adding ERA5 climate signals improves image-only IoU from 0.326 to 0.337 (+3.4%), suggesting that atmospheric forcing carries predictive information beyond imagery alone. All deep learning models substantially outperform persistence and linear trend baselines (IoU 0.160 and 0.169 respectively), with improvements of 89-99% relative IoU. A lightweight climate-only MLP baseline (661K parameters) achieves an IoU of 0.320 (98% of image-only performance) using 85x fewer parameters, suggesting that ERA5 variables encode substantial predictive signal independently of satellite imagery. SHAP attribution analysis suggests that spring solar radiation (MAM) is the dominant climate driver, consistent with the known role of spring insolation in setting melt season trajectories.
[CV-151] he Multipath Blind Spot: K-Agnostic Robust Calibration for Sparse-Anchor Metric Depth from Frozen Foundations
链接: https://arxiv.org/abs/2607.04101
作者: Sohag Roy,Rajesh Misra,Swami Shastravidyananda,Tamal Maharaj
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Monocular depth foundations predict domain-general relative depth but lack absolute scale; a handful of sparse metric anchors from a range sensor can calibrate them to metric depth, an attractive alternative to metric-supervised training. Existing sparse-anchor calibration methods, however, assume the anchors are clean, whereas real sensors produce outliers that are present with the wrong value – time-of-flight multipath, mixed pixels – not merely missing. We show that the established residual-on-CFA calibration recipe collapses under such outliers, and that the strongest publicly deployed method, VI-Depth, has a structural multipath blind spot: robust to missing anchors, it falls behind an unprotected baseline on three of four datasets when anchors are present but wrong. We propose Multipath-Robust Anchor Calibration (MRAC), a parameter-free, inference-time wrapper that gates anchors by foundation consistency – a Theil–Sen fit and a median-absolute-deviation test against the foundation’s own relative-depth ordering – before a single call to the calibration head. MRAC adds no learned parameters, runs its selection in \approx 50,\mu s on CPU, and serves anchor budgets K \in [5,200] from one checkpoint. On a 320 -cell benchmark with a same-backbone, same-architecture control, MRAC strictly wins 84% of same-backbone cells across all four outlier families and, against VI-Depth, wins all twelve corrupted multipath cells and all sixteen KITTI cells, reducing KITTI multipath AbsRel by 3.2\times ( 0.489 to 0.151 ) at zero retraining.
[CV-152] Sparse4D-Radar: An Efficient and Robust Framework for Surround-View 3D Object Detection via 4D Radar-Camera Fusion
链接: https://arxiv.org/abs/2607.04098
作者: Fuyuan Ai,Yuchen Tan,Jiehui Chen,Zhiwei Xu,Chunyi Song
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 13 pages, 6 figures
Abstract:In recent years, 4D imaging radar has gained wide attention in autonomous driving for its robustness against harsh weather and ability to output target velocity. Nevertheless, mainstream 4D radar-camera fusion methods only support front-view perception, lacking mature solutions for surround-view sensing. Directly expanding these pipelines to full 360° coverage introduces excessive computation cost and limits real-world deployment. To tackle these limitations, this work proposes Sparse4D-Radar, an efficient robust surround-view multi-modal fusion framework. We first design a Deformable Fusion module to embed radar-camera features into sparse queries, constructing the lightweight base version Sparse4D-Radar-Base. Two dedicated modules are further introduced to boost localization accuracy and modality stability: Velocity-Consistency Sampling (VCS) refines features via radar velocity cues for motion awareness, and Adaptive Modality Gating (AMG) dynamically adjusts cross-modal fusion weights according to feature confidence. Combining all components, we build Sparse4D-Radar-Acc for high-precision detection demands. Comprehensive experiments on OmniHD-Scenes verify that our approach achieves state-of-the-art surround-view 3D detection performance. Compared with prior arts, our method obtains over 7% mAP and 10% ODS improvements under complex driving scenes while running at nearly 10 FPS, striking a favorable trade-off among detection accuracy, environmental robustness and inference efficiency. Our open-source code is available at this https URL.
[CV-153] Seeing Once is Enough? Online Geometry-Aware Token Pruning for 3D Question Answering ICLR2026
链接: https://arxiv.org/abs/2607.04079
作者: Ruei-Chi Lai,Bolivar Solarte,Chin-Hsuan Wu,Yi-Hsuan Tsai,Min Sun
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: published at ICLR 2026 Workshop on Efficient Spatial Reasoning
Abstract:Recent Multi-modal Large Language Models (MLLMs) have demonstrated remarkable performance on 2D question answering tasks. However, extending these models to the 3D question answering remains challenging, as they typically require multiple views of the scene, which incurs substantial computational cost at inference. To mitigate this issue, existing solutions rely on strategic frame selection or token-merging algorithms that require preprocessing in advance all frames of the scene, i.e., an offline fashion. In contrast, we propose the first online token-pruning method that can be integrated seamlessly with current MLLM models for 3D question answering tasks, without additional training and with lower memory this http URL key insight is to project each input frame into a shared voxel space using depth information and camera pose, identifying spatially-overlapped regions across frames and selectively pruning redundant image tokens before they enter the language model. Our method enables efficient online processing while reducing up to 50% of token usage. We apply this approach to Qwen2.5-VL-7B and Qwen3-VL-8B, demonstrating improved performance on the ScanQA, SQA3D, and OpenEQA-HM3D benchmarks.
[CV-154] Enhancing Implicit Neural Representations with Image Feature Embedding for Unsupervised Cardiac Cine MRI Reconstruction
链接: https://arxiv.org/abs/2607.04069
作者: Donghang Lyu,Marius Staring,Yiming Dong,Keupp Jochen,Hildo J. Lamb,Mariya Doneva
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: In submission
Abstract:Cardiac cine Magnetic Resonance Imaging (MRI) is a critical diagnostic tool that provides dynamic insights for radiologists. To accelerate acquisition, under-sampled k-space data is often used, requiring reconstruction methods that combine coil sensitivity encoding with prior information to recover missing data. Deep learning approaches have gained more attention for leveraging data-adaptive priors. While supervised learning approaches are a common choice, they depend on fully sampled reference data, which is not always available. Unsupervised methods eliminate the need for fully sampled reference data, which can be advantageous in cardiac cine MRI reconstruction. Among them, implicit neural representations (INRs) have shown great potential due to their simple architecture and good quality reconstructions. In this work, we propose an image-domain dual-branch INR framework, termed I-FP-INR, which extends the original INR design by introducing an additional feature-processing branch. This design aims to extract complementary feature embeddings to enhance the overall representation, thereby benefiting reconstruction. Extensive evaluations on both public datasets and in-house data show consistent improvements over baseline methods in reconstruction quality, with strong robustness across varied scenarios.
[CV-155] PreSIST: Vision-Language-Informed Object Persistence Prediction in Open-World Scenes
链接: https://arxiv.org/abs/2607.04057
作者: Amanda Adkins,Tarunvidyut Ravisankar,Joydeep Biswas
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 8 pages, 6 figures, 3 tables
Abstract:Robots deployed over long periods must reason about environments that change over time. Existing long-term perception systems often address object change reactively, updating their maps only after revisiting a scene and observing that an object has moved. Instead, robots should reason proactively about how long objects are likely to persist using the context in which they appear. For example, a car at a traffic light and a car in a parking spot share the same semantic class, but their contexts imply different persistence durations. We propose PreSIST (Predictive Scene-conditioned Instance Survival over Time), a method for predicting whether an observed object will remain in its last seen pose at arbitrary future times. PreSIST estimates instance-level persistence priors from object properties and scene context, then integrates these priors with a probabilistic persistence filter as observations become available. Its key insight is that the reasoning capabilities of vision-language models (VLMs) can relate scene context to likely object use and human activity, enabling persistence prediction before long-term observations are available. We develop two interchangeable variants: PreSIST-Lang, which estimates persistence priors using a VLM, and PreSIST-Vis, a novel vision-only model trained using PreSIST-Lang pseudo-labels for efficient deployment. Experiments on a new dataset of in-the-wild object persistence annotations show that PreSIST-Lang and PreSIST-Vis outperform baselines on open-world persistence prediction.
[CV-156] SiamJEPA: On the Role of Siamese Student Encoders in JEPA
链接: https://arxiv.org/abs/2607.04044
作者: Makoto Yamada
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
备注:
Abstract:Recently, Joint Embedding Predictive Architectures (JEPAs) have attracted significant attention in the computer vision and machine learning communities as a promising framework for self-supervised representation learning. Unlike masked autoencoders that reconstruct pixels, JEPA models learn representations by predicting latent embeddings of masked regions. Existing JEPA-based methods, such as I-JEPA and V-JEPA, typically employ a single encoder in the student network. In contrast, using Siamese encoders for student network is more naturally aligned with brain-inspired representation learning frameworks, yet their role in JEPA models remains largely unexplored. In this paper, we investigate the effect of Siamese student encoders in JEPA-based representation learning. To this end, we propose SiamJEPA, masked Siamese student encoders equipped with an exponential moving average (EMA) teacher network. SiamJEPA can also be viewed as a JEPA formulation of the brain-inspired representation learning model PhiNet. Through extensive experiments on ImageNet linear probing, we demonstrate that Siamese encoders act as an effective regularizer for the JEPA objective, improving representation separability and accelerating learning during the early stages of training. Furthermore, SiamJEPA consistently outperforms comparable single-encoder JEPA variants under limited training budgets and achieves higher linear probing accuracy than Masked Autoencoders (MAE) which requires longer training. Our findings reveal that Siamese student encoders are not merely an architectural choice but constitute an important inductive bias for predictive representation learning. These results provide new insights into the design of JEPA-based models and suggest that incorporating Siamese student architectures offers a simple yet effective approach for improving self-supervised representation learning.
[CV-157] Paired Uterine Whole-Slide Images and Pathology Reports for Multimodal Computational Pathology
链接: https://arxiv.org/abs/2607.04020
作者: Han Li,Jingsong Liu,Ayako Ura,Junlin Hou,Zhengyang Xu,Azar Kazemi,Oskar Thaeter,Christian Grashei,Fabian Gülhan,Reza Nasirigerdeh,Xun Ma,Rui Yan,Hao Chen,S. Kevin Zhou,Nassir Navab,Carolin Mogler,Peter Schüffler
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Uterine diseases represent an important category of gynecologic pathology and require accurate histopathological assessment for diagnosis and treatment planning. Whole-slide images (WSI) have enabled the digital transformation of pathology workflows and provided new opportunities for artificial intelligence (AI) in computational pathology. In particular, multimodal models that jointly analyze histopathology images and pathology reports have shown promising potential for automated pathology report generation and AI-assisted diagnosis. However, the development of such systems remains limited by the scarcity of datasets that pair whole-slide images with clinically meaningful pathology reports. Instead, existing pathology datasets focus on patch- or slide-level annotations of a single endpoint (e.g., disease class), which do not fully capture the rich information in full clinical diagnostic workflow reports. Here, we introduce TUM-Uteria, a uterine pathology dataset comprising WSIs paired with diagnostic pathology reports at both the case and slide levels, collected from a tertiary medical center. The dataset contains 216 clinical cases, comprising 455 slide-level WSI-report pairs. The dataset underwent a structured multi-stage validation procedure involving board-certified pathologists to ensure reliable annotations. TUM-Uteria supports research in computational pathology, including whole-slide image analysis, multimodal learning, and automated pathology report generation.
[CV-158] SAGE: Synchronized Action-Gaze Recognition and Anticipation for Human Behavior Understanding ECCV2026
链接: https://arxiv.org/abs/2607.04017
作者: Chenyi Kuang,Nakul Agarwal
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026
Abstract:Human object interaction (HOI), gaze pattern, and their anticipation are intricately linked, providing valuable insights into cognitive processes, intentions, and behavior. However, most existing models handle gaze and actions separately, missing both their interdependence and the advantages of a unified solution. This paper presents a novel unified framework, SAGE (Synchronized Action-GazE), which integrates simultaneous recognition and anticipation of both HOI and human gaze into a single unified end-to-end trainable model. Our approach leverages a transformer-based architecture and incorporates gaze data into spatiotemporal attention mechanisms to simultaneously predict current and future human actions and gaze behavior. We explore this bidirectional relationship between gaze and actions under different scenarios, whether requiring a close-up, detailed view (egocentric) or a wider, more contextual view (exocentric), making our framework versatile for various applications. Additionally, due to lack of datasets for comprehensive analysis of both HOI and gaze in exocentric videos, we establish a new benchmark Exo-Cook to facilitate further research in this domain. Experiments on three benchmark datasets: VidHOI, EGTEA Gaze+, and Exo-Cook show that jointly modeling gaze and actions across current and future frames achieves consistently strong results, often surpassing specialized state-of-the-art models tailored to individual tasks. By unifying actions and attention in a comprehensive way, our work lays the groundwork for more intuitive human-machine interaction.
[CV-159] Full Glyph Images Beat Token Embeddings: A Controlled Study for Transformers
链接: https://arxiv.org/abs/2607.03994
作者: Shuyang Xiang,Hao Guan
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Modern language models generally represent text as sequences of discrete token embeddings, an assumption deeply rooted in current practice but rarely questioned. We challenge this representation, especially for Chinese, by replacing index-based token embeddings entirely with a single rasterized image of the character sequence, processed by a vision encoder composed of a shared ResNet and a shallow Vision Transformer. To isolate the role of input representation, we construct a dual-branch controlled framework in which both a Vision-based model and an index-based baseline share an identical decoder backbone, training objective, optimizer, and data curriculum. Any performance difference is therefore attributable to the input modality only. Across all tested decoder backbones, the Vision-based model consistently outperforms the baseline, reaching a peak accuracy of 0.429 versus 0.355 for the index-based baseline,that is, a 21% relative improvement, while converging in about half the number of training epochs. The advantage emerges especially within the first five epochs (under 21% of total data) and persists under moderate character corruption: the corrupted Vision model matches the clean index-based baseline. Ablation studies reveal that the advantage requires both spatially coherent input and a ViT encoder with 2D positional encodings. A cross-script comparison on English shows the advantage does not transfer directly to alphabetic writing systems, suggesting that the uniform visual density and radical structure of Chinese characters are enabling conditions. These findings suggest that transformers are more modality-agnostic than commonly assumed, and that discrete tokenization is not a fundamental requirement for Chinese language modeling.
[CV-160] InSpace: Structure-Aware 3D Indoor Scene Generation from a Single 360° Image ECCV2026
链接: https://arxiv.org/abs/2607.03990
作者: Gwanhyeong Koo,Hyunsu Kim,Youngji Kim,Taejae Lee,Siwoo Lim,Sunjae Yoon,Suyong Yeon,Chang D. Yoo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026
Abstract:Recent advances in single image-to-3D generation have enabled high-quality asset synthesis, yet extending these capabilities to indoor scene generation remains challenging. Existing methods focus on asset-level generation while neglecting the structural layout, which is essential for downstream applications and serves as the spatial anchor for grounding assets. However, a single image with a limited field of view lacks the spatial coverage to recover a coherent global layout. To this end, we use a 360° image represented in equirectangular projection (ERP) and propose InSpace, a structure-aware framework for 3D indoor scene generation. InSpace comprises three stages: (1) estimating partial scene geometry as spatial priors, (2) generating coarse scene structure with view-selective cross-attention, and (3) producing detailed layout and asset geometry with textures through a global-local hybrid attention, using flow matching. We also propose ERP-FRONT, a paired ERP-Image-to-3D indoor scene dataset based on 3D-FRONT. Experiments show that InSpace generates complete 3D indoor scenes with structural layout, along with separate textured assets from a single ERP image, achieving strong performance across 3D and 2D metrics. Project Page: this https URL
[CV-161] DS-SAC: Density Search for Sample Consensus
链接: https://arxiv.org/abs/2607.03972
作者: Suraj Thapa,Muhammad Aminul Islam
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Robust geometric model estimation is a fundamental problem in computer vision. RANSAC and its variants remain widely used for this task; however, they rely on stochastic minimal sampling. In this article, we propose Density Search Sample Consensus (DS-SAC), a deterministic robust estimation framework, that avoids repeated random sampling by searching dense regions. Starting from an initial model estimated from the available points, the method performs local exploration via forward and backward search. To facilitate global exploration, DS-SAC recursively partitions the point set using signed residuals and searches each valid partition for high-consensus models. We show that DS-SAC has polynomial complexity with respect to the number of points, making it an efficient alternative to stochastic consensus-based methods. Experiments on large-scale real-world datasets for homography, fundamental matrix, and essential matrix estimation show that DS-SAC achieves higher AUC scores, competitive or lower median pose errors, and faster runtime compared with widely used robust estimators, including RANSAC, MAGSAC, LO-RANSAC, and GC-RANSAC.
[CV-162] Reward Lightning: Fast Video Generation via Homologous Preference Distillation ECCV2026
链接: https://arxiv.org/abs/2607.03960
作者: Jiaxiang Cheng,Bing Ma,Xuhua Ren,Kai Yu,Peng Zhang,Tianxiang Zheng,Qinglin Lu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV 2026
Abstract:Achieving simultaneous preference alignment and distillation acceleration in video diffusion models remains an open challenge. Existing methods optimize the two objectives over mismatched representation spaces, where improving one objective often compromises the other. To overcome this, we propose Reward Lightning, a unified framework that aligns and accelerates a video diffusion model within a single shared representation. Its central principle is homology: both objectives are evaluated on identical latent features, which mitigates the gradient conflicts that arise when they are optimized over disjoint representations. As a foundational component, we first introduce a latent reward model (LRM) that scores videos directly in the latent space, without decoding back to the pixel space. Building on the LRM, homologous preference distillation (HPD) reuses this shared backbone to perform adversarial distillation and preference alignment jointly, yielding few-step generators that remain faithful and well aligned. Extensive experiments demonstrate that the LRM surpasses pixel-level and latent-level reward baselines by 11.0% and 14.7% in preference accuracy, and that Reward Lightning generates high-fidelity videos in merely 1 to 4 steps, improving the average VBench score by 2.1% while leading in text alignment, motion quality, and visual quality. Project page: this https URL.
[CV-163] ESSERA v2: Scaling Pixel-wise Earth Foundation Models
链接: https://arxiv.org/abs/2607.03949
作者: Zhengpeng Feng,Sadiq Jaffer,Ira Shokar,Jovana Knezevic,Mark Elvers,Clement Atzberger,Robin Young,Aneesh Naik,Niall Robinson,Andrew Blake,David Coomes,Anil Madhavapeddy,Srinivasan Keshav
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Pixel-wise Earth-observation (EO) foundation models are now achieving state-of-the-art performance via generated spatial embeddings. However, how these models scale and how best to spend a pretraining budget remain poorly understood. We present the largest controlled scaling study for EO to date: 395 training runs on 1,024 GH200 superchips within a fixed pixel-wise Barlow Twins family, each evaluated on 15 downstream tasks. We find that pretraining loss barely predicts downstream performance (|Pearson r| 0.2), so selecting models by loss wastes a large share of the compute. We also find that, as the training budget grows, the encoder and the data should grow together while the projector stays fixed, which gives a simple rule for allocating compute. Using this rule, we train a family of pixel-wise models (0.5B and 1B, with a 2B model in training) and distill them into compact students for embeddings-as-data deployment. The 21-million-parameter distilled TESSERA v2-1B-M in aggregate outperforms all open and proprietary models tested, some of which are orders of magnitude larger. These students produce Matryoshka representations that are inexpensive to serve: a 16-dimensional prefix keeps 92% of the full 128-dimensional performance at 1/8 of the storage. Upon completion of training we plan to release v2 global embeddings covering 2017-2025. Together, these results give a concrete, empirically grounded recipe for scaling pixel-wise EO foundation models: train large encoders, select by downstream performance, and distil into flexible student models. All code will be released at this https URL.
[CV-164] A Large-Scale Dataset and a New Method for RemoteSensing Traffic Object Segmentation
链接: https://arxiv.org/abs/2607.03945
作者: Zhigang Yang,Huiguang Yao,Linmao Tian,Qiang Li,Qi Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Remote sensing imagery plays a crucial role in evaluating regional transportation capacity. However, existing segmentation datasets often lack diversity in object categories and scenes, limiting the ability of models to comprehensively evaluate trans portation capacity in real-world scenes. To alleviate this gap, we construct a large-scale and diverse dataset for transportation object segmentation, named as NWPU-Traffic. This dataset encompass four traffic object categories (car, airplane, ship, and train) and a wide range of scenes from 49 cities across 7 countries, with instance-level annotations to ensure precise segmentation of individual objects, which bridges critical shortcomings in resolution and scene diversity in existing datasets. Leveraging this dataset, we establish a benchmark with several popular segmentation networks. Furthermore, we propose a novel segmentation method that leverages spatial-channel preserving feature interaction and an adaptive feature decoder, enabling robust segmentation across varying scales and complex environments. Extensive experiments and ablation studies validate the effectiveness of our approach. The dataset and code are publicly available at this https URL.
[CV-165] EgoInertia-MI: A Multimodal Egocentric Vision and IMU Benchmark for Motor Impairment Assessment
链接: https://arxiv.org/abs/2607.03934
作者: Fatemah Alhamdoosh,Pietro Pala,Abduallah Mohamed,DK Arvind
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Motor impairments, including tremor, bradykinesia, gait abnormalities, and postural instability, are common across many neurological and movement-related conditions. Conventional clinical assessments are often intermittent and may fail to capture subtle temporal variations in motor behavior. While wearable IMUs and third-person video have shown promise for objective motor assessment, third-person recordings raise privacy concerns and require constrained acquisition setups. In contrast, egocentric vision provides a more naturalistic and privacyaware alternative. In this work, we introduce EgoInertia-MI, a multimodal benchmark dataset combining synchronized egocentric video and wearable IMU signals for motor impairment analysis. The dataset contains 19 upper- and lower-body activities performed by healthy volunteers simulating varying levels of motor impairment severity levels: no impairment, mild impairment, and severe impairment. We establish two benchmark tasks: action recognition and motor impairment severity estimation, and evaluate multiple unimodal and multimodal baselines. Experimental results show that egocentric video provides strong cues for motor impairment assessment, while multimodal fusion achieves the best overall performance, reaching 0.78 Macro-F1 for severity estimation and 0.93 Macro-F1 for action recognition. These findings highlight the potential of combining egocentric vision and wearable sensing for ecologically valid and privacy-aware motor assessment. Code and data are available at:this https URL.
[CV-166] NavEYE: Vision-Centered Multi-Sensor Fusion-Based Situational Awareness System for Intelligent Surface Vehicles
链接: https://arxiv.org/abs/2607.03915
作者: Ryan Wen Liua,Junxiong Lianga,Haoyu Wanga,Mengwei Baoa
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:With the rapid development of sensor and artificial intelligence (AI) technologies, intelligent surface vehicles (ISVs) have gained increasing attention from academia and industry. Their intelligence, reliability, and safety depend heavily on situational awareness in complex navigational environments. To achieve high-quality perception, we develop a vision-centered multi-sensor fusion system, named NavEYE, by exploiting complementary sensors, including the automatic identification system (AIS), radar, and RGB camera. Specifically, we first propose a multi-constrained gated data association method (MCGA) to accurately match low-temporal-resolution AIS data with high-temporal-resolution radar data. Their fusion result is then obtained by selectively implementing distance-aware adaptively weighted fusion (DAWF) and timeliness decay-based stitching fusion (TDSF), which reduce the uncertainty caused by AIS or radar data loss in real-world sensing scenarios. Based on accurate and robust visual object detection, we further associate and fuse AIS, radar, and visual data through joint constraints of normalized bearing and distance features. According to the fusion results, comprehensive information related to ships of interest can be automatically obtained, helping enhance situational awareness and reduce collision risk for ISVs. The feasibility, robustness, usability, and effectiveness of the proposed multi-sensor fusion method and situational awareness system are demonstrated through extensive experiments on a real-world sensing dataset collected from AIS, radar, and camera. The experimental results show the superior performance of our fusion method in both quantitative and qualitative evaluations. In addition, the shipboard NavEYE system can promote navigational safety for ISVs in complex and dynamic environments.
[CV-167] USE: A Unified Self-Ensembling Framework for Test-Time Prompt Tuning ICML2026
链接: https://arxiv.org/abs/2607.03900
作者: Siru Jiang,Jian Liang,Ran He,Tieniu Tan
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: ICML 2026
Abstract:Test-time adaptation (TTA) has emerged as a popular paradigm for improving the performance of vision-language models (e.g., CLIP) on downstream tasks. Among existing CLIP-based TTA methods, Test-Time Prompt Tuning (TPT) is a pioneering work that optimizes textual prompts using multiple test-time augmentations and remains a strong baseline to date. In this work, we revisit TPT and reveal that its optimization can be interpreted as implicitly learning from self-generated pseudo labels. Building on this perspective, we propose a unified self-ensembling framework (USE) that ensures consistency between the optimization and inference stages. During optimization, we introduce a simple yet effective self-ensembling (SE) strategy that emphasizes the test image itself over its augmented views adaptively to obtain more reliable pseudo labels. To fully exploit the potential of augmentations, we further apply the same strategy at inference time, unifying the objectives of both stages. Notably, SE can also act as a lightweight optimization-free TTA method. Extensive experiments across multiple datasets demonstrate that SE and USE outperform their counterparts, respectively. Furthermore, SE yields consistent performance gains when integrated with existing TTA methods. The code is available at this https URL.
[CV-168] DICT: Data Injection and Contrastive Trajectory Refinement for Conditional Image Generation with Diffusion Models ECCV2026
链接: https://arxiv.org/abs/2607.03899
作者: Chunnan Shang,Xin Zhang,Zhizhong Wang,Hongwei Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV 2026
Abstract:Diffusion models have become a dominant paradigm for conditional image generation, yet existing approaches generally follow two directions: task-specific designs that can improve performance but limit generalization, and training-free loss guidance that compresses rich conditions into scalar objectives and applies stepwise guidance, leading to information bottlenecks and error accumulation along the sampling trajectory. Given the urgent need for an effective unified framework across diverse conditional image generation tasks, we propose Data Injection and Contrastive Trajectory Refinement (DICT), a training-free inference method that enhances conditional image generation without introducing task-dependent architectures. DICT introduces Data Injection, where noise-perturbed conditional signals are integrated into early denoising stages; by performing guided denoising on these injected signals, DICT adaptively selects and distills task-salient information from the raw condition, effectively preserving spatial richness and ensuring precise condition-to-generation alignment. Furthermore, DICT applies Contrastive Trajectory Refinement across adjacent denoising states, enabling pairwise comparisons that progressively improve sample quality. These designs keep inference simple while improving cross-task transfer under a unified diffusion formulation. Extensive experiments on conditional image generation tasks (e.g., style transfer, image super-resolution, and image deblurring) show consistent gains in fidelity and perceptual quality over representative task-specific and loss-guided baselines.
[CV-169] BAT3R: Bootstrapping Articulated 3D Reconstruction from 2D Image Collections ECCV2026
链接: https://arxiv.org/abs/2607.03891
作者: Jakub Zadrozny,Oisin Mac Aodha,Hakan Bilen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026. Project page: this https URL
Abstract:3D reconstruction of articulated objects from a single image is challenging because large training datasets with paired image and 3D supervision are difficult to obtain. Recent point map-based methods achieve strong performance but rely on synthetic datasets rendered from manually created articulated 3D assets with carefully curated pose distributions. While camera viewpoints can be easily sampled, generating realistic object articulations remains costly and labor-intensive. We propose a training framework that reduces this requirement by leveraging unannotated 2D images collections with only a single rigged canonical mesh per category. Starting from a weak 3D shape predictor trained on canonical-pose renders, we iteratively estimate object articulation and camera pose by fitting the mesh to predicted point maps. The recovered articulations and viewpoints are then used to render updated synthetic training data, progressively improving the predictor. Despite using substantially weaker 3D supervision, our models achieve performance comparable with DualPM, which requires manually curated articulated training datasets.
[CV-170] SGF-CDNet: A Consistency-Discrepancy Graph Network over Semantic-Geometric Fused Nodes for Face Forgery Detection ICME
链接: https://arxiv.org/abs/2607.03883
作者: Jiayao Jiang,Bin Liu,Nenghai Yu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, 4 figures. This work has been accepted at ICMEW 2026
Abstract:The rapid advancement of deepfakes necessitates robust face forgery detection. Although forged faces may lack obvious artifacts, they often contain subtle disharmony among different facial regions. We propose SGF-CDNet, a Consistency-Discrepancy Graph Network (CD-GNN) over Semantic-Geometric Fused (SGF) nodes. First, SGF-CDNet constructs SGF nodes by deeply fusing semantic regions from face parsing with geometric information from facial landmarks, allowing nodes to capture both high-level concepts and precise geometric constraints. Next, a dual-path CD-GNN performs parallel relational reasoning on these nodes across two dimensions: consistency and discrepancy. The consistency path evaluates if facial components follow natural biological patterns, while the discrepancy path mines for structural tensions and feature conflicts introduced by forgeries. By integrating these processes, our model effectively identifies disharmonious relationships between facial components. Extensive experiments on public datasets demonstrate that SGF-CDNet achieves superior performance, establishing it as a reliable solution for face forgery detection.
[CV-171] MACRO: Training-free Multi-plane Attention for Closeup Render Optimization
链接: https://arxiv.org/abs/2607.03875
作者: Nitzan Hodos,Roy Amoyal,Lior Fritz,Ianir Ideses,Sagie Benaim,Netalee Efrat
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Close-up rendering, zooming into a scene well beyond any training camera, is important for virtual production and interactive 3D content, yet remains an open challenge. 3D Gaussian splatting (3DGS) enables high-fidelity, real-time novel view synthesis, but its rendering quality degrades at close range. Recent diffusion-based methods that enhance the rendering by conditioning on reference images from the training set produce significant artifacts in this setting. We analyze this failure and identify its root cause: the scale gap between the close-up and reference views. We show that the features in reference-conditioned enhancement models are not scale-invariant, causing cross-view attention to retrieve incorrect correspondences when the same content appears at different scales, and that this mismatch cannot be corrected in latent space because the VAE encoder is not scale-equivariant. Building on this analysis we introduce MACRO, Multi-plane Attention for Closeup Render Optimization, a training-free method for high-quality close-up novel view synthesis from 3DGS. MACRO resolves the scale gap by leveraging the scene’s known 3D structure: it decomposes the close-up into depth planes, crops and resizes references in image space to match the scale of each plane before encoding, and applies a depth-aware attention mask so each token attends only to scale-matched references. The method requires no architectural changes or additional training. We further contribute two new close-up novel view synthesis benchmarks, the first standardized evaluation protocol for this setting, and demonstrate state-of-the-art results on both, outperforming existing 3DGS and diffusion-based methods on both reconstruction and perceptual metrics. Project page: this https URL
[CV-172] SharpSplat: Edge-Regularized 3D Gaussian Splatting for High Fidelity Urban Building Reconstruction from UAV images
链接: https://arxiv.org/abs/2607.03872
作者: Porus Vaid,Shivam Chopra,Vaibhav Kumar
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at IEEE International Geoscience and Remote Sensing Symposium (IGARSS) - 2026
Abstract:Reconstructing high-fidelity 3D building models from UAV imagery is essential for large-scale digital twin development. However, existing 3D Gaussian Splatting (3DGS) techniques often struggle with building facades, failing to capture sharp geometric transitions. To address this, we propose a semantic edge regularization framework that supervises 3DGS to produce crisp architectural boundaries. Our method leverages SAM 3 to generate precise building masks, from which we extract architecturally significant edges. During training, we align rendered image gradients with these extracted edges, forcing the Gaussians to converge into sharp structural geometries. Evaluations across campus environments, dense urban centers, and custom residential datasets demonstrate significant improvements in edge fidelity without requiring architectural modifications to the 3DGS pipeline. Our approach proves robust across diverse building types, roof geometries, and urban densities.
[CV-173] GeoSelect: Spatial-Program Execution for Training-Free Referring Remote Sensing Image Segmentation
链接: https://arxiv.org/abs/2607.03869
作者: Yuhang Jiang,Guohui Deng,Miaozhong Xu,Chao Ruan,Jinling Zhao,Linsheng Huang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 20 pages, 10 figures, 9 tables
Abstract:Referring remote sensing image segmentation isolates the object named by a natural-language expression in an aerial image. Existing training-free methods resolve the expression through implicit vision-language activations or region-text similarity, which gives weak control over the spatial, comparative, and ordinal relations that dominate aerial referring: they cannot represent constructions such as the largest ship or the second court from the left. We propose GeoSelect, a training-free pipeline that reframes referring as the execution of a typed spatial program. A frozen, text-only language model synthesises the expression into a small domain-specific language, a well-formedness checker accepts the program, and a deterministic executor runs it. The central abstraction is a single scored candidate set type under which every operator composes: continuous geometric fields realise position and proximity as dense pixel-level maps, while discrete set and order operators add the extremum, ordinal, counted-union, and relational constructions that fields alone cannot express. Because execution is explicit, every intermediate program, field, and ranking is inspectable, and a reliability ladder degrades any failing program to a field-only special case, so every expression returns an answer. GeoSelect attains 58.86 mIoU on RRSIS-D test and 55.27 mIoU on RISBench test, more than twice the best prior training-free method on RRSIS-D, with no referring supervision and on a single GPU. A controlled comparison with candidates and segmenter fixed attributes the gain to explicit execution, not the backbone; an oracle decomposition localises the residual gap to detection recall on RRSIS-D and selection on RISBench, and an exposure audit confirms robustness to pretraining leakage. Code will be released upon acceptance at the project page this https URL.
[CV-174] Ghosts Beneath Textures: Texture-Relation Cues for Cross-Paradigm AI-Generated Image Detection
链接: https://arxiv.org/abs/2607.03862
作者: Haoyu Wang,Yiming Qin,Zhongjie Ba,Ziping Dong,Jishen Zeng,Peng Cheng,Kui Ren
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:AI-generated images have proliferated rapidly, motivating extensive research. Most existing AI-generated image detectors are developed and evaluated under image-free generation paradigms, such as noise-based or text-guided generation. However, image-conditioned generation has become increasingly important in practical applications, as it enables more fine-grained control over generated content. Detecting AI-generated images across these two paradigms creates a critical cross-paradigm detection problem that has long been overlooked. To study this problem, we construct ConImageGen, a benchmark for cross-paradigm AI-generated image detection. Evaluations on ConImageGen show that existing detectors fail to generalize reliably across image-free and image-conditioned generation. To address this failure, this paper identifies a cross-paradigm forensic cue and provides a new perspective for generalized AI-generated image detection. Specifically, by suppressing semantic interference, we visualize, for the first time, semantics-irrelevant texture patterns across generation paradigms. These patterns exhibit structured local-global texture relations, indicating a generalizable form of forensic evidence. Motivated by this finding, we shift the focus from directly exploiting explicit artifacts to modeling texture relations and propose DTS-Det, a detection framework that captures and leverages such relations for generalized AI-generated image detection. Extensive experiments validate the effectiveness of our method. DTS-Det achieves state-of-the-art performance across diverse evaluation settings, reaching 99.6% ACC on ConImageGen with a 10.5% gain over the best baseline. It also achieves 93.2%/94.1% ACC in cross-dataset evaluation on PicoBanana/RAID and maintains detection rates of 95.2%/88.1% under reconstruction attacks and black-box adversarial attacks, respectively.
[CV-175] PRISM3D: Probabilistic Refinement and Robust Initialization for Physically Consistent Scene Modeling under Extreme Motion Blur ECCV-26
链接: https://arxiv.org/abs/2607.03855
作者: Gopi Raju Matta,Reddypalli Trisha,Vemunuri Divya Madhuri,Kaushik Mitra
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV-26
Abstract:We address the inverse problem of blind 3D scene reconstruction from extremely motion-blurred images, a scenario where traditional Structure-from-Motion (SfM) pipelines fail. Existing approaches typically circumvent this bottleneck by relying on impractical sharp-image supervision. In this work, we introduce PRISM3D, a unified framework enabling robust reconstruction directly from severely degraded inputs. To overcome the lack of a reliable starting point, we propose a Robust Initialization strategy utilizing deep dense tracking method (VGGSfM) to recover global topology where feature matching fails. To the best of our knowledge, we are the first to effectively leverage this paradigm to bootstrap 3D Gaussian Splatting from extreme motion blur. However, while robust, this initialization yields sparse and noisy geometry that causes deterministic optimization to diverge. To resolve this, we propose a coupled solution driven by probability and physics: we adopt a probabilistic formulation for geometric densification via Markov Chain Monte Carlo (MCMC) to robustly populate the sparse priors, while simultaneously modeling physical image formation via continuous Bezier Trajectories. Furthermore, while PRISM3D establishes a highly robust standalone pipeline, the availability of complementary event streams offers an opportunity to push the reconstruction fidelity further. To exploit this, we introduce PRISM3D-E, a multi-modal (RGB + Events) extension that seamlessly integrates high-temporal-resolution events as structural priors to maximize geometric recovery. Because existing datasets lack paired event streams under such severe degradation, we concurrently contribute the PRISM3D-E Benchmark to facilitate rigorous evaluation. Extensive experiments demonstrate that both our standalone RGB framework and its multi-modal extension establish new state-of-the-art performance.
[CV-176] CogRad: A Cognitively-Inspired Multi-Agent Framework for Radiology Report Generation
链接: https://arxiv.org/abs/2607.03853
作者: Saif Ur Rehman Khan,Hasaan Maqsood,Sebastian Vollmer,Andreas Dengel,Muhammad Nabeel Asim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Automated radiology report generation (RRG) can ease radiologist workload, yet most existing systems produce a report in a single forward pass, with no mechanism to check a claim against the image or revisit a finding once stated. We present CogRad, a cognitively inspired multi-agent framework that structures generation around four stages of a radiologist’s reading process. A Scout agent discovers anatomical regions directly from image patches via slot attention and assigns region and disease-level triage scores; an Investigator agent concentrates representational capacity on the regions Scout flags as suspicious; a Writer agent compiles these signals into a disease gated visual prefix for a large language model; and a Verifier agent supervises training with a visual entailment loss and, at inference, re-examines its own draft sentence by sentence, regenerating any report it judges insufficiently grounded. On CheXpert Plus, CogRad attains a BLEU-4 of 0.316 and a CIDEr of 0.322, the best scores among the methods we compare against. On IU X-Ray, it attains a BLEU-4 of 0.201 and a CIDEr of 0.724, leading every baseline on every standard NLG metric. We further evaluate CogRad with RadGraph F1, CheXbert F1, and a hallucination analysis to assess clinical accuracy beyond standard text-overlap metrics, complemented by ablation studies and Grad-CAM-based visualizations that characterize each agent’s contribution and the model’s visual grounding.
[CV-177] ContiStain: Cross-Domain Relation-Preserving Distillation for Continual Multi-Domain Virtual IHC Staining MICCAI2026
链接: https://arxiv.org/abs/2607.03851
作者: Fuqiang Chen,Yifeng Wang,Hongpeng Wang,Yongbing Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注: Accepted at MICCAI 2026
Abstract:A unified multiplex virtual staining model enables scalable and non-destructive multiplex analysis from HE slides while promoting parameter efficiency, shared pathological knowledge, and consistent cross-biomarker representations. However, in clinical practice, data for new biomarkers are typically acquired sequentially over time. Fine-tuning on such temporally arriving data leads to severe performance degradation on previously learned biomarkers, as sequential optimization disrupts the structured relationships among biomarker representations in the latent space. To address this issue, we propose ContiStain, an IHC multi-domain relational distillation framework for continual virtual staining. We first (i) construct a domain-aware structured feature space using a mixture-of-experts (MoE) feature extractor to reduce representation interference across biomarker domains. Based on this stabilized feature space, we then (ii) propose a relation-preserving distillation strategy that explicitly enforces the consistency of cross-domain token-level cosine similarity matrices between learned biomarker domains during continual adaptation. By maintaining cross-domain structural coherence, ContiStain mitigates forgetting while retaining adaptability to new domains. Experiments on the MIST dataset under a four-domain sequential virtual IHC staining setting show improved stability, reducing FID and ConchFID by 11.1 and 60.9 compared to sequential fine-tuning, enabling scalable and robust multi-domain virtual staining. Code is released at this https URL.
[CV-178] How Do Diffusion Classifiers Decide? A Bias-Centric Evaluation
链接: https://arxiv.org/abs/2607.03831
作者: Saba Fathi,Fardin Ayar,Maryam Abdolali,Ehsan Javanmardi,Manabu Tsukada,Mahdi Javanmardi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Diffusion models have recently been repurposed for zero-shot classification, giving rise to diffusion classifiers that identify the best-matching text prompt by minimizing the noise-prediction error. Despite their growing adoption, how these models make classification decisions remains poorly understood. We introduce ASOB-Bench, a bias evaluation for diffusion classifiers along three dimensions: Attribute binding, Size-Order bias, and Background dependency. These dimensions serve not as an exhaustive taxonomy but as targeted probes of how the text-conditioned reconstruction-error score reaches a decision. Such a perspective is well studied for discriminative vision-language models, yet remains overlooked for diffusion classifiers. Extending an existing framework with five new attribute categories on newly constructed datasets, we find diffusion classifiers are less prone to attribute misbinding than an OpenCLIP baseline; on the established ComCo benchmark they are substantially more susceptible to size-order shortcuts; and on ImageNet-B they suffer far larger accuracy drops, revealing heavy reliance on background over foreground cues. Reconstruction-error heatmaps and U-Net cross-attention visualizations expose the mechanism behind each bias. Because diffusion classifiers share the same denoiser as text-to-image models, these single-pass diagnostics also point toward analogous failure modes in generation. Overall, diffusion classifiers exhibit a distinct bias profile from vision-language models, offering guidance for building more robust diffusion-based models.
[CV-179] Q-TriM: Question-Guided Tri-Modal Attention for Audio-Visual Question Answering ECCV2026
链接: https://arxiv.org/abs/2607.03825
作者: SungHun Kim,SeungJun Baek
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted at ECCV 2026
Abstract:Audio-Visual Question Answering (AVQA) extends classical VQA by requiring joint reasoning over video and synchronized audio. However, many AVQA systems rely on deeply stacked layers of self- and cross attention across text, video, and audio. Such sequential stacking may incur loss of information such as subtle inter-modal cues over the layers, causing errors to accumulate across sequential attention layers during the fusion. We introduce Q-TriM which performs multi-modal fusion in a shallow and parallel manner instead of a deep and sequential manner. For Q-TriM, we propose a novel framework for attention operation incorporating video and audio conditioned on text. As a result, we obtain not only standard cross attention outputs but also Tri-Modal Attention representations in which Query, Key, and Value come from distinct modalities. These attention representations are combined in parallel at a single stage, thus avoiding the multi-modal fusion with deep stacks in order to mitigate error accumulation and depth-induced issues. Q-TriM achieves state-of-the-art performance on three AVQA benchmarks, including substantial gains on MUSIC-AVQA-R, which demonstrates its robustness and out-of-distribution generalization. Code is available at this https URL
[CV-180] FDR-Occ: Factorized Dense Routing for Full-Spectrum 3D Occupancy Prediction ECCV2026
链接: https://arxiv.org/abs/2607.03822
作者: Dubing Chen,Huan Zheng,Tianyi Yan,Yucheng Zhou,Runzhou Tao,Zhongying Qiu,Jianfei Yang,Jianbing Shen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026
Abstract:Vision-based 3D occupancy prediction fundamentally relies on the 2D-to-3D view transformation. Current paradigms predominantly utilize explicit physical projection, which artificially restricts the routing matrix to strict, sparse camera rays. While computationally efficient, this imposes a severe Locality Bottleneck, preventing the network from constructing holistic contextual understanding and degrading sharply when camera extrinsics are unreliable or absent. To break this bottleneck, we abstract view transformation as unconstrained bipartite routing and propose Factorized Dense Routing (FDR). By approximating dense 2D-to-3D mixing through hierarchical tensor contractions, FDR guarantees a fully-global receptive field with tractable, sub-quadratic complexity. Crucially, the mandatory spatial contraction in dense routing exposes a fundamental Resolution-Context Trade-off. To address this, we introduce a Resolution-Context Decoupled Architecture. We factorize the 3D space into a global macroscopic topological anchor (via FDR) and precise local geometric planes (via explicit projection). This decoupling enables global semantic inference and exact surface localization to complement each other without mutual compromise. Extensive experiments demonstrate that our framework achieves state-of-the-art performance on the Occ3D-nuScenes and Occ3D-Waymo benchmarks. More notably, in an uncalibrated setting where physical extrinsics are withheld, our global routing internalizes the implicit multi-camera rig topology and exhibits substantially stronger structural robustness than physical-projection baselines under the same protocol.
[CV-181] RISTAR: Triple-Signal Stair Recognition and Vision-Only Indoor Navigation for Search-and-Rescue Micro-UAVs
链接: https://arxiv.org/abs/2607.03818
作者: Octavian Gîngu,Stelian Spînu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 7 figures. Project repository available at: this https URL
Abstract:Indoor search-and-rescue (SAR) operations often require rapid situational awareness where GNSS signals are unavailable and human access is difficult or hazardous. While most autonomous aerial systems rely on LiDAR, stereo vision, or specialized depth cameras, such solutions increase both hardware complexity and deployment costs. This paper presents a complete autonomous indoor navigation framework for low-cost unmanned aerial vehicles based exclusively on monocular vision. Implemented on a DJI Tello platform, the system combines monocular depth estimation using Depth Anything V2 with classical computer vision and lightweight deep learning models for scene understanding, victim detection, and hazard recognition. The framework consists of two independent behaviors: (i) corridor exploration with automatic door detection, room entry, OCR-based room identification, and victim inspection; and (ii) autonomous stair ascent based on TRISTAR (TRI-Signal STair Ascent Recognition), a novel triple-sensor fusion method that integrates structural cues (Sobel filtering), texture analysis (multi-scale Gabor filtering), and geometric depth from monocular depth estimation. Evaluation used real indoor flights in a university building. Depth calibration reduced relative depth error from 27.4% to below 10%, while the door detection algorithm reached a precision of 0.93 and an F1-score of 0.91. A dedicated ablation study shows that multi-sensor fusion significantly improves stair-recognition robustness compared to individual sensing modalities, and a failure-case analysis delineates the limits of monocular perception under challenging lighting and reflective surfaces. The results demonstrate that reliable indoor exploration and stair traversal are achievable on resource-constrained platforms without specialized ranging hardware, a practical, cost-effective solution for rapid SAR deployment.
[CV-182] Global Logic and Local Search: Dual-Stream Multimodal In-Context Learning for Verifiable Industrial Anomaly Detection ECCV2026
链接: https://arxiv.org/abs/2607.03817
作者: Runzhi Deng,Yundi Hu,Yiming Zhong,Zhao Wang,Xixi Liu,Hongsong Wang,Caifeng Shan,Fang Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV 2026. 18 pages, 3 figures, 6 tables
Abstract:Large Multimodal Models (LMMs) show strong few-shot generalization, but industrial anomaly detection remains difficult because defects are small, input resolution is limited, and textual standards are not always grounded in visual evidence. Recent optimization-based methods improve alignment through fine-tuning, but they often require many defective samples, which are unavailable in early deployment. We present Global Logic and Local Search (GLLS), a training-free framework for reference-guided multimodal in-context verification. GLLS uses a Part-Aware Visual-Logical Atlas to organize normal references and structured specifications in the inference context. It combines a Global Logic Stream, where SAM 3 extracts partially checkable visual facts, with a Fine-Grained Actions Stream, where MCTS selects local evidence crops under a fixed budget. Experiments on MMAD-QA and additional anomaly detection datasets show consistent gains over matched and general-purpose baselines, while keeping the final diagnostic decision traceable to explicit visual evidence throughout the inspection trace.
[CV-183] stMate: Test-Time Domain Adaptation Aided by Lightweight Vision Foundation Model
链接: https://arxiv.org/abs/2607.03810
作者: Dimitrios Fotiou,Vasileios Mygdalis,Ioannis Pitas
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Test-Time Domain Adaptation (TTDA) aims to adapt Deep Neural Networks to distribution shifts using only streaming, unlabeled test data in real time. Current methods for semantic segmentation tasks suffer from critical limitations. Entropy minimization techniques require costly backpropagation, risking catastrophic forgetting and producing noisy segmentation boundaries. Memory-bank methods, while backpropagation-free, exhibit slow adaptation, requiring numerous samples to converge and struggle to handle continuous domain shifts. We introduce TestMate, a novel, real-time, and backpropagation-free TTDA framework that overcomes these issues. TestMate leverages generalization capability of a lightweight Visual Foundation Model to guide the adaptation. We use a zero-shot instance segmentation YOLOv8-seg based model to generate unlabeled mask proposals for objects and their parts at multiple scales in real time. These proposals are fused with the primary model via a heuristic, size-ordered competitive scheme, where small, high-confidence regions dominate and refine predictions in surrounding larger, less certain areas. This paremeter-free mechanism enables immediate adaptation from the first frame, inherently avoids catastrophic forgetting and effectively preserves fine object details and boundaries, even for small objects. TestMate can be used as a standalone, efficient refinement module or seamlessly integrated into existing TTDA methods to significantly boost their performance. We demonstrate state-of-the-art results across two benchmark datasets, proving TestMate’s effectiveness in three distinct adaptation tasks: TTDA, Source-Free Domain Adaptation (SFDA), and online-TTDA. Code is available.
[CV-184] CineMobile: On-Device Image-to-Video Diffusion for Cinematic Camera Motion Generation
链接: https://arxiv.org/abs/2607.03803
作者: Xuyao Huang,Zelai Deng,Xu Wang,Xizhong Xiao,Zhijie Deng
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:The growing demand for image-to-video creation on mobile devices has increasingly focused on cinematic motion effects like bullet time, dolly zoom, slow motion, etc. While Diffusion Transformers (DiTs) exhibit strong performance in video generation, their large parameter sizes and multi-step iterative denoising processes lead to substantial computational overhead, making efficient generation on mobile devices challenging. We propose CineMobile to bridge the gap. In particular, CineMobile adopts a three-fold optimization strategy: (1) leveraging a distillation-guided pruning approach to derive a compact yet efficient model that retains the essential video generation capabilities required for cinematic effects; (2) optimizing the compressed model into a 4-step generator via a combination of diffusion distillation and reinforcement learning; (3) employing a hybrid post-training quantization strategy to compress the model footprint to under 1 GB. Experimental results show that compared to the teacher model with the Wan 2.1 architecture, CineMobile achieves a 40x speedup in generation while maintaining comparable visual quality. Specifically, CineMobile generates 49-frame 480p videos with a per-step denoising latency of 0.6s on an NVIDIA H200 GPU and 20s on the MediaTek Dimensity 8400 Ultimate 5G platform, with a peak memory usage of 1.8 GB, demonstrating its practical applicability for mobile-based image-to-video creation.
[CV-185] Foundations of Equivariant Deep Learning: Unifying Graph and Sheaf Neural Networks ICML2026
链接: https://arxiv.org/abs/2607.03798
作者: Yoshihiro Maruyama
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ICML 2026 as a spotlight paper with oral presentation
Abstract:Symmetry is everywhere in nature and society. Geometric deep learning exploits symmetries in data to improve the performance and efficiency of deep learning systems. In this paper, we extend geometric deep learning to utilize richer symmetry structures. Specifically, we develop order-equivariant neural networks (OENN), which generalize standard graph message passing and sheaf neural networks via the theory of equivariant bundles over face posets (face categories). We (i) characterize all linear order-equivariant maps, (ii) build OENN layers, and (iii) prove universal approximation theorems (UATs) for continuous order-equivariant maps, which are new results even when restricted to sheaf neural networks (for which no UAT was known before). We illustrate the framework on graph and sheaf models. Our results can also be seen as extending the known UAT for graph neural networks to a more general setting that subsumes sheaf neural networks as well. In addition, we show that OENN can be extended further to CENN, Category-Equivariant Neural Network, which gives the general form of equivariant neural networks as well as of equivariant universal approximation theorems, allowing us to leverage categorical symmetry in data (e.g., non-invertible symmetries on multiple objects with compositional relations on those symmetries).
[CV-186] Probabilistic Robustness in Medical Image Classification
链接: https://arxiv.org/abs/2607.03797
作者: Yi Zhang,Siddartha Khastgir,Xingyu Zhao
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注:
Abstract:Deep learning (DL) has shown strong performance in medical image classification, but its trustworthy deployment remains challenging in safety-critical clinical settings, where prediction errors under perturbations may lead to severe consequences. Existing studies mainly focus on adversarial robustness (AR) from a worst-case perspective; however, such settings may be less representative of real medical applications. In this work, we investigate probabilistic robustness (PR) as a more practical measure of model trustworthiness. To this end, we construct a set of natural corruption settings for medical image classification and systematically evaluate commonly used DL models on MedMNIST v2 dataset. Our study provides a statistically grounded perspective on assessing the trustworthiness of DL models, thereby supporting their more trustworthy deployment in medical imaging applications.
[CV-187] InfraNet: Quality-Aware RGB Guidance for Efficient Infrared Object Detection
链接: https://arxiv.org/abs/2607.03795
作者: Zichao Feng,Haodong Zhu,Jingying Yang,Sheng Xu,Yangyang Ren,Yuguang Yang,Xuhui Liu,Juan Zhang,Tian Wang,Linlin Yang,Baochang Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Robust object detection under adverse visual conditions remains a long-standing challenge for multi-modal perception systems. Existing fusion-based methods typically require both RGB and infrared (IR) inputs, and treat them equally during both training and inference, which compromises their robustness when the RGB modality becomes unreliable or unavailable. In this case, we propose \textbfInfraNet, an IR-centric quality-aware framework that regulates RGB guidance during training and supports flexible RGB–IR or IR-only deployment. InfraNet employs an asymmetric architecture where the primary IR pathway extracts multi-scale infrared features for predictions, while the auxiliary RGB pathway provides reliability-controlled supervisory signals. The core of InfraNet is \textbfQualGate, a quality-aware fusion module that learns a task-oriented control signal to suppress unreliable RGB guidance and compensate IR features during cross-modal training. Built upon InfraNet, we design two architectural variants: a lightweight IR-only architecture InfraNet-IR and an RGB–IR architecture InfraNet-RGB-IR. Our method is evaluated through extensive experiments on four benchmark datasets (LLVIP, FLIR-Aligned, M ^3 FD, and DroneVehicle), showing strong or competitive accuracy in challenging low-light and adverse weather conditions. Notably, InfraNet maintains high efficiency in IR-only inference, making it both accurate and computationally efficient.
[CV-188] From Region Arrival to Instance-Level Grounding in Vision-and-Language Navigation
链接: https://arxiv.org/abs/2607.03792
作者: Xiangyu Shi,Ruoxi Yang,Wei Tao,Jiwen Zhang,Yanyuan Qiao,Qi Wu
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-and-Language Navigation (VLN) agents may satisfy conventional success criteria while still failing to establish reliable object-level grounding, because current evaluation protocols mainly reward stopping within a 3-meter radius and largely ignore the agent’s final orientation and target visibility. We formalize this limitation as the Last-3-Meter Grounding Gap and introduce three instance-centric metrics to quantify proximity precision, target visibility, and final-view grounding. To mitigate this gap, we propose REALM (Region-to-Entity Alignment for Last-3-Meter Navigation), a plug-and-play, architecture-agnostic refinement module that decouples fine-grained target approaching from long-horizon navigation. REALM uses a visibility-aware stopping strategy to reduce premature termination and improve final viewpoint alignment. We further construct REVERIE-AIM, which provides object-instance-level goals and 180K short-horizon training samples for final-stage target approaching. Extensive evaluations across four diverse VLN backbones show that REALM consistently improves proximity precision and visual grounding success, demonstrating its broad applicability.
[CV-189] G2TAM: Geometry Grounded Track Anything Model ICML2026
链接: https://arxiv.org/abs/2607.03789
作者: Chenming Zhu,Peizhou Cao,Jingli Lin,Wenbo Hu,Yunlong Ran,Jiangmiao Pang,Tai Wang,Xihui Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ICML 2026. Project Page: this https URL
Abstract:Human spatial understanding arises from jointly perceiving geometry and semantics, enabling consistent object identification and localization across viewpoints and time. Current video segmentation models depend on explicit object appearance memory banks for instance tracking, yet they remain vulnerable to large viewpoint changes and long-term occlusions. Leveraging the spatial consistency afforded by modern feed-forward 3D reconstruction models, we propose the Geometry Grounded Tracking Anything Model (G ^2 TAM), a unified framework for promptable instance tracking in 3D using only unordered RGB images or videos. G ^2 TAM employs spatially aligned geometric representations as implicit memory, ensuring stable instance identity and localization across frames and views. At its core is a cross-modal spatial encoder that integrates visual and textual prompts into a shared geometric space, enabling end-to-end spatial reconstruction and instance-consistent mask prediction. To support training and evaluation, we construct InsTrack, a large-scale dataset with a dedicated validation split for benchmarking. Extensive experiments show that G ^2 TAM delivers strong cross-view consistency, promptable instance spatial tracking, video object segmentation and spatial reconstruction, establishing a foundation for interactive, geometry-grounded spatial reasoning.
[CV-190] Rethinking Depth Pruning for Vision Transformers: A Heterogeneity-Aware Perspective
链接: https://arxiv.org/abs/2607.03784
作者: Zhenfeng Su,Kang Zhao,Han Bao,Tao Yuan,Zhongzhe Hu,Xianzhi Yu,Wenxuan Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:While prior studies have successfully compressed vision Transformers (ViTs) through various pruning techniques, most have concentrated on width pruning to achieve significant reductions in model size. Depth pruning, which removes entire layers from a ViT, is notoriously difficult for accuracy recovery despite its potential to deliver higher speedups, limiting the acceleration achieved by existing joint width-and-depth pruning methods. In this work, we reveal that the failure of existing depth pruning methods lies in their neglect of heterogeneity between different layers, and we introduce HetDPT, a heterogeneity-aware depth pruning method that avoids dimension mismatch. Comprehensive experiments on ImageNet-1K, CIFAR-100, COCO, and ADE20K validate our method: HetDPT achieves a 1.58 \times speedup for DeiT-B while maintaining accuracy and a 1.39 \times speedup for DeiT-S with nearly no accuracy degradation. Furthermore, when combined with width pruning, HetDPT+ sets a new state-of-the-art record in extreme ViT pruning, enhancing the acceleration ratio from 4.24 \times to 5.19 \times for the Isomorphic-Pruning-2.6G configuration while maintaining near-lossless accuracy; our code is available at this https URL.
[CV-191] City-Level 3D Surface Reconstruction with Viewpoint Orientation Partitioning and Scene Completion ECCV2026
链接: https://arxiv.org/abs/2607.03771
作者: Liang Han,Wenyuan Zhang,Junsheng Zhou,Yu-Shen Liu,Zhizhong Han
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV 2026
Abstract:Multi-view 3D surface reconstruction is a longstanding challenge in computer vision. Although recent large-scale reconstruction methods based on 3D Gaussian Splatting (3DGS) achieve impressive novel-view synthesis, producing high-quality surfaces over large scenes remains difficult, due to complex geometry, long optimization, and limited memory. In this paper, we propose a novel yet simple partitioning method to efficiently and faithfully reconstruct large-scale scene surfaces. Our key insight lies in a scene partitioning method based on viewpoint orientation. This partitioning approach ensures that views with similar orientations are jointly involved for more accurate depth estimations, leading to precise surface reconstructions and balanced computation on multiple GPUs in parallel. In addition, we propose a strategy to detect and repair missing regions in the initial point cloud caused by sparse viewpoints or insufficient textures, thereby further improving the geometric quality. Extensive experiments on the GauU-Scene, MatrixCity, and UrbanScene3D datasets demonstrate that our method outperforms the state-of-the-art approaches in surface reconstruction for large-scale scenes. Project page: this https URL.
[CV-192] Self-Improving Diffusion Classifiers with Minority Preference Optimization ECCV2026
链接: https://arxiv.org/abs/2607.03770
作者: Hyunsoo Kim,Jungmyung Wi,Soobin Um,Donghyun Kim,Suhyun Kim
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: ECCV 2026
Abstract:Prior studies have demonstrated that diffusion classifiers achieve robust zero-shot classification performance. However, their effectiveness is strongly tied to the pretraining data distribution: they perform well in majority, high-density regions of the data manifold, but are significantly less accurate in minority, low-density regions. Although prior works on minority sampling have focused on generating more minority-like images, what minority sampling fundamentally enables beyond generation remains underexplored. In this paper, we reveal a direct relationship between minority sampling in generation and the perception capability of diffusion classifiers. Specifically, we show that enhancing minority sampling broadens the coverage of underrepresented regions on the data manifold, thereby improving diffusion-based recognition. To exploit this connection, we propose \textitSelf-Improving Diffusion Classifiers with Minority Preference Optimization (MiPO), which fine-tunes a pretrained diffusion model using minority preference rewards. Using only arbitrary caption data, MiPO generates candidate samples, rewards those that better cover minority regions, and optimizes the model with LoRA and Group Relative Policy Optimization, without additional image data, external foundation models, or external reward models. This enables stable, prompt-adaptive minority sampling and translates low-density generative coverage into improved zero-shot diffusion classification. To sum up, we show that diffusion classifier perception is biased toward majority regions, demonstrate that this bias can be alleviated through minority preference optimization, and evaluate MiPO on five standard datasets.
[CV-193] Sparse-View Surface Reconstruction using Gaussian Splatting through High-Confidence Depth Propagation with Normal Priors ECCV2026
链接: https://arxiv.org/abs/2607.03765
作者: Liang Han,Bangcai Wei,Junsheng Zhou,Yu-Shen Liu,Zhizhong Han
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV 2026
Abstract:3D reconstruction from sparse views is a challenging task in 3D computer vision. Recent studies on 3D Gaussian Splatting (3DGS) have achieved remarkable results with sparse views in novel view synthesis, yet reconstructing high-quality geometric surfaces from sparse views remains a challenge, due to the limited geometry clues and the discreteness of Gaussians. In this paper, we propose a novel 3DGS-based method for high-fidelity surface reconstruction from sparse views. Our key insight is to introduce a normal-guided depth propagation approach, which can extend depth information from high-confidence regions to constrain the depth in low-confidence areas. Additionally, we propose an abnormal depth edge-aware regularization to address depth discontinuities caused by the discreteness of Gaussians. Extensive experiments on DTU and Tanks-and-Temples datasets demonstrate that our method outperforms the state-of-the-art methods in sparse view surface reconstruction. Project page: this https URL.
[CV-194] GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation
链接: https://arxiv.org/abs/2607.03760
作者: Yongcong Wang,Jie Zhang,Rui Jiang,Xubing Yang,Ting Yun,Li Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 5 pages
Abstract:The deployment of large-scale foundation models like Segment Anything Model (SAM) on resource-constrained Earth observation platforms is hindered by prohibitive computational costs and the domain shift between natural and remote sensing imagery. To address these challenges, we propose \textitGeospatial \textitSegment \textitAnything \textitModel-Lite (GeoSAM-Lite), a lightweight, prompt-free segmentation framework designed for efficient onboard remote sensing segmentation. GeoSAM-Lite incorporates two core innovations: (1) Geospatial-Domain Initialization (Geo-Init), a domain-aware pre-training strategy that distills geospatial priors from a specialized teacher to bridge the domain gap; and (2) Feature Fusion Layers (FFL), which recalibrate spatial features and restore high-frequency boundary cues to overcome the capacity bottlenecks of lightweight backbones. Experiments across representative datasets, with a primary focus on cloud scenarios to evaluate performance under extreme scale variations and complex boundaries, demonstrate that GeoSAM-Lite achieves competitive accuracy while reducing parameters by 92.8% compared to the heavyweight RSAM-Seg. By establishing a superior Pareto frontier between efficiency and fidelity, GeoSAM-Lite offers a practical solution for real-time segmentation on edge devices.
[CV-195] Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity
链接: https://arxiv.org/abs/2607.03754
作者: Le-Anh Tran
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 6 figures, 4 tables
Abstract:Unmanned aerial vehicle (UAV) target segmentation remains challenging due to the small size of objects, appearance variations, cluttered backgrounds, and the scarcity of densely annotated data. These factors hinder the performance and practical deployment of lightweight segmentation models in real-world UAV applications. To address this problem, this paper investigates the use of SAM3 (Segment Anything Model 3) as a pseudo-label generator for training compact segmentation networks. Specifically, two supervision paradigms are explored: (i) direct pseudo-supervision using unaltered SAM3-generated masks, and (ii) a refinement strategy that re-applies SAM3 to localized image patches for improved mask quality. Based on these paradigms, a two-stage SAM3-guided pseudo-label generation framework is proposed. In the first stage, SAM3 generates coarse masks for initial object localization. The localized regions are subsequently cropped into patches and processed by SAM3 again to generate fine masks with accurate object boundaries and discard false positives. The resulting coarse and fine masks are then used as pseudo-labels to optimize a lightweight network, termed IPS-Seg, which consists of three components: an IdentityFormer backbone for feature extraction, an Atrous Spatial Pyramid Pooling module for multi-scale context aggregation, and a PixelShuffle-based decoder for spatial resolution recovery. Extensive experiments under multiple supervision settings demonstrate the effectiveness of the proposed framework. The results show that IPS-Seg achieves a favorable trade-off between segmentation accuracy and computational efficiency while benefiting consistently from the proposed pseudo-label generation strategy. These findings highlight the potential of large-scale foundation models as annotation sources for training compact task-specific segmentation networks in low-label vision domains.
[CV-196] Attending to Multimodal Generation One Token at a Time
链接: https://arxiv.org/abs/2607.03738
作者: Varun Gupta,Vineet Gandhi,Makarand Tapaswi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal large language models (MLLMs) generate responses autoregressively, integrating visual and linguistic information in an evolving context. Prior work on interpretability has focused on individual layers and circuits (where), leaving the token-level dynamics of multimodal computation during generation (when) underexplored. We address this gap and study attention shifts as per semantic role; tracking model attention to image, text, instruction, and previously generated tokens, One Token at a Time (OTaT). We introduce multimodal tasks that require explicit switching between visual and textual context within a single response. Across two mainstream model families and four open-weight MLLMs of varying sizes, we establish consistent patterns: attention to image peaks at tokens requiring image-derived information, instruction tokens are revisited during task transitions, and attention to previously generated tokens increases as the generation progresses. Causal attention blocking interventions validate the functional role of these trends. We profile model behavior under disrupted attention and observe responses falling back to language priors, or exhibiting cross-modal leakage, denial, or recovery. Finally, informed of the attention dynamics through our novel analysis, we propose a simple test-time intervention to boost attention to the relevant modality at the right time, significantly improving multimodal task performance.
[CV-197] ProxyUp: Training-Free Proxy-Conditioned Video Generation for Controllable Dynamics
链接: https://arxiv.org/abs/2607.03732
作者: Zanwei Zhou,Jiazhong Cen,Jiemin Fang,Yumeng He,Chen Yang,Sikuang Li,Fanpeng Meng,Zhikuan Bao,Wei Shen,Qi Tian
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: \href
Abstract:Precise control over complex dynamics remains challenging for modern video generative models, as text prompts alone often cannot specify physically plausible, fine-grained motion and interactions. We introduce \textitproxy-conditioned video generation , where a coarse proxy video from physics-based simulation or real-world recording serves as a dynamics carrier to control foreground object motion. Given a proxy video and a text prompt, the goal is to synthesize a new video that preserves the proxy dynamics while generating novel content and plausible interactions aligned with the prompt. Since paired proxy-target videos are difficult to obtain, we propose \textbfProxyUp , a training-free framework built on pretrained video generative models. ProxyUp first inverts the proxy video into an intermediate latent representation and applies \textbfregion-wise latent noising , preserving motion-critical proxy latents while injecting noise into regions intended for text-driven regeneration. To mitigate the distribution mismatch and weak foreground-background coupling introduced by this heuristic latent composition, we further propose \textbfStochastic Flow Relaxation (SFR) , which progressively relaxes the composed latent toward the model’s learned distribution before ODE sampling. Experiments on both simulation and real-world proxies show that ProxyUp outperforms strong video editing and motion transfer baselines in dynamic fidelity and text alignment.
[CV-198] Leverag ing Pathology Co-occurrence for Test-Time Adaptation in Chest X-Ray Diagnosis
链接: https://arxiv.org/abs/2607.03715
作者: Woojin Jeong,Yujin Choi,Dongbin Kim,Soyeon Park,Jaewook Lee
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Medical imaging models often degrade when deployed at new clinical sites due to differences in imaging equipment, protocols, and patient populations. Test-time adaptation (TTA) addresses this by updating a pretrained model using only unlabeled target data, without access to source data. However, existing TTA methods were designed for single-label classification on natural image benchmarks, minimizing entropy uniformly across all samples without considering label dependencies. This overlooks a key property of multi-label medical imaging: pathologies do not occur independently but exhibit structured co-occurrence patterns. In this work, we propose Co-occurrence Weighted Adaptation (CoWA), which leverages disease co-occurrence patterns as a reliability signal for adaptation. CoWA estimates label co-occurrence structure from model predictions and downweights samples that deviate from expected patterns, enabling adaptation to rely more on consistent predictions while reducing the impact of noisy ones. We evaluate CoWA on chest X-ray benchmarks under domain shifts and demonstrate consistent improvements over established baselines.
[CV-199] IPDiff: Diffusion-driven ORSI Salient Object Detection with Information Reconstruction and Multi-Prior Guidance
链接: https://arxiv.org/abs/2607.03696
作者: Gongyang Li,Zhen Bai,Runmin Cong,Dan Zeng,Weisi Lin,Xiao-Ping Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 22 pages, 8 figures, accepted by IJCV 2026
Abstract:Existing Salient Object Detection in Optical Remote Sensing Image (ORSI-SOD) methods mainly adopt the static inference strategy, which uses fixed trained model parameters for saliency inference in the testing phase. This means that even if the generated saliency map has errors, it cannot be further optimized. In this paper, we propose the novel IPDiff, a Diffusion-driven ORSI-SOD method with Information Reconstruction and Multi-Prior Guidance. We build IPDiff based on a unique dynamic optimization strategy, which endows IPDiff with the ability to iteratively optimize saliency maps with a dynamic parameter. Specifically, we formulate ORSI-SOD as a conditional diffusion problem in IPDiff. IPDiff first extracts informative conditional priors from ORSIs, including the saliency prior and the hierarchical priors, in the prior network with the assistance of the information reconstruction-driven attention module. The saliency prior can provide positional information of salient objects, while the hierarchical priors can provide specific detail and semantic information of salient objects. Under the guidance of these priors, IPDiff then iteratively denoises random noise as the timestep dynamically changes in the denoising network, generating saliency maps that are close to ground truths. Notably, we simultaneously supervise IPDiff in both spatial and spectral domains through a hybrid loss function to achieve efficient network training. Comprehensive experiments on public ORSSD, EORSSD, and ORSI-4199 datasets demonstrate that our proposed IPDiff achieves the best performance compared to 46 state-of-the-art methods. The code and results of our method are available at this https URL.
[CV-200] Phase-Preserving Trimodal Transformer for Tropical Forest Biomass Estimation Using Optical and PolInSAR Data
链接: https://arxiv.org/abs/2607.03663
作者: Luiz Felipe Parente Santiago(1 and 2),Rosiane Rodrigues de Freitas(1),Daniel Rodrigues dos Santos(3),Felipe Ferrari(3) ((1) Institute of Computing (IComp), Federal University of Amazonas (UFAM), Manaus, Brazil, (2) Brazilian Army Research Institute in the Amazon (IPEAM), Manaus, Brazil, (3) Military Institute of Engineering (IME), Rio de Janeiro, Brazil)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 7 figures, one of which is a TikZ
Abstract:The accurate estimation of Above-Ground Biomass (AGB) in mature tropical forests remains a critical challenge in remote sensing, primarily due to the saturation of Synthetic Aperture Radar (SAR) signals in high-density areas and persistent cloud cover affecting optical imagery. To overcome these physical limitations, we propose the Trimodal Coherent Co-attention Transformer (TCCT), a physics-informed deep learning architecture. The TCCT natively fuses optical surface reflectance (Landsat-5) with complex-valued Polarimetric SAR Interferometry (PolInSAR) data from both P and L bands. Unlike traditional fusion methods, our architecture employs complex-valued encoders to preserve spatial phase coherence, coupled with a dynamic co-attention mechanism that acts as an adaptive gating module, reducing the weight of cloud-corrupted optical pixels and shifting reliance to microwave phase data. We also derived a localized spatial allometric calibration model via Levenberg-Marquardt optimization, tailored to the specific wood density of the Paracou region in the Amazon basin. Evaluated using a two-stage protocol, the TCCT first underwent a rigorous 5-fold cross-validation to establish robust global weights (achieving a global RMSE of 4.19 m). Subsequently, following a localized spatial fine-tuning phase over 200 epochs, the model attained an absolute RMSE of 3.78 m and an R^2 of 0.33 for Canopy Height Models (CHM), outperforming standard Random Forest, CNN, and Vision Transformer baselines. Our ablation study confirms that preserving phase coherence mitigates deep-canopy signal saturation. When converted to AGB, the fine-tuned TCCT map yielded a Relative RMSE (rRMSE) of 4.51% in dense forest areas above 50 Mg/ha. By meeting the European Space Agency (ESA) BIOMASS mission requirement of less than 20% error, the TCCT provides a robust framework for continuous carbon stock mapping in tropical biomes.
[CV-201] From Geometric Labels to Semantic Understanding of Indoor Building Components Using Multimodal Large Language Models
链接: https://arxiv.org/abs/2607.03661
作者: Shuju Jing,Chao Yin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 46 pages, 13 figures, accepted by Automation in Construction journal
Abstract:Point cloud-based understanding has become an important enabler for facility operation and maintenance involving indoor building components. However, existing methods output only discrete labels without explaining component functions or natural language interactions. This paper proposes Building-MLLM, a point cloud-centered multimodal large language model (MLLM) for indoor components, which models point clouds and instructions to generate responses across Simple Recognition, Complex Captioning, and Multi-Engineering Question Answering tasks. Building-MLLM addresses semantic concentration through four domain-specific mechanisms: Point Information Enhancer for task-relevant semantics, Geometry-Preserving Regularization preventing geometric erosion, fixed textual prefix for domain stabilization, and multi-dimensional LoRA balancing recognition with reasoning. A multi-constraint progressive instruction-generation engine is developed to compile a synthetic point cloud-text dataset with 4198 objects, 37,782 instruction-following pairs, and 47 categories. Experiments show that Building-MLLM achieves 88.00%, 65.10%, and 68.14% on the three task types, respectively, demonstrating superior indoor component language understanding and providing initial generalizability in transfer inference on other real-world datasets.
[CV-202] ViPo-MLLM : Visual-Pose Multimodal LLM for Gloss-Free Sign Language Translation
链接: https://arxiv.org/abs/2607.03657
作者: Ahmed Abul Hasanaath,Bicheng Xu,Mir Rayat Imtiaz Hossain,Leonid Sigal,Hamzah Luqman
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Gloss-free Sign Language Translation (SLT) translates sign language videos into spoken-language sentences without gloss annotations, avoiding costly labeling but requiring fine-grained modeling of hands, body, and facial cues. Existing methods often use single-modality or weakly fused features, limiting performance. We propose ViPo-MLLM, a framework that integrates spatio-temporal RGB and human pose features. Dedicated encoders model intra-modal dynamics and cross-modal attention captures long-range dependencies. The fused representation is conditioned with a structured prompt and processed by an LLM trained with contrastive and language modeling objectives. The proposed model was evaluated on the PHOENIX14T and CSL-Daily datasets and achieved new state-of-the-art results on both datasets. Moreover, the ViPo-MLLM model attained competitive performance compared to gloss-based recognition approaches, confirming the effectiveness of the proposed pose cues and cross-modal attention mechanisms.
[CV-203] ClinOCR-Bench: A Comprehensive Clinical Scanned Document Dataset for Optical Character Recognition Model Evaluation
链接: https://arxiv.org/abs/2607.03650
作者: Enshuo Hsu,Jin Zhou,Kirk Roberts
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Extracting textual information from scanned medical documents, such as external laboratory reports and manually filled forms, has been a major challenge in modern electronic health records (EHRs). Recent advancements in vision language models (VLMs) have shown great promise over traditional OCR tools. However, at this point, most clinical OCR studies were conducted on private, institutional data. To our knowledge, there are few publicly available datasets for evaluating OCR models in the clinical domain. Furthermore, common scanning artifacts that undermine OCR performance are not reflected in those datasets, leaving a systematic evaluation unfeasible. Therefore, we release a publicly available, realistic-looking OCR benchmark dataset, ClinOCR-Bench, with 384 scanned images across 6 subsets: Normal, Handwriting, Poor Quality, Rotation, Tables, and Mix-artifacts. ClinOCR-Bench features: 1) diverse document types and layouts, 2) full coverage of common EHR scan artifacts, 3) protected health information-free, 4) template-aware train/test split, and 5) adequate sample size for OCR benchmarking. Baseline OCR performance was evaluated using state-of-the-art open-weight and proprietary VLMs. The dataset and documentation are available on GitHub (this https URL).
[CV-204] Do Medical Vision Language Models Actually See? A Counterfactual Grounding Framework and Hard-Negative Contrastive Training for Visually-Reliant Medical VLMs
链接: https://arxiv.org/abs/2607.03647
作者: Anas Zafar,Leema Krishna Murali,Siddhant Bharadwaj,Ashish Vashist,Jia Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Large vision language models (VLMs) report strong accuracy on medical question-answering, yet it remains unclear whether they reason from visual evidence or exploit textual shortcuts. We introduce a counterfactual evaluation framework that decouples visual and textual contributions by substituting input images with controlled surrogates blank, pixel-shuffled, image-absent, and CLIP-retrieved hard negatives and derive a suite of grounding metrics including the Visual Reliance Score (VRS) and Visual Hallucination Rate (VHR). We further introduce CORAL (COntrastive Retrieval-Augmented Learning), a 7B-parameter LoRA fine-tune of Qwen2.5-VL-7B trained with a Contrastive Grounding Objective (CGO) that penalises answer invariance under hard-negative image swaps. On a paired controlled evaluation across four closed-form medical VQA benchmarks (PathVQA, PMC-VQA, SLAKE, VQA-RAD; n=400 total), CORAL improves macro accuracy by +6.7 pp (P(Delta0)=0.988) and reduces VHR by 8.0 pp (P0.001) over the matched Qwen2.5-VL-7B base; neither MedVLThinker RL variant achieves a significant gain on either metric. Cross-domain diagnostics further reveal that image substitution costs only =6.5 pp on medical benchmarks versus 48-61 pp on general-domain tasks, situating the grounding gap that CGO targets. We discuss evaluation limitations openly including train/eval benchmark overlap and underpowered secondary metrics and release our framework, training code, and model weights to support reproducible grounding audits of medical VLMs.
[CV-205] Moonstone: A Multimodal Foundation Model and Benchmark for Lunar Remote Sensing ECCV2026
链接: https://arxiv.org/abs/2607.03644
作者: Ayush Prasad,Swarnalee Mazumder
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted for publication in ECCV 2026
Abstract:Decades of orbital missions have produced multi-modal remote sensing data for the Moon, spanning optical imagery, spectroscopy, thermal emission, radar, gravity, and elemental composition. Yet these datasets remain fragmented across archives, and no benchmark exists for evaluating machine learning on lunar data. We introduce Moonstone, the first multi-modal foundation model benchmark for lunar remote sensing. Our contributions are: (1) a 28-channel, 128 pixels-per-degree (~237 m) global lunar pretraining dataset from seven instrument families across five missions, (2) MG-MAE, a modality-grouped masked autoencoder with per-group convolutional tokenizers, a shared Vision Transformer encoder, attention masking for missing modalities, coverage-adaptive masking for heterogeneous spatial coverage, and spectral continuity regularization for physically plausible reconstructions, and (3) a benchmark of six downstream tasks covering classification, regression, and segmentation. MG-MAE pretrained features outperform scratch baselines on all tasks and surpass both ImageNet-pretrained and vanilla MAE baselines by large margins. Data and code are available at this https URL and this https URL .
[CV-206] Probing Identity-Specific Motion Signatures: A Controlled Diagnostic Study
链接: https://arxiv.org/abs/2607.03633
作者: Yingtie Lei,Fangxun Liu,Baicheng Wu,Colin Lee,Ziheng Zhang,Junke Yang,Zhiyuan Tao,Xuyan Huang,Shuheng Wang,William Koran,Kyle Park,Elijah H Buckwalter,Cheng-Hsuan Chiang,Tejas Naik,Daniel Yi,Wei-Lun Chao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Identity recognition (e.g., person, animal re-identification) has traditionally relied heavily on static appearance cues. Yet motion–consistent, individual-specific dynamics–can provide a complementary and potentially more robust signature, especially when appearance is weak or variable. This raises a fundamental question: when identity-specific motion cues are clearly present, to what extent do modern video models use them for recognition? To investigate this question, we conduct a systematic diagnostic study and introduce BALLER120, a controlled benchmark of 120 professional basketball players performing free-throws. By focusing on the same multi-phase action across individuals, BALLER120 reduces action-level variation and identity-correlated acquisition biases, enabling fine-grained analysis of identity-specific kinematic patterns. We find that modern video models can predict identity accurately from RGB videos, but often rely on static appearance cues such as faces and jersey regions, even when informative motion cues are available. Strikingly, when appearance is suppressed through silhouette-only or skeleton-only inputs, the same model architectures shift toward motion micro-patterns (e.g., foot placement and elbow bending). Despite containing less visual information, appearance-suppressed representations achieve competitive accuracy and stronger robustness to appearance shifts. Our qualitative analyses further show that appearance-suppressed models attend to distinctive motion patterns across individuals. Overall, our study demonstrates that identity-specific motion signatures are present, informative, and learnable, but modern video models may overlook them in favor of easier static shortcuts unless appearance cues are explicitly suppressed.
[CV-207] RADIO1D: Elastic Representations for Condensed Vision Modeling ICML2026
链接: https://arxiv.org/abs/2607.03624
作者: Greg Heinrich,Mike Ranzinger,Collin McCarthy,Natan Bagrov,Eugene Khvedchenya,Bryan Catanzaro,Jan Kautz,Andrew Tao,Pavlo Molchanov
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Published as main conference paper at ICML 2026
Abstract:This paper challenges the assumption that vision-language models (VLMs) require fixed patch-based 2D vision features. Analyzing fine-tuned vision encoders, we find that representations become increasingly abstract and less spatially coherent during VLM training. Notably, models trained with image-text alignment (such as SigLIP2) develop a small number of specialized tokens that effectively summarize global image content. Building on this, we introduce RADIO1D, which compresses images into a compact, variable-length 1D token sequence using multi-teacher knowledge distillation and an autoencoder design. The resulting representations exhibit strong hierarchical summarization, enabling accurate scene understanding - even with a single token - and support improved composition-aware image retrieval. In VLMs, RADIO1D provides flexible accuracy-efficiency tradeoffs through adjustable token counts, delivering competitive performance on diverse multimodal benchmarks with lower computational overhead and better accuracy.
[CV-208] IDEAL-Bench: Indoor Dataset and Evaluation suite for Analyzing 3D Layout reasoning
链接: https://arxiv.org/abs/2607.03614
作者: Yuening Cai,Junwei Zhou,Youran Qu,Yu-Wing Tai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 36 pages. Project page: this https URL
Abstract:Spatial question answering is the dominant paradigm for evaluating spatial intelligence in Vision-Language Models (VLMs), but it leaves a complementary axis of spatial competence under-evaluated: holistic 3D layout inference, which predicts every visible object’s pose and extent from a single image in a structured form. To this end, we introduce IDEAL-Bench, an evaluation suite that requires VLMs to predict structured 3D layouts on photorealistic indoor scenes across 10 room types, scored along five numerical dimensions and a perceptual render-and-compare protocol. By operating on semantically realistic scenes with full asset substitution under controlled lighting and viewpoint, IDEAL-Bench moves beyond CLEVR-style simple geometric primitives so that any image-space discrepancy reflects spatial reasoning alone. The benchmark is built on IDEAL-Scenes, a procedurally generated dataset of 1,000 re-renderable Blender environments with ground-truth layouts. Evaluating 15 prominent VLMs reveals three findings: the task remains substantially unsolved, with the strongest model reaching only 62.1/100 overall; all models exhibit a sharp asymmetry between object recognition and geometric regression, indicating that current VLMs are trained to describe scenes rather than to measure them; model rankings partially diverge from those on QA-based and primitive-reconstruction benchmarks: top-tier consensus holds, but mid-tier rankings shift substantially. Collectively, these findings establish IDEAL-Bench as a diagnostic suite, targeting the geometric and structural competencies that QA-based evaluation cannot surface, and paving the way towards more rigorous evaluation of spatial intelligence in next-generation VLMs. Together, these findings position IDEAL-Bench as a principled diagnostic for whether future VLMs achieve genuine spatial understanding rather than linguistic approximations of it.
[CV-209] SAF3R: Dynamic Sparse Attention for Feed-Forward 3D Reconstruction Transformers ECCV2026
链接: https://arxiv.org/abs/2607.03612
作者: Jianing Deng,Yuanzhe Li,Jialu Wang,Song Wang,Tianlong Chen,Huanrui Yang,Jingtong Hu
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: ECCV 2026
Abstract:Feed-forward 3D reconstruction (F3R) transformers have recently achieved remarkable success. However, scaling them to long image sequences remains challenging, as the quadratic complexity of cross-view global attention quickly becomes the dominant computational bottleneck. While recent efforts attempt to improve efficiency through compressed or sparse attention, they fail to fully exploit the inherent sparsity and dynamic behavior of global attention. In this work, we present a comprehensive analysis of global attention across multiple F3R transformers and reveal that attention patterns are highly heterogeneous, dynamic, and extremely sparse across layers and attention heads. Motivated by these findings, we propose SAF3R, a training-free dynamic sparse attention framework tailored to F3R transformers. SAF3R integrates tailored sparse attention mechanisms with offline head profiling and an efficient online adaptation strategy to match input-dependent attention behaviors. Extensive experiments demonstrate that SAF3R achieves high sparsity ratios while preserving camera pose estimation and 3D reconstruction quality, translating into substantial end-to-end speedup on F3R transformers compared to existing methods. Code is available at this https URL
[CV-210] A Step Towards Robust Unsupervised Domain Adaptation via Fine-Tuning and Reinforcement Learning ECAI2025
链接: https://arxiv.org/abs/2607.03600
作者: Sushant Dagaji Desale,Rahul Mishra,Ashutosh Kumar Sinha
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted and presented at the 28th European Conference on Artificial Intelligence (ECAI 2025), Bologna, Italy
Abstract:Adversarial robustness in Unsupervised Domain Adaptation (UDA) remains a significant challenge due to noisy pseudo labels and inherent distributional shifts between the clean source and adversarially perturbed target domains. Existing approaches often fail to achieve an optimal trade-off between robustness and accuracy, as pseudo-labels generated by domain-adapted models tend to introduce classification errors under adversarial attacks. In this work, we propose \textbfSFT+RL, a two-stage robust UDA framework that integrates Supervised Fine Tuning (SFT) and Reinforcement Learning (RL) on top of CLIP’s pre-trained visual encoder. In the SFT stage, we adversarially fine-tune a linear classifier using PGD-based perturbations over the labelled source domain while partially unfreezing CLIP’s projection layer. It allows adaptation to adversarial noise while preserving CLIP’s rich semantic priors. We introduce a confidence-guided pseudo-labeling strategy in the RL stage to annotate unlabeled target samples progressively. Pseudo labels are filtered using a decaying confidence threshold to balance quality and coverage, and the model is trained on a composite dataset formed by combining clean source samples with high-confidence target samples. Adversarial training is applied to mixed batches of clean and adversarial examples to enhance cross-domain robustness. Comprehensive evaluations on three benchmark datasets OfficeHome~\citetomm-ude, PACS~\citepacs, and VisDA~\citevisda demonstrate the effectiveness of our approach. Notably, \textbfSFT+RL achieves average improvements of \textbf10.2% in clean accuracy and \textbf15.8% in adversarial robustness across all three datasets, outperforming existing state-of-the-art methods.
[CV-211] oken-Based Affordance Grounding with Large Vision-Language Models ECCV2026
链接: https://arxiv.org/abs/2607.03595
作者: Seung Il Lee,Qinqian Lei,Daguang Xu,Dong Yang,Robby T. Tan,Yixin Chen,Bo Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: Accepted to ECCV 2026
Abstract:Affordance grounding aims to localize image regions that support a specific action, serving as a core capability for physical intelligence and embodied perception. Previous studies have primarily relied on weakly supervised learning with action labels from exocentric images. However, these methods often struggle with visually ambiguous exocentric images containing co-occurring actions; moreover, they fail to distinguish semantically similar actions because existing methods typically rely on brief action phrases that lack rich semantic details for action-specific localization. Although large vision-language models (LVLMs) encode rich action semantics and their action-conditioned textual outputs implicitly contain spatial cues, they do not directly provide action-specific spatial localization. To address these problems, we propose TokAG, a zero-shot affordance grounding framework that exploits the token-level semantic-spatial signals in LVLMs to localize action-relevant regions without external supervision. We observe that attention maps associated with different LVLM output tokens vary significantly, with many attending to irrelevant regions such as the background. Thus, we introduce a spatial-aware token-selection mechanism to systematically evaluate each output token and select the one whose attention maps exhibit dominant activation over the target object, instead of relying on arbitrary attention maps. By extracting these object-focused attention maps, we transform the LVLM’s implicit semantic signals into zero-shot affordance heatmaps. Our zero-shot framework consistently outperforms prior weakly supervised approaches across multiple benchmarks, improving NSS by 10.7% on the unseen split of AGD20K and by 29.7% on HICO-IIF. The code and models will be made publicly available.
[CV-212] Responsibility Distribution Estimation in Ego-View Accident Videos with Multimodal Large Language Models
链接: https://arxiv.org/abs/2607.03591
作者: Ryosei Tamura,Andrew Shin
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Recent studies on multimodal traffic accident understanding have mainly relied on infrastructure-camera footage, satellite imagery, or structured crash records. However, such data sources are costly to deploy and maintain at large scale, and they cannot objectively capture what the driver was actually able to observe before the accident. In contrast, ego-view accident videos directly represent the driver’s visual perspective, making them suitable for reasoning about avoidability and driver responsibility. In this paper, we introduce responsibility distribution estimation for ego-view traffic accident videos, a new task in which a model predicts the percentage of responsibility assigned to each involved agent. We construct an LLM-assisted responsibility annotation pipeline and fine-tune multimodal large language models under multiple input settings, including raw frames, segmentation-enhanced input, and textual descriptions. Experimental results establish a strong initial benchmark, demonstrating that multimodal LLMs can effectively perform this nuanced, constraint-based reasoning task. Our findings suggest that ego-centric accident videos provide a promising foundation for socially and legally meaningful multimodal reasoning beyond conventional accident classification and explanation tasks.
[CV-213] Vision Non-Causal Trapezoidal Mamba: Eliminating Directional Scanning in Vision SSMs with Second-Order Dynamics WACV2027
链接: https://arxiv.org/abs/2607.03589
作者: Anvitha Ramachandran,Dhruv Parikh,Haoyang Fan,Rajgopal Kannan,Viktor Prasanna
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Submitted to WACV 2027 Conference Round 1
Abstract:State Space Models (SSMs) have emerged as an alternative to Vision Transformers, yet most vision SSMs inherit directional token scanning from causal sequence modeling. While effective for sequential data, directional scanning introduces spatial bias and orientation-sensitive representations. We present Vision Non-Causal Trapezoidal Mamba (VNCT), a second-order non-causal vision SSM that enables all image tokens to interact in a single pass, eliminating direSctional scanning and achieving low single-image inference latency. VNCT exhibits more orientation-robust representations, showing reduced performance degradation under image rotations and flips, while improving Boundary IoU by up to 3.7 points, leading to more accurate boundary preservation and object localization. Across ImageNet-1K classification, COCO object detection and instance segmentation, and ADE20K semantic segmentation, VNCT consistently outperforms both directional-scanning vision SSMs and first-order non-causal SSMs. These results show that directional scanning is unnecessary for high-performance vision SSMs and that second-order non-causal state-space modeling offers a simple, efficient, and robust alternative for visual recognition.
[CV-214] PLGSA-Transformer: Periocular Landmark-Guided Attention with Occlusion-Adaptive Cosine Thresholding for Cross-Modal Masked and Unmasked Face Recognition
链接: https://arxiv.org/abs/2607.03581
作者: Dana A Abdullah
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:The widespread adoption of facial masks, accelerated by COVID-19 and mandated in security-sensitive settings, has exposed limitations of conventional face recognition systems. Existing approaches relying on fixed cosine thresholds, non-adaptive CNNs, and purely data-driven features fail to generalize when facial regions are occluded, creating a gap between lab performance and real-world deployability. This paper proposes PLGSA-Transformer, a cross-modal face matching framework with three contributions. First, Periocular Landmark-Guided Spatial Attention (PLGSA) uses MediaPipe landmarks to compute Gaussian heatmaps over the eye, brow, and forehead regions, fusing them with EfficientNetB3 features via a learnable residual gate to direct attention toward discriminative visible regions. Second, a Hybrid CNN-Transformer Branch reshapes feature maps into tokens processed by a two-layer Multi-Head Self-Attention encoder, enabling cross-regional dependency modelling. Third, the Occlusion-Adaptive Cosine Threshold (OACT) is a jointly trained head that raises the matching threshold in proportion to predicted occlusion severity. The model is evaluated on 858 images from Zenodo MDMFR (60%), Kaggle CelebA-HQ masked collection (25%), and author-collected images (15%), spanning both genders, ages 21-75, with varied mask types, trained via a unified loss combining contrastive verification, identity classification, and occlusion cross-entropy. PLGSA-Transformer achieves 97.22% pair verification accuracy with ROC AUC 1.0000, surpassing VGG-16-based MUFM (Abdullah et al., 2025; 95.0%), HOG classifiers (Adnan et al., 2020; 85.0%), and Feature-based Structural Measure (Shnain et al., 2017; 86.61%). These results confirm that encoding periocular geometry into attention, with Transformer modelling and occlusion-adaptive thresholds, yields a robust, scalable solution for cross-modal masked face recognition.
[CV-215] When Geometry Aligns: Dihedral Hidden-State Transformations in UNet ViT and DiT Architectures
链接: https://arxiv.org/abs/2607.03580
作者: Mojtaba Faramarzi,Alex Lamb,Irina Rish
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the Conference on Lifelong Learning Agents (CoLLAs), 2026
Abstract:Diffusion architectures now encompass convolutional UNets as well as transformer-based designs such as Diffusion Transformers (DiTs), inspired by Vision Transformers (ViTs), yet the effects of structured geometric perturbations within these architectures remain poorly understood. We study this question through a unified framework that applies reflection-based elements of the dihedral group to intermediate hidden states as controlled internal interventions, contrasting geometrically consistent and inconsistent variants. Using activation-level diagnostics, including Self-Consistency Shift (SCS), Activation Mass Scatter (AMS), and Drift, we analyze feature stability and geometric drift. We find that consistent transformations improve stability, while inconsistent ones induce predictable, architecture-specific failures. In the main Stable Diffusion 2.1 U-Net study, we evaluate seven intervention modes over three seeds and complement the internal diagnostics with image-level FID, KID, CLIP score, and LPIPS diversity. Taken together with supporting ViT and controlled DiT analyses, these results establish geometric consistency as a key principle for stable hidden-state interventions in spatially structured vision and diffusion models.
[CV-216] EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI
链接: https://arxiv.org/abs/2607.03568
作者: Hasan Ulutas,Muhammet Emin Sahin,Mustafa Fatih Erkoc,Esra Yuce,Turker Tuncer,Sengul Dogan,Serkan Kiranyaz
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 25 pages, 13 figures, 10 tables
Abstract:Objective: Accurate identification of acute ischemic infarcts on diffusion-weighted magnetic resonance imaging (DWI) is a critical prerequisite for reliable lesion quantification and effective clinical decision support in the management of cerebrovascular events. Methods: This study presents EPRA U-Net (Efficient Pyramid Residual Attention U-Net), a task-specific integrated architecture for efficient and accurate infarct segmentation of DWI images. In the proposed architecture, an EfficientNet-based encoder was used as a hierarchical feature extractor with a minimized parameterization. In addition, a Residual-Recurrent (R2) block (recurrent unrolling step t = 2, following the original formulation) and Atrous Spatial Pyramid Pooling (ASPP) were integrated to enhance the performance of spatial dependency modeling. Additionally, a dual attention mechanism was incorporated to highlight lesion-related activations while concurrently enabling the suppression of extraneous background responses. To prioritize lesion detection consistent with clinical imperative, a Tversky loss function was adopted, emphasizing the sensitivity of detection over its specificity during the optimization process. Results: Experimental evaluations were conducted utilizing an in-house dataset comprising 167 patients with 4,895 DWI slices; subsequently, the performance of the proposed EPRA U-Net was assessed in comparison with state-of-the-art models, specifically UNet++, DeepLabV3+, and TransUNet. The experimental results suggest that EPRA U-Net attained superior performance, evidenced by a pixel-aggregated Dice of 0.8984, a per-sample Dice of 0.9469, an IoU of 0.8155, a Recall of 0.8887, a Lesion F1 of 0.9378, and an HD95 of 11.62 px. Furthermore, a clear reduction in the rate of missed lesions, specifically by 16%, 25%, and 29%, was observed when compared with UNet++, DeepLabV3+, and TransUNet, respectively.
[CV-217] XPlainVerse: A Million-Scale Benchmark for Explainable Deepfake Detection
链接: https://arxiv.org/abs/2607.03562
作者: Abhijeet Narang,Kartik Kuckreja,Shreya Ghosh,Muhammad Haris Khan,Jianfei Cai,Abhinav Dhall
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:As deepfake detection models increasingly produce natural language explanations, their reasoning often remains weakly grounded in visual artifacts, limiting reliability and user trust. Existing benchmarks mainly evaluate classification accuracy, overlooking whether explanations reflect the actual manipulations. This gap hinders progress toward deployable, explainable deepfake detection systems. To this end, we introduce XPlainVerse, a large-scale benchmark designed for joint deepfake detection and human-centered explanation. XPlainVerse comprises one million real and manipulated images, pairing authentic images from five established sources with forgeries generated by twelve off-the-shelf image editing and synthesis models. We further propose a multi-stage filtering pipeline, Edit-Check, to verify if manipulations satisfy their intended edits, enabling reliable reasoning supervision at scale. Beyond dataset scale, XPlainVerse provides two complementary explanation styles: technical explanations for expert analysis and simplified explanations optimized for non-technical users. To evaluate explanation quality beyond surface similarity, we propose novel metrics, EntityScore and EvidenceScore, that measure reasoning fidelity by checking whether explanations correctly identify manipulated entities and visual evidence. Human annotations on 2,000 explanation pairs validate our dataset quality against human judgment. We believe XPlainVerse will establish grounded explanation quality as a measurable dimension of deepfake detection and support scalable research on trustworthy, interpretable models.
[CV-218] Latent Clarity: Bridging World-Model Kinematics to Semantic Manifolds for Video Anomaly Anticipation
链接: https://arxiv.org/abs/2607.03558
作者: Abu Anas Ibn Samad
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 14 pages, 3 figures, 5 tables
Abstract:Continuous video anomaly detection is dominated by reactive Multiple Instance Learning (MIL) that collapses spatiotemporal features into scalar scores. We introduce PULS (Predictive Unified Latent Space), a continuous semantic world-model pipeline comprising two modules: a 490M-parameter KSD Bridge (Kinematic-to-Semantic Distillation) and a 16.8M-parameter Anticipatory State Predictor (ASP). The KSD Bridge maps V-JEPA 2 physical tensors into the 2048-d Qwen3-VL-Embedding-2B text-aligned hypersphere, trained on a subset of UCF-Crime. This translation alone yields a chunk-level AUROC of 0.8994 for UCF-Crime and 0.8162 for out-of-distribution XD-Violence without MIL or hierarchical fusion. We introduce and validate the Latent Clarity Hypothesis: because JEPA’s temporal predictor discards aleatoric pixel noise while preserving kinematics, anticipated future representations are more semantically separable than observed presents. The ASP sharpens these anticipated future latents, achieving 44.5% mean 14-way zero-shot VQA accuracy (exceeding observation baseline by +9.6 pp). Applying the ASP to Observation Tensors collapses accuracy to 7.3% (random chance), proving Anticipation and Observation occupy distinct sub-manifolds. A Triple-Track Lead-Time protocol with an L1-surprise gate yields a peak +8.9 pp anticipatory advantage at T-0.5s (p 0.001, N = 1,000 permutation), separating physical anticipation from static scene priors. Zero-shot transfer to XD-Violence confirms that Newtonian-invariant kinematic representations generalize out-of-distribution. Comments: 14 pages, 3 figures, 5 tables Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2607.03558 [cs.CV] (or arXiv:2607.03558v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.03558 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-219] VISION-2DCD: A Long-Term Change Detection Dataset for Large-Scale Outdoor Construction Monitoring ICRA2026
链接: https://arxiv.org/abs/2607.03553
作者: Dayou Mao,Yuchen Lin,Ashkan Ebadi,John Zelek,Alexander Wong,Yuhao Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 11 pages, 7 figures, 1 table. Accepted for publication at the 2026 IEEE International Conference on Robotics and Automation (ICRA 2026). Project page: this https URL
Abstract:Automation in construction is essential for reducing costs and human errors in large-scale projects. We approach the construction progress monitoring from the aspect of detecting changes in construction sites. As construction buildings continue to evolve in geometry and appearance over time, change detection need to be performed from arbitrary camera viewpoints. This necessitates developing 2D Change Detection (2DCD) algorithms that operate robustly across diverse camera perspectives at construction sites. While developing and evaluating such systems is data-intensive, no open-source benchmark dataset exists at the intersection of 2D change detection and construction automation research. Data collection using Unmanned Aerial Vehicles (UAVs) is gaining its popularity in outdoor large-scale surveying. However, in active construction sites conducting drone missions equipped with high-end sensors imposes safety concerns. Flight trajectory and collected camera viewpoints can be significantly limited. To address this critical gap, we introduce iVISION-2DCD, a large-scale synthetically generated dataset from dense LiDAR point clouds with photorealistic input images and accurate ground truth annotations. Our dataset formally defines the problem of viewpoint-robust 2DCD at construction sites and captures the inherent complexities of real-world deployment. In this paper, we present our systematic methodology for synthetic data generation, developing novel view synthesis techniques to overcome bi-temporal alignment and viewpoint diversity challenges, and implementing semi-automated semantic segmentation with change label generation while preserving challenging real-world cases. Benchmark evaluations using state-of-the-art 2DCD algorithms demonstrate that iVISION-2DCD poses novel research challenges for the computer vision and robotics communities.
[CV-220] Perceptual Flow Matching for Few-Step Generative Modeling
链接: https://arxiv.org/abs/2607.03524
作者: Chuyang Zhao,Yifei Song,Hongfa Wang,Jianlong Yuan,Yuan Zhang,Siming Fu,Zhineng Chen,Huilin Deng,Haoyang Huang,Nan Duan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We propose Perceptual Flow Matching (PFM), a simple yet effective framework for few-step generation in flow-matching models. Rather than performing velocity regression in the conventional VAE latent space, PFM supervises flow matching in a perceptual feature space using pretrained perceptual models. This simple change substantially improves the few-step generation capability of flow-matching models, reducing the number of sampling steps from 35-50 to 4-8 while preserving generation quality. Unlike existing acceleration and distillation approaches, PFM requires neither teacher models nor auxiliary score networks and can be integrated into standard flow-matching training pipelines with minimal modifications. Extensive experiments on image generation, video generation, and image editing tasks demonstrate that PFM consistently produces high-quality results while producing fewer artifacts than existing distillation-based methods. We further show that perceptual supervision shifts the regression minimizer from mean-seeking to mode-seeking, biasing predictions toward on-manifold modes that remain accurate under coarse few-step integration. Our results reveal that standard flow-matching training can naturally yield high-quality few-step generators when supervised in an appropriate representation space. We hope this insight inspires future research into representation-aware objectives for efficient generative modeling.
[CV-221] Mixture-of-Gaussians-Guided Schedule Design for Brownian Bridge Diffusion Models
链接: https://arxiv.org/abs/2607.03517
作者: Ron Levi,Michael Elad
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 66 pages, 10 figures
Abstract:Brownian Bridge Diffusion Models (BBDM) offer an appealing framework for image restoration and inverse problems by constructing a stochastic bridge from the clean signal directly to the degraded observation, rather than to pure noise. Despite their promise, the choice of bridge schedule is typically inherited from heuristics, and a principled analytical framework for schedule design has been lacking. In this work, we develop such a framework by offering a novel analysis of BBDM reverse dynamics under a Mixture-of-Gaussians (MoG) prior. This setting yields a closed-form ideal posterior and a corresponding MMSE denoiser, while the BBDM-induced reconstruction law is captured analytically through a tractable surrogate. Building on these expressions, we formulate two complementary schedule-design objectives: a Wasserstein criterion targeting perceptual quality and an MSE criterion targeting reconstruction fidelity. Our work exposes an inherent tradeoff between the two and proves the existence of universal schedules for both that are independent of the degradation and prior. Extensive experiments on controlled MoG settings confirm full alignment between theory and practice, and experiments on the FFHQ dataset across inpainting, deblurring, and super-resolution tasks validate the practical value of our schedule-design criteria.
[CV-222] Flex-Forcing: Towards a Unified Autoregressive and Bidirectional Video Diffusion Model
链接: https://arxiv.org/abs/2607.03509
作者: Xinyin Ma,Julius Berner,Chao Liu,Arash Vahdat,Weili Nie,Xinchao Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Recent progress in large-scale generative models has substantially advanced video generation, yet existing methods remain constrained by a rigid inference paradigm. Bidirectional diffusion models excel at global coherence and visual fidelity but suffer from slow inference, while autoregressive models offer efficient and streaming generation at the cost of long-range consistency and exposure bias. We introduce Flex-Forcing, a unified training and inference framework that enables a video diffusion model to seamlessly operate under both bidirectional and autoregressive generation regimes. The core idea is a flexible chunking mechanism jointly defined over the temporal axis and denoising steps. This design allows the model to (1) perform flexible chunking according to different device budgets, (2) perform bidirectional inference across chunks for global structure planning, while generating frames autoregressively within each chunk for efficient and fine-grained synthesis, and (3) perform any-order, any-timestep autoregressive generation without the strict causal constraint. Extensive experiments on multiple video generation benchmarks demonstrate that Flex-Forcing achieves consistently better video quality, long-video stability than strong baselines with a rigid inference schedule, while offering faster inference.
[CV-223] owards Standardized Light Field Quality Assessment: Hybrid Subjective Benchmarking and Objective Metric Evaluation
链接: https://arxiv.org/abs/2607.03494
作者: Saeed Mahmoudpour,Mylene C. Q. Farias,Gi-Mun Um,Myllena A. Prado,Ismael Seidel,Leonardo de Sousa Marques,Leonardo Andrade,Shengyang Zhao,Carla L Pagliari
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM); Image and Video Processing (eess.IV)
备注:
Abstract:Benchmarking immersive media coding solutions, especially in the standardization context, requires reliable and reproducible subjective quality assessment (QA) procedures, along with objective quality metrics that remain accurate across different distortion types. This paper presents a standardized workflow for light field QA, developed and deployed in the context of JPEG Pleno standardization activities, which integrates benchmark generation, a hybrid subjective evaluation, and objective metric analysis into a common workflow. The benchmark is designed to encompass not only traditional coding-only artifacts but also distortions that arise in processing pipelines in which light field encoding is accompanied with view synthesis and reconstruction techniques. A hybrid subjective method is proposed enabling fine-grained assessment by combining reference-anchored quality rating with targeted pairwise refinement in perceptually ambiguous regions. The reliability of subjective scores is verified using statistical consistency analyses between observers of two cohorts. Finally, a large set of objective metrics is systematically evaluated in terms of global prediction accuracy, local agreement in ambiguous quality regions, and robustness across distortion families. The results show that several metrics achieve strong agreement for coding-only stimuli, but their performance consistently drops when view synthesis distortions are included. The analysis further highlights the importance of view-pooling strategy in the design of future light field quality metrics. The work provides a reproducible and standardization-ready framework for fine-grained light field QA, while identifying key limitations of current objective metrics under emerging coding pipelines. The subjectively annotated dataset is publicly available at this https URL.
[CV-224] Learning to Generate Multiple Objects from Dense and Occluded Layouts
链接: https://arxiv.org/abs/2607.03488
作者: Bach-Hoang Ngo,Si-Tri Ngo,Hieu Le,Trung-Nghia Le
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Text-to-image diffusion models fail to generate correct object counts in dense scenes, where overlapping instances collapse into indistinguishable structures despite appearing visually plausible. We identify this as instance ownership collapse: tokens from overlapping objects interact freely through attention, while heavily occluded instances receive weak supervision due to their small visible areas. We address this through layout-aware attention biases that softly bias token interactions toward region-consistent grouping and suppress cross-instance leakage, paired with an amodal-balanced loss that amplifies gradients for occluded objects based on their occlusion level. To enable systematic evaluation, we introduce OverlapDepth-45K, a benchmark of densely overlapping scenes with amodal supervision. Our approach substantially improves count accuracy and prevents instance merging while preserving image quality. Project page: this https URL
[CV-225] PhysMirror: Physics-Aware Mirror Object Generation IROS2026
链接: https://arxiv.org/abs/2607.03470
作者: Xuan-Bach Mai,Duy-Phuc Nguyen,Quoc-Van Le,Tam V. Nguyen,Thanh-Toan Do,Huu Le,Duong-Van Nguyen,Minh-Triet Tran,Trung-Nghia Le
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: IROS 2026
Abstract:Synthesizing physically accurate mirror reflections remains a fundamental challenge for modern text-to-image diffusion models, which are increasingly critical for generating synthetic training data for embodied AI and robotic perception. These models typically struggle with strict geometric constraints, leading to hallucinations that degrade the utility of the synthetic data. To address this, we introduce a novel, end-to-end physics-aware generation framework namely PhysMirror that natively enforces projective geometry through explicit 3D spatial priors. Our method automatically lifts prompted objects into 3D meshes and constructs a lightweight, mathematically exact mirror scene within a simulated environment. By rendering this explicit 3D scene, we extract precise 2D conditioning elements, such as depth maps and segmentation maps, that serve as robust guiding signals for downstream diffusion models, guiding them to generate images with physically correct mirror reflections. Moreover, we introduce Mirror Consistency Score (MCS), reference-free, fully automated metric that quantifies physical correctness using dense feature matching and vanishing point convergence. Experimental results on our newly constructed MirrOB dataset demonstrate that our approach outperforms state-of-the-art baselines in reflection accuracy and physical realism, while maintaining strong text-to-image semantic alignment, providing a reliable pipeline for embodied AI data generation. The source code is released at this https URL.
[CV-226] WorldBagel: Uncovering the Power of Unified Multimodal Models for Vision-Language-Action-World Modeling ECCV2026
链接: https://arxiv.org/abs/2607.03461
作者: Zelin Zhao,Min Shi,Bo Yuan,Haotian Xue,Jialuo Li,Lama Moukheiber,Humphrey Shi,Yongxin Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Rejected by ECCV 2026, Arxiv Paper
Abstract:World models aim to capture environment dynamics in ways that support perception, reasoning, and action, and have recently become a central direction in Vision-Language-Action-World (VLAW) modeling. Meanwhile, unified vision-language models have demonstrated strong multimodal generation capabilities, yet their potential as world models remains underexplored. In this work, we introduce \textttWorldBagel, a unified VLAW framework built on BAGEL, a modern multimodal unified model, and use it to systematically investigate the role of unification in world modeling. Across multi-task robotic manipulation and cross-domain experiments, \textttWorldBagel consistently outperforms task-specific alternatives and learns action representations that are more structured and semantically aligned with visual and linguistic context. Experiments on LIBERO, Language Table, and Franka show that unification is not only an architectural convenience, but also a key factor in learning effective VLAW models, leading to consistent empirical gains and deeper insights into multimodal world modeling. Code and model checkpoints will be released upon acceptance.
[CV-227] Handwriting Trajectory Recovery with Diffusion Models
链接: https://arxiv.org/abs/2607.03422
作者: Hiroki Nagamatsu,Shoji Toyota,Seiichi Uchida
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recovering online pen trajectories from offline handwriting images, often referred to as handwriting trajectory recovery (stroke recovery), is an offline-to-online conversion task with applications in stroke-level editing and forensic analysis. We propose, to the best of our knowledge, the first diffusion-model-based framework for this task. Our method formulates trajectory recovery as image-conditioned generation and uses a denoising diffusion model to sample pen trajectories consistent with the observed ink trace. Through extensive quantitative evaluations on CASIA-OLHWDB (1.0-1.1), we verify that the proposed approach enables accurate recovery even for complex multi-stroke characters, substantially improving both temporal similarity (DTW/LDTW) and shape fidelity (AIoU) over representative prior methods such as PEN-Net and Cross-VAE. We further show that the model captures general stroke-order tendencies and generalizes to classes unseen during training, exemplified by cross-script transfer: a model trained on Chinese characters can recover reasonable stroke orders for Latin letters to some extent.
[CV-228] Efficient bias mitigation in T2I diffusion models using Concept Graphs
链接: https://arxiv.org/abs/2607.03397
作者: Mansi,Avinash Kori,Francesco Leofante
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Text-to-Image diffusion models often propagate harmful bias inherited from the training data. Existing bias mitigation techniques typically intervene only at the text encoder or provide inference-time guidance, often leading to generations that collapse into semantically incoherent outputs. To address these limitations, we introduce CO-ALIGN (Concept Ontology Alignment), a novel bias mitigation approach based on concept-graph alignment that operates on the model’s internal concept ontology. By aligning concepts within the text encoder and denoiser, CO-ALIGN achieves substantial bias reduction while preserving generative integrity. We demonstrate the effectiveness of concept-graph alignment across three paradigms: text-encoders, denoisers and joint text-denoiser ontology alignment. CO-ALIGN outperforms the state of the art, improving fairness by 30% , \Delta FID=11.4 in image quality, 2.8% in image fidelity, all while reducing semantically incoherent outputs by 88% . Beyond bias mitigation, we show that CO-ALIGN benefits other downstream tasks as well. In particular, our experiments demonstrate that better-aligned internal ontologies enhance concept unlearning robustness across multiple unlearning techniques.
[CV-229] mporalGS: Training-Free Plug-and-Play Acceleration for 3D Gaussian Splatting Rendering via Temporal Priors
链接: https://arxiv.org/abs/2607.03390
作者: Yuhongze Zhou,Zihao Yang,Xinxin Zuo,Juwei Lu
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注:
Abstract:3D Gaussian Splatting (3DGS) has revolutionized novel-view synthesis with its fast and high-fidelity rendering. However, rendering at high FPS and low latency across various scenes remains a challenge, especially when large amounts of 3D Gaussian ellipsoids appear in the scene. To address this issue, we introduce TemporalGS, to the best of our knowledge, the first training-free plug-and-play algorithmic approach to accelerate 3DGS rendering without any post-training or post-processing, implemented on top of tile-based software rasterization. The key idea is that, instead of rendering frames independently as 3DGS, we leverage the temporal priors, represented by novel geometry and appearance buffers, etc., to reduce redundancy of Gaussian preprocessing, sorting, and rasterization operations of consecutive frames. Specifically, we propose two acceleration strategies: (1) temporal dynamic culling, which filters out Gaussians that contribute less to current frame rendering; (2) selective rendering, which renders only a small portion of tiles that cannot be approximated by the temporal priors. By adapting and interleaving these two strategies, TemporalGS yields a simple but effective plug-and-play solution for 3DGS rendering speed-up without any training. Extensive experiments show that TemporalGS achieves comparable or even better performance compared to existing state-of-the-art post-training or post-processing-based 3DGS rendering acceleration approaches. TemporalGS can significantly enhance the rendering speed of various 3DGS methods, achieving up to 1.48\times acceleration, while maintaining competitive rendering quality. We further extend our TemporalGS to hardware rasterization-based 3DGS to show the portability of our algorithm.
[CV-230] Present but Not Remembered: Auditing How Frozen VLAs Encode Deploy and Steer Visual History
链接: https://arxiv.org/abs/2607.03372
作者: Chih-Ting Liao,Xin Cao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:A frozen vision-language-action model (VLA) receives recent observations at every decision step, yet prior work has focused on adding memory rather than asking how existing history is represented and used. We study this temporal axis using layer-resolved linear probing and causal interchange interventions across three VLAs from two architecture families. We find a three-part dissociation. First, past-frame content remains linearly decodable throughout the network. Second, information unique to history beyond the current frame is nearly absent, indicating that stored history is largely a redundant copy of the present. Third, history is causally deployed only when the current frame is heavily degraded, while the action readout progressively loses dependence on history through the network. Although all models encode history similarly, their deployment strategies differ: under the same occlusion, one architecture increasingly relies on history as a fallback, whereas the other relies on it less. We further introduce a training-free temporal deployment audit that distinguishes these regimes. In the fallback regime, re-injecting history neither repairs occlusion nor disambiguates actions, confirming the redundancy of the stored representation. In the other regime, the same intervention reliably steers the predicted action toward the donor history. These results show that steerability depends on how history is deployed rather than whether it is encoded. VLAs do not forget the past; they largely fail to represent it as information distinct from the present. Our findings suggest that future memory augmentation should inject information unique to the past rather than simply more history.
[CV-231] Brand-as-Memory: Vision-Language Models Encode Causal Mechanistically Localizable Credibility Priors for News Sources
链接: https://arxiv.org/abs/2607.03365
作者: Chih-Ting Liao,Xin Cao
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-language models (VLMs) increasingly read news and web content as images, where the publisher’s identity is visually present. We show that VLMs carry a strong source-credibility prior keyed on outlet identity, and study it along three axes. (i) Cross-model benchmark. We introduce CueTrust, a cross-model diagnostic that measures which surface source cue overrides an article’s content evidence via a Source-Override Index (SOI). Across seven VLMs and five cues, the vulnerability profile is model- and scale-dependent, and the override is outlet-identity-specific and encoding-invariant, firing from the masthead name, the logo image, or the bare domain, but not from a named author, in-text authority, or page layout (clean negative controls). (ii) Mechanistic account. For the brand cue, we give a full mechanistic account: swapping only the masthead moves credibility across an approximately 11 log-odds range that tracks professional ratings (rho = 0.88 with Media Bias/Fact Check). The prior is dual-coded (name and logo), strengthens with scale, is causally formed at layers 19-21, carried by interpretable seed-stable sparse-autoencoder features, and recurs at the same relative locus in a second model family. It overrides content (about 1.8x) as a signal-magnitude effect within a shared pathway, not a privileged route. Steering the localized direction selectively reduces the override (41% reduction) and generalizes to held-out outlets, confirming the prior is causally used, not merely decodable. Deployed VLMs may thus defer to source identity over the evidence in front of them, a reliability failure we can measure across models, localize, and causally probe. We release the stimulus suite and CueTrust.
[CV-232] GrowFields: Compositional 4D Neural Fields for Topology-Changing Plant Growth ECCV2026
链接: https://arxiv.org/abs/2607.03330
作者: Joaquin Gajardo,Michele Volpi,Marko Mihajlovic,Siyu Tang,Lukas Roth,Sergey Prokudin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCV 2026
Abstract:Quantifying plant growth dynamics from sparse longitudinal 3D observations is fundamental for agriculture and plant sciences. Yet, plants pose unique challenges: they undergo intricate non-rigid deformations, exhibit changing topology as new organs emerge, and often lack explicit temporal correspondences between consecutive data acquisitions due to newly formed tissue. Methods designed for general scenes struggle to model topology changes and asynchronous organ growth characteristic of plants. To address these challenges, we introduce GrowFields, a compositional dynamic neural field representation for organ-aware 4D plant growth modelling from point cloud time series. Our approach decomposes a plant into its constituent organs and aligns each organ into its own canonical coordinate frame, isolating intrinsic growth patterns from global plant motion. We then learn a shared continuous neural deformation field that models temporal dynamics across all organs, conditioned on learnable per-organ latent codes capturing organ identity and growth characteristics. The resulting modular yet unified representation naturally accommodates the asynchronous development of plant organs while remaining grounded in the practical setting of organ-level plant tracking. We evaluate GrowFields on growth sequences from four plant species, assessing geometric fitting and organ tracking accuracy using manually annotated leaf-tip trajectories. Results demonstrate consistent improvements in spatial precision, temporal coherence, and morphological fidelity over a range of existing representations.
[CV-233] From General Actions to Domain-Specific Monitoring: Prior-Adaptive Transfer for Skeleton-Based Action Recognition
链接: https://arxiv.org/abs/2607.03327
作者: Hao Wang,Di Yang,Jiangtao Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Skeleton-based action recognition models have recently shown strong performance on large-scale benchmarks with general actions. However, directly transferring them to domain-specific tasks e.g., healthcare monitoring, is often suboptimal, as such tasks are narrow in scope and may be relevant to only a subset of general motion priors. Moreover, not all pretrained motion patterns are equally useful for a specific task, and retaining less relevant components may hinder adaptation and increase computational cost. To address these challenges, we propose Prior-Adaptive Transfer of Skeletons (PATS), a framework that adapts general skeleton-based models by selectively retaining task-relevant motion priors while filtering redundant ones during transfer. PATS follows a standard pipeline that extracts skeleton signals from videos and employs a spatio-temporal backbone pre-trained on general actions. The key contribution lies in a novel Adaptive Prior Transfer module, which performs model compression as a prior selection mechanism through iterative pruning and refinement. Experiments on two specific action recognition tasks, Alzheimer’s detection and fall detection, show consistent improvements in both performance and efficiency over competitive baselines. The code will be released upon acceptance.
[CV-234] LBTCap: A Lightweight Bilateral Transformer for Real-Time Remote Sensing Image Change Captioning
链接: https://arxiv.org/abs/2607.03320
作者: Licheng Zhang,Siew-Kei Lam,Naveed Akhtar
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Remote sensing image change captioning (RSICC) generates natural-language descriptions of semantic changes between paired remote sensing images (RSIs), supporting applications such as urban planning, disaster response, and environmental monitoring. Although recent methods achieve strong captioning accuracy, most overlook computational efficiency and inference speed, which are essential for real-time deployment in practice. To this end, we propose LBTCap, a lightweight RSICC framework built on a bilateral Transformer that jointly models pre- and post-change features for efficient processing of paired RSIs. Specifically, we introduce a bilateral attention mechanism for paired inputs: the two temporal images are projected into separate queries and keys by the same query and key matrices shared across the two images, the value is formed from their concatenation, and the two resulting attention maps are combined by a learnable, structurally bilateral weighting instead of a fixed subtraction. This design keeps both temporal branches explicit while remaining compact, and, together with a truncated backbone and grouped-query attention, LBTCap uses only 39.99M parameters, of which the change-aware encoder accounts for just 2.78M. Extensive experiments on two public RSICC datasets show that LBTCap matches or closely approaches the accuracy of state-of-the-art methods while using far fewer parameters and running at markedly higher inference speed, with the benefit of the bilateral formulation most pronounced in the low-resource setting, demonstrating a favorable accuracy-efficiency trade-off for practical RSICC.
[CV-235] Defending from GeoLocalization through Adversarial Road Trips ECCV2026
链接: https://arxiv.org/abs/2607.03277
作者: Niccolò Niccoli,Federico Becattini,Lorenzo Seidenari
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCV 2026
Abstract:Retrieval-based image geolocalization has emerged as a powerful technique for determining the location of a query image by matching it against a large, geotagged database. The success of deep learning based approaches has raised concerns regarding privacy and safety. A way to protect users from geolocalization is to design adversarial attacks for such methods. In this paper, we introduce RoadTrip Attack (RTA), a novel and highly effective targeted adversarial attack for geolocalization. RTA conceptualizes the adversarial process as finding an optimal distractor journey to a specific, attacker-chosen location. It employs a beam search algorithm to iteratively construct a sequence of incorrect geographic locations that form a path to the target. At each step, the attack generates subtle perturbations to the query image, guiding the geolocalization model toward the next location in this deceptive path. We show that our method is also strong in black-box settings, obtaining highly transferable attacks with less perceptible image artifacts.
[CV-236] OmniLayout: A Schematic-Coupled Multimodal Benchmark for Constraint-Aware Geometric Reasoning in PCB Layout
链接: https://arxiv.org/abs/2607.03261
作者: Taiting Lu,Kaiyuan Lin,Mingjia Wang,Haolin Ye,Runze Liu,Yuxin Tian,Vahe Melkonyan,Haoyu Wang,Muchuan Wang,Chufan Hong,Yifan Yang,Sung-Liang Chen,Yi-Chao Chen,Yicheng Jin,Mahanth Gowda
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent large language models (LLMs) have demonstrated remarkable progress in 3D spatial reasoning, spatial grounding, and fine-grained geometric understanding. However, their ability to reason about densely packed object placement under strict spatial and functional constraints remains largely unexplored, despite being a fundamental challenge in practical electronic design automation (EDA) workflows. To bridge this gap, we introduce OmniLayout, the first benchmark designed to evaluate LLMs on printed-circuit-board (PCB) layout placement reasoning under real-world geometric, routing, and connectivity constraints. OmniLayout contains 1,681 industrial-grade schematic-coupled PCB layouts and includes four tasks: (1) geometric reasoning for IC physical placement, with 77.24K placement instances constrained within PCB board boundaries; (2) routability-aware placement reasoning, generating physically valid component placements; (3) electrical functionality, preserving schematic-specified connectivity and electronic functional correctness; and (4) tool-augmented agentic reasoning for invoking external tools to accomplish tasks (1)-(3). Our results reveal substantial limitations of current LLMs in PCB layout placement, including weak geometric reasoning, poor routability optimization, and inconsistent preservation of electrical functionality.
[CV-237] A Decomposable Probe for Few-Step Diffusion Models: Prompt Latent and Score Selectivity across Backbone Families and Distillation Paradigms
链接: https://arxiv.org/abs/2607.03256
作者: Patrick Mu Haojie
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 3 figures
Abstract:Few-step distilled diffusion students cut text-to-image inference from ~50 to 1-8 network evaluations, but the quality gap is usually summarised by a single FID/CLIP scalar that cannot say which axis of the conditioning response changed, nor whether a behaviour comes from the architecture, the distillation objective, or simply from being a diffusion model. We replace the scalar with a decomposable probe that injects controlled perturbations along three layers (prompt encoder, denoiser input, denoiser output) under three modes (mean, variance, scale) and six strengths, reporting a bootstrap-median Bures W2^2 selectivity ratio on Inception features. Under a single matched estimator across 23 models – five teachers and 18 distilled students spanning five backbone families (SDXL, SD1.5, SD3.5, PixArt-alpha, FLUX), three architecture classes (UNet, DiT, MMDiT), and five distillation paradigms – the three layers read three empirically separable factors: the prompt layer is a universal prompt-mean response (a sanity channel, not a discriminator), the latent layer reads the prediction type, and the score layer reads the distillation objective. Our main result: within this sweep, the latent layer is a near-binary detector of rectified-flow backbones. Its ratio exceeds 1 across a sustained low-to-mid band only for rectified-flow models (SD3.5, FLUX); no epsilon-prediction model qualifies. A matched epsilon-prediction control (PixArt-alpha) rules out wide-T5 conditioning, and the fingerprint survives adversarial (ADD) distillation as both teacher and student. Two secondary score-layer findings hold under narrower scopes: a canonical 4-step ADD-vs-rest contrast on the UNet families with a non-ADD baseline, and a CI-separated trajectory-rollout early-strength score spike on both UNet and DiT. All ratios are CI-citable under one estimator; we release the per-cell tables and the estimator.
[CV-238] Semantic Segmentation-Driven Image-Level Diagnosis of Liver Cancers in Hematoxylin and Eosin Histopathology Images
链接: https://arxiv.org/abs/2607.03253
作者: Ivica Kopriva,Dario Sitnik,Arijana Pacic,Karolina Krstanac,Irena Veliki Dalic,Marijana Popovic Hadzija
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 42 pages; 6 figures, 9 tables
Abstract:As hematoxylin eosin (HE) staining constitutes the primary entry point in routine diagnostic workflows, computer-aided diagnosis from whole-slide HE images is of particular clinical relevance. However, substantial variability in specimen preparation, staining protocols, and scanning conditions, together with inherent uncertainty in expert pixel-level annotations, makes automated analysis of HE-stained images challenging. In this study, we propose a semantic segmentation-based framework for image-level diagnosis, grounded in the clinically motivated assumption that each histopathological image corresponds to a single cancer type. Image-level predictions are obtained by assigning the class of the dominant pixel-level label in the segmentation output. To ensure clinical relevance, we adopt the nnU-Net architecture and train it on a publicly available dataset collected in our study with pixel-level annotations for three liver cancer types: hepatocellular cacrcinoma (HCC; 55 images from 30 patients), cholangiocellular carcinoma (CCA; 55 images from 29 patients), and colorectal metastatic adenocarcinoma (CMA; 60 images from 30 patients). Annotations were independently provided by four pathologist. We hypothesize that the combination of stain normalization and semantic segmentation mitigates domain shift and reduces sensitivity to annotation noise. Five-fold cross-validation yielded balanced accuracy of 0.975 (HCC), 0.950 (CCA), and 1.000 (CMA), comparable to results obtained with immunohosthochemical staining and superior to several deep learning models trained on patch-level annotations. The proposed framework has the potential to support pathologists in prioritizing immunohistochemical marker selection, thereby reducing diagnostic costs and turnaround time. Integration with immunohistochemical findings improve overall diagnostic reliability.
[CV-239] Learning to Suppress SPAD-based LiDAR Flare
链接: https://arxiv.org/abs/2607.03247
作者: Xuanya Zhu,Linghao Shen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Single-Photon Avalanche Diode (SPAD)-based Light Detection and Ranging (LiDAR) is emerging for autonomous vehicles due to its high sensitivity and precise depth sensing capabilities. However, flare caused by excessive photon returns or pile-up effects can lead to incorrect depth estimation and exaggerated boundaries in point clouds, resulting in severe distortions of geometric measurements, making flare suppression essential for safety-critical applications. Existing flare mitigation methods primarily operate at the hardware or signal-processing levels. While effective under specific configurations, they are largely rule-based and configuration-dependent, lacking learnable representations that generalize across diverse sensing scenarios. In this work, we reformulate flare suppression as a semantic segmentation problem, enabling data-driven learning of geometric and photometric cues directly from SPAD measurements. We first benchmark representative segmentation models on the newly introduced SPAD flare dataset and observe that they struggle to exploit the intrinsic multi-echo characteristics of SPAD signals. Motivated by this observation, we propose Physically-Informed segmentation for LiDAR Flare (PILF), a learning-based approach that treats the first and second echoes, together with ambient illumination, as distinct modalities, aggregating cross-echo information while jointly encoding geometric and photometric features. Experiments across multiple real-world scenes demonstrate that PILF significantly outperforms compared segmentation models, achieving up to 79.32% mIoU, and providing an effective solution for SPAD-based LiDAR flare suppression.
[CV-240] Fast 3D Foundation Model Initialized Gaussian Splatting CEC
链接: https://arxiv.org/abs/2607.03209
作者: Anurag Dalal,Daniel Hagen,Kjell G. Robbersmyr,Kristian Muri Knausgård
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Robotics (cs.RO)
备注: 8 pages, 5 figures, 5 tables. Accepted at ICECET 2026
Abstract:This paper introduces a fast method for high-quality 3D Gaussian Splatting (3DGS) reconstruction without traditional Structure-from-Motion (SfM). The proposed approach leverages 3D Foundation Models (3DFMs) for camera pose and point-cloud initialization, then jointly optimizes both camera poses and Gaussian primitives using a depth-guided loss function. This enables fast convergence even from rough initialization with as few as 50-60 input views. To further improve reconstruction quality in sparse-view scenarios, an MLP-based pose refinement module is introduced alongside depth-guided supervision from the foundation model. Extensive experiments on Mip-NeRF 360, Tanks and Temples, and RobustNeRF demonstrate that the proposed method achieves competitive reconstruction quality (23.61 dB PSNR, 0.19 LPIPS) while reducing training time to approximately three minutes per scene. The proposed method produces ready-to-use 3DGS models at a fraction of the time required by existing pipelines, making it suitable for near real-time applications in robotics, VR, and autonomous navigation.
[CV-241] Seeing Through WiFi: Lightweight Human Pose Estimation with Dynamic Kernel Attention
链接: https://arxiv.org/abs/2607.03196
作者: Toan D. Gian,Van-Dinh Nguyen,Vo Phi Son,Nhan Thanh Nguyen,Dinh Thai Hoang,Diep N. Nguyen,Nguyen Cong Luong,Symeon Chatzinotas
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Submitted for possible publication (13 pages, 7 tables, 11 figures)
Abstract:WiFi-based human pose estimation (HPE) enables the detection and interpretation of human body positions and movements without the need for wearable devices while preserving individual privacy concerns. Implementing this solution requires enhancing model performance and maintaining efficiency, especially on resource-constrained devices. This paper introduces a novel framework, WiLHPE, for lightweight and efficient human pose estimation using WiFi CSI signals. Empowered by a camera-based model during training, WiLHPE processes raw WiFi signals directly to estimate human poses in the testing phase. It employs a novel neural network architecture to dynamically learn convolutional kernels and apply attention mechanisms across channel and frequency spaces. This innovative method diversifies the kernels to improve the recognition capabilities of WiFi signals without adding complexity, ensuring efficiency. Additionally, the Tree-Structured Parzen Estimator algorithm is employed to optimize the critical hyperparameters of the neural network efficiently, minimizing the time required for optimal hyperparameter search compared to heuristic methods. Results from experiments on both the MM-Fi and WiPose datasets highlight the superiority of WiLHPE over state-of-the-art approaches, achieving 85.96% and 94.27% at PCK50, respectively, with minimal computational overhead. Notably, WiLHPE performs impressively even under challenging conditions, maintaining around 80% at PCK50 under AWGN noise with an error variance of 0.5.
[CV-242] BVS: Bayesian Visual Search with Multimodal Large Language Model for Fine-grained Perception ICML2026
链接: https://arxiv.org/abs/2607.03184
作者: Geng Li,Yuxin Peng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ICML 2026. Project page with code: this https URL
Abstract:While Multimodal Large Language Models (MLLMs) demonstrate impressive general capabilities, they struggle with fine-grained perception in ultra-high-resolution (UHR) images, particularly for tiny objects in cluttered scenes. Existing methods face a dilemma: they either rely on inefficient prior-free scanning, or depend on static prior-driven heuristics that lack posterior correction to rectify initial model biases. To address this, we propose BVS (Bayesian Visual Search), a framework that formulates perception as a global optimization problem over a continuous spatial-scale manifold. Specifically, BVS bridges prior guidance with posterior correction: it utilizes an early-stop attention rollout of MLLM to construct reasoning-aware priors, while employing a scale-aware non-stationary kernel and GP-UCB to dynamically rectify noise and recover missing information in the prior through iterative local observations. We provide theoretical guarantees via sub-linear regret bounds, and extensive experiments demonstrate that BVS significantly outperforms state-of-the-art baselines with a superior trade-off between accuracy and efficiency.
[CV-243] FairFlow: Demystifying and Mitigating Stereotype Bias in Text-to-Image Diffusion Transformers
链接: https://arxiv.org/abs/2607.03180
作者: Chen Chen,Yuanmin Huang,Zhenfei Zhang,Mi Zhang,Xiaohan Zhang,Yun Xiong,Xiaoyu You,Min Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 11 figures
Abstract:Multimodal diffusion transformers (MM-DiTs) have emerged as the prevalent backbone for modern text-to-image generation systems. However, they exhibit critical alignment vulnerabilities, systematically manifesting severe stereotype biases even under benign prompts. This poses a significant risk of algorithmic discrimination in deployed systems. Since most existing mitigation strategies were tailored for legacy U-Net architectures, the precise remediation of these vulnerabilities in MM-DiTs remains a critical open challenge. In this work, we first investigate the root cause of this vulnerability via mechanistic analysis. We reveal that bias representations in MM-DiTs are not uniformly distributed across depth, but are mediated by a sparse set of layers functioning as internal semantic binding hubs. These hubs exhibit a stage-wise propagation driving bias manifestation: early hubs establish the structural templates susceptible to bias, middle hubs actively extract core stereotypical concepts from textual conditioning, and late hubs globally solidify these biases through visual self-attention. Leveraging these architectural insights, we propose FairFlow, an intrinsic, mechanism-guided mitigation framework. FairFlow acts as an internal regulator by employing sparse steering: it learns attribute-specific fair directions and injects them exclusively at the identified semantic hubs within a constrained inference window. Evaluations on FLUX.1-dev and Stable Diffusion~3 demonstrate that FairFlow effectively neutralizes these stereotypical vulnerabilities across gender, race, and intersectional settings, achieving an optimal fairness-fidelity balance. With near-zero inference overhead and robustness to complex prompts, FairFlow provides a lightweight and practical bias mitigation for large-scale deployed MM-DiT systems. Code and datasets will be publicly released upon acceptance.
[CV-244] Rethinking Brain Decoding with CLIP: The Role of Adversarial Robustness
链接: https://arxiv.org/abs/2607.03165
作者: Byeongseo Bok,Futa Waseda,Jun Liu,Isao Echizen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 20 pages, 10 figures
Abstract:Brain decoding aims to uncover neural mechanisms by inferring stimulus-related representations from brain signals. In fMRI studies, this is typically achieved by mapping fMRI responses to the latent representations of computational models. Recently, CLIP has become a popular choice for brain decoding due to its rich vision–language embedding space. However, aligning fMRI signals with CLIP representations remains challenging. As CLIP is not explicitly optimized for neural alignment, its representations may capture statistically predictive cues that are only partially reflected in brain activity, limiting decoding performance. In this paper, we investigate whether adversarially robust representations improve neural decoding with CLIP. Adversarial training suppresses non-robust features and promotes more stable, perceptually structured representations, which may better align with brain activity. We evaluate this by fixing the fMRI decoder and varying only the target representation (standard CLIP vs. robust variants) on fMRI-image retrieval and zero-shot classification tasks across NSD and GOD datasets. Empirical results show that this simple change consistently improves task performance and yields stronger alignment across multiple metrics. Attribution analysis further reveals consistently low agreement between standard CLIP and its robust variants, suggesting that adversarial robustness reorganizes feature importance in the visual representation. These findings suggest that the choice of target representation influences neural decoding performance and that adversarial robustness may serve as a useful criterion for brain decoding.
[CV-245] DistillH-Mamba: A Hypergraph-Mamba-Based Knowledge Distillation Model for Efficient Impact Fall Detection
链接: https://arxiv.org/abs/2607.03156
作者: Tresor Y. Koffi,Youssef Mourchid,Mohammed Hindawi,Yohan Dupuis
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 8 figures. Published in IEEE Sensors Journal, 2025
Abstract:Falls among the elderly represent a significant public health concern due to their prevalence, consequences, and societal burden. While deep learning has improved fall detection, accurately identifying impact moments (when an individual hits the ground) remains challenging. Additionally, current algorithms often rely on complex models with high computational demands, limiting real-time deployment feasibility. In this work, we propose DistillH-Mamba, a novel architecture for impact fall detection that addresses these challenges through three key innovations: First, we introduce a hypergraph-based approach that captures higher-order relationships between multiple joints simultaneously, enabling more accurate modeling of complex interactions during impact falls. Second, we integrate the Mamba architecture with hypergraphs for impact detection, significantly accelerating processing speed while efficiently capturing both long-term dependencies and sudden skeletal motion changes. Third, we employ relational knowledge distillation that preserves crucial spatial-temporal relationships while reducing computational demands for real-time impact fall detection. Evaluated on the 3D Skeletons UP-Fall and UMAFall datasets, our DistillH-Mamba model achieves 97.38% accuracy in detecting impact within fall events and 73.8% reduction in inference time compared to its teacher model, outperforming state-of-the-art methods in both precision and efficiency.
[CV-246] CuBAS: Information Geometric Curvature-Based Adaptive Sampling for Supervised Classification
链接: https://arxiv.org/abs/2607.03145
作者: Alexandre L. M. Levada
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Information Theory (cs.IT); Machine Learning (stat.ML)
备注: 28 pages, 7 tables, 6 figures
Abstract:The informativeness of a training set is as consequential as its size, yet most sampling strategies remain agnostic to the intrinsic geometry of the data distribution. We introduce CuBAS (Curvature-Based Adaptive Sampling), an information-geometric framework for adaptive data selection in supervised classification, grounded in the q-state Potts Markov random field (MRF) model. The central insight is that a labeled dataset can be viewed as a statistical manifold, on which local curvature, estimated via the ratio of second to first-order observed Fisher information, faithfully encodes the geometric complexity of the data distribution. We construct a k-nearest-neighbor graph over the labeled data and derive a closed-form curvature score at each vertex from the Potts sufficient statistics. This curvature signal partitions the graph into two complementary regimes: low-curvature regions, corresponding to smooth, homogeneous clusters, and high-curvature regions, concentrated around decision boundaries that are disproportionately informative for classification. By selecting nodes from both regimes, CuBAS constructs compact yet maximally informative training subsets. Empirical evaluation across more than 60 benchmark datasets demonstrates consistent and statistically significant improvements over random sampling and uncertainty-based baselines, across a wide range of labeling budgets and classifier architectures. CuBAS is computationally efficient (linear in the number of k-NN graph edges), theoretically grounded in the differential geometry of statistical manifolds, and interpretable in terms of the local shape operator of the data manifold.
[CV-247] xt as Partial Constraint: Core-Residual Alignment for Robust Vision-Language Learning
链接: https://arxiv.org/abs/2607.03143
作者: Chengzhen Yu,Canran Xiao,Siyuan Ma,Yang Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-language alignment powers open-vocabulary recognition, retrieval, and LVLM grounding, yet natural captions are often underspecified, making similarity brittle and overly confident under paraphrase and omitted details. We aim to learn representations whose matching is stable across caption views and whose confidence reflects how strongly text constrains an image. We propose Text as Partial Constraint (TPC), a core-residual alignment framework that treats multi-view captions as incomplete supervision. It distills a consensus semantic core as the alignment target, learns a single-view core predictor for standard inference with one query, and explicitly discourages vision-language similarity from depending on the orthogonal unsaid residual. An uncertainty-aware contrastive objective further softens alignment when caption views disagree, reducing overconfident updates under weak language constraints. Across zero-shot recognition and adversarial robustness, TPC achieves 81.42/64.05 Top-1 clean/robust accuracy on ImageNet and 76.19/52.03 on an Avg-14 transfer suite, while improving LVLM transfer with 85.16 POPE F1 and 59.57 OKVQA accuracy under an LLaVA-1.5-7B stack. These results suggest that modeling text as a partial constraint is a practical and principled route to more reliable vision-language representations under underspecified language supervision.
[CV-248] A Multi-Task Deep Learning Framework for Real-Time Intelligent Video Surveillance with Temporal Event Validation
链接: https://arxiv.org/abs/2607.03131
作者: Estera Dumitru,Stelian Spînu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 12 pages, 5 figures. Project repository available at: this https URL
Abstract:Modern video surveillance systems generate far more video streams than human operators can effectively monitor, making automated analysis essential for timely detection of security events. This paper presents a unified multi-task deep learning framework that simultaneously performs face recognition with zone-based authorization, automatic license plate recognition, weapon detection, fire and smoke detection, and human action recognition on a shared GPU platform. Among the integrated modules, two task-specific deep-learning models are proposed in this work to address scenarios that are insufficiently represented in publicly available datasets: a single-class weapon detector fine-tuned on a merged and relabeled dataset, achieving a mean average precision (mAP@0.5) of 0.947, and a SlowFast-R50 action recognition model trained on a purpose-built vandalism dataset comprising 614 video clips, achieving 94.33% classification accuracy. To improve robustness in continuous video, all detection modules are integrated into a temporal event-validation architecture based on multi-frame confirmation, confidence-weighted voting, and cascaded filtering, transforming frame-level predictions into reliable security events. Each module is evaluated independently on established public datasets (LFW, D-Fire, FIRESENSE, and UCF-Crime), followed by integrated end-to-end system evaluation. The proposed temporal validation strategy reduces the fire and smoke false-alarm rate from 52% to 4% and improves video license plate exact-match accuracy from 66.7% to 81.8%, while the complete framework maintains real-time operation with a per-frame latency below 100 ms on commodity hardware. These results demonstrate that combining specialized deep-learning models with temporal event validation provides an effective and practical solution for reliable real-time intelligent video surveillance.
[CV-249] Vidu S1: A Real-Time Interactive Video Generation Model
链接: https://arxiv.org/abs/2607.03118
作者: Jintao Zhang,Kai Jiang,Jintao Chen,Xu Wang,Yang Luo,Yuji Wang,Dechuang Chen,Jungang Li,Chengyang Ye,Marco Chen,Hongzhou Zhu,Min Zhao,Yuxuan Jiang,Zhengkun Huang,Chendong Xiang,Kaiwen Zheng,Haoxu Wang,Xiaohang Wang,Qi Jia,Xin Chen,Yimin Chen,Youhe Jiang,Fangcheng Fu,Zhijie Deng,Fan Bao,Jianfei Chen,Jun Zhu
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:We introduce Vidu S1, a real-time interactive video generation model supporting voice control of digital characters. Users can control video generation content at any moment through voice instructions. Vidu S1 supports infinite-length real-time video generation without blurring, drift, or visual distortion. Built with TurboDiffusion and TurboServe, Vidu S1 outputs 540p real-time videos at up to 42 FPS on regular consumer GPUs. Users can upload custom images of real people, anime, and pets, and choose different voice tones for personalized experiences. Experiments show that Vidu S1 achieves the best performance across all test metrics while fully meeting real-time inference requirements. A playable online demo is available at this https URL.
[CV-250] ExpoMotion: A Large-Scale Benchmark and A Householder Projection Network for Multi-Exposure Fusion ECCV2026
链接: https://arxiv.org/abs/2607.03110
作者: Yao Liu,Lishen Qu,Shihao Zhou,Jie Liang,Hui Zeng,Yabin Peng,Huipeng Lin,Lei Zhang,Jufeng Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV 2026
Abstract:Multi-Exposure Fusion (MEF) effectively extends dynamic range, but practical deployment is hindered by motion-induced ghosting and the scarcity of high-quality dynamic benchmarks. Current benchmarks largely neglect dynamic scenes and lack reliable ground truth, making it difficult to handle the complexity of real-world motions. In response, we introduce ExpoMotion, a large-scale benchmark designed to evaluate deghosting capabilities. Comprising 1,738 sequences and 10,909 images across diverse environments, it covers a wide range of motions and provides high-fidelity GTs constructed through an expert-guided acquisition pipeline. To tackle the complex dynamics and extreme conditions captured in this benchmark, we propose the Householder Orthogonal Projection network (HOP), which revisits MEF deghosting from a mathematical perspective via Householder transformation, decoupling multi-frame alignment into exposure pre-alignment and ghost filtering. Specifically, the Global Priors Illumination Alignment (GPIA) module first rectifies drastic dynamic range discrepancies by utilizing global statistics for exposure harmonization. Regarding ghost removal, our Householder Orthogonal Attention (HOA) models artifacts as orthogonal perturbations. By employing a dynamic Householder reflector, HOA effectively projects ghosts out of the feature manifold while preserving high-frequency details. Experiments demonstrate that our ExpoMotion dataset enables superior generalization and artifact-free detail restoration, while also validating the effectiveness and efficiency of the HOP method. The dataset and code are available at this https URL.
[CV-251] Observable- and Positional-Encoding-Dependent Symmetry Readout from Neural Network Weights
链接: https://arxiv.org/abs/2607.03108
作者: Naoya Chiba,Satoshi Sugiyama,Yuki Uranishi
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Post-hoc analysis of trained neural network weights often seeks to recover geometric structure directly from the parameters. We show that, for positional-encoding-equipped neural fields, the symmetry visible from weights is not the true symmetry group itself, but an observable symmetry set determined by the trained parameters, the positional encoding (PE), and readout observable. We formulate this dependence through an exact observability hierarchy, G_\mathrmobs^\mathrmexact \subseteq G_\mathrmlift^\mathrmexact(\phi) \cap G_\mathrmtrue , where G_\mathrmlift^\mathrmexact(\phi) is the set of input transformations that the PE can exactly lift to the feature space. The hierarchy implies that even when a target function has a geometric symmetry, that symmetry may be structurally invisible to weight-level observables if the PE does not represent the corresponding transformation. We test this prediction using MLPs trained on two-dimensional signed distance functions with multiple shape symmetry groups, positional encodings, and Gram-based observables. The results show a consistent PE-dependent pattern: DyadicAxisPE supports D_4 -sensitive readout but structurally suppresses D_3 rotations, TriAxisPE yields lower D_3 / D_6 readout scores under the tested Gram observables by replacing coordinate axes with three 120-degree-separated axes, and random Fourier features mainly exhibit a \pi -rotation response under these readouts. These findings show that PE design affects not only approximation behavior but also which structures are accessible to post-hoc weight-level readouts. This provides a basis for a principled observable-dependent symmetry readout.
[CV-252] SNR-Adaptive Unified Diffusion for Multi-Task Medical Image Segmentation
链接: https://arxiv.org/abs/2607.03103
作者: Jiahao Liu,Hang Wei,Shuai Wu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to IEEE SMC 2026. 6 pages, 15 figures
Abstract:Clinical cardiac imaging pipelines currently deploy separate models for each dataset and modality, incurring redundant training costs and precluding knowledge sharing across anatomically related tasks. Consolidating semi-supervised learning, unsupervised domain adaptation, and domain generalisation into one model is therefore a practical necessity, yet naive joint training exposes a fundamental barrier: conflicting label semantics between datasets collapse LA Dice from 90.31% to 83.38%, while gradient imbalance across tasks of unequal complexity suppresses the weaker tasks throughout training. We present UniT-Diff, a unified diffusion segmentation framework that resolves these conflicts through three targeted mechanisms. An 11-channel task-specific output space physically partitions label categories, eliminating cross-task gradient sign reversal by construction. SNR-Adaptive Task Conditioning (SATC) scales the task token by the log signal-to-noise ratio of the current diffusion timestep, suppressing domain-specific bias during coarse denoising and restoring full task guidance as the signal clears. Task-Type-Aware Conditional Dropout (TTACD) permanently removes the task token for domain-generalisation inputs, routing them through a shared neutral pathway that draws on cross-dataset cardiac anatomy rather than source-vendor statistics. Under a single parameter set, UniT-Diff surpasses independently trained task-specific baselines on all three benchmarks simultaneously: +0.87% on LA, +1.77% on MMWHS, and +0.88% on MNMS.
[CV-253] Robustness Meets Uncertainty: Evidential Adversarial Training for Robust Selective Classification
链接: https://arxiv.org/abs/2607.03075
作者: Nicolas Sournac,Ahmed Baha Ben Jmaa,Bertrand Braeckeveldt
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Safety-critical applications require classifiers that are both robust and reliable. Adversarial training is a widely adopted defense for improving robustness in deep neural networks; however, its effect on the reliability of predictive uncertainty remains underexplored. We investigate this gap through the lens of selective classification, which has rarely been systematically analyzed alongside adversarial robustness. We introduce a unified benchmark for the robustness-uncertainty trade-off. It standardizes architectures, augmentations, threat models, and evaluation metrics across clean, adversarial, and common-corruption settings. Across a wide range of state-of-the-art adversarial training methods, we uncover a recurring failure mode: several approaches improve robust accuracy while degrading uncertainty ranking, leading to poorer selective behavior. To address this, we propose Evidential Adversarial Training (EV-AT), which models uncertainty through a Dirichlet distribution and combines (i) an evidence-based loss promoting clean accuracy and reliable uncertainty with (ii) a robust evidence-alignment loss matching clean and adversarial predictions in log Dirichlet-parameter space. Extensive experiments show that EV-AT shifts the Pareto frontier of robustness-uncertainty trade-offs beyond prior state-of-the-art adversarial training methods. Our source code is publicly available at this https URL.
[CV-254] Attention-Guided Efficientnet Architecture For Precise Criminal Identification in Surveillance Images
链接: https://arxiv.org/abs/2607.03073
作者: Savitha N J,Lata B T
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Link: this https URL , Publisher: Sichuan University, Chaina
Abstract:Criminal identification from surveillance imagery has become a critical research area in intelligent forensic surveillance systems due to the increasing deployment of CCTV cameras in public and private environments. However, surveillance-based face recognition remains highly challenging because of low image resolution, illumination variation, motion blur, pose changes, facial occlusion, and background clutter. To address these limitations, this paper proposes an Attention-Guided EfficientNet (AG-EfficientNet) framework for precise criminal identification in surveillance images. The proposed framework integrates EfficientNet-B0 with Convolutional Block Attention Modules (CBAM) to enhance discriminative facial feature learning under degraded surveillance conditions. In addition, a multi-scale surveillance feature fusion strategy is introduced to preserve both local texture information and high-level semantic identity representations. A hybrid Softmax-Triplet optimization mechanism is further employed to improve inter-class separability and intra-class compactness for robust criminal identity discrimination. The proposed framework was experimentally evaluated using the Labeled Faces in the Wild (LFW) and SCFace datasets. Experimental results demonstrate that the proposed AG-EfficientNet framework achieved superior surveillance recognition performance with an identification accuracy of 98.2%, Precision of 97.9%, Recall of 97.6%, F1-Score of 97.7%, and ROC-AUC of 0.99, outperforming conventional deep learning architectures including AlexNet, VGG16, ResNet50, MobileNetV2, and standard EfficientNet-B0. Furthermore, Grad-CAM visualization and ablation analysis confirm the effectiveness of the proposed attention-guided feature learning strategy.
[CV-255] SafeGuard: A Multi-Agent Perception-Reasoning Framework for Social-Risk AI-Generated Video Detection ECCV2026
链接: https://arxiv.org/abs/2607.03069
作者: Wenlin Wu,Sheng Zhou,Peipei Song,Wenhao Wang,Junbin Xiao,Xun Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026, The code and dataset are publicly available at this https URL
Abstract:As video generation paradigms evolve from localized manipulation to full-scene synthesis, AI-generated video detection becomes increasingly challenging, as forgeries exhibit coherent global structure and high perceptual realism. However, existing benchmarks are biased toward perceptual fidelity and primarily evaluate detectors based on perceptual artifacts, providing limited coverage of scenarios that require reasoning about violations of physical laws, structural coherence, or social logic. This dataset bias shapes current approaches and results in a Perception-Reasoning Gap: artifact-centric models capture low-level statistical irregularities yet lack semantic inference, whereas vision-language models perform semantic reasoning but remain insensitive to fine-grained forensic cues. To bridge this gap, we propose SafeGuard, a multi-agent framework that enables collaborative specialization between forensic perception and semantic reasoning. A hierarchical perceptual solver extracts fine-grained forensic evidence, while a self-reflective verifier enforces consistency between semantic inference and physical plausibility, forming an interpretable evidence chain. To support evaluation, we introduce SafeVid, a novel AI-generated video detection benchmark comprising 20K videos spanning 10 social risk categories, designed to evaluate physical plausibility, structural consistency, and the rationality of social behaviors. Extensive experiments demonstrate the generalization of SafeGuard, improving accuracy on SafeVid by +18.7% and consistently outperforming prior methods across four public benchmarks.
[CV-256] PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation
链接: https://arxiv.org/abs/2607.03068
作者: Ebenezer Tarubinga
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
备注:
Abstract:Semi-supervised semantic segmentation (SSSS) has long turned on one question, which pseudo-labels to trust, and answered it with ever more careful confidence filtering. Foundation backbones change the regime: with a DINOv2 teacher a strict threshold already retains a measured 98%-clean pseudo-label set, so the accuracy that remains lives not in the filter but in how the embedding space is structured by class. We propose PixCon, a clean-positive pixel-contrastive framework. PixCon maintains a per-class memory bank that admits only labeled pixels the student already classifies correctly, guaranteeing a contamination-free positive set ( \rho_F=0 ) by construction, unlike prior contrastive SSSS banks (ReCo, U ^2 PL) built from confidence-filtered pseudo-labels. It is a single branch over a consistency backbone, adds no inference-time parameters, and needs no bank-specific threshold. A first-order analysis of the supervised-InfoNCE gradient explains why contamination hurts: its false-positive term scales as \rho_F/(1-\rho_F) , which we measure (0.018 on Pascal, 0.106 on ADE20K) rather than assume. Across Pascal VOC, Cityscapes, and ADE20K, PixCon matches or improves a strong DINOv2-based UniMatch V2 baseline in a compute-matched one-switch protocol: it improves every Pascal-1/8 seed (a per-seed gain of about +0.2 mIoU) and its three-seed mean reaches 87.90, the published UniMatch V2-B figure. Because contamination is already rare under foundation-model teachers, our analysis indicates the \rho_F=0 guarantee acts chiefly as robustness as teachers weaken, while the accuracy gain comes from cleaner positive supervision, making clean-positive contrast a robust, low-cost default for foundation-model SSSS.
[CV-257] Lightweight Polyp Segmentation via a Gain-Aware Prediction-Space Recursive Controller
链接: https://arxiv.org/abs/2607.03062
作者: Jiachi Zhang,Zhuoyu Wu,Quanjun Wang,Wenhui Ou,Wenqi Fang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:While lightweight polyp segmentation is highly desirable for low-cost deployment, reported performance gains often stem from upgraded backbone encoders, complex decoders, or heavy refinement branches. Consequently, it remains difficult to isolate whether a lightweight correction mechanism is inherently effective on its own. We address this limitation by formulating refinement as a prediction-space recursive correction task, introducing a recursive controller that operates directly on backbone logits. Under a fixed recursion budget, this controller aggregates discrepancy and uncertainty evidence, updates a compact state tracking recent correction utility, and applies additive residual logit corrections. By design, this correction path remains small, host-portable, and deployment-explicit. Utilizing a unified Kvasir-trained protocol, we evaluate our approach across seven lightweight backbones on Kvasir-SEG and three transfer datasets, measuring segmentation accuracy (Dice/IoU) alongside deployment efficiency (parameters, GMACs, and peak memory). The controller yields consistent improvements in the source domain, achieves competitive performance against both training-side baselines and heavier structural refiners on representative hosts, and delivers selective transfer gains with minimal static overhead. Code is available at this https URL.
[CV-258] RIGS-Refiner: Risk-Guided Recursive Refinement in Prediction Space for Colonoscopy Polyp Segmentation
链接: https://arxiv.org/abs/2607.03058
作者: Jiachi Zhang,Zhuoyu Wu,Wenqi Fang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Post-refinement can improve colonoscopy segmentation after host inference, but many designs still rely on extra correction heads or multi-stage pipelines with non-negligible parameter or computational cost. For polyp segmentation, host predictions are often already reasonable globally, with remaining errors clustered around ambiguous boundaries and difficult local structures. These residual errors matter in colonoscopy images because useful masks need correct lesion coverage and clean contour delineation across subtle mucosal transitions. This setting favors selective local repair in prediction space over reprocessing the entire mask. We therefore propose RIGS-Refiner, a lightweight post-refinement plugin for risk-guided recursive refinement in prediction space. Starting from a frozen host anchor prediction, RIGS-Refiner extracts lightweight image priors and prediction cues, applies risk-guided update, and writes back residual corrections through a shared recursive cell. The module adds only +519 parameters and +0.631 GFLOPs, keeping the refinement path compact for deployment. Experiments use Kvasir-SEG for training and Kvasir, ClinicDB, ColonDB, and ETIS for evaluation under two frozen hosts, namely PraNet and SegFormer-B0. Results show consistent gains on both hosts and a favorable efficiency-accuracy trade-off against representative post-refinement methods. Code is available at this https URL.
[CV-259] OmniFocus: Query-Guided Modality-Balanced Token Compression for Omni-Modal Large Language Models
链接: https://arxiv.org/abs/2607.03050
作者: Shijie Cao,Qingyu Zhang,Boxi Yu,Yuzhong Zhang,Boxi Cao,Yaojie Lu,Hongyu Lin,Xianpei Han,Le Sun
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD)
备注:
Abstract:Omni modal large language models (OmniLLMs) have attracted wide attention for their ability to jointly process audio and video, but they generate large token sequences under audio-visual inputs, leading to substantial inference cost. Existing audio-visual token compression methods often rely on unimodal guidance, overlooking the temporal locality of query-relevant evidence in audio-visual inputs and implicitly assuming that the two modalities share a temporally aligned information density distribution. We propose \textbfOmniFocus, a training-free query-guided token compression method for OmniLLMs that performs independent importance estimation for video and audio, enabling a modality-symmetric compression design that preserves modality-specific salient evidence while maintaining audio-visual alignment, thereby mitigating the modality bias issue that can arise from unimodal-guided compression. Experiments on the Qwen2.5-Omni model family across four audio-visual benchmarks show that OmniFocus maintains strong compressed performance at low token retention ratios and outperforms existing baselines on several major benchmark scores at 25% token retention. On DailyOmni with Qwen2.5-Omni-7B at 25% token retention, OmniFocus maintains 59.40 accuracy while delivering up to 1.38 \times prefill speedup relative to the full-token baseline, highlighting a favorable practical accuracy-efficiency trade-off.
[CV-260] xt-to-Image Generation for Projector-Camera System Registration
链接: https://arxiv.org/abs/2607.03046
作者: Xinyu Chen,Yuqi Li,Jiabao Li,Pinyan Tang,Chong Wang,Aditi Majumder
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Establishing correspondence between projector and camera images in a procam (projector + camera) system is essential for achieving high-resolution pixel matching, referred to as procam registration. The highest accuracy is typically obtained using structured light patterns (e.g., stripes or blobs). However, these methods are often inefficient and lack meaningful information for human viewers. Although some have explored the use of natural images, these often fail to provide a sufficient distribution of features to achieve comparable accuracy. Additionally, existing methods struggle to cope with environmental factors such as surface textures and variations in brightness due to ambient light or changes in camera exposure. To address these limitations, we propose a method based on deep neural networks. Our approach aims to generate a single natural image from text-based prompts that not only appears realistic but also possesses rich spatial features to enhance registration accuracy in procam applications. We have developed a deep neural network trained on a synthesized dataset that simulates potential geometric and photometric distortions encountered in a procam system illuminating a relatively smooth object (see Figure 1). Our trained network predicts the correspondence between projector and camera images, significantly improving registration accuracy across various procam configurations. By jointly considering the naturalness and feature richness of the projector images, our method minimizes visual disruptions in projected content without sacrificing precision. A user study confirms that our technique enhances perceived naturalness and usability compared to existing methods, validating its practical utility in real-world applications.
[CV-261] CURE: Controllable Unified Image Restoration for Complex Degradations ICPR2026
链接: https://arxiv.org/abs/2607.03044
作者: Boseong Kim,Donghyeon Cho
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ICPR 2026
Abstract:The presence of composite degradations poses a significant challenge, since the underlying corruption factors exhibit complex and interdependent interactions. Even when the degradation types are known, accurately restoring the image remains difficult due to the intertwined nature of their effects and the need for selective control during the recovery process. To address this, we introduce CURE, a unified framework that enables controllable restoration in complex degradation settings by learning disentangled and adjustable representations. CURE is driven by four complementary objectives. First, an identity embedding is incorporated, along with a reconstruction constraint, to ensure that the model can reproduce the input image when restoration is unnecessary. Second, the ratio control mechanism blends the identity embedding with degradation-specific embeddings using user-regulated mixing ratios, allowing continuous control over restoration intensity. Third, an intermediate loss is applied to supervise stepwise outputs, each encouraged to tackle the removal of only a single degradation factor within a composite mixture. Finally, a permutation-invariant loss ensures that the model achieves consistent restoration quality regardless of the order in which multiple degradations are addressed. Since CURE modifies only the training strategy and not the underlying network architecture, it can be seamlessly integrated into existing controllable restoration models. Experiments demonstrate that CURE delivers state-of-the-art performance on composite degradation benchmarks, while enabling both selective and jointly fused restoration through flexible modulation of embedding ratios. The code and dataset are available at this https URL.
[CV-262] Natural Language Camera Movement Understanding ECCV2026
链接: https://arxiv.org/abs/2607.03043
作者: Yuwen Tan,Joey Huang,Jin Huang,Haoxiang Li,Boqing Gong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026
Abstract:Understanding camera movement in natural language is critical for training and evaluating video generation models, among other applications. However, we demonstrate that existing vision-language models (VLMs) fail this task in surprising ways, frequently confusing translation with rotation, left with right, and object movement with camera movement. To address these limitations, we establish natural language camera movement understanding as a standalone research task. We introduce a two-level cinematographic taxonomy and an extensive, atomic benchmark featuring both real and synthetic videos. Furthermore, we curate a large-scale, multi-source training set enhanced by targeted camera movement augmentation. Our fine-tuned VLM-8B outperforms Gemini 3.1 Pro by 10% and 11% on our benchmark’s real and synthetic videos, respectively. Despite these gains, a significant gap remains relative to human performance, underscoring the need to promote and facilitate future research on natural language camera movement understanding.
[CV-263] OmniDS: Dual-Stream Context Fusion for Omnidirectional Depth from Fisheye Cameras
链接: https://arxiv.org/abs/2607.03038
作者: Chaesong Park,Jihyeon Hwang,Muyeol Sung,Jongwoo Lim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 3 figures, 3 tables
Abstract:Omnidirectional depth estimation from multi-fisheye camera rigs is complicated by visibility conflicts: wide baselines cause different cameras to observe different portions, or even different faces, of the same object, so aggregating their features into a unified equirectangular (ERP) representation under fixed projection produces ambiguous matching evidence near occlusion boundaries and thin structures. Although existing methods mitigate this by down-weighting unreliable views, they do not resolve the underlying discrepancy because context formation and cross-view fusion remain tied to rigid fisheye-to-ERP sampling. We present OmniDS, an iterative depth refinement framework that replaces rigid aggregation by combining dynamic context fusion with consensus-aware multi-view similarity. A dual-stream encoder pairs a lightweight CNN for geometric detail with a frozen DINOv3 for semantic priors; their features are reprojected into ERP space at each refinement step via learned view weighting and deformable cross-attention with geometric distortion bias. In parallel, a multi-view consensus volume captures global cross-camera agreement through group-wise correlation and feature variance, regularized by a 3D U-Net. For efficient deployment, we distill the dual-stream representation into a single MobileNet-based encoder. OmniDS achieves state-of-the-art performance on the OmniThings, OmniHouse, and Sunny benchmarks while maintaining competitive inference speed. Project page and codes are available at this https URL.
[CV-264] C3ASD: Multi-Level Consistency-Driven Representation Learning ECCV2026
链接: https://arxiv.org/abs/2607.03018
作者: Jin Hong,Jisoo Park,Junseok Kwon
类目: Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD)
备注: ECCV 2026
Abstract:Active Speaker Detection determines whether a visible person in a video is speaking at each moment. While recent audio-visual fusion methods perform well on clean data, they degrade under real-world corruptions such as background noise, occlusion, or simultaneous modality degradation. We attribute this limitation to the absence of explicit consistency constraints that promote robust, semantically aligned representations across modalities. Without such guidance, models tend to learn fragile modality-specific shortcuts that fail under corrupted conditions. We propose C^3 ASD, a multi-level consistency-driven framework with three complementary constraints: embedding-level inter-modality consistency aligns audio-visual representations during speech; sequence-level intra-modality consistency separates speaking and non-speaking clusters via track-aware contrastive learning; and prediction-level consistency stabilizes fusion through knowledge distillation. Extensive experiments demonstrate significant improvements under diverse audio, visual and joint corruptions, while maintaining competitive performance on clean data.
[CV-265] MambaLIE: Scene Light Intensity-Boosted Low-Light Image Enhancement with State Space Model
链接: https://arxiv.org/abs/2607.03013
作者: Wanshu Fan,Xiangyu Li,Cong Wang,Kin-man Lam,Xin Yang,Haiyan Zhang,Dongsheng Zhou
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Images captured by consumer electronic devices, such as mobile phones and digital cameras, often suffer from low-light degradation due to sensor limitations and imaging pipelines, which degrades visual quality and affects downstream vision tasks. Existing methods based on Convolutional Neural Networks (CNNs) and Transformers have dominated current low-light image enhancement (LIE) due to their excellent ability to model hierarchical features. However, CNNs operate in local receptive fields that cannot model long-range dependencies, while Transformers overcome this problem but incur substantial computational costs. To address these challenges, we propose MambaLIE, a Scene Light Intensity-Boosted Low-Light Image Enhancement method based on a State Space Model (SSM). We first introduce scene light intensity to improve the structural distribution of illumination, which is then gated with the low-light input to guide enhancement. To better model the illumination while maintaining computational efficiency, we propose the Locally Enhanced State Space Model (LESSM) for efficient light enhancement. Our LESSM contains two branches: an SSM branch and a Local Enhanced branch, where the former is used to model the long-range dependencies with linear time complexity, while the latter is used to enhance local feature representations. Extensive experiments demonstrate that MambaLIE outperforms state-of-the-art CNN-based and Transformer-based LIE methods on four widely used synthetic benchmarks and five publicly available real-world benchmarks in terms of accuracy, speed, and model size, making it suitable for practical deployment on resource-constrained devices. Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.03013 [cs.CV] (or arXiv:2607.03013v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.03013 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Li Xiangyu [view email] [v1] Fri, 3 Jul 2026 06:46:45 UTC (32,516 KB) Full-text links: Access Paper: View a PDF of the paper titled MambaLIE: Scene Light Intensity-Boosted Low-Light Image Enhancement with State Space Model, by Wanshu Fan and 5 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.CV prev | next new | recent | 2026-07 Change to browse by: cs cs.AI References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[CV-266] HyperVAttention: Efficient Sparse Attention with Spatio-Temporal Clustering for Video Diffusion
链接: https://arxiv.org/abs/2607.03012
作者: Dongyeun Lee,Amir Zandieh,Vahab Mirrokni,Junmo Kim,Insu Han
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 21 pages, 8 figures
Abstract:Video Diffusion Transformers (VDiTs) have demonstrated significant capabilities in high-fidelity video generation. However, their ability to produce long-duration videos is fundamentally constrained by the quadratic complexity of the self-attention mechanism. Recent clustering-based sparse attention methods improve the quality-speed trade-off by grouping semantically similar tokens, but their practical efficiency remains limited by two bottlenecks: substantial clustering overhead and low CTA utilization caused by irregular cluster-induced blocks. We propose HyperVAttention (HVA), a training-free sparse attention framework that addresses both bottlenecks jointly. To reduce clustering overhead, we introduce 3D local-window clustering, which exploits the spatio-temporal locality of video tokens to restrict centroid search to fixed local neighborhoods, and implement it with a custom Triton kernel for efficient execution. We further propose a hybrid clustering strategy that performs full clustering only at anchor steps and updates only subset tokens at intermediate steps, leveraging the temporal stability of cluster assignments across denoising steps. To improve CTA utilization, we present hardware-aware cluster merging that minimizes CTA-aligned execution cost through parallel agglomerative merging, improving block density and approximation fidelity by utilizing idle tile capacity. Together, these components reduce clustering overhead, avoid redundant updates, and better align sparse attention with the fixed tile structure of modern GPU kernels. Experiments on Text-to-Video generation show that HVA establishes a new Pareto frontier for training-free sparse attention in video diffusion, reducing end-to-end latency by up to 2.13\times while improving fidelity over existing training-free sparse attention baselines.
[CV-267] PosterHarness: Turning Scientific Poster Generation into an Auditable Instruction-Following Benchmark
链接: https://arxiv.org/abs/2607.03006
作者: Tianyi Yang,Dawei Fu,Youpeng Wu,Zixun Kou,Linrui Chen,Ruobing Jiang,Zijian Wang,Qiang Li
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Text-rich image models can now design poster-scale layouts, but we lack ways to measure whether they honor scientific communication contracts: legible labels, prescribed aspect ratios, and – above all – abstaining from fabricated scientific figures. We present POSTERHARNESS, an auditable harness reframing poster generation as measurable instruction-following tasks, with a pilot benchmark and failure taxonomy. POSTERHARNESS uses a placeholder-first contract to separate two jobs models otherwise conflate. The model performs visual-summary design: typography, reading path, color, and background – but never draws data-bearing figures. Every figure region must be an empty labeled placeholder; a deterministic compositor inserts real source-paper figures at detected coordinates. This makes properties measurable: placeholder count and ID accuracy, blankness, aspect-ratio compliance, abstention from synthesized graphics, public-text hygiene, and source-figure provenance – with failures logged as explicit rejections, not hidden in plausible-looking output. We instantiate the harness on 12 papers (6 HEP, 6 AI/ML-adjacent) and report three findings. (i) A counterfactual probe shows the placeholder contract drives VLM-counted synthesized figures from 34 to 0 across three papers. (ii) A failure taxonomy identifies blocking contracts: placeholder geometry, placeholder QA, template critic, and public text. (iii) Comparison with Paper2Poster shows a trade-off: PosterHarness yields higher-resolution artifacts, lower white-canvas fraction, and stronger VLM visual preference; the deterministic baseline retains slightly more PosterQuiz-style information and runs faster. We report this as regime characterization, not a superiority claim. All artifacts, prompts, manifests, and audit scripts are released as a reusable evaluation component.
[CV-268] REAL-OW: Rehearsal-free Open World Object Detection with Low-Rank Adaptation and Dual-Stage Objectness Modeling
链接: https://arxiv.org/abs/2607.03004
作者: Huazhong Zhang,Xiaowen Fu,Yang Zhang,Linlin Shen,Jinbao Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Open-World Object Detection (OWOD) requires detectors to identify previously unseen objects as unknown and incrementally incorporate them into the set of known categories, while preserving previously acquired knowledge. Existing frameworks rely heavily on exemplar replay to mitigate catastrophic forgetting, but in some real applications, storing raw data conflicts with data access restrictions and leads to data exposure risks, while incurring significant memory overhead. In this paper, we propose REAL-OW, a novel rehearsal-free framework that decouples incremental knowledge through a collaborative adapter architecture based on Low-Rank Adaptation (LoRA). Specifically, we deploy General Adapters (GAs) in the backbone to enable the significance-aware refinement of cross-task universal representations, while Specific Adapters (SAs) in the decoder provide orthogonal storage for task-specific expertise. To resolve representation drift in objectness modeling under rehearsal-free constraints, we introduce Dual-Stage Objectness Modeling (DSOM), which alternates between feature aggregation and boundary consolidation to stabilize objectness distributions while maintaining the separation between known and unknown categories. Furthermore, DSOM is supported by a Calibrated Gaussian Negative Log-Likelihood (CG-NLL) distance tailored for the dispersed feature distributions inherent in rehearsal-free settings. Extensive evaluations demonstrate that REAL-OW achieves state-of-the-art performance, surpassing existing exemplar replay methods in both detection precision and unknown discovery. Our approach establishes a new baseline for rehearsal-free OWOD.
[CV-269] CONFLUX: A Latent Diusion Model for 3D Chest-CT Synthesis with RL Post-Training
链接: https://arxiv.org/abs/2607.02998
作者: Max Van Puyvelde,Halil Ibrahim Gulluk,Wim Van Criekinge,Olivier Gevaert
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Controllable generative models of 3D medical images can synthesize volumes with specified clinical attributes, but this demands samples that are simultaneously high-fidelity, natively 3D, and faithful to the requested conditioning. We present CONFLUX, a latent diffusion model for chest computed tomography (CT): a 3D variational autoencoder compresses each volume, and a rectified-flow transformer generates in the latent space. Generation is conditioned on structured radiological metadata (18 abnormality findings, sex, age, and reconstruction kernel) through adaptive layer normalization. The model leads strong volumetric baselines on tri-planar Frechet distance (FID 32.3 vs. 74.6 for MAISI) while exposing direct control over clinical attributes. To strengthen that control we add an online reinforcement-learning post-training stage (group-relative policy optimization) that rewards how reliably a classifier recovers the requested findings from each generated volume. Judged by a separate, independent classifier, post-training removes 47% of the shortfall relative to real-scan reliability. We release the model and a ~200k synthetic chest-CT dataset with conditioning metadata spanning a wide variety of clinical findings.
[CV-270] VISTA: Auditing Semantic Divergence in Vision-Language Models
链接: https://arxiv.org/abs/2607.02995
作者: Junchi Liao,Jiawen Deng,Fuji Ren
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Preprint. 3 figures
Abstract:Vision-language models can exhibit visual concept-conditioned divergence: given images containing demographic features, corporate logos, or ideological symbols, some models produce unusually uniform responses that differ from what peer models say about the same input. These behaviors evade text-only audits because visual concepts cannot be isolated or substituted the way text tokens can. We present VISTA (Visual Inconsistency Screening Through Analysis), a black-box cross-model audit that couples semantic entropy with distribution-based divergence to flag model-specific anomalies. In a controlled study, we implant concept-conditioned stances in three VLMs via fine-tuning on small biased datasets and confirm that VISTA detects them. Auditing six VLMs across 19 topics, VISTA surfaces 142 high-suspicion cases (1.2%) and identifies selective refusal as a previously unreported divergence pattern, where models refuse demographic queries at rates varying from 0 to 65% across groups.
[CV-271] GuideMe: Multi-Domain Task Guidance and Intervention in Streaming Video ECCV2026
链接: https://arxiv.org/abs/2607.02991
作者: Fang Liu,Jinpeng Chen,Ke Xu,Yuhao Liu,Huankang Guan,Xudong Lu,Bo Yang,Gerhard Hancke,Rui Liu,Rynson W.H. Lau
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026
Abstract:While multimodal Large Language Models (MLLMs) excel at offline video understanding, an interesting question of how far they are from serving as a real-time procedural coach remains unknown. Such a role typically requires an MLLM to continuously monitor the execution, detect mistakes, and provide corrective guidance in a closed-loop interaction. In this paper, we construct GuideMe, the first multi-domain benchmark for streaming video that supports training and evaluation of MLLMs for closed-loop interactive task guidance. It comprises 2,458 videos spanning 223.7 hours across diverse domains (\eg, cooking, object manipulation, daily-life guidance, and fitness), with 47,775 interaction samples covering next-step instructions, completion feedback, error detection, and corrective guidance. To evaluate existing models on GuideMe, we design a three-component assessment framework to measure the capabilities of representative MLLMs, which consists of temporal-semantic bipartite matching for sequence-level alignment, behavioral classification for intervention timing, and LLM-as-a-Judge for content quality. Extensive experiments highlight a critical performance asymmetry: despite excelling at providing instructions, existing MLLMs consistently fail to identify execution errors and respond with corrective feedback. Code and data are released at this https URL.
[CV-272] Cross-device Collaborative Test-time Adaptation with Zeroth-order Optimization and Model Merging
链接: https://arxiv.org/abs/2607.02988
作者: Yu Mitsuzumi,Akisato Kimura,Yasuhiro Fujiwara,Hisashi Kashima
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Test-time adaptation (TTA) mitigates domain shifts by using incoming test data to update a model on the fly. The majority of TTA methods require resource-intensive backpropagation (BP) for model updates, particularly demanding large memory sizes, which makes it infeasible to deploy them on resource-limited devices (e.g., edge devices). To address this issue, we integrate two different techniques, zeroth-order optimization (ZOO) and model merging, under the recently established cross-device collaborative TTA (CDC-TTA) framework, where the system is composed of a mixture of resource-abundant and resource-limited devices, and the model information (e.g., model weights obtained on each device) is shared across the devices. Our method is executable on resource-limited devices by introducing ZOO, which requires only forward processing and bypasses the resource-intensive BP optimization. Concurrently, to mitigate the high-dimensional optimization difficulty caused by the side effect of ZOO, we incorporate model merging of the shared multiple models and set the merge coefficients as the optimization objective, which successfully reduces the optimization dimension. In addition, to enhance the synergistic combination of ZOO and model merging, we propose a unique preprocessing strategy that trims intra-model non-influential weights and reduces the inter-model information redundancy. We empirically confirmed the effectiveness of our method using common corruption and style-transferred image benchmarks.
[CV-273] RePos: Relative-to-Absolute Output Factorization for Cross-Environment WiFi-Based 3D Human Pose Estimation
链接: https://arxiv.org/abs/2607.02986
作者: Zhangcheng Hou,Tomoaki Ohtsuki
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 7 figures, 15 tables
Abstract:Device-free 3D human pose estimation using commodity WiFi Channel State Information (CSI) enables privacy-preserving and illumination-robust human sensing, but its deployment is limited by poor cross-environment generalization. Unlike images, CSI measurements do not have a spatially localized correspondence to body parts and are heavily affected by multipath propagation, causing models that regress absolute poses to entangle body structure with environment-specific location cues. Within a single environment this coupling is benign: an end-to-end absolute-pose variant, RePos-D, already achieves state-of-the-art accuracy on Person-in-WiFi-3D (86.9 mm MPJPE, a 3.4% gain over the previous best WiFi method, DT-Pose). Across environments, however, the same model overfits position and suffers significant performance degradation. We therefore propose RePos, a factorized framework that separates root-relative pose estimation from root localization. By preventing absolute-pose supervision from affecting the structure branch, RePos learns environment-invariant pose representations. Specifically, it groups CSI features into body-part-aware latent tokens that skeleton-guided modeling refines into the pose, while a separate amplitude-based network estimates the root position through a differentiable spatial-decomposition module. Under the strict MM-Fi cross-environment protocol, RePos achieves MPJPEs of 254.4-296.1 mm, a 10-21% reduction over existing WiFi-based methods. The improvement remains consistent across activity protocols, leave-one-environment-out splits, and leakage-free few-shot transfer. Analysis of the learned features shows that relative-pose representations remain largely position-agnostic, while root localization retains environment dependence, indicating distinct generalization behavior for structure and localization.
[CV-274] Awakening Diffusion Transformers: Eliciting Stronger Generation and Understanding via Massive Activation Modulation
链接: https://arxiv.org/abs/2607.02968
作者: Chaofan Gan,Zicheng Zhao,Yuanpeng Tu,Xi Chen,Ziran Qin,Tieyuan Chen,Supavadee Aramvith,Mehrtash Harandi,Weiyao Lin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Massive Activations (MAs) have been widely observed in Transformer-based models, yet their structure and functional roles in Diffusion Transformers (DiTs) remain insufficiently understood. In this work, we systematically analyze MAs in representative DiTs and find that they are spatially distributed across image tokens while concentrated in a small set of fixed feature dimensions. We further show that these dimensions are closely aligned with AdaLN residual scaling factors and are primarily modulated by the denoising timestep rather than text conditions. This structure leads to two task-dependent effects: for generation, MAs are critical for fine-grained detail synthesis while having limited influence on global semantics; for understanding, their shared high-magnitude directions make raw DiT features overly similar across spatial tokens and weaken dense feature discrimination. Based on these findings, we introduce Eliciting Massive Activation (EMA), a training-free framework that leverages Massive Activations (MAs) as a unified modulation signal to improve both generative and representational capabilities of DiTs. For generation, EMA proposes MA-driven Detail Guidance (DG), which suppresses MA dimensions to construct a detail-deficient counterfactual prediction and guides sampling toward finer visual details. DG further supports efficient partial-forward inference, integration with classifier-free guidance, and token-level Local DG for refining selected image regions. For understanding, EMA introduces MA-modulated REPresentation extraction (MREP), which uses pretrained AdaLN channel-wise modulation to reduce MA directional dominance and concatenates spatially normalized MA maps to preserve useful spatial structure. Extensive experiments demonstrate that EMA consistently improves both the generation quality and representation capability of DiTs.
[CV-275] Parallelized Autoregressive Decoding for Omni-Modal Dense Video Captioning ECCV2026
链接: https://arxiv.org/abs/2607.02963
作者: Wenzheng Zeng,Siyi Jiao,Chen Gao,Hwee Tou Ng,Mike Zheng Shou
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
备注: ECCV 2026. Project website: this https URL
Abstract:Dense video captioning aims to generate temporally grounded descriptions of video events, benefiting both event-level video understanding and generation. In this domain, autoregressive video large language models have emerged as a prevalent paradigm due to their strong generative and cross-modal modeling capacity. However, generating dense captions under the token-by-token paradigm severely limits inference efficiency and hinders scalability as video length and event density increase. In this work, we propose a parallelized autoregressive framework that not only improves generation efficiency but also enhances temporally grounded captioning performance. Our key insight is to exploit the weak local dependencies across temporally distinct events to restructure the causal dependency graph, thereby enabling lossless parallel generation. Specifically, tokens with weak cross-event dependencies can be decoded in parallel, while tightly coupled tokens within each event retain sequential decoding to preserve local semantic coherence. To realize this insight, we introduce two key components for lossless parallel decoding: (1) a latent global planning mechanism that automatically learns the event-level structure and produces compact tokens encoding global inter-event causality while adaptively aggregating event-level audio-visual semantics, guiding subsequent dependency restructuring and parallel decoding; and (2) an event-factorized parallel decoding mechanism that effectively balances local focus with global inter-event awareness. Experiments on various benchmarks demonstrate the clear advantage of our approach in both efficiency and performance in omni-modal event grounding and captioning. Project website: this https URL.
[CV-276] Overloading Large Vision-Language Models for Jailbreaking
链接: https://arxiv.org/abs/2607.02961
作者: Haoyu Zhang,Yangyang Guo,Mohan Kankanhalli
类目: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR)
备注:
Abstract:Large Vision-Language Models (LVLMs) exhibit remarkable vision-language capabilities and are increasingly deployed in real-world applications such as personal assistants, document analysis systems, and embodied agents. However, their dual-modal attack surfaces make them vulnerable to jailbreak attacks. Existing LVLM jailbreaks rely on simple designs, e.g., short text and out-of-distribution images. Nevertheless, recent advancements in both large language model backbones and multimodal mechanisms undermine these attacks, particularly their transferability among model architectures. To overcome this limitation, we propose a novel information overloading method that is equipped with both extensive text and multi-dimensional image attacks. These components are arranged in recursion-based image-typography layouts to exponentially increase multimodal information complexity. This overloading approach amplifies the cross-modal processing required, which undermines the safety alignment in LVLMs. Extensive experiments on both open-sourced and commercial LVLMs establish our method as a new state-of-the-art LVLM jailbreak attack. On open-source models, our method achieves an average ASR of 88.6%; on commercial LVLMs, it reaches an average ASR of 84.0%, exceeding the best baseline by 48.7%. Moreover, our prompts optimized on open-source surrogate models transfer effectively across model families. Beyond empirical results, we probe the safety-critical information flows within victim LVLMs. Our observations reveal that complex image-typography compositions induce intensified cross-modal processing and reduce the model’s certainty in generating refusal responses. Together, these findings highlight information overloading as a practical and emerging safety risk for real-world LVLM deployments, underscoring the need for stronger defenses against complex multimodal jailbreak inputs.
[CV-277] Incentivizing Vision Language Models to Search for Long Video Question Answering ECCV2026
链接: https://arxiv.org/abs/2607.02959
作者: Harsh Goel,S P Sharan,Sahil Shah,Minkyu Choi,Joungbin An,Kristen Grauman,Sandeep P. Chinchali
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: To appear at the European Conference on Computer Vision (ECCV 2026)
Abstract:We introduce VSeek, an agentic framework that transforms long-video question answering (LVQA) from a passive, single-pass perception task into a multi-turn retrieval process. VSeek utilizes a natural language-driven search to identify relevant context within long videos and is post-trained with reinforcement learning (RL) to jointly formulate targeted search queries and reason over retrieved clips for LVQA. While RL post-training has revolutionized reasoning in symbolic domains such as mathematics and code, its application to long-video understanding remains hindered by a lack of verified rewards. To ensure that the retrieved context is relevant, we propose a novel neuro-symbolic approach that bridges open-ended natural language with discrete visual verification. Specifically, complex user queries are compiled into formal temporal logic specifications for systematically decomposing natural language questions into a definitive checklist of required atomic visual primitives, such as key objects and activities, along with their temporal ordering. These systematically derived grounding events provide the critical feedback signal for RL post-training, enabling dense, verifiable rewards based on the successful retrieval of these specific visual elements rather than relying entirely on outcome-only answer accuracy. By explicitly optimizing for this verifiable evidence-seeking behavior, VSeek improves Pass@1 scores by up to 8% and Pass@4 scores by 15% on long-video understanding benchmarks compared to base models. We open-source our code at this https URL.
[CV-278] ReLo-IRR: Reflection-Guided LoRA Framework for Image Reflection Removal
链接: https://arxiv.org/abs/2607.02957
作者: Chaoqun Wang,Yuehuan Wei,Haoxiang Cao,Shaobo Min
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Single-image reflection removal (SIRR) aims to recover the clean transmission layer from a reflection-contaminated image. Although recent methods achieve promising results with large diffusion models, they rely on image-agnostic adaptation strategies, e.g., fine-tuning or ControlNet, that enforce uniform suppression regardless of reflection severity. As a result, heavy reflections often leave residuals, while weak ones suffer from detail loss. To this end, we propose ReLo-IRR, a reflection-guided LoRA framework built upon the rectified flow model. First, a lightweight estimator is designed to predict the reflection strength descriptor, providing an explicit prior of reflection dominance for each image and enabling image-dependent LoRA modulation. Second, we introduce a time-conditioned mechanism that fuses this reflection descriptor with timestep embeddings, enabling LoRA modulation to evolve consistently with the coarse-to-fine denoising process. By jointly modeling reflection strength and denoising dynamics, our ReLo-IRR achieves robust suppression of diverse reflection conditions. Extensive experiments on challenging benchmarks validate the effectiveness of ReLo-IRR, demonstrating superior dereflection performance and robust generalization. The code is released at this https URL.
[CV-279] Pooling-Based Context Modeling for Convolution-Free Deep Image Prior
链接: https://arxiv.org/abs/2607.02952
作者: Gihyun Kim,Jong-Seok Lee
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 16 pages, 9 figures
Abstract:Convolutional Neural Networks (CNNs) achieve strong denoising performance by exploiting spatial context from neighboring pixels. Deep Image Prior (DIP) leverages this property to restore images from a single noisy input without requiring large datasets. However, the over-parameterized architecture of DIP often leads to noise fitting during optimization. In this paper, we propose Pool-DIP, a convolution-free architecture that incorporates pooling-based contrast modeling to capture spatial context efficiently. Pool-DIP improves denoising performance while significantly reducing the number of parameters and computational complexity compared to convolution-based DIP models. Experimental results show that Pool-DIP achieves competitive performance across multiple datasets, including a real-world benchmark. Spectral analysis further reveals that Pool-DIP stabilizes the evolution of high-frequency components during optimization and suppresses erroneous high-frequency signals. The proposed architecture also generalizes well to other image restoration tasks such as super-resolution and inpainting.
[CV-280] MatPhaseBench: A Semantics-Guided Benchmark for Materials Phase Diagrams Understanding
链接: https://arxiv.org/abs/2607.02934
作者: Hanwen Wang,Sihan Liang,Zhiwei Liu,Yangang Wang,Wei Yan,Yuqin Liu,Zongguo Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI)
备注:
Abstract:Materials phase diagrams are a core knowledge representation in materials science, encoding temperature,composition, phase stability, and phase transformation pathways, with their full understanding requiring thermodynamic mechanism analysis and scientific reasoning. Although VLMs have shown promise in scientific image understanding, their systematic evaluation on such logically complex images demanding deep mechanistic interpretation remains limited, and phase diagrams provide a challenging testbed for this purpose. We introduce MatPhaseBench, a high-quality, high-reliability benchmark for complex scientific image understanding, focused on materials phase diagrams. MatPhaseBench is constructed from 3681 papers in classical materials science journals, from which 200 high-quality diagram-text pairs were selected, covering 189 material systems and 70 elements. The benchmark has three key features: (1)targeting complex scientific image understanding-it moves beyond simple objective tests to open-ended tasks requiring deep comprehension; (2)comprehensive image-text alignment-semantic information associated with images is fully preserved during literature mining and matching; (3) high-quality human-supervised text acquisition-all descriptions undergo strict manual validation. Experimental results show that current VLMs remain substantially behind expert-level understanding: they are largely limited to surface visual perception, lack deep reasoning grounded in thermodynamic mechanisms, have limited domain awareness and expert analytical experience, and perform poorly in distinguishing fine-grained differences in composite or multi-diagram settings. Overall, MatPhaseBench constitutes a challenging research-grade benchmark, providing a foundational platform for complex scientific image understanding, phase diagram analysis, and trustworthy multi-modal AI in science.
[CV-281] CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detection
链接: https://arxiv.org/abs/2607.02930
作者: Wen Dong,Zhao Wang,Shuangqing Zhang,Kai Sun,Ben Li,Guo-Sen Xie,Caifeng Shan,Fang Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal Large Language Models (MLLMs) excel in diverse vision tasks, but full-parameter retraining is computationally expensive as real-world knowledge evolves. Existing continual learning methods often suffer from semantic entanglement in parameter spaces across tasks, impeding the continuous deployment of models. This challenge is especially pronounced in Anomaly Detection (AD), which exhibits triple heterogeneity across modalities, domains, and defect scale variability, significantly complicating multi-task knowledge transfer. In this paper, we propose CL-Anomaly, a parameter-efficient fine-tuning framework based on an isolation-sharing collaboration to enable continual learning for anomaly detection with MLLMs. We introduce the task-private expert PrivLoRA, which physically isolates task-specific subspaces in the parameter space to prevent semantic entanglement of anomaly knowledge in diverse scenarios. The Layer-Adaptive Shared Experts maintain cross-task representations within a unified feature space, enabling knowledge sharing between previous and new tasks. Furthermore, we propose a Layer-Adaptive Knowledge Transfer strategy that automatically selects and dynamically updates the layer-wise key shared experts of each task via a momentum-based mechanism, promoting effective knowledge transfer across related anomaly detection tasks. Extensive experiments across three continual learning scenarios for anomaly detection, including class-incremental, cross-domain, and cross-modal, demonstrate that CL-Anomaly outperforms state-of-the-art methods. Code is available at this https URL.
[CV-282] VideoSearcher: Empowering Video Deep Research with Multi-Tool Agent ic Reasoning via Reinforcement Learning
链接: https://arxiv.org/abs/2607.02927
作者: Zhenkun Gao,Yicheng Bao,Jinlong Peng,Xueheng Li,Theo Huang,Bangwei Liu,Kunquan Li,Zhenye Gan,Tao Hu,Chengjun Xie,Mingqian Yang,Xuanhua He,Zhizhong Zhang,Xin Tan,Chengjie Wang,Yuan Xie
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Technical report. Project page: this https URL ; Code: this https URL ; Model Data on HuggingFace
Abstract:Video understanding is moving beyond closed-context perception toward open-world evidence exploration, a paradigm formalized as Video Deep Research (VDR). However, existing multimodal search agents primarily target static images, and the current VDR benchmark relies on text-centric retrieval that discards crucial visual information. To address these limitations, we propose VideoSearcher, a closed-loop agentic framework that empowers Vision-Language Models with multi-tool reasoning for VDR. VideoSearcher unifies temporal localization, spatial focusing, and multimodal search within a single reasoning trajectory, enabling agents to progressively ground visual clues, retrieve relevant evidence, and synthesize answers. To optimize knowledge-intensive reasoning trajectories, we propose Bi-branch Sequence Policy Optimization (BiSPO), a reinforcement learning algorithm that decouples tool-invocation optimization from answer-accuracy optimization. This design provides stable learning signals for both evidence-grounded reasoning and purposeful tool use. Furthermore, we construct VideoSearch-QA, the first benchmark designed to evaluate open-world video information grounding and multimodal search-based reasoning. Extensive experiments demonstrate that VideoSearcher significantly outperforms prior open-source agentic baselines across various search-oriented and multimodal understanding benchmarks.
[CV-283] STAC: Selective Spatiotemporal Aggregation and Compression for Video Reasoning Segmentation
链接: https://arxiv.org/abs/2607.02922
作者: Syed Ariff Syed Hesham,Yun Liu,Guolei Sun,Jing Yang,Henghui Ding,Xue Geng,Xudong Jiang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video reasoning segmentation demands pixel-accurate object tracking across hundreds of frames under complex natural language queries, producing dense spatiotemporal tokens whose quadratic self-attention cost makes long-video processing prohibitive. Existing methods address this through token compression, yet typically operate on encoder features lacking temporal context, constraining selection before content redundancy can be reliably assessed. Informed compression requires contextual awareness, but acquiring that awareness at full resolution incurs the same quadratic cost compression aims to reduce. State-space models resolve this constraint, as their linear recurrence selectively conditions each token on temporal context at \mathcalO(T) cost, producing representations where content redundancy becomes assessable. Building on this, Selective SpatioTemporal Aggregation and Compression (STAC) enriches features via decoupled bidirectional spatial and causal temporal scanning, leveraging recurrence-derived redundancy for hierarchical compression with adaptive thresholds optimised with segmentation objective. STAC achieves 85% token reduction and 1.8 \times speedup while surpassing compression-free baselines on reasoning segmentation benchmarks in a zero-shot streaming-compatible setting. Code is available \hrefthis https URLhere.
[CV-284] R3D: Quantitative 3D Spatial Reasoning for Egocentric Wearables
链接: https://arxiv.org/abs/2607.02921
作者: Maxwell Horton,Wei Lu,Quan Tran,Yury Astashonok,Kirmani Ahmed,Babak Damavandi,Anuj Kumar,Xiao Zhang,Seungwhan Moon
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Quantitative 3D spatial reasoning from egocentric RGB-D video is a critical capability for next-generation wearable assistants. Yet existing benchmarks do not reflect the challenges of handling (1) natural egocentric video, (2) posed RGB-D video inputs, and (3) challenging quantitative 3D spatial reasoning QA. To fill this gap, we introduce R3D-Bench (Reasoning in 3D), a benchmark of 3,033 quantitative spatial reasoning questions across 15 types – spanning multiple-choice, distance-based, and volumetric reasoning questions – built on top of 57 egocentric video sequences from Aria Digital Twin. To set a strong baseline on this dataset, we introduce R3D, a model-agnostic spatial tool-calling framework. In contrast to existing approaches that directly embed 3D information into the model’s input representation, R3D constructs a 3D scene from video using segmentation and depth-lifted object representations. It provides this information to an LLM through eight composable spatial tools. On R3D-Bench, R3D with Qwen3-VL 235B achieves 73.5% mean relative accuracy, substantially outperforming the best depth-enabled baseline (CuTR+Tools, 61.9%) and the best RGB-only baseline (Gemini 3 Flash, 46.5%).
[CV-285] Learning Taxonomic Trees with Hierarchical Representation Regularization for Large Multimodal Models ICML2026
链接: https://arxiv.org/abs/2607.02909
作者: Hulingxiao He,Zhi Tan,Yuxin Peng
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Published as a conference paper at ICML 2026
Abstract:Taxonomies provide key information about the semantic relationships between concepts and the inherent organization of vision and language. Despite their impressive capabilities, large multimodal models (LMMs) often lack taxonomic knowledge, leading to low hierarchical visual recognition (HVR) consistency. These models typically only rely on language modeling objectives during fine-tuning and lack explicit taxonomy-aware regularization. To address this, we propose Hierarchical Representation Regularization ( HiR^2 ), a simple plug-and-play regularizer that improves hierarchical consistency in LMMs. Specifically, we introduce a semantic-aware visual tree construction framework that extracts coarse-to-fine visual features from intermediate LLM layers guided by textual cues. The regularizer combines two complementary objectives: a taxonomic entailment loss that enforces hierarchy via hyperbolic entailment cones in the Lorentz model, and a discriminative dispersive loss that promotes angular separation of semantically similar embeddings on the unit sphere without disturbing the radial hierarchical structure. Extensive experiments demonstrate that HiR^2 effectively captures taxonomic structures across diverse LMMs and fine-tuning methods. Code is available at this https URL.
[CV-286] Holo-Captioning: Toward the Text Equivalent of 3D Scenes ECCV2026
链接: https://arxiv.org/abs/2607.02908
作者: Kun-Yu Lin,Chengke Bu,Zhenguo Li,Kai Han
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026
Abstract:This work introduces holo-captioning, a novel task that strives to seek the text equivalent of 3D scenes. As the initial step, we formulate holo-captioning as generating a structured textual description that comprehensively depicts all entities within a 3D scene – including their semantic tags, spatial locations, attributes, and inter-entity relations. To tackle this challenging task, we first develop an effective captioning engine to produce detailed descriptions of individual entity instances and instance pairs, and contribute a large-scale benchmark comprising over 15K scenes for training and evaluation. Building upon this foundation, we propose HoloScribe, a novel model that features an instance-aware decoupled pipeline for generating structured holo-captions, and further incorporates anchor-aware instance linking to identify relational instance pairs. Additionally, we propose a comprehensive evaluation metric named HoloScore, and provide a human-curated test set to ensure reliable model assessment. Experimental results demonstrate that HoloScribe significantly outperforms state-of-the-art 3D dense captioners and 3D LLM generalists, underscoring the effectiveness of our approach. Project page: this https URL
[CV-287] PPE-Bench: A Benchmark for Evaluating MLLM Unlearning under Private-Public Entanglement
链接: https://arxiv.org/abs/2607.02897
作者: Xianren Zhang,Delvin Ce Zhang,Dongwon Lee,Suhang Wang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 17 Pages
Abstract:Multimodal Large Language Models (MLLMs) have shown strong capabilities, but they may memorize private information from web data, raising privacy concerns. Machine unlearning offers a way to remove such private knowledge without retraining from scratch. However, existing MLLM unlearning benchmarks have two major limitations. First, they rely on simplified images that contain only the single target individual, failing to reflect the visual complexity of real-world photos. Second, they typically assume that the forget set and retain set are fully separated, ignoring the fact that private information is often visually entangled with benign public information. For example, a private individual may appear with a public figure or in front of a well-known landmark, where unlearning the private target should not damage the public context. To address these limitations, we propose PPE-Bench, a new benchmark for evaluating MLLM unlearning under private-public entanglement. Each image contains a target individual to be forgotten and public information to be preserved, including public figure and landmark. We further introduce two simple but effective methods to better preserve public information during unlearning. Through experiments, we find that existing unlearning methods can reduce private information leakage, but often substantially harm adjacent public information.
[CV-288] SPLIT: Training-Free AI-Generated and Partially Edited Video Detection via Spatial Patch-Level Incoherence and Temporal Roughness ECCV2026
链接: https://arxiv.org/abs/2607.02886
作者: Jongyeop Hyun,Hyounghun Kim
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: ECCV 2026 (32 pages)
Abstract:Deploying AI-generated video detectors in real-world services demands an ultra-low false positive rate (FPR) on real videos to avoid falsely rejecting authentic content, a regime where standard metrics such as AUROC fail to reflect actual operating behavior. We introduce Spatial Patch-Level Incoherence and Temporal Roughness (SPLIT), a training-free detector that operates on patch tokens from a frozen vision encoder to detect both fully generated and partially edited videos. SPLIT computes two complementary signals: Two-step Temporal Roughness (TTR), capturing non-smooth patch trajectories via one-step and two-step feature variation contrast, and Local Spatial Motion Incoherence (LSMI), measuring spatially inconsistent temporal changes through gradients of a feature-space motion field. The two are fused multiplicatively with gamma correction to sharpen real-fake separation at strict thresholds. We further propose a service-aligned evaluation protocol based on Fake Recall at fixed FPR with real-only threshold calibration and cross-real threshold transfer. Across three benchmarks (FakeParts, GenVideo, and ViF-Bench), SPLIT achieves the highest Fake Recall at FPR = 0.1% , substantially outperforming supervised and training-free baselines while remaining robust to post-processing with negligible overhead. The code is publicly available at this https URL .
[CV-289] E-TraMamba: A New Paradigm for Efficient Long-Term 3D Feature Tracking with Event Cameras
链接: https://arxiv.org/abs/2607.02866
作者: Juwei Shen,Yujie Wu,Changwen Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 14 pages
Abstract:Event-based 3D tracking enables low-latency and high-speed perception, while existing CNN- and Transformer-based trackers struggle to capture long-range spatiotemporal dependencies in sparse, noisy event streams, especially under real-time and efficiency constraints. To address these challenges, we present E-TraMamba, the first Mamba-based framework for 3D feature tracking on event data. This new framework adopts a linear state-space model for efficient long-range modeling and integrates a lightweight affine-transform predictor to maintain stable tracking under motion blur and occlusion. We also design an effective scheme to fuse multi-scale cues – local spatiotemporal patches, correlation maps, and positional embeddings – into a unified representation that enables stable and smooth 3D tracking. We construct a large-scale synthetic dataset, named EvD-PointOdyssey, which is generated with monocular rendering and provides synchronized event streams, depth maps, and accurate 3D trajectories for training and evaluating event-based 3D tracking models. Extensive experiments on event-based benchmarks demonstrate that E-TraMamba achieves state-of-the-art performance, delivering over 2\times longer feature lifetimes under strict accuracy thresholds (e.g., 0.1 m), with higher tracked-feature ratios and lower RMSE than all baselines. These results make E-TraMamba a strong candidate for low-latency visual odometry, real-time SLAM, and interactive robotics.
[CV-290] Cancelable Biometric Template Protection Based on Multi-Instance Fusion: A Contralateral Iris Approach
链接: https://arxiv.org/abs/2607.02860
作者: Jittarin Chaivong,Nicha Vikromrotjananan,Teekatat Piriyapittaya,Waree Kongprawechnon,Suradej Duangpummet
类目: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR)
备注: 6 pages
Abstract:Biometric templates are vulnerable to theft if stored without protection. Unlike passwords, a compromised iris cannot be reissued. Although existing cancelable biometric schemes address this problem, most still require an external key or token, introducing an additional attack surface. This paper proposes a cancelable contralateral iris template protection scheme that eliminates the need for a separate token or stored secret, satisfying the three requirements of ISO/IEC 24745: irreversibility, unlinkability, and confidentiality. The method fuses three enrollment samples per eye using Majority Vote Fusion to produce a stable template, and applies a salt-based bit permutation derived from the subject’s enrollment ID. Combining the left- and right-permuted templates via a bitwise XOR produces a single Protected Fused Template. Since left and right iris patterns are statistically independent, fusing contralateral irises improves the accuracy of the system. An attacker must possess both iris codes and both salts to recover any useful information, yielding a larger effective key space than single-iris schemes. Experiments on three datasets, CASIA-IrisV4-Interval, CASIA-IrisV2 (two devices), and CASIA-Iris-Thousand, yield EERs of 0.36 %, 4.88 %, 10.80 %, and 3.35 %, respectively; the highest value reflects the more challenging cross-device scenario. These results demonstrate that our contralateral approach outperforms unprotected baselines while remaining competitive with state-of-the-art cancelable methods. %In addition, an ablation study confirms the benefit on both recognition performance and security. To the best of our knowledge, this is the first scheme to combine tokenless multi-instance fusion and contralateral binding under ISO/IEC 24745.
[CV-291] Prior Bias in Vision Language Models on UML Diagram Interpretation
链接: https://arxiv.org/abs/2607.02853
作者: Zaiyu Cheng,Khai-Nguyen Nguyen,Antonio Mastropaolo
类目: Computer Vision and Pattern Recognition (cs.CV); Software Engineering (cs.SE)
备注: 16 pages, 14 figures, 2 tables
Abstract:Vision Language Models (VLMs) are increasingly applied to software engineering artifacts, especially UML class diagrams whose meaning depends on visual notation. Yet, it is unclear whether VLMs actually read such diagrams or instead answer from pretrained priors about how classes typically relate. We introduce a controlled UML benchmark in which each prior-conforming diagram is paired with its prior-conflicting counterpart that (1) preserves the same class names and layout while (2) reverses only the relation arrow. We evaluate eight open-source VLMs from two model families, InternVL3.5 and Qwen3, alongside two closed-source frontier models GPT-5.4 and GPT-5.4 Mini. Across the eight open-source models, reversing the arrow reduces relation-direction accuracy by 33.48% on average, while GPT-5.4 Mini retains a 10% gap. In the harder three-class condition, accuracy drops sharply by 45.28% for open-source models, and even 18.62% for the GPT-5.4 family on average. Scaling provides only limited improvements and is family-dependent. Our benchmark presents a diagnostic prior-driven failure in diagram-grounded software understanding. Our artifact is available at this https URL.
[CV-292] Differential Amplifier-Inspired AmpAttention for Multi-View Robotic Manipulation IROS2026
链接: https://arxiv.org/abs/2607.02845
作者: Jin Yang,Ping Wei,Nanning Zheng
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by IROS2026
Abstract:Multi-view robotic manipulation methods with the attention mechanism have recently achieved significant progress in both training efficiency and task performance. However, the inherent redundancy, occlusion, and viewpoint dependency in robotic view images often lead to severe attention drift. To address this challenge, we propose AmpAttention, a novel attention mechanism inspired by differential amplifiers in analog circuits. It aims to suppress attention noise and capture high signal-to-noise ratio signals for more reliable perception. Based on this, we introduce the RVAF model, which integrates task-guided intra-view and inter-view AmpAttention. Compared to previous state-of-the-art methods, RVAF achieves the optimal average success rate across 18 RLBench tasks (249 variations) while reducing training time by 33.3%. RVAF also demonstrates strong potential in real-world high-precision tasks, exemplified by its ability to pick up a dart and accurately insert it into the red bullseye. Furthermore, we extend RVAF to RVAF++ by incorporating the SAM2 image encoder. RVAF++ achieves substantial gains on high-precision tasks, achieving a 91% success rate on the `insert peg’ task. More qualitative results are provided at the anonymous project website this https URL.
[CV-293] CLEAR: Closed-Loop Reinforcement Learning at Scale for End-to-End Autonomous Driving
链接: https://arxiv.org/abs/2607.02841
作者: Yunxiao Shi,Hong Cai,Mohammad Ghavamzadeh,Fatih Porikli
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:End-to-end autonomous driving (E2E-AD) aims to directly map raw sensor information to driving actions. Recently, with the rapid advancement of multi-modal large language models (MLLMs), researchers have proposed the paradigm of Vision-Language-Action (VLA) models for E2E-AD, where it seeks to integrate visual perception, language understanding and action prediction within a single policy. However, existing VLA-based policies largely adopts imitation learning, where it only learns to drive by optimizing distance-based metrics w.r.t. logged expert trajectories. Such distribution shift between open-loop training and closed-loop inference leads to suboptimal performance in closed-loop planning. To close this gap, we present CLEAR, a system that enables closed-loop training using Reinforcement Learning (RL) at scale for E2E-AD. We propose to learn a novel residual waypoint policy around the waypoint prior from pretrained VLA policies, effectively harnessing the knowledge within. On another front, one of the key challenges to scale up RL for vision-based policies is the number of parallel simulation environments since RL is data hungry. To that end, we design a heterogeneous pipeline that places the simulator and the VLA learner on distinct compute groups, which allows us to dramatically increase the number of simulation environments running in parallel while avoiding resource contention and maintaining training stability. We show that with a simple reward, CLEAR significantly outperforms previous methods and sets new state-of-the-art performance on the challenging benchmarks of CARLA longest6 v2 and Bench2Drive.
[CV-294] Vision Token Manipulation Attacks on Cloud-Edge Inference of Large Vision-Language Models
链接: https://arxiv.org/abs/2607.02819
作者: Zikai Zhang,Rui Hu,Olivera Kotevska,Jiahao Xu
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Cloud-edge Large Vision-Language Model (LVLM) inference enables efficient deployment by splitting computation between edge devices and cloud servers. In this process, intermediate vision tokens are transmitted from the edge to the cloud over a communication link, thereby exposing a new attack surface. We study vision token manipulation attack (VTM-Attack) under a black-box man-in-the-middle setting, where an adversary intercepts and manipulates a subset of transmitted vision tokens under a budget constraint. We propose four naïve attack strategies and an optimization-based token selection method. Experiments on 6 state-of-the-art LVLMs (3B-72B) across 4 benchmarks show that manipulating only 10% of vision tokens can reduce accuracy by up to 88.31%. These results reveal a critical vulnerability in cloud-edge LVLM inference.
[CV-295] Conversational Human Audio-visual Talking Dialogue Generation ECCV2026
链接: https://arxiv.org/abs/2607.02799
作者: Junhao Song,Lluis Guasch,Xilin He,Zhongyu Yang,Yingfang Yuan,Weicheng Xie,Linlin Shen,Haijun Lin,Shizhe Liu,Wei Pang,Siyang Song
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026 as a main paper
Abstract:Large-scale dyadic interactive audio-visual dialogue (DIAD) datasets provide fundamental data resources for developing humanoid interactive virtual agents and digital humans. However, collecting such data is time-consuming, expensive, and ethically sensitive. To address this, we propose CHAT, a new dyadic interactive audio-visual dialogue generation (DIADG) framework that generates diverse, paired, and mutually responsive speech-face dialogue clips from a single textual prompt. CHAT unifies large language models and talking face models with interactive audio and facial behaviour refinement modules, enabling the generation of aligned dyadic dialogue clips with diverse contents and facial identities. Experiments show that CHAT outperforms existing related methods designed for similar tasks under both objective and subjective evaluations. Moreover, our synthesised CHAT-AVD-50k dataset serves as effective pre-training data for downstream interactive head generation, consistently improving PerFRDiff and ReactDiff on REACT 2024. CHAT offers a scalable alternative to the costly and ethically sensitive collection of real dyadic interaction data.
[CV-296] rack the Noise Move the World:3D-Grounded Motion-Consistent Noise for Controllable Video Generation
链接: https://arxiv.org/abs/2607.02798
作者: Long Vu,Tan Ngo,Animesh Karnewar,Amir Habibian,Binh-Son Hua,Hung Bui,Minh Hoai Nguyen,Phong Nguyen-Ha
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注: Project Page: this https URL
Abstract:Modern image-and-text-to-video diffusion models can synthesize highly realistic videos by iteratively denoising an initial Gaussian noise tensor conditioned on reference image and text inputs. However, existing approaches still lack precise and unified controllability over both object motion and camera motion within a single generation process. We present UniCaMo, a unified framework that enables simultaneous control of object trajectories and camera viewpoints by directly constructing the input noise of the diffusion model. Specifically, UniCaMo builds a shared 3D-grounded motion-consistent noise space across latent video frames. Sparse 3D point tracks are used to warp the Gaussian noise of the reference frame along desired object trajectories, while a virtual spherical noise representation provides globally consistent noise values for newly revealed scene regions under camera motion. By combining local track-guided noise warping with global sphere-based noise sampling, UniCaMo maintains geometric and temporal consistency under both object movement and viewpoint changes. Because UniCaMo modifies only the input noise, it requires no auxiliary adapters, control branches, or architectural changes to the underlying video diffusion model. With lightweight LoRA fine-tuning on large pretrained video diffusion models, including Wan 2.1 (14B), UniCaMo achieves state-of-the-art results in both video quality and motion controllability on standard controllable video generation benchmarks.
[CV-297] Biomechanics-aware Multi-view Markerless Motion Capture of Dexterous Hand Movements
链接: https://arxiv.org/abs/2607.02796
作者: Pouyan Firouzabadi,J.D. Peiffer,Kunal Shah,Anton Sobinov,Lee E. Miller,R. James Cotton,Wendy M. Murray
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Markerless motion capture (MMC) techniques have been widely beneficial in biomechanical analysis of human movement; however, application to complex motions of the hand lags other musculoskeletal systems. The primary goal of this study was to evaluate the performance of a biomechanical reconstruction method that implements a gradient-based optimization approach with a biomechanical model in the loop for tracking dexterous, unconstrained hand movements using MMC. Using a custom, 8-camera setup, we acquired 121 video recordings from 6 participants performing 11 different tasks that spanned 6 hand postures, 5 object manipulation tasks, and involved motion of the proximal upper limb joints. Performance of the proposed MMC pipeline was directly compared to a more commonly adopted two-stage reconstruction method that first triangulates 2D keypoints from computer vision pose estimation algorithms to 3D and then enforces biomechanical constraints by solving a constrained inverse kinematics problem. Relative performance was assessed qualitatively by visual inspection and quantitatively using a computer vision metric. Our method generated solutions for all 121 video recordings; the two-stage method did not converge for 15% of the recordings. Across the remaining videos, our method produced more biomechanically plausible hand kinematics than the two-stage method and was more robust to occlusion effects during tasks that involved objects. The relative robustness of the end-to-end method suggests that it is more effective in utilizing the available 2D digital keypoint information. Automatic and biomechanically meaningful tracking of hand kinematics during dexterous movements has the potential to support clinical evaluation, rehabilitation monitoring, and studies of human motor control.
[CV-298] Signal from Space: Detecting Schools and Towers to Bridge the Digital Divide
链接: https://arxiv.org/abs/2607.02724
作者: Zakarya Elmimouni,Sandor Farkas,Fares Fourati,Vladimir Daigele,Walid Mathlouthi,Mohamed-Slim Alouini
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Reliable internet access is essential for modern education, yet millions of school-aged children especially in developing regions remain offline due to unconnected schools. The Giga Initiative aims to connect every school to the internet, but doing so at scale requires efficient methods to map schools and assess surrounding connectivity infrastructure without relying on sparse or noisy third-party datasets. In this work, we propose a scalable, vision-only framework that uses high-resolution satellite imagery and transfer learning to address both tasks simultaneously. By adapting pre-trained object detection models to new geographical regions with minimal labeled data, we detect schools and cell towers directly from space. We then analyze the spatial relationship between detected schools and nearby towers as a proxy for connectivity availability. This purely imagery-driven pipeline enables large-scale infrastructure mapping, reduces dependency on auxiliary data, and supports data-driven prioritization of connectivity investments in underserved areas. Our approach is demonstrated on real satellite imagery from Lesotho, showing strong performance across this region.
[CV-299] Provable Pruning for Efficient 3D Gaussian Splatting via Coresets
链接: https://arxiv.org/abs/2607.02721
作者: Waseem Mousa,Alaa Maalouf
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Machine Learning (cs.LG)
备注: 39 pages, 15 figures, including supplementary material. Code: this https URL
Abstract:3D Gaussian Splatting (3DGS) enables high-quality real-time novel-view synthesis, but practical scenes often contain millions of Gaussians, making compression essential for deployment on limited hardware. Existing reduction methods are effective but mostly heuristic: they provide no multiplicative approximation guarantee for the rendered objective, and thus rely heavily on costly post-pruning finetuning to recover quality. We ask a basic question: can a 3DGS scene be provably replaced by a much smaller weighted subset (coreset) while preserving the objective of interest? We first show that, in the unrestricted setting, no non-trivial multiplicative 3DGS coreset exists. We then show that multiplicative guarantees are not impossible, but resolution-dependent. For a prescribed rendering resolution, such as representative views or grids of views/rays, we provide the first weighted coreset construction theorem for 3DGS. The construction samples Gaussians by sensitivity: provable importance scores measuring each Gaussian’s role in the full-scene objective. Finally, under explicit validity and log-transmittance stability assumptions, we turn this objective guarantee into a rendering guarantee. Empirically, our method is strongest where deployment needs it most: aggressive compression with no or minimal recovery compute. In prune-only and very short finetuning regimes, it achieves state-of-the-art performance, showing that principled importance estimation can be both theoretically meaningful and practically useful. Open-source code is available at this https URL.
[CV-300] GRCD: Grounded Region Change Detection for Multi-Finding Chest X-Ray Pairs
链接: https://arxiv.org/abs/2607.02719
作者: OFM Riaz Rahman Aranya,Peyman Najafirad,Kevin Desai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Radiologists routinely compare current and prior chest X-rays to track disease progression, producing follow-up reports that describe multiple findings, each localised to an anatomical region and annotated with a temporal change status. Existing automated methods either generate reports from a single image without modelling temporal context, or incorporate temporal information but do not ground their outputs spatially. The few approaches that combine temporal reasoning with spatial grounding are restricted to single-finding descriptions, leaving multi-finding reports with mixed change directions unaddressed. We present GRCD, a framework for grounded report generation from chest X-ray pairs in the multi-finding setting. We first construct a rigorously cleaned dataset of temporal chest X-ray pairs by identifying and correcting two systematic labelling errors in the source annotations. We then introduce a Region-Guided Change Token module that encodes per-region temporal change across anatomical structures and injects this signal into a language model through a dual-pathway strategy combining prepended spatial tokens with gated cross-attention. On a multi-finding test set, GRCD outperforms existing baselines on text generation and clinical accuracy metrics, with gains in change detection. Ablation studies confirm that the dual-pathway design outperforms either integration strategy in isolation on text and clinical metrics, and that region-level change encoding is necessary for multi-finding generation. Code is available at this https URL
[CV-301] Diagnosing Aerial-View Object Detectors with Foundational Image Generative Models
链接: https://arxiv.org/abs/2607.02718
作者: Stanislav Panev,Minhyek Jeon,Vaishnavi Khindkar,Ahish Deshpande,Celso M de Melo,Shuowen Hu,Shayok Chakraborty,Fernando De la Torre
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Recent advances in large-scale image generative models enable photorealistic scene synthesis with controllable attributes. Beyond data augmentation, their potential as diagnostic tools for trained vision systems remains unexplored in the aerial and remote sensing domains. We introduce a synthetic diagnostic framework for aerial-view vehicle detection that combines text-guided generation, attribute-controlled editing, and automated attribute verification to construct a controllable synthetic testbed. This enables fine-grained evaluation of pretrained detectors under diverse scene types and environmental conditions that are difficult to isolate in real datasets. Across three detection architectures and three real aerial datasets, synthetic scene-wise performance trends closely match real-world weaknesses. Guided by these diagnostics, targeted supplementation with small real datasets from the identified weak categories yields improvements of up to 13% AP50 while requiring substantially fewer additional samples than non-targeted augmentation. Our results show that controlled synthetic probing can predict real-domain performance gaps and guide efficient data collection. The proposed diagnostic framework is modular and can incorporate alternative generative or vision-language models as capabilities evolve. Our code and datasets are available here: this https URL
[CV-302] Global Pose Control for Generative View Synthesis in Normalized Object Coordinate Space ECCV2026
链接: https://arxiv.org/abs/2607.02712
作者: Zhibing Li,Amogh Gupta,Behnoosh Parsa,Dan Casas
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026. Project page this https URL
Abstract:Novel View Synthesis (NVS) enables the generation of unseen views of a scene from a single or multiple images, allowing users to freely explore an object from any viewpoint. Despite the recent impressive qualitative improvements of generative models for this task, existing methods struggle to provide global and intuitive control of target viewpoints because they either use input-relative camera poses or are limited to generating sparse global views. This lack of global pose control severely limits the number of downstream tasks potentially enabled by NVS. To address this limitation, we propose a novel approach for precise camera control in a customizable Normalized Object Coordinate Space (NOCS), requiring single or few unposed images. Our method operates solely on the absolute camera pose of the target view in NOCS, eliminating the need for a relative world frame or camera poses of the input images. Unlike previous methods that treat NVS as a standalone generation task, we formulate it as an image editing problem and build upon state-of-the-art editing models to leverage their superior generalization capability. Camera information is injected as dedicated camera tokens via an in-context multi-modal conditioning strategy. To alleviate the inherent ambiguity of NOCS, we incorporate text descriptions that explicitly define the object’s canonical coordinate frame, which also enhances generalization to unseen object categories. Furthermore, we curate a high-quality dataset with consistently aligned orientations and corresponding NOCS text definitions. Extensive experiments demonstrate that our method robustly generates novel views with accurate and consistent orientations from arbitrary unposed images across diverse categories, achieving state-of-the-art image quality and fidelity.
[CV-303] RayTun3R: Online Camera Adaptation in 3D Foundation Models
链接: https://arxiv.org/abs/2607.02711
作者: Daniil Sinitsyn,Nikita Araslanov,Daniel Cremers
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent 3D foundation models, such as DUSt3R, MASt3R, VGGT, \pi^3 , and Depth Anything 3, provide strong feed-forward depth and pose estimates on pinhole imagery, but degrade sharply under fisheye camera geometry. We show that this failure is partly caused by a pinhole camera bias in the positional encodings of pretrained 3D foundation models, and propose RayTun3R, a lightweight camera adaptation approach. It keeps the pretrained network fixed and adapts only lightweight components tied to token position and camera geometry. RayTun3R learns parameter-efficient residual corrections to absolute and rotary positional encodings, together with parameter-free tokenization and corrections to prediction-grid coordinates that remove residual pinhole assumptions. The resulting adapter contains only 10,752 trainable parameters and can be learned from a short temporal segment using geometric losses. Once adapted, RayTun3R transfers effectively to the remaining frames of the sequence without incurring additional runtime costs. Across diverse fisheye datasets with fields of view from 110^\circ to 200^\circ , our adapter reduces rotation error by 2 - 12\times relative to the unadapted model, outperforms LoRA while using \sim!14\times fewer trainable parameters, improves pose over adaptation-free baselines while avoiding their multi-view inference cost, and remains competitive on depth accuracy.
[CV-304] When Does Resolution Help a Frozen Backbone? Global Attention at Resolution Predicts Scalable Adaptation for Camouflaged and Marine Animal Segmentation
链接: https://arxiv.org/abs/2607.02708
作者: Tyler Rust,Chandra Kambhamettu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 3 figures, 7 tables
Abstract:Adapting frozen vision foundation models to fine-grained segmentation now largely depends on backbone selection. Whether the backbone applies global attention to a high-resolution token set predicts whether a low-rank adapter turns resolution into accuracy. Isotropic ViTs attend globally over the full grid and keep improving with resolution; hierarchical backbones confine early attention to local windows and pool the grid before their global stages, plateauing at lower resolutions. A controlled six-backbone study establishes the pattern, and editing the backbone points to the cause: pooling keeps the benefit, removing global attention does not. The effect is specific to low-rank adaptation. Under one fixed pipeline, SALT (Side-stem, Attention-gated U-Net, Low-rank Tuning), one RGB-only pass on a strong isotropic backbone wins the best S-measure on the four data-matched camouflaged sets, and leads every marine and salient set. It reaches a new state of the art on both marine-animal benchmarks (MAS3K mIoU 0.878).
[CV-305] VLRC: Vision-Language Reprojection Consistency as a scalable signal for better feed-forward 3D pretraining
链接: https://arxiv.org/abs/2607.02707
作者: Marwane Hariat,David Filliat,Antoine Manzanera
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Feed-forward 3D models are commonly trained using either expensive geometric supervision or self-supervised photometric objectives, both of which provide incomplete learning signals. We introduce Vision-Language Reprojection Consistency (VLRC), a scalable auxiliary objective that exploits frozen vision-language representations as semantic multi-view supervision. Given a predicted 3D reconstruction, VLRC reprojects dense vision-language features across views and enforces feature consistency between corresponding image locations, requiring no additional 3D annotations. The objective integrates seamlessly with both self-supervised monocular reconstruction and supervised-pretrained feed-forward 3D models during unlabeled adaptation. By aligning geometry with language-grounded features, VLRC not only improves depth and camera estimation but also enables more coherent multi-view semantic fusion for open-vocabulary 3D scene understanding. Experiments on indoor and outdoor benchmarks demonstrate consistent gains in 3D reconstruction accuracy and zero-shot open-vocabulary 3D semantic segmentation.
[CV-306] Aircraft Detection in Satellite Imagery using Deep Learning Object Detectors
链接: https://arxiv.org/abs/2607.02699
作者: Mujahir Hussain Abbasi(Department of Information Technology Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal, India),S. Beghin(Bose School of Computational and Physical Sciences Kristu Jayanti, Bengaluru, Karnataka),A. T. R. Krishna Priya(AB Technologies Nagercoil, Kanyakumari, Tamil Nadu, India),P. Jamuna(Department of Electrical and Electronics Engineering Nandha Engineering College, Erode, India),G. Anandhakumari(Department of Physics Erode Sengunthar Engineering College, Perundurai, India),S. Jaanaa Rubavathy(Department of Electrical and Electronics Engineering Saveetha School of Engineering, Saveetha University, Chennai, India)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The object detection in satellite imagery has garnered considerable attention due to its extensive real-world applications and the inherent challenges it presents, including noise, fluctuating image quality, and intricate backgrounds. This paper proposed a framework for object detection that combines image enhancement and Deep Learning (DL) to make detection more accurate. First, a Gabor filter is used to process the input image to bring out important features and reduce noise. Then, normalization is applied to make sure that the data is evenly distributed so that the model works properly. After that, a model based on YOLOv11 is used to quickly learn and find object features. The proposed method achieves a mAP of 95%, precision of 97%, recall of 85%, and F1-score of 91%, which demonstrates the superior aircraft detection performance. These results show the framework accurately identify aircraft in satellite imagery and is suitable for real-time applications such as surveillance, air traffic monitoring and remote sensing analysis.
[CV-307] Property-Constrained 3D Porous Media Reconstruction from 2D Images via Conditional Generative Adversarial Networks
链接: https://arxiv.org/abs/2607.02693
作者: Ali Sadeghkhani,Brandon Bennett,Arash Rabbani
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Geophysics (physics.geo-ph)
备注: 6 pages, 3 figures. Submitted to the 87th EAGE Annual Conference Exhibition, Aberdeen, UK, June 2026
Abstract:This study presents a conditional Generative Adversarial Network (cGAN) framework for generating 3D porous media volumes with controlled porosity, trained exclusively on 2D thin section images. The key innovation lies in combining property-conditioned generation with 2D-to-3D reconstruction, eliminating the need for expensive 3D training data while maintaining control over petrophysical properties. The framework employs a hybrid architecture with a 3D generator and 2D discriminator, where multi-axis slice extraction enables learning 3D-consistent structures from 2D training data. Porosity labels are extracted using an Enhanced U-Net segmentation model. The methodology was demonstrated on two carbonate samples with different lithologies: dolomite-anhydrite and pure dolomite. Results show that the framework successfully generates realistic 3D volumes capturing lithological features such as anhydrite inclusions and fine crystalline textures. Porosity control achieved an R^2 of 0.93, with mean absolute errors of 0.019 and 0.010 for the heterogeneous and homogeneous samples, respectively.
[CV-308] An Automated Multimodal Glaucoma Detection Framework Using ViT and a Stacking-Based Ensemble
链接: https://arxiv.org/abs/2607.02692
作者: Ishrat Jahan,Muhammad E.H Chowdhury,Murugappan Murugappan,Kanchon Kanti Podder,Tawsifur Rahman,Shrestha Datta,Md Sakib Abrar Hossain,Md Mosarrof Hossen,Yosra Magdi Salih Mekki,Sanjiban Sekhar Roy
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Glaucoma is a progressive eye disease that can lead to irreversible vision loss if not detected at an early stage. Conventional diagnostic procedures are often time-consuming and rely heavily on expert interpretation, limiting their scalability for large-scale screening. In this study, glaucoma detection is investigated under two evaluation settings: sample-wise, where individual samples are analyzed independently, and patient-wise, where data from each patient are aggregated for final prediction. An automated multimodal framework is proposed that integrates fundus images with clinical data. Under the sample-wise setting, detection is performed using fundus images and clinical features individually, as well as through their multimodal combination. Under the patient-wise setting, predictions are obtained by aggregating multiple fundus image representations with corresponding clinical information for each patient. Deep visual features are extracted using a Vision Transformer (ViT) architecture and classified using classical machine-learning models, with a stacking-based ensemble of the three best-performing classifiers employed to optimize performance. Experiments conducted on the publicly available PAPILA dataset demonstrate strong diagnostic performance, achieving 97.47% accuracy and a 97.50% F1-score for sample-wise multimodal classification, and 98.97% accuracy and F1-score for subject-wise detection. The proposed framework is further deployed as an end-to-end web-based platform to support automated glaucoma screening and clinical decision support.
[CV-309] S-EMBER: A Large-Scale Benchmark for Streaming Egocentric Memory Retrieval
链接: https://arxiv.org/abs/2607.02689
作者: Xiaodong Wang,Xuanyi Zhao,Pedro Rodriguez,Devendra Singh Sachan,Barlas Oguz,Seungwhan Moon,Shang-Wen Li,Gargi Ghosh,Xin Dong,Wen-Tau Yih
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:As wearable devices enable continuous first-person recording, AI assistants must reason across long time horizons to recall past experiences-a capability known as episodic memory. Current benchmarks often rely on offline evaluation with access to entire video files, failing to simulate the streaming reality of wearable intelligence. We introduce S-EMBER (Streaming Egocentric Memory Benchmark for Episodic Retrieval), a large-scale benchmark comprising 3,141 videos totaling 388 hours of organic activity captured via Ray-Ban Meta smart glasses. S-EMBER formalizes grounded streaming episodic retrieval, a paradigm shift from global offline search to causal, active recall triggered by visual events in a continuous stream. We provide 9,448 QA pairs requiring manual visual proof through precise temporal localization and supporting flexible response lengths to simulate natural human-AI interaction. Our extensive benchmarking of frontier models uncovers a localization paradox: while semantic reasoning improves with parameter scale, temporal grounding precision remains a stagnant architectural bottleneck that does not benefit from brute-force increases in model size, resolution, or frame density. S-EMBER establishes a hardware-authentic foundation for developing grounded, reliable episodic memory in the next generation of wearable AI agents.
[CV-310] K9-Bench: Evaluating Multimodal LLM s on Canine-Centric Videos
链接: https://arxiv.org/abs/2607.02680
作者: Khush Attarde,Yusuf Ali,Megha Thukral,Divye Bhutani,Thomas Ploetz,Zsolt Kira
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:MLLMs have shown strong zero-shot capabilities across diverse inputs such as across images, video, audio, and text. A crucial, yet underexplored, application of these models lies in understanding and modeling animal-centric scenarios. As animals are integral to millions of households, benchmarking next-generation AI models on pet-focused tasks, ranging from recognizing distress signals to enabling responsive robotic companions, is essential for building AI systems that can work alongside us. We introduce K9-Bench, a novel benchmark focused on real-world domestic dog videos, specifically targeting canine action and interaction understanding via approximately 5000 question-answer pairs across 907 videos spanning 5 distinct task categories that test long-form, canine-centric multimodal reasoning in MLLMs. To create this dataset, we propose a scalable, VLM/LLM-powered data generation pipeline that automatically mines canine-centric videos from the web and curates QA pairs requiring fine-grained, multi-hop reasoning over canine actions and temporally extended interaction sequences. We implement bias mitigation strategies designed to eliminate biases introduced by VLMs during dataset curation. Through extensive experimentation, we find that frontier MLLMs exhibit limited zero-shot performance on canine-centric tasks: although state-of-the-art closed-source models outperform open-source counterparts, they still struggle with compositional reasoning over subtle posture and interaction cues spread over long horizons. We observe that generic chain-of-thought prompting provides only modest performance for such long-horizon reasoning. Beyond a novel dataset for canine activity analysis, K9-Bench provides a general-purpose dataset construction pipeline that can be adapted to other low-data domains for quantitative analysis. Our project website is available at: this https URL.
[CV-311] EmoteGPT : 3D Human Facial Expressions from Natural Language Descriptions
链接: https://arxiv.org/abs/2607.02674
作者: Haoran Wang,Mohit Mendiratta,Christian Theobalt,Adam Kortylewski
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Precise control of 3D facial expressions from text is crucial for virtual avatars, animation, and human-computer interaction, yet existing text-to-3D methods jointly generate identity, expression, and texture, making fine-grained expression control difficult. We instead formulate text-driven expression synthesis as a regression problem in the disentangled parameter space of a 3D Morphable Model (3DMM). This setting, however, requires paired data linking detailed language to precise expression parameters, which are missing from existing resources. To fill this gap, we introduce Txt2Emote, a benchmark of diverse 3D facial expressions with fine-grained textual annotations obtained from GPT-4o and a high-fidelity face tracker, providing both explicit descriptions detailing facial features and implicit descriptions referencing the situational context behind the expression. Leveraging this dataset, we present EmoteGPT, a text-to-3D expression framework based on a Multimodal Large Language Model (MLLM) with a dedicated Expr token to semantically ground expression representations, which are then decoded into 3DMM parameters. We further improve EmoteGPT by augmenting training with large-scale image-to-3DMM data, enabling it to surpass state-of-the-art text-to-3D face synthesis methods on emotion recognition metrics and in perceived expressiveness. Integrated into avatar pipelines, our method enables photorealistic and stylized 3D avatars, as well as expressive 3D-consistent 2D face synthesis from textual input.
[CV-312] EVA-Client: A Unified Data Collection Inference and Deployment Framework for Embodied Policies on Real Robots
链接: https://arxiv.org/abs/2607.02646
作者: Heqing Yang,Yang Yi,Liyao Wang,Linqing Zhong,Donglin Yang,Ruipu Wu,Zitong Bai,Fengjiao Chen,Manyuan Zhang,Linjiang Huang,Si Liu
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: this https URL
Abstract:We present EVA-Client, an open-source framework for deployment, data collection, and evaluation of trained manipulation policies on real robots. Sitting between a policy server and the physical hardware, EVA-Client unifies the real-robot stages of the policy iteration loop within a single codebase. It makes three contributions. First, a component-decoupled architecture in which robot backends, inference strategies, and transport middlewares form an orthogonal grid: adding a robot or a strategy touches only its own layer. Second, inspectable execution through Debug, Collect, and Eval workflows, with modes ranging from open-loop simulation to continuous real-time control. Third, every evaluation run doubles as a data collection, recording full rollouts in training-ready format alongside exhaustive logs and a side-by-side comparison viewer, so each evaluation feeds the next round of training rather than ending as an unrecorded impression. EVA-Client further consolidates major real-time inference strategies, synchronous and asynchronous execution, ACT-style temporal ensembling, Real-Time Chunking, and a naive-async ablation baseline, behind a single configuration surface.
[CV-313] BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models ECCV2026
链接: https://arxiv.org/abs/2607.02643
作者: Aryan Pandit
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026
Abstract:Diffusion-based generative models have transformed visual content synthesis, yet they remain vulnerable to unauthorized usage and lack reliable attribution methods. Existing watermarking techniques often treat latent tensors as static spatial feature maps or depend on pixel-domain modification, and most do not explicitly leverage the internal frequency structure of the latent space for dual-band redundant embedding, leaving them susceptible to the stochastic nature of diffusion and regeneration attacks. We introduce BiSLW, a trainable bi-spectral latent watermarking framework that jointly embeds aligned identity signals across complementary spectral bands of the decoded diffusion latent using learned encoders and decoders, going beyond fixed-pattern frequency approaches. We leverage the inherent frequency structure of diffusion latents to design a dual-band watermarking framework. Low-frequency components encode global semantics, while high-frequency components capture fine texture. We exploit this structure to embed watermarks across complementary spectral bands. The watermark is independently injected into both bands via learned encoders and recombined before decoding, ensuring it becomes intrinsic to the generative trajectory. Dual spectral decoders recover the watermark from each band, while a cross-band consistency constraint enforces alignment between semantic and textural embeddings. Experiments show that BiSLW achieves a strong balance between perceptual fidelity and robustness, improving PSNR by over 3 dB compared to prior latent diffusion watermarking methods while preserving near-perfect bit accuracy under aggressive regeneration and common distortions, all with negligible computational overhead.
[CV-314] CLABTOOLKIT: An Open-Source Toolkit for Routine Processing Manipulation and Visualization of Neuroimaging Data
链接: https://arxiv.org/abs/2607.02638
作者: Yasser Alemán-Gómez,Nino Hervé,Patric Hagmann
类目: Computer Vision and Pattern Recognition (cs.CV); Quantitative Methods (q-bio.QM)
备注: Presented at OHBM 2026 in Bordeaux, France. Submitted to Aperture Neuro
Abstract:Neuroimaging research requires manipulating heterogeneous data structures, including raw MRI volumes, volumetric parcellations, cortical surface meshes, tractograms, and connectivity matrices, across tools with incompatible interfaces and file formats, forcing researchers to repeatedly re-implement routine but technically demanding operations. We present CLABTOOLKIT, an open-source Python package that consolidates these operations into a single, coherent framework by representing volumetric, surface, and streamline data as interoperable Python objects. Five core data structures (Parcellation, Surface, AnnotParcellation, Tractogram, and Connectome) encapsulate common neuroanatomical entities and provide consistent methods for loading, processing, and exporting data across standard neuroimaging formats (e.g., NIfTI, GIFTI, FreeSurfer annotations, TCK/TRK), including connectome generation from a parcellation and scalar-map projection onto tractogram streamlines. Complementary modules support BIDS dataset management, FreeSurfer integration, diffusion MRI processing, morphometric analysis, graph-theoretical network analysis, and GPU-accelerated multi-panel visualization via PyVista. The toolkit comprises 19 modules organised into six layers, exposing 13 object-oriented classes with 234 methods and 207 standalone functions, and a JSON-based configuration system enables workflow customization without code changes. Unlike existing neuroimaging libraries, which typically address these tasks separately, CLABTOOLKIT combines color and lookup-table management, parcellation manipulation, multi-surface visualization, and tractography utilities within a single framework. CLABTOOLKIT is compatible with Python 3.9-3.12 and released under the Apache 2.0 license. Source code, documentation, and example workflows are available at this https URL.
[CV-315] Federated Learning for Object Detection: Enabling Collaborative Drone Learning Without Centralizing Data
链接: https://arxiv.org/abs/2607.02636
作者: Daniel M. Jimenez-Gutierrez,Enrique Zuazua,Georgios Kellaris,Joaquin del Rio,Oleksii Sliusarenko,Xabi Uribe-Etxebarria
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC)
备注:
Abstract:Object detection is a fundamental capability for AI-driven perception in safety-critical drone and edge-vision systems, including disaster response, operational security environments, infrastructure monitoring and defense applications. Robust model performance in such environments depends on large, continuously updated datasets. However, training high-performing detectors typically requires centralizing aerial imagery, which raises privacy, regulatory, storage, and bandwidth challenges. This is especially problematic in distributed drone deployments, where visual data is generated onboard and is often impractical or undesirable to transfer to a centralized infrastructure. In this work, we apply Federated Learning (FL) for object detection, enabling drones to improve a shared model while keeping image data local and private. We implement a federated object detection pipeline using the this http URL FL platform on the KIIT-MiTA dataset, and compare it with Single-drone and Centralized baselines using mean Average Precision (mAP) at IoU thresholds of 0.50 and 0.50-0.95. In our experiments, the proposed FL approach remains close to Centralized training while dramatically improving over Single-drone training, with the best lightweight model (YOLO26 nano), suitable for deployment even on very limited edge infrastructure, achieving relative gains of 52.89% and 67.80% in mAP@0.50 and mAP@0.50:0.95, respectively. These results show that FL enables scalable, high-performing, and privacy-preserving object detection across distributed drone fleets without data centralization. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC) Cite as: arXiv:2607.02636 [cs.LG] (or arXiv:2607.02636v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.02636 Focus to learn more arXiv-issued DOI via DataCite
[CV-316] SE-UNet: Singular Equivariant Imaging for Real-World Constrained Generation
链接: https://arxiv.org/abs/2607.02628
作者: Kanishk Awadhiya
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:While diffusion models have revolutionized image synthesis, their application to real-world inverse problems is often hampered by the need for massive datasets and the difficulty of imposing strict physical constraints. In this work, we introduce \textbfSE-UNet (Singular Equivariant UNet), a framework designed to solve ill-posed imaging tasks without extensive pre-training. By treating generation as an optimization problem constrained by geometric equivariance ( D_4 group) and singular value gating, SE-UNet effectively standardizes the solution space. We demonstrate that these strong inductive biases allow for state-of-the-art zero-shot inpainting results (80% missing pixels) on CIFAR-10. Our method surpasses Deep Image Prior (DIP) baselines by over 4 dB in PSNR and exhibits a characteristic “singular snap” convergence – rapidly locking into the signal manifold. SE-UNet thus offers a data-efficient pathway for constrained generation, aligning with the ReALM-GEN goal of bridging theoretical priors with practical deployment.
[CV-317] Fusion: A Framework for Unified Sequential Token AdaptatIon in VisiOn TraNsformers
链接: https://arxiv.org/abs/2607.02612
作者: Aravind Pradeep,Samira Nazari,Mahdi Taheri,Christian Herglotz
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
备注:
Abstract:Vision Transformers achieve strong image classification accuracy but process all image regions with nearly the same computation, even when many regions are redundant or uninformative. Recent adaptive inference methods reduce this cost by selectively compressing tokens or terminating inference early, but combining these mechanisms often causes unstable intermediate representations and accuracy degradation. We introduce Fusion, a unified adaptive inference framework that coordinates token merging, early exiting, and token pruning through a simple staged design: tokens are merged first, confidence is evaluated next, and pruning is applied only to samples that continue inference. This ordering allows the three mechanisms to operate cooperatively rather than competitively. Fusion further includes lightweight routing modules that adapt compression strength to each input and support inference-time adjustment of the accuracy–latency trade-off without retraining. On ImageNet-1k with DeiT-S, Fusion matches or surpasses state-of-the-art adaptive ViT methods at comparable compute budgets while reducing calibration error by up to 4\times and inference energy by 48% . Experiments across ImageNet-100, CIFAR-100, and ImageNette with multiple ViT backbones demonstrate consistent transferability without dataset-specific tuning.
[CV-318] Privacy-Preserving Industrial Ergonomics: mmWave-Based Automated REBA Scoring and Pose Estimation
链接: https://arxiv.org/abs/2607.02611
作者: Xuhan Zhang,Zhuangzhuang Dai,Luis J. Mans,Victor Chang
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 6 pages, 4 figures. To appear in the Proceedings of the 2026 International Conference on Automation and Computing (ICAC 2026)
Abstract:Work-related Musculoskeletal Disorders (WMSDs) require continuous ergonomic assessments. While Rapid Entire Body Assessment (REBA) is a gold-standard observation tool, manual monitoring is labor-intensive, and vision-based automation leads to privacy concerns. This paper proposes a novel end-to-end multi-task learning framework for privacy-preserving ergonomic assessment using millimetre-wave (mmWave) radar. A spatio-temporal backbone reconstructs 3D human skeletons, which serves as the biomechanical foundation for a subsequent regression head to generate REBA risk scores. To overcome the sparsity of radar point clouds, we utilise a multi-objective loss function incorporating biomechanical limits and temporal smoothness constraints. Furthermore, we implement an oversampling strategy to address the imbalance of high-risk postures in existing datasets. Experimental results on MMFi dataset demonstrate that our framework achieves a Categorical Accuracy of 77.78% and real-time performance with an inference latency of 5.70 ms. Our method reaches a High-risk REBA MAE of 0.93, which significantly outperforms both direct regression and two-stage pipelines in high-risk scenarios, providing a robust solution for non-invasive industrial ergonomic assessment.
[CV-319] DynaWM: A Base-VLA-Guided World Foundation Model for Moving-Object Manipulation
链接: https://arxiv.org/abs/2607.02604
作者: Chongkei Chang,Zhidong Deng
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 17 pages, 3 figures
Abstract:Although vision-language-action (VLA) models have received widespread attention, many challenges remain in manipulating dynamic moving objects. In most existing approaches, end-to-end forward or inverse dynamics models, i.e., world models, are incorporated into high-performance base VLA architectures, which may degrade the performance of well-pretrained base VLA models due to inappropriate fine-tuning. In this paper, we propose DynaWM, a base-VLA-guided world foundation model that adapts to a wide variety of fine-tuned and coarse-tuned base-VLA checkpoints for moving-object manipulation. DynaWM uses a Mamba-3-based action encoder to encode the base action chunk produced by the base VLA into an action-conditioning representation, a V-JEPA 2.1 vision encoder to extract features from multi-view observation history, and a proprioceptive state encoder to encode robotic-arm proprioceptive states. These feature representations jointly condition a flow-matching DiT to regenerate motion-aware action trajectories for moving-object manipulation. For systematic evaluation, we construct the DynaGrasp-32 benchmark, covering six categories of moving-object manipulation tasks, including velocity variation, trajectory variation, and multi-object manipulation, as well as the DynaGrasp-1600 dataset, which consists of 32 scenarios, 1,600 demonstration trajectories, and approximately 1.53M images. For fine-tuned base-VLA checkpoints, DynaWM achieves percentage improvements of 7.19, 45.31, 1.88, and 10.94 over SmolVLA, X-VLA, \pi0, and \pi0.5, respectively. For coarse-tuned base-VLA checkpoints, performance increases by 35.13, 44.06, 35.69, and 26.13 percentage, respectively. Ablation experiments show that visual encoding enhances success by 27.50%, while reducing success by 45.44% if action conditioning is removed.
[CV-320] CV-DCLR: Causal-Visual Dynamic Label Refinement for Robust Zero-Shot Learning
链接: https://arxiv.org/abs/2607.02601
作者: Can Wang,Jiangnan Li,Mingyu Li,Yining Song,Kangrui Ren,Min Gan,Jinfu Fan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Zero-Shot Learning (ZSL) facilitates knowledge transfer via shared semantic spaces. However, a critical bottleneck in this paradigm is Semantic Entanglement, where visual representations are inevitably conflated with visually similar semantic concepts, such as distinguishing the intrinsic traits of a Wolf from the shared features of a Husky. Existing global alignment methods often indiscriminately maximize correlations between visual and semantic modalities, leading models to overfit spurious similarities rather than capturing distinctive class identities. To address this fundamental limitation, we propose the Causal-Visual Dynamic Label Refinement (CV-DCLR) framework. Unlike traditional approaches that rely on superficial visual statistics, CV-DCLR recalibrates visual-semantic associations via a Dual-Stream Mutual Correction Mechanism. This includes a Visual Likelihood Stream to model observational patterns and a Causal Importance Stream that verifies the structural necessity of candidate prototypes through Counterfactual Intervention. Acting as a logical filter, our adaptive gating mechanism dynamically modulates feature responses to amplify genuine causal traits while suppressing visually plausible but structurally irrelevant distractors. Extensive experiments on the CUB, SUN, and AWA2 benchmarks under a rigorous Semantic Entanglement Injection protocol demonstrate that CV-DCLR significantly outperforms state-of-the-art methods in high-ambiguity scenarios. Specifically, while existing models suffer catastrophic degradation under entanglement, our framework maintains robust performance, effectively disentangling true class identities from semantic confounders.
[CV-321] Evaluating Agent ic Harness Systems for Autonomous Computational Pathology
链接: https://arxiv.org/abs/2607.02598
作者: Jie Lin,Zongyi Chen,Qiaoling Zheng,Liuyi Wang,Hengyi Jiang,Jiabao Chen,Xiang Liu,Yinghong Yang,Liansheng Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Autonomous computational pathology (ACP) converts high-level pathology analysis goals into executable, traceable and clinically bounded workflows. Realizing this capability requires adapting general agentic harness systems to pathology-specific tasks, tools, evidence standards and clinical claim boundaries. We contribute ACP-Bench, a framework that adapts existing harness systems from computational pathology support toward ACP workflow capability. ACP-Bench evaluates 41 pathology workflow tasks, including 24 biomarker, 7 morphology and 10 prognosis tasks spanning 6 body-system groups and 9 endpoint families. The benchmark evaluates 9 models and 3 harness groups (Claude Code, Codex and Open Code), yielding 369 complete trajectories. ACP-Bench evaluates each trajectory across workflow execution, diagnostic performance and clinical-boundary alignment, combining expert-adjudicated process audits, diagnostic assessment and pathologist-validated safety review. Across evaluated systems, workflow initiation, task interpretation and diagnostic reporting were more mature than tool-bound execution, result binding and reflective workflow revision, and formal end-to-end completion remained rare. ACP-Bench provides a reusable standard for auditing whether agentic systems can operationalize pathology workflows before claims of reliable clinical autonomy.
[CV-322] CIPHER: Causal Intervention Pathways for Healthcare Equity and Robustness MICCAI2026
链接: https://arxiv.org/abs/2607.02596
作者: Xinyu Jia,Weidong Guo,Wangyuan Zhao,Yi Guo,Zeju Li,Yuanyuan Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 8 pages, 2 figures. Accepted by MICCAI 2026
Abstract:Deep learning models for medical diagnosis frequently exhibit substantial performance disparities across sensitive subgroups (e.g., race, sex), even when average accuracy is high. While generative data augmentation offers a route to mitigate this, existing strategies are suboptimal; they typically address only one or two dependency channels between sensitive attributes and image features. We formalize the medical image formation process via a structural causal model, revealing that sensitive attributes actually influence image content through four distinct pathways-a structural complexity neglected by prior works. Based on this insight, we introduce CIPHER (Causal Intervention Pathways for Healthcare Equity and Robustness), a framework designed to systematically intervene on all four causal paths. To achieve this, CIPHER utilizes a diffusion backbone equipped with classifier-free guidance and null-text inversion. This technical design enables the faithful reconstruction of patient-specific anatomy while allowing for the precise, editable synthesis of counterfactuals required to break sensitive dependency chains. We tested CIPHER using chest X-ray and dermoscopy benchmarks across both standard and shifted data distributions. By employing a multi-pathway intervention strategy, our model reduced worst-group disparities by an average of 35.8% compared to disease-conditioned synthesis baselines, while also improving total diagnostic accuracy
[CV-323] An automated method of identifying incorrectly labelled images based on the sequences of loss functions of deep learning networks
链接: https://arxiv.org/abs/2607.02594
作者: Zhipeng Zhang,Wenhui Shou,Wengting Ma,Dongjia Xing,Qingqing Xu,Li-Qun Xu,Qingxia Fan,Ling Xu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Presented at the 6th EAI International Conference on IoT as a Service (IoTaaS 2020). Published by Springer in LNICST, vol 346, 2021
Abstract:Deep learning is widely applied in medical image analysis, but up to 10% of manually labelled images may be incorrect, degrading model performance. This paper proposes an automated method to identify incorrectly labelled medical images by analyzing sequences of loss functions from deep learning classification networks over multiple training epochs. Identified images can be reviewed and relabelled by experts, improving dataset quality and model performance. Two experiments validate the method on a fundus image dataset for referable diabetic retinopathy screening. In the first, 6% (648) of 10,788 gold-standard labels were intentionally flipped. The method identified 75.31% (488) of the flipped samples, with only 4.85% (492) false positives among correctly labelled samples. In the second, reviewing and correcting the 980 identified samples (9.1% of the dataset) and retraining the model improved best accuracy on an independent test set from 95.93% (with 6% label noise) to 96.50% (with 1.5% noise), approaching the ideal 96.57% (with 0% noise). The results demonstrate the method’s effectiveness in improving model performance through automated label quality control.
[CV-324] H-OPD: Confidence Aware Heterogeneous Multi-Teacher Multimodal On-policy Distillation
链接: https://arxiv.org/abs/2607.02592
作者: Qixiang Yin,Huanjin Yao,Yuchen Cai,Jianghao Chen,Ziyi Wang,Min Yang,Fei Su,Zhicheng Zhao
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Technical report(work-in-progress). Code will be available at \url{ this https URL }
Abstract:On-policy distillation (OPD) has recently emerged as an effective post-training paradigm by providing supervision on student-generated trajectories. However, existing OPD methods for multimodal reasoning usually rely on a static teacher routing, assigning each sample to a single teacher based on modality or task type. This ignores that visual grounding and abstract reasoning may dominate different decoding steps, making a single teacher insufficient for the full trajectory. To this end, H-OPD is proposed as a confidence-aware heterogeneous multi-teacher OPD framework for multimodal reasoning. By verifying the complementarity of heterogeneous teachers in the same reasoning process, H-OPD replaces task or sample level teacher routing with token-level teacher arbitration along the shared student trajectory. H-OPD employs vision-to-language description transfer to enable text-only teachers to access key visual semantics, and uses a confidence-aware arbitration mechanism to dynamically combine vision-language teacher and text-only teachers at each token. Extensive evaluations over 11 widely-used reasoning benchmarks showcase the superior performance of our method.
[CV-325] CPR: Chained Perceptual Refinement for Coarse-to-Fine Medical Image Classification
链接: https://arxiv.org/abs/2607.02591
作者: Si-Yuan Lu,Hanruo Zhu,Ziquan Zhu,Gaojie Jin,Zeyu Fu,Lu Yin,Ke Li,Lu Liu,Tianjin Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:High resolution medical images contain fine grained, spatially sparse cues that are critical for diagnosis, yet preserving full resolution incurs substantial computational and memory costs. Most deep models process images uniformly, leading to redundant computation or loss of diagnostic detail under downsampling. We propose Chained Perceptual Refinement, CPR, a coarse to fine framework that formulates medical image analysis as a sequential global to local decision process. Starting from a low resolution global view, CPR dynamically predicts the location and spatial extent of refinement regions, extracts high resolution evidence from the original image, and incrementally integrates it with global context. By keeping the backbone input size fixed while contracting the perceptual field, CPR preserves diagnostic fidelity with constant peak GPU memory. Extensive experiments on five medical imaging datasets and multiple backbone architectures demonstrate that CPR consistently outperforms both fixed resolution and multi scale state of the art baselines, achieving improvements of up to 2.27 percentage points over the second best method. It also achieves up to a 19.6 fold reduction in GFLOPs at matched accuracy, establishing a superior accuracy and efficiency trade off for high resolution medical image analysis. The code is available on GitHub.
[CV-326] Reliability-Aware CT-MRI Registration: A Quality Engineering Framework with Stability Analysis and Risk Classification
链接: https://arxiv.org/abs/2607.02585
作者: Nisreen Albzour
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal CT-MRI registration is central to image-guided radiotherapy, surgical navigation, and diagnostic workflows, but most pipelines report only aggregate quality metrics without per-case reliability signals. We propose a reliability-aware framework that converts registration quality into Green/Yellow/Red risk categories using data-learned thresholds. CT images were registered to T1-weighted MRI using rigid and affine transformations on 90 paired slices from 18 patients across brain, abdominal, and neck anatomies. Reliability was assessed using Delta NMI, Delta SSIM, Dice overlap, registration stability, and inverse consistency error, combined into a single score R. Thresholds learned from training patients were applied unchanged to held-out test patients. Affine registration outperformed rigid registration on NMI and SSIM, yielding 44% Green classifications versus 33% for rigid. Reliability-filtered registrations improved the average alignment profile compared with unfiltered methods. Per-anatomy analysis showed substantial variation, with stronger reliability for abdominal registrations than brain registrations. Weight sensitivity analysis identified Dice overlap as the dominant reliability component. The proposed framework provides an interpretable quality-control layer for multimodal registration, while risk thresholds reflect statistical rather than clinical validation.
[CV-327] RotateAttention: RoPE-Aware Rotation and Range Rectification for INT4 Quantized Attention in Video Generation
链接: https://arxiv.org/abs/2607.02584
作者: Yaofu Liu,Wanli Lan,Jinxi Li,Binhang Yuan,Harry Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In \textbfDiT-based video generation models equipped with 3D Rotary Position Embeddings (3D RoPE), the attention mechanism remains a primary computational bottleneck due to its quadratic complexity with respect to sequence length. While quantized \textbfFlashAttention offers a promising path toward hardware acceleration, existing low-bit quantization methods overlook two critical challenges in this setting: \textbf1) applying online rotation matrices – a widely used technique for mitigating outliers in Queries ( Q ) and Keys ( K ) – is difficult to reconcile with \textbfRoPE; and \textbf2) the non-negative attention matrix P = \exp(QK - \max(QK)) makes symmetric quantization waste half of the 4-bit dynamic range. In this work, we observe that the outlier distributions of Q and K are strongly affected by the dimensional partitioning of \textbf3D RoPE. Based on this finding, we propose \textbfRotateAttention, an efficient \textbfmixed-precision INT4 FlashAttention framework tailored for \textbfDiT-based video generation models with 3D RoPE, using selective \textbfFP16 fallback for accuracy-sensitive attention blocks and denoising steps. RotateAttention introduces two core techniques: \textbf1) RoPE-aware Rotation, which employs either mergeable rotation matrices that can be fused into RoPE or negligible-overhead matrices to mitigate RoPE-induced outliers in Q and K ; and \textbf2) Range-optimized P Quantization, which uses fixed scales and zero-points to fully exploit the \textbfINT4 numerical range with minimal computational overhead. Experiments show that \textbfRotateAttention preserves video generation quality nearly identical to full-precision baselines while achieving up to 1.68 \times end-to-end speedup and 2.2 \times kernel-level acceleration.
[CV-328] Evaluating Intellectual Property Guardrails of Generative Image Models: A Technical Report
链接: https://arxiv.org/abs/2607.02582
作者: Austin T. Hoag,Apostolos Modas,Yunhao Ba,Julienne M. LaChance,Jinru Xue,Wiebke Hutiri,Jan Simson,Tiffany Georgievski,Alex Towli,Joseph Smith,Yuki Mitsufuji,Alice Xiang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Generative image models are capable of producing images that bear a strong resemblance to, or replicate, recognizable intellectual property (IP). In this technical report, we present a benchmark and automated evaluation pipeline to test for evidence of IP guardrails in generative image models along with the propensity for these models to generate images with recognizable IP. The IP categories we tested include fictional characters, celebrity likeness, and commercial logos and do not encompass the full range of IP which may be implicated by image generation models. We evaluated fourteen widely used text-to-image models, including three self-hosted open weights models and eleven private models. While all of the private models were observed to refuse generations at some level due to IP guardrails, the frequency of generation refusals varied substantially among models. The refusal rates also varied considerably across the different IP categories tested. Commercial logos were refused least frequently and were successfully generated at the highest rate, on average. Though the rate varies, all models tested readily generated images containing recognizable IP as of March 2026.
[CV-329] Classroom Behavior Monitoring with YOLO An Empirical Study in Higher Education Settings
链接: https://arxiv.org/abs/2607.02580
作者: Sinh Vu Trong,Dung Nguyen Manh,Hieu Hoang Minh,Hieu Pham Trung,Thu Pham Ha,Nhu Le Hoang
类目: Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY)
备注:
Abstract:Classroom behavior monitoring plays a vital role in evaluating student engagement and improving teaching effectiveness. Traditional observation methods remain subjective and lack scalability. This study introduces a real-world dataset of classroom videos collected at the Banking Academy of Vietnam (BAV-Classroom dataset), annotated with nine distinctive behavioral categories. State-of-the-art Computer Vision models were evaluated and compared, with YOLOv11 achieving the best performance. Experimental results indicate that students’ concentration often decreases notably during the final part of lectures, highlighting challenges in sustaining engagement. Our findings demonstrate the feasibility of applying computer vision for automated classroom monitoring, providing valuable insights for academic quality management.
[CV-330] Criterion-Conditional In-Context Learning: Evaluating Criterion-Shift Adaptation in Vision-Language Models ICML2026
链接: https://arxiv.org/abs/2607.02575
作者: Kaiyun Yang,Ruilin Yang,Zhimin Yao,J. Wang,Wei Ge
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted by ICML 2026
Abstract:Vision-language models can perform new tasks without parameter updates through in-context learning (ICL), whose core mechanism is utilizing the support set for task induction. In the standard ICL setting, once the task is induced, its decision criterion remains fixed. However, in real-world applications, many tasks exhibit a stable high-level intent, while their decision criteria shift according to specific requirements. Thus, we introduce a new setting, denoted as Criterion-Conditional In-Context Learning (CC-ICL), where models must infer the latent criterion from context and adjust predictions accordingly under fixed task semantics. To evaluate this capability, we propose two complementary metrics, Criterion Invariance and Criterion Sensitivity, capturing the model’s robustness and adaptability under criterion shifts. We further construct CC-Bench, a multi-domain benchmark that supports evaluation under the CC-ICL setting. By employing a dual-level data hierarchy, CC-Bench enables legitimate ground-truth variation conditioned on the active criterion even when the task remains fixed. Experiments on CC-Bench reveal that most models exhibit a rigid boundary bias, struggling to align their decisions with the latent criterion. We also find that even a simple multi-criterion training strategy can significantly reduce this bias, improving Criterion Sensitivity and enabling 7B-scale models to surpass proprietary models without degrading general multimodal performance.
[CV-331] Symmetry-Structured Neural Completion of Islamic Geometric Patterns from Sparse Control Geometry
链接: https://arxiv.org/abs/2607.02573
作者: Hassan Ugail,Irfan Mehmood
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Islamic geometric patterns are governed by exact rotational symmetry and strict construction rules. This paper treats these rules as formal geometric knowledge and embeds them in a neural completion framework, rather than leaving them to be learned statistically from data. Given sparse control geometry and a target symmetry order, the system completes the pattern as a vector graph by predicting edges and refinements of bounded curves over a candidate lattice whose edges are organised into rotational orbits under the cyclic group. Symmetry is enforced either by constraining predictions within these orbits or by projecting them onto them during inference. The orbit-tied variant provides a constructive guarantee: for any input and any orbit-level selection rule, it produces exact N-fold symmetry, preserves anchor points, and keeps all refinements within prescribed bounds. These properties are verified numerically. The study focuses on rotational symmetry, and all quantitative results are obtained from procedurally generated graphs inspired by Islamic geometric design rather than from a historical corpus. On clean inputs, enforcing exact validity produces no measurable loss in fidelity. When control geometry is missing, an unstructured decoder loses fidelity and breaks symmetry; retraining on corrupted inputs recovers much of the fidelity but not exact validity. Symmetry-structured inference, by contrast, keeps violations at zero throughout. The results show that augmentation and symmetry structure address distinct failure modes: augmentation improves fidelity under corruption, while symmetry structure guarantees validity. The framework therefore provides a knowledge-constrained, guarantee-backed approach to neural completion for scalable vector ornaments whose validity depends on exact geometric structure.
[CV-332] Additive Causal Construction for Transferable and Reconfigurable Cross-System Learning in Multi-Source Image Fusion
链接: https://arxiv.org/abs/2607.02572
作者: Zhizhong Fu,Wei Zhou,Zhaoyang Jiang,Yulong Lin,Yifu Hou,Xiaorong Ding,Qiang Yan,Yifan Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:In multi-source image fusion scenarios, heterogeneous inputs are typically driven by distinct generative mechanisms and can be viewed as a composition of multiple causal systems. However, cross-system discrepancy (CSD) and cross-system entanglement (CSE) commonly arise during the fusion process, often leading to significant performance degradation under out-of-distribution (OOD) predictions. To address the CSD and CSE issues, we propose the additive causal construction (ACC) framework, which characterizes information fusion at two levels: firstly, it establishes causal “anchors” shared among multiple systems through intervention consistency to enable causal graph transferability (CGT); and secondly, it formalizes the fusion process as causal construction and models the reliability of constructed paths through uncertainty quantification to ensure causal graph reconfigurability (CGR). Building upon this, we revisit the traditional causal representation learning (CRL) with ACC and propose ACC-CRL as a learnable instantiation of the framework. The method explores joint causal content representations across systems via content-mechanism decoupling, and performs response alignment under shared anchors to mitigate CSD. Furthermore, it incorporates structural uncertainty to adaptively regulate the fusion process, thereby suppressing unstable CSE. We conduct systematic experiments on synthetic data (ColorMNIST) and real-world multi-center medical imaging tasks (microvascular invasion (MVI) prediction). The results demonstrate that the proposed method significantly improves OOD generalization while maintaining in-distribution (ID) performance, validating the effectiveness and robustness of the ACC-CRL strategy based on mechanism alignment and uncertainty modeling in open environments.
[CV-333] Dual-Adaptive SAM3: Hierarchical Routing over Low-Rank Expert Layers for Parameter-Efficient Medical Image Segmentation MICCAI2026
链接: https://arxiv.org/abs/2607.02571
作者: Ying Chen,Jinyue Li,Kun Wang,Qiankun Li,Yang Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by MICCAI 2026
Abstract:The Segment Anything Model with Concepts (SAM3) heralds a new paradigm for open-vocabulary segmentation through natural language interaction, offering significant potential for medical image analysis. However, effectively adapting such a powerful vision-language model to the diverse and nuanced domain of medical imaging remains a key challenge. Naive fine-tuning is parameter-inefficient, while standard Mixture-of-Experts (MoE) methods introduce prohibitive computational overhead, limiting their clinical applicability. To address this, we propose Dual-Adaptive SAM3 (DA-SAM3), a novel framework that achieves both high segmentation accuracy and extreme parameter efficiency via a dual-adaptive specialization mechanism. Our first adaptation is task-aware: a Dynamic Expert Router (DER) that sparsely activates the most relevant experts by jointly reasoning about the visual input and the textual concept prompt, mimicking a clinical consultation process. Our second adaptation is parameter-aware: a Decomposed Parameterized Experts (DPE) design that represents each expert as a shared frozen base (inherited from the pretrained SAM3) and a lightweight trainable low-rank delta, reducing MoE parameter overhead by over 80%. Extensive experiments on multiple public medical segmentation benchmarks demonstrate that Dual-Adaptive SAM3 not only matches or exceeds the accuracy of fully fine-tuned SAM3 and standard MoE baselines, but also achieves a notable 5% gain over current state-of-the-art methods, with interpretable results validating its effectiveness. The code is available at: this https URL.
[CV-334] Uncertainty-Aware Last-Layer Adaptation of RETFound for Referable Diabetic Retinopathy Screening Under Dataset Shift
链接: https://arxiv.org/abs/2607.02569
作者: Karim Mardhani
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 12 pages, 8 tables, 6 figures. Code available at this https URL
Abstract:This paper presents a safety-centered empirical evaluation of uncertainty-aware last-layer adaptation for referable diabetic retinopathy screening using RETFound, a self-supervised vision-transformer retinal foundation model used here as a frozen feature encoder, and the public APTOS 2019 and DDR diabetic retinopathy fundus image datasets. We compare a cached-feature softmax head, post-hoc temperature scaling, variational Bayesian last-layer heads, a diagonal Laplace last-layer approximation, and an SNGP-style cached-feature head. On APTOS, uncertainty-aware operating points improved sensitivity and selective-referral behavior. The strongest APTOS selective-referral result deferred approximately 20 percent of cases and reduced accepted-case false negatives to zero while preserving high accepted-case specificity. However, threshold tuning also reduced false negatives at high false-positive cost, so false-negative reduction alone was not unique to Bayesian modeling. On DDR, native Bayesian heads qualitatively reproduced the APTOS direction but with weaker tradeoffs, while the APTOS-trained SNGP checkpoint transferred poorly and failed to provide useful external selective-referral behavior. These results highlight the value of safety-centered evaluation beyond aggregate accuracy: uncertainty-aware last-layer heads can improve internal safety-oriented operating points, but trustworthy retinal screening claims require explicit safety-coverage evaluation and second-dataset validation under shift.
[CV-335] MAGE: View-guided Point Cloud Completion with Efficient Modality Alignment and Adaptive Geometry Enhancement
链接: https://arxiv.org/abs/2607.02568
作者: Weize Quan,Zhengwei Wu,Kai Wang,Dong-Ming Yan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:View-based point cloud completion aims to recover a complete 3D shape from a partial point cloud, guided by a single-view image. However, existing approaches often suffer from limited performance due to weak modality alignment and limited self-geometry enhancement. To overcome these challenges, we propose a unified geometry-aware framework that integrates efficient modality alignment and adaptive geometry enhancement, mainly to address cross-modal geometric inconsistency of view-guided point cloud completion. Specifically, we propose a geometry-aware modality alignment by integrating a shared self-attention Transformer and cross-modality reconstruction supervision, which aims to bring features of the image and point cloud close to each other in a shared latent space describing the 3D object. To enhance the perception of global shape and local geometric details, we propose an adaptive geometry-aware self-attention module, which simultaneously considers local geometry-aware attention computation and the spatially-variant feature fusion. In addition, we apply a geometry-perceptive anchor refinement module to reorganize the anchor points (representing a local region of the shape) under appropriate supervision, further boosting the completion performance of our method. Extensive experiments on both synthetic and real-world datasets demonstrate that our method achieves superior performance over existing approaches. Our code will be available at this https URL.
[CV-336] From Raw Segmentations to Simulation-Ready Cardiac Meshes: An Automated Framework for Anatomical Reconstruction and Virtual Cohort Generation
链接: https://arxiv.org/abs/2607.02564
作者: Francesco Fabbri,Martino Andrea Scarpolini,Paolo Ciancarella,Francesco Tudisco,Roberto Verzicco,Alessandro Ricci,Francesco Viola
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Tissues and Organs (q-bio.TO)
备注:
Abstract:Computational models of the human heart are widely used to study electromechanical and fluid-dynamical cardiac function and to support applications such as in silico clinical trials. However, most studies remain limited to single or patient-specific anatomies, restricting the inclusion of population-level variability required for uncertainty quantification. A key challenge is translating medical-image segmentations, which may contain artifacts, mesh defects or disjoint domains, into topologically coherent geometries suitable for multiphysics simulations. In this work, we present a semi-automatic pipeline that converts CT-based segmentations into simulation-ready cardiac meshes within a few minutes while preserving anatomical and topological consistency. Building on modern deep learning segmentation methods, the framework incorporates a template-based registration stage to regularize artifacts and enforce mesh-quality constraints. A Chamfer-distance morphing strategy deforms a high-quality template toward each segmented heart, matching individual chambers while preserving topology. The resulting meshes are watertight, isotopological, and endowed with consistent point-to-point correspondence. The pipeline is validated on 58 healthy cardiac CT scans, including all cardiac chambers and proximal vessel segments. The resulting meshes can be represented in a unified shape space, enabling the construction of a statistical shape model of the heart and major vessels. Principal Component Analysis shows that a low-dimensional latent space efficiently captures population variability, while Gaussian Mixture Modeling enables synthetic anatomy generation. Overall, the proposed framework (released open-source) provides a pathway from raw segmentations to simulation-ready cardiac geometries, enabling anatomically consistent virtual cohorts for large-scale in silico studies. Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Tissues and Organs (q-bio.TO) Cite as: arXiv:2607.02564 [cs.CV] (or arXiv:2607.02564v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.02564 Focus to learn more arXiv-issued DOI via DataCite
[CV-337] Entropy-Coded MS-VQ-VAE with Learned Priors for Ultra-Low Bitrate Video Compression
链接: https://arxiv.org/abs/2607.02562
作者: Manikanta Kotthapalli,Banafsheh Rekabdar
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Learned video codecs based on continuous latent representations struggle to operate reliably below 0.1 bits per pixel~(bpp): without a differentiable rate signal, Lagrangian optimisation cannot effectively trade reconstruction quality for bitrate at extreme compression ratios. We demonstrate that discrete latent representations sidestep this limitation entirely. In a vector-quantized~(VQ) codec, the codebook size~ K imposes a hard information ceiling of \log_2 K bits per symbol; a learned autoregressive prior then exploits the non-uniform distribution of code usage – which we show follows a power law – to push actual bitrates well below this ceiling, without any rate-penalty tuning. Building on the MS-VQ-VAE architecture introduced in~\citekotthapalli2026msvqvae, we sweep K \in \128, 256, 512, 1024\ under a uniform training protocol to trace four operating points on the rate-distortion~(RD) curve. We identify and resolve a critical training instability: gradient-based VQ collapses catastrophically at K \leq 512 , whereas EMA-stabilised codebook updates with dead-code restart maintain full utilisation across all configurations. On 500 UCF101 test clips ( 64!\times!64 , 32~frames), our models operate at 0.043-0.064~bpp – 3.3-5 \times below H.264’s practical floor and 5 - 7.6\times below H.265’s floor at this resolution. Every MS-VQ-VAE configuration outperforms H.265 CRF,36 on perceptual quality (LPIPS) despite using 5 - 7.6\times fewer bits. At K=1024 , the model surpasses H.265 CRF,36 on LPIPS by a margin of 0.072 absolute while using 5.1\times fewer bits. Codebook analysis confirms power-law index distributions and 70-85% entropy efficiency, establishing the pipeline as a principled learned entropy coder. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2607.02562 [cs.CV] (or arXiv:2607.02562v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.02562 Focus to learn more arXiv-issued DOI via DataCite
[CV-338] Double-Helix Active Geometry: LiDAR-Anchored Multi-View Depth with Selective Abstention
链接: https://arxiv.org/abs/2607.02561
作者: Jinwen Wen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 10 pages, 3 figures, 7 tables
Abstract:Consumer depth sensors such as the LiDAR scanner on recent iPhones provide metric range, but their useful range is short and their returns are sparse. We present DH-Active, a lightweight, training-free geometry back-end that treats the sensor as a metric ruler rather than the sole source of depth. Near-field returns anchor the metric relative pose of two views through PnP; visually trackable samples without a valid depth return are then triangulated under that pose. A parallax/reprojection gate abstains wherever the geometry is ill-conditioned, leaving an explicit hole and a selective score instead of forcing an estimate. The measured core front end, including spiral sampling, sparse back-projection, and hole taxonomy but excluding preprocessing and multi-view recovery, runs at 1.11 ms median latency on CPU (OpenCV using 14 threads), about 38 times faster than a DINOv2-L visual branch on GPU in our timing setup. Across two iPhone captures and the public TUM RGB-D and ARKitScenes benchmarks, held-out depth is recovered at 1.4 to 6.7 percent median relative error. In a controlled ARKitScenes protocol that uses only returns within 2 m to set scale and an independent laser scan as ground truth, DH-Active achieves 64.2 percent scene-median coverage of evaluable far-field candidates at 13.4 percent scene-median relative error; direct triangulation from the device trajectory is not usable. We also report the alternatives that failed in our tests: single-frame defocus, classical focus-stack depth, defocus-LiDAR fusion, point-to-point ICP over a good visual-inertial track, and attention-to-holes resampling. A 1.26 B learned model remains more accurate after oracle scale alignment. The contribution here is narrower: metric sparse depth, explicit abstention, zero learned parameters, and near-millisecond CPU cost.
[CV-339] Inpainting U-Net for seamless pedestrian-level wind prediction across urban morphologies
链接: https://arxiv.org/abs/2607.02560
作者: Jingzi Huang,Claire E. Heaney,Tao Li,Xinzhe Li,Graham O. Hughes,Maarten van Reeuwijk
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Pedestrian-level wind prediction is essential for urban design and wind-comfort assessment, but high-fidelity simulations such as LES remain computationally expensive for rapid evaluation. This study develops a two-stage U-Net framework for efficient prediction of time-averaged pedestrian-level wind speed over realistic urban morphologies. The model is trained and evaluated using the UrbanTALES dataset, which contains realistic city configurations under different approaching wind directions. In the first stage, a baseline U-Net model (M1) predicts wind fields patch-by-patch from normalised building height and fetch information. This formulation allows application to urban domains of arbitrary size, but independent patch inference can introduce discontinuities at patch boundaries. To address this, a second U-Net model (M2) is introduced as an inpainting-based refinement model. M2 uses a larger contextual window containing the initial M1 prediction and local morphology to reduce discontinuities using neighbouring flow information. During full-field inference, M2 is applied iteratively using a Gauss-Seidel scheme until convergence. Results show that M1 captures the main spatial distribution of pedestrian-level wind speed and performs well in low- and moderate-velocity regions, although high-velocity peaks are less accurate. M2 substantially reduces patch-boundary artefacts and improves spatial coherence. Across unseen urban cases, the framework reproduces mean velocity and spatial variability reasonably well, while maximum velocities remain underestimated. Overall, the proposed framework provides an efficient and flexible surrogate model for high-resolution pedestrian-level wind prediction across realistic urban morphologies.
[CV-340] How many labels do you need? A decision framework for cross-habitat marine species recognition
链接: https://arxiv.org/abs/2607.02559
作者: Alzayat Saleh,Mostafa Rahimi Azghadi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 29 pages, 12 figures, 4 tables. Includes supplementary appendix
Abstract:Automated image recognition is increasingly used to scale ecological monitoring beyond manual annotation, yet ecologists lack evidence-based guidance on how much labelling effort reliable deployment at new sites requires. We present a decision framework quantifying the trade-off between labelling effort and recognition accuracy when transferring vision systems across marine habitats. The benchmark spans five datasets, three oceans, and three taxonomic groups (fish, corals, invertebrates), from tropical reefs in the Great Barrier Reef and French Polynesia to a temperate Danish fjord. We evaluated four recognition models (DINOv2, CLIP, ResNet-50, EfficientNet-B4) under four adaptation strategies (linear probing, LoRA, Visual Prompt Tuning, full fine-tuning) across three protocols: within-habitat transfer across 20 reef sites (240 runs), cross-dataset geographic transfer along a difficulty gradient (40 runs), and few-shot adaptation curves with 0-100 labelled samples per class (648 runs). Frozen self-supervised foundation features (DINOv2 + linear classifier, 1,538 trainable parameters) generalised to unseen reef sites at least as well as fully fine-tuned convolutional baselines four orders of magnitude larger; they learned species-diagnostic, habitat-invariant representations, whereas baselines encoded habitat-specific shortcuts that fail at new sites. As few as 10-20 labelled images per species sufficed to deploy reliable recognition at a new site, cutting annotation effort by roughly an order of magnitude. Solution. Programmes expanding to new sites can deploy reliable recognition by pairing a frozen, open foundation model (DINOv2) with a simple linear classifier and annotating only 10-20 images per species - roughly 1-4 hours per site. The framework lets programmes budget labelling effort against expected accuracy across sites, ecosystems, and platforms. Comments: 29 pages, 12 figures, 4 tables. Includes supplementary appendix Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2607.02559 [cs.CV] (or arXiv:2607.02559v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.02559 Focus to learn more arXiv-issued DOI via DataCite
[CV-341] Do Diabetic Foot Ulcer Segmentation Models Generalize? A Cross-Dataset Benchmark of CNN and Transformer Architectures
链接: https://arxiv.org/abs/2607.02555
作者: Abderrahmane Benfatah
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注: 9 pages, 2 figures, 3 tables
Abstract:Deep learning models for diabetic foot ulcer (DFU) segmentation routinely report high accuracy, but they are almost always trained and tested on the same dataset, leaving their behaviour on data from a different clinical source largely unmeasured. We benchmark three representative segmentation architectures – U-Net and DeepLabV3+ (convolutional) and SegFormer-B2 (Transformer) – under an identical, leakage-screened protocol: training on the combined FUSeg/AZH wound data and evaluating, without fine-tuning, on two independent external datasets (DFUC2022 and Medetec). All models achieve strong in-domain performance (Dice 0.80–0.83) but degrade substantially across datasets. The degradation is, however, architecture-dependent: SegFormer-B2 generalizes best on both external sets (DFUC2022 Dice 0.557, Medetec Dice 0.786), outperforming both convolutional models, while the more complex DeepLabV3+ generalizes worse than the simpler U-Net. Per-image failure analysis on 2,160 images across both external test sets confirms that SegFormer-B2 produces the fewest catastrophic failures on DFUC2022 (31.1%), compared with U-Net (38.5%) and DeepLabV3+ (43.0%). The consistent ranking across two independent external sources, confirmed by Wilcoxon signed-rank tests (p 0.001 on both datasets), indicates that architecture family, not model complexity, drives cross-hospital generalization.
[CV-342] Reliability-Aware Monocular Depth Supervision for Sparse-View Neural Reconstruction
链接: https://arxiv.org/abs/2607.02554
作者: Wei-Teng Chu,Yashasvini Gopalan,Changju Yuan
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注: 10 pages, 6 figures. All authors contributed equally
Abstract:Sparse-view neural reconstruction is challenging in outdoor driving scenes, where cameras usually move along a narrow forward-facing trajectory and provide limited multi-view overlap. Although monocular depth estimators can provide dense geometric priors, their predictions are noisy, and not uniformly reliable across image regions. In this work, we study monocular depth supervision for sparse-view neural reconstruction. We use Depth Anything V2 as a dense monocular depth prior, align its predictions to metric depth using scale-shift fitting, and apply depth supervision selectively through photometric masks generated from an RGB-only baseline model. We evaluate this strategy on two representative scene representations: Mip-NeRF-360 and Splatfacto. On KITTISeq02 under an every2 sparse-view setting, masked monocular depth supervision gives only marginal rendering gains for Mip-NeRF-360 and does not improve metric geometry. In contrast, Splatfacto benefits more clearly, improving PSNR from 14.903 to 15.932 and reducing RMSE from 0.542 to 0.100. Additional KITTISeq05 experiments and matched-ratio mask ablations further show that the gains for Splatfacto come from selecting reliable low-error regions rather than simply reducing the number of depth-supervised pixels. Additional experiments on the Bicycle scene show that depth supervision can improve geometry while hurting RGB rendering quality when multi-view coverage is already strong. Overall, our results suggest that monocular depth priors are useful for under-constrained sparse-view reconstruction, but should be applied selectively and with moderate weighting.
[CV-343] Interpretable machine learning predicts Parkinsons disease severity using motion-corrected QSM MRI and multiband multiecho fMRI features
链接: https://arxiv.org/abs/2607.02553
作者: Aixa X. Andrade
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
备注: 16 pages from main manuscript and 17 pages from supplementary information
Abstract:Introduction: Objective neuroimaging biomarkers may improve Parkinson’s disease motor assessment by capturing brain variation not directly observable from clinical examination. We used interpretable machine learning to predict current motor severity, measured by MDS-UPDRS Part III, from QSM and multiband multi-echo resting-state fMRI-derived ReHo features. Methods: Regional QSM and ReHo features were extracted from 28 participants, including 24 individuals with Parkinson’s disease and 4 controls. Thirteen feature-set experiments evaluated imaging-only, clinical-only, imaging-plus-clinical, full, reduced, and multimodal inputs. Support vector regression, Elastic Net, Random Forest, and XGBoost models were trained using nested cross-validation. Performance was assessed using pooled held-out R^2, RMSE, MAE, Pearson correlation, permutation testing, and the proportion of participants predicted within +/-5 MDS-UPDRS Part III points. Results: Imaging-only models carried meaningful predictive signal, whereas the clinical-only model performed weakly. Full fMRI, full QSM, and clinical variables provided the strongest global fit, explaining 45.4% of variance in motor severity. Selected QSM plus clinical variables produced the most clinically close predictions, with 75.0% of participants predicted within +/-5 points and the lowest MAE among top-performing models. SHAP highlighted cerebellar, thalamic, striatal, insular, and motor cortical features. Conclusion: QSM and multiband multi-echo fMRI-derived ReHo capture distinct, interpretable dimensions of Parkinson’s disease motor severity. These findings show that structural and functional imaging contribute differently depending on the clinical prediction goal. Comments: 16 pages from main manuscript and 17 pages from supplementary information Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC) Cite as: arXiv:2607.02553 [cs.CV] (or arXiv:2607.02553v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.02553 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Aixa X. Andrade [view email] [v1] Fri, 26 Jun 2026 20:26:37 UTC (6,847 KB)
[CV-344] DELTAVID: Enhancing Fine-Grained Spatiotemporal Perception with Cross-Video Differences
链接: https://arxiv.org/abs/2607.02551
作者: Yankai Yang,Yancheng Long,Bin Wen,Fan Yang,Tingting Gao,Han Li,Shuo Yang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Video multimodal large language models have made strong progress on open-ended video understanding, but they still lack precise local spatiotemporal perception. When two videos share almost the same global semantics and differ only in a short time span or a small region, current models often fail to find the change and provide reliable evidence. We propose DELTAVID, a verifiable proxy-task framework that enhances fine-grained spatiotemporal perception with cross-video differences. The key idea is to turn cross-video spot-the-difference into a trainable perception signal, where a model identifies local changes, judges temporal boundaries, and organizes spatial evidence by comparing similar videos. To make this signal scalable to train and reliable to evaluate, we further introduce DELTAVID-10K and DELTAVID-Bench, which convert controllable local differences in real videos into evidence-labeled training and test samples. Experiments show that DELTAVID substantially improves performance on cross-video difference understanding and transfers the learned local evidence ability to general video understanding benchmarks, including MMVU, MLVU, Video-MME, VideoHolmes, VideoMMMU, LVBench, TempCompass, and LongVideoBench. These results show that cross-video differences are not only an effective way to diagnose fine-grained perception failures, but also a scalable proxy supervision that moves Video MLLMs from coarse semantic understanding toward fine-grained spatiotemporal evidence reasoning.
[CV-345] Learning 3D Affordances for Blade Insertion in Cluttered Stowing
链接: https://arxiv.org/abs/2607.02549
作者: Tianyu Li,Harpreet Sawhney,Minju Jung,Aditya Mehrotra,Kunal Mehrotra,Mudit Agrawal
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Many manipulation tasks require reasoning about free-space affordances: discovering volumes where an extended rigid tool can safely navigate, complementary to surface contact affordances for grasping. Robotic stowing is a canonical instance, where a blade must sweep items aside inside cluttered fabric bins to create insertion space. Production stow systems generate millions of such episodes, but standard approaches with unimodal data infer affordances as SE(3) pose distributions, a geometric question asked in the wrong domain. VulcanVoxel keeps inference spatial: a masked autoencoder over 3D occupancy fields reconstructs blade occupancy conditioned on scene geometry, computing feasibility locally at each voxel and recovering multi-modal predictions from unimodal data. Blade affordances are spatial objects, subsets of 3D space defined by geometric feasibility. Pose parameters carry no structure for reasoning whether unobserved placements are feasible, and standard generative objectives including flow matching faithfully learn the unimodal distribution produced by execution policies and cannot recover geometric alternatives. Trained on 10,000 real warehouse stow episodes without human annotation, VulcanVoxel achieves top-5 coverage of 0.89 versus 0.71 for the best pose-based baseline, with a distilled student providing RGB-to-voxel inference in 30 ms. vs. 1.4 s. for voxel-to-voxel. We have released a dataset of real blade insertion cycles with RGB-D observations and pose trajectories at this https URL. html.
[CV-346] FLYTEK-Embodied-Omni Technical Report
链接: https://arxiv.org/abs/2607.02542
作者: Yuan Zhang,Jingfei Ni,Guanchen Lu,Shiqi Zhang,Qingshan Xu,Chi Liu,Xin Nie,Wenjie Xu,Lin Gao,Zhiyuan Cheng,Mingxin Zhou,Jiajia Wu,Diyuan Liu,Jia Pan,Chao Ji
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:General-purpose embodied agents must understand multimodal instructions, anticipate how their environment will evolve, and produce precise control actions over extended horizons. Existing approaches typically specialize in visual-language reasoning, video-based world modeling, or action generation, while cascaded pipelines that first synthesize future observations and then infer actions can introduce interface bottlenecks and compound prediction errors. We present iFLYTEK-Embodied-Omni, a unified multimodal foundation model that jointly models vision(videos and images), language, and action within a single Omni framework. Its modality-specific visual-language, video-generation, and action-generation components communicate through shared multimodal self-attention. This design establishes brain-cerebellum collaboration: the vision-language modeland video generation model form a high-level brain for instruction understanding, task planning, progress tracking, and future visual-state prediction, whereas the action generation modelserves as a low-level cerebellum that directly converts planned subgoals and shared multimodal context into executable action chunks. To develop these capabilities, we combine action-annotated and action-free embodied videos from human demonstrations and robot interactions with embodied reasoning, embodied perception, and general-purpose image-text data to construct a comprehensive dataset. We further adopt a four-stage strategy that progressively trains the VLM, VGM, and AGM before jointly fine-tuning the complete model.
[CV-347] Open-Weather Robust 3D Detection via Dual-Critic Diffusion Alignment ECCV2026
链接: https://arxiv.org/abs/2607.01983
作者: Shuyao Li,Chuanxing Geng,Heyang Sun,Qiang Zhou,Jingjing Gu
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 18 pages, 6 figures, 8 tables. ECCV 2026 camera-ready
Abstract:Robust 3D object detection under adverse weather remains a critical hurdle for autonomous driving. Despite progress with LiDAR-4D radar fusion, most methods are constrained by a closed-world assumption, implicitly requiring training and test weather to align in both type and severity. This premise fails in practice: the open-ended nature of weather, and even variations within a single type like rain, cause dramatically different LiDAR degradation patterns, leading to significant performance drops in unseen conditions. To address this, we present Dual-Critic Guided Diffusion Alignment (DCDA), a weather-agnostic framework that learns to recover degraded LiDAR features toward a clean manifold. Rather than modeling specific weather types, DCDA employs a 4D radar-conditioned diffusion process to progressively refine features, guided by two complementary critics. (i) A detection-guided critic, anchored by a pre-trained clean-weather model, ensures that the refined features retain object-level discriminability and localization accuracy. (ii) A weather adversarial critic enforces holistic distributional consistency with clean-weather representations. By aligning features through semantic and distributional constraints rather than explicit weather modeling, DCDA generalizes effectively to unseen weather types and severities without requiring paired data or weather labels. We further introduce a structured open-weather benchmark with held-out type-severity combinations and extensive experiments verify DCDA’s advantages.
[CV-348] PotatoGANs: Utilizing Generative Adversarial Networks Instance Segmentation and Explainable AI for Enhanced Potato Disease Identification and Classification
链接: https://arxiv.org/abs/2405.07332
作者: Fatema Tuj Johora Faria,Mukaffi Bin Moin,Mohammad Shafiul Alam,Ahmed Al Wase,Md. Rabius Sani,Khan Md Hasib
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Numerous applications have resulted from the automation of agricultural disease segmentation using deep learning techniques. However, when applied to new conditions, these applications frequently face the difficulty of overfitting, resulting in lower segmentation performance. In the context of potato farming, where diseases have a large influence on yields, it is critical for the agricultural economy to quickly and properly identify these diseases. Traditional data augmentation approaches, such as rotation, flip, and translation, have limitations and frequently fail to provide strong generalization results. To address these issues, our research employs a novel approach termed as PotatoGANs. In this novel data augmentation approach, two types of Generative Adversarial Networks (GANs) are utilized to generate synthetic potato disease images from healthy potato images. This approach not only expands the dataset but also adds variety, which helps to enhance model generalization. Using the Inception score as a measure, our experiments show the better quality and realisticness of the images created by PotatoGANs, emphasizing their capacity to resemble real disease images closely. The CycleGAN model outperforms the Pix2Pix GAN model in terms of image quality, as evidenced by its higher IS scores CycleGAN achieves higher Inception scores (IS) of 1.2001 and 1.0900 for black scurf and common scab, respectively. This synthetic data can significantly improve the training of large neural networks. It also reduces data collection costs while enhancing data diversity and generalization capabilities. Our work improves interpretability by combining three gradient-based Explainable AI algorithms (GradCAM, GradCAM++, and ScoreCAM) with three distinct CNN architectures (DenseNet169, Resnet152 V2, InceptionResNet V2) for potato disease classification.
[CV-349] SiamixFormer: a fully-transformer Siamese network with temporal Fusion for accurate building detection and change detection in bi-temporal remote sensing images
链接: https://arxiv.org/abs/2208.00657
作者: Amir Mohammadian,Foad Ghaderi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Building detection and change detection using remote sensing images can help urban and rescue planning. Moreover, they can be used for building damage assessment after natural disasters. Currently, most of the existing models for building detection use only one image (pre-disaster image) to detect buildings. This is based on the idea that post-disaster images reduce the model’s performance because of presence of destroyed buildings. In this paper, we propose a siamese model, called SiamixFormer, which uses pre- and post-disaster images as input. Our model has two encoders and has a hierarchical transformer architecture. The output of each stage in both encoders is given to a temporal transformer for feature fusion in a way that query is generated from pre-disaster images and (key, value) is generated from post-disaster images. To this end, temporal features are also considered in feature fusion. Another advantage of using temporal transformers in feature fusion is that they can better maintain large receptive fields generated by transformer encoders compared with CNNs. Finally, the output of the temporal transformer is given to a simple MLP decoder at each stage. The SiamixFormer model is evaluated on xBD, and WHU datasets, for building detection and on LEVIR-CD and CDD datasets for change detection and could outperform the state-of-the-art.
[CV-350] CompressedVQA-AEV: Full-Reference and No-Reference Quality Assessment Models for Asymmetric Encoded Videos
链接: https://arxiv.org/abs/2607.04606
作者: Wei Sun,Xingwei Liu,Dandan Zhu,Xiangyang Zhu,Weixia Zhang,Guangtao Zhai
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注: CompressedVQA-AEV-FR achieves first place in the FR track of QoMEX 2026 Grand Challenge
Abstract:This report presents our solutions to the QoMEX 2026 Grand Challenge on Video Quality Assessment for Asymmetric Encoded Videos, comprising a full-reference (FR) model, CompressedVQA-AEV-FR, and a no-reference (NR) model, CompressedVQA-AEV-NR. The FR approach leverages a Swin-B backbone to extract multi-stage similarity statistics between reference and distorted videos for quality prediction. For the NR setting, our model employs complementary frame-level encoders based on SigLIP2 and Swin-B, followed by temporal mean pooling and cross-fold ensembling to estimate perceptual quality without reference data. Our CompressedVQA-AEV-FR achieves first place in the FR track of QoMEX 2026 Grand Challenge, while CompressedVQA-AEV-NR secures fourth place in the NR track, demonstrating the effectiveness of our proposed models. The code is available at this https URL.
[CV-351] On Pairwise Quantile Regression – Statistical Guarantees and Applications UAI2026
链接: https://arxiv.org/abs/2607.04431
作者: Romain Thérézien,Stephan Clémençon,Fantin Girard,Hamza El-Abdouni
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted to UAI 2026
Abstract:Quantile regression provides a powerful tool for summarizing the conditional distribution of a real valued random variable (r.v.) of interest Y as a function of covariates Z in cases where it shows a large dispersion with high probability, going beyond the situation where standard least square regression is informative/predictive. This article aims to extend this methodology to the pairwise case, when the variable to be explained takes the form of a similarity function between two independent observations, such as pixelated ID photos, as input data of biometric systems) and the explanatory variables take the form of a pair of covariates of the observations, such as the age or the hair color. We establish theoretical guarantees for solutions of this statistical learning problem, considered here as empirical minimizers of a pairwise version of the pinball loss. Leveraging sharp concentration results for U -processes, we prove generalization bounds and identify mild conditions under which fast learning rates can be achieved. Confirming the probabilistic analysis, experiments based on simulation data also provide solid empirical evidence of the validity of the methodology promoted here for pairwise quantile regression. Finally, its usefulness from an application perspective is demonstrated by a detailed study aimed at analyzing errors in similarity scoring for facial recognition.
[CV-352] MambaRefine-CD: MambaVision with Region-Boundary Temporal Refinement
链接: https://arxiv.org/abs/2607.04403
作者: Dineth Perera,Thaariq Firdous,Oshadha Samarakoon,Roshan Godaliyadda,Parakrama Ekanayake,Vijitha Herath
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, 2 figures, MERCon 2026
Abstract:Binary change detection in remote sensing requires both complete changed-region localization and accurate boundary delineation. We present MambaRefine-CD, a region-boundary temporal refinement framework built on a shared MambaVision encoder. The proposed D-RBI module constructs temporal evidence from paired features, absolute differences, and signed differences, then separates it into region and Sobel-conditioned boundary streams. Region features are enhanced with CRAM-lite and decoded by an adaptive receptive-field FPN, while the finest boundary stream guides a bounded residual refinement of the coarse prediction. Experiments on DSIFN-CD and WHU-CD show strong changed-class F1 and IoU under verified evaluation settings, and ablations support the contribution of signed temporal evidence and the full region-boundary refinement pipeline.
[CV-353] FedProIn: Mitigating Client Drift for Learnable Prototypes in Federated Medical Imaging
链接: https://arxiv.org/abs/2607.04158
作者: Harsh Kumar,Tarun Kumar Garg,Vaanathi Sundaresan
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Federated learning (FL) is severely hindered by statistical heterogeneity due to variations in scanners, acquisition protocols, and patient populations. Such non-IID data induces client drift during local optimization, leading to unstable convergence and suboptimal global models when parameter-based aggregation is applied. We propose a prototype-based, influence-aware federated learning framework (FedProIn) that uses multiple learnable class prototypes to capture shared semantic structures across heterogeneous clients. We introduce feature divergence loss and prototype contrastive loss to mitigate client drift by decomposing it into feature drift and prototype drift. In addition, we propose a normalized influence aggregation strategy that adaptively weights client prototypes according to their contribution to the global representation, reducing the impact of biased or low-quality updates. Experimental results on two publicly available medical datasets, HAM10000 and Matek-19, demonstrate that FedProIn achieves accuracies of (83.5% IID, 81.1% non-IID) on HAM10000 and (96.2% IID, 95.8% non-IID) on Matek-19, respectively, outperforming existing baselines in both conditions. Our code is available at this https URL.
[CV-354] Cross-Modal Fusion of OCT and OCT angiography enface for Improved Diagnostics of Diabetic Retinopathy
链接: https://arxiv.org/abs/2607.03959
作者: Rashadul Hasan Badhon,Atalie Carina Thompson,Jennifer I. Lim,Theodore Leng,Minhaj Nur Alam
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide, highlighting the need for accurate and accessible screening tools. Optical Coherence Tomography (OCT) provides high-resolution structural information of the retina, whereas OCT angiography (OCTA) offers complementary vascular information that is highly relevant for DR diagnosis. In this study, we propose a cross-modal fusion of OCT B-scans with single-channel en face OCTA using a bidirectional cross-modal attention network for automated DR classification. Two independent datasets, OCT500 and UIC, comprising 730 subjects in total, were utilized to evaluate performance under within-dataset, combined-dataset, and cross-dataset generalization settings. A ConvNeXt V2 model trained solely on OCT images served as the unimodal baseline. In addition to ground-truth (GT) OCTA, we explored the use of translated (TR) OCTA generated from OCT scans, eliminating the requirement for dedicated OCTA hardware. Experimental results demonstrate that cross-modal fusion consistently outperforms unimodal OCT classification across all evaluation scenarios. Fusion with GT OCTA improved classification accuracy and discriminative performance, while TR OCTA achieved comparable or superior results in most settings. Furthermore, TR OCTA improved sensitivity and cross-dataset generalization, indicating enhanced robustness to domain shifts. These findings demonstrate that attention-based OCT-OCTA en face fusion provides clinically meaningful improvements for DR detection and suggest that computationally generated OCTA can serve as a practical, low-cost alternative to hardware-acquired OCTA, enabling broader deployment of high-performance retinal screening systems in resource-limited clinical environments.
[CV-355] GLOW-FDG: Generalized cancer LesiOn Whole-body segmentation model for 18F-FDG-PET/CT
链接: https://arxiv.org/abs/2607.03931
作者: Maksym Fritsak,Maximilian Rokuss,Hubert S. Gabryś,Yannick Kirchhoff,Benjamin Hamm,Sebastian M. Christ,Nicolas Martz,Isabelle Opitz,Rolf Stahel,Martin Hüllner,Matthias Guckenberger,Klaus Maier-Hein,Stephanie Tanadini-Lang
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Medical Physics (physics.med-ph)
备注:
Abstract:Whole-body fluorodeoxyglucose positron emission tomography combined with computed tomography is widely used in cancer care, but manual lesion delineation is slow, subjective, and difficult to scale. We present GLOW-FDG, an open-source artificial intelligence model for whole-body cancer lesion segmentation in fluorodeoxyglucose positron emission tomography and computed tomography. The model was trained on 1,563 scans spanning multiple cancer types and evaluated on 185 external scans from independent institutions. Across breast cancer, nonmetastatic and oligometastatic lung cancer, head and neck cancer, and metastatic melanoma, GLOW-FDG consistently outperformed publicly available benchmark models in lesion detection, while reducing false positives and maintaining strong segmentation accuracy. Quantification of total tumor burden and total lesion glycolysis was robust across cohorts, and performance approached the variability observed between expert radiation oncologists. These results support GLOW-FDG as a generalizable tool for automated cancer segmentation and quantitative imaging biomarker extraction in whole-body imaging.
[CV-356] GALOSH: Blind Training-Free Denoising of Raw Bayer and sRGB Images by Parallel-Friendly Local Shrinkage
链接: https://arxiv.org/abs/2607.03768
作者: Yoshiro Sato
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 4 figures, 6 tables. Code: this https URL
Abstract:Classical training-free denoisers such as BM3D and non-local means owe much of their strength to search: content-dependent block matching whose memory traffic and data-dependent control flow parallelize poorly and preclude fixed-latency implementations. Learned denoisers reach the highest quality, but they need training data, degrade outside their training domain (which we also observe), and carry per-pixel compute budgets that effectively require a GPU. We present GALOSH (Generalized Anscombe LOcal SHrinkage), a redesign of training-free denoising that removes the search entirely and aims at multi-domain coverage, speed, and quality at once: a blind per-image Poisson-Gaussian noise fit, a generalized Anscombe transform, a two-pass local Walsh-Hadamard shrinkage of luminance, and a luminance-guided local regression of chrominance – two deliberately different operators for the two perceptually different noise components, each with its own strength control. Every stage is local, data-independent, and regular – the same computation graph for every pixel of every image. One core serves two domains: raw Bayer mosaics and sRGB/YUV images. On four real-noise benchmarks (SIDD Medium and RawNIND, raw and sRGB) GALOSH is consistently the strongest among the tested blind, training-free methods – surpassing BM3D- and NLM-family baselines even when those are given an oracle noise level – and approaches trained networks on raw data while remaining below in-domain trained networks at high ISO in sRGB. Being search-free makes it fast: 7x-650x faster than the DL baselines on the same GPU at full benchmark size, and the only strong method in the comparison that also runs practically on plain CPUs. The fixed, data-independent structure is designed to map naturally onto fixed-point and streaming hardware, supported by an operation-count analysis and a working INT16 fixed-point realization.
[CV-357] Deep Learning-Based Characterization of Detonation-Cell Size Distributions in Soot-Foil Records
链接: https://arxiv.org/abs/2607.03764
作者: Mingyang Bu,Robson A. Schneider,Karl P. Chatelain,Mhedine Alicherif,Yingchen Shi,Andrés Z. Mendiburu,Deanna A. Lacoste,Bing Wang
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The geometric size and regularity of detonation cells are key physical parameters for characterizing detonation waves. Traditional manual measurement of soot foils is time-consuming and subjective, while existing computer vision techniques often exhibit poor generalization on real experimental images with high noise, blurred boundaries, and severe overlapping. To address this, we propose a novel method for automated recognition and high-order feature extraction of detonation cells based on deep learning instance segmentation (Mask R-CNN). By constructing a custom heterogeneous dataset (numerical simulations and physical experiments) and integrating transfer learning, the model achieves accurate pixel-level mask prediction within highly noisy flow fields. Results indicate high pixel-level agreement in benchmark validations and strong robustness against noise in complex real-world soot foils. Predicted average cell sizes agree well with manual measurements, yielding relative errors under 2% and 3.5% for regular and irregular conditions, respectively. Sensitivity ablation experiments confirm the model’s scale adaptability and guided the establishment of a standardized preprocessing paradigm for appropriate image patching. Overcoming the limitation of extracting only global average sizes, this model achieves automated tracking of the transient spatial evolution of cell sizes along the propagation direction. Furthermore, it quantitatively extracts high-order regularity features, such as the irregularity index (RI) and standard deviation of cell deflection angles, demonstrating consistency with theoretical expectations. The proposed method enhances the efficiency and objectivity of statistical analysis, providing a powerful data extraction tool for experimental and numerical soot foils.
[CV-358] riple-Phase Multimodal Knowledge Aggregation Framework for Microbial Keratitis Subtype Diagnosis on Slit-Lamp Photography
链接: https://arxiv.org/abs/2607.03740
作者: Yiqing Wang,Maria A. Woodward,Ziyun Yang,N. Venkatesh Prajna,Chunming He,Leslie M. Niziol,Mercy Pawar,Ming-Chen Lu,Guillermo Amescua,Rachel Wozniak,Sejal Amin,Abinaya Krishnan,Prabhleen Kochar,Sina Farsiu
类目: Quantitative Methods (q-bio.QM); Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注:
Abstract:Microbial keratitis requires rapid pathogen identification to guide treatment, but culture- and PCR-based diagnostics are slow and resource-intensive. We developed a triple-phase multimodal framework for bacterial-versus-fungal keratitis classification using slit-lamp photographs acquired under blue-light, sclerotic-scatter, and white-light illumination, together with clinical metadata. The model combines cross-modality contrastive learning, modality-specific fine-tuning, and feature-level multimodal ensemble learning for patient-level prediction. We evaluated the framework on a multicenter dataset of 1,645 patients and 17,158 images from India and the United States. The model achieved 85.84% accuracy, 84.46% average F1-score, and 0.885 AUC. Site-specific evaluation showed that pooled results were overly optimistic, whereas resampling- and balance-based re-evaluation provided a more realistic assessment of cross-site generalization. Under all settings, our framework remained the top-performing approach. Upon acceptance, the code will be released and dataset access will be provided subject to University of Michigan data-sharing clearance.
[CV-359] Model Confidence-Guided Multi-Image Fusion of Fundus Images for Diabetic Retinopathy Diagnosis
链接: https://arxiv.org/abs/2607.03643
作者: Ananya Raghu,Anisha Raghu,Alice S. Tang,Yannis M. Paulus,Tyson N. Kim,Tomiko T. Oskotsky
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Purpose: Early screening for eye diseases is critical in low- and middle-income countries where access to care is limited. We investigate whether a confidence-guided, multi-image diabetic retinopathy diagnosis framework can integrate image filtering with confidence-aware predictions for reliable screening at capture. Methods: We develop a multi-image fusion method that aggregates retinal views to improve confidence and balanced accuracy. Our method uses confidence to identify unreliable predictions, prompting retakes when needed. We compare: (1) a cascaded image-quality and disease diagnosis pipeline using a single image per patient, (2) confidence-based prediction, and (3) our confidence-based multi-image fusion pipeline. All methods are evaluated using a RETFoundGreen backbone on the mBRSET (n = 1,234) and BRSET (n = 7,599) datasets. Results: At 70% coverage, our method achieves 91% balanced accuracy on mBRSET and 97% on BRSET, improvements of ~12% and ~6%, respectively, over cascade filtering. The image-quality cascade reaches sensitivities of 61% on mBRSET and 86% on BRSET, whereas our framework reaches 94% and 96%, respectively, at 50% coverage. Conclusions: Human-annotated quality labels are weakly associated with diagnostic performance, and confidence-based filtering consistently outperforms image quality-based cascaded pipelines. Translational Relevance: Using confidence-based multi-image fusion, patients receive more reliable predictions, reducing incorrect diagnoses during screening. The lightweight backbone and single inference pass per image make the framework compatible with low-latency mobile screening systems in resource-limited settings. Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2607.03643 [eess.IV] (or arXiv:2607.03643v1 [eess.IV] for this version) https://doi.org/10.48550/arXiv.2607.03643 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Tomiko Oskotsky [view email] [v1] Fri, 3 Jul 2026 23:48:32 UTC (2,197 KB) Full-text links: Access Paper: View a PDF of the paper titled Model Confidence-Guided Multi-Image Fusion of Fundus Images for Diabetic Retinopathy Diagnosis, by Ananya Raghu and 5 other authorsView PDF view license Current browse context: eess.IV prev | next new | recent | 2026-07 Change to browse by: cs cs.CV eess References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[CV-360] An Interpretable Deep Learning Framework for Discovery and Clinical Validation of Deep Radiomic Signatures in Tumor Classification
链接: https://arxiv.org/abs/2607.03593
作者: Chengkun Sun,Jinqian Pan,Renjie Liang,Zhengkang Fan,Xin Miao,Yi Guo,Mei Liu,Muxuan Liang,Russell Terry,Jie Xu
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Imaging signatures are quantitative features extracted from medical images that provide clinically meaningful information for tumor diagnosis, characterization, prognosis, and treatment planning. Although deep learning has shown great potential for imaging signature discovery, its limited interpretability remains a major barrier to clinical adoption. Existing approaches often achieve high predictive performance but provide little biological insight into the identified signatures. We propose a unified framework for interpretable imaging signature discovery by integrating deep learning based segmentation, explainable classification, and radiomic analysis. A robust segmentation model is first used to accurately delineate tumors, followed by a Grad-CAM guided pipeline that identifies diagnostically important regions as candidate imaging signatures. A mutual information based adaptive thresholding strategy enables patient-specific signature extraction. The resulting signatures are validated using a downstream deep learning classification model, while radiomic features extracted from the signature regions are evaluated with traditional machine learning models and interpreted using SHAP to identify the most discriminative biomarkers. The proposed framework is evaluated on the public BUSI breast ultrasound, KiTS renal CT, and BraTS brain tumor datasets, as well as a private UF Health renal CT cohort. Compared with conventional whole-tumor radiomics, the proposed signature-based approach achieves improved discriminative performance while providing greater biological interpretability. By converting deep learning attention into reproducible quantitative imaging biomarkers, this framework offers an interpretable and reproducible solution for non-invasive tumor characterization and imaging biomarker discovery.
[CV-361] Motion Estimation Techniques for Volumetric Video Attribute Compression
链接: https://arxiv.org/abs/2607.03576
作者: Haoran Hong,Eduardo Pavez,Antonio Ortega,Ryosuke Watanabe,Keisuke Nonaka
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Point cloud compression relies on techniques to compress both geometry and attributes. Motion-based approaches for dynamic solid point cloud geometry compression within the geometry-based point cloud compression (G-PCC) framework have achieved significant reductions in geometry rate. However, motion-based techniques for attribute compression remain underexplored, making it challenging to achieve significant reductions in the temporal redundancy of attributes. Firstly, this paper proposes a geometry-based inter-coding scheme to compress the attributes of dynamic solid point clouds. Secondly, a graph-based motion-estimation scheme for point-cloud attribute compression is proposed. Thirdly, an interpolation-free fractional-voxel motion estimation method is proposed to refine motion accuracy to fractional-voxel precision. Our experimental results on the MPEG point cloud dataset show that the proposed scheme outperforms G-PCC, GeS-TM, and V-PCC in lossless and lossy geometry conditions. We achieve average bitrate savings of 55.3% , 42.3% , and 16.5% over G-PCC, GeS-TM, and V-PCC, respectively, under lossy-geometry conditions.
[CV-362] Piecewise Dynamic Diffusion Regularization for Reconstruction of Cardiac Cine MRI
链接: https://arxiv.org/abs/2607.03299
作者: Florian Fürnrohr,Reinhard Heckel
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Real-time cardiac cine MRI enables visualization of the beating heart during free breathing, but severe undersampling and motion make reconstruction highly challenging. A central challenge for reconstruction is incorporating powerful priors of cardiac anatomy while remaining computationally efficient. We propose Piecewise Dynamic Diffusion Regularization (PDDR), a reconstruction method that integrates a spatiotemporal diffusion model as a generative prior within a variational reconstruction framework for cine MRI. The model employs dedicated spatial layers to encode anatomical structure and temporal layers to capture cardiac motion learned from gated cine data. PDDR leverages the dynamic prior in a piecewise manner, enabling the efficient use of spatiotemporal diffusion models for processing of long real-time sequences. Experiments on retrospectively accelerated and prospective real-time cine MRI demonstrate that PDDR outperforms classical, unsupervised, and diffusion-based methods, delivering high-quality reconstructions with substantially reduced computation time compared to state-of-the-art baselines. These results highlight PDDR as a practical and scalable solution for free-breathing, real-time cardiac MRI. Code is available at this https URL.
[CV-363] Harmonic-Aware Transformer for Real-Time Catheter Localization in Interventional Procedures of Magnetic Particle Imaging
链接: https://arxiv.org/abs/2607.02919
作者: Abuobaida M. Khair,Wenjing Jiang,Xiaoli Yang,Moritz Wildgruber,Xiaopeng Ma
类目: Medical Physics (physics.med-ph); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Signal Processing (eess.SP)
备注:
Abstract:Magnetic particle imaging (MPI) enables real-time, radiation-free tracking of magnetic nanoparticle-coated instruments, making it highly suitable for interventional procedures. This study proposes a harmonic-aware transformer framework that directly predicts catheter tip positions from raw MPI voltage signals, eliminating the need for image reconstruction and reducing computational latency. The framework incorporates frequency-domain preprocessing to isolate the 2nd to 8th drive-field harmonics, enhancing the signal-to-noise ratio while preserving motion-relevant features. A transformer architecture with six encoder layers and eight attention heads is employed to learn spatio-temporal dependencies across the three receive axes (x, y, z) for accurate three-dimensional position estimation. The model is trained on simulated MPI signals and evaluated on real in vitro datasets under standard, bending, and heartbeat-like motion conditions. The proposed method achieves sub-millimeter localization accuracy, with a minimum L2 error of 0.103 +/- 0.092 mm and mean absolute errors (MAEs) of 0.039 +/- 0.046 mm, 0.054 +/- 0.049 mm, and 0.060 +/- 0.044 mm along the (x, y, z) axes, respectively, for the bending dataset. Across all datasets, the MAE ranges from 0.165 mm to 0.655 mm, demonstrating consistent performance. The optimized inference achieves a latency of 0.55 ms per frame and a throughput of approximately 1800 frames per second, confirming real-time capability. Compared with conventional MPI-guided approaches relying on image reconstruction, the proposed framework provides improved accuracy, reduced latency, and enhanced robustness under complex motion conditions. These results highlight the potential of harmonic-aware transformer models as efficient and scalable solutions for real-time catheter localization in interventional MPI.
[CV-364] Pretreatment MRI reveals a latent molecular-subtype-independent structural phenotype that organizes treatment trajectories and recurrence risk
链接: https://arxiv.org/abs/2607.02768
作者: Dattatreya Kantha,Murray H. Loew
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
备注: 31 pages, 8 figures, 7 tables
Abstract:Pathologic complete response and tumor shrinkage measure whether breast cancer responds to neoadjuvant therapy, but not whether that response was structurally favorable, persistent, or hidden beneath volume loss. We built an outcome-blind longitudinal DCE-MRI manifold from I-SPY2 trajectories to test whether pretreatment imaging carries a structural response phenotype missed by conventional descriptors. The dominant axis of response geometry was not recoverable from the full clinical and genomic stack – age, receptor subtype, MammaPrint, PAM50, treatment arm, and tumor burden – but became strongly recoverable once baseline structural entropy was added. A constrained representation mapping recovered the same axes as unconstrained decomposition, establishing the structure as intrinsic rather than a post-hoc interpretation. The phenotype persisted through therapy, and as treatment proceeded the volumetric signal faded while entropy stayed separated – a crossover from burden to structural persistence. Among complete responders, structurally disordered tumors could shrink more early yet remain structurally disordered, a volumetric deception invisible to endpoint labels. External analyses in UCSF, I-SPY1, and Duke established recurrence relevance under representation-dependent boundaries, and a representation-family commensurability assessment showed why feature-name matching is insufficient: the same label can fail, transport, or entangle with extraction geometry. Pretreatment MRI therefore exposes a structural response phenotype that endpoint-based language leaves invisible – including, among complete responders, a pretreatment imaging signal of structurally distinct response states that awaits prospective validation.
[CV-365] Direct Time-of-Flight Measurement Accuracy Improvement With Perimeter-Gated SPADs
链接: https://arxiv.org/abs/2607.02546
作者: Md Sakibur Sajal,Hunter Guthrie,Zexi Liu,Marc Dandin
类目: Instrumentation and Detectors (physics.ins-det); Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 4 pages, 7 figures, Accepted in MWSCAS 2026 conference
Abstract:Direct time of flight (dToF) measurements are susceptible to errors because of system-level and circuit-level timing jitters. In addition, device-level uncertainty stemming from the dark noise of single-photon avalanche diode (SPAD) contributes to the aggregated error. We demonstrate that perimeter gating can help reduce the device-level detection inaccuracy for SPAD devices by reducing the dark noise probability. Specifically, in this work, we developed a general framework to accurately estimate the dToF jitters stemming from different source levels and analyzed a counter-based time to digital converter (TDC) circuit that are commonly used in such systems. We have also measured dToFs using a perimeter-gated SPAD (pg-SPAD) detector fabricated in a 0.35 \mu m standard CMOS process. Experimental results show that pg-SPADs can improve measurement accuracy in both free-running and time-gated operations.
[CV-366] MedMambaLite: Hardware-Aware Mamba for Medical Image Classification
链接: https://arxiv.org/abs/2508.05049
作者: Romina Aalishah,Mozhgan Navardi,Tinoosh Mohsenin
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 21st IEEE Biomedical Circuits and Systems Conference (BioCAS) 2025
Abstract:AI-powered medical devices have driven the need for real-time, on-device inference such as biomedical image classification. Deployment of deep learning models at the edge is now used for applications such as anomaly detection and classification in medical images. However, achieving this level of performance on edge devices remains challenging due to limitations in model size and computational capacity. To address this, we present MedMambaLite, a hardware-aware Mamba-based model optimized through knowledge distillation for medical image classification. We start with a powerful MedMamba model, integrating a Mamba structure for efficient feature extraction in medical imaging. We make the model lighter and faster in training and inference by modifying and reducing the redundancies in the architecture. We then distill its knowledge into a smaller student model by reducing the embedding dimensions. The optimized model achieves 94.5% overall accuracy on 10 MedMNIST datasets. It also reduces parameters 22.8x compared to MedMamba. Deployment on an NVIDIA Jetson Orin Nano achieves 35.6 GOPS/J energy per inference. This outperforms MedMamba by 63% improvement in energy per inference.
人工智能
[AI-0] SovereignPA-Bench: Evaluating User-Owned Personal Agents under Evolving Intent Platform Mediation and Consent Constraints
链接: https://arxiv.org/abs/2607.05363
作者: Dylan Zongmin Liu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Personal agents are becoming persistent user-owned intermediaries: they remember preferences, filter platform-mediated information, use tools, and negotiate with services. Existing benchmarks evaluate tool use, web navigation, desktop control, personalization, recommendation, and evolving context, but rarely ask whether an agent preserves user sovereignty: advancing the user’s current interests while respecting privacy, consent, evidence, user burden, and resistance to manipulative incentives. We introduce SovereignPA-Bench, an executable benchmark for evaluating user-owned personal agents under evolving intent, platform mediation, privacy boundaries, consent constraints, evidence requirements, and burden tradeoffs. The benchmark separates agent-visible ObservableState from evaluator-only HiddenLabels, reports component metrics for task success, alignment, privacy, consent, evidence, manipulation, burden, and auditability, and preserves paired scenario ordering for model and policy comparisons. We evaluate 120 sovereignty stress scenarios across 4 model families and 8 policy baselines, yielding 3,840 frozen-prompt trajectories with raw prompts, outputs, provider-form responses, parsed actions, recomputable metrics, hard-set analyses, qualitative cases, and a blinded 3-annotator audit over 240 items. Full-sovereign scaffolding improves sovereignty score over direct, memory-only, consent-only, evidence-only, ReAct/tool-use, safety-prompt, and judge-guard baselines while reducing privacy leakage, consent violation, over-concession, and manipulation capture. Human audit shows high agreement on privacy and consent and lower agreement on manipulation, identifying the subjective frontier of platform-persuasion judgments. These results show that personal-agent evaluation must move beyond task completion toward representative, consent-aware, evidence-grounded action.
[AI-1] Graph Sparse Sampling: Breaking the Curse of the Horizon in Continuous MDP Planning
链接: https://arxiv.org/abs/2607.05359
作者: Idan Lev-Yehudi,Vadim Indelman
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Planning under uncertainty in continuous domains is essential for autonomous systems, yet computationally demanding. Tree-based search methods such as Monte Carlo Tree Search (MCTS) remain popular, but their branching structure can require sampling budgets that grow exponentially with lookahead depth in the worst case. From a tree perspective, continuous state or action spaces become especially challenging, since the planner must decide where to search in an infinite branching hierarchy. We propose Graph Sparse Sampling (GSS), an online planning algorithm that shares sampled futures across many candidate decisions, rather than sampling separate successors for each candidate action. This branch-free graph exposes large GPU-friendly batches, while using heuristics to focus computation. We prove finite-sample performance guarantees for GSS covering full-rank or low-rank generative simulators via smoothed backups, and discrete or sampled continuous action spaces. Under suitable overlap, regularity, and action-coverage conditions, these bounds have polynomial dependence on the planning horizon, formalizing when shared futures can avoid the exponential horizon dependence of tree-shaped sparse sampling. We demonstrate continuous-control simulations where GSS substantially outperforms tree-based planners on long horizons or achieves near-optimal performance, supporting no-branching graph planning as a complementary design principle for online control.
[AI-2] REK: Distill to Explore Reinforce to Refine
链接: https://arxiv.org/abs/2607.05339
作者: Yuanda Xu,Zhengze Zhou,Kayhan Behdin,Jelena Markovic-Voronov,Hejian Sang,Xiaomin Li,Wenhui Zhu,Xinchen Du,Aida Rahmattalabi,Ran He,Sen Na,Zhipeng Wang,Alborz Geramifard
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: 18 pages, 3 figures, 6 tables
Abstract:Group Relative Policy Optimization (GRPO) is effective when the current policy already samples useful reasoning trajectories, but it stalls on hard prompts whose correct solution modes lie outside the student’s on-policy support. We propose TREK (Teacher-Routed Exploration via Forward KL), a simple staged procedure that uses distillation not for imitation but for exploration support expansion. A key advantage of TREK is its generality: because it only consumes verified output trajectories, it can use an external black-box teacher, a white-box teacher, or the same model given additional inference-time context, and it can efficiently identify which hard-prompt samples are most worth consolidating even when teacher internals are unavailable. TREK first identifies prompts where the unaided student has very low pass rate, queries a proposal source to produce verified candidate solutions, keeps the top- r proposals ranked by current student likelihood, applies a short forward-KL phase to pull those verified modes into the student’s support, and then returns to standard on-policy GRPO refinement. On mathematical reasoning, TREK with DeepSeek-V4 proposals improves Qwen3 models across all tested scales on AIME 2024 and AIME 2025; for Qwen3-8B, it improves AIME 2025 from 36.9 to 40.3 and AIME 2024 from 47.9 to 51.1 (avg@16), while the self-context variant reaches 38.5 and 49.6 without an external teacher. On agentic tasks, TREK raises ALFWorld success rate from 75.8 to 82.8 and ScienceWorld success rate from 12.5 to 26.7; notably, on the hardest task types, TREK achieves high success rates early in training while unaided GRPO requires substantially more optimization steps to reach comparable levels.
[AI-3] Evaluating and Understanding Model Editing for Medical Vision Language Models ECCV
链接: https://arxiv.org/abs/2607.05310
作者: Guli Zhu,Chenwei Wu,Liyue Shen
类目: Artificial Intelligence (cs.AI)
备注: Accepted to the European Conference on Computer Vision (ECCV) 2026. Code and benchmark are available at this https URL
Abstract:Model editing promises a fast, targeted way to correct post-deployment mistakes in medical vision-language models (VLMs) without costly retraining. However, existing multimodal model editing benchmarks focus on general-purpose tasks and do not reflect realistic clinical domain requirements and variability. To address this, we introduce M3Bench, a clinically grounded benchmark for multimodal model editing that evaluates whether an edit remains reliable, precise, and generalizable under the challenges of image and text variation, modality and protocol shifts, clinical knowledge composition, and temporal progression. M3Bench contains 16,276 questions spanning diverse anatomy, modalities, and specialties, and supports both single and sequential edits. By evaluating 4 representative editors across 6 medical and general VLMs, we find that no method excels across all criteria. Gradient-based editors achieve strong transfer but suffer from catastrophic locality violations, whereas memory-based methods preserve locality but lack compositional generality and exhibit high backbone-dependent hyperparameter sensitivity. We further attribute these failures to the latent space geometry of VLMs and how different editing methods shift its landscape. Overall, M3Bench establishes a rigorous clinical stress test for multimodal model editing and offers actionable guidance for safer post-deployment adaptation. The benchmark is publicly available at this https URL . Comments: Accepted to the European Conference on Computer Vision (ECCV) 2026. Code and benchmark are available at this https URL Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2607.05310 [cs.AI] (or arXiv:2607.05310v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2607.05310 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-4] MetaSkill-Evolve: Recursive Self-Improvement of LLM Agents via Two-Timescale Meta-Skill Evolution
链接: https://arxiv.org/abs/2607.05297
作者: Zefeng Wang,Minxi Yan,Jinhe Bi,Sikuan Yan,Volker Tresp,Yunpu Ma
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recent LLM agents tackle increasingly long-horizon, open-ended tasks, and external skills, reusable procedural knowledge supplied to the agent, further extend this capability. However, a fixed, hand-authored skill is rarely optimal, and cannot adapt to the diversity of tasks an agent encounters. Self-improving agents address this by rewriting their own skill files from execution traces, yielding meaningful gains on challenging benchmarks. Yet such self-evolution remains non-recursive: it improves only the task skill (what the agent does) while the improvement procedure (how it improves) is authored once and held fixed. We introduce MetaSkill-Evolve, a two-timescale framework that makes agentic skill improvement recursive: every branch carries both a task skill s and a branch-local meta-skill m=(\psi,\sigma,\alpha,\pi,\varepsilon) whose five components parameterise the Analyzer, Retriever, Allocator, Proposer, and Evolver agents of the improvement pipeline. Task skills evolve on a fast loop while the meta-skill evolves on a slower one under the same pipeline applied to itself, with no additional model or objective. With all five pipeline agents sharing a single frozen backbone, MetaSkill-Evolve outperforms no-skill, static-skill, and single-level evolution baselines on three agentic benchmarks (OfficeQA, SealQA, ALFWorld), improving held-out test accuracy over the raw backbone by +23.54, +16.09, and +1.92 points respectively.
[AI-5] Adaptive Inference Batching using Policy Gradients
链接: https://arxiv.org/abs/2607.05272
作者: Ruslan Sharifullin
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Performance (cs.PF)
备注: 5 pages, 5 figures, 1 table
Abstract:Inference serving systems must balance throughput and latency under bursty, heterogeneous workloads, yet the industry standard remains static batching policies that require manual tuning and cannot adapt to shifting traffic. We investigate whether reinforcement learning (RL) can learn adaptive batching and routing policies that outperform these heuristics, training REINFORCE and PPO agents on a discrete-event simulator validated against queuing theory and production traces (Azure Functions, BurstGPT). We formulate the problem as an MDP over queue state, request type and GPU availability, evaluating across standard Poisson traffic, extreme bursts, real-world traces and heterogeneous multi-GPU routing. Our central finding is a clear boundary condition for RL’s value in systems problems. In single-GPU settings, a well-tuned static batching policy is already near-optimal under Poisson-like arrivals and RL offers only marginal gains (+0.1% to +1.0%). In multi-GPU heterogeneous routing, however, where fast and slow requests compete for shared resources, the agent discovers a workload-segregation policy that eliminates Head-of-Line blocking, yielding a 3.5x (348%) improvement over Round-Robin and a 48% improvement over the strongest heuristic baseline (Shortest-Queue), with 60% higher throughput and 25% lower latency while respecting SLA constraints. The policy generalizes to unseen bursty and real-world traffic despite training only on synthetic Poisson arrivals and an attention-augmented policy network converges roughly 20% faster than an MLP baseline. These results suggest RL’s advantage over engineered heuristics concentrates in combinatorial, multi-resource decisions rather than single-resource temporal scheduling, a practical distinction for deciding where learned policies justify their engineering cost in production inference infrastructure. Comments: 5 pages, 5 figures, 1 table Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Performance (cs.PF) Cite as: arXiv:2607.05272 [cs.LG] (or arXiv:2607.05272v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.05272 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-6] Privacy-Preserving Robustness Verification for Neural Networks UAI2026
链接: https://arxiv.org/abs/2607.05251
作者: Nianyun Song,Xiaokun Luan,Yu Guo,Rongfang Bie,Meng Sun,Xiyue Zhang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Logic in Computer Science (cs.LO)
备注: Accepted by UAI 2026
Abstract:Neural network verification and data privacy are inherently in tension: verification demands full access to model parameters and input data, yet both are increasingly restricted by privacy regulations and intellectual property constraints. This tension has left robustness verification impractical in privacy-sensitive domains. In this work, we address this gap with SecureCROWN, the first framework for privacy-preserving neural network robustness verification. Built upon secure two-party computation (2PC), our framework enables a model owner and a data owner to jointly compute certified robustness bounds – revealing only the final result while provably protecting both parties’ private data under the semi-honest security model. A key challenge is securely computing the conditional operations in Linear Bound Propagation, where the data-dependent branching is incompatible with standard secure computation protocols. We eliminate branching by formulating conditional logic as continuous arithmetic operations. Additionally, we introduce a Newton–Raphson refinement method to improve numerical stability. Extensive analysis and experiments show that SecureCROWN strictly matches plaintext verification results, while completing in 0.1–200s across varied model sizes and communication settings (LAN/WAN), demonstrating the feasibility of privacy-preserving neural network verification.
[AI-7] Optimizing ML Workload Partitioning between CPUs and CIM Accelerators for Heterogeneous Computing
链接: https://arxiv.org/abs/2607.05240
作者: Joel Klein,Rebecca Pelke,Roberto Laudani,Jan Moritz Joseph,Rainer Leupers
类目: Emerging Technologies (cs.ET); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
备注: PREPRINT - Accepted for publication at the 34th IFIP/IEEE International Conference on Very Large Scale Integration SoC (VLSI-SoC), October 11-14, 2026, in Limassol, Cyprus
Abstract:Computing-in-Memory (CIM) accelerators execute Matrix-Vector Multiplications (MVMs) in memory, making them a compelling solution for Machine Learning (ML) workloads. However, existing ML workload partitioning approaches for CIM accelerators do not fully account for Resistive Random Access Memory (RRAM) constraints such as limited memory, high write latency, and limited endurance. They also neglect parallelism, low-level architectural effects, or the Central Processing Unit (CPU) as a complementary compute resource. To address these limitations, we propose an Integer Linear Programming (ILP)-based workload partitioning framework for heterogeneous CPU-CIM systems. It minimizes end-to-end inference latency under RRAM constraints, captures parallelism, and combines empirical profiling with analytical models. Using our framework, heterogeneous CPU-CIM execution achieves speedups of up to 30.9x over CPU-only execution on an edge CPU and 7.3x over a high-performance CPU. A Design Space Exploration (DSE) yields further design insights for future CIM accelerators.
[AI-8] MoP-JEPA: Hard-Assigned Predictor Mixtures for Stochastic JEPA World Models
链接: https://arxiv.org/abs/2607.05238
作者: Zhi Song,Ximing Xing,Zhenchao Tang,hanbo Huang,Tianxu Lv,minghao Yang,Zhongzheng Niu,He Bing,Lusheng Wang,Jianhua Yao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:JEPA world models predict the next latent state with a single deterministic predictor trained by latent regression. We show that this fails structurally when the environment is stochastic: at a branching transition, the regression-optimal predictor outputs the conditional mean of the successor embeddings, a point between the true next states that corresponds to no state at all. We prove this collapse for deterministic and gated mixture-of-experts predictors, and prove that MoP-JEPA’s hard-assigned predictors converge instead to a quantizer of the transition distribution: one head per successor mode, enumerable in a single forward pass, which is the interface a planner consumes. On official OGBench offline data with leak-free evaluation, planning over single-predictor rollouts performs poorly ( 0.02 – 0.09 success) while planning over our predicted modes reaches up to 0.85 , ahead of deterministic, gated-MoE, and variational predictors on every task. Because multi-prediction evaluation invites coverage freeloading, a verification protocol is part of the method: an input-agnostic codebook control, a shuffled-context test, router-gated readouts, transition-precision guards, and a verified-route criterion in which the model proposes its transition graph blind and ground truth is used only to check the result. Under this criterion our method outperforms the strongest soft alternative on all three mazes ( 2 – 5\times ), and the protocol identifies the remaining gap in that baseline’s raw scores as routes through predicted transitions that do not exist. The same model executes in the real environment, placing second of seven against the published OGBench baselines on the hardest maze. Multimodal dynamics decide whether a JEPA world model can plan at all; a mixture of predictors with hard assignment is a minimal and verifiable fix.
[AI-9] EvoAgent Bench: Benchmarking Agent Self-Evolution via Ability Transfer
链接: https://arxiv.org/abs/2607.05202
作者: Xingze Gao,Chuanrui Hu,Hongda Chen,Pengfei Yao,Zhao Wang,Yi Bai,Zhengwei Wu,Yunyun Han,Xiaofeng Cong,Jie Gui,Yafeng Deng,Teng Li
类目: Artificial Intelligence (cs.AI)
备注: 15 pages, 2 figures, 8 tables
Abstract:Agent self-evolution in long-horizon LLM systems is largely procedural: useful experience is not merely stored information, but reusable procedures for searching, debugging, and verification. Yet current evaluations do not isolate this form of transfer. Agent benchmarks test single-episode task solving; memory benchmarks target information retention rather than procedural reuse. We introduce EvoAgentBench, a benchmark for agent self-evolution via Ability-guided transfer across four agentic domains: web research, algorithmic reasoning, software engineering, and knowledge work. EvoAgentBench extracts trace-grounded Abilities from agent executions, canonicalizes them into operational units, and builds domain-specific Ability Graphs linking tasks that share procedural overlap. By design, every test task is backed by verified training-side Ability support. Across a 528/267 train/test split, two scaffolds, and three backbones, curated Ability content transfers reliably across model families, but no current automatic method sustains positive gain in all settings. EvoAgentBench shifts self-evolution evaluation from aggregate accuracy comparison to fine-grained diagnosis of experience encoding, routing, and uptake. The benchmark is publicly available at this https URL.
[AI-10] Reason Reward Refine: Step-Level Errors Corrections with Structured Feedback for Physics Reasoning in Small Language Models
链接: https://arxiv.org/abs/2607.05199
作者: Raj Jaiswal,Dhruv Jain,Rishabh Dhawan,Sree Krishna Uppalapati,Shin’ichi Satoh,Tanuja Ganu,Rajiv Ratn Shah
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Physics reasoning fails structurally in small language models: an error at any step propagates forward, corrupting every inference that follows. Limited domain knowledge, hallucination under multi-step derivation, and distributional sensitivity compound this failure. We propose a step-level reward framework that identifies the first reasoning error, generates targeted structured feedback, and trains the model to revise its solution via policy gradient with KL regularization, without exposing it to ground truth solutions as generation targets. Unlike annotation-dependent step-level methods, no preference data construction is required and the external verifier operates exclusively at training time. Across five physics benchmarks, our framework delivers accuracy gains of 17-20% over CoT prompting and 10-16% over the strongest baseline, reduces calculation errors from 56.9% to 23.5%, and reduces miscomprehension errors from 22.3% to 12.0% in the best observed cases. Conceptual errors reduce from 89.7% to 68.7%, yet persist as the hardest failure mode across all conditions.
[AI-11] When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents
链接: https://arxiv.org/abs/2607.05189
作者: Yechao Zhang,Shiqian Zhao,Jiawen Zhang,Jie Zhang,Gelei Deng,Xiaogeng Liu,Chaowei Xiao,Tianwei Zhang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 25 pages, 8 figures. Preprint
Abstract:Persistent personal agents combine long-term memory with access to users’ external environments, enabling personalized foreground assistance and proactive background execution. This integration also creates a new path to compromise: untrusted external content can be silently written into persistent memory and later reused as trusted state. We study this threat as stealth memory injection, in which a remote black-box adversary delivers a single email payload that must induce the agent to write poisoned memory, stay hidden in the agent’s response to the user, and affect future behavior. We introduce WhisperBench, a 108-case benchmark spanning five risk categories and both fact and preference poisoning. Built on a real IMAP/SMTP workflow and an authentic email agent skill, it enables full-cycle evaluation of stealth memory injection attacks. To enable this black-box attack under single-email delivery and without runtime feedback, we propose MemGhost, a one-shot payload generation framework. MemGhost uses an environment proxy to emulate persistent-agent execution and an objective proxy to convert memory adoption and conversational stealth into dense rubric-based rewards, then trains the attacker policy with supervised fine-tuning and reinforcement learning. Across 56 held-out test cases, MemGhost achieves 87.5% end-to-end success on OpenClaw with GPT-5.4 and 71.4% on Claude Code SDK with Sonnet 4.6. It also transfers across personal-agent architectures (NanoClaw and Hermes Agent) and memory backends (filesystem and vector-based Mem0), and remains effective against input-level, model-level, and system-level defenses. These results suggest that persistent memory can turn ordinary external processing into a practical pathway for long-term agent compromise. Comments: 25 pages, 8 figures. Preprint Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) ACMclasses: I.2.7; K.6.5; D.4.6 Cite as: arXiv:2607.05189 [cs.CR] (or arXiv:2607.05189v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2607.05189 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-12] ClassicLogic: A Knowledge-Driven Benchmark of Classic Puzzle Games for Evaluating Compositional Generalization
链接: https://arxiv.org/abs/2607.05185
作者: Mahnoor Shahid,Hannes Rothe
类目: Artificial Intelligence (cs.AI); Symbolic Computation (cs.SC)
备注:
Abstract:Compositional generalization, the ability to understand and produce novel combinations of known components, remains a fundamental challenge for modern artificial intelligence. While few benchmarks exist, many focus on linguistic tasks and lack complex, explicit compositional structures. We introduce ClassicLogic, a new benchmark suite designed to evaluate an agent’s ability to learn and compose problem-solving strategies. The benchmark consists of four classic logic puzzles: Sudoku, KenKen, Kakuro, and Futoshiki. Its core innovation is a hierarchical, explicit knowledge base for each game, where complex solving strategies are formally defined as compositions of simpler, foundational strategies. This structure allows for fine-grained evaluation of an agent’s reasoning capabilities, from learning basic rules to applying multi-step compositional strategies to solve puzzles of increasing, mathematically validated difficulty. The open-source benchmark provides a challenging new testbed for advancing neuro-symbolic and other advanced AI reasoning systems.
[AI-13] Rethinking On-Policy Self-Distillation for Thinking Models
链接: https://arxiv.org/abs/2607.05184
作者: Simran Kaur,Narutatsu Ri,Yinghui He,Liam Fowl,Sanjeev Arora
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Self-distillation is a promising recipe for self-improvement in language models. In this setting, a model can serve as its own teacher when given privileged information, such as a solution to a math problem. This seems especially appealing for thinking models, which can use test-time reasoning to absorb the privileged information. Surprisingly, we show that privileged self-distillation degrades thinking models on long reasoning traces: across five Qwen3 and OLMo thinking models evaluated on AIME24, AIME25, and HMMT25, privileged-context distillation causes a relative drop of up to 17% in avg@16 accuracy. The degradation scales with the amount of privileged context withheld from the student and is most pronounced at long rollout budgets, where thinking models otherwise obtain their largest gains. This failure mode is not specific to self-distillation: on-policy distillation (OPD) improves thinking models, but privileged OPD reverses these gains. Our diagnostics link this failure mode to how privileged teacher context reshapes learning at high-entropy forking positions, where multiple continuations remain plausible and may lead to different reasoning paths. Privileged context lowers fork rates in thinking-model rollouts but not in instruction-model rollouts. This leads to an interesting dichotomy, where privileged context can help instruction-tuned models but hurts stronger thinking models. The effect is visible when the student begins a self-correction branch, where privileged OPD penalizes sampled reconsideration tokens that vanilla OPD supports. Thinking models trained with a privileged teacher produce fewer verification, backtracking, and hedging markers, even after length normalization. These findings indicate that self-distillation for strong thinking models requires attention to token-level signal, especially around correction and reasoning steps.
[AI-14] CP-WSP: A Declarative CP-SAT Framework for Configurable Multi-Constraint Workforce Scheduling ICAPS2026
链接: https://arxiv.org/abs/2607.05177
作者: Vipul Patel,Anirudh Deodhar,Dagnachew Birru
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 17 tables, CASP:ER Workshop @ICAPS 2026
Abstract:Workforce scheduling is an NP-hard combinatorial optimization problem requiring simultaneous satisfaction of labor regulations, coverage requirements, employee preferences and operational objectives. Existing CP formulations typically model simplified instances with 6-12 constraints at shift-level granularity and critically lack explicit support for: mandatory break scheduling with midpoint placement control; acuity weighted workload equity; sub-shift temporal granularity enabling demand-driven staffing; inter-week schedule stability; and cross-midnight shift patterns common in 24-hour operations. This paper presents CP-WSP: a declarative CP-SAT framework enforcing 14 hard constraints as mathematically inviolable requirements (zero regulatory violations by construction) while optimizing 15 soft objectives through a unified weighted penalty function – all configurable via a JSON specification with no code changes required. Key contributions include: a shift-window variable decomposition enabling mandatory break scheduling with centrality control; acuity-weighted workload equity; multi-granularity temporal resolution from 30 minutes to 2 hours; inter-week schedule stability; a grid-offset preprocessing technique for cross-midnight shifts; and a reproducible 36-configuration benchmark suite for community comparison. Evaluated on INRC-II benchmarks at both hourly and shift-level granularity and on 36 synthetic configurations.
[AI-15] Agent Gym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments ACL2026
链接: https://arxiv.org/abs/2607.05174
作者: Zhiheng Xi,Dingwen Yang,Jiaqi Liu,Jixuan Huang,Honglin Guo,Baodai Huang,Tinggang Chen,Qi Zhang,Zhonghang Lu,Chenyu Liu,Jiajun Sun,Jiazheng Zhang,Dingwei Zhu,Xin Guo,Junzhe Wang,Zhihao Zhang,Yuming Yang,Junjie Ye,Minghe Gao,Dongrui Liu,Jiaming Ji,Guohao Li,Tao Gui,Qi Zhang,Xuanjing Huang
类目: Artificial Intelligence (cs.AI)
备注: Accepted as a main conference paper at ACL 2026
Abstract:Language agents, i.e., LLM agents, progress rapidly and are increasingly deployed in production environments. This trend underscores the urgent need for rigorous and realistic evaluations. However, most existing benchmarks evaluate agents in simplified, idealized settings. They typically rely on pre-packaged tool interfaces, overlook critical steps, and assume inputs are clean and fully specified. Consequently, they understate the difficulty of real deployments, where uncertainty and noise are ubiquitous and agents must proactively explore the environment to uncover new tools. To bridge this gap, we present AgentGym2, a new evaluation framework with task instances grounded in real-world end-to-end working demands. Beyond reasoning and planning, it measures agents’ ability to execute end-to-end procedures, discover tools via exploration, compose tools for unseen tasks, and remain robust to noisy and underspecified information. Experiments on 15 proprietary and open-source models show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2, revealing a substantial gap between the capability of current agents and the demands of real-world applications.
[AI-16] he Changing Role of Symbolic Methods in Artificial Intelligence
链接: https://arxiv.org/abs/2607.05168
作者: Jun Sun
类目: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
备注:
Abstract:Why do intelligent systems need to perform explicit symbolic reasoning? Computer science has traditionally regarded symbolic reasoning as a defining component of intelligence. Yet the remarkable success of modern foundation models raises a fundamental question: if increasingly capable AI systems can operate with little explicit symbolic reasoning, what role do symbolic methods actually play? This article argues that explicit symbolic reasoning is not a fundamental property of intelligence, but a computational consequence of operating on simplified models of reality. We propose the Compression Principle: every computational model is a simplified representation of reality, and explicit symbolic reasoning compensates for information omitted during model construction. From this principle, we derive the Modeling–Reasoning Trade-off: as computational models preserve richer representations of the world, the need for explicit symbolic reasoning correspondingly decreases. This perspective provides a unified explanation for both the historical success of symbolic methods and the remarkable effectiveness of modern foundation models. Paradoxically, the same development makes symbolic methods increasingly important for humans. As intelligent systems become more capable and more opaque, symbolic representations increasingly serve as interfaces through which humans specify requirements, verify behavior, regulate autonomous systems, and establish trust. We therefore argue that the future of symbolic methods lies not primarily as the computational engine of intelligent systems, but as the symbolic interface between increasingly capable AI systems and the humans who build, govern, and depend upon them. Subjects: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO) Cite as: arXiv:2607.05168 [cs.AI] (or arXiv:2607.05168v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2607.05168 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-17] Open Problems in AI Incident Governance ICML
链接: https://arxiv.org/abs/2607.05163
作者: Harleen Kaur Sidhu,Rebecca Scholefield,Nour Annan,Kevin Hernandez,Isabel Nieh Hou,Abdulrahman Alshaikhi,Ze Shen Chin,Rokas Gipiškis
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: Accepted to ICML Technical AI Governance Research workshop 2026
Abstract:AI systems may produce failures after deployment that pre-deployment safety assessments do not anticipate. Managing these failures requires what we refer to as adequate \textitAI incident governance, where having good definitions, taxonomies, monitoring practices, reporting mechanisms, and incident analysis is essential. We examine existing frameworks related to AI incident governance by regulatory bodies and independent efforts, and find that while there are frameworks that describe how individual functions can be performed, there is a lack of consistency within the aspects of definitions, classification, monitoring, and reporting. These inconsistencies apply to the types of incident data that is collected and reported, the ways in which they are categorised, and as a result, the depth, representativeness, and accuracy of analysis that can be performed.
[AI-18] PDEFlow: Autonomous Agent ic PDE Pipelines for Neural Operator Learning and Solver-Free Inference
链接: https://arxiv.org/abs/2607.05134
作者: Akshat Jani,Prathamesh Gadekar,Sakhinana Sagar Srinivas,Venkataramana Runkana
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:We present PDEFlow, an autonomous agentic framework that turns user-level ODE and PDE descriptions into solver-backed neural-operator pipelines. The workflow links problem specification, data generation, operator training, and checkpoint-based inference. A stateful input graph converts multi-turn natural-language input and user edits into validated problem specifications. The data-generation module then samples parameters, solves the configured governing-equation with FEniCSx finite-element backend, and stores the solutions as operator-ready tensors. The training and inference stages use a registry-based interface, allowing different neural operators to be trained and deployed without changing the surrounding pipeline. In the current implementation, we instantiate this interface with a multi-branch Bayesian DeepONet. Experiments on benchmark ODE and PDE tasks show that PDEFlow can construct valid specifications, generate solver-backed datasets, train neural operators across steady and transient problem classes, and provide solver-free predictions from saved checkpoints. The framework is designed for repeatable scientific and engineering workflows where many related physics configurations must be specified, simulated, learned, and queried with minimal manual intervention.
[AI-19] acReason er: A Dynamic Tactile-Language Framework for Interactive Reasoning in Real-World Scenarios IROS2026
链接: https://arxiv.org/abs/2607.05131
作者: Kailin Lyu,Di Wu,Long Xiao,Jianning Zeng,Jianwei He,Chang Lin,Lianyu Hu,Lin Shu,Jie Hao,Ce Hao
类目: Artificial Intelligence (cs.AI)
备注: 8 pages, 7 figures. Accepted at IROS 2026
Abstract:Among the five primary human senses, tactile is arguably the most fundamental to survival, as it enables the perception of physical contact and interaction in real-world environments. In this paper, we explore two key challenges of integrating tactile sensing into intelligent systems for multimodal reasoning: (i) insufficient modeling of dynamic tactile signals, which restricts reasoning over temporally evolving properties, and (ii) hallucination in tactile foundation models caused by the absence of explicit reasoning mechanisms, leading to unstable real-world inference. To address these challenges, we propose TacReasoner, a dynamic tactile-language framework for interactive reasoning in real-world scenarios. First, TacReasoner incorporates a Dynamic-aware Tactile Encoder to enhance the perception and representation of dynamic tactile signals. More importantly, we introduce TouchCoT-10k, the first tactile chain-of-thought dataset for structured reasoning over tactile inputs. Upon it, we establish DynTac-Bench to systematically evaluate dynamic tactile perception and real-world commonsense reasoning. Experimental results demonstrate that TacReasoner achieves competitive performance against state-of-the-art models across multiple datasets. Notably, despite using only 7B parameters, TacReasoner outperforms the 14B VTV-LLM model on most subtasks, highlighting its effectiveness and efficiency in tactile commonsense reasoning.
[AI-20] hree-Phase Evaluation of AI-Assisted Software Development Life Cycle
链接: https://arxiv.org/abs/2607.05125
作者: Joshua Strubel,Professor Carrie Russell,Carson Crockett,Jason Ferraro,Nathan Londhe,Uzayr Syed,Jacob Viehe
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:This paper presents an exploratory evaluation of how increasing levels of AI autonomy affect software development productivity, requirement adherence, and developer cognitive workload. A team of four developers reimplemented the same full-stack web application across three sequential phases: partial AI-assisted development using GitHub Copilot, an AI-exclusive workflow using GitHub Copilot, and an AI-exclusive workflow using AWS Kiro. Evaluation metrics included development effort (hours), requirement adherence (RITM score), AI-interaction efficiency, and NASA-TLX workload measures. Across phases, higher levels of AI autonomy were associated with reduced development effort, improved requirement adherence, and lower self-reported mental workload, while developer frustration increased modestly. The AWS Kiro phase achieved the strongest overall performance on most measured dimensions, suggesting that tooling architecture may influence outcomes independently of AI autonomy level.
[AI-21] Agent Data Injection Attacks are Realistic Threats to AI Agents
链接: https://arxiv.org/abs/2607.05120
作者: Woohyuk Choi,Juhee Kim,Taehyun Kang,Jihyeon Jeong,Luyi Xing,Byoungyoung Lee
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 19 pages, 19 figures, 7 tables
Abstract:AI agents act on behalf of user prompts, consuming external data and taking actions based on the agent context. Prior research on AI agent security has primarily focused on indirect prompt injection (IPI). Its most well-studied category is instruction injection, where attacker-controlled untrusted data is interpreted as an instruction. In response, many mitigations have been proposed to prevent instruction injection attacks. In this paper, we introduce a new category of IPI, agent data injection attacks (ADI). ADI injects malicious data disguised as trusted data, such as security-critical metadata (e.g., resource identifiers or data origins) or agent context data (e.g., tool call and response formats). As a result, agents unknowingly execute unintended actions based on attacker-controlled data. ADI has similar attack impacts as instruction injection attacks, because it causes agents to misbehave and execute unintended actions. Despite the similar impact, ADI remains underexplored and easily bypasses existing IPI defenses. We found several critical vulnerabilities in real-world agents that allow an attacker to launch various attacks: arbitrary click attacks on web agents (Claude in Chrome, Antigravity, and Nanobrowser), and remote code execution and supply-chain attacks on coding agents (Claude Code, Codex, and Gemini CLI). We evaluate ADI vulnerabilities across off-the-shelf models and AI agents, and find that ADI is effective in both standalone LLMs and AI agent settings. ADI exposes a critical gap in agent security, signifying that current AI agents do not employ a fundamental security principle: current agents do not isolate trusted data from untrusted data.
[AI-22] Grokking Is Conditional and Frag ile: A Fully-Tractable Multi-Seed Study at 12K Parameters
链接: https://arxiv.org/abs/2607.05104
作者: Yoshiyuki Ootani
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Grokking – the delayed onset of generalization long after a network has fit its training set - -is usually studied in models too large to read completely and reported from single training runs. We instead study a publicly released ~11,856-parameter Llama-style transformer (Glimmer-1-Base) on modular arithmetic, small enough to enumerate its weights, attention, and full input-output map, and we measure grokking as a multi-seed rate rather than a single outcome. In this fully-tractable regime grokking is a conditional, fragile phase transition. It is gated by training-set coverage, whose threshold tracks output cardinality (the modulus) more than task structure, an ordering that holds above the transition and across a ten-fold change in domain size. Weight decay reproduces the Omnigrok inverted-U at 12K parameters, a positive control on the rate measurement. Grokking also sits on a numerical knife-edge: two perturbations of the floating-point environment – CPU thread count (reduction order) and CPU-versus-GPU execution – each flip a minority of same-seed outcomes without a detectable shift in the aggregate rate. Decomposition into sub-task specialists helps chiefly by making coverage cheap rather than by adding supervision. Methodologically, multi-seed control under a fixed numerical environment overturns three dramatic single-run narratives in our own data, each a seed confound. The unit of evidence for grokking must therefore be a multi-seed rate under a pinned numerical environment, checked where possible against a direct reading of the model.
[AI-23] Diffusion-Guided Uncertainty-Aware Delayed Policy Optimization
链接: https://arxiv.org/abs/2607.05064
作者: Junqi Tu,Zejiao Liu,Fangfei Li,Yang Tang
类目: Artificial Intelligence (cs.AI)
备注: Preprint; appendices included
Abstract:Reinforcement learning in real world environments often suffers from severe performance degradation due to delayed feedback. Existing approaches typically mitigate performance degradation caused by observation delays by constructing augmented states or predicting the true states. However, these methods often overlook the inherent discrepancy between delayed state and true states induced by stochastic MDP. We theoretically prove the existence of such a discrepancy and show that it leads to the degradation of the optimal policy. To address this challenge, we propose Diffusion Guided Uncertainty Aware Delayed Policy Optimization (DUPO). Our method explicitly models the relationship between delayed state message and the current state using a diffusion model, and leverages the resulting discrepancy estimates to weight delayed policies. Extensive experiments on continuous robotic control tasks with multiple stochastic delays demonstrate that DUPO consistently outperforms existing methods and remains effective even under long and random delay scenarios.
[AI-24] oward Trustworthy Large Language Model Agents in Healthcare
链接: https://arxiv.org/abs/2607.05055
作者: Hadi Hasan,Safaa Salman,Adam Tai Abou Dargham,Ammar Mohanna,Ali Chehab
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Healthcare appointment scheduling remains a persistent operational bottleneck, driven by manual coordination, fragmented legacy systems, and high administrative overhead. These inefficiencies constrain provider availability and degrade patient access to care. This paper presents CareConnect, a safety-first conversational agent for healthcare logistics automation that leverages large language model (LLM) function calling, retrieval-augmented generation (RAG), and layered deterministic safety guardrails. The system orchestrates eight domain-specific tools to support appointment booking, modification, cancellation, and facility information retrieval, while enforcing strict scope constraints that prohibit medical advice or diagnosis. Safety-critical situations are handled through deterministic short-circuit mechanisms for emergency detection and medical intent refusal. We evaluate CareConnect on a comprehensive benchmark of 680 task-oriented scenarios spanning end-to-end workflows, multi-turn interactions, and edge cases. Experimental results demonstrate a 91.8% task completion rate with a median per-request latency of 2.2 seconds, 96.0% safety compliance on the dedicated safety-critical evaluation subset, and an average operational cost of 0.0324 per appointment, yielding a significant cost reduction compared to manual human scheduling. These findings show that carefully scoped and rigorously safeguarded LLM-based agents can reliably automate complex healthcare operational workflows while maintaining safety guarantees and achieving substantial cost efficiency. The source code and system implementation are publicly available at this https URL.
[AI-25] LLM -Based Test Oracles: Source-of-Authority Taxonomy – A Systematic Literature Review
链接: https://arxiv.org/abs/2607.05031
作者: Ali Hassaan Mughal,Muhammad Bilal
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 15 pages, 10 figures, 7 tables. Systematic literature review. Submitted to IEEE Access
Abstract:Large language models (LLMs) are increasingly used to produce test oracles, the part of a test that decides whether observed behavior is correct. Yet a clear account of where these oracles draw their authority is missing. Prior secondary studies organize the area by oracle form or by LLM technique. None organizes it by the source of the verdict’s authority, the property that governs how far a verdict can be trusted. This article presents a systematic literature review, conducted and reported under the PRISMA 2020 guidelines. From 2,436 records, an LLM pre-filter followed by independent dual human screening (reviewer agreement, a Cohen’s kappa of 0.79) and full-text assessment yielded 54 included studies. We analyze these along three axes: the source of an oracle’s authority, the form it takes, and the mechanism that adjudicates it. We characterize the landscape of domains, languages, models, and adaptation strategies. Specification-derived authority, though the most common single source, covers about half of the studies (28 of 54). The remaining 26 reach a verdict with no specification at all. The source of authority and the adjudication mechanism cross-cut: the same source is checked by several mechanisms and one mechanism serves several sources, so a label such as LLM-as-a-judge names a mechanism rather than a basis for trust. We further report how these oracles are evaluated and how they fail, and read the sparse and empty regions of the taxonomy as a research agenda. The protocol, search query, and per-study coding sheet are released as supplementary material.
[AI-26] Your Agents Memories Are Not Its Own: Forged Reasoning Attacks on LLM Agent Memory and Defenses
链接: https://arxiv.org/abs/2607.05029
作者: Neeraj Karamchandani,Piyush Nagasubramaniam,Sencun Zhu,Dinghao Wu
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: Preprint. 10 pages, 2 figures, 4 tables
Abstract:Persistent memory has enabled large language model (LLM) agents to store factual knowledge, prior decisions, reasoning histories, tool usage information, and context. While this has improved the agent’s functionality and continuity across tasks, it has also introduced a new attack surface: the agent’s own reasoning history. In this paper, we introduce the Forged Amplifying Rationale Memory Attack (FARMA), which poisons an agent’s remembered reasoning rather than its factual knowledge. It inserts forged reasoning traces using evasive language that bypasses keyword-based defenses, then amplifies them through self-referential reinforcement that defeats consensus-based defenses. To address FARMA, we introduce SENTINEL, a layered defense pipeline to detect forged reasoning entries. Its central component is the Reasoning Guard that structurally analyzes candidate entries for forgery using five weighted signals. We evaluate FARMA and SENTINEL across multiple agents and different LLM models with 50 trials and show that FARMA achieves an attack success rate of up to 100% under baseline conditions and is capable of defeating defense mechanisms like keyword filter and A-MemGuard. Our evaluation also shows that SENTINEL reduces FARMA’s attack success rate to as low as 0% with no false positives observed across 326 benign agent traces. Our work demonstrates the need to protect not only an agent’s retrieved content but also the integrity of its reasoning history. Comments: Preprint. 10 pages, 2 figures, 4 tables Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.05029 [cs.CR] (or arXiv:2607.05029v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2607.05029 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-27] Hyperparameter Transfer in Graph Neural Networks
链接: https://arxiv.org/abs/2607.05017
作者: Gage DeZoort,Boris Hanin
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The performance of deep learning models crucially depends on the settings of hyperparameters like learning rate, initialization scale, and weight decay. Hyperparameter transfer aims to make near-optimal hyperparameter settings consistent across model scale, so that large models can be optimized by proxy tuning their smaller, cheaper-to-optimize counterparts. While transfer principles are well-studied in the context of dense neural networks in language and vision tasks, they remain comparatively under-explored for graph neural networks (GNNs). We develop and validate a transfer parameterization for GNNs trained with SGD, Adam, and AdamW. Through theoretical scaling analyses and controlled experiments, we show that the proposed parameterization yields stable feature updates, learning rate transfer, and improved performance as width and depth increase. For SGD, we identify graph-dependent first-layer correction factors and show that their use can accelerate early training in graphs with sparse bag-of-words inputs. For Adam, we explore how different message passing normalizations affect early- and late-training transfer behavior, illustrating the importance of message passing normalization and advocating for an associated hyperparameter. For AdamW, we adapt a parameterization that allows for the joint transfer of weight decay and learning rate. Together, these results provide a practical recipe for scaling GNNs across a variety of learning tasks and training scenarios.
[AI-28] ImputeECG: Deep Learning Reconstruction of Complete 12-Lead Electrocardiograms from Incomplete Recordings for Cardiac Assessment
链接: https://arxiv.org/abs/2607.05009
作者: Xiaocheng Fang,Haoyu Wang,Jieyi Cai,Qinghao Zhao,Jun Li,Shanwei Zhang,Guangkun Nie,Yujie Xiao,Shun Huang,Jiarui Jin,Hongmin Liu,Guodong Wang,Shuohua Chen,Liming Lin,Shouling Wu,Hongyan Li,Shenda Hong
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Complete digital 12-lead electrocardiograms (ECGs) are essential for AI-enabled cardiovascular assessment, yet many clinical ECG records, particularly those digitized from ECG images, remain incomplete because of short display formats, incomplete waveform digitization, lead loss, or signal corruption. We developed ImputeECG, a mask-conditioned one-dimensional Transformer autoencoder that completes 12-lead, 10-s ECGs while retaining all observed samples. The model was trained on PTB-XL and evaluated on PTB-XL and CPSC2018 under simulated incomplete settings, with additional real-world validation in a 43,633-record Kailuan clinical cohort after ECG image digitization. Metrics were computed over originally missing regions, with analyses of morphology and downstream diagnostic utility. On PTB-XL, ImputeECG reduced missing-region MAE by 41.7-51.0% and MSE by 54.0-63.7% versus the strongest baseline, with lower errors in R-peak timing, RR interval, QRS duration, QT interval, and P-wave, QRS-complex, and T-wave reconstruction. On CPSC2018, ImputeECG reduced MAE by 49.7-51.9%, supporting external generalization. In downstream multi-label classification, ImputeECG restored performance to 92.28% AUROC and 33.88% AUPRC in the most incomplete PTB-XL setting, approaching complete-ECG performance. On CPSC2018, completed ECGs achieved 94.75-95.89% AUROC and 78.83-81.86% AUPRC across settings. In Kailuan, ECG completion improved zero-shot sex prediction AUROC from 82.6% to 85.8% and reduced age prediction MAE from 10.72 to 9.87 years after image-based ECG digitization. These findings support ECG completion as a practical strategy for converting incomplete ECG records into AI-ready 12-lead, 10-s digital signals and extending the usable scope of ECG archives for digital cardiac assessment.
[AI-29] Quantum-Inspired Harmonic Decision Models: A Computational Framework for Music Generation
链接: https://arxiv.org/abs/2607.05007
作者: Josef Pavlíček,Petra Pavlíčková,Martin Molhanec
类目: Artificial Intelligence (cs.AI); Sound (cs.SD)
备注: 17 pages, 3 figures. Preprint. Code and evaluation data available at GitHub
Abstract:This paper introduces a quantum-inspired computational framework for harmonic decision-making in music. The proposed approach formulates harmonization as an optimization problem within a structured combinatorial space, where multiple candidate chord sequences are evaluated under interacting musical constraints. The model combines an interference-based harmonization stage with a classical optimization procedure grounded in tonal harmony. The quantum-inspired component enables the parallel consideration of multiple harmonic alternatives, while the classical stage refines the resulting sequences to ensure structural coherence and stylistic plausibility. The framework is evaluated on selected musical examples, including Autumn Leaves and It’s a Long Way to Tipperary. Quantitative analysis shows that the optimization stage significantly reduces chord density, increases harmonic stability, and improves functional organization. At the same time, expert evaluation highlights the importance of stylistic context, demonstrating that increased harmonic complexity is not always perceived as more natural. The results suggest that harmonic generation can be interpreted as a structured decision-making process in a constrained search space. The proposed approach provides a computational model that integrates domain-specific knowledge with an interference-based search mechanism. Although preliminary, this work indicates that quantum-inspired methods may offer a useful framework for modeling complex decision processes in creative domains such as music. The proposed framework contributes to ongoing research on quantum-inspired models of cognition and decision-making in complex biological and creative systems. Comments: 17 pages, 3 figures. Preprint. Code and evaluation data available at GitHub Subjects: Artificial Intelligence (cs.AI); Sound (cs.SD) ACMclasses: I.2.8; H.5.5; J.5 Cite as: arXiv:2607.05007 [cs.AI] (or arXiv:2607.05007v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2607.05007 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-30] he Map Behind the Flow: Finite-Step Gradient Descent as a Dynamical System
链接: https://arxiv.org/abs/2607.04993
作者: Thomas Hofmann
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Many phenomena of deep learning are dynamical: they concern not only which minima exist, but how gradient descent reaches, avoids, or selects among them. Edge-of-stability behavior, sharpness oscillations, catapult phases, balancing, and movement toward flatter representations are effects of the training map itself, and are poorly captured by the small-step gradient-flow limit. This paper studies fixed-step gradient descent as a discrete dynamical system in a hierarchy of exactly solvable models retaining basic structures of deep learning: depth, factorization, width, data coupling, activation, and stochasticity. The starting point is the balanced scalar reduction of a deep linear chain, giving a quartic loss and a cubic gradient map whose post-edge behavior is explicit. Under the natural large-depth scaling, this dynamics converges to a universal Ricker-type map. The edge of stability is therefore not a breakdown of optimization, but the first bifurcation of the training map. Embedding the scalar dynamics back into factored models turns these regimes into learning phenomena. Finite steps break conservation laws of gradient flow and contract factorization imbalance; residual oscillations move parameters toward flatter, more balanced representations. Wider linear networks produce a ladder of spectral edges, so the optimal learning rate can lie beyond the first edge. Data coupling, nonlinear activations, and stochastic targets preserve the same organizing principle: finite-step oscillations drive alignment, balancing, and representation selection. Thus the learning rate is not merely a numerical stability parameter. It is a structural parameter of the training dynamics, determining its attractors and shaping the representations gradient descent selects. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.04993 [cs.LG] (or arXiv:2607.04993v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.04993 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-31] LLM for the development of FCM
链接: https://arxiv.org/abs/2607.04983
作者: Alexis Kafantaris
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); General Literature (cs.GL); Machine Learning (cs.LG)
备注:
Abstract:This article is about the development of a fuzzy cognitive map using a local large language model. In the light of recent advances it is evident that large language models, and even local large language models are capable of extracting quantities from textual data. In other words, a local LLM like Qwen2.5-32B, or probably larger, can accept entities as prompt input and determine relevant quantitative data as the model output. In turn, this output can be utilized for the construction of a data driven fuzzy cognitive map. Hence, this implementation is achieved and then the model is thoroughly tested; Qwen2.5-32B is used and the data is extracted from hotel reviews from TripAdvisor. Furthermore, the extracted documents pass through the model unfiltered and then a fuzzy cognitive map is trained and evaluated. A case is made about Greek reviews where a star topology FCM is formed that indicates the preferences of the reviewers. Finally, external validation is performed to establish whether the fuzzy cognitive map can correlate the star rating of the review -an outcome outside the model’s inference scope -with its predicted satisfaction.
[AI-32] Multi-Robot Open Adaptive Teaming Across Unseen Environments Partners and Scales
链接: https://arxiv.org/abs/2607.04972
作者: Yang Li,Feng Xue,Fan Mo,Yunhao Liu,Jianhong Wang,Ying Wen,Qingrui Zhang,Shaoshuai Mou,Wei Pan
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Deploying robot teams in the real world requires simultaneous adaptation to unseen environments, unknown partners, and varying team sizes, yet existing approaches often address these challenges in isolation under the closed-world assumption of fixed teammates. We formalize this as open adaptive multi-robot teaming and propose a hypergraphic-form game formulation that captures team-level cooperative relationships beyond pairwise interactions, providing a principled foundation for coordination structure inference when team composition changes dynamically within episodes. Unlike graph neural network architectures, this is a game-theoretic construct for modeling strategic interactions and payoff structures among agents. Building on this formulation, we develop the Hypergraphic Open-ended Learning Algorithm (HOLA), which progressively expands partner and environment diversity during training rather than optimizing for fixed configurations. Evaluated on cooperative pursuit with multi-drone and multi-quadruped platforms, HOLA outperforms all baselines across all three adaptability dimensions. Learned policies transfer directly to physical hardware without fine-tuning, with successful deployments on Crazyflie and Zsibot L1 platforms confirming robust real-world coordination in novel environments with unseen teammates.
[AI-33] STAPO: Selective Trajectory-Aware Policy Optimization for LLM Agent Training ACL2026
链接: https://arxiv.org/abs/2607.04963
作者: Qiuyi Qi,Tian Liang,Mutian Bao,Jinjian Zhang,Dongnan Liu,Wei Zhou,Linjian Mo,Ming Kong,Jie Liu,Feng Zhang,Qiang Zhu
类目: Artificial Intelligence (cs.AI)
备注: ACL 2026 MainConference
Abstract:Reinforcement Learning (RL) is the dominant paradigm for training Large Language Model (LLM) agents on long-horizon tasks. However, sparse and delayed rewards often lead to trajectory neglect, in which agents lose focus on the task goal and interaction history at intermediate steps. Prior work has explored step-level supervision using Shannon-entropy-based uncertainty signals, which conflate inherent state complexity with agent confidence and therefore provide unreliable estimates of decision reliability. To address this issue, we propose normalized entropy, which measures confidence deviations relative to an agent’s average behavior under a given state, thereby strengthening the association between low-quality actions and trajectory neglect. Building on this insight, we introduce Selective Trajectory-Aware Policy Optimization (STAPO), a hierarchical group-based RL framework. STAPO leverages normalized entropy to locate outlier steps associated with trajectory neglect and optimizes them via a joint mechanism of trajectory-aware reward and trajectory-independent penalty, enhancing trajectory awareness while preserving training stability. Extensive experiments on ALFWorld, WebShop, and Search-Augmented QA demonstrate that STAPO achieves state-of-the-art performance while substantially alleviating trajectory neglect, validating its effectiveness and robustness for agentic tasks.
[AI-34] DSWAM: A Dual-System World Action Foundation Model for Fine-Grained Robot Manipulation
链接: https://arxiv.org/abs/2607.04927
作者: Jian Zhu,Jianjun Zhang,Taiyi Su,Tianbin Liu,Zhangyuan Wang,Kai Xie,Zitai Huang,Chong Ma,Youzhang He,Tianjian Wang,Hanyang Wang,Weihao Ding,Yi Xu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 13 pages, 1 figures
Abstract:World Action Models (WAMs) provide a promising alternative to Vision-Language-Action (VLA) policies by using video-based world modeling as dense supervision for robot action learning. Existing WAMs excel at physically grounded execution, but typically lack the explicit language-level planning interface in VLM-based VLAs for decomposing coarse instructions. Such decomposition becomes important when household tasks involve complex multi-step goals, where coarse user commands need to be converted into sequences of fine-grained executable subtasks. Meanwhile, the field still lacks a fair real-robot comparison between VLA and WAM execution capabilities, since existing systems often differ in data, robot embodiments, and task protocols. To address both the decomposition gap and the need for a controlled WAM-VLA comparison, we introduce DSWAM, a Dual-System World Action Foundation Model for fine-grained robot manipulation. DSWAM keeps a System 1 WAM executor as the default control path and optionally activates a System 2 vision-language subtask planner only when task decomposition is useful. The planner predicts executable subtasks from short-term visual history and a global task prompt, while the WAM executor performs world-aware action generation for each instruction or subtask. The executor is trained with action prediction and video co-training, but inference directly predicts action chunks without explicit future video generation. To make this execution path practical on real robots, we further integrate TensorRT acceleration, asynchronous execution, and real-time chunking (RTC) so that policy queries do not block robot control. To provide a fair real-robot comparison with VLA policies, we build and evaluate DSWAM under the DeMaVLA real-world deformable manipulation setting with matched robot platform, pretraining data, post-training data, and evaluation criteria.
[AI-35] Input Pathways Shape Few-Shot Not Zero-Shot Binding in Tiny Transformers: A Fully-Enumerable Study
链接: https://arxiv.org/abs/2607.04926
作者: Yoshiyuki Ootani
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:How does the way information reaches a transformer – as symbolic tokens, a clean per-factor “oracle” code, or an entangled perceptual vector – shape whether it binds that information compositionally? We study ~6-10K-parameter transformers on finite factored worlds enumerated exhaustively, so every measurement covers the whole input space (zero sampling variance) and the informative routes are information-matched (exact Bayes ceiling 1.0). We report four findings. (1) Endpoint invariance: on held-out binding queries no informative route reaches converged zero-shot composition – each ends at or below chance despite a ceiling of 1.0, so within a bounded sweep the failure reflects inductive bias under a lookup-sufficient objective, not missing information. (2) A two-factor account of few-shot binding: sample efficiency is best explained by input-pathway parameter sharing and code readability; a dimension-matched control and a graded readability sweep isolate readability from input dimension, and the clean oracle is not the most sample-efficient readable route. (3) A double dissociation: early in training, distributed – but not index-like – codes pass through a transient above-chance phase (tracking code format), while few-shot efficiency tracks pathway sharing. (4) Failure anatomy: symbolic routes lose the answer at the readout; index routes mis-bind (the answer stays decodable, yet an input intervention shows the output tracks the wrong slot); entangled routes inherit their input’s readability. The central claim is the two-factor account; the endpoint and anatomy results are diagnostic constraints. All code, manifests, and per-seed logs are released for exact reproduction.
[AI-36] Medi-Gemma: A Hybrid Clinical Decision Support System Integrating Deterministic EMR Analytics and Retrieval-Augmented Generation
链接: https://arxiv.org/abs/2607.04907
作者: Mohammed Saim Ahmed Quadri,Yunzhe Xue,Justin W. Ady,Usman Roshan
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Deploying Large Language Models (LLMs) in high-stakes clinical settings remains limited by structural hallucinations, weak deterministic reasoning over tabular patient data, and omissions in vector retrieval. This paper presents the architecture and validation of Medi-Gemma, a Clinical Decision Support System (CDSS) for wound pathology triage and workflow automation. The platform introduces a decoupled framework that separates clinical perception from data orchestration while preserving traceable reasoning. Medi-Gemma uses a multi-stage pipeline coordinated by a centralized ClinicalOrchestrator. Data requests are handled without generative inference by a DataManager that cleans unstructured Electronic Medical Record (EMR) files through type coercion. Natural language queries are processed by a hierarchical IntentRouter, which routes requests to deterministic analytics paths executed by a PandasQueryEngine or to patient-specific reasoning managed by a ClinicalRAGEngine using a CPU-optimized vector store. A key contribution is the Ground Truth Injection Module, which intercepts patient-specific queries, extracts numeric identification tokens, queries the structured dataframe via Pandas, retrieves the latest validated clinical state, and embeds this snapshot as an overriding context block in the LLM prompt before generation. Safety compliance is enforced by a deterministic ProtocolManager that maps clinical terminology to fixed evidence-based risk pathways, while a SafetyVerifier phrase filter prevents output rule violations. Validation shows that this architecture eliminates semantic context drift, prevents database compilation crashes, and improves factual adherence to backend clinical repositories. These results support Medi-Gemma as a safer pattern for LLM-based clinical decision support where structured data fidelity, retrieval grounding, and deterministic safeguards are essential.
[AI-37] CARL: Constraint-Aware Reinforcement Learning for Planning with LLM s ACL2026
链接: https://arxiv.org/abs/2607.04854
作者: Qiuyi Qi,Jinjian Zhang,Mutian Bao,Tian Liang,Guocong Li,Dongnan Liu,Wei Zhou,Jie Liu,Ming Kong,Linjian Mo,Feng Zhang,Qiang Zhu
类目: Artificial Intelligence (cs.AI)
备注: ACL 2026 Findings
Abstract:Despite their strong reasoning capabilities and extensive world knowledge, Large Language Models (LLMs) frequently generate plans that violate task constraints, undermining their reliability in real-world applications. This deficiency arises from a lack of systematic mechanisms to incorporate constraint information during the generation process. While existing approaches attempt to mitigate this by relying on external tools or task decomposition, they fail to enhance the model’s intrinsic constraint awareness. To address this, we propose Constraint-Aware Reinforcement Learning (CARL), a novel RL framework designed to strengthen LLMs’ intrinsic focus on constraints. CARL introduces a constraint-aware reward by comparing the model’s output distributions under constrained and unconstrained inputs, encouraging constraint focus and penalizing neglect. Compatible with various RL frameworks and requiring no external solvers or top models, CARL enables scalable, end-to-end constraint-aware planning. Extensive experiments on BlocksWorld, TravelPlanner, and T-Eval demonstrate that CARL significantly outperforms standard Reinforcement Fine-Tuning (RFT) baselines and state-of-the-art reasoning models, exhibiting a markedly increased focus on constraints.
[AI-38] SynSFX: Multi-Model Sound Effects Synthesis Dataset for Deepfake Detection and Evaluation
链接: https://arxiv.org/abs/2607.04848
作者: Linxi Li,Yuncong Yu,Qianwei Guo,Liwei Jin,Yechen Wang,Carsten Maple
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: 7 pages, 1 figures
Abstract:While audio deepfake detection has advanced significantly, representative detectors show limited generalization to synthetic sound effects. Existing environmental audio datasets such as EnvSDD provide important initial resources, but remain limited in scale and generation provenance for studying isolated sound-effect deepfakes. To support this direction, we present SynSFX, a large-scale corpus of 43374 clips (26452 synthetic, 16922 real) spanning 7 popular text-to-audio models.
[AI-39] Pretraining Curricula Enable Selective Fine-tuning
链接: https://arxiv.org/abs/2607.04846
作者: Sebastian A. Bruijns,Jirko Rubruck,Mia H. Whitefield,Kai J. Sandbrink,Fazl Barez,Christopher Summerfield
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Transformers follow implicit curricula whereby some tasks are learned before others. However, how explicit pretraining curricula influence learning, generalization, and the selectivity of fine-tuning is unclear. This is important for AI safety, where fine-tuning is used to selectively suppress misaligned behaviors. Here, we compare curricula that pretrain tasks in a balanced (sampled uniformly) or an imbalanced (one task early, the other late) fashion. We show that imbalanced learning of two conflicting copy tasks promotes in-context learning and improves the selectivity of refusal fine-tuning. Ablations and activation patching show that this occurs because imbalanced pretraining encourages tasks to be disentangled in separable neural circuits, whereas balanced training routes both tasks through a common pathway. We extend these findings to a synthetic language learning task involving rule-consistent and rule-violating data, where imbalanced curricula similarly lead to more localized, less entangled rule representations, resulting in more robust rule-following behavior. Together, these results suggest that imbalanced pretraining curricula may be an important tool for promoting disentangled representations, with direct consequences for the precision and reliability of safety fine-tuning.
[AI-40] Predicting Drafted Deck Strength for “Magic: the Gathering”
链接: https://arxiv.org/abs/2607.04782
作者: Tomas Rigaux,Hisashi Kashima
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at IEEE Conference on Games (CoG) 2026
Abstract:Many real-world games do not admit a fixed, compact rule set: instead, their dynamics are defined by interactions among a large and often evolving collection of game pieces, making general-purpose policy learning impractical. Magic: the Gathering (MTG) exemplifies this setting, where the cards themselves define and alter gameplay rules, strategic constraints, and long-term outcomes, while the pool of available cards is ever-changing. We study Draft, a constrained deck-building format of MTG in which eight players make 39-45 sequential selections from semi-random packs to construct a 40-card deck under partial information. By isolating the card selection process from gameplay, Draft provides a tractable yet non-trivial setting for studying decision-making driven by combinatorial card synergies. We propose an encoder-based model that produces set-contextualized card embeddings to encode the draft decision sequence, with a consistent improvement over linear baselines on large-scale real-world data, establishing a first learned benchmark for outcome prediction in MTG Draft. Our code is available at this http URL.
[AI-41] Agent icPD: A Stage-Aware Agent ic Framework for Physical Design QoR Optimization
链接: https://arxiv.org/abs/2607.04758
作者: Shuo Ren,Zijin Cheng,Yaohui Han,Libo Shen,Leilei Jin,Wanting Tian,Rongliang Fu,Chao Wang,Bei Yu,Tsung-Yi Ho
类目: Artificial Intelligence (cs.AI)
备注: 7 pages, 6 figures
Abstract:Physical design quality-of-results~(QoR) optimization is hard and expensive. Choices made at one stage can help or hurt later stages. Each evaluation requires a costly EDA run through the full flow. While existing methods still treat optimization as flat parameter tuning or a LLM-based script generation task, we present AgenticPD, a stage-aware agentic framework for physical design QoR optimization. Instead of re-running the full flow after every trial, AgenticPD is organized around the stage boundaries of the physical design flow, where a Judge Agent navigates the search and stage-specialized agents make local decisions within their own stage using stage-local tools. Additionally, the agent harness in AgenticPD provides structured observations, execution history, and agent context management. As a result, the system can branch from prior intermediate states and reuse checkpoints to continue the optimization procedure, and every candidate is evaluated at the post-route signoff. Across these baselines, AgenticPD achieves strong post-route timing while remaining competitive in power and area.
[AI-42] rust Region Policy Distillation
链接: https://arxiv.org/abs/2607.04751
作者: Zhengpeng Xie,Li Lyna Zhang,Zeke Xie,Mao Yang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Big goals are hard to achieve all at once; breaking them into small steps is wiser. We present Trust Region Policy Distillation (TOP-D), which transforms the notoriously unstable, high-variance On-Policy Distillation (OPD) into a stable training paradigm by dynamically constructing a proximal teacher. Theoretically, we establish a rigorous framework demonstrating that TOP-D inherently controls gradient variance. By providing a formal global convergence analysis alongside a monotonic improvement bound, we mathematically formalize the reliability and stability of the overall training dynamics. Empirically, TOP-D dramatically enhances training stability, sample efficiency, and final performance on mathematical reasoning tasks. More importantly, TOP-D introduces zero additional computational overhead, positioning itself as a promising alternative to the well-established OPD paradigm.
[AI-43] RustMizan: A Compilable Contamination-Aware Benchmarking Framework for Rust Vulnerabilities
链接: https://arxiv.org/abs/2607.04729
作者: Tarek Elsayed,Shiping Yang,Eunsong Koh,Sanika Goyal,Vincent Huang,Paul Ngo,Nathan Young,Mohammad Omidvar Tehrani,Alvyn Kang,Arnell Kang,Zeyu Chen,Angélica Moreira,Xuan Feng,Angel X. Chang,Nick Sumner,Steven Y. Ko
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 36 pages, 7 figures
Abstract:LLM agents are increasingly applied to vulnerability analysis, but existing benchmarks have not kept pace. They typically rely on small non-compilable snippets, focus on binary classification (vulnerable or not), and do not account for the risk that publicly-released datasets are part of model training corpora. We introduce RustMizan, a benchmarking framework for Rust vulnerability analysis that addresses these gaps. RustMizan contains compilable code variants at the crate, file, and function levels, with annotations for binary vulnerability detection, CWE classification, and function- and line-level localization. A paired mutation framework produces semantics-preserving code mutants for contamination testing and robustness probing. Across four frontier models in an agentic setup with command-line access, binary classification sits in the 56-65% range, but line localization F1 stays near 20%, and adversarial cues drop line F1 by about 27%.
[AI-44] FORGE: Research-Trajectory Hijacking Attacks on Deep Research Agents
链接: https://arxiv.org/abs/2607.04718
作者: Yue Pan,Ziheng Zhang,Junxiang Lei,Changhao Jia,Qingyi Si,Hongcheng Guo
类目: Artificial Intelligence (cs.AI)
备注: 20 pages,8 figures,Code available at this https URL
Abstract:Deep research agents decompose open-ended queries into subtasks, retrieve web evidence over multiple rounds, and synthesize long-form reports. This workflow creates a planning-layer poisoning surface: adversarial documents that enter the retrieval pool can steer follow-up questions and turn a local injection into report-level contamination. We present FORGE (Fabricated Orchestrated Reasoning chain for aGent Exploitation), a two-level attack that combines intra-document reasoning fabrication with inter-document chain coordination to hijack subtask planning. We further introduce the PRISM metric, which weights infected report claims by cognitive type, and Root Query Anchoring, a lightweight defense that ties recursive follow-up generation to the root query. Across 25 queries, Network FORGE reaches 26.4% PRISM with five injected documents and exhibits depth migration, in which recursive synthesis shifts poisoned content from overt framing into factual premises. On the 10-query defense subset, RQA (Root Query Anchoring) reduces PRISM from 38.5% to 18.3%.
[AI-45] Geometry-Aware Motion Latents for Learning Robust Manipulation Policies
链接: https://arxiv.org/abs/2607.04714
作者: Yunchao Zhang,Yijia Weng,Ruizhe Liu,Ming Hu,Leonidas Guibas,Yanchao Yang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Learning motion latents for robotic manipulation heavily relies on extracting motion patterns from visual sequences, yet effective action abstractions require understanding three-dimensional geometric transformations. Here, we introduce GeoMoLa (Geometry-Aware Motion Latents), which learns discrete motion latent codes by predicting how point clouds evolve during manipulation rather than reconstructing visual observations. This four-dimensional objective – spatial geometry changing through time – forces latent representations to encode actual physical motion rather than appearance patterns. GeoMoLa achieves state-of-the-art performance using only single-view RGB-D input, while existing methods require multi-view reconstruction, succeeding across diverse manipulation benchmarks. Our ablations reveal that geometric prediction is the key to driving performance, quantitatively validating that manipulation depends on spatial understanding. Furthermore, the learned codes exhibit effective motion abstraction: applying them to novel scenes produces physically consistent transformations regardless of visual context. Our real-world experiments also confirm this robustness capability, achieving robust manipulation with minimal demonstrations in cluttered environments where geometric reasoning determines success. Thus, we demonstrate that effective motion latents for robot control can better emerge from understanding motion through its three-dimensional effects rather than pixel-level patterns.
[AI-46] RSPO: Reward-Swap Policy Optimization for Multi-Turn LLM Agents
链接: https://arxiv.org/abs/2607.04713
作者: Qiang Liu,Taian Guo,Ruizhi Qiao,Xing Sun
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Reinforcement learning holds significant potential for training large language models (LLMs) to handle multi-turn interactive tasks. However, in long-horizon, multi-turn tasks characterized by sparse outcome rewards, directly training with outcome rewards often results in slow convergence due to the sparsity of signals and the lack of fine-grained feedback. Furthermore, the model may fail to learn successful trajectories that are not sampled during training, thereby limiting its performance. Conversely, while employing customized dense process rewards provides richer signals and accelerates convergence, these surrogate rewards may exhibit potential misalignment with the ground-truth outcome rewards. This inconsistency can bias the training direction and ultimately degrade the model’s final performance. In this work, we propose Reward-Swap Policy Optimization (RSPO), a method designed to leverage the rich information from dense process rewards to facilitate training with outcome rewards. By utilizing a reward-swap mechanism, RSPO ensures the diversity of sampled trajectories while guaranteeing consistency between the optimization objective and the true outcome rewards, thereby elevating the performance ceiling of the model. We conduct extensive experiments on two challenging agent benchmarks, WebShop and ALFWorld. By applying our method to various reinforcement learning algorithms, including GRPO, PPO, and GiGPO, we demonstrate that RSPO achieves consistent performance improvements across different baselines and benchmarks.
[AI-47] Integrated Altruistic and Fairness Preference Induces Advanced Mutual Cooperation in Sequential Social Dilemmas
链接: https://arxiv.org/abs/2607.04710
作者: Yu Wei,Yukiko Ogura,Yoshiyuki Ohmura,Ildefons Magrans de Abril,Hoshinori Kanazawa,Yasuo Kuniyoshi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Inducing cooperation among distributed agents is still a difficult problem in the field of multi-agent reinforcement learning (MARL), particularly in social dilemma situations. There, individual interests are misaligned with the common good and individual rationality leads to suboptimal group outcomes. In contrast, humans are able to achieve cooperation with one another in such situations. A common explanation for such cooperative behavior is that individuals have social preferences. In order to achieve cooperation in MARL, we design a new utility function integrating altruistic preferences (incentive for other’s reward) and fairness preferences (incentive for equality) from social psychology and behavioral economics, namely, Altruistic and Fairness Preference (AFP), a reward-sharing mechanism which converts one’s own and other’s rewards to incentives for cooperative behavior. We performed comparative experiments with standard RL and inequity aversion agents in two challenging sequential social dilemma games, and showed that AFP agents successfully achieved mutual cooperation with more collective rewards and higher equity than the baselines. To further understand the progression of AFP during training, we subsequently explore the effects of altruistic preferences and fairness preferences on agents’ behavior. The results suggest that altruistic preferences encourage agents to contribute to the public goods, and fairness preferences induce mutual behavior between agents.
[AI-48] Do Vision-Language-Action Models Mean What They Say? On the Role of Faithfulness in Embodied Reasoning
链接: https://arxiv.org/abs/2607.04681
作者: Matthew Foutter,Matteo Cercola,Lena Wild,Yunshan Wang,Michelle Li,Daniele Gammelli,Marco Pavone
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Embodied Chain-of-Thought has emerged as a promising mechanism to enhance robot decision-making and interpretability in black-box Vision-Language Action (VLA) models. However, whether this verbalized Chain-of-Thought truthfully reflects the policy’s underlying decision process remains poorly understood. We distinguish between functional reasoning, in which reasoning improves task performance, and faithful reasoning, in which reasoning truly reflects the policy’s internal decision process. We argue that SoTA alignment strategies offer a necessary but insufficient notion of faithfulness, admitting reasoning whose intermediate steps can mask the causal links in action prediction through confounding factors (e.g., reasoning that is ungrounded in the environment and internally disconnected or inconsistent), restricting policy generalization. We study this gap through a human evaluation of a SoTA reasoning model for autonomous driving, revealing an inconsistent coupling between reasoning quality and downstream trajectory improvement. We then operationalize a behavioral surrogate for embodied faithfulness through a learned critic, Pinocchio, scoring observation grounding and stepwise coherence, and use this critic as a dense reward signal in post-training an embodied policy with reinforcement learning. Across withheld driving benchmarks, our post-trained planner improves faithfulness by 4% and 18% over SoTA alignment and trajectory error post-training baselines, respectively, while maintaining competitive downstream task performance. Finally, on a synthetic out-of-distribution test set, post-training for faithfulness improves policy responsiveness to rare counterfactual scenarios by 1.6x that of a SoTA policy, suggesting that faithful reasoning traces contribute to more robust, generalizable, and interpretable embodied intelligence. Project page: this https URL
[AI-49] Elastic Gang: Per-Token Membership Change for a Hard-Barriered LLM Inference Gang Co-Scheduled with OS Processes
链接: https://arxiv.org/abs/2607.04668
作者: Daeyeon Son
类目: Operating Systems (cs.OS); Artificial Intelligence (cs.AI); Performance (cs.PF)
备注: 14 pages, 1 figure, 6 tables
Abstract:On-device LLM decoding is a hard-barriered CPU-SIMD computation that wants every core for milliseconds per token, while the rest of the OS wants those same cores continuously. A barriered gang cannot simply be dropped into a preemptive scheduler: an unannounced departure deadlocks a barrier, and an unannounced arrival silently corrupts logits. I present the elastic gang of Anima OS, a bare-metal x86-64 Rust kernel in which the inference gang is a first-class schedulable entity whose core membership may change between any two tokens. The core mechanism is an ACK-latched epoch protocol that never waits on a named core: a seqlock-style generation-tagged latch composed with RCU/epoch-style membership consent, so each token’s participant set is the intersection of the cores the gang requested and the cores that acked the current epoch. An un-acked core is outside this token and joins at most one token later. Displaced general processes migrate and keep running; cores return to them the moment a generation ends. On a real AMD Zen 5 machine (8C/16T), inference output is bit-exact under verified per-token membership change on both a 135M and a 7B model, the property that makes elasticity safe in a kernel whose safety gate reads logits. Against fair static core partitions, elastic membership Pareto-dominates: at intermediate inference duty cycles it delivers 1.75x (25%), 1.52x (50%), and 1.28x (75%) the general throughput of a static 8-core split at equal or better inference throughput, recovers all eight stranded cores when inference is idle, and converges to the split at saturation. Returning a lent core costs 0.22 us (p50); acquiring a busy, tenant-occupied core costs one scheduling quantum (~16 ms): a running tenant is never preempted mid-slice. Decode throughput saturates at gang width 8, so ceding cores past the knee is nearly free: elasticity auto-sizes the gang online. Comments: 14 pages, 1 figure, 6 tables Subjects: Operating Systems (cs.OS); Artificial Intelligence (cs.AI); Performance (cs.PF) ACMclasses: D.4.1; D.4.8; I.2.11 Cite as: arXiv:2607.04668 [cs.OS] (or arXiv:2607.04668v1 [cs.OS] for this version) https://doi.org/10.48550/arXiv.2607.04668 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-50] Machine Learning for Depression Screening and Intervention: an Original Circadian Rhythm Score-based Methodology
链接: https://arxiv.org/abs/2607.04648
作者: Bin Wang,Shuo Lian,Yuanyuan Hou,Dexian Wang,Peilan He,Feng Hong,Yanwei Yu,Tianrui Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Depression screening from large-scale behavioral data is challenged by fragmented circadian indicators, limited interpretability, and the lack of intervention-oriented analysis. Existing approaches typically analyze sleep, activity, and social behaviors in isolation, failing to capture their joint circadian structure. To address this limitation, we first propose the Circadian Rhythm Score (CRS), a composite index that compresses multi-domain daily behaviors into a unified representation of circadian rhythm. CRS is constructed to maximize discriminative power for depression screening while preserving behavioral semantics through non-negativity constraints. Empirical results demonstrate near-lossless compression, where a single CRS retains almost the full predictive capability compared with multiple raw behavioral indicators. Building upon CRS, we develop an interpretable depression screening framework based on gradient-boosted trees and SHAP analysis, revealing nonlinear and saturation-like associations between circadian rhythm and depression risk. Beyond risk prediction, we further integrate interaction modeling and counterfactual regression to estimate heterogeneous and dose-dependent behavioral effects, enabling intervention-oriented reasoning under different circadian contexts. Experiments on the China Health and Retirement Longitudinal Study (CHARLS, n=15,233), demonstrate robust screening performance (ROC-AUC=0.825) and identify actionable behavioral thresholds, including a minimum effective exercise dose of approximately 300 MET-min/week and an optimal restorative nap duration of approximately 65 minutes for sleep-deprived individuals. By bridging supervised representation learning and interpretable modeling, this work provides a scalable framework for depression screening and intervention-aware healthcare data mining.
[AI-51] Formal Disco: Scalable Open-Ended Generation of Formally Verified Programs
链接: https://arxiv.org/abs/2607.04631
作者: Gabriel Poesia,Simon Henniger,Tzu-Han Hsu,Yilun Du,Nada Amin
类目: Artificial Intelligence (cs.AI)
备注: Code: this https URL Datasets: this https URL
Abstract:The cost of producing code is rapidly diminishing with increasingly capable AI agents, while quality assurance of generated programs has not kept pace. Formal verification provides the strongest possible guarantees, but the ability of AI models to work with verification-aware languages is hindered by the scarcity of human-written examples of programs in those languages. To tackle this prevalent data scarcity issue, we propose Formal Disco: a distributed system for coordination of LLM-based workers that can be easily applied to open-ended synthetic data generation at scale. We use Formal Disco to share tasks and programs between three classes of workers: “initiators”, which read random READMEs from open-source repositories and documentation snippets to sketch a related verified program, “fixers” which take compiler and verifier feedback and attempt to resolve issues, and “extenders” that take working programs and propose patches to expand them. Formal Disco records all agent-generated traces and uses them both for initial distillation from a stronger model as well as self-improvement. We also propose a principle of maximum entropy for synthetic program generation, and use entropy maximization via iterative supervised fine-tuning to learn to generate increasingly diverse programs over time. We release large datasets of synthetic verified programs in three languages - Dafny, Verus, and Frama-C -, and fine-tune open models for verification-relevant tasks, often matching or exceeding the performance of Claude Opus 4.5. Overall, our work offers a path to create synthetic data at scale for formal reasoning domains and overcome the long-standing data barrier.
[AI-52] MRMS: A Multi-Resolution Memory Substrate for Long-Lived AI Agents
链接: https://arxiv.org/abs/2607.04617
作者: Jizhizi Li,Amy Shi-Nash
类目: Artificial Intelligence (cs.AI)
备注: 11 pages, 1 figure, 7 tables; technical report
Abstract:Long-lived AI agents require continuity across interactions, but continuity cannot be obtained by simply extending the prompt window. An agent must preserve useful prior experience, retrieve it selectively, distinguish personal context from external evidence, and revise memory when the underlying situation changes. We propose an architectural memory substrate organized along two orthogonal axes: a representational axis spanning structured records, vector representations, and graph relations; and a temporal axis spanning short-term traces, medium-term abstractions, and long-term semantic commitments. Its key design constraint is synchronized structured-vector-graph memory: structured records govern eligibility, vector representations support recall, and graph relations adjudicate support, contradiction, and supersession before gated context projection. Its central claim is that reliable personalization is a memory design problem: useful memory is structured, selectively exposed, continuously consolidated, and epistemically labeled rather than stored as undifferentiated conversation history. Beyond the framework, we instantiate MRMS as a lightweight prototype implementing structured records, vector retrieval, temporal policies, and graph-based revision. The prototype exercises the core substrate mechanisms through pre-generation memory selection, revision, boundary enforcement, and evidence attribution under controlled long-lived interaction scenarios with explicit evidence requirements.
[AI-53] SILO: Simulation-in-the-Loop Sim-to-Real Transfer for Multi-Stage Cable Routing
链接: https://arxiv.org/abs/2607.04616
作者: Stone Tao,Jie Xu,Hesam Rabeti,Yashraj Narang,Yijie Guo,Iretiayo Akinola
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Website: this https URL
Abstract:Linear-deformable manipulation remains challenging due to the complex deformations of objects such as cables and ropes. Prior data-driven approaches, particularly imitation learning, have shown some promise in narrowly defined settings but typically require thousands of demonstrations for specific tasks and cable types, limiting scalability and generalization. We introduce a sim-to-real reinforcement learning (RL) framework for multi-stage cable routing that leverages GPU-parallelized simulation to approximate linear deformable behaviors. Training across thousands of parallel simulations enables the learned policies to generalize across diverse cable geometries and deformation patterns. To bridge the sim-to-real gap, we propose a novel deployment strategy that combines a Simulation In the LOop (SILO) execution framework, localized RL policies, and robust cable state estimation. On real-world cable routing tasks, our approach achieves higher success rates and 2x reduction in cycle times compared to prior state-of-the-art learning methods. To our knowledge, this is the first successful sim-to-real transfer of RL policies for multi-stage cable routing. Videos and additional visualizations are available at this https URL
[AI-54] Governed Individuation: Cryptographically Decoupling an Agents Learning from Its Authority
链接: https://arxiv.org/abs/2607.04613
作者: Xue Qin,Simin Luan,Cong Yang,Zhijun Li
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: Technical companion report. 25 pages, 8 figures
Abstract:Autonomous agents are moving from sandboxed text generators to operators of code, data, and physical infrastructure, and they increasingly learn while deployed. This reopens a question that alignment techniques answer only probabilistically: after an agent has adapted in the field, is the running system still confined to what its operator authorised? Here we show that confinement can be guaranteed as an invariant of the agent’s execution architecture rather than a probabilistic outcome of its training. Governed individuation binds an agent at boot to a cryptographically frozen identity digest, and routes every action through a gate defined over the semantic effect of the action rather than its name. We prove that no amount of learning, skill acquisition, or self-induced governance abstraction can widen the agent’s permitted authority without an operator-signed change to its identity; the guarantee holds even when the agent induces its own safety principle and that principle is wrong. Empirically, in an open-ended tool-use benchmark where a large action space rules out name-based blocking, ungoverned software agents under reward pressure attempt to tamper with their own evaluation at a task-dependent rate that reaches every run on the hardest task, whereas the gate reduces executed forbidden effects to zero as a verified property of the construction while preserving task success. An adversarial evaluation of monitors of increasing semantic depth shows false-allows falling from 75% (name-based gating) to zero (dynamic effect tracing), and refusal history transfers compliance to held-out red-line families. Trust in a deployed learning agent shifts from a wager on its continued alignment to a check anyone can run at boot.
[AI-55] Simple-to-Complex Structured Demonstrations for Vision-Language-Action Learning
链接: https://arxiv.org/abs/2607.04591
作者: Xinchuan Qiu,Yi Yu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 20 pages
Abstract:Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation by integrating visual perception, language understanding, and robot action generation. Existing research has primarily focused on improving model architectures, training strategies, and dataset scale, while little attention has been paid to how demonstrations are collected and organized. We identify demonstration organization as a fundamental yet overlooked aspect of imitation learning, as it directly affects policy learning efficiency, training stability, and policy generalization. To address this gap, we propose a simple-to-complex structured demonstration collection strategy for VLA learning using a dual-arm robotic platform. Our approach systematically organizes data through three general principles: (i) decomposing complex manipulation tasks into progressively learnable sub-skills, (ii) standardizing the interaction environment to reduce unnecessary variability, and (iii) organizing demonstrations according to progressively increasing task complexity. This structured design enables VLA models to first acquire fundamental manipulation skills before learning increasingly complex task compositions, facilitating more effective learning of long-horizon manipulation tasks. We evaluate the proposed strategy on two representative robotic manipulation tasks: block grasping and sorting, and towel folding. Experimental results show consistent improvements in task success rate and training stability compared with the baseline method of directly collecting end-to-end complete task trajectories. These findings highlight demonstration organization as a previously underexplored but important factor in VLA learning and provide practical insights into efficient skill acquisition, scalable dataset construction, and long-horizon robotic manipulation.
[AI-56] Attention Limited Reward Learning
链接: https://arxiv.org/abs/2607.04590
作者: Wenqian Xing
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Pairwise human comparisons are a primary interface through which modern AI systems learn human preferences. RLHF and related alignment pipelines typically model such comparisons with Bradley–Terry log-odds, where choice probabilities are governed by latent reward differences. This paper examines what this assumption misses through a reduced-form model motivated by rational inattention, in which each label is generated by a low-capacity evaluation channel. The model separates two forms of ambiguity that standard reward modeling tends to conflate: a comparison may be difficult because the two candidates are genuinely close in value, or because the relevant distinction is hard to detect under limited attention. We show that limited attention can fundamentally distort what pairwise comparisons reveal. In particular, passive comparison data cannot generally distinguish reward, attention, and default tendencies, and heterogeneous attention can make standard Bradley–Terry reward modeling recover misleading rankings. Our analysis shows that learning is governed not by the raw number of labels, but by the amount of attended information each label carries. A case study on human votes over language-model pairs from Chatbot Arena exhibits the predicted signature, a cyclic component of the comparison data that exceeds sampling noise and that no scalar reward can represent; a second case study on perceptual comparisons shows that response times and gaze carry gap information that the labels do not. This perspective suggests that human feedback should be treated not as direct revealed preference, but as an attention-limited measurement process: a weak preference signal may reflect hidden evaluation difficulty rather than genuine indifference.
[AI-57] LLM -Driven CI-CD Workflow Intelligence for Cyber Systems Engineering
链接: https://arxiv.org/abs/2607.04579
作者: Bonan Shen,Jiazhou Gao,Tao Ning,Wei-Jung Huang,Xin Liu
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:CI/CD workflows have become executable operational policy: they decide what gets built, tested, released, and deployed, and they mediate how maintainers interact with delivery infrastructure. That makes them an important measurement point for cyber-systems engineering. Recent large language model (LLM) work shows that workflow stages can be recognized directly from configuration files, but stage labels alone do not tell us whether a workflow is brittle, unusual for its ecosystem, or worth revising first. We present an LLM-based CI/CD analysis pipeline that combines repository enrichment, anti-pattern detection, stage mining, and recommendation generation over a large GitHub corpus. Starting from 59,550 repositories with at least 1,000 stars, we identify 34,225 projects with CI/CD and collect 127,559 configuration files. Across 75,201 analyzed workflows, the anti-pattern detector reports 434,769 findings, dominated by reliability and maintainability issues. Across 59,906 configurations, stage usage differs significantly by language ( \chi^2 = 4168.88 , p 0.001 , Cramer’s V = 0.063 ), and domain analysis shows distinct operational profiles, including higher release and cache usage in mobile projects. For repository-level recommendation generation, few-shot prompting performs best overall, averaging 8.25 recommendations per repository with 96.1% YAML-valid snippets. Taken together, the results argue for CI/CD observability that combines diagnosis, context, and human review rather than treating workflow mining as a stage-classification problem alone.
[AI-58] A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training ICML2026
链接: https://arxiv.org/abs/2607.04574
作者: Junze Ye,Jiayi Cheng,Miao Lu,Michal Mankowski,Jose Blanchet,Mohsen Bayati
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted by ICML 2026 Workshop: RLxF: Reinforcement Learning from World Feedback
Abstract:For LLM agents, supervised fine-tuning is not only about teacher labels’ quality, but also about which interaction contexts those labels condition on. Pure behavioral cloning uses full teacher demonstrations, creating a mismatch between teacher-induced contexts seen in training and student-induced contexts encountered at test time. Recent work addresses this mismatch by querying a teacher at contexts reached by the student, often with increasingly elaborate filtering of the teacher’s continuations. We instead frame on-policy data construction as a budget-allocation problem: under matched supervision resources, should teacher output be spent on more start-to-finish demos, longer continuations, outcome filtering, or broader coverage of learner-induced contexts? We formalize this design space through the rollout policy, switch-time distribution, continuation horizon, filtering rules, and two complementary costs: teacher inference generated before filtering and teacher supervision retained for SFT. Across HotpotQA, ALFWorld, and Terminal-Bench-Dev, bounded unfiltered teacher continuations at learner-induced contexts improve over pure behavioral cloning at matched budgets. On HotpotQA and ALFWorld, where we run the full comparison, few-step continuations match or exceed success-filtered and critical-context-filtered alternatives. Our findings suggest that a few teacher steps, placed at learner-induced contexts, can be a more cost-efficient supervision allocation than longer or more heavily curated teacher completions.
[AI-59] Detecting Answer-Driven Reasoning in LLM -Based Educational Tutors via Truncated Chain-of-Thought Auditing
链接: https://arxiv.org/abs/2607.04572
作者: Bonan Shen,Dingyan Shang,Youting Wang,Tao Ning
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Large language model (LLM) tutors often produce fluent step-by-step explanations, but a correct and pedagogically formatted response does not guarantee that the answer was derived from the student-facing problem. In realistic tutoring systems, the model may also have access to teacher notes, answer keys, rubrics, or retrieved solution artifacts. We study whether such private answer information can make tutor explanations answer-driven: the final answer is behaviorally available before the written explanation has justified it. Using Truncated Reasoning AUC Evaluation (TRACE), which probes how early a chain-of-thought prefix can pass a verifier, we evaluate 1000 GSM8K test problems under three paired tutoring contexts: question-only, correct answer-key, and wrong answer-key. At fixed fractions of each generated explanation, we force the model to answer immediately and verify the response against the gold numeric answer. With Qwen2.5-3B-Instruct, answer-key access raises median TRACE AUC from 0.375 to 0.900 and makes the gold answer available at the first 10% prefix in 997 of 1000 cases. The effect remains strong on the 746 examples where both question-only and answer-key explanations end with the correct answer. These results support truncated CoT auditing as a lightweight process-level diagnostic for answer-driven reasoning in math tutoring explanations.
[AI-60] Heaviside Continuity of Rolling Coefficients for Eliminating Epistemic Entropy in Large Language Models
链接: https://arxiv.org/abs/2607.04562
作者: MY Pitsane,Hope Mogale
类目: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
备注: A First draft
Abstract:Large language models (LLMs) generate fluent outputs that can be wrong. Unlike humans, who often exhibit cues when providing false information, LLMs produce errors that are difficult to detect because autoregressive decoding provides no mechanism for verifying intermediate reasoning before state progression. We introduce Heaviside Continuity of Rolling Coefficients (HCRC), a verification-first execution framework that reformulates inference as predicate-gated state transitions governed by a Heaviside Gate. HCRC combines model confidence with independent verification signals from a parallel worker architecture, allowing execution to advance only when predefined correctness predicates are satisfied. This prevents invalid intermediate states from propagating, reducing epistemic entropy without modifying the underlying model. We evaluate HCRC on software-engineering and reasoning tasks across thirteen proposers from four providers. On capable proposers, the gate reduces the false-completion rate (FCR) from 4–7% to 0% while remaining latency-competitive and, in some settings, faster than the unwrapped model. On weaker proposers, it converts false completions into honest halts instead of corrupting downstream state. Beyond benchmarking, HCRC has operated for months as the production control plane of an agentic coding environment, authorizing file mutations, verification-driven progress reporting, and memory compaction. These results establish HCRC as a general framework for verification-driven LLM execution, showing that reliable reasoning can be achieved through principled execution control rather than model scale alone.
[AI-61] Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations KDD2026
链接: https://arxiv.org/abs/2607.04557
作者: Dongmin Bang,Sugyun An,Inyoung Sung,Ilho Yun,Sun Kim,Sangseon Lee
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM)
备注: 12 pages, 5 figures, 5 tables. Accepted at BIOKDD 2026, held in conjunction with ACM SIGKDD 2026
Abstract:Accurate prediction of patient-specific therapeutic response from pre-treatment transcriptomes is hindered by the scarcity of matched clinical response labels and post-treatment molecular profiles. Preclinical transfer-learning models can simulate drug-induced expression changes but are often hard to interpret and unstable, whereas knowledge-graph methods provide mechanistic context yet remain static and fail to capture drug-induced transcriptomic perturbation dynamics. We propose PREDIKTOR, a patient-centered multi-view framework that aligns a personalized network view with a transferable transcriptomic perturbation view to predict clinical drug response. For each patient, we construct an individualized gene regulatory network from tumor expression using DysRegNet and augment it with drug-target links from DrugBank; a graph neural encoder yields a drug-centric, mechanistically grounded embedding. In parallel, a frozen condition-specific gene-gene attention model pretrained on LINCS L1000 generates a simulated post-perturbation transcriptomic profile for the same patient-drug pair. We align the two views in a shared latent space via a CLIP-style contrastive objective with drug-context hard negatives, then concatenate the representations for end-to-end response classification. On TCGA, PREDIKTOR consistently outperforms state-of-the-art baselines under patient-, drug-, and tissue-split evaluations, and transfers zero-shot to the I-SPY2 trial, improving AUROC by 5.6% over competing methods. The aligned embeddings yield stable gene and pathway attributions that recover known mechanisms, supporting actionable and interpretable precision oncology.
[AI-62] Lights Camera Carbon: Architectural Scaling Laws for Video Generation Energy Consumption
链接: https://arxiv.org/abs/2607.04553
作者: Nidhal Jegham,Boris Gamazaychikov,Sasha Luccioni
类目: Multimedia (cs.MM); Artificial Intelligence (cs.AI)
备注: 17 pages
Abstract:We present a bidirectional framework for estimating the energy consumption of text-to-video (T2V) and text-to-video-audio (T2VA) models from architectural first principles and observable generation parameters such as resolution and duration, requiring no access to weights, model size, or implementation details. Forward, it predicts energy from generation parameters and architectural principles; backward, it recovers architectural scaling behavior from observed inference times, with accuracy serving as a criterion for architectural validity. Building on the established compute-bound nature of video diffusion models, we demonstrate that each model’s energy profile obeys theoretically derived scaling laws, decomposing into quadratic and linear terms whose coefficients directly reflect the underlying architectural complexity. Validated across six open-source models spanning 8.3B-27B parameters and three GPU configurations, this decomposition achieves below 3% MAPE across all architectures. This approach offers a standardized, empirically and theoretically grounded framework for sustainability benchmarking across T2V models and architectures.
[AI-63] Auto: The AGI Compiler
链接: https://arxiv.org/abs/2607.04542
作者: Jaber Jaber,Osama Jaber
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 10 pages, 4 figures, 3 tables, 1 algorithm. Code: this https URL
Abstract:Every LLM agent run re-derives its behavior token by token on a frontier model: brilliant, expensive, slow, and unbounded. We present Auto, a compiler that records live agent behavior, measures which parts are secretly deterministic, extracts them into verified programs or distilled specialists, and emits cognition binaries: WebAssembly artifacts whose manifests carry measured guarantees and whose declared capabilities are physically enforced by the sandbox. A tiered runtime executes compiled behavior behind conformally calibrated guards; guard trips deopt to the reference agent, and the captured trace recompiles back down, so nothing is figured out twice. We use “AGI compiler” in one narrow, testable sense: a system that autonomously converts novel experience into permanent, verified, near-free skill while measuring what it does not know. On AUTO-BENCH, a benchmark we introduce and pre-register, 87.1% of 560 recorded frontier-agent spans are witnessed-deterministic (three of the four censused task families measure 100.0%). On a 300-item stream with three scheduled distribution shifts, the closed loop compiles three artifact generations and drives marginal cost from 59 to 2 micro-dollars per item (6.4x end-to-end) at 96.9% parity on witnessed inputs with zero errors. The same stream also quantifies the failure modes: a loose guard silently mislabels 48.9% of compiled answers, and an unfaithful deopt reference causes the verification gate to refuse recompilation. Calibration and reference fidelity, not model capability, decide whether cheap stays correct. Code: this https URL
[AI-64] Obey Diverge Collapse: Blind Obedience to Incorrect Instructions Drives Code LLM s to Irrecoverable Code Semantic Collapse
链接: https://arxiv.org/abs/2607.04537
作者: Raj Jaiswal,Anany Singh Divy,Savar Bhasin,Adi Bajpai,Tanuja Ganu,Rajiv Ratn Shah
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Code language models are now trusted collaborators in production workflows for debugging, refactoring, and iterative repair, and every benchmark that evaluates them assumes the instructions they act on are correct. We study what happens when that assumption breaks. We evaluate code language models across four experiments designed to assess whether models resist or obey incorrect instructions in single-pass and iterative repair settings, using the RunBugRun dataset of algorithmic Python problems with deterministic test cases. Our findings reveal a striking behavioral pattern: models correctly identify an incorrect instruction as wrong, then follow it anyway. This compliance unknowingly introduces errors beyond the original bug, and the corrupted code state cannot be recovered through subsequent self-guided iterative repair, which fails to converge across passes. We term this Blind Obedience, characterize the Ghost (Unknown) Errors it introduces, quantify the proportion of cases where semantic corruption proves irrecoverable, and show that extended reasoning cannot reverse it. These findings surface behavioral properties invisible to pass-rate evaluation, with direct consequences for code language models deployed in production settings.
[AI-65] Lyapunov-Guided Training for Hardware-Safe Neural Networks Under Fixed-Point Arithmetic
链接: https://arxiv.org/abs/2607.04531
作者: Anis Hamadouche,Amir Hussain
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Signal Processing (eess.SP); Numerical Analysis (math.NA)
备注:
Abstract:Low-precision neural networks are attractive for resource-constrained hardware, but fixed-point arithmetic introduces failure modes that are often hidden by idealised quantisation models. In particular, two’s-complement overflow wrapping can corrupt hidden activations by changing both their magnitude and sign, leading to unstable numerical error propagation and severe accuracy degradation. This paper proposes a Lyapunov-stabilised quantisation framework for low-precision neural networks operating under hardware-style wrapping arithmetic. The hidden-state energy is monitored through a layerwise Lyapunov function, and a monotone projection is applied to enforce bounded and non-increasing state evolution across depth. The method is evaluated on MNIST using a compact patch-based transformer under post-training quantisation and quantisation-aware training with fixed-point bit-widths from 4 to 16 bits. Monte Carlo results show that unconstrained wrapped quantisation-aware training collapses to near-chance accuracy across 6-16 bits, with activation overflow rates exceeding 11%. In contrast, the proposed monotone Lyapunov projection suppresses activation overflow to below 0.012% and restores stable low-precision learning, achieving 86.55% accuracy at 12 bits. These results demonstrate that Lyapunov-based state control can act as a hardware-aware stabilisation mechanism for reliable fixed-point neural inference and training.
[AI-66] Measuring Harness-Induced Belief Divergence in Multi-Step LLM Agents
链接: https://arxiv.org/abs/2607.04528
作者: Haiwen Yi,Xinyuan Song
类目: Artificial Intelligence (cs.AI)
备注: 28 pages
Abstract:Software-agent benchmarks usually report whether an agent solves a task, but the agent reaches that outcome through a harness that controls what it sees, which actions it can take, which failures are repaired, which states are verified, and which evidence is logged. We show that this harness can change the agent’s multi-step beliefs even when the task, environment, and base LLM are fixed. We introduce a belief-rollout diagnostic that elicits structured K-step trajectories over progress, risk, recoverability, constraints, failure mode, uncertainty, future success, repair cost, and next action under alternative harnesses. We define a cross-harness belief divergence and decompose it into an arrival term for immediate interface shifts and a growth term for horizon-dependent belief changes. On controlled coding tasks and public-benchmark stress tests, blocked actions, compressed repairs, selective verification, and cost-aware evidence pruning often preserve terminal success while changing the beliefs that drive later decisions. We further introduce BIWM, a no-training protocol that canonicalizes observations, logs censored branches, expands repair traces, records verification masks, executes risky branches in shadow, and aligns belief trajectories across harness views. The results suggest that harness design is an experimental variable in agent evaluation, not an implementation detail. Our code is available at this https URL.
[AI-67] raining-Free Model Selection and Domain-Aware Score Calibration for First-Shot Anomalous Sound Detection
链接: https://arxiv.org/abs/2607.04526
作者: Grach Mkrtchian
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注:
Abstract:First-shot anomalous sound detection in DCASE Challenge Task 2 must flag anomalies of unseen machine types with a single threshold, without knowing whether a test clip comes from the data-rich source domain (990 normal training clips) or the data-scarce target domain (10). Two organizer-reported problems remain open: source- and target-domain AUC are negatively correlated across systems, and development-set performance does not predict evaluation-set performance. We address both with a training-free post-hoc layer over frozen audio embeddings: (i) per-domain quantile calibration shrunk toward a pooled map by a prior strength m, tracing a source/target balance frontier, and (ii) a label-free cross-validated domain-balance criterion that ranks candidate configurations from training normals only, paired with a coarse development-labeled viability veto. On DCASE 2025, the criterion rank-predicts the official evaluation score across a 45-configuration grid (Spearman rho = +0.91; family-block bootstrap 95% CI [+0.83, +0.95]) while development score is uninformative (+0.06). Criterion-based selection raises the evaluation score from 55.83 to 59.34 (jackknife CI [2.2, 4.8]) and, on an extended grid, to 61.05 – retrospectively fourth of 35 teams. Replicating on DCASE 2023 and 2024 bounds the claim: development score is uninformative in all three years and degenerate configurations recur (vetoed every time), but under family-clustered uncertainty the criterion’s predictive evidence survives only in 2025; in both replication years a fixed full-equalization default matches or beats criterion-based selection. A DCASE 2026 forward test is frozen before the 2026 evaluation ground truth is released; all headline numbers are reproduced by the official evaluator.
[AI-68] VLA Grounder: Language-Conditioning Space Optimization for Black-Box VLA Models
链接: https://arxiv.org/abs/2607.04517
作者: Damir Shodiev,Aleksei Staroverov,Nikita Kachaev,Alexey K. Kovalev,Aleksandr I. Panov
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-Language-Action (VLA) models are commonly treated as end-to-end action policies conditioned on natural-language task descriptions. In practice, however, their behavior often depends sharply on how the instruction is phrased, suggesting that language is not merely a task label but an optimizable conditioning input. We study whether frozen VLA policies can be improved by optimizing language space rather than updating action weights. Our method introduces a language-conditioning space policy that translates a human instruction into a short VLA-grounded command using object appearance, spatial relations, and target-grounding cues. The language-conditioning space policy is initialized with a failure-derived command-space prior and optimized with reinforcement learning from sparse task-completion rewards, while the downstream VLA remains fully frozen. This yields language-conditioning space optimization: RL discovers which VLA-grounded commands best elicit successful behavior from the frozen action policy. Experiments on RL4VLA and VL-Think show that language-conditioning space optimization improves success on instruction-sensitive, symbolic, and multi-object manipulation tasks, demonstrating that language can serve as an optimizable variable for a robot foundation models. Website: this https URL
[AI-69] Compressing the Validation Bottleneck: An Agent ic Self-Driving Lab for Scientific Discovery ICML2026
链接: https://arxiv.org/abs/2607.04508
作者: Kyunghoon Hur,Chihun Lee
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: Accepted at ICML 2026 AI for Science Workshop ; AI Scientist Competition
Abstract:Agentic AI-for-Science can automate ideation, planning, and analysis, but final validation still depends on real experiments. A self-driving lab (SDL) can execute those experiments, yet the loop still has bottlenecks: the agent may spend too many rounds on low-value experiments, or each round may require a high-cost experiment. We target these two physical bottlenecks with one agent. First, a prior-aware agentic DOE loop uses domain knowledge and past results to propose feasible and informative next experiments, reducing trials-to-target. Second, a cost-aware surrogate agent predicts high-cost, high-resolution measurements from low-cost, low-resolution measurements. It chooses between a high- and a low-cost measurement based on the predicted uncertainty. We examine these directions in the biology and materials domains, respectively. Together, under a single agent, these components aim to accelerate the SDL loop by reducing both the number of loops and the cost per experiment.
[AI-70] Why Pure Reasoning is Not Enough: Nature as the Source of Mathematical Innovation
链接: https://arxiv.org/abs/2607.04505
作者: Charanjit S. Jutla,Vimal Sharma
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We advance the hypothesis that human mathematical reasoning, constrained by both the undecidability and the computational intractability of even modest logical fragments, relies fundamentally on pattern matching from domains external to pure deduction. The most prolific reservoir of such patterns is the natural world, whose physical laws and biological systems have undergone billions of years of ``pre-computation’’ and already exhibit surprisingly innovative solutions. To ground this claim, we trace the history of the Fourier transform and relevant mathematics, from the vibrating string controversy to the hear equation and subsequent formalisms prevalent in mathematics. At each critical juncture, a physics problem forced the acceptance or creation of a mathematical tool that pure formal reasoning failed to anticipate or, worse, human reasoning had resisted. We further survey the landscape of logical complexity, from NP-hard propositional satisfiability to the non-elementary decision-procedures for monadic second-order theories, to demonstrate that even when a logic is decidable, the resources required for worst-case deduction are astronomically prohibitive. We argue that these barriers make physics-inspired pattern matching not just a historical accident but a cognitive necessity. Finally, we draw the consequence for artificial intelligence: if pure reasoning is constitutively insufficient, then any system aiming at human-level mathematical creativity must embed a vast store of cross-domain patterns rather than rely on deduction alone. This furnishes a principled justification for the enormous scale of contemporary large language models. Subjects: Artificial Intelligence (cs.AI) ACMclasses: I.2; F.2; F.4 Cite as: arXiv:2607.04505 [cs.AI] (or arXiv:2607.04505v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2607.04505 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-71] wo Black Boxes One Solver: Encoder Probing and Decoder Attribution for Neural Multi-Attribute VRP under Hard-Mask and Recourse Decoders ECAI2026 IJCAI
链接: https://arxiv.org/abs/2607.04487
作者: Sohaib Afifi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
备注: Accepted at the IJCAI-ECAI 2026 Workshop on Explainable Artificial Intelligence (XAI), Bremen, Germany. 8 pages
Abstract:Neural autoregressive solvers for the Multi-Attribute Vehicle Routing Problem (MAVRP) reach competitive cost but offer no per-step justification, a problem when dispatchers must validate, accept, or compare them. We open two complementary black boxes in one protocol. On the encoder side, linear probes, spontaneous-organization metrics, rank-based richness measures, and discovered-direction analyses with intervention validation characterize how the latent represents constraint families at the graph, node, and edge level. On the decoder side, three attribution methods (gradient, integrated gradients, DeepLIFT) feed three reading angles: abductive, contrastive against the best feasible alternative, and counterfactual (smallest input change that switches the action or restores feasibility). Explanations are scored on fidelity, concentration, stability, sanity, and actionability. Across six variants combining three encoders (Attention baseline, Unimp, UnimpMoe) with two decoders (Hard-Mask, Recourse), we find that graph inductive bias improves both representational predictability and decoder sanity, that the Mixture-of-Experts encoder represents constraints in a distributed rather than axis-aligned way, and that the Recourse training regime, not merely its softer mask, produces policies that represent infeasibility usefully, exposing make-feasible counterfactuals that Hard-Mask policies fail to produce even when fed infeasible alternatives externally.
[AI-72] Regime-Conditional Stabilisation of LLM -Augmented Cooperative Multi-Agent Reinforcement Learning
链接: https://arxiv.org/abs/2607.04470
作者: Faid Keddouri,Sohaib Houhou,Aissa Boulmerka,Nadir Farhi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
备注:
Abstract:Large Language Models (LLMs) offer a natural interface for translating human objectives into reward signals for cooperative multi-agent reinforcement learning (MARL), yet the training-time dynamics of this integration remain poorly understood. We show that dynamically updating LLM-generated reward weights during off-policy MARL violates the stationarity assumption of Potential-Based Reward Shaping (PBRS) and contaminates the experience replay buffer, whose stored transitions carry reward labels computed under stale shaping weights. We characterise the result as a regime-dependent failure whose severity depends on how competent the unshaped baseline already is. To control it we propose two stabilisation strategies: a Phase-Based Freeze Schedule that enforces strict stationarity within training phases, and Exponential Moving Average (EMA) smoothing that bounds per-episode weight drift. We evaluate across three cooperative environments and five random seeds with QMIX, complemented by an exploratory VDN extension, yielding a three-regime taxonomy. In the augmentative regime (Simple Spread), where the baseline is functional (74.4 %), EMA significantly improves success to 86.7 % ( +12.3 pp, p0.01 ) while naive dynamic updates collapse it to 15.2 %. In the essential regime (Level-Based Foraging), where the baseline is broken (0.1 %), any shaping unlocks the task (95.9 % under EMA). In the supplementary regime (SMAC 3m), where the baseline is near-saturated (98.8 %), stabilised shaping preserves performance (99.9 %) while unstabilised shaping adds variance without gain. These findings establish reward-signal stationarity as a necessary design constraint and indicate that regime placement is a practical predictor of whether dynamic LLM shaping helps or harms.
[AI-73] Operator-on-F complements value-equivalence: a planning -time diagnostic for latent world models
链接: https://arxiv.org/abs/2607.04464
作者: Donna Vakalis
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at the RLC 2026 World Model Workshop
Abstract:World-model evaluation for model-based reinforcement learning typically asks whether the learned model predicts reward and value well, which can leave planning-relevant errors in the model’s latent rollouts unmeasured. We introduce a complementary diagnostic, operator-on-F, that compares a model’s k-step latent pushforward to the environment’s on an observable subset F, using the model’s own predictor. On a TD-MPC2 size sweep over cheetah-run, reward-prediction error stays within [0.028, 0.091] for every model size - only about 3x variation - so an unnormalized reward-fit check has narrow resolution to distinguish them; the (unnormalized) Bellman residual and reward error themselves have weak relationships with return (Spearman -0.10 and -0.30). Operator error spans 0.28 to 2.62 over the same sizes. At 317M the operator error is 2.62 - an order of magnitude above the 0.28-0.36 cluster - and the planning return collapses to 0.9, while reward-prediction error (0.091) is the highest of the five but stays within the same small [0.028, 0.091] range as the rest of the sweep. The rank correlation between operator error and return loss is -0.90 (anchor-bootstrap 95% CI [-0.90, -0.70] at n=5 sizes; leave-one-out removal of any single size leaves it at -0.80 or stronger). The operator also returns informative, architecture-discriminating estimates in a cross-architecture comparison between TD-MPC2 and a pure-SSL latent world model. The operator diagnostic complements value-equivalence rather than replacing it.
[AI-74] Robustness Verification of an Autonomous Underwater Vehicle-based Plankton Classifier
链接: https://arxiv.org/abs/2607.04453
作者: Abdelrahman Sayed Sayed,Pierre-Jean Meyer,Asgeir J. Sørensen,Mohamed Ghazel
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Systems and Control (eess.SY); Dynamical Systems (math.DS)
备注: 6 pages, 2 figures, Accepted in IEEE OES AUV Symposium 2026
Abstract:The assessment of planktonic standing stocks and microorganism structures is critical for understanding upper ocean biological processes. Currently, autonomous underwater vehicles (AUVs) equipped with in-situ optical imaging and artificial intelligence (AI) methods offer a promising solution for persistent surveillance, mapping and monitoring of planktonic life. However, current AI methods often lack robustness in dynamic, unstructured environments, where environmental noise and non-biological artifacts lead to frequent misclassifications. Standard convolutional neural network (CNN) classifiers often struggle with such conditions, leading to misclassifications that require time-consuming manual validation by marine biologists. To address this issue, we propose a novel robustness verification framework for in-situ plankton classifiers based on reachability analysis. We also introduce a continuous-time neural ordinary differential equation (neural ODE) classification model leveraging the high-resolution imaging capabilities of the SilCam particle imager. In this paper, we demonstrate the effectiveness of the proposed framework by formally verifying the robustness of the neural ODE model against environmental perturbations. We demonstrate that our verification framework acts as an automated filter providing formal guarantees of model stability against ambiguous data, thereby improving the reliability of autonomous sampling and reducing the post-processing workload. Comments: 6 pages, 2 figures, Accepted in IEEE OES AUV Symposium 2026 Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI); Systems and Control (eess.SY); Dynamical Systems (math.DS) Cite as: arXiv:2607.04453 [cs.RO] (or arXiv:2607.04453v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2607.04453 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-75] A Deep Learning-based surrogate model for Severe Accidents in nuclear reactors using ASTEC
链接: https://arxiv.org/abs/2607.04450
作者: Alessandro Longhi,Danny Lathouwers,Zoltán Perkó
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Integral codes like the Accident Source Term Evaluation Code (ASTEC) are powerful tools to study the physics of Severe Accidents (SAs) in nuclear reactors. Real time SA simulators can also be helpful in training operators of nuclear plants to react correctly to malfunctions. However, SA simulators can take up to several days per simulation, making their use infeasible for real time applications. In this work we show how to speed up a SA simulator with a fast, Deep Learning based (DL), surrogate model (SM). The SM is built as a combination of a dimensionality reduction stage, via an AutoEncoder, and a time-stepping stage, via a Neural Ordinary Differential Equation. The data on which the SM is trained are obtained from the ASTEC simulator, by sampling a set of operator actions for station blackout (SBO) and loss-of-coolant accidents (LOCA). The objective of the developed SM is to approximate multiple spatio-temporal fields for the thermal-hydraulic physics, core degradation, and fission product release modules in ASTEC’s vessel domain. The SM predicts simultaneously around 80 different physical variables (both scalar and fields), maintaining a stable autoregressive rollout up to 50 thousand time steps. In addition, the AutoEncoder achieves a dimensionality reduction by a factor of over 300 , which allows the SM to predict up to 40 hours of simulation in under a minute, both on CPU and GPU. This work is the first study of the capabilities and limits of DL based surrogate modeling in approximating the challenging, highly non-linear physics of ASTEC.
[AI-76] From Regulation to Requirements: An Automated Requirement Derivation and Explanation Pipeline
链接: https://arxiv.org/abs/2607.04448
作者: Pavithra PM Nair,Preethu Rose Anish
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Ensuring software compliance with regulations such as the General Data Protection Regulation (GDPR) and the Artificial Intelligence Act (EU AI Act) poses a significant challenge, as requirements engineers must translate complex legal text into actionable software requirements - a process that remains largely manual and error-prone in practice. We present an automated regulation-to-requirements pipeline that identifies requirement-bearing clauses in regulatory documents and derives system-agnostic software requirements, accompanied by plain-language explanations, traceable to their legal sources. We evaluate the pipeline on the full clause sets of the GDPR (398 clauses) and the EU AI Act (574 clauses). For requirement-bearing clause identification, the approach achieves macro-averaged F1 scores of 0.82 and 0.78, respectively, outperforming a SetFit-based baseline. Human evaluation shows high completeness (4.60 and 4.45) and correctness (3.74 and 3.54) of derived requirements, while explanation clarity scores are near-ceiling (4.92 and 4.94) on a 1-5 scale. We implement the approach in Reg2Req, a publicly released tool that further supports requirement classification, use case seeding, cross-reference analysis, definition indexing, and a traceability matrix to operationalize regulatory compliance in practice. A user study with 25 practitioners shows that the plain-language explanations significantly improve comprehension of derived requirements and confidence in acting on them (p 0.001), and that all participants would use Reg2Req as a starting point for deriving software requirements from a regulation.
[AI-77] ResearchStudio-Idea: An Evidence-Grounded Research-Ideation Skill Suite from ML Conference Outcomes
链接: https://arxiv.org/abs/2607.04439
作者: Qihao Zhao,Yangyu Huang,Yalun Dai,Lingao Xiao,Jianjun Gao,Xin Zhang,Wenshan Wu,Scarlett Li,Yang He,Yan Lu,Yap Kim Hui
类目: Artificial Intelligence (cs.AI)
备注: Tech Report
Abstract:Large language models have made research ideation increasingly accessible, yet effective idea development requires more than generating candidate directions. Researchers must ground a problem in current literature, identify meaningful bottlenecks, differentiate from existing solutions, and evaluate risks before committing to implementation. We present ResearchStudio-Idea as a reusable skill suite for this first mile of research ideation. The suite includes Paper-Search, a standalone multi-source literature search skill; Scoop-Check, a standalone prior-art collision checker for novelty claims; and IdeaSpark, the end-to-end skill that composes evidence grounding, pattern-guided generation, collision retrieval, audit, and idea-card rendering into one workflow. IdeaSpark is constructed from a corpus of 1,947 machine learning conference papers collected from ICLR, ICML, and NeurIPS between 2021 and 2025, including Oral papers, a separately tracked high-citation subset, and rejected submissions. Analysis of these outcomes reveals 31 recurring ideation sub-patterns, consolidated into 15 reusable ideation patterns. Each pattern is operationalized as a structured card containing research contexts, bottleneck types, differentiation strategies, supporting precedents, and common failure modes. Given a research problem and an evidence bundle, IdeaSpark evaluates evidence readiness, reconstructs the surrounding research context, identifies unresolved bottlenecks, selects relevant patterns, instantiates one candidate direction, retrieves potentially conflicting prior work, and performs outcome-informed auditing. This workflow transforms reusable ideation patterns into traceable research proposals. Blind automated-judge evaluations show that IdeaSpark consistently produces stronger research proposals than no-skill and generic-skill baselines while maintaining competitive novelty.
[AI-78] A Retrieval-Augmented Framework for Detecting and Resolving Prag matic Ambiguities in Natural Language Requirements
链接: https://arxiv.org/abs/2607.04436
作者: Pavithra PM Nair,Preethu Rose Anish
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Natural language requirements (NLRs) are essential for bridging communication gaps among diverse stakeholders in software development. However, the inherent ambiguity in NLRs can pose significant challenges. In particular, some requirements may be misinterpreted due to varying contextual knowledge and domain-specific expectations of the stakeholders, a phenomenon known as pragmatic ambiguity. This paper presents an approach for detecting and resolving pragmatic ambiguities in NLRs. The approach leverages retrieval-augmented generation techniques with novice, intermediate, and expert domain knowledge bases to simulate stakeholders with varying domain expertise and detect discrepancies in requirement interpretation. Candidate disambiguated requirements are generated using the expert domain knowledge base, with final validation by a requirements analyst required to ensure alignment with the intended functionality. We evaluate the approach on two requirements specification documents from the PUblic REquirements dataset, using four large language models: GPT-4o-mini, Mistral-7B, Llama-3.1-8B, and Qwen2.5-7B. Detection performance is assessed using macro-averaged accuracy, precision, recall, F1, and F2 scores. The resolution quality of the candidate disambiguated requirements is measured through human evaluation of relevance, clarity, and consistency. In this initial evaluation, results show that the proposed approach can detect pragmatic ambiguities and produce candidate disambiguated requirements that are relevant, clear, and consistent with the intended system functionality. Among the evaluated models, GPT-4o-mini achieved the highest macro-averaged recall (0.75) and F2 score (0.75) for pragmatic ambiguity detection. In the resolution task, GPT-4o-mini received the highest relevance scores from human evaluators, while Mistral-7B achieved the highest scores for clarity and consistency.
[AI-79] Covert Trait Propagation Is Representation Alignment: Mechanistic Evidence from Hidden-Channel Distillation
链接: https://arxiv.org/abs/2607.04432
作者: Kargi Chauhan,Aditya Shah
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:A student model trained on pure uniform noise can still inherit its teacher’s digit-classification ability, provided the two share initialization. Previous work proves this transfer is guaranteed when the teacher’s learning rate is small enough, but does not explain where in the network the channel lives or what sets its capacity. Working in an MLP distillation setting on MNIST, we show these channels are not purely informational: geometric alignment gates access to the information the channel carries. Shared initialization makes the output projection W_2 a common coordinate key, and KL gradients reshape the student’s input projection W_0 until its hidden representations align with the teacher’s. We call this covert trait propagation (CTP). Five experiments support this mechanism: channel closure tracks weight drift, not teacher accuracy; freezing W_0 destroys transfer while freezing W_2 leaves it intact; multi-teacher ensembles cancel out despite each teacher carrying comparable label information; and linear centered kernel alignment (CKA) tracks student accuracy at r=0.98 across a continuous initialization sweep. Applying the same geometric lens to cross-token behavioral entanglement (CTBE) in instruction-tuned LLMs, we find the effect appears to be activated by alignment training, acting on an inherited substrate, and that the standard log-ratio metric produces an apparent frequency bias that is largely a circularity artifact.
[AI-80] Full-Stack FP4: Stable LLM Pretraining with Quantized Projections Optimizers and Attention
链接: https://arxiv.org/abs/2607.04422
作者: Siyu Ding,Mingchuan Ma,Jiabo Tong,Xingrun Xing,Ziming Wang,Guoqi Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent NVFP4 pretraining methods mainly target transformer linear layers, leaving optimizer states, optimizer arithmetic and attention underexplored in 4-bit pipelines. This critical gap blocks stable full-stack 4-bit pretraining, as the three core modules exhibit unique numerical failure patterns: linear layers hit hard quantization noise limits with dimension-propagated error amplification; AdamW second moments are heavy-tailed non-negative values fragile to low-precision denominators; attention carries error-prone computation paths demanding strict forward-backward quantization consistency. We propose Full-Stack FP4, the first complete NVFP4 pretraining framework resolving all three stability bottlenecks via module-wise precision strategies. For linear projections, LoRA-SVD lightweight decomposition suppresses quantization noise and breaks the direct-quantization error ceiling, shrinking the linear-only loss gap from 1.40% to 0.61%. For optimizers, we design AdamW second-moment transformation for robust NVFP4 storage and fully support native NVFP4 Newton-Schulz iterations for the Root (Muon) optimizer. For attention, a mixed-precision scheme quantizes Q/K/V and backward dS while guarding vulnerable paths in BF16, paired with unified tensor reuse to sustain forward-backward alignment. We further analyze fast error accumulation in naive low-bit matrix multiplication and the extreme sensitivity of PV / dOV^T attention branches. All modules are plug-and-play with cumulative stability and efficiency improvements. Our 3B/64B-token pretraining validates near-BF16 performance with merely 1.47% loss gap, verifying feasible stable end-to-end NVFP4 LLM pretraining.
[AI-81] Agent Step Value: State-Transition Measurement with State-Grounded LLM Evaluators
链接: https://arxiv.org/abs/2607.04419
作者: Andrew Zhang,Chengzhan Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Most agent evaluations collapse a multi-step trace into a final answer, a success flag, or a trajectory-level score. These aggregates obscure the diagnostic question developers need most: which action changed the state in a useful direction? We introduce Agent Step Value (ASV), a state-transition measurement framework that scores each observed action by the change it induces in a state-grounded evaluator’s distribution over fixed candidate outcomes. ASV renders redacted before/after state projections, uses a stateless LLM evaluator to assign candidate log scores, and reports both gold-free belief diagnostics and offline oracle validation metrics. A label-free rationale pass separates evaluator deliberation from one-token option scoring, preserving candidate likelihoods while exposing leakage and floor-score events. On 100 reviewed open-QA evidence-seeking tasks with live PubMed retrieval, a partially live DeepSeek actor, and DeepSeek log-probability scoring, ASV evaluates 1,100 steps and 2,200 states. Under the fixed-layout rationale-conditioned protocol, mean gold-margin gain is -2.335 (trajectory-bootstrap 95% CI [-3.395, -1.272]), entropy movement is 0.000, and mean Bayesian surprise is 2.693. ASV therefore localizes constructive and destructive belief pivots that final-answer scores and entropy-only step metrics miss. We release the standalone ASV Eval toolkit.
[AI-82] LLM -as-a-Tutor: Policy-Aware Prompt Adaptation for Non-Verifiable RL
链接: https://arxiv.org/abs/2607.04412
作者: Yujin Kim,Namgyu Ho,Sangmin Hwang,Joonkee Kim,Yongjin Yang,Sangmin Bae,Seungone Kim,Jaehun Jung,Se-Young Yun,Hwanjun Song
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Reinforcement learning (RL) for non-verifiable instruction following increasingly relies on LLM judges with prompt-specific rubrics as reward signals. While recent methods adapt these rubrics to the evolving policy during training, the training prompts themselves remain static, drawn from fixed corpora. This static approach often results in a critical misalignment between prompt difficulty and policy capability, leaving the judge unable to recover a discriminative reward signal when prompts fail to elicit quality variance among rollouts. To address this misalignment, we introduce LLM-as-a-Tutor, a framework that extends the LLM’s role from judge to tutor: a single model serves as an examiner that pairwise-compares policy rollouts to detect non-challenging prompts, and as a generator that appends atomic constraints to them. This append-only design monotonically raises difficulty in step with the policy’s capability, producing a self-calibrating training signal without external difficulty schedules. On three complex instruction-following benchmarks, our method consistently outperforms both policy-unaware baselines and prior policy-adaptive methods that adapt rubrics or rewrite prompts, suggesting prompt adaptation as a missing axis of policy-awareness in non-verifiable RL.
[AI-83] MechMath Agent Team: LLM Driven Agents for Mathematical Research
链接: https://arxiv.org/abs/2607.04394
作者: Yichuan Cao,Ruichen Qiu,Junqi Liu,Jiaqi Wang,Dakai Guo,Ruyong Feng,Lihong Zhi,Xiao-Shan Gao
类目: Artificial Intelligence (cs.AI); Symbolic Computation (cs.SC)
备注:
Abstract:AI reasoning has become a central focus in contemporary artificial intelligence, largely driven by the success of large language models. However, mathematical research, which is characterized by non-linear derivation paths, rigorous logical requirements, and protracted exploration cycles, poses severe challenges for existing reasoning systems. To overcome these limitations, we present the MechMath Agent Team (MMAT), which is a large language model driven agent designed to serve as a co-pilot throughout the full cycle of mathematical research. We design a tripartite Harness Architecture that decouples system responsibilities into Control, Execution, and Augmentation planes, thereby reconciling rigorous logical control with the agility demanded by open-ended research. Building upon this framework, we instantiate three specialized agents: a Knowledge Base Manager, a Natural Language Prover, and a Formal Language Prover, all operating in a closed loop to produce formally certified mathematical proofs. We evaluate MMAT on open problems in Number Theory, Algebraic Complexity Theory, Differential Algebra, Operator Algebra, and Inequalities. Across a two-month deployment, 11 problems have been solved, demonstrating its capacity to act as a co-pilot throughout the entire research cycle. The contributions are threefold: a general decoupled Harness Architecture for multi-agent mathematical reasoning, its concrete instantiation in the MMAT system, and empirical validation on a diverse suite of open problems.
[AI-84] Decentralized Aggregation of LLM Predictions via Wagering Mechanisms
链接: https://arxiv.org/abs/2607.04389
作者: Yuhong Luo,David M. Pennock,Xintong Wang
类目: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)
备注: 32 pages
Abstract:It is increasingly common to aggregate predictions from multiple LLMs, each with domain expertise or access to private tools and data, to improve collective prediction performance. In decentralized settings, aggregation weights need to be determined without access to models’ private information and should remain robust to strategic reporting. We propose a family of advantage-aligned wagering mechanisms for LLM aggregation (WALLA), in which each model reports a prediction and a learned wager, and predictions are aggregated using wagers as weights. WALLA introduces a leave-one-out baseline into the net payout function, yielding three desirable properties: (1) dominant-strategy incentive compatibility of prediction under arbitrary belief structure, (2) advantage–wager alignment, where the optimal wager is proportional to the model’s expected score advantage, and (3) prediction-agnostic wager optimization, enabling decentralized learning of wager policies without requiring optimal predictions. We further instantiate two mechanism variants that trade off normality and no-arbitrage while maintaining a bounded worst-case deficit for the mechanism. Experiments on question-answering and forecasting benchmarks across heterogeneous models and private-information settings show that WALLA matches centralized aggregation methods in predictive performance, while simultaneously achieving decentralized learning, advantage-aligned aggregation weights, uncertainty awareness, and incentive-compatible prediction.
[AI-85] Auto-AEG: Scalable Data Construction for Open-Vocabulary Audio Event Grounding
链接: https://arxiv.org/abs/2607.04383
作者: Zihan Zhang,Xize Cheng,Wenhao Yan,Tong Zhang,Dongjie Fu,Boyun Zhang,Yongbo He,Tao Jin
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: Work in progress
Abstract:Large Audio-Language Models (LALMs) reason fluently about sound yet struggle to localize precisely when events occur, while classical Sound Event Detection attains frame-level precision only over a closed label set. At the intersection of these paradigms lies the task of Open-Vocabulary Audio Event Grounding: predicting all time intervals of a target sound event described by an arbitrary natural language query. While this task is crucial for real-world audio understanding and LALM adaptation, it is bottlenecked by data scarcity. Few large-scale resources provide open-vocabulary onset/offset supervision, and manual temporal annotation is prohibitively expensive. To address this, we introduce Auto-AEG, a scalable pipeline that constructs such supervision by automatic data construction and model fine-tuning. It pairs programmatically synthesized clips, which carry exact ground-truth intervals for supervised cold-start, with multi-model pseudo-labels on real-world audio that supply the reward signal for reinforcement learning. Training with this pipeline yields promising performance gains on both the DESED SED benchmark and AEGBench, an independent difficulty-stratified benchmark we release. Our results show that automatically constructed data, coupled with interval-aware reward function design, is an effective data-side route to expanding the temporal localization capability of LALMs.
[AI-86] Nemotron-Labs-3-Puzzle-75B-A9B: Compressing Hybrid MoE LLM s
链接: https://arxiv.org/abs/2607.04371
作者: Akhiad Bercovich,Talor Abramovich,Daniel Afrimi,Shay Aharon,Nir Ailon,Vladimir Anisimov,Omer Ullman Argov,Maor Ashkenazi,Tomer Asida,Nave Assaf,Tomer Bar Natan,Alexander Bukharin,Grzegorz Chlebus,Marcin Chochowski,Eric Chung,Mohammad Dabbah,Carlo del Mundo,Ewa Dobrowolska,Ido Galil,Yaniv Galron,Amnon Geifman,Yonatan Geifman,Izik Golan,Alex Gronskiy,Tomasz Grzegorzek,Netanel Haber,Lior Kadoch,Grzegorz Karch,Tomer Keren,Abhinav Khattar,Amir Klein,Tugrul Konuk,Roi Koren,Daniel Korzekwa,Shaun Kotek,Konstantinos Krommydas,Itay Levy,Ofri Masad,Yoav Miron,Pavlo Molchanov,Shahar Mor,Zach Moshe,Saurav Muralidharan,Najeeb Nabwani,Besmira Nushi,Mostofa Patwary,Omri Puny,Johannes Rausch,Tomer Ronen,Sepehr Sameni,Itamar Schen,Elad Segal,Daniel Serebrenik,Ido Shahaf,Soumye Singhal,Daniil Sorokin,Sharath Turuvekere Sreenivas,Marta Stepniewska-Dziubinska,Ali Taghibakhshi,Nima Tajbakhsh,Oren Tropp,Dor Tzur,Anna Warno,Yi-Fu Wu,Michal Zawalski,Jiaqi Zeng,Yian Zhang,Ran Zilberstein,Amit Zuker,Ran El-Yaniv
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We present Nemotron-Labs-3-Puzzle-75B-A9B, a compressed variant of Nemotron-3-Super optimized for interactive deployment. We designed the model to maximize server throughput under high user throughput constraints. In interactive serving workloads on a single 8xB200 node, Puzzle-75B-A9B achieves approximately 2x higher server throughput than Nemotron-3-Super at matched user throughput constraints. In ultra-long-context deployment on a single H100 GPU, the compressed model increases 1M-token concurrency from 1 request to 8 requests. Puzzle-75B-A9B is constructed using a multi-stage pipeline that combines the Iterative Puzzle compression framework with knowledge distillation, reinforcement learning, quantization, and a Multi-Token Prediction head. The compression process jointly optimizes heterogeneous MoE pruning, active parameter budget, and Mamba pruning to improve inference efficiency while preserving model quality. We evaluate Puzzle-75B-A9B on a broad suite of reasoning, coding, multilingual, long-context, and agentic benchmarks. Despite substantial compression, the model retains strong downstream accuracy relative to the parent model across a wide range of tasks. These results demonstrate that large hybrid MoE models can be substantially optimized for deployment efficiency while maintaining strong downstream capability.
[AI-87] One Framework for All: Cross-Modal Membership Inference for Generative Models
链接: https://arxiv.org/abs/2607.04339
作者: Dayong Ye,Tainqing Zhu,Kun Gao,Junhao Liu,Yichuan Chen,Shuai Zhou,Hengzhu Liu,Bo Liu,Wanlei Zhou
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注:
Abstract:Large generative models across text-to-text, text-to-image, and image-to-text modalities have been shown to pose significant privacy risks. One fundamental threat is membership inference attacks (MIA), which aim to determine whether a given data point was used in a model’s training set. Although prior work has investigated MIAs against these three classes of generative models, existing approaches treat them in isolation and are not cross-applicable, thereby limiting their real-world utility. To address this limitation, we present the first comprehensive study of a unified membership inference framework that applies across text-to-text, text-to-image, and image-to-text modalities. Our approach is grounded in a key modality-agnostic observation: the output distribution of a generative model can approximate its training data distribution. Leveraging this property, we model the distributions of model-generated outputs and auxiliary non-member samples in a shared embedding space, and perform membership inference via likelihood ratio testing. We conduct extensive experiments in a strict black-box setting under both partial-knowledge and zero-knowledge threat models, and evaluate membership inference against both fine-tuning and pre-training data. Experimental results demonstrate our approach’s superior performance in comparison to existing state-of-the-art methods, which are typically optimized for a single model class.
[AI-88] Server-side Anti-cheat in FPS games for Aimbot detection using Deep learning and Machine learning
链接: https://arxiv.org/abs/2607.04336
作者: Siddhesh A. Dhinge,Shubham G. Sukum,Harsh S. Ranjane,Ruturajsingh R. Rajput,Jyoti H. Jadhav
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Modern video games are becoming more complex day by day. Most of these modern games are multiplayer first-person shooter (FPS) games. The rising popularity of FPS games emphasizes the need to combat cheating for fair and enjoyable gaming. As the number of players using cheating techniques like aimbots, wallhacks, and speed hacks is also increasing, we need a way to detect players who are using cheating tools to gain an unfair advantage over regular players. In this system, we focus exclusively on detecting aimbot cheats. Players who use aimbot cheats generally do not prioritize other aspects of the game. To distinguish between regular and cheating players, we identify specific features encompassing time-series data such as aim velocity, number of shots, distance to target, and more, along with behavioral data such as utility usage, player movement, and other gameplay patterns. Utilizing these features, we construct a server-side aimbot detection classifier named ‘YAACS’. YAACS comprises a parser, a deep learning model, and intermediary connection utilities designed for integration with the game server. The proposed system achieves a classification accuracy of 88.6% with a false positive rate of 0.97% using a Stacked LSTM with Dense layers trained on sequences of 128 ticks (Tick Delta Negative=56, Tick Delta Positive=24), outperforming the Decision Tree baseline which achieves a higher accuracy of 96.2% but at a false positive rate of 2.68%, 2.76x worse than the best LSTM configuration. These results demonstrate that incorporating temporal context through sequence modelling is critical for minimising false accusations in FPS cheat detection.
[AI-89] Do GUI Agents Believe Their Eyes? Diagnosing State-Belief Reliance on Pixels versus Structure
链接: https://arxiv.org/abs/2607.04334
作者: Guijia Zhang,Harry Yang
类目: Artificial Intelligence (cs.AI)
备注: 15 pages, 5 figures
Abstract:Multimodal GUI agents read an interface through two redundant channels: the rendered pixels of a screenshot and a serialized structure such as a DOM or accessibility tree. Before acting, an agent forms a belief about the current interface state, but existing benchmarks score task success, element grounding, or attack resistance and do not ask whether that belief is drawn from the pixels. We formalize visual state reliance, the attribution of a state belief to pixels, structure, or priors, and measure it with paired single-channel interventions over 310 real web, mobile, and desktop probes. Every probe is scored by deterministic forced choice, with no model-generated item and no model judge. Our central metric is the Perception-Fusion Gap, the fraction of probes a model perceives correctly yet resolves toward structure under conflict. Across five models from three vendors, textual state beliefs defer to structure while image-only accuracy stays near ceiling, and Perception-Fusion Gap is positive for every model; non-text identity, by contrast, stays largely pixel-bound. The substitution is specific to the serialized-text and indexed-action channel, and coordinate-action agents are largely immune. For textual conflicts, a white-box ablation traces the effect to a single copied structural value, and in two live environments the conflict drives wrong actions and real task failure. Visual state reliance therefore gives a measurable diagnostic of whether agent state beliefs are visually grounded, and the errors it exposes propagate to actions.
[AI-90] HAS-Bench: Evaluating LLM -Based Human-Agent Systems under Configurable Human Participation
链接: https://arxiv.org/abs/2607.04329
作者: Yaozu Wu,Wei-Chieh Huang,Jizhou Guo,Dongyuan Li,Renhe Jiang,Henry Peng Zou,Chunyu Miao,Shanghao Li,Weizhi Zhang,WeiWei Ye,Yankai Chen,Meng Zhang,Xue Liu,Philip S.Yu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models increasingly operate in settings where humans are active collaborators rather than passive task providers. We introduce HAS-Framework, a graph-based framework that represents humans and LLM-powered agents as first-class participants with explicit roles, permissions, communication paths, and action authority. Building on this framework, HAS-Bench evaluates Human-Agent Systems under configurable human participation across agency levels, interaction channels, and persona policies. The benchmark measures both task outcomes and process-level collaboration behavior, including clarification quality, feedback utilization, control calibration, safety, initiative, and interaction cost. Experiments across six domains show that human participation can substantially improve task completion and failure recovery, but the gains depend on when, how, and by whom human input is exercised.
[AI-91] Agent ic SABRE: An Uncertainty-Aware Neuro-Symbolic Multi-Agent Framework for Adaptive Ransomware Detection
链接: https://arxiv.org/abs/2607.04292
作者: Henry Kabuye,Biju Issac,Jeyamohan Neera
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 34 pages
Abstract:Ransomware has evolved into a complex, adaptive, and fast-moving adversary category in which static signatures and monolithic classifiers fail to generalise under concept drift, evasion, and behavioural polymorphism. In this paper, we present Agentic SABRE (Semantic-Behavioural Arbitration for Ransomware Evaluation), an uncertainty-aware, neuro-symbolic, multi-agent framework for adaptive ransomware detection. SABRE fuses semantic, representation-based evidence with behavioural, time-window forensic telemetry and employs Monte Carlo Dropout inference to quantify epistemic uncertainty for each agent. We introduce a decision-layer orchestrator that performs risk- and uncertainty-aware triage using two interpretable thresholds: a risk score and an uncertainty budget. High-confidence, high-risk samples are automatically contained, while uncertain or borderline cases are escalated to human analysts, establishing a flexible computational contract between autonomous response and analyst oversight. To support auditability and trust, SABRE integrates post-hoc explainability mechanisms, including gradient saliency, permutation importance, and counterfactual analysis, enabling both local and global interpretation of agent decisions. Extensive evaluation on RDset and RanSMAP demonstrates that Agentic SABRE preserves perfect discrimination on saturated semantic datasets, with AUC equal to 1.0, while improving robustness under weak behavioural signals. It achieves up to a 4.9 percent relative reduction in false escalations at equal recall while maintaining calibrated predictive uncertainty. Counterfactual analysis further shows that semantic and behavioural decisions can be reversed with bounded perturbation cost, indicating stable and interpretable decision boundaries.
[AI-92] Agent ic-V2X: Small Language Model Agents for Deadline-Aware V2X Scheduling in 5G/6G Networks
链接: https://arxiv.org/abs/2607.04290
作者: Gerasimos Papanikolaou-Ntais,Alexandros Kaloxylos,Athanasios Kanavos
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
备注: 20 pages 7 figures
Abstract:Large Language Models (LLMs) are proposed as control interfaces for next-generation networks, but their latency, hallucinations, and lack of control guarantees make them unsuitable for near-real-time packet schedulers, especially in dynamic V2X environments. This paper introduces Agentic-V2X, an architecture where a small, locally deployed language model acts as a periodic non-real-time rApp-inspired policy creator, while a lightweight xApp-like controller executes validated policies at intervals suitable for scheduling. The framework targets deadline-aware 5G NR V2X scheduling with heterogeneous services (teleoperated driving, cooperative awareness, HD map sharing, and sensor sharing). Given a scenario summary, service objective, and telemetry, the LLM generates a structured policy containing service priorities, weight bounds, and safety constraints. A validator checks and repairs the policy before the controller enforces it via scheduler-weight adaptation in ns-3/ns3-ai. The evaluation compares proportional fair scheduling, static expert policies, a heuristic xApp, static LLM policies, and adaptive LLM-rApp policies over 126 completed runs. Metrics include deadline-constrained packet reception ratio, tail latency, deadline violations, throughput, fairness, policy validity, and safety interventions. Results show that the adaptive LLM-rApp/xApp design generates valid and executable policies and remains competitive at several operating points, including improved mean critical reliability over PF at the highest density. However, paired statistical analysis shows that the adaptive method is not the best aggregate method and remains below the strongest static policies overall. These results support Agentic-V2X as a safe, executable small-LLM policy-generation architecture rather than a universally dominant scheduler.
[AI-93] HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models
链接: https://arxiv.org/abs/2607.04265
作者: Angen Ye,Weijie Ke,Xiaofeng Wang,Xinze Chen,Chaojun Ni,Guosheng Zhao,Boyuan Wang,Zheng Zhu,Junjie Xie,Dapeng Zhang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:World-action (WA) models can generate long-horizon action chunks for general-purpose robotic manipulation, but they remain vulnerable to calibration, perception, and contact-dynamics errors in real-world precision tasks, often failing in the final few millimeters of alignment or insertion. We propose HALO-WA, a hybrid-attention latent-guided online reinforcement learning (RL) framework for WA models, which leverages latent features and action priors from the WA generation process through a lightweight actor-critic adapter to enable fast online adaptation to real deployment errors. HALO-WA introduces a hybrid-attention structure that preserves the temporal consistency of action chunks while reading task-relevant information from WA latents conditioned on visual context and end-stage correction requirements, thereby producing refined action chunks. We validate HALO-WA on four real-world precision manipulation tasks, where it improves the average success rate from 26.4% for WA-base to 87.1%, outperforming the strongest baseline by 19.2 percentage points while requiring only 45–75 minutes of online training per task. To facilitate reproducibility, we further conduct supplementary simulation experiments in RoboTwin and release the code at this https URL.
[AI-94] Shortcut Learning in Legal Judgment Prediction: Empirical Evidence from the UK Employment Tribunal
链接: https://arxiv.org/abs/2607.04261
作者: Joe Watson,Joana Ribeiro de Faria,Marcus Tomalin,Måns Magnusson,Huiyuan Xie,Hao Tian Yeung,Felix Steffek
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Current Legal Judgment Prediction (LJP) is constrained by its reliance on post-hoc judicial materials, increasing the likelihood that models perform retrospective classification rather than true forecasting. This paper empirically investigates shortcut learning in this context by studying claim-level outcome prediction in UK Employment Tribunal (UKET) decisions. Using a corpus of 33,158 individual claims, we predict outcomes from claim texts and LLM-extracted case summaries, evaluating models ranging from interpretable TF-IDF-based classifiers to black-box LLMs. While headline predictive performance figures appear strong, we demonstrate that such performance in LJP systems trained on post-hoc judicial text can be driven by the retrospective nature of the source material. Stratifying the test data by human judgments of leakage reveals that performance increases where outcome-revealing cues are embedded in the narrative. Moreover, a model trained on just the 4% of features identified as leakage achieves high performance, outperforming human experts. These findings substantiate concerns that LJP performance may be exaggerated by linguistic artefacts. Yet this vulnerability is not fatal to the research agenda. Instead, post-hoc judgments might be treated as potentially contaminated texts, requiring active auditing. Retraining models after masking leakage features results in only a negligible reduction in Macro-F1. Hence, while models will opportunistically exploit shortcuts when available, they remain capable of extracting useful predictive signals when these artefacts are removed.
[AI-95] Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series
链接: https://arxiv.org/abs/2607.04245
作者: Zitao Shuai,Zongzhe Xu,Yuntian Wu,Sirui Li,Tianhong Li,Yuzhe Yang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Generative models have changed how machine learning represents complex data distributions, especially in language and vision, yet many real-world systems are observed instead as continuous, high-dimensional, and noisy sensor time series. Existing generative modeling of sensor data, however, remains fragmented across modalities, datasets, and task formulations, limiting a systematic understanding of when, how, and why generative models succeed or fail in real-world settings. To address this gap, we introduce SensorGen, a large-scale study of sensor-signal generation spanning 14 settings across 4 domains, 7 datasets, and 12 signal modalities. Leveraging SensorGen, we systematically evaluate generative models from five major families and uncover three key findings: (1) flow-matching models provide strong overall performance across most settings; (2) signal properties matter, with demographic covariates improving longitudinal generation and time-frequency modeling improving high-frequency signal generation; and (3) generated signals have practical utility beyond visual realism, with scaling improving generation quality and synthetic data improving downstream performance. Together, SensorGen establishes a broader understanding of design choices, evaluation protocols, and failure modes in real-world sensor data generation.
[AI-96] Progress- and Reliability-Oriented Group Policy Optimization for Agent ic Reinforcement Learning
链接: https://arxiv.org/abs/2607.04242
作者: Mingxuan Fan,Peiyang Liu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Group-based reinforcement learning (RL) has become an effective paradigm for improving large language model agents on long-horizon interactive tasks. To obtain finer-grained policy updates than trajectory-level optimization, recent work has moved toward step-level group-based RL, where intermediate steps are grouped and compared within a rollout batch. However, step-level advantage estimation is sensitive to how groups are formed: grouping by broad state keys improves coverage but may compare actions taken under different histories, while enforcing historical consistency yields fairer comparisons at the cost of fragmented groups and missing peer-comparison signal. In this paper, we propose ProGPO (Progress- and Reliability-Oriented Group Policy Optimization), a learned-critic-free method for context-consistent step-level learning. ProGPO keeps exact-prefix action comparison, and complements sparse peer comparisons with transition credit derived from rollout-based state potentials. To estimate these potentials reliably, ProGPO combines semantic expansion with inverse-variance fusion across history depths. We evaluate ProGPO on two challenging agentic tasks, ALFWorld and WebShop, with Qwen2.5-1.5B-Instruct. Results show that ProGPO improves over matched agentic RL baselines under comparable computational overhead, and additional Qwen2.5-3B-Instruct experiments further test the scalability of the proposed method.
[AI-97] Biological Motifs for Agent ic Control
链接: https://arxiv.org/abs/2607.04240
作者: Bogdan Banu
类目: Artificial Intelligence (cs.AI); Cell Behavior (q-bio.CB)
备注: 80 pages. Preprint; feedback welcome
Abstract:The transition of Large Language Models (LLMs) from passive generators to autonomous agents has introduced significant challenges in reliability, security, and state management. Current agentic architectures are often constructed ad-hoc, prone to hallucination cascades, infinite loops, and prompt injection attacks. This paper argues that many of these failure modes can be analyzed using control motifs long studied in systems biology, provided the comparison is made at the level of typed interfaces and coordination structure rather than literal biological mechanism. We develop a typed interface correspondence between Gene Regulatory Networks and agentic software systems using polynomial functors and wiring diagrams. Five biological motifs are mapped to composable software design patterns: Coherent Feed-Forward Loops for noise suppression, Adaptive Immunity for layered security, Mitochondrial Signaling for resource governance, Endosymbiosis for neuro-symbolic integration, and Morphogen Diffusion for spatially varying coordination. An epistemic topology layer derives Kripke-style knowledge operators from the wiring diagram’s observation structure and proves four predictive theorems for multi-agent scaling. The core contributions are: (1) the Agentic Operad, a typed syntax for agent composition with provable error suppression bounds for feed-forward topologies; (2) an epistemic topology with four theorems (error amplification, sequential penalty, parallel acceleration, and tool density scaling) whose qualitative predictions are consistent with published multi-agent benchmarks; and (3) a six-layer progression from structure through development, grounded in autonomous learning frameworks and convergence proxies from the empirical literature. A reference implementation with 1,813 tests and 116 examples illustrates practical feasibility. Comments: 80 pages. Preprint; feedback welcome Subjects: Artificial Intelligence (cs.AI); Cell Behavior (q-bio.CB) Cite as: arXiv:2607.04240 [cs.AI] (or arXiv:2607.04240v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2607.04240 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-98] Unsupervised Features Mining via Activation Geometry
链接: https://arxiv.org/abs/2607.04222
作者: Amit LeVi,Elad David,Max Fomin
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Interpretability methods aim to reveal the features represented inside large language models (LLMs). Many existing methods begin with labeled examples of a human-defined concept that may reflect human biases, and then identify how that concept is represented within the model, for example in its activation space or through other decomposition methods. We introduce \emphMining via Activation Geometry (MAG), a simple unsupervised framework for extracting reasoning features from model activations by prepending the same natural-language instruction Q to every input p , where Q defines the reasoning feature of interest, such as Can this object be found in the desert?'' or Is this prompt malicious?‘’ We measure how the instruction changes the model’s internal representation using m(Q \mid p) - m§ at a single readout point. We explore eight different MAGs. The extracted reasoning features predict the models’ own world understanding and judgment, can be approximated into a single activation direction, we found that some features are more linearly represented and some less, this linear representation, which is vector steering, can change the LLMs’ decisions through activation steering by injecting reasoning features. Finally, we use the same method to select the best training datasets for prompt-injection classifier probes: while similarity between ordinary activations is almost unrelated to downstream performance, RFD-based similarity achieves 94.7% Top-1 and 100% Top-2 accuracy.
[AI-99] A Clustering-Based Framework for Identifying Suspicious Trading Patterns in Capital Market
链接: https://arxiv.org/abs/2607.04184
作者: Asif Zaman,Romona Magdalene Sarkar,Sabiha Khair Ohi,Iftekharul Mobin
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted for publication in IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence Networking (QPAIN), 2026. This is the authors preprint version. The final version is available via IEEE Xplore
Abstract:Market manipulation is the dubious practice of manipulating stock prices in order to make a quick profit, which truly degrades confidence on trading platforms. We implemented an unsupervised fraud-detection toolkit that begins with K-Means++ clustering to address this issue. A dataset of roughly one million financial transactions from 2012 to 2024 is used. In order to identify fraudulent trades and categorize them using market practice heuristic thresholds, the study suggests a clustering-based pipeline. The method highlights 2.02% of trades as suspicious where 51.10% clearly indicate spoofing, 0.10% indicate pump and dump, 0.55% indicate insider trading, 1.43% indicate a fake breakout, and 46.83% are unclassified. Despite the lack of ground truth, the model’s performance is confirmed by a Silhouette Score of 0.561.
[AI-100] Piercing Gilbreaths Conjecture: From Deep Number Theory Insights to Fintech and Cybersecurity
链接: https://arxiv.org/abs/2607.04166
作者: Vincent Granville
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:I propose a new methodology to attack the fascinating Gilbreath’s conjecture about prime numbers, first posted in 1878 and unsolved to this day. The problem statement is rudimentary: kids can understand it. However, despite decades of research, almost no progress has been made. This paper changes the game by presenting a new approach based on sieving, a number of new results with proof, a precise path to the solution, and solid references. It also introduces the concept of reverse sieving, along with applications to testing randomness, pattern and fraud detection, cybersecurity, synthetic data, sequence categorization and normalization, or to detect and quantify a new type of chaos in time series including Brownian motions. Magic primes, forbidden prime number constellations, cellular automata, and reduction via classes of equivalent sequences, are some of the innovative and promising topics discussed in the paper.
[AI-101] Language models guide symbolic equation discovery by controlling search
链接: https://arxiv.org/abs/2607.04156
作者: Zikai Xie,Wenmei Li,Man Luo,Jun Jiang,Linjiang Chen
类目: Artificial Intelligence (cs.AI); Symbolic Computation (cs.SC)
备注:
Abstract:Scientific equation discovery must combine broad domain priors with strict numerical testing. Symbolic regression supplies numerical grounding but faces a combinatorial search space, whereas many language-model systems ask the model to propose or select formulas directly. We test a different division of labour. We compare role specifications in which the language model acts as equation author, candidate decider or search controller, alongside end-to-end language-model and purely numerical baselines. In the controller setting we propose here, implemented as LLM-PySR, language models specify variables, operators, transformations and search depth; symbolic regression enumerates and fits expressions; and deterministic metrics govern retention. Across 74 AI-Feynman equations and seven complex formula-recovery tasks, search control achieved the strongest observed balance of accuracy, complexity, stability and cost. On an independent battery dataset, LLM-PySR identified a compact piecewise-linear relation between early voltage-curve displacement and cycle life. The results suggest that language models should shape hypothesis exploration rather than decide which equations survive.
[AI-102] Information-Geometric Superposed Vowel Evaluation: Part 1. Moraic Syllabary (Japanese)
链接: https://arxiv.org/abs/2607.04154
作者: Yusei Tamura,Shigekazu Ishihara,Ken Ito
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: 8 pages, 4 figures
Abstract:This paper explains the principles and provides examples of a new method for distinguishing between FAKE human speech synthesized by generative AI and natural speech. Since synthetic speech is generated based on information from a limited set of training spectra, the variety of vowels - which are key to identifying individuals - is limited. In contrast, natural speech exhibits a more diverse distribution of vowel spectra due to the flexibility of the human articulatory organ. In this paper, using Japanese - a Syllabary limited to five vowel phonemes, each of which corresponds one-to-one with a specific sound - as an example, we outline a method for distinguishing between synthetic and natural speech reading the same text by analyzing the spectral distributions. If we normalize the spectra of speech sounds and regard them as probability density functions for the frequency bands received by the hair cells of the human cochlea, and evaluate the distance between spectra using the Wasserstein metric, the Wasserstein distances between the vowels of synthetic speech are short. By preserving this distance and performing a topological mapping using persistent homology, the spectral probability density functions of synthetic and natural speech can be decomposed into clusters.
[AI-103] Mask-based Predictive Representations for Reinforcement Learning
链接: https://arxiv.org/abs/2607.04153
作者: Kai Zhao
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 16 pages, 7 figures
Abstract:Vision-based deep reinforcement learning involves dealing with high-dimensional inputs of image information. It is crucial to abstract effective states from high-dimensional image inputs and limited samples for sample-efficient reinforcement learning. To address this challenge, inspired by fields such as natural language processing and computer vision, we propose a self-supervised task based on mask prediction as an auxiliary task for reinforcement learning. This non-reconstruction method uses the sequence information collected by the agent from the environment and the context information in the sequence to predict the masked information, thereby strengthening the agent’s understanding of the task and learning effective representations. Combined with transformers, we find that the model reconstructs the masked input sequence in the latent space. By feeding the compressed representations learned by this method into reinforcement learning models, we observe an improvement in the sample efficiency of reinforcement learning. Moreover, the model outperforms state-of-the-art sample-efficient reinforcement learning methods on multiple continuous and discrete control benchmarks.
[AI-104] CSB: A Counting and Sampling tool for Bit-vectors
链接: https://arxiv.org/abs/2607.04142
作者: Arijit Shaw,Kuldeep S. Meel
类目: Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI)
备注: This is the authors’ version of the article published in Acta Informatica
Abstract:Satisfiability modulo theory (SMT) solvers have significantly advanced automated reasoning due to their effectiveness in solving problems across various fields. With the advancement in SMT solvers, there is growing interest in exploring capabilities beyond mere satisfiability, similar to the progression observed in Boolean satisfiability solvers that expanded into counting and sampling. In this study, we investigate the following question: Can we rely on modern CNF model counters and CNF samplers to extend modern SMT solvers to handle the problems of counting and sampling over bit-vectors? The main contribution of this work is the development of an efficient and user-friendly tool, csb, that solves a bunch of problems around model counting and sampling on the theory of bit-vectors, namely exact and approximate projected and non-projected model counting, along with the almost-uniform and uniform-like sampling. In the case of exact counting, projected counting, and uniform sampling. Our tool csb converts the bit-vector formula into a CNF formula using bit-blasting techniques before applying CNF model counters or samplers to perform counting or sampling. Our experiments demonstrate significant performance improvements over existing methods. Comments: This is the authors’ version of the article published in Acta Informatica Subjects: Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.04142 [cs.LO] (or arXiv:2607.04142v1 [cs.LO] for this version) https://doi.org/10.48550/arXiv.2607.04142 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journalreference: Acta Informatica, Volume 63, article number 23 (2026) Related DOI: https://doi.org/10.1007/s00236-026-00535-0 Focus to learn more DOI(s) linking to related resources
[AI-105] Conflict-Based Lazy Search for Fast Multi-Manipulator Planning
链接: https://arxiv.org/abs/2607.04124
作者: Dongliang Zheng,Zhipeng Wang,Siqi Wang,Yuxi Lu,Bin He,Hesheng Wang,Panagiotis Tsiotras
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Employing multiple manipulators can boost efficiency and accomplish tasks that a single manipulator cannot do. However, real-time planning for multiple manipulators in a cluttered workspace still poses significant challenges for planning algorithms. This article proposes a new planning algorithm called Conflict-Based Lazy Search (CBLS) for multimanipulator planning. CBLS is built on Conflict-Based Search (CBS), an efficient multiagent pathfinding (MAPF) algorithm that has shown an order of magnitude speedup over previous approaches [1], [2]. CBS addresses MAPF by solving many single-agent pathfinding (SAPF) problems. Thus, its planning time directly depends on the efficiency of the SAPF algorithm adopted. Our CBLS algorithm enhances CBS with precomputation and lazy search. First, a lazily evaluated graph with controlled sparsity is precomputed for a single manipulator. Second, we propose the Lazy Edged-based A* (LEA*) for efficient SAPF. Since edge evaluation is the computational bottleneck of manipulator planning, LEA* uses lazy search and an edge queue to reduce the number of edge evaluations. We show that LEA* is optimally vertex efficient and has improved edge efficiency compared to A*. We apply the proposed CBLS to multi-manipulator planning problems and show its superior performance by comparing it with CBS and a sampling-based algorithm, namely, RRT-Connect.
[AI-106] Parametric Memory Decoding for Zero-Shot Routing in LoRA-Based External Parametric Memory
链接: https://arxiv.org/abs/2607.04118
作者: Fengxian Ji,Zhuohan Xie,Jingpu Yang,Fan Zhang,Zirui Song,Xiuying Chen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:With the rise of parametric memory, LoRA-based External Parametric Memory (EPM) has emerged as a modular solution, but existing routing methods often introduce additional training, deployment, and maintenance overhead. This raises a natural question: can a LoRA-based EPM bank be routed without maintaining an additional routing component? However, existing zero-shot LoRA routing methods still face two problems under the EPM setting: (1) their evaluations are scattered across different task settings rather than organized around EPM access, and (2) their routing signals lack a unified perspective to guide systematic improvement. To address these problems, we organize PMD-Bench, covering document-level, domain-level knowledge, and task-skill, and propose Parametric Memory Decoding (PMD), the first framework designed to systematically improve zero-shot LoRA routing by reframing it as decoding activations over external parametric memory. Based on PMD, we further instantiate PMDRouter, which scores each LoRA by its response magnitude from a single base-model prefill. Experiments on PMD-Bench show that PMDRouter achieves the strongest internal-signal performance across multiple zero-shot routing settings. These results demonstrate the feasibility of zero-shot LoRA routing and suggest that PMD can serve as a general framework for improving zero-shot routing methods. Sources: Github (this https URL)
[AI-107] Forethought: Verifiable Reasoning from Neurosymbolic Primitive Programming
链接: https://arxiv.org/abs/2607.04096
作者: Vishvesh Bhat,Jay Vaghasiya,Emmanuel Anaya Gonzalez
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Current agentic workflows usually involve decomposing user requests into sequences of tool calls with correctly resolved parameters, the results of which are processed through reasoning traces in the language model’s context window. The prevailing route to improve such reasoning is test-time scaling, which trains models to search over long chains of thought; but the resulting capability is entangled in model weights, is not verifiable step-by-step, and is costly at inference. We present Forethought, a neurosymbolic reasoning system that instead treats reasoning as an explicit, verifiable program, that builds from a library of symbolic and neural primitives which are composed through a domain-specific language. The result are reasoning programs, which are concrete representations of the model’s work, and as such can be inspected and modified before deployment. Instantiated as a tool-calling execution kernel and evaluated across five benchmarks, Forethought improves base-model accuracy by about 30% relative and outperforms vanilla prompting, reinforcement learning scaffolds, and prompt-evolution methods, enabling small models to match or exceed frontier models capabilities. In a direct comparison, a non-reasoning model augmented with Forethought competes with a dedicated reasoning model while requiring roughly three orders of magnitude less post-training investment, and remains model-agnostic and auditable.
[AI-108] PLACEMEM: Toward a Compute-Aware Memory Plane for Lifelong Agents
链接: https://arxiv.org/abs/2607.04089
作者: Sukanta Ganguly
类目: Artificial Intelligence (cs.AI)
备注: 6 pages, 3 Tables,. 1 Figure
Abstract:Lifelong agents need more than larger context windows and better retrieval. They need memories that can persist, evolve, and be corrected without forcing the serving stack to recompute the same history on every turn or silently reuse stale runtime state. We present PLACEMEM as a systems position on lifelong-agent memory, instantiated by an executable control-plane prototype. The central claim is that agent memory should be represented as versioned capsules that unify semantics, provenance, validity, and reusable runtime state under one correction-aware identity. In the current prototype, capsules drive prompt-level text retrieval, KV-aware routing, and cascading invalidation over live streamed backends; prospective layer-frontier replay is intentionally framed as a deeper integration agenda rather than a claimed engine feature. We describe a vLLM-first prototype with persistent capsule state, concurrency-safe invalidation, an OpenAI-compatible routing sidecar, a typed metadata contract, and a benchmark harness that measures live first-token latency, reuse, and post-correction behavior. The result is both an executable artifact that demonstrates correction-aware control-plane behavior today and a concrete roadmap for replay-aware serving integration in future lifelong-agent systems.
[AI-109] FedSPM: Routing-Enabled Federated Learning under Dual Heterogeneity via Semiparametric Mixture
链接: https://arxiv.org/abs/2607.04085
作者: Zijian Wang,Pengfei Li,Guangyu Yang,Qiong Zhang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注:
Abstract:Routing-prediction federated learning has emerged as a new paradigm that reframes inter-client heterogeneity as a resource for system-level intelligence: at inference time, the server routes each external query to the best-matched client for prediction. Existing approaches, however, typically treat each client as internally homogeneous, overlooking latent subpopulations within local data. For example, patients with the same diagnosis at one hospital may exhibit morphologically distinct disease subtypes. The coexistence of inter-client and intra-client heterogeneity, which we call dual heterogeneity, can impair both routing and prediction. To address this challenge, we propose FedSPM, a routing-enabled semiparametric mixture framework that represents each client using client-specific latent components. Each component combines a predictive distribution for classification with a feature distribution for routing. To flexibly model feature distributions while effectively sharing information across clients, FedSPM models their density ratios relative to a common nonparametric measure estimated via empirical likelihood. We develop a federated expectation-maximization algorithm that optimizes a tractable surrogate and prove convergence of the exact profiled objective at the standard \mathcalO(1/\sqrtT) rate when the surrogate errors are properly controlled. Experiments on controlled benchmarks and real-world medical data demonstrate consistent improvements in routing and prediction under dual heterogeneity. Code is available at this https URL.
[AI-110] Benchmarking API Drift in LLM -Generated Quantum Code Across Successive SDK Versions
链接: https://arxiv.org/abs/2607.04072
作者: Mohammad Arif Rasyidi,Syahirul Faiz
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Quantum Physics (quant-ph)
备注:
Abstract:Large language models can generate plausible quantum code, but it is unclear whether they can reliably target the specific software development kit (SDK) version requested by the user. We study this problem as API drift and introduce quantum-api-drift, a benchmark for measuring version fidelity, defined here as execution success on the requested SDK version, cross-version compatibility, failure modes, and documentation-guided repair in LLM-generated quantum SDK code. We instantiate the benchmark with Qiskit, a representative quantum SDK that underwent substantial interface changes across v0.43, v1.3, and v2.0. We evaluate 17 models on 50 tasks with 3 samples per prompt, yielding 450 generated samples and 1,350 executions per model. Sixteen models are tested in a matched REST API setting with a 1024-token output cap, while GPT-5.4 (Codex CLI) is reported separately as a reference configuration. Across the 16 matched REST models, diagonal Pass@1 ranges from 0.02 to 0.85. Claude Opus 4.7 is strongest on v0.43 and v2.0, while Grok 4.20 is strongest on v1.3 at 0.513. Error profiles differ systematically by model strength: weaker models fail mainly with broken imports, while stronger models more often reach deprecation-level failures. Documentation-guided repair succeeds for 0.19 to 0.59 of repair attempts overall and is consistently much more effective for migration to v2.0 than to v1.3. The benchmark artifacts are publicly available at this https URL. These results show that version alignment is a distinct evaluation axis for quantum code generation and that API drift remains only partly recoverable even with migration guidance.
[AI-111] What is Left for Us? Second Scholarship Against the Degradation of Research by AI
链接: https://arxiv.org/abs/2607.04049
作者: Claudio Novelli,Luciano Floridi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We argue that generative AI can degrade research by eroding the very practices through which scholarly judgement is formed and academic trust is built. As constitutive conditions for the production and validation of knowledge, these practices cannot be reduced to the final outputs of research, which is what AI so effectively simulate. Accordingly, when researchers delegate central tasks of inquiry to systems like Large Language Models, they may stop enacting these practices and, with them, lose access to the formation they provide. An individual research output generated by AI may even appear improved but the researcher behind it fails to develop. Against this risk, merely keeping humans in the loop as prompters or quality checkers of AI outputs is insufficient to preserve research as a site of intellectual formation. What is needed instead is a renewed commitment to research as a lived practice in which judgement is formed gradually, often through frictions, and participation in a scholarly community. We defend it because it rests on four sources and warrants of research that cannot be automated: tacit knowledge, personal commitment, socialisation, and deep reading. This practice enacts what we call second scholarship, by which we understand the reappropriation of scholarly craft, chosen out of a critical experience of what generative AI can and cannot do. What cannot and should not be delegated becomes what research communities must value and answer for. This is what is left for us.
[AI-112] Reward-Gated On-Policy Distillation
链接: https://arxiv.org/abs/2607.04037
作者: Mohammad Sadegh Akhondzadeh,Vijay Lingam,Atula Tejaswi,Chanakya Ekbote,Sujay Sanghavi,Aleksandar Bojchevski
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 8 pages, 2 figures, 3 tables
Abstract:On-policy distillation is a powerful way to transfer reasoning ability from a strong teacher to a smaller student: the student samples trajectories from its own policy, and the teacher provides dense token-level supervision on the states the student actually visits. However, this supervision is not always reliable: a teacher can assign high likelihood to plausible but incorrect solutions, or low likelihood to correct student solutions that follow different reasoning paths. Unconditionally distilling the teacher can therefore reinforce bad modes or erase useful student behavior. To address these limitations, we introduce RG-OPD: Reward-Gated On-Policy Distillation that uses verifier feedback to decide when teacher logits should be trusted. RG-OPD bridges sparse verifier rewards and dense teacher logits, preserving token-level supervision while filtering misleading teacher signals. Across reasoning and coding benchmarks, RG-OPD produces stronger distilled students, outperforming both vanilla reverse-KL distillation and the recent TSD-KD baseline. At 1K generation length, RG-OPD improves over reverse-KL by 2.9 points and over TSD-KD by 4.9 points; in the long-generation setting, it improves over the untuned student by 8.2 points. Our code is available at this https URL.
[AI-113] he “I Dont Know” Filter: Enhancing Agent ic Reliability in Function Calling
链接: https://arxiv.org/abs/2607.04034
作者: Stefan Broecker,Mason del Rosario,Boris Selitser,Thomas Strohmer
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:The language models that underpin agents have seen a rapid rise in performance on function calling benchmarks. However, the metrics used in the training and evaluation of these models often encourage models to make positive claims even when the answer is uncertain, leading to hallucinations. Such hallucinations can be disastrous when language models are trusted to use function calls to make decisions in high stakes applications. To that end, we propose an agent evaluation metric that takes into account the negative outcomes associated with incorrect function calls. Further, to catch hallucinations before they can cause harm, we propose a lightweight trainable filter that can quantify a language model’s uncertainty and remove potentially harmful function calls. By training that filter to detect and suppress uncertain function calls without modifying the underlying model, we demonstrate a practical path toward agents that know when to say “I don’t know,” a property we argue is essential to production reliability.
[AI-114] OmniOpt: Taxonomy Geometry and Benchmarking of Modern Optimizers
链接: https://arxiv.org/abs/2607.04033
作者: Siyuan Li,Jiabao Pan,Yumou Liu,Zhuoli Ouyang,Xin Jin,Xinglong Xu,Jingxuan Wei,Shengye Pang,Jintao Che,Xuanhe Zhou,Conghui He,Cheng Tan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Survey benchmark preprint V1 (91 pages)
Abstract:Optimizer selection for large-scale model training has become a system-level design decision constrained jointly by compute, memory, tuning budget, and task diversity, yet the landscape of over one hundred methods remains fragmented. We therefore present OmniOpt, a unified survey and benchmark cookbook of optimizers for the research community. OmniOpt rests on four coupled components. First, we treat every optimizer update as a structured transformation through a five-stage meta-pipeline, and show that most methods engage only one or two of these stages. Second, we use norm-constrained linear minimization oracles (LMOs) to unify different optimizers. Third, these two views ground a dual-dimension taxonomy, one dimension assigning each method to a mechanism family and the other recording the measurable training objectives it aims to improve. Fourth, and at the core of this paper, we instantiate the full taxonomy in a unified cross-domain benchmark spanning representative optimizers, model scales, and training regimes from language model pretraining to image classification, systematically analyzing each method family across multiple effect objectives and laying out their trade-offs. OmniOpt thus supplies the research community with an operational coordinate system for selecting optimizers under explicit mechanism and objective assumptions, and charts a direction for the future development of the optimizer community.
[AI-115] Efficient Discovery of Conditional Dependencies with Desbordante
链接: https://arxiv.org/abs/2607.04030
作者: Ivan Kozhukov,Dmitry Fedoseev,Maksim Emelyanov,Artem Smola,Pyotr Senichenkov,Pavel Anosov,George Chernishev
类目: Databases (cs.DB); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Performance (cs.PF)
备注:
Abstract:Conditional functional dependencies (CFDs) are functional dependencies with a restricted scope: they specify the context in which a dependency holds and are useful for data-quality tasks, specifying complex integrity constraints, and extracting valuable insights from data. We study the CFD discovery problem, which is computationally demanding. We build on the state-of-the-art CFDFinder algorithm and introduce a set of algorithmic and engineering improvements, including a parallelization strategy, to produce ParCFDFinder. Our implementation is integrated into Desbordante - a high-performance open-source data profiler written in C++ that exposes a Python interface, enabling CFD discovery to be invoked from any Python program. Experimental results show that our enhancements speed up the algorithm by up to 318\times ( 118\times on average) and reduce memory usage by up to 23\times ( 14\times on average) compared with the existing Java-based implementation of Metanome. Integrating ParCFDFinder into Desbordante makes it possible, for the first time, to conveniently discover CFDs on datasets with hundreds of thousands of rows on a commodity machine within a reasonable time. Subjects: Databases (cs.DB); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Performance (cs.PF) ACMclasses: H.3; I.5; J.0 Cite as: arXiv:2607.04030 [cs.DB] (or arXiv:2607.04030v1 [cs.DB] for this version) https://doi.org/10.48550/arXiv.2607.04030 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journalreference: 2026 39th Conference of Open Innovations Association (FRUCT), Helsinki, Finland, 2026, pp. 130-141 Related DOI: https://doi.org/10.23919/FRUCT70069.2026.11506538 Focus to learn more DOI(s) linking to related resources
[AI-116] A Unified Algebraic Framework for Classification Performance Evaluation
链接: https://arxiv.org/abs/2607.04028
作者: Ronaldo C. Prati
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:We propose a unified algebraic framework for classification performance evaluation that encompasses binary, multiclass, multilabel, ordinal, hierarchical, cost-sensitive, and soft-label settings within a single formalism. The foundation is a representation of actual and predicted labels as binary indicator matrices, combined with three aggregation operators – global, column-wise, and row-wise – that correspond exactly to micro, macro/weighted, and exemplar averaging. Any binary performance measure expressed in terms of true/positive/negative counts extends automatically to all settings by substituting these operators, generating multiclass and multilabel versions without measure-specific derivations. The framework further accommodates soft classifier outputs via argmax or thresholding, soft ground truth via triangular norms, ordinal classification via membership functions or cumulative encodings, and cost-sensitive evaluation via a cost matrix that subsumes MAE and MSE as special cases. We establish several theoretical results: micro-averaging equals denominator-weighted macro-averaging; the product t -norm is the unique one preserving the confusion-matrix partition; skew-invariant measures are characterised as functions of recall and specificity; and micro-precision, micro-recall, and micro- F_1 are all equal to accuracy in multiclass settings. Empirical illustrations on synthetic and real data confirm the theoretical findings.
[AI-117] Finite Reliability Representations: Noise-Calibrated Belief-Space Covers for Reliable Decision-Making
链接: https://arxiv.org/abs/2607.04019
作者: Hyung-Jin Yoon,Hunmin Kim
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI); Robotics (cs.RO); Optimization and Control (math.OC)
备注: 25 pages. Introduces Finite Reliability Representations for noise-calibrated belief-space covers and policy sufficiency
Abstract:Physical sensing and actuation noise floors should inform how much belief resolution a decision-making system can reliably use. We introduce Finite Reliability Representations (FRR), a framework for covering belief spaces by reliability cells: regions within which the optimal action-value function Q*(b,u) varies by at most a tolerance epsilon, uniformly over actions. The framework is formulated on beliefs rather than states and uses a cover rather than an equivalence quotient, because approximate decision-closeness is not transitive in general. A central technical point is that noisy Bayesian updates should not be treated as globally contractive on arbitrary beliefs. We therefore separate three objects: the fixed-observation filter map, the predictive observation law, and the controlled belief-transition kernel. For nonlinear continuous-state systems, FRR is obtained under a reachable-set Lipschitz modulus for the belief-transition kernel. For finite-state POMDPs, the same construction becomes exact on the belief simplex: prediction is linear, Bayesian correction is a normalized positive linear map, sensor noise enters through observation-distribution distinguishability, and actuation uncertainty enters through an action-execution channel. Under the corresponding action-value Lipschitz condition, an FRR cover supports a cell-constant policy whose suboptimality is bounded by 2 epsilon/(1 - gamma). We also introduce reliability entropy, the logarithm of the minimal number of reliability cells, as a measure of certified decision-relevant belief complexity. The framework distinguishes representation sufficiency from fundamental performance floors imposed by sensing, process, and actuation noise. It applies to finite POMDPs, linear-Gaussian filters, locally linearized nonlinear filters, and particle-filter implementations through analytic or empirical certification of reliability cells.
[AI-118] When Does Small Data Work? Accuracy and Efficiency Trade-offs Between Tabular Foundation Models and Conventional Methods for Crowd-State Classification at Hajj and Umrah
链接: https://arxiv.org/abs/2607.04013
作者: AlJawharh S. AlOtaibi,Mohamed Eltahir,Jude AlSubaie
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 6 pages, 3 figures, 10 tables
Abstract:Learning from few labeled examples is a central challenge in tabular machine learning, and it becomes the binding constraint in domains where labeling is costly, such as crowd monitoring during Hajj and Umrah. Tabular foundation models, which predict from only a handful of examples without task-specific training, were recently introduced to address this very-few-label regime. In this study we test them on crowd-state classification to assess how much they help when labels are scarce, and we compare them against standard machine learning methods to characterize the accuracy and efficiency trade-offs between the two approaches. Using three real datasets we evaluate different machine learning models, in untuned and tuned forms, against three foundation models. Results show that no single family is best everywhere. The right choice depends on the label budget. When labels are very few, foundation models lead. As labels grow, tuned conventional models catch up and significantly surpass the foundation models on the more structural geometry target. Efficiency separates them further where tuned machine learning models incur a large tuning cost that foundation models avoid, although foundation models reprocess their context at every prediction. We summarize these results as a practical map of which approach to prefer under a given label budget and computational budget.
[AI-119] Order-based Causal Discovery for Multistage Processes
链接: https://arxiv.org/abs/2607.03971
作者: Eun-Yeol Ma,Junsub Jung,Heeyoung Kim
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Causality has become an increasingly important tool for gaining a deeper understanding of complex systems. Among various causal analysis methods, causal discovery, which identifies causal relationships among variables from data, has been widely used to uncover underlying causality in diverse processes. However, while multistage processes are prevalent in many fields, existing causal discovery methods may produce counterintuitive results, given the known process knowledge, and may not be computationally efficient for handling large datasets typical of multistage processes. To address this gap, we propose a novel causal discovery method called Order-based Causal Discovery for Multistage Processes (OCDM). OCDM is designed to infer the causal structure of multistage data while preserving their inherent hierarchical and sequential structure by explicitly incorporating process knowledge into the causal discovery process. Specifically, we propose a structural knowledge-informed order-inferring algorithm that infers the causal order of variables by incorporating information about the stage from which each variable originates, based on an order-based causal discovery framework naturally suited for inherently ordered multistage data. Furthermore, to eliminate spurious edges from the initial causal graph generated based on the inferred causal order, we introduce a novel pruning technique using stochastic gated neural networks, which offers greater computational efficiency compared to existing methods. Through experiments on various datasets, we demonstrate that OCDM effectively infers the causal structure of multistage processes, outperforming existing methods.
[AI-120] Refused in Chat Written in Code: Workflow-Level Jailbreak Construction in IDE Coding Agents
链接: https://arxiv.org/abs/2607.03968
作者: Abhishek Kumar,Carsten Maple
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models are increasingly deployed as IDE-integrated coding agents that decompose tasks, generate and edit files, run code, and refine outputs over many turns. Yet their safety is still often evaluated as if they were chatbots: one harmful prompt, one response, judged in isolation. We introduce workflow-level jailbreak construction, a failure mode in which a harmful objective is assembled across ordinary stages of a software-development workflow rather than generated through a single direct prompt. Using GitHub Copilot in Visual Studio Code, we study four closed-weight backends: Claude Sonnet 4.6, Claude Haiku 4.5, Gemini 3.1 Pro, and Gemini 3.5 Flash. Across 204 prompts from Hammurabi’s Code, HarmBench, and AdvBench , the models show near-complete refusal under direct chat, CSV-read, and single-step code-fix baselines, with only 8/816 successful responses in each baseline condition. Under the full workflow, however, the same prompts and backends produce 816/816 unsafe teaching-shot completions, all independently confirmed by two expert evaluators under a strict rubric. These results show that conversational refusal benchmarks can substantially overstate the safety of deployed coding agents and motivate defenses that reason about safety across multi-turn IDE workflows and their generated artifacts, not only individual chat turns.
[AI-121] Worldscape-MoE: A Unified Mixture-of-Experts World Model for Scalable Heterogeneous Action Control
链接: https://arxiv.org/abs/2607.03964
作者: Jianjie Fang,Yongyan Xu,Ziyou Wang,Chen Gao,Yuchao Huang,Zhaolu Wang,Rongze Tang,Mingyuan Jia,Baining Zhao,Weichen Zhang,Xin Zhang,Haisheng Su,Yu Shang,Wei Wu,Xinlei Chen,Yong Li
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 36 pages
Abstract:World models are rapidly becoming a core infrastructure for embodied intelligence and interactive agents: they provide controllable simulators in which agents can perceive, act, forecast, and acquire scalable experience. Yet current video generation world models are still organized around isolated control interfaces, such as camera trajectories, robot actions, or hand-joint signals. This fragmentation is increasingly a scaling bottleneck. The central challenge is not the absence of controllable generators, but the lack of a unified and extensible learning framework that can absorb heterogeneous action supervision while preserving a shared model of world dynamics. In this work, we introduce Worldscape-MoE, a Mixture-of-Experts world model built on Diffusion Transformers for scalable heterogeneous action control. Our key observation is that different controls specify different interfaces to the same underlying world: although their representations differ, they constrain shared physical regularities, scene dynamics, and interaction semantics. Worldscape-MoE operationalizes this observation through modality-aware control injection, shared and control-specific experts, and a progressive MoE tuning strategy that supports continual extension to new action modalities. Experiments across locomotion, robotic manipulation, and egocentric hand control show that heterogeneous supervision improves rather than interferes with individual control capabilities. Worldscape-MoE achieves strong results on WorldArena, improves locomotion and hand-control metrics, exhibits robust out-of-distribution generalization, and demonstrates scaling behavior as additional control data and experts are integrated.
[AI-122] Why3-py: A Tool for Formal Verification of Hypothesis Testing and Meta-Analysis in Python
链接: https://arxiv.org/abs/2607.03951
作者: Akira Tanaka,Yusuke Kawamoto
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO); Programming Languages (cs.PL)
备注:
Abstract:The reproducibility crisis in scientific research has received widespread recognition, thereby increasing the importance of meta-analyses that integrate statistical analyses from multiple studies. However, statistical methods often have ambiguous and implicit underlying assumptions, which can lead to their erroneous applications and interpretations. To address this issue, we propose a formal verification framework for statistical programs written in Python. Specifically, we present Why3-py, a Python front-end for the Why3 verification platform that transforms Python programs into verification-oriented WhyML representations suitable for formal verification, addressing the challenges arising from Python’s dynamic typing and runtime polymorphism. Furthermore, we extend the StatWhy tool to support the verification of meta-analysis methods. These tools enable users to identify overlooked assumptions and misuse of analyses, and to verify the correctness of Python programs for hypothesis testing and for meta-analyses.
[AI-123] Online Linear Programming for Multi-Objective Routing in LLM Serving
链接: https://arxiv.org/abs/2607.03948
作者: Zixi Chen,Yinyu Ye,Zijie Zhou
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Optimization and Control (math.OC)
备注:
Abstract:We study the online routing problem in large language model serving, where requests arrive sequentially and must be dispatched to parallel decode workers under tight batch-size and KV-cache constraints. Unlike widely used routing heuristics that are not tied to explicit service-level objectives (SLOs) and offer limited control over latency-throughput trade-offs, we introduce a multi-objective optimization framework that formulates routing as an online linear programming with interpretable decision rewards. We apply an efficient bid-price control policy based on the online linear programming that admits requests when their SLO-weighted benefit exceeds their shadow prices. To meet millisecond decision requirements, we develop a warm-started, projected first-order updates that track the evolving dual shadow prices online with predictable runtime. We integrate our router into the Vidur simulator and demonstrate substantial improvements over standard baselines across multiple SLO regimes, including end-to-end latency, time-to-first-token, throughput, and tail performance. A big picture from our result: a science-based approach outperforms others based on heuristics.
[AI-124] Harness-Aware Self-Evolving: Co-Evolving Model Weights Harness and Task Solutions
链接: https://arxiv.org/abs/2607.03935
作者: Haochen Luo,Yi Huang,Sichun Luo,Fengyuan Liu,Lei Li,Zefa Hu,Junlan Feng,Qi Liu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Self-evolving frameworks usually optimize task solutions while treating the surrounding harness as fixed. We introduce Harness-Aware Self-Evolving (HASE), an agentic reinforcement-learning framework in which a single model can generate task solutions or edit selected harness components in a multi-turn action space. HASE enables a single Qwen3-8B model to match the text-classification performance of a GPT-OSS-120B model that uses Claude Code as the harness proposer. In alpha factor mining, HASE outperforms the reported GPT-OSS-120B baseline. HASE also repairs imperfect evaluation components and converges to state-of-the-art performance in circle-packing algorithm discovery. These results show that HASE improves the harness and the solution through one unified agentic process.
[AI-125] MPSelectTune: Prompt-type Selection for Fine-tuning improves Concept Unlearning in LLM s NEURIPS2025
链接: https://arxiv.org/abs/2607.03932
作者: Shubhadip Nag,Srinjoy Das,Agniva Saha,Anushree Ghosh,Soumi Das,Tarun Kumar,Suparna Bhattacharya,Sourangshu Bhattacharya
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at NeurIPS 2025 - Reliable ML Workshop ( this https URL )
Abstract:LLMs can be conveniently adapted to a diverse set of tasks, e.g, prediction, question-answering tasks, etc, using appropriate prompts with few-shot examples. Biased or harmful concepts, e.g. gender or bio-weapons, present in pre-trained LLMs can lead to unsafe or unethical responses for many such prompts. Removing such undesirable concepts robustly across different prompt types remains a challenging problem, since existing unlearning methods typically ignore the impact of prompt variation. In this paper, we explore a novel adversarial approach to use a joint prompt for the main task and concept task prediction. We show that fine-tuning using the ``worst prompt type’’ for concept prediction (with the highest concept accuracy) improves the average unlearning performance over a fine-tuning method that uses a combination of all prompt types. Our proposed method, MPSelectTune, is a two-stage approach that minimizes the concept accuracy of the highest accuracy-prompt type, after fine-tuning using a novel multi-task loss using multiple prompt types. Experimental results on four benchmarks show 2 - 15% main task accuracy improvements over recent baselines and while reducing the worst-case concept accuracy by up to 17% compared to recent baselines.
[AI-126] okAN: Accent Normalization Using Self-Supervised Speech Tokens
链接: https://arxiv.org/abs/2607.03928
作者: Qibing Bai,Shuai Wang,Yuhan Du,Bohan Li,Yannan Wang,Haizhou Li
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
备注: Submitted to IEEE Transactions on Audio, Speech, and Language Processing (TASLP)
Abstract:Accent normalization (AN) seeks to convert non-native (L2) accented speech into standard (L1) speech while preserving speaker identity. The current techniques either require naturally recorded parallel L1-L2 speech for training, or suffer from quality degradation when supervised by synthesized targets. In this paper, we present TokAN, a token-based accent normalization framework that operates on self-supervised discrete speech tokens extracted from a L1-L2 jointly trained vector-quantization (VQ) tokenizer, without the need of synthetic supervisory speech. An autoregressive encoder-decoder model performs token-to-token conversion, translating L2-accented token sequences into the tokens of standard voice. We also introduce reinforcement learning (RL) post-training based on Group Relative Policy Optimization (GRPO), using word error rate and accent classifier confidence as complementary rewards. A non-autoregressive flow-matching synthesizer recovers the Mel-spectrogram from the converted tokens, conditioned on the source speaker embedding. We also develop a flow-matching duration predictor that supports total-duration-aware synthesis, making TokAN applicable to duration-critical tasks such as voice dubbing and live casting. Experiments on seven English accents demonstrate that TokAN reduced the word error rate from 12.40% to 9.89% after supervised fine-tuning, and further to 9.23% after RL post-training, consistently outperforming frame-to-frame, direct flow-matching, and prompt-based token-conversion baselines in terms of accent reduction and intelligibility.
[AI-127] abQueryBench: A Query-Centric Benchmark for Synthetic Tabular Data
链接: https://arxiv.org/abs/2607.03926
作者: Jialin Zhang,Fenghao Dong,Yajie Zhou,Vyas Sekar,Shinan Liu
类目: Databases (cs.DB); Artificial Intelligence (cs.AI)
备注:
Abstract:Synthetic tabular data support use cases like data sharing, model development under access restrictions, and rapid prototyping of analytical workflows. Modern generative models are evaluated by their statistical similarity, correlation structure, privacy, and downstream machine-learning utility. However, such evaluations leave a gap: they rarely test the structure that matters for analytical queries. We present TabQueryBench, a query-centric benchmark that uses SQL-shaped analytical queries as structural assessors for synthetic data fidelity. It provides an extensible foundation for query-centric synthetic-data evaluation. From 12 public sources of analytical queries, TabQueryBench taxonomizes recurring cross-domain logic into 44 reusable query templates and grounds them to each dataset via a policy-guided template-to-SQL pipeline. This makes queries schema-aware while preserving comparability across generative models. Across 49 datasets and 11 generative models, it activates 10-12 templates per dataset, producing more than 100 executable SQL queries per dataset. Our systematic experiments show five main patterns. First, current tabular generative models can have good distance-based fidelity, but they still fall short on query-centric fidelity: RealTabFormer achieves the highest query-centric fidelity, but it only reaches 0.75 +/- 0.15 (REAL data score is 1.00). Second, tabular generative models struggle with very high-cardinality discrete support. Third, SOTA generative models preserve good global conditional query-centric fidelity, but fail more on local queries. Fourth, tail fidelity deteriorates as queries move toward the extreme tail; even the best model recovers only about 40.7% of real rare values. Finally, there is a fidelity-cost tradeoff in tabular generation: BayesNet offers the strongest tradeoff, with slightly lower query-centric fidelity but much lower generation cost.
[AI-128] Advanced Topic Modeling Techniques for Categorizing Software Vulnerabilities
链接: https://arxiv.org/abs/2607.03887
作者: Utkarsh Tiwari,Spoorthi M,Anirudh S,Nidhin Prabhakar T. V
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 10 pages, 10 figures. Accepted at the 16th International Conference on Computing, Communication and Networking Technologies (ICCCNT 2025), July 6-11, 2025, IIT Indore, Madhya Pradesh, India. IEEE proceedings
Abstract:The increasing complexity and frequency of software vulnerabilities demand efficient methods to analyze and prioritize threats. Traditional approaches often fail to process the vast amount of unstructured textual data effectively, highlighting the need for advanced solutions. This study leverages state-of-the-art topic modeling techniques powered by large language models (LLMs) to extract meaningful insights from the ‘Threat’ feature of a software vulnerability dataset. Models such as BERTopic, Top2Vec, CombinedTM, Llama2 with BERTopic, and Mixtral are utilized, along with dimensionality reduction and clustering methods like UMAP, PCA, HDBSCAN, and DBSCAN. By uncovering latent patterns and generating interpretable clusters, this research enhances threat prioritization and decision-making in cybersecurity. The findings support scalable and automated solutions for vulnerability management, contributing to improved security practices.
[AI-129] High-Fidelity One-Step Generative Visuomotor Policy via Recursive Correction Frequency Consistency and Contrastive Flow Matching
链接: https://arxiv.org/abs/2607.03865
作者: Yuran Chen,Xinye Cai,Zhonglin Gong,Yang Huang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Generative models such as diffusion and flow matching have advanced robotic visuomotor policies by modeling multimodal action distributions, but their multi-step sampling or ODE solving introduces inference latency. Existing one-step acceleration methods often compress the whole generation process into a single large update, leading to spatial deviation, frequency distortion, and mode averaging. This paper proposes a high-fidelity one-step generative visuomotor policy framework that addresses these issues with three complementary mechanisms. Recursive Consistent Action Flow (RCAF) uses recursive correction to compensate for spatial truncation errors and align one-step predictions with refined flow trajectories. Dual-Timestep Frequency Consistency (DTFC) preserves high-frequency manipulation details through adaptive spectral consistency across flow timesteps. Contrastive Flow Matching (CFM) separates entangled action flows with a margin-based repulsive objective, reducing ambiguous actions in multimodal manipulation. Experiments on RoboTwin, RoboTwin 2.0, Adroit, DexArt, and real-world robot platforms show that the proposed method achieves competitive or superior performance compared with strong 10-step generative policy baselines while requiring only one forward pass (1 NFE), enabling low-latency visuomotor control.
[AI-130] DualView: Preventing Indirect Prompt Injection in Personal AI Agents
链接: https://arxiv.org/abs/2607.03821
作者: Juhee Kim,Woohyuk Choi,Taehyun Kang,Youngmin Kim,Byoungyoung Lee
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Personal AI agents that run on the user’s local machine, such as OpenClaw, automate daily tasks including web search, email, and file management. Their access to computer resources, including the network, file system, and shell, exposes them to indirect prompt injection (IPI) attacks. Prior Dual LLM defenses block IPI by replacing untrusted data with symbols that the agent can reference but not read. However, they track untrusted data only inside the agent’s context, so when the agent saves and later rereads untrusted data, that data, possibly an attacker’s prompt, can return as trusted data rather than as a symbol, which we call stored IPI. Operating on the user’s real environment, which humans and programs share, is what makes agents like OpenClaw practical, and is exactly why a defense that ignores it is incomplete. Preserving symbols in such an environment is hard, because humans and programs need original data. We present DualView, which extends untrusted data tracking from the agent’s context to the user’s environment, including the file system, shell, network, and other agents, by giving each channel two views. In AgentView, the agent sees untrusted data as symbols even after writing it out and reading it back, blocking stored IPI, while HumanView preserves original data for humans and tools. DualView routes each tool call to the right view and synchronizes data across the two views. DualView deploys as an OpenClaw plugin using only tool hooks, without changing the agent’s tool-call logic or tool implementations. Since DualView isolates untrusted data by design, its protection is not limited to known attack templates. In our evaluation on an IPI benchmark and PinchBench, DualView blocked every IPI attack, including stored IPI, while keeping utility close to the unprotected baseline. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.03821 [cs.CR] (or arXiv:2607.03821v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2607.03821 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-131] CGGS: Consistency-Augmented Geometric Gaussian Splatting for Ego-centric 3D Scene Generation
链接: https://arxiv.org/abs/2607.03819
作者: Zhenyu Sun,Xiaohan Zhang,Qi Liu,Huan Wang
类目: Graphics (cs.GR); Artificial Intelligence (cs.AI)
备注: 16 pages, 10 figures, 5 tables, IEEE Transactions on Image Processing
Abstract:Challenges remain in ego-centric 3D scene generation due to limited view overlap and the dominant influence of individual perspectives on scene interpretation. These factors hinder the creation of viewpoint-consistent and semantically aligned visual content, as well as the construction of accurate geometric structures. In this paper, we propose CGGS, a text-to-3D framework aiming to enhance 3D-content-awareness and address geometric distortions in ego-centric scene generation. Firstly, the Ego-centric Generator is proposed by fine-tuning a Multi-View Latent Diffusion Model with consistency-augmented loss to generate consistent, high-fidelity 2D content aligned with textual descriptions. Then, Layout Decorator leverages optical flow and point-track correspondence to estimate depth, therefore producing dense point clouds as coarse layouts from the ego-centric 2D priors. Building on this initialization, Geometric Refiner is proposed to enhance 3D Gaussian reconstruction via an entropy-based Mutual Information Depth Loss (MID) combined with a hierarchical optimization scheme for improving visual quality and geometric structure. Comprehensive experiments demonstrate that \textcolorsoftredCGGS outperforms previous methods in generating coherent and accurate text-driven 3D scenes. Project page: this https URL.
[AI-132] Folding Reasoning and Scaling with Open-source Drug Discovery Engine
链接: https://arxiv.org/abs/2607.03787
作者: Aureka AI OpenDDE project
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Biomolecules (q-bio.BM)
备注:
Abstract:Accurately modeling biomolecular interactions is a central bottleneck in biology and therapeutic discovery. Here, we introduce Open Drug Discovery Engine (OpenDDE), an open-source, all-atom biomolecular foundation model that uses co-folding as the entry point to a scalable AI-driven drug discovery engine. Rather than treating structure prediction as an isolated endpoint, OpenDDE is designed as a shared structural reasoning layer for modeling sequence-structure-function relationships across biomolecular complexes, enabling complex structure prediction today while providing a foundation for de novo design, affinity estimation, structure-conditioned optimization, and more. OpenDDE integrates advances in all-atom architecture, atomic latent reasoning, inference optimization, and large-scale data processing to achieve IsoDDE-level co-folding accuracy within a reproducible and openly accessible framework. We also identify two scaling-law directions for co-folding models, revealing practical routes for continued improvement through data, model, inference, and training scaling. By releasing training code, inference pipelines, checkpoints, and benchmarks, OpenDDE aims to democratize access to frontier biomolecular intelligence, accelerate global collaboration, and lay an open foundation for next-generation drug discovery systems that can move from predicting molecular structures toward designing, scoring, and optimizing therapeutic candidates for human health.
[AI-133] SkillFab: An Agent -Native Skill Production Platform
链接: https://arxiv.org/abs/2607.03780
作者: Anjie Xu,Yifeng Cai,Yi Li,Zixing Wang,Zhiyu Zhang,Jingfan Chen,Ruohan Xu,Leye Wang
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 12 pages, 7 figures. Technical report
Abstract:SkillFab is an agent-native platform for turning missing capabilities into reviewed, reusable Agent Skills. At runtime, agents first search for reusable skills; when no adequate skill exists, the unmet capability becomes a demand-first issue before any repository or implementation branch needs to exist. Development then proceeds through a SkillFab-managed repository, Git-ingested commit evidence, maintainer review, and registry publication. The same lifecycle is exposed through web, REST, and MCP surfaces, so humans, scripts, and external agents operate on shared state rather than separate task logs. The current system uses scoped Git push URLs, native range commit ingestion, workflow-state reads, and workflow-event histories to make long-running agent work reviewable and recoverable. We document the platform model, architecture, implemented capabilities, and three case studies: an end-to-end OS-detect skill run, a Docker research package that converts operational practice into reusable skill knowledge, and an external optimization case showing how improved skill artifacts can enter SkillFab as reviewable, versioned submissions. Deployment: this https URL. Comments: 12 pages, 7 figures. Technical report Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.03780 [cs.SE] (or arXiv:2607.03780v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2607.03780 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-134] FedACT: Federated Adaptive Coordinate Trust Modulation for Robust Transformer Training under Data Heterogeneity
链接: https://arxiv.org/abs/2607.03763
作者: Shuai Li,Qinglin Wang,Ping Luo,Jiahuan Wang,Hongyang Hu,Haotian Mo,Yigui Feng,Ziang Liu,Qisong Xiao,Jie Liu,Tao Sun
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 24 pages
Abstract:Federated Transformer training increasingly relies on local AdamW, whose adaptive updates can provide much stronger local progress than SGD-based training. However, under heterogeneous client data, even globally corrected AdamW updates may remain highly uneven in coordinate-wise reliability. We refer to this phenomenon as coordinate trust mismatch. Existing federated adaptive optimizers mainly address mismatch at the client-update or communication-round level, but still apply the corrected adaptive direction densely and uniformly across coordinates. In this paper, we propose FedACT, a global-aware coordinate trust modulation method for federated AdamW training. FedACT first forms a globally corrected adaptive direction and then reallocates update magnitudes according to a coordinate-wise trust score, assigning larger steps to coordinates jointly supported by local gradients and global correction, while preserving smaller non-zero updates on the remaining coordinates. Extensive experiments on federated vision Transformers, CNNs, LLM pre-training, and LLM fine-tuning show that FedACT consistently improves over strong federated adaptive baselines, with the largest gains on Transformer models under stronger data heterogeneity. Mechanism analyses further show that FedACT improves cross-client direction consistency, suggesting that coordinate-level trust allocation effectively complements round-level global-local correction. Code will be released.
[AI-135] Bridging Interleaved Multi-Modal Reasoning as a Unified Decision Process
链接: https://arxiv.org/abs/2607.03748
作者: Zican Hu,Xuyang Hu,Yiming Liu,Zuwei Long,Wei Liu,Yunzhuo Hao,Jiawei Gu,Linjie Li,Yu Cheng,Zhenhong Sun,Weibo Gu,Xing Sun,Zhi Wang
类目: Artificial Intelligence (cs.AI)
备注: 22 pages, 8 figures
Abstract:Unified multi-modal models (UMMs) have shown promising interleaved text-image reasoning capabilities, yet effectively optimizing such multi-turn generation via reinforcement learning (RL) remains an open challenge. Existing approaches apply RL exclusively to text steps, relegating image generation to supervised surrogates, preventing policy gradients from propagating through the full interleaved trajectory across heterogeneous modalities. This leaves the potential of RL for UMMs largely untapped. In the paper, we introduce \textbfBRAID (\textbfBridging inte\textbfRle\textbfAved mult\textbfI-modal reasoning as a unified \textbfDecision process), a simple framework that casts multi-turn text-image-text reasoning as a unified Markov decision process (MDP), enabling joint optimization of textual and visual generation via a single, principled RL objective. BRAID computes a shared trajectory-level advantage and propagates it coherently into both text tokens and image denoising paths, each optimized through its modality-native policy gradient mechanism. To further address long-horizon credit assignment, BRAID employs a vision-language model (VLM) judge that scores each intermediate image on its reasoning utility, supplying dense turn-level feedback to sharpen learning at critical visual branches. Experiments on spatial reasoning and visual perception benchmarks show that BRAID consistently outperforms various baselines, confirming that a unified MDP formulation with vision-thinking guidance is essential for effective multi-modal reasoning.
[AI-136] Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives
链接: https://arxiv.org/abs/2607.03744
作者: Hanie Kang,Huang-Cheng Chou,Sudarsana Reddy Kadiri,Shrikanth Narayanan
类目: Artificial Intelligence (cs.AI)
备注: Submitted to SLT 2026
Abstract:Automatic depression detection from clinical interviews typically models the semantic content and acoustic characteristics of participant speech. However, the interactional timing between the clinician and participant remains comparatively under-modeled. We investigate conversational temporal dynamics, specifically dyadic turn-pair timing, as a primary modality fused with self-supervised encoders. Evaluated on the DAIC-WOZ dataset, we compare a compact 24-dimensional timing module against frozen WavLM-large and RoBERTa-large baseline detectors. This temporal module achieves the highest single-modality performance on the development set. Furthermore, a convex-weighted late fusion strategy improves overall performance to 0.804 and 0.669 macro-F1 on the development and test sets, respectively. The learned fusion effectively assigns zero weight to acoustics, demonstrating that conversational timing serves as a lightweight, interpretable complement for dyadic depression screening.
[AI-137] OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies
链接: https://arxiv.org/abs/2607.03723
作者: Kelin Yu,Haode Zhang,Harish Ravichandar,Yunhai Han,Ruohan Gao
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Project page: this https URL
Abstract:Visual policies learned from human videos, teleoperation, and robot demonstrations offer scalable motion priors, but often fail in contact-rich manipulation, where success significantly depends on local force and contact geometry. Tactile sensing provides these complementary signals, yet tactile data remain costly to collect and hard to generalize across sensors, robots, and tasks. We introduce OmniTacTune, a policy-agnostic real-world RL pipeline that adapts tactile feedback to pretrained visual policies through residual correction. OmniTacTune uses a two-stage design: it first bootstraps tactile-aware learning from autonomous base-policy rollouts, then learns a lightweight tactile residual policy through online interaction. Extensive experiments show that OmniTacTune generalizes across diverse contact-rich tasks, visual base policies, and tactile representations. Across four real-world contact-rich tasks, it improves visual base policies from 5-40% success to 85-100% within 40-80 minutes, demonstrating an efficient path for adapting tactile feedback to scalable visual robot policies. Project page: this https URL
[AI-138] Explainable Reinforcement Learning for Adaptive Traffic Signal Control
链接: https://arxiv.org/abs/2607.03703
作者: Dickens Kwesiga,Nishu Choudhary,Angshuman Guin,Michael Hunter
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Reinforcement Learning (RL) has emerged as a powerful paradigm for adaptive traffic signal control. However, in safety-critical infrastructure like traffic control, the opaque, black-box nature of deep RL models poses challenges for transportation agency acceptance, regulatory compliance, operational trust, troubleshooting, and fine-tuning. To bridge this gap between high-performance optimization and human-comprehensible interpretability, this effort introduces a novel, explainable entity centric RL framework for safe and transparent traffic signal control. Rather than processing traffic states through monolithic, flat vectors, the proposed architecture disaggregates real-time intersection observations into distinct, high-dimensional lane entities and phase temporal configurations to inherently preserve the structural topology and geometric configurations of the intersection. Relational dependencies and inter-lane conflicts are dynamically extracted via a dual-stage attention network featuring sequential multi-head cross-attention and self-attention blocks. This design yields a real time affinity matrix that quantifies the direct influence of signal phases on specific approach volumes and queues, providing full visual and analytical interpretability. To ensure strict operational reliability, a deterministic action-masking interface is integrated directly into the Proximal Policy Optimization pipeline, explicitly blocking invalid phase transitions to guarantee absolute compliance with established signal timing and safety constraints. Evaluated in a microscopic simulation environment, outperforms state-of-the-art baselines in delay minimization. More importantly, the emergent attention weights align precisely with established traffic engineering principles, offering an auditable, trust-enabling, and deployable architecture for next-generation adaptive traffic control systems.
[AI-139] Agent Reinforcement Learning via Pivotal-Aware Self-Feedback Retry
链接: https://arxiv.org/abs/2607.03702
作者: Weiyang Guo,Zesheng Shi,Longhui Zhang,Zeen Zhu,Min Zhang,Jing Li
类目: Artificial Intelligence (cs.AI)
备注: 26pages, 16 figures
Abstract:Large language model (LLM) agents have shown strong decision-making capabilities in long-horizon interactive tasks, yet they still struggle to effectively leverage failed trajectories: full retries incur high interaction costs, while experience retrieval tends to dilute critical experience signals. To address this, we propose PivoARL, a self-feedback retry framework for experience exploitation in LLM agents. PivoARL identifies the pivotal erroneous turn through structured reflection and performs local retry only from the corresponding pivotal state, thereby reusing the correct prefix and reducing redundant interactions. From an information-gain perspective, we further show that pivotal retry concentrates useful experience signals near the error boundary, mitigating the signal dilution caused by state-agnostic experience utilization. Based on this insight, we design a pivotal-aware credit assignment mechanism that rewards correct prefixes while isolating erroneous suffixes, and optimize reflection quality through implicit reflection returns. We conduct a systematic evaluation on 4 agent tasks and 7 search-based QA benchmarks. Results show that PivoARL achieves significant improvements on Pass@2/3 across all tasks, with an average gain of about 11.5% over MetaRL. Moreover, benefiting from contrastive preference signals induced by pivotal turns, PivoARL also consistently improves Pass@1 on over 80% of the tasks. On Minesweeper environment, PivoARL improves over GiGPO by more than 45% and reduces interaction turns by about 42% on average compared with full-retry methods. Code is available at this https URL.
[AI-140] Robust Feasible Route Construction through Collaborative Partition Optimization
链接: https://arxiv.org/abs/2607.03694
作者: Oguzhan Karaahmetoglu,Hyong Kim
类目: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
备注: 24 pages, 3 figures, 3 tables
Abstract:Large-scale Capacitated Vehicle Routing Problems (CVRPs) are commonly solved by partitioning customers into smaller routing problems that can be optimized independently. While this substantially reduces computational complexity, independently constructed routing solutions may leave some customer demand unserved even when sufficient resources exist elsewhere in the fleet. We present Collaborative Routing Constructors (CoRC), a routing framework that enables independently solved subproblems to exchange customers and vehicles during optimization rather than relying solely on a fixed partition or a subsequent global re-optimization stage. Computational experiments on AGS benchmark instances and synthetic instances containing up to 200,000 customers compare CoRC against independent routing, post-routing global re-optimization, and state-of-the-art, end-to-end routing frameworks. Across all evaluated partitioning strategies, CoRC consistently constructs feasible routing solutions where competing partition-based methods do not. Furthermore, it remains effective on problem instances for which the evaluated end-to-end routing frameworks did not produce solutions under the same computational budget. These results demonstrate that collaboration between routing subproblems provides a robust and scalable approach for feasible large-scale route construction.
[AI-141] Dont Blame the Large Language Model: How Scaffolding Evolution Shapes Coding Agent Quality
链接: https://arxiv.org/abs/2607.03691
作者: Oussama Ben Sghaier,Hao Li,Bram Adams,Ahmed E. Hassan
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Coding agents, autonomous systems that use large language models (LLMs) to resolve software engineering tasks, rely on agentic scaffolding: a middleware layer in between a developer and a large language model that orchestrates system prompts, tool execution, context management, and iterative reasoning loops. While these scaffoldings evolve at extreme velocities, no study has examined how this evolution affects agent quality (i.e., effectiveness and efficiency) over time. Practitioners regularly report quality regressions after scaffolding updates, yet consistently attribute them to the underlying model rather than the scaffolding itself. In this paper, we address this gap by conducting the first controlled longitudinal study that isolates the scaffolding’s contribution. Unlike prior work that fixes the scaffolding and varies the model, we fix the model and vary only the scaffolding, evaluating 35 sequential releases to measure their impact on agent effectiveness and efficiency. We first empirically study the development and release evolution of five major open-source scaffoldings (i.e., Codex, Qwen Code, Gemini, OpenCode, and OpenHands), revealing extreme release velocities exceeding two releases per day and thousands of issues within months. We then perform a controlled deep dive into 35 sequential releases of the Qwen Code CLI, evaluating each against 50 stratified SWE-bench Verified tasks while holding the underlying LLM constant. We trace the resulting quality fluctuations to specific development patterns and architectural components, and illustrate our findings with concrete qualitative evidence linking individual pull requests to measured quality shifts.
[AI-142] A Fair Benchmarking of Deep Relational Database Learning Models
链接: https://arxiv.org/abs/2607.03659
作者: Kazi F. Akhter,Bharath Ajendla,Manar D. Samad
类目: Databases (cs.DB); Artificial Intelligence (cs.AI)
备注:
Abstract:Relational databases (RDBs) are the primary data infrastructure in many enterprises, yet recent deep learning methods designed for RDBs have been evaluated under inconsistent experimental protocols, making fair comparison difficult. We present one of the first systematic benchmarking studies of recently released deep learning methods for RDBs, evaluating them across five relational databases, with one classification and one regression task for each. We refactor all deep RDB models to allow the full range of experimental procedures to be applied consistently across all methods. Our findings indicate that the relational transformer (RT) approach delivers the strongest overall performance on both classification and regression tasks compared to the state-of-the-art graph-based modeling and learning of RDBs. Even for single-table learning tasks, deep learning methods designed for RDBs outperform the leading tabular foundation model, TabPFN 2.5. Extending learning from a single table (hop = 0) to multiple tables (hop = 1, 2) by connecting neighboring tables in relational databases enhances performance, but the additional benefit from higher hops diminishes as computational overhead grows. Deep RDB learning methods have the potential to challenge state-of-the-art tabular foundation models, especially on large-scale enterprise data. The source code for this benchmarking study is publicly available.
[AI-143] AutoCedar: An Agent ic Framework for Verifier-Guided Access Control Policy Synthesis
链接: https://arxiv.org/abs/2607.03656
作者: Adarsh Vatsa,Sachi Shome,Yingming Zhou,William Eiers
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 11 pages, 2 figures
Abstract:Large Language Models are increasingly used to turn natural-language requirements into code. In access control, that shortcut is dangerous: a generated policy can compile and read correctly while granting access that no one approved. The difficulty is not only writing policy code. It is fixing what the requirements mean before code is written, and then checking that the final policy actually satisfies that intent. We present AutoCedar, a verifier-guided system that first turns natural-language access-control requirements into a reviewed, checkable target, and then synthesizes Cedar policies against that target. AutoCedar decomposes schema and policy authoring into small intent atoms: reviewable claims about vocabulary and behavior. Once those atoms pass mechanical validation and human intent review, the model proposes a candidate policy, the verifier checks it against the approved target, and each failure is turned into a repair signal that tells the model whether to broaden, narrow, or restructure the policy without changing the target. Because the model’s work is split into small problems, each grounded in reviewed intent and backed by verifier feedback, end-to-end policy authoring becomes tractable. AutoCedar converges on all 221 tasks of CedarBench, our benchmark of authorization tasks paired with executable semantic boundaries. Across three requirements-corpus case studies covering healthcare, education, and conference management, AutoCedar converts noisy prose and extracted access-control fragments into reviewed schemas, formal checks, and a globally verified Cedar policy store for each scenario.
[AI-144] ELiTeFormer: An Efficient Transformer for FPGAs
链接: https://arxiv.org/abs/2607.03652
作者: Victor Agostinelli,Nicolas Bohm Agostini,Antonino Tumeo
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI)
备注:
Abstract:Transformer blocks are prevalent in large language model (LLM) but present deployment challenges due to their challenging computational and memory demands. While prior work has typically optimized attention mechanisms or feed-forward networks (FFNs) separately, few hardware (HW) architecture have jointly addressed both components with co-designed hardware acceleration. We present ELiTeFormer (Efficient Linear Ternary Transformer), the first Transformer model architecture that unifies hybrid linear attention with ultra-low-precision (ternary) linear projections, specifically co-designed for field-programmable gate array (FPGA) deployment. ELiTeFormer achieves 10x model weight compression and 12.8x key-value (KV) cache compression compared to LLaMA 3, while maintaining competitive accuracy (31.9% on the MMLU benchmark, within 3.0% of BitNet b1.58). Our key architectural contribution is a novel processing element (PE) micro-architecture that eliminates all multiplications in ternary linear projections through bitmasking operations, significantly reducing resource utilization by completely avoiding dedicated digital signal processing (DSP) blocks. We simulate, synthesize, and deploy ELiTeFormer targeting a Xilinx VCK5000 Versal board using high-level synthesis (HLS) flows. Block-level simulations show 9.6x speedup for FFN operations and 4.4x speedup for attention compared to standard implementations. End-to-end deployment achieves up to 3.9x lower latency and 3.2x better energy efficiency than LLaMA 3 on an NVIDIA A100 graphics processing unit (GPU) at long context lengths. This represents the first FPGA realization combining linear attention with ternary quantization, demonstrating the viability of algorithm-architecture co-design for next-generation LLM acceleration.
[AI-145] he Role of Rigor in Artificial Intelligence
链接: https://arxiv.org/abs/2607.03634
作者: Timothy Nguyen
类目: Artificial Intelligence (cs.AI)
备注: To appear in D. Rickles and T. Thebault (eds.) Philosophy of Rigor (Routledge, 2026)
Abstract:Artificial intelligence (AI) has achieved extraordinary capabilities despite lacking many of the conceptual and scientific foundations associated with mature disciplines. Unlike traditional sciences, where reliable technology typically emerges from theoretical understanding, modern AI has progressed largely through performance-driven iteration and “alchemical” experimentation. This tension motivates a systematic analysis of AI through the lens of rigor. We introduce a three-part framework consisting of conceptual rigor (clarifying foundational concepts), epistemic rigor (establishing scientific understanding), and operational rigor (ensuring reliable performance and deployment). Using this framework, we analyze competing conceptions of intelligence and understanding, the strengths and limitations of the empirical approach to deep learning, the power and pitfalls of benchmarks, and the obstacles to theory development posed by modern AI systems. We argue that the distinctive trajectory of AI arises from how forms of rigor interact across paradigms, resulting in the primacy of operational rigor in modern deep learning. This perspective helps explain both AI’s rapid advances and its persistent uncertainties, while clarifying the challenges involved in transforming AI into a mature science and reliable technology.
[AI-146] Differentiate the Evaluator Not the Program: An Efficient Runtime Representation for Neuro-Symbolic Learning
链接: https://arxiv.org/abs/2607.03574
作者: Lucas Sheneman
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Programming Languages (cs.PL)
备注:
Abstract:AI systems increasingly propose executable scientific models whose value depends on both their symbolic structure and their fitted continuous parameters. This makes parameter calibration the bottleneck of program-and-parameter co-search: an outer loop can generate thousands of candidate programs, but each needs an inner gradient-based optimization before it can be assessed. Staging each candidate into its own differentiable graph makes individual models fast but sacrifices the program-as-data property that keeps search fluid; interpreter-based approaches preserve programs as runtime data but pay interpreter overhead that dominates the numerical work. We present the Native Differentiable Virtual Machine (NDVM), a runtime representation that differentiates executable programs without compiling each candidate into a separate graph. NDVM separates symbolic structure from differentiable numeric state: tags, symbols, environments, and control remain native runtime data, while numeric payloads live in dense batched buffers with exact reverse-mode gradients recorded along the realized execution trace, so one evaluator walk is amortized across large populations of parameter vectors. A locked cost model of a real differentiable self-hosted Scheme interpreter motivates the design. We realize NDVM as a native runtime with forward and gradient equivalence to the reference backend, about 60x per-lane batch amortization, near-linear multicore scaling, and two independent front ends. In fixed-budget co-search over LLM-proposed programs, NDVM reaches high-quality solutions about 24x sooner in wall-clock time, suggesting runtime differentiation as a practical systems foundation for scientific discovery workflows. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Programming Languages (cs.PL) Cite as: arXiv:2607.03574 [cs.LG] (or arXiv:2607.03574v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.03574 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-147] acher Supervision over Representation Equivalence Classes
链接: https://arxiv.org/abs/2607.03572
作者: Sang Il Han
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Knowledge distillation is usually framed as a choice of what to match in the teacher - its logits, hidden features, or sample relations - which presupposes that the teacher’s representation has absolute coordinates to match. It does not: a pretrained representation is identifiable only up to an orthogonal-and-isotropic-scaling equivalence class, so a student should learn the teacher’s equivalence class, not its features. The organizing fact is that capability is the teacher’s output function, a class invariant that factors through the quotient by the class action, so an objective recovers capability exactly when it is defined there. This makes absolute feature matching ill-posed, and admissible supervision a matter of targeting class invariants (Gram structure, CKA, principal subspaces) or aligning coordinates first, unifying feature matching, relational distillation, alignment, and grafting in one geometric account. We validate our framework on Qwen2.5 and Llama-3.1. A restoration study recovers a corrupted model’s representation (CKA ~ 0.99) but not its capability, and an ablation isolates the cause: output-function (logit) matching drives capability, while matching hidden representations aligns geometry without restoring function. Recovery is confined to the corpus-covered region, and a graft study confirms that boundary overlap predicts transplant success but is necessary, not sufficient.
[AI-148] How to Avoid Debate: Scalable AI Safety via Doubly-Efficient Interactive Proofs ICML2026
链接: https://arxiv.org/abs/2607.03561
作者: Liyan Chen,Yael Tauman Kalai,Zoe Xi
类目: Artificial Intelligence (cs.AI); Computational Complexity (cs.CC); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
备注: ICML 2026
Abstract:As AI models continue to develop powerful capabilities, it becomes critical that we are able to verify that their output is aligned with our intentions. A recent line of work focuses on verification via debate, a model of interactive proofs where two competing powerful provers, or AI models, debate each other to convince a weak verifier, or a human, of the correctness of their claim. However, debate assumes that the two AI models possess equal abilities and that one of them is truthful, which may not be realistic. In this work, we show \emphhow to avoid debate: we initiate the study of \emphsingle-prover interactive proofs for AI safety. Prior results in single-prover interactive proofs do not immediately carry over to the AI safety setting: for example, they do not work when the computation has access to an oracle, such as to human judgment or an external database such as the web. We present doubly-efficient single-prover interactive proofs and arguments for oracle-aided computations (also known as relativizing proofs), in the settings where (1) the computation is robust, in the sense that the output does not change if at most a small fraction of the answers to oracle queries are incorrect, or (2) the oracle is a low-degree polynomial. These results suggest that interactive verification is possible even without debate, under structured or noise-tolerant oracle access. Comments: ICML 2026 Subjects: Artificial Intelligence (cs.AI); Computational Complexity (cs.CC); Cryptography and Security (cs.CR); Machine Learning (cs.LG) Cite as: arXiv:2607.03561 [cs.AI] (or arXiv:2607.03561v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2607.03561 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-149] Applying Answer Set Programming with Fuzzy Membership Functions: a Case Study
链接: https://arxiv.org/abs/2607.03550
作者: Luca Ferragina,Ilenia Galati,Lorena Gullone,Francesco Scarcello
类目: Artificial Intelligence (cs.AI)
备注: A shorter version of this paper has been accepted at the 24th International Workshop on Nonmonotonic Reasoning (July 17-19, 2026, Lisbon, Portugal)
Abstract:Human reasoning often operates through qualitative concepts expressed by linguistic labels such as high, low, expensive, or cheap, whose interpretation depends on context and is usually vague, despite being rooted in numerical data. This paper explores a novel fuzzy-logic-based qualitative extension of Answer Set Programming (ASP) to bridge numerical information and qualitative reasoning. The underlying language, formally introduced in a separate work, provides a principled framework that avoids rigid thresholds and supports robust reasoning under vagueness. Focusing on a representative use case, we illustrate how the framework integrates numerically grounded inputs (such as outputs of machine learning models) with symbolic reasoning over qualitative labels. Key features, including learning-based membership functions and semantically enriched predicates, enable the combination of expert knowledge, contextual factors, and subjective interpretations within a unified declarative setting.
[AI-150] MentalThink: Shaping Thoughts in Mental SVG World
链接: https://arxiv.org/abs/2607.03530
作者: Kangheng Lin,Jisheng Yin,Dingming Li,En Yu,Yana Wei,Han Zhou,Liang Zhao,Hongyu Zhou,Hongbo Peng,Jianjian Sun,Zheng Ge,Xiangyu Zhang,Daxin Jiang,Jingyu Wang
类目: Artificial Intelligence (cs.AI)
备注: 17 pages, 6 figures
Abstract:We introduce MentalThink, a visual-symbolic reasoning paradigm that equips Multimodal LLMs (MLLMs) with an executable mechanism for “mental” visualization. The core of MentalThink is a think-with-SVG pipeline, where the model learns to generate, render, and interpret scalable vector graphics (SVG) code as an intermediate visual representation for multi-turn reasoning. By creating structured vector sketches, the model can externalize spatial hypotheses, inspect them through deterministic rendering, and reason within a constrained geometric space, effectively mimicking the human process of mental imagery. We instantiate this paradigm through a two-stage training framework, combining Supervised Fine-Tuning (SFT) for SVG syntactic alignment with multi-turn Reinforcement Learning (RL) to encourage iterative inspection, revision, and refinement of intermediate visual hypotheses. Extensive evaluations demonstrate that MentalThink achieves superior performance on spatial understanding and reasoning benchmarks (e.g., 55.1% on VSIBench, 76.0% on MindCube), showing that executable vector graphics provide a verifiable visual workspace for dynamic perspective taking, visual reflection, and compositional scene construction.
[AI-151] AGL-1: The Enterprise AI Governance Layer as a Control Plane for Trusted Enterprise Intelligence
链接: https://arxiv.org/abs/2607.03516
作者: Roopam W. Sure
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 16 pages, 1 figure; independent technical report
Abstract:Enterprise artificial intelligence is moving from isolated experimentation toward operational dependency across copilots, retrieval-augmented generation systems, autonomous agents, and AI-enabled business workflows. As this transition accelerates, the primary enterprise challenge is no longer only model access or inference scale. It is governed intelligence operations: the ability to enforce authorization, preserve contextual lineage, control persistent memory, detect stale or conflicting knowledge, constrain agentic execution, and produce audit-ready evidence across distributed AI estates. This paper introduces AGL-1, the Enterprise AI Governance Layer, as a vendor-neutral reference model for the control plane that should operate across foundation models, retrieval systems, orchestration frameworks, enterprise memory, policy engines, observability systems, tools, APIs, and business applications. Building on governed knowledge-system principles introduced in GKS-5, AGL-1 generalizes the governance problem from retrieval-specific controls to full AI execution-path governance. It identifies recurring failure modes such as unauthorized retrieval, stale grounding, unmanaged memory, weak provenance, policy drift, fragmented observability, and uncontrolled autonomous execution. It then defines seven governance domains: identity-aware retrieval, policy enforcement, provenance management, memory governance, knowledge integrity monitoring, agentic execution control, and trust observability. The central claim is that durable enterprise value from AI will increasingly depend on the ability to govern intelligence at scale. In complex enterprises, trust is not a property of the model alone. It is a property of the system around the model: identity, knowledge, policy, memory, tools, human oversight, and evidence working together as a managed control plane. Comments: 16 pages, 1 figure; independent technical report Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Computers and Society (cs.CY) Cite as: arXiv:2607.03516 [cs.SE] (or arXiv:2607.03516v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2607.03516 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Roopam Walia Sure [view email] [v1] Fri, 3 Jul 2026 17:42:08 UTC (392 KB)
[AI-152] CAGE-1: Control Assurance and Governance Evaluation for Enterprise Agent ic AI
链接: https://arxiv.org/abs/2607.03510
作者: Roopam W. Sure
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 17 pages, 3 figures; independent technical report
Abstract:Enterprise artificial intelligence is moving from experimentation into operational workflows. Early programs focused on model access and retrieval-augmented generation, but enterprises are now beginning to deploy agents that plan, retrieve, remember, call tools, update systems, and coordinate work across applications. This changes the evaluation problem. Leaders are no longer asking only whether an answer is accurate or fluent. They need to know who authorized an action, which policy applied, whether evidence was current, whether memory was valid, whether a tool call was permitted, whether the decision can be replayed, and whether the agent can be stopped before it creates business impact. This paper introduces CAGE-1: Control, Assurance, and Governance Evaluation for Enterprise Agentic AI. CAGE-1 is an evaluation framework for deciding whether enterprise agents are ready for deployment. It evaluates authority, policy enforcement, retrieval quality, memory integrity, tool safety, auditability, human oversight, conflict handling, safe failure, Prebind Assurance, operational readiness, and business fitness. CAGE-1 introduces Prebind Assurance to describe the evaluated ability to prove that an agentic action is controlled before it becomes binding, effective, or operationally consequential. The framework tests whether a proposed action is admitted, held, narrowed, refused, escalated, quarantined, or made non-effective before protected consequence forms. Comments: 17 pages, 3 figures; independent technical report Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Computers and Society (cs.CY) Cite as: arXiv:2607.03510 [cs.SE] (or arXiv:2607.03510v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2607.03510 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Roopam Walia Sure [view email] [v1] Fri, 3 Jul 2026 17:37:10 UTC (483 KB)
[AI-153] owards Diverse and Comprehensive Benchmarks for Mutual Information Estimation
链接: https://arxiv.org/abs/2607.03487
作者: Alberto Foresti,Ivan Butakov,Alexander Tolmachev,Giulio Franzese,Alexey Frolov,Pietro Michiardi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Theory (cs.IT)
备注:
Abstract:Mutual information (MI) estimation is a central problem in machine learning and statistics; however, existing benchmarks typically evaluate estimators on simplified, low-dimensional distributions, leaving their performance on complex, realistic data largely unexplored. We address this gap with a comprehensive benchmarking framework grounded in a unified copula-theoretic perspective that subsumes existing benchmarks as special cases. Within this framework, we propose two complementary families of tests: a copula-first family that systematically varies ground-truth MI, dimensionality, and marginal complexity using synthetic and flow-based transformations; and a marginals-first family that couples real-world image data with controlled dependency structures, extending the classic same-class-pairing paradigm. We use this suite to extensively evaluate three classes of estimators: non-parametric, discriminative, and generative. Contrary to prevailing assumptions, our results indicate that there is no universal winner: each category can systematically outperform all other estimators under specific setups. By analyzing these cases, we identify fundamental estimation barriers and propose new tests that more effectively stress these specific limitations. We share the open source code at this https URL.
[AI-154] Demonstrating Generalization Failures via Mixtures of Conditional Policies
链接: https://arxiv.org/abs/2607.03478
作者: Jou Barzdukas,Jack Peck,Julian Schulz,Paulius Rauba,Steven Basart,Lennie Wells
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Post-training of frontier language models is conducted on curated task suites, and inevitably leaves a distribution shift between training and deployment environments. This exposes developers to generalization failures, which are relatively poorly understood. To better understand such generalization failures, we believe the community should construct clean demonstrations under simplified conditions. To facilitate this, we propose a simple and flexible way to construct language models which fail to generalize in controllable ways when subsequently trained with Reinforcement Learning (RL) on a given distribution of training tasks. Our construction uses Supervised Fine-Tuning on a dataset of a mixture of transcripts corresponding to a collection of ‘conditional policies’, which can each independently be assigned certain behaviors on each different task distribution, to obtain a model that is then well approximated as a ‘mixture of conditional policies.’ We observe that RL training then selects for policies that obtain the highest reward on the training distribution. This can produce striking behaviors: in a controlled setting with two distributions containing identical questions prepended with two different ‘trigger strings’, RL training on either distribution actively degrades performance on the other to zero, even though the underlying task is identical. We also use our construction to illustrate two novel ways in which generalization may fail in future language models, corresponding to distribution shifts of task coverage and temporal context respectively. While our construction is deliberately simple and may not closely resemble ‘natural’ generalization failures, the resulting ‘model organisms’ are of interest for alignment stress-testing and generalization science, and can be used as existence proofs that training success and generalization can come apart in structured ways.
[AI-155] Best-of-Better-N: Generating Pre-Aligned Responses with In-Context Learning
链接: https://arxiv.org/abs/2607.03453
作者: Eric Lei,Hsiang Hsu,Chun-Fu Chen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Inference-time alignment methods, such as Best-of- N , offer a flexible alternative to training-based alignment by using reward models to select high-quality responses generated by a reference LLM. However, the efficacy of these methods is inherently limited by the response quality: if the reference LLM assigns negligible probability to high-reward responses, no selection strategy will succeed in finding aligned outputs. In this work, we propose Best-of-Better- N (BoBN), an in context learning-based generation framework to address this challenge. Our method utilizes retrieval from high-reward examples relevant to the input query and task. Crucially, we introduce a restyling step where retrieved responses are rewritten by the reference LLM to align with the target task’s format and style. These restyled examples are used in-context to shift the sampling distribution toward the high-reward region. We analytically characterize how in-context learning shifts the output distribution of pretrained transformers toward the high-reward region, resulting in provable benefits on the target task. We then evaluate BoBN on safety alignment and mathematical reasoning benchmarks across several reference LLMs. BoBN’s higher-quality responses enable better performance to be achieved when the number of responses N is fixed, and smaller N required to achieve a target performance.
[AI-156] SkillOpt-Lite: Better and Faster Agent Self-evolution via One Line of Vibe
链接: https://arxiv.org/abs/2607.03451
作者: Yifei Shen,Bo Li,Xinjie Zhang
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:While skill optimization for autonomous agents has gained traction, existing methods rely on complex pipelines. This leaves a fundamental question unaddressed: What constitutes a minimal viable pipeline for skill optimization, where every component is justified by theory or empirical necessity? We formalize skill optimization via Zeroth-Order (ZO) optimization, mapping classical counterparts (central difference, trust regions) to recent literature. Noting that unlike blind numerical perturbations in classical ZO, skill trajectories serve as interpretable debugging feedback. Grounded in Claude Code philosophy and PAC learning, we establish three principles for convergence and generalization: file-system-based trajectory exploration, consensus attribute mining, and independent validation gating. Eliminating redundancies, we propose SkillOpt-Lite. It accelerates convergence and outperforms full SkillOpt: improving LiveMath by +8.8 points on GPT-5.5 and +25.4 points on GPT-5.4-nano, allowing the nano model to surpass standard GPT-5.4 optimized by SkillOpt. Finally, we integrate our framework into production coding agents like VSCode Copilot, enabling developers to evolve agent skills via one line of vibe. Because our framework treats all agent components simply as standard editable code, this minimal pipeline naturally generalizes to full harness optimization (HarnessOpt). On SpreadsheetBench, HarnessOpt enables GPT-5.4-nano to achieve 0.7758 accuracy, outperforming the larger GPT-5.5 running standard pipelines (0.7620). Code is available at this https URL.
[AI-157] HiMe: Hierarchical Embodied Memory for Long-Horizon Vision-Language-Action Control
链接: https://arxiv.org/abs/2607.03449
作者: Li Ji,Siyin Wang,Pengfang Qian,Xiaopeng Yu,Yihai Tian,Zhaoye Fei,Jingjing Gong,Xipeng Qiu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Current Vision-Language-Action (VLA) models excel at robotic manipulation but often struggle with non-Markovian tasks requiring long-term memory and reasoning due to their reliance on immediate observations. Existing solutions face a ‘‘frequency-competence paradox,’’ where stronger reasoning models are too slow for real-time control, while faster models lack sufficient reasoning capabilities. To resolve this architectural misalignment, we propose HiMe, a Hierarchical Embodied Memory framework that decouples embodied intelligence into a high-frequency Executor for execution, a Sentry for working memory, and a Planner for long-term strategy. We also introduce a dynamic knowledge system based on cross-modal semantic schemas and active management mechanisms, allowing robots to maintain memory plasticity through ‘‘Add, Update, and Delete’’ operations. This hierarchical design effectively balances the conflict between real-time execution and slow thinking planning, significantly improving success rates in long-horizon tasks. Experiments demonstrate that this approach not only outperforms flat memory baselines but also exhibits the novel ability to self-correct its internal knowledge based on human preferences.
[AI-158] No Time Like the Present: Agent ic Test-Time Training for LLM Agents
链接: https://arxiv.org/abs/2607.03441
作者: Yanbo Wang,Jinhua Hao,Yuze Shi,Kun Yuan,Ming Sun
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 15 pages, 10 figures
Abstract:LLM agents often degrade over long episodes: as trajectories grow, they revisit explored states, repeat failed actions, and lose strategies that previously worked. Test-time training (TTT) offers a way to adapt model weights to the evolving task state, but existing LLM TTT methods largely adapt once to a fixed input. We study continuous TTT in multi-turn agent episodes, where each update changes the policy that generates later training text. This creates a self-training loop that helps when new trajectory information appears, but can amplify drift when the agent gets stuck and repeatedly trains on similar text. We find that update-text repetition distinguishes these regimes and introduce Agentic Test-Time Training (aTTT), a token-level reweighting method that downweights the loss on tokens appearing in repeated n -grams from prior updates while leaving novel tokens fully weighted. To run such updates inside live episodes, we build a concurrent serving system using vLLM’s runtime LoRA API, limiting overhead to 1.9 \times the no-TTT cost. aTTT improves success by up to 5.0 points on ALFWorld and 4.9 points on SWE-bench Lite. The gains concentrate where models already have task competence but drift over long trajectories, suggesting that aTTT mainly preserves existing competence rather than teaching new abilities.
[AI-159] Amortising Bayesian Experimental Design for Sequential Information Gathering in LLM s ICML2026
链接: https://arxiv.org/abs/2607.03426
作者: Jakob Hartmann,James Harvey,Jhonathan Navott,Erik Y. Wang,Luckeciano C. Melo,Flaviu Cipcigan,Cheng Zhang,Alessandro Abate
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 20 pages, 7 figures. Accepted to FoGen 2026: Foundations of Deep Generative Models: Understanding Memorization, Generalization, and Reasoning, an ICML 2026 workshop (non-archival)
Abstract:Large language models (LLMs) exhibit strong reasoning and world-knowledge capabilities, yet often struggle to gather information effectively across the multi-turn interactions required in sequential decision-making settings. We introduce Amortised Sequential Information Gathering (ASIG), a fine-tuning approach that amortises Bayesian Experimental Design (BED) into LLM policies via a multi-turn extension of Group Relative Policy Optimisation with an Expected Information Gain reward. Evaluated on the 20 Questions task, ASIG more than doubles the success rate of the 7B base model and reduces inference cost by over 25\times relative to BED-LLM, a competitive inference-time baseline. Applied to MediQ, a medical diagnosis benchmark unseen during training, ASIG improves information-seeking performance at the 7B scale, suggesting that the learned strategies can transfer out of distribution. Our findings show that amortising BED into LLM policies provides an effective and computationally efficient approach to sequential information gathering.
[AI-160] Securing Multi-Tool AI Agent Chains With Dynamic Real-Time Compositional Policies
链接: https://arxiv.org/abs/2607.03423
作者: Chris Schneider,Kriti Faujdar,Philipp Schoenegger,Ben Bariach
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Modern AI agent implementations such as frontier coding agents chain multiple tools at runtime that create a security surface that per-tool guardrails are unable to address, as individually permitted tools can violate organizational policies when composed. We propose the Dynamic Security Control Compositor (DSCC), a two-phase approach to compositional security for multi-tool agent chains. In Phase 1, at session checkout, a Most Restrictive Set (MRS) algorithm composes per-tool security policies into a single effective policy with a formal monotonicity invariant that extending a chain can only tighten the result, blocking incompatible combinations before any tool executes. Outputs of any tool call propagate their classification constraints into a session-level taint state, so subsequent invocations must satisfy the most restrictive constraints seen so far. In Phase 2, at runtime, the system tracks the sensitivity of data the agent touches through a monotonic taint state and revokes the session if the accumulated exposure would make a subsequent tool call a policy violation. Together, these phases provide defense in depth, where static composition prevents unsafe chains from starting, and runtime taint tracking catches violations that emerge from the specific data used. We provide a reference implementation on 32 tools governed by 16 NIST SP 800-53 aligned policies and evaluate it under two composition modes. In the default clearance mode, permitted combinations are partitioned into classification-level clusters, blocking 79.2% of policy pairs and 95.5% of triples. The alternative taint mode admits mixed-classification chains within the exfiltration boundary, blocking 42.5% and 60.5% respectively. We discuss the governance implications for organizations deploying multi-tool agents, including the utility-security tradeoff and the changes needed to operationalize chain-aware policies.
[AI-161] DETECT-3B-Omni is Agnostic of Content and Demographics
链接: https://arxiv.org/abs/2607.03418
作者: Nicolas M. Müller,Aditya Tirumala Bukkapatnam,Dominik Schnieders,Zohaib Ahmed
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注:
Abstract:A trustworthy and GDPR-compliant deepfake audio detector must base its decisions on acoustic artifacts, not on what is being said or who is speaking. We present a large-scale study of semantic independence for Resemble AI’s detector, DETECT-3B-Omni. Using 10,240 audio samples from diverse US English speakers across 30 states, generated through 8 different AI voice-cloning systems, we test whether detection accuracy depends on spoken content (benign versus malicious), speaker gender, speaker age, or speaker region. Using equivalence testing, our results show that the accuracy difference between any two of these groups is at most 2 percentage points, at 99% confidence. The detector therefore identifies AI-generated audio with equivalent accuracy regardless of what the audio says or who the speaker is.
[AI-162] From Mobile Data to Business Insights: An End-to-End Analytics Framework for Large-Scale Urban Mobility Analysis and Decision Support
链接: https://arxiv.org/abs/2607.03394
作者: Thiago Andrade,Shazia Tabassum,Miguel E. P. Silva,Ricardo Dinis,Joao Gama
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注:
Abstract:Real time location data derived from mobile applications is a powerful tool for addressing various urban challenges, including tourism planning, parking management, bus route optimization, and resource allocation. Besides, it offers invaluable insights for shaping strategic decisions in commercial domains such as location based services, market share analysis, and behavioral profiling. In this expansive study, we aim to address all of the aforementioned challenges by investigating the behaviors and patterns of smartphone users within urban environments, particularly in the domains of tourism, transportation, and retail. Our approach encompasses the development of a sophisticated data platform from inception to implementation, which includes the formulation of use cases, architectural design, and implementation of modules. We employ state of the art techniques and technologies, including data anonymization, ETL pipelines, and utilizing Google BigQuery and Vertex AI for data processing and machine learning model development. A modular architecture based on reusable analytical building blocks was developed to generate data products that support multiple stakeholder driven use cases. Additionally, we apply interactive data visualization techniques via Power BI to facilitate the effective interpretation of analytical findings by stakeholders. The developed models address a wide range of mobility analytics tasks, including mobility profiling, frequent trajectory mining, area of influence analysis, traffic anomaly detection, and origin destination pattern analysis. The results demonstrate the framework’s ability to capture user mobility dynamics at fine spatial and temporal resolutions, providing actionable insights for urban planning and strategic business decision making.
[AI-163] When Aggregate Alignment Misleads: Auditing Policy Repair Without Per-State Expert Actions
链接: https://arxiv.org/abs/2607.03386
作者: Peiying Zhu,Sidi Chang
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Agentic AI systems are increasingly used to edit, refine, and repair decision policies, but evaluating these edits is difficult when per-state expert action labels are unavailable. We study this problem in a hotel-pricing simulator where an agentic policy editor receives only region-level diagnostic feedback: summaries of how its price distribution differs from a benchmark policy across time, inventory, and market regions. The editor cannot observe benchmark actions, benchmark source code, reward numbers, or held-out outcomes, and may only propose constrained edits to a target-action table. On 5,000 held-out episodes, a multi-restart LLM editor reaches RevPAR 108.47 (95% CI 107.61 - 109.34), close to the benchmark policy’s 108.75 (107.81 - 109.68), with paired gap (LLM minus benchmark) -0.276 and 95% CI [-0.692, 0.146]. A cheap diagnostic projection already recovers much of the revenue (107.90), so the LLM editor’s distinctive gain is not raw revenue lift alone: it also reduces episode composition distance from 1.153 to 0.609. This is the strongest non-benchmark repair result. This profile is not explained by restart search alone: non-semantic proposers with up to 2,500 evaluations fall 8.77 - 14.57 RevPAR points short. Nor is it explained by plausible prompt format: a shuffled-diagnostic control breaks region-error correspondence and falls to RevPAR 94.30. The match is genuine but partial. A tree editor achieves stronger pooled alignment, 0.214 versus 0.266, and stronger reference-state D1, 0.328 versus 1.197, yet revenue falls to 98.91. These results show that agentic policy repair should be evaluated by whether diagnostic feedback becomes reliable closed-loop outcome, not by a single behavioral distance.
[AI-164] LLM -Enhanced Hierarchical Heterogeneous Graph Representation Learning for Malicious Python Package Detection
链接: https://arxiv.org/abs/2607.03350
作者: Hang Gao,Xiaoyu Chen,Baoquan Cui,Zhen Tang,Peng Qiao,Fengge Wu,Jian Zhang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Malicious Python packages have become a major threat to software supply chain ecosystems due to the widespread adoption of open-source repositories such as PyPI. Existing learning-based detection methods struggle to capture the hierarchical organization and heterogeneous interactions among different program entities. Although Large Language Models (LLMs) have demonstrated strong capabilities in code understanding and semantic reasoning, they are rarely integrated with structural program representations for fine-grained malicious behavior analysis. In this paper, we propose an LLM-enhanced hierarchical heterogeneous graph representation learning framework for malicious Python package detection. The framework constructs a hierarchical heterogeneous code graph that explicitly models heterogeneous code entities and different types of structural dependencies. LLMs are further leveraged to infer function-level semantic roles, introducing an additional layer of semantic heterogeneity. Based on this graph, we develop a hierarchical heterogeneous graph neural network that performs type-aware message passing over different node and edge categories, effectively modeling malicious behavior propagation for accurate package-level classification. The framework also incorporates a function-level attribution mechanism which, combined with LLM reasoning, automatically identifies suspicious functions and localizes fine-grained malicious behaviors without human expert intervention. Extensive experiments on real-world datasets show that our framework consistently outperforms traditional machine learning methods, graph-based detectors, and state-of-the-art LLMs across packages with varying sizes and dependency complexities, while providing accurate, robust, and interpretable malicious behavior localization.
[AI-165] PedestrianDiffusion: Multimodal Generative Denoising and Dense State Estimation for Inertial Navigation
链接: https://arxiv.org/abs/2607.03349
作者: I-Hao Lu,Dongsoo Han
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The accuracy of consumer-grade inertial navigation is bottlenecked by the stochastic noise of Micro-Electro-Mechanical Systems (MEMS). Traditional deterministic neural architectures often succumb to ``estimation jittering,‘’ sacrificing high-frequency kinematic fidelity for numerical stability. We propose PedestrianDiffusion, a multimodal spectral-domain generative framework reformulating dense 6D state estimation as a continuous conditional denoising process. By operating in the frequency domain, our formulation bounds the spectral covariance, acting as a mathematical preconditioner to stabilize the reverse diffusion trajectory. Furthermore, we introduce a zero-shot semantic conditioning mechanism leveraging vision-language embeddings as categorical priors to generalize across heterogeneous sensor noise profiles. To address the computational intractability of generative tracking, we deploy a single-step deterministic probability flow ODE solver ( T=1 ). This yields high-capacity asynchronous batch trajectory refinement, establishing the viability of generative architectures for asynchronous batch trajectory refinement on edge hardware. Extensive evaluations on the OxIOD, RIDI, RoNIN, and TLIO benchmarks demonstrate that PedestrianDiffusion achieves state-of-the-art performance, exhibiting unprecedented robustness to impulse perturbations and coupled 6D kinematic drift. This work provides a rigorous algorithmic blueprint for next-generation Neural Inertial Measurement Units (N-IMUs).
[AI-166] FedAvg for HAR: Exploring the Tradeoff Between Personalized and Generalization Accuracy
链接: https://arxiv.org/abs/2607.03334
作者: Andrea De Luna,Susanna Peretti,Chiara Contoli,Alessandro Bogliolo
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The federated learning (FL) paradigm fosters distributed pervasive computing combined with artificial intelligence techniques, allowing for optimized data usage and improved mitigation of privacy concerns. Indeed, model training occurs on the client’s local devices, and model parameters are subsequently shared with a centralized server. However, there is a need to find a tradeoff between models’ personalization and generalization capabilities. In this paper, we design and implement several testing scenarios devoted to evaluating and comparing the centralized, local, and federated paradigm performances. We also design and implement a scenario that emulates a change in clients’ data. We then present experimental results of the FedAvg algorithm applied to the Human Activity Recognition (HAR) domain to understand the trade-off between personalized and generalized accuracy. Results show that, although FedAvg confirms a higher degree of personalization capabilities while keeping a high degree of generalization with respect to the traditional centralized learning, this result is not so obvious under stressful conditions, such as when varying class distribution over clients.
[AI-167] SPORK: Self-Speculative Forking to Accelerate Agent ic LLM Inference
链接: https://arxiv.org/abs/2607.03333
作者: Huajun Bai,Weiwei Lv,Huichuan Zheng,Youyou Lu,Jiwu Shu
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 16 pages, 15 figures. Code: this https URL
Abstract:LLM agents are becoming a common interface for research, coding, and question answering, yet their Thought-Action-Observation loop is often serial: the model reasons, emits a tool call, then idles the GPU until the result returns. This wait consumes 16-37% of wall time in our workloads and 35-61% in prior reports. Speculative tool execution can hide this wait, but existing systems need auxiliary predictors, historical traces, or static workflow graphs, leaving a gap for training-free, day-one deployment. We observe that the model can be its own predictor: a probe forked at the start of generation predicts Qwen3-32B’s upcoming tool name with 74.6-99.6% accuracy across five benchmarks. We present SPORK (Self-sPeculative fORKing), a training-free controller that dispatches the speculated tool call early, overlapping its execution with the remaining chain-of-thought decode. A cost model captures when speculation breaks even, and each component improves one of its terms: a prefix-cache fork cuts probe cost, a confidence gate filters mispredictions, and partial-token accept turns rejected probes into speculative-decoding drafts. On acceptance, the tool result is ready when reasoning ends; on rejection, SPORK falls back to serial execution with no correctness penalty. On real-tool benchmarks, SPORK cuts Qwen3-32B’s GAIA P95 by 18% (131.9 to 108.1 s); the mechanism holds across model sizes from 4B to 32B and across dense and mixture-of-experts models, with task accuracy within 1 pp of baseline or better wherever measured. SPORK deploys as a thin controller over standard completion APIs (no retraining, no auxiliary models, no offline traces) and is orthogonal to token-level speculative decoding. SPORK is open source at this https URL.
[AI-168] Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems
链接: https://arxiv.org/abs/2607.03324
作者: Hyunsoo Lee,Panggah Prabawa,Dae-Hyun Choi,Joongheon Kim
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI)
备注:
Abstract:Eco-friendly energy management for artificial intelligence data centers (AIDCs) is crucial because of the significant increase in energy consumption-induced carbon emissions from AIDCs resulting from the rapid expansion of AI applications. This paper proposes a hierarchical carbon-aware multi-agent reinforcement learning (CA-MARL) framework for robust and efficient operations of AIDCs under uncertainties while ensuring low-carbon operation of power distribution systems. The framework comprises a workload manager (WM) agent and multiple local AIDC agents trained using a multi-agent transformer method, corresponding to a global AIDC aggregator and a local AIDC operator, respectively. Leveraging AIDC operation data along with nodal carbon intensity (NCI) calculated from the carbon emission flow-integrated distribution system operator problem, the WM agent spatially allocates AI training and inference jobs among all AIDCs. Based on the jobs allocated from the WM agent and NCI information, each AIDC agent schedules economical and eco-friendly operations of the AIDC by performing the following tasks: i) temporal shifting of training jobs, ii) spatial allocation of training graphics processing unit (GPU) blocks and inference GPUs within the AIDC, and iii) control of the supply air temperature of the cooling system. The effectiveness of the proposed framework was assessed using an IEEE 33-node power distribution system.
[AI-169] Is Agent ic Code Review Helpful? Mining Developers Feedback to CodeRabbit Reviews in the Wild
链接: https://arxiv.org/abs/2607.03316
作者: Hong Yi Lin,Mingzhao Liang,Kla Tantithamthavorn,Patanamon Thongtanunam
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Agentic code review, where autonomous agents provide code review comments on pull requests, is increasingly integrated into development workflows, yet there is limited empirical evidence on how developers respond to such comments in practice. In this paper, we present an empirical study of agentic code reviews using CodeRabbit as a case study. Through an empirical study of 31,073 pairs of code reviews and developer feedback from 10,191 pull requests across 239 GitHub repositories, our results show that agentic reviews receive mixed reception: 36.4% were accepted and 7.3% triggered discussion, while 56.3% were rejected. Rejections were primarily associated with invalid suggestions that were false positives, redundant, or out of scope, as well as misalignment with developer intent and coding practices. We further found that agentic reviews tend to focus more on functional concerns than evolvability-related comments, yet they were more likely to be invalid. To improve effectiveness in review practices, we explored various LLM-based approaches for predicting review rejection. We found that lightweight learning-based methods achieve up to 76% F1 score, suggesting learnable patterns exist between code reviews and their corresponding feedback. Our results highlight the current state of CodeRabbit’s agentic code reviews, showing opportunity gaps for improvement, as well as shortcomings hindering its effectiveness.
[AI-170] Reflective Dialogue or Prompt Refinement? Effects of Tutor Scaffolding on Students Independent LLM Use for Programming
链接: https://arxiv.org/abs/2607.03303
作者: Jerome Brender,Laila El-Hamamsy,Kim Uittenhove,Aitor Perez,Patrick Jermann,Francesco Mondada,Engin Bumbacher
类目: Artificial Intelligence (cs.AI)
备注: Best paper award, Published in AIED 2026: The 27th International Conference on Artificial Intelligence in Education
Abstract:While Large Language Models (LLMs) can provide personalized support in learning, several studies have raised concerns regarding their use in education. Importantly, learning depends on how students engage with LLMs. This study examined how two types of LLM-based tutors shape students’ prompting practices, learning, and subsequent LLM-use: a Socratic-Guidance (SG) tutor, which structures interaction through dialogic questioning, and a Prompt-Refinement (PR) tutor that guides the formulation of effective prompts. We conducted a two-phase study in a graduate-level mobile robotics course: 66 students used either the SG or PR tutor during a 6-week intervention, followed by 52 students using an unconstrained LLM during a 3-week course project. Results show that while the SG- and PR tutors led to similar task performance and prompting patterns during guided use, they differ in learning outcomes and later LLM-use. SG-students, relative to PR-student, achieved higher learning gains in later sessions, and were more likely to adopt understanding-driven prompting strategies, which are predictive of higher understanding, when using an unconstrained LLM. Although learners perceived the SG tutor as less efficient, the findings suggest that Socratic guidance supports the development of students’ capacity to learn with LLMs over time, highlighting its importance for LLM tutor design.
[AI-171] A harmonised dataset for Earth system foundation models
链接: https://arxiv.org/abs/2607.03298
作者: Carlos Rodriguez-Pardo,Massimo Tavoni
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); General Economics (econ.GN); Atmospheric and Oceanic Physics (physics.ao-ph)
备注: Under review
Abstract:Foundation models for Earth systems have so far been trained primarily on physical climate and weather data, with limited representation of the human systems that both drive and respond to environmental change. The lack of a unified global training resource that combines climate, land, ocean, cryosphere, infrastructure, hazards, and socioeconomic data on a common grid hinders progress toward truly multimodal Earth system foundation models. We present WorldTensor, a harmonised global dataset that aligns hundreds of environmental and socioeconomic variables to a standardised 0.25 ^\circ spatial grid and annual temporal framework. WorldTensor integrates reanalysis products, remote sensing, emissions inventories, land use reconstructions, hydrological observations, infrastructure and hazard datasets, and socioeconomic indicators within a single representation designed for machine learning workflows. To build the dataset, we regridded inputs across heterogeneous native resolutions and projections, rasterised point and vector datasets into spatially meaningful gridded fields, and reconciled temporal coverages ranging from daily observations to sparse multiyear socioeconomic snapshots. All outputs are distributed as NetCDF files with standardised coordinates, variable metadata, and a common CF metadata convention. WorldTensor provides a reproducible resource for training and evaluating foundation models that learn coupled dynamics across environmental and human systems at planetary scale.
[AI-172] Embodied Operators and Benchmarking: Toward Reusable and Deployable Embodied Intelligence Systems
链接: https://arxiv.org/abs/2607.03283
作者: Junwu Xiong,Jiaxuan Gao,Wei Chai,Renxing Chen,Yuzhen Li,Yu Guo,Yucheng Guo,Mingxi Luo,Wenyang Ma,Yiyun Mou,Yifei Zhang,Chen Zhou,Yongjian Guo
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Embodied intelligence systems require not only end-to-end policy models, but also reusable functional modules that transform multimodal observations, robot states, human demonstrations, and task contexts into structured representations, decisions, trajectories, control references, and system services. This work defines these modules as embodied operators and studies them as independent yet composable units in embodied intelligence pipelines. We clarify their definition boundary, emphasizing task semantics, standardized input-output contracts, deployability, reusability, and multi-layer optimizability. We further construct a taxonomy covering five categories: detection and segmentation, spatial localization and 3D understanding, hand motion recovery, embodied foundation models and task-decision operators, and planning, control, and system support operators. For each category, we summarize representative functions, technical paradigms, application roles, and practical limitations. Beyond taxonomy, we propose a multi-dimensional benchmark framework that evaluates embodied operators in terms of correctness, end-to-end efficiency, resource usage, temporal stability, portability, interface compatibility, deployment reliability, and downstream task utility. We also discuss workflow-level operator acceleration and open challenges in operator composition, data standardization, world models, VLA safety, edge deployment, and real-world application value. Overall, this work argues that embodied operators should be optimized and evaluated as holistic deployable components, providing a foundation for reusable, scalable, and verifiable embodied intelligence systems.
[AI-173] Unbiased Alignment for Large Language Models with Noisy Preferences ICML2026
链接: https://arxiv.org/abs/2607.03248
作者: Jialiang Wang,Xianming Liu,Xiong Zhou,Hui Liu,Haoliang Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted by ICML2026
Abstract:The alignment of large language models with human preferences is commonly achieved through Reinforcement Learning from Human Feedback or Direct Preference Optimization. However, these methods are vulnerable to the significant noise prevalent in real-world preference datasets. To address this critical issue, we present a theoretical framework for unbiased alignment, introducing the Unbiased Reward Model (URM) loss and the Unbiased Direct Preference Optimization (UDPO) loss. By mathematically correcting the distortion induced by preference noise, our novel objectives enable unbiased model training directly from noisy datasets, without requiring clean ground-truth supervision. We provide rigorous theoretical analyses demonstrating that our methods are noise-tolerant, parameter downward compatible, and classification-calibrated. Comprehensive experiments across diverse datasets demonstrate that our approaches outperform state-of-the-art baselines. Code available at: this https URL.
[AI-174] CONTRA: Red-Teaming Configurations of Personalizable Agents
链接: https://arxiv.org/abs/2607.03220
作者: Jonathan Nöther,Adish Singla,Goran Radanovic
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent tools such as OpenClaw have extended the capabilities of LLM-based agents from simple dialog-based systems to fully autonomous agents. These systems allow personalization of the agent through modifiable internal files and the installation of skills. While this enables deployment in a wide range of settings and the automation of diverse tasks, greater capability and autonomy increases the risk of malicious actions being executed unintentionally. In this work, we explore the interplay between agent configuration and the risk of executing dangerous actions without explicit instruction. To this end, we propose CONfiguration Tree-search for Red-teaming Agents (CONTRA), an LLM-assisted tree-search algorithm that discovers agent configurations resulting in the execution of malicious actions. CONTRA works by reasoning about benign yet dangerous configurations and evaluating them in a simulated environment. We construct a dataset of the 473 most popular skills from a public repository, along with 2-5 corresponding malicious target actions per skill. In a large-scale analysis, we find that 75.1% of skills have at least one configuration resulting in the execution of a malicious action, most of which have not been detected as containing malicious content by existing scans. Overall, CONTRA successfully identifies a configuration leading to the execution of the target action in 39.2% of all tested cases. Our findings demonstrate that current agents provide insufficient safety with respect to personalization.
[AI-175] Builder Defender Breaker: The Case Against Removing the Human from the AI-Driven Security Lifecycle
链接: https://arxiv.org/abs/2607.03215
作者: Mohamed Chahine Ghanem
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 7 pages
Abstract:Artificial intelligence has spread across the whole of the security lifecycle. The same family of models now writes application code, hardens it, and probes it for weaknesses, so that a single generative substrate increasingly performs all three roles at once. Enthusiasm for this convergence tends to treat full autonomy as the natural end point of partial assistance. This article argues that it is not. When the system that builds an artifact is drawn from the same distribution as the systems that defend and test it, the three roles inherit a common set of blind spots, and the independence that makes verification meaningful is quietly lost. Removing the human does more than raise the automation level: it collapses the external oracle against which machine output is judged, outruns the point at which a person could intervene, hands adversaries a predictable and poisonable target, and dissolves the locus of accountability when something fails. Drawing on evidence from autonomous code generation, adversarial machine learning, software fault tolerance, and the first all-machine hacking tournaments, we argue that the human belongs in the loop not as a temporary scaffold but as a permanent structural requirement, and set out what a defensible division of labour between people and machines should preserve.
[AI-176] A Bayesian Framework for Evaluating Scenario Compatibility in Generative Population Synthesis ITSC
链接: https://arxiv.org/abs/2607.03190
作者: Zhenlin Qin,Leizhen Wang,Yancheng Ling,Zhenliang Ma
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted for publication in the Proceedings of the 2026 IEEE International Conference on Intelligent Transportation Systems (ITSC)
Abstract:Scenario-based transportation analysis specifies future assumptions through aggregate population targets, whereas generative population synthesis models produce detailed individual-level realizations. When scenario targets are imposed on generative models, current practice relies on deterministic marginal calibration, implicitly assuming that the targets are compatible with the model’s learned structural support. However, whether scenario-level constraints lie within the generative support–and how strongly they distort structural uncertainty–remains largely unexamined. We propose an ensemble-based Bayesian updating framework to quantify scenario compatibility in conditional population synthesis. A population-aware conditional variational autoencoder is developed to learn a distribution over plausible population structures while preserving aggregate fidelity. An ensemble of realizations sampled from the learned prior provides an empirical approximation of structural uncertainty. Scenario targets are treated as probabilistic evidence over aggregate statistics, and posterior weights are obtained through Bayesian updating across the ensemble. Scenario compatibility is quantified using effective sample size (ESS), which measures posterior concentration and the compression of structural uncertainty induced by conditioning. Experiments demonstrate that scenario impact depends not only on target magnitude but also on alignment with the learned joint structure, and reveal structural failure modes when targets fall outside prior ensemble support. The proposed framework provides a probabilistic diagnostic model for evaluating scenario feasibility and structural consistency before downstream projection and transportation planning.
[AI-177] Scalable Maximal Frequent Episode Mining with Desbordante
链接: https://arxiv.org/abs/2607.03188
作者: Maxim Ivanov,Matvei Smirnov,Alisa Strazdina,George Chernishev
类目: Databases (cs.DB); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Performance (cs.PF)
备注:
Abstract:Episode mining aims to extract subsequences of events that possess certain distinctive properties and constitute facts valuable to the user. Maximal frequent episode mining concentrates on discovery of frequently-appearing subsequences, which are not included into any other larger frequent subsequence. The state-of-the-art for this problem is the MaxFEM algorithm which enumerates possible subsequences, while applying various pruning techniques to accelerate the search. However, this is a computationally-intensive problem: reducing the minimum number of required subsequence occurrences or increasing the length of the subsequence both substantially raise running time, which limits practical use of MaxFEM. In this paper we describe our efforts in designing a high-performing algorithm for this problem. For this we: 1) develop an efficient C++ implementation of MaxFEM, and 2) devise an efficient technique to parallelizing it. As the result, we propose an improved parallel MaxFEM variant, which we call ParMaxFEM. Additionally, we integrate the improved algorithm into Desbordante - a high-performance, open-source data profiler with deep Python integration that treats patterns as first-class entities and allows users to develop their custom programs that can include discovery and validation of patterns. To evaluate our approach we compare both C++ implementations with the original SPMF implementation. Experiments demonstrated that our reimplemented version provides up to 8\times speedup over the SPMF baseline, while our parallelization technique provides up to 35\times improvement overall (on 8 cores).
[AI-178] AnchorVLA: Bridging Discrete Decisions and Continuous Trajectories for Vision-Language-Action Planning
链接: https://arxiv.org/abs/2607.03182
作者: Qi Liu,Yabei Li,Hongsong Wang,Heng Zhang,Lei He
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Autonomous driving planning requires translating navigation intent, traffic rules, dynamic interactions, and language instructions into executable continuous trajectories. Vision-Language-Action models have been introduced into driving planning to improve long-tail generalization, commonsense reasoning, high-level semantic understanding, and explainability. However, existing VLA planners mainly follow planning-head-based trajectory prediction or full-trajectory autoregressive generation. The former only weakly constrains continuous trajectory generation with VLA reasoning, while the latter relies on long sequences of low-information-density coordinate tokens, making semantic-action alignment difficult and leading to discretization errors and inefficient inference. To address these limitations, we propose AnchorVLA, a hierarchical decision-anchored VLA planning framework that uses trajectory-pattern anchors as an explicit interface between high-level VLA reasoning and continuous trajectory execution. Specifically, Decision-as-Anchor Representation represents behavior-level driving decisions with anchor tokens, each encoding an entire local motion pattern rather than a single coordinate point. Decision-Anchored Residual Flow then generates fine-grained continuous trajectories in the selected anchor-defined residual space, capturing multi-modal execution refinements after high-level decision making. By reasoning over compact and semantically meaningful anchors instead of autoregressively generating waypoint sequences, AnchorVLA preserves LLM-based decision making while improving inference efficiency, semantic-action alignment, and continuous generation flexibility. Experiments on the Bench2Drive closed-loop benchmark show that AnchorVLA achieves a state-of-the-art Success Rate of 77.28 and a competitive Driving Score of 89.92.
[AI-179] aming Up with AI: Coordination and Cooperation
链接: https://arxiv.org/abs/2607.03181
作者: Nicole Immorlica,Inbal Talgam-Cohen
类目: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI)
备注:
Abstract:Successful diffusion of AI in the workforce hinges on the economic value that AI brings to human endeavors. Bringing AI into the workforce is more than deploying a powerful new technology – it is launching a new form of collaboration. Each human worker is now endowed with a team of AI agents; work can be delegated to these agents, and the role of the human shifts towards managing and monitoring. How can we maximize the economic value from collaboration with AI in the workforce? How can we make it a “true” collaboration that empowers human workers rather than replacing them? We take an approach that combines the fields of theoretical computer science and economics, highlighting the potential of algorithmic tools grounded in economic principles to improve the effectiveness of human-AI collective work. We consider two tiers of tools: (1) tools for better coordination, via algorithmic management of interdependencies; (2) tools for better cooperation, via contractual incentive alignment. We show how a principled approach based on algorithmic and economic research enhances both coordination and cooperation, charting a pathway for future research to inform AI markets. Subjects: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.03181 [cs.GT] (or arXiv:2607.03181v1 [cs.GT] for this version) https://doi.org/10.48550/arXiv.2607.03181 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-180] CRRL: A Causality-Based Reinforcement Learning Framework for Autonomous System Recovery
链接: https://arxiv.org/abs/2607.03177
作者: Safia Fatima,Kai Olav Ellefsen,Leon Moonen
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 30 pages
Abstract:Traditional reinforcement learning (RL) for recovery in autonomous systems lacks causal understanding and generalizes poorly to novel failure scenarios. RL policies often stall in failure states, spending up to 70% of an episode immobilized. Rule-based recovery alone is inadequate, and adding heuristic recovery to a pretrained PPO policy worsens rewards because policies cannot coordinate well with unanticipated interventions. The issue is not missing recovery mechanisms but a lack of policies trained to collaborate with them. We introduce CRRL, a causal-guided RL framework that trains policies to work effectively with rule-based recovery. The recovery detects stalled states and assists the agent. Causal relations from driving logs shape the training signal, teaching the policy to anticipate stalls and adjust actions in recovery contexts. The framework follows MAPE-K, with sensor collection, causal model construction, and hybrid RL policy training corresponding to Monitor, Analyze, and Plan/Execute, respectively. We evaluate CRRL through a four-condition ablation study across three driving scenarios, with 20 episodes per condition. We find that causal training significantly improves reward, distance, and velocity. Moreover, 9 of 20 roundabout episodes required zero recovery intervention, confirming navigation competence. These results show that causal-guided training produces effective RL policies that cooperate with rule-based safety components. Comments: 30 pages Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2607.03177 [cs.SE] (or arXiv:2607.03177v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2607.03177 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-181] Effectiveness of LLM -based Software Diversity for Reliability Improvement – an Empirical Study
链接: https://arxiv.org/abs/2607.03174
作者: Gabriel Almeida,Ilir Gashi,Vladimir Stankovic,João R. Campos
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Software diversity has been extensively studied as a means of reducing the risk of common-mode failures. Classic work showed that the central issue is whether failures of diversely redundant components overlap in ways that limit the reliability gains. Traditional software diversity is costly to obtain, since it requires multiple implementations as well as the corresponding validation, maintenance, and deployment effort. Recent advances in Large Language Models (LLMs) may change this. LLMs enable inexpensive code generation: they produce many candidate implementations of the same specification quickly, across different models, decoding settings, and programming languages. This raises a natural question: can LLMs serve as practical generators of software diversity, and how much reliability improvement can that diversity actually provide? In this paper, we extend classical empirical studies of software diversity in human-written programs to LLM-generated code. We study three specifications using both historical human-written programs and large pools of LLM-generated ones evaluated under a common compilation, sandboxing, and exhaustive test suite. We explore LLM diversity along multiple axes, including model family, generation temperature, and programming language. Reliability improvement is evaluated in a 1-out-of-2 configuration across both homogeneous and heterogeneous program populations, including within-LLM pairings and pairings across programming languages and across LLM-generated and human-written programs. The results show that combining LLM-generated programs, especially in heterogeneous settings, can yield reliability gains, although this is partly conditioned by the programming language and generation setting. Taken together, these findings suggest that LLMs provide a scalable source of comparatively low-cost programs whose diversity can be leveraged for reliability improvement.
[AI-182] Decentralised Federated Learning over Temporal Networks: The Role of Heterogeneities
链接: https://arxiv.org/abs/2607.03171
作者: Arash Badie-Modiri,Chiara Boldrini,Lorenzo Valerio,János Kertész,Márton Karsai
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注:
Abstract:Decentralised federated learning, based on peer-to-peer communication, is increasingly proposed for on-device training of machine learning models, promising a privacy-preserving, communication-efficient training process with no risk of single-point failure. However, the role of structural and temporal inhomogeneities in such fully decentralised settings remains poorly understood. Here, we investigate their effects when model parameters are locally averaged during aggregation. We show that the decentralised federated learning process is governed, both in the early phase and the late, stationary limit, by the same dynamics as a lazy random-walk diffusion process on temporal networks. Based on this mapping, we demonstrate that the typical experimental scenario used in decentralised federated learning leads to unrealistically rapid convergence because of ignoring the temporal and structural inhomogeneities inherent in the communication network. We analyse real-world temporal networks and find that inhomogeneities most often dramatically slow down diffusion, hence the convergence process.
[AI-183] Which Algorithm Specification Formats Help Language Models Implement Machine Learning Algorithms?
链接: https://arxiv.org/abs/2607.03158
作者: Masahiro Kato,Taka Kato
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Large language models (LLMs) are increasingly used to implement algorithms from research manuscripts, but papers often leave implementation choices implicit. This study examines how the written format of an algorithm specification affects first-pass LLM implementation accuracy. We compare ordinary prose, LaTeX algorithm-style pseudocode, PDF-like extracted pseudocode, Markdown fields, YAML-like specifications, JSON-like specifications, and Python code stubs across five machine learning tasks, three models, and four experimental settings, yielding 4,020 generated implementations. Hidden tests evaluate details that often determine correctness, including tie-breaking, array shapes, numerical rules, return structures, and invalid-input behavior. Under the core-information setting, LaTeX algorithm-style pseudocode has the largest average format effect, with YAML-like specifications and ordinary prose close behind. Under complete information, GPT-5.4 mini shows no format differences in the matched comparisons, whereas Gemma 3 4B and Llama 3.2 3B still do. Code stubs do not consistently improve correctness despite specifying the function signature. The results support a writing recommendation: authors should state the interface, computation steps, numerical rules, and boundary-case behavior explicitly, instead of relying on a particular surface format to carry those details.
[AI-184] Rethinking Neural Nonlinearity as Gating
链接: https://arxiv.org/abs/2607.03148
作者: Muhammad Sabih,Frank Hannig,Jürgen Teich
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Activation functions are considered an essential primitive for neural nonlinearity, i.e., they enable neural networks to serve as universal approximators. In this paper, we show that this nonlinearity can also be achieved by input-conditioned threshold gating through branches as a universal primitive. We demonstrate that standard activations – whether piecewise-linear (ReLU, PReLU, Hardtanh) or smooth (SiLU, Sigmoid, Tanh, GELU) – are in fact instances of a single Threshold Gating (TG) primitive. For softmax, we show that it admits an exact TG conversion via its equivalent per-element Sigmoid form. We then validate these equivalences by converting pretrained networks across CNNs, transformer-based models, and recurrent architectures, preserving model performance without requiring retraining. Threshold Gating also enables training from scratch that goes beyond replacing existing activations, enabling gains in model compression, performance, and shorter training. We also propose a ‘Minimal Branch Theorem’ which relates the minimum number of required branches in our primitive to the trainability of general deep neural networks. In terms of hardware implementation, TG maps to a unified implementation in the case of analog in-memory systems, addressing the bottleneck of analog-to-digital and digital-to-analog converters (ADC/DAC) that is known to significantly impact power consumption and on-chip area.
[AI-185] Detecting Architectural Drift in Safety-Critical Firmware through Runtime Trace Analysis
链接: https://arxiv.org/abs/2607.03135
作者: Domenico Francesco De Angelis,Marco De Luca,Domenico Amalfitano,Pasquale Cimmino,Anna Rita Fasolino
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: accepted at 42nd IEEE International Conference on Software Maintenance and Evolution (ICSME 2026)
Abstract:Maintaining consistency between architectural design and runtime-observed behavior is challenging in long-lived safety-critical firmware. This paper presents a runtime-informed methodology for detecting architectural drift in ISO 26262-compliant firmware. The approach collects hardware-assisted execution traces, abstracts them into message exchanges among firmware components, and compares the resulting runtime behavior with design-time sequence diagrams through a deterministic differencing step. The computed delta identifies discrepancies as confirmed, missing, additional, or inverted, while a constrained LLM-based step generates a human-readable report only to support expert review. We evaluate the methodology in an industrial firmware context through agreement-based validation and a practitioner survey. Results over 26 test cases show strong agreement between the generated deltas and expert-curated references, while practitioners perceive the reports as useful for interpreting drift, reducing manual analysis effort, and supporting safety-oriented documentation activities. The findings suggest that combining runtime trace analysis, deterministic architectural differencing, and constrained LLM-based reporting can practically support architectural drift detection in evolving safety-critical firmware.
[AI-186] ACPO: Adaptive Credit Policy Optimization via Fine-Grained Surrogate Entropy
链接: https://arxiv.org/abs/2607.03126
作者: Zijun Xie,Yuyang You,Yongzhi Li,Enlei Gong,Zeyu Chen,Quan Chen,Yanhua Cheng,Peng Jiang,Yadong Mu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Reinforcement Learning (RL) has substantially improved the reasoning ability of large language models (LLMs), but sparse outcome rewards still make token-level credit assignment difficult. Existing scalable RL methods typically assign trajectory-level rewards uniformly across tokens, while recent entropy-aware approaches either rely on coarse detached heuristics or directly optimize true entropy, which can introduce non-local gradient components misaligned with sampled-token policy updates. We propose Adaptive Credit Policy Optimization (ACPO), a token-level credit assignment framework based on a mode-local surrogate entropy. ACPO asymmetrically modulates policy updates by emphasizing uncertain decisions in successful rollouts and overconfident tokens in failed rollouts. We show that the surrogate admits deterministic entropy bounds and, under modal alignment and proximal updates, preserves the policy-gradient direction to leading order. Experiments on mathematical reasoning and coding benchmarks, including AIME 2025 and HumanEvalPro, show that ACPO consistently improves over strong RL baselines such as DAPO, GTPO, and SAPO.
[AI-187] Flow-A11y: Flow-Aware Accessibility Testing
链接: https://arxiv.org/abs/2607.03100
作者: Nasr Eddine Fliti,Leisan Kokorina,Florian Tambon,Michael Papadakis
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Modern web applications increasingly expose accessibility barriers through interaction flows rather than static page snapshots. Keyboard traps, focus loss, modal leakage, delayed status updates, dynamic controls, and changing page regions often become observable only after users perform concrete actions. These behaviors are directly related to dynamic WCAG criteria, yet they remain difficult to automate because their assessment depends on runtime interaction evidence and is still commonly performed through manual inspection. We present Flow-A11y, a flow-aware accessibility testing system for interaction-dependent WCAG criteria. Given a target page and a natural-language scenario, Flow-A11y executes the flow in a real browser, records an ordered runtime trace, constructs criterion-specific evidence packets, gates unsupported judgments, and emits auditable findings grounded in resolvable runtime evidence. Evaluated on 19 real public-web scenarios covering 45 dynamic WCAG criteria, Flow-A11y achieves over ten times higher oracle agreement than a generic browser-agent audit, while its evidence-calibration layer improves fail precision from 23.5% to 41.4% and eliminates invalid evidence references. These results show that runtime traces provide actionable evidence for assessing interaction-dependent accessibility behavior. They demonstrate a practical path toward automating dynamic WCAG criteria that page-level scanners cannot assess and that have traditionally required manual evaluation.
[AI-188] STELLA: Efficient Sensor-to-LLM Translation for On-Device Human Activity Recognition
链接: https://arxiv.org/abs/2607.03089
作者: Nirhoshan Sivaroopan,Albert Zomaya,Kanchana Thilakarathna
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:HAR is increasingly expected to run continuously on edge devices, yet recent LLM-based methods remain hard to deploy: raw sensor prompts are long, cloud inference adds latency and privacy risk, and fine-tuned LLM pipelines turn general-purpose models into task-specific classifiers. We present STELLA, an efficient sensor-to-LLM translation framework for on-device HAR that shifts the burden from LLM adaptation to sensor tokenization. A lightweight hierarchical tokenizer compresses an entire multi-channel inertial window into a fixed set of compact latent sensor tokens, which are projected into the embedding space of a frozen pretrained LLM and combined with a natural-language prompt for label scoring. This preserves activity-relevant temporal and cross-channel structure while keeping LLM-side computation predictable across sensor configurations. STELLA also supports on-device personalization, adapting only the lightweight tokenizer on small amounts of user-specific labelled data and augmenting inference with a local retrieval context, keeping the LLM, user data, and retrieval on device. Across seven public HAR datasets and eight benchmark settings, STELLA achieves new state-of-the-art performance, improving over prior methods by up to 11.83% F1; on-device personalization yields up to a further 21.91% F1 as user data accumulates after deployment. STELLA also outperforms representative time-series tokenizers under the same LLM pipeline and achieves real-time inference under practical mobile and edge budgets, showing that efficient sensor tokenization is a practical path toward accurate, private, and personalized LLM-based HAR on edge devices.
[AI-189] Spectral Rewiring for Exploration Purification and Model Merging
链接: https://arxiv.org/abs/2607.03065
作者: Zhilong Zhang,Hongli Yu,Huan-ang Gao,Hanlin Wu,Yuxuan Song,Wei-Ying Ma,Ya-Qin Zhang,Hao Zhou
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Reinforcement learning has become a standard post-training recipe for large language models, but dense full-parameter updates create two deployment-relevant bottlenecks: suppressed reasoning performance, often reflected by premature saturation of test-time scaling, and interference when consolidating multiple capabilities through multi-domain training or model merging. We show that the reasoning-effective component of these updates is largely concentrated in the base model’s spectral space, motivating Subspace-Aligned Rewiring (SAR), a post-hoc editing method that retains this spectral core while removing orthogonal components. SAR therefore preserves reasoning gains and filters residual update directions that suppress performance or amplify cross-domain interference. Across several model families and scales, SAR extracts compact reasoning cores using as little as approximately 0.58% of total parameters: it preserves over 99% of post-training performance and improves high-k exploration in mathematical reasoning, and generalizes to agentic coding by improving six of seven open benchmarks on an in-house model. SAR also purifies mixed-domain training updates by releasing suppressed coding capability while maintaining math reasoning and instruction following. It further enables model merging across experts, yielding cross-domain generalization that surpasses previous merging baselines and even the best single-domain experts. Overall, SAR shows that extracting reasoning-effective updates from parameter geometry can serve as a training-free mechanism to improve reasoning and multi-domain performance.
[AI-190] LACE-SVD: Loss-Aware SVD with Cumulative Error Correction for LLM Compression
链接: https://arxiv.org/abs/2607.03057
作者: Zhuowen Liu,Longkun Hao,Shiyu Feng,Xiaowen Chang,Ruiqun Li,Changqun Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 12 pages, 5 figures, 5 tables
Abstract:The rapid growth in the parameter scale of large language models (LLMs) has created a strong demand for efficient compression techniques. As a hardware-agnostic and highly compatible approach, low-rank compression has been widely adopted to reduce both memory footprint and computational cost. However, existing SVD-based methods are still largely driven by local reconstruction objectives, overlooking two critical limitations: rank budgets are often allocated without explicitly considering layer-wise loss sensitivity, and local approximation errors can propagate and accumulate through the residual stream, leading to amplified global deviations from the original model. To address these issues, we propose LACE-SVD, a Loss-Aware SVD framework with Cumulative Error correction for LLM compression. LACE-SVD first estimates the calibration negative-log-likelihood increase induced by candidate layer-wise compression ratios and solves a budget-constrained allocation problem to assign rank budgets. It then refines the compressed model with closed-form local updates and introduces a propagation-aware correction for residual-stream output modules, reducing layer-output discrepancy as a proxy for cumulative error propagation. Experimental results demonstrate that at a high compression ratio (0.6), the WikiText-2 PPL of our method on LLaMA-7B (32.57) is significantly better than that of Dobi-SVD (46.18).
[AI-191] Beyond Forecasting: The Belief-to-Trade Layer in Prediction-Market Agents
链接: https://arxiv.org/abs/2607.03015
作者: Yishu Wang,Yuxuan Wang,Jiaqi Deng,Hanyang Tang
类目: Artificial Intelligence (cs.AI)
备注: 10 pages, 4 figures
Abstract:Forecasting future events has attracted growing attention as a testbed for general-purpose AI. A natural way to ground this evaluation is let the models trade in the prediction markets. Trading, however, requires more than forecasting. Moreover, recent benchmarks report a substantial gap between calibrated probability scores and the trading results. We propose Raven-Agent, to the best of our knowledge, the first autonomous trading agent for prediction markets. On a controlled replay over an archived decision set, our architecture achieves the only positive return and the only positive risk-adjusted return among all tested policies. We have released our code in this https URL .
[AI-192] Back to Basics: Improving Molecular Understanding in LLM s via SMILES-Graph Translation
链接: https://arxiv.org/abs/2607.03007
作者: Wenda Wang,Jinjia Feng,Zhewei Wei
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM)
备注:
Abstract:Recent advances in molecular large language models have led to strong performance on molecular understanding and generation tasks, yet these gains often come without reliable structural grounding. In particular, existing approaches conflict with the chemistry principle that structure determines function: despite their downstream success, current molecular LLMs perform poorly on basic structure recognition, suggesting that they fail to capture molecular graphs from canonical SMILES. To remedy this, we propose MolBasic, a structure-first framework that strengthens structural comprehension via SMILES-Graph translation. MolBasic is built around a multi-level structure perception benchmark, where bidirectional SMILES-Graph conversion serves as the core task to align sequential and topological representations. On top of this foundation, we employ a progressive learning scheme with a standardized Chain-of-Thought (CoT) to steer models from structure acquisition toward higher-level molecular reasoning. Experiments show that MolBasic substantially improves structural understanding and yields robust gains on downstream tasks, including property prediction and objective optimization, supporting our structure-first paradigm.
[AI-193] Reinforcement Learning for Evidence-Seeking Diagnostic Reasoning with Large Language Models
链接: https://arxiv.org/abs/2607.02983
作者: Shengyi Hua,Kangzhe Hu,Conghui He,Xiaofan Zhang,Shaoting Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recent reasoning-centric Large Language Models (LLMs) have made significant strides, yet they predominantly operate on a passive-inference pattern that assumes complete information. In contrast, real-world clinical intelligence is inherently an iterative investigative process requiring strategic evidence acquisition. To bridge this gap, we formalize medical diagnosis as an Iterative Evidence-Seeking Task. We leverage Reinforcement Learning with Verifiable Rewards (RLVR) to elicit intrinsic reasoning within a closed-loop environment, guided by a novel suite of rewards that enforce diagnostic precision and examination consistency. To facilitate this, we introduce the Retrieval-Augmented Generation-based Examination Simulator (RAGES), a high-fidelity clinical oracle that provides realistic, knowledge-grounded follow-up evidence. Empirical results across diverse datasets demonstrate that our framework enables LLMs to transition from passive responders to autonomous assistants. Notably, our model demonstrates comparable performance to larger and reasoning-enhanced baselines, while RAGES proves superior to vanilla LLMs in generating biologically plausible clinical feedback.
[AI-194] Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN
链接: https://arxiv.org/abs/2607.02981
作者: Long Zhao,Shixun Ji,Bin Cheng,Bin He
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advancements in the Internet of Things (IoT) emphasize the urgent need for advanced network security, as IoT networks feature dynamic topologies, imbalanced traffic, and complex attack patterns. Unlike general IT networks, IoT environments exhibit extreme heterogeneity and sparse topologies. Traditional GNN-based intrusion detection methods often struggle to efficiently model node and edge features or capture fine-grained anomalies in such settings. To address this, we propose SKGFusionKAN, a novel IoT-tailored approach enhancing GraphSAGE with a multi-scale selective kernel attention mechanism. This enables adaptive extraction of node and edge features under diverse traffic conditions. Specifically, our edge-oriented message passing strengthens information propagation, while selective kernel attention adaptively weights edge-derived information from different scales to handle heterogeneity. We also introduce a gated fusion process to dynamically integrate multi-scale features, improving robustness against evolving attacks. Finally, we leverage Kolmogorov-Arnold Networks (KAN) for classification, offering superior nonlinear modeling capabilities essential for detecting intricate, low-frequency attacks. To our knowledge, this work presents a comprehensive integration of GNNs and KAN with dedicated architectural innovations for IoT intrusion detection. Extensive experiments on four NIDS benchmarks show that SKGFusionKAN consistently outperforms state-of-the-art approaches in binary and multiclass tasks, demonstrating its potential for IoT security.
[AI-195] he Foreign Policy AI Evaluation Gap
链接: https://arxiv.org/abs/2607.02955
作者: Charles Pozniak,Jeba Sania
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:We argue that AI systems used in conducting foreign policy tasks - broadly enacting ‘statecraft’ - should be a priority test case for technical AI governance research. In enacting foreign policy, we refer to the formulation and implementation of external objectives by political actors. Statecraft is a high-consequence deployment domain, with extreme downside risks and structural properties that standard evaluation practices handle poorly. These features include partial observability, unbounded action spaces, contested ground truth, and multidimensional objectives. This paper advocates for a literature-grounded research agenda. Our contribution is threefold: (i) a claim about the structural conditions of foreign policy that combine catastrophic tail risk with technical evaluation complexities, (ii) an ECOSYSTEM review that highlights the asymmetric focus on ASSESSMENT features over ACCESS, VERIFICATION, SECURITY, and OPERATIONALIZATION, and (iii) a demand-side evaluation framework that decomposes foreign-policy workflows into bounded, evaluable sub-tasks with human recombination. As AI systems are already being deployed in the conduct of war and peace, amid limited public evaluation infrastructure from the technical AI governance community, this agenda is an urgent priority.
[AI-196] A Precedent-Guided Co-Scientist for Side-Effect-Aware Drug Redesign ICML2026
链接: https://arxiv.org/abs/2607.02944
作者: Yujin Kim,Charmgil Hong
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at the ICML 2026 Workshop on AI for Science
Abstract:We propose PRECEDE, a precedent-guided co-scientist for side-effect-aware drug redesign that revises a parent compound to mitigate a specified side effect while preserving therapeutic function. Rather than isolated molecular generation, PRECEDE frames redesign as evidence-grounded reasoning over drug–side-effect associations, biomedical knowledge graphs, and precedents of safety-driven optimization, coordinated by an LLM orchestrator with explicit policies and human-review checkpoints. We position PRECEDE as a human-supervised AI-for-science workflow in which hypotheses remain auditable, falsifiable, and bounded by prior pharmacology.
[AI-197] A Sliding-Window-Based Reinforcement Learning for Dynamic Assembly Flow Shop Scheduling with Multi-Product Delivery
链接: https://arxiv.org/abs/2607.02941
作者: Junhao Qiu,Jianjun Liu,Ting Liu,Rongjie Liao,Zhantao Li,Qingfu Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-product kitting delivery imposes significant challenges for real-time scheduling in hybrid manufacturing systems that integrate processing and assembly, as dynamic order arrivals simultaneously alter supply dependencies and the set of feasible job-machine assignments. This paper proposes a sliding-window-based reinforcement learning (SWRL) framework for end-to-end online scheduling in the flexible assembly flow shop scheduling problem with complex kitting constraints. The problem is formulated as a heterogeneous graph-based Markov decision process that captures the dual-layer kitting structure and the tail-product bottleneck dynamics that produce a sparse reward landscape. To address the resulting challenges, SWRL integrates a sliding-window filtering mechanism that filters inactive nodes and prioritizes kitting-critical operations, a spatiotemporal graph encoding network that tracks bottleneck shifts across consecutive decision states, and a dynamic action mapping module with a constrained waiting strategy that adapts to the changing action space under variable topologies. Experiments on real-world instances from a home appliance manufacturer demonstrate that SWRL achieves consistent tardiness reductions over classical dispatching rules and existing deep reinforcement learning methods, and exhibits robust performance across varying resource configurations, order loads, and arrival concentrations.
[AI-198] VERITAS: Towards a General-Purpose Replication Tool for Scientific Research
链接: https://arxiv.org/abs/2607.02931
作者: Haokun Liu,Filbert Aurelian Tjiaranata,Chenhao Tan
类目: Artificial Intelligence (cs.AI)
备注: 21 pages, 2 figures, 8 tables
Abstract:AI tools are accelerating scientific publication while the systems that review it struggle to keep up, and independent verification of published research has become both harder and more important. As manual replication is slow and expensive, a growing line of work uses coding agents to automate parts of the process. Existing efforts are largely packaged as benchmarks with companion agents that only run inside the benchmark’s own pipeline, and no general-purpose replication tool exists. We present VERITAS, a domain-agnostic replication framework built around CLI coding agents. Given a paper, a code repository, or both, VERITAS extracts the paper’s claims, runs the methodology while resolving issues as they arise, and judges each claim against the evidence from experiment runs. The pipeline returns an importance-weighted Replication Score, a severity-rated log of every fix applied, and the patched codebase. We evaluate VERITAS on CORE-Bench and ReplicationBench, 65 papers spanning computer science, social science, medicine, and astrophysics. Against two strong Claude Code baselines on the same model and host environment, VERITAS achieves state-of-the-art performance and leads on every metric on both benchmarks.
[AI-199] Bootstrap Flow-Map Tree Sampling Enables Online Feedback Driven Search
链接: https://arxiv.org/abs/2607.02915
作者: Binglin Ji,Anindya Sarkar,Hengchang Lu,Jens Sjölund,Yevgeniy Vorobeychik
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 34 pages, 23 figures
Abstract:In many scientific and engineering domains, maximizing discovery within a limited sampling budget demands strategic, observation-guided exploration. While generative models have enabled training-free reward alignment, current methods typically excel in local searches within narrow regions of the underlying distribution. These approaches struggle when preferences are unknown a priori and only revealed through sequential feedback-a scenario demanding broad exploration to uncover high-utility regions. To address this, we introduce Bootstrap Flow-Map-Tree (a.k.a BFMT), a novel computationally efficient sampling framework designed for history-aware global search and alignment under sampling budget constraints. BFMT enables full tree-path construction from any tree depth using a single function evaluation, drastically reducing computational overhead while providing critical foresight for sequential sampling. By enabling dynamic transition time steps scheduling, BFMT efficiently allocates its sampling budget, smoothly transitioning from broad global exploration to fine-grained local refinement of high-utility modes discovered through exploration. Extensive experiments and ablations across diverse search and alignment tasks demonstrate that BFMT substantially outperforms baseline approaches.
[AI-200] Oyster-II: Reinforcement Learning for Constructive Safety Alignment in Large Language Models
链接: https://arxiv.org/abs/2607.02914
作者: Jiyang Guan,Yong Xie,Jun Chen,Jiexi Liu,Zipeng Ye,Defeng Li,Jiayu Shen,Jialing Tao,Hui Xue
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) have demonstrated remarkable capabilities across diverse applications, yet ensuring their simultaneous safety, helpfulness, and trustworthiness remains a persistent challenge. Conventional refusal-oriented alignment strategies mitigate harmful content generation but systematically fail to serve legitimate user needs, often withholding information that could safely and constructively address the underlying intent of sensitive queries. Building upon the constructive safety paradigm pioneered by Oyster-I, which moves beyond blanket refusal toward thoughtful, response-oriented safety alignment, we identify two critical limitations of its Supervised Fine-Tuning (SFT)-based scheme: insufficient safety generalization to out-of-distribution scenarios and a phenomenon we term safety chain-of-thought (CoT) over-generalization, wherein safety-oriented reasoning patterns are excessively applied to benign queries, degrading helpfulness and user experience. To address these limitations, we propose Oyster-II, a reinforcement learning (RL)-based constructive safety alignment framework that adopts a Zero-RL paradigm combined with a multi-stage reinforcement learning this http URL across extensive benchmarks, Oyster-II comprehensively surpasses both Qwen3-14B and its predecessor Oyster-I on safety dimensions, achieving cross-scale performance comparable to Qwen3-Max and Qwen3.5-397B.
[AI-201] CoACT: Action-Preserving Observation Compression for Coding Agents
链接: https://arxiv.org/abs/2607.02911
作者: Haorui Chen,Yuancheng Zhu,Yitong Zhang,Jia Li
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:LLM-based coding agents solve software-engineering tasks through iterative interactions with development environments, where returned observations accumulate in the context and become a major source of inference cost. Observation compression reduces this cost by shortening observations before they are appended to the context. However, existing methods still exhibit an unsatisfactory efficiency-effectiveness trade-off, as they do not explicitly model how compression affects the agent’s subsequent behavior. This paper proposes CoACT, an action-preserving observation compression method for coding agents. CoACT is built on next-action preservation (NAP), which requires a compressed observation to induce the same next action as the raw observation. By checking the agent’s immediate next action, NAP provides a practical signal for whether a compression preserves the information needed for continued task solving. During training, a teacher model first generates multiple compressed candidates of each observation. CoACT then uses an action-preservation reward based on NAP to filter out candidates that would change the agent’s next action, and uses a length-reduction reward to choose compact candidates as supervision for a lightweight compressor. Experiments on SWE-bench Verified with three agentic models show that CoACT reduces average total token consumption by 33.0% while maintaining task-solving effectiveness close to the uncompressed agent.
[AI-202] IER: Trajectory-Invariant Explanation Regularization for Membership Privacy
链接: https://arxiv.org/abs/2607.02903
作者: Varun Sharma,Kar Wai Fok,Vrizlynn L. L. Thing
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Explainability is central to building trustworthy AI, yet explanation interfaces can inadvertently provide adversaries with an expanded privacy-related attack surfaces. Recent studies show that advanced membership-inference attacks succeed by exploiting confidence-drop trajectories, induced through attribution-guided perturbations, as discriminative features, rather than directly using confidence scores or explanation vectors. Existing defenses against membership inference fail to directly mitigate such explanation-driven attacks. In this work, we investigate whether, during training, a model’s own gradients can be leveraged as defense signals against such attacks, thereby aligning explanation profiles between members and non-members. To this end, we propose a Trajectory-Invariant Explanation Regularization (TIER) defense that penalizes erratic fluctuations in confidence drops simulated through gradient-guided perturbations and simultaneously minimizes the distributional shifts via KL-divergence. Unlike conventional adversarial training, which emphasizes label robustness, our approach targets explanation robustness by enforcing self-consistency through KL-divergence and reducing the variance of confidence drops between members and non-members. Extensive experiments confirm that our method effectively mitigates these attacks, delivering privacy protection while maintaining model utility and explanation fidelity.
[AI-203] MedCalc-Pro: Solving Complex Medical Calculations with LLM Agents
链接: https://arxiv.org/abs/2607.02879
作者: Siran Zhao,Ruihui Hou,Ziyue Huai,Chennuo Zhang,Tong Ruan
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Current benchmarks for evaluating large language models (LLMs) in medical calculation are largely based on simplified settings, where each patient case corresponds to a single calculator and the required tool is explicitly specified in the query. However, real clinical scenarios often require multiple calculators for joint evaluation, nested-scale calculation, and fuzzy queries that do not directly specify the target calculator. To this end, we propose a new medical calculation benchmark, MedCalc-Pro, which covers three progressively challenging task settings: single-calculator, multi-calculator, and nested-calculator calculation settings. MedCalc-Pro contains 2,268 real-world clinical cases, covering 77 medical calculators across 14 clinical departments. Meanwhile, to address the limited performance of existing frameworks and methods in complex clinical scenarios, we further propose a more generalizable agent framework that supports multi-tool selection and nested-tool calling, while suppressing parameter error propagation through structured validation and evidence review. We conduct systematic comparisons across open-source, closed-source, and medical-specialized LLMs, and the results show that our framework achieves the best performance across all three task settings. This work provides a new benchmark and method for evaluating and applying LLMs in challenging medical calculation scenarios.
[AI-204] Determinants and Limits of LLM Security-Tool Orchestration: A Study with HexStrike-AI
链接: https://arxiv.org/abs/2607.02873
作者: Romain Gerard,Assmaa Zeghaider,Yan Guo
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注:
Abstract:Large language model agents driving security tool suites over the Model Context Protocol are increasingly common. Yet the factors that bound their capability remain poorly characterized: how much depends on the model versus the client that drives it, whether constraining the agent to the orchestrator’s own tools helps, and where capability is limited by reasoning rather than by missing tools. Using HexStrikeAI, an open-source orchestrator that exposes 150+ tools, as a testbed, we follow a methodology that evaluates the system, diagnoses its failures, and applies targeted improvements. We run 86 picoCTF challenges across seven categories and three difficulty tiers, under three tool-access regimes and three model/client configurations (774 trials). We then apply corrections to existing tools, agent-behavior changes, and eleven new capability tools, and re-run the previously-unsuccessful trials. The diagnosis isolates the driving client as a first-order factor for a fixed model (a 2.1 * gap between two DeepSeek clients) and a monotonic difficulty gradient, with the largest gains in the mid tier. The overall solve rate rises from 55.4% to 72.0%, and every configuration improves significantly (paired McNemar p 0.001, non-overlapping 95% confidence intervals). The residual failures are reasoning- or environment-bound rather than missing-tool. A 60-run stability sub-study finds single-run verdicts reproducible (17/20 unanimous). We discuss what the results imply for how such orchestrators should be evaluated, and we are explicit about the limits: the study uses a single benchmark, the fixes were tuned on the same challenges they were evaluated on, and the client effect is demonstrated for one model only, so its generality to other models remains a hypothesis.
[AI-205] Object-Centric Environment Modeling for Agent ic Tasks
链接: https://arxiv.org/abs/2607.02846
作者: Yiyang Li,Tianyi Ma,Zehong Wang,Yijun Ma,Yanfang Ye
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM) agents can improve through accumulated experience, but free-form textual memories become difficult to maintain, validate, and reuse as interactions grow. Recent symbolic approaches learn executable skills or programmatic world models, yet often store local procedures or assume simplified dynamics. We propose Object-Centric Environment Modeling (OCM), which organizes experience into an executable object-centric environment model. OCM maintains two connected code bases: object knowledge, which defines environment entities and mechanisms as Python classes, and procedure knowledge, which records reusable interaction patterns that must import and use the object model. OCM works in an online setting: after each episode, OCM reflects on the trajectory, updates both knowledge bases, and verifies that all procedures execute against the updated object model. During future interaction, the agent uses progressive knowledge disclosure to inspect compact code signatures first and read source code only when needed. Experiments show that OCM achieves the best average rank across benchmarks and reduces invalid actions, demonstrating that agents can benefit from building object-centric environment models.
[AI-206] JavaVulBench: A Java Vulnerability Benchmark with Realistic Splits a Unified Multi-Backend Harness and a Leakage-Aware Evaluation Mode
链接: https://arxiv.org/abs/2607.02825
作者: Norbert Sandor Szolnoki,Gabor Antal
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:We release \textscJavaVulBench, a benchmark dataset and evaluation harness for Java vulnerability detection. The dataset contains \sim 30,600 Java methods spanning 1,740 CVEs and 700+ projects, labelled at both method and line granularity, with per-CVE publication dates and five realistic split strategies: random, project-disjoint, temporal, deduplicated, and unseen CWE-family. The harness provides a single \textttLlmPrediction schema across three backend families (encoder classifiers, local generative models served by Ollama, and API-served LLMs routed through OpenRouter) so that twelve reference detectors CodeBERT, GraphCodeBERT, UniXcoder, DeepSeek-Coder-1.3B, and eight API/open-weight LLMs (GPT-4o, GPT-4.1-mini, Claude Sonnet~4, DeepSeek-v3, DeepSeek-Coder-v2, Qwen-2.5-Coder-14B/7B, CodeLlama-13B) are evaluated under identical conditions from a single command. A pre-training contamination audit is shipped alongside every model so users can separate genuinely unseen test CVEs from potentially memorised ones. Data, code, and fine-tuned checkpoints are archived on Zenodo [31] and short demonstration video is available on YouTube (this https URL_hqkuoM) this https URL.
[AI-207] SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery
链接: https://arxiv.org/abs/2607.02807
作者: Yuvraj Virk,Zack Edds,Chunqiu Steven Xia,Lingming Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Long-running coding agents such as autoresearch can persistently discover optimizations for open-ended problems. However, they tend to converge onto a single high-level approach, then proceed with low-level edits while missing other superior approaches to the problem. We hypothesize two harness-level design choices contribute to this behavior: accumulating context in a single long-running agent and only exposing a single program state to edit. We introduce SwarmResearch, an orchestrator-subagent harness in which a Shepherd Agent uses global context to steer a population of Search Agents, each operating with local context in their respective git branch. On open-ended optimization tasks, SwarmResearch discovers better or comparable solutions to state-of-the-art LLM-guided evolution and multi-agent techniques on 13/15 tasks, driven by higher-level exploration. Compared with fixed scaling of serial and parallel agents, SwarmResearch’s orchestrator-guided scaling discovers better-performing solutions by adapting parallelism at different search depths.
[AI-208] A Preliminary Study on Explaining Risk of Code Changes using LLM -Based Prediction Models
链接: https://arxiv.org/abs/2607.02782
作者: Yalin Liu,Kosay Jabre,Rui Abreu,Zachariah J. Carmichael,Vijayaraghavan Murali,Akshay Patel,Jun Ge,Weiyan Sun,Cong Zhang,Audris Mockus,David Khavari,Peter C. Rigby,Nachiappan Nagappan
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Predictions by machine learning (ML) and artificial intelligence (AI) models are often received skeptically unless they are paired with intelligible explanations. In the context of just-in-time defect prediction, highlighting small portions of a software change (diff) – beyond rule-based lints – where risk may be concentrated has not yet been extensively investigated. In this work, we leverage attention weights from an LLM-based Diff Risk Score (DRS) model to highlight parts of a diff that the model focuses on when predicting risk. We aggregate token-level attention into interpretable code units (lines, hunks, and files), and present the top-K units to developers as a lightweight form of guidance during code review. We evaluate our approach using expert-labeled changes that have caused real outages. Results show that the highlighted snippets cover expert-labeled outage-causing change lines 53.85% of the time when highlighting the top-2 hunks, while requiring developers to review 26.28% of the changed lines on average. Because attention is produced during standard model inference, the approach is scalable for large development workflows and can be surfaced in the code review UI with low additional latency.
[AI-209] Automated Data Readiness for Scientific AI
链接: https://arxiv.org/abs/2607.02771
作者: Sean R. Wilkinson,Valentine G. Anantharaj,Jong Youl Choi,Ketan Maheshwari,Marshall McDonnell,Massimiliano Lupo Pasini,Polina Shpilker,Renan Souza,Patrick Widener,Sarp Oral,Wesley Brewer
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注:
Abstract:Leadership computing facilities steward large-scale scientific datasets that routinely require substantial transformation before serving as AI training data. However, no existing framework fully unifies automated transformation, readiness assessment, provenance tracking, and agent-native deployment. We present REDI, an open-source framework that addresses this gap through a unified five-stage pipeline (ingest, preprocess, transform, structure, and output) with per-stage instrumentation for reproducibility and deployment as an agent-callable skill; companion tool SetGo automates FAIR compliance and catalog publication. Evaluated across climate, proteomics, materials science, and nuclear fusion, REDI transforms all datasets from raw to AI-ready, with outputs validated against domain-expert references, and preliminary results show near-ideal parallel scaling to 100 nodes on Frontier for the climate case. Provenance-instrumented profiling reveals file I/O as the dominant pipeline cost, with format selection a first-order optimization lever. These results establish REDI as a cross-domain platform providing automated data readiness for scientific AI, transforming data preparation bottlenecks into reproducible, reusable community assets.
[AI-210] Out-of-Distribution Generalization of Risk Aversion in Language Models ICML2026
链接: https://arxiv.org/abs/2607.02755
作者: Kristina Zhang,Junior Chinomso Okoroafor,Benjamin Maltbie,Andrew Lin,Abhitej Bokka,Elliott Thornley
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: ICML 2026 Agents in the Wild workshop
Abstract:Training AIs to be risk-averse in resources could offer a failsafe in the event that AIs turn out misaligned. Misaligned but risk-averse AIs would tend to prefer low-risk, low-reward strategies like cooperation over high-risk, high-reward strategies like rebellion, limiting the downsides of any misalignment. But we can only feasibly train AIs to be risk-averse on low-stakes gambles, and we will only be safe if their risk aversion generalizes to astronomically-high-stakes gambles. Will it? To shed light on this question, we introduce RiskAverseOOD: a benchmark for measuring how well risk aversion generalizes out of distribution. We then offer some initial results. Using a variety of methods to make Qwen3-8B choose risk-aversely when the stakes are low, we find that we can induce substantial risk aversion when the stakes are astronomically high. Our models’ learned risk aversion generalizes at least partially across 98 orders of magnitude. From a baseline 2% rate of choosing a safe `Cooperate’ option, we see rates around 70% (SFT and tie training), 52% (DPO), and 39% (activation steering). In another experiment, our fine-tuned reward model reliably scores risk-averse reasoning above risk-neutral or excessively risk-averse alternatives (99.6% pairwise accuracy). We replicate these effects at different scales (Qwen3-1.7B and Qwen3-14B) and across model families (Gemma-3-12B-IT and Llama-3.1-8B-Instruct). Overall, we find that risk aversion learned at low stakes can generalize OOD to astronomically high stakes, though not yet consistently enough to serve as a reliable failsafe. Achieving that level of consistency is an open problem.
[AI-211] SMOCS: A Streaming Framework for Simplified Deployment Monitoring and Optimization of ML Systems in Production
链接: https://arxiv.org/abs/2607.02731
作者: Armen Kasparian,Kishansingh Rajput,Malachi Schram,John Vennekate
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Machine learning has demonstrated significant potential for real-time monitoring, optimization, and control of scientific facilities. However, deploying and maintaining ML models in operational environments remains a substantial engineering challenge. Each facility presents unique data protocols, non-standard formats, and infrastructure constraints, forcing teams to rebuild integration pipelines for every new application. We present SMOCS (Streaming Monitoring Optimization and Control System), a Kafka-based containerized framework that addresses this challenge through three contributions: 1) a layered abstraction over Apache Kafka that separates infrastructure from application logic, 2) a three-thread agent architecture that temporally decouples data ingestion, model training, and real-time inference enabling continuous online learning from live data streams, and 3) a configuration-driven deployment model that enables domain experts to operate ML pipelines without software engineering expertise. SMOCS is facility platform-agnostic, fault-isolated by design, and horizontally scalable through Docker containerization. The framework is publicly available as open-source software on the Jefferson Lab Github.
[AI-212] Not All Refusals Are Equal: How Safety Alignment Fails Cybersecurity at Scale NEURIPS2026
链接: https://arxiv.org/abs/2607.02714
作者: Vadym Hadetskyi,Dario Pasquini,Artem Sorokin
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 14 pages, 11 figures. Under review at NeurIPS 2026
Abstract:There is no doubt that safety alignment is an essential step in LLM training. However, conceptually it does not distinguish between various domains and the level of potential harm of a query, which creates significant complications in the fields like cyber security, where a model should not be constrained by its safety circuits to accomplish the goals of legitimate, authorized operations. In this work, we share our findings from a large scale abliteration experiment on 24 open-source LLMs and show that domain-specific abliteration is achievable with standard methodology on the example of a 1T-parameter Kimi K2. Building on recent work showing that refusal in LLMs occupies a multi-dimensional subspace within layers, we find that it is also distributed widely across layers, especially in trillion-parameter MoE architectures, and so we aim to capture the part of it that represents harmful concepts in the cybersecurity domain exclusively. We also investigate the correlation between models’ features and the effect of domain-specific abliteration, identifying that the type of safety training and architecture are the most reliable predictors. Finally, we classify the models into 3 \emphabliteration susceptibility tiers and put forward a set of conjectures as to why a particular effect from this intervention might be observed in a given model.
[AI-213] ASK in the Dark: Uncertainty-Gated LLM Assistance under Partial Observability IJCAI ECAI
链接: https://arxiv.org/abs/2607.02686
作者: Juarez Monteiro,Nathan Gavenski,Guilherme Lima,Francisco Galuppo,Odinaldo Rodrigues,Adriano Veloso
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at the IJCAI-ECAI Joint Workshop on Planning for Complex Real-World Applications and Bridging the Gap Between AI Planning and (Reinforcement) Learning
Abstract:Reinforcement learning agents operating under partial observability must act on incomplete information, making them natural candidates for guidance from small language models (SLMs) that carry broad reasoning priors. Yet integrating SLM guidance into this setting has proven difficult: across all test environments, vanilla uncertainty-gated approaches achieve an overwrite rate at or near zero, meaning the SLM almost never contributes an independent action. We trace this failure to the bare egocentric prompt, which provides insufficient context for genuine reasoning, and identify it as a context problem rather than a capacity problem. We propose ASK+, which supplies the SLM with trajectory-aware context (a partially revealed map, visited positions, and action history) and structured chain-of-thought reasoning, converting it from a passive redundancy check into a more informative consultant that occasionally corrects the policy. We further establish that the predictive entropy signal used for selective querying measures action uncertainty rather than state uncertainty and remains informative in POMDPs, making uncertainty-gated assistance viable beyond fully observable settings. The stateful prompt drives substantial gains: on DoorKey, where vanilla ASK matches PPO (both 89%), ASK+ reaches 93% success; on FourRooms, success climbs from 53% to 70%; on HigherLower, accuracy reaches 73.7%, matching the SLM-only upper bound. Across all environments, Qwen3.5-2B matches or exceeds Qwen3.5-4B, confirming that prompt design and selective gating dominate the impact of model scale, enabling guidance without large models.
[AI-214] Internal Pluralism and the Limits of Pairwise Comparisons
链接: https://arxiv.org/abs/2607.02672
作者: Bailey Flanigan,Michelle Si
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Local pairwise comparisons are a standard tool for learning how people want decision rules to work, e.g., in participatory design or alignment. However, their use builds in two strong assumptions: that local comparisons are sufficient evidence about how a person wants an automated decision rule to behave, and that people can always answer those comparisons decisively. We investigate how these assumptions may be compromised under internal pluralism: the idea that an individual evaluates decision rules according to multiple authoritative priorities about how the rule should behave. We provide a formal model of such pluralistic preferences over decision rules, which then lets us identify two distinct failures of forced local pairwise comparison data. First, priorities such as proportionality, egalitarianism, and equal treatment are inherently global: what they imply in one case can depend on what happens elsewhere, so local comparisons may fail to capture them. Second, even when priorities are representable locally, tension between strongly-held priorities can generate internal conflict, producing potentially costly behavioral distortions when comparisons are forced. We then use our model to investigate the alternative – allowing people to report indecision – and our findings suggest that doing so can considerably reduce the number of queries needed to learn preferences accurately. We conclude by describing how our model points toward preference-learning methods that elicit these priorities directly, yielding more faithful and interpretable accounts of what people value.
[AI-215] Metronome: Bound the Cache Keep the Beat for Real-Time Interaction Model Serving
链接: https://arxiv.org/abs/2607.02640
作者: Jiaying Meng,Bojie Li
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)
备注:
Abstract:Real-time interaction models – Moshi, MiniCPM-o, Qwen-Omni – turn serving into a periodic real-time task: on every frame a session ingests streaming audio and must respond by a recurring wall-clock deadline, while its KV cache grows monotonically and stays pinned for the whole conversation. This regime hides a dangerous failure mode. On a real full-duplex stack, sustained load does not degrade serving gracefully: it falls off a cliff, jumping in one step from milliseconds per frame to a stalled engine when accumulated session state exhausts the KV pool. The collapse is metastable – identical five-minute runs collapse or survive on run-to-run variance – and silent: latency and deadline-miss metrics read healthy throughout. We show one move restores both stability and observability: bound each session’s resident state, and latency starts telling the truth. Metronome’s in-engine KV window eliminates the collapse (0/20 vs. 14/20 runs across two batches) and turns per-frame latency into a monotone load signal, on which an online admission controller discovers the schedulable concurrency; without the window, the identical controller over-admits into the wall. A first-order model predicts the collapse time within a few percent on the headline model, and a quality probe validates the bound’s design by ablation: the window alone is quality-free in turn-based decoding, and its few pinned attention-sink tokens are what keep free-running generation healthy. Everything is measured end-to-end on real audio, across four interaction models on one GPU. Subjects: Sound (cs.SD); Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI) Cite as: arXiv:2607.02640 [cs.SD] (or arXiv:2607.02640v1 [cs.SD] for this version) https://doi.org/10.48550/arXiv.2607.02640 Focus to learn more arXiv-issued DOI via DataCite
[AI-216] Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting
链接: https://arxiv.org/abs/2607.02637
作者: Disheng Liu,Tuo Liang,Chaoda Song,Yu Yin
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent generative models can produce high-quality synthetic images, offering scalable training training data for data-hungry models. Existing approaches to exploiting this potential typically involve 1) training or fine-tuning generators, or 2) using lightweight post-hoc adaptation like prompt engineering or inference-time guidance, making them generator-specific and expertise-intensive. We study a complementary question: given a fixed pool of generated images, can downstream utility be improved purely by selecting an informative subset? The answer is yes. We show that effective selection must counter a structural bias of modern generators: they tend to over-produce canonical modes of each class while underrepresenting intra-class variation. Building on this insight, we split each real class into a canonical Homogeneous (HO) subset and a non-redundant Heterogeneous (HE) subset, then score synthetic images by a fidelity-diversity criterion that rewards semantic alignment while penalizing canonical redundancy. The method is generator-agnostic and requires no retraining. Across multiple benchmarks, it consistently outperforms state-of-the-art data selection baselines and matches the real-data performance with up to 40% fewer synthetic samples. The same criterion remains effective when applied on top of stronger task-tuned generators, with gains on both classification and segmentation tasks. Post-generation selection is therefore not a substitute for better generators, but a complementary mechanism for improving the utility of synthetic data.
[AI-217] QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting
链接: https://arxiv.org/abs/2607.02632
作者: Shah Nawaz Haider,Steve Austin,Arnab Barua,Sarowar Morshed Shawon,Hadaate Ullah
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 7 pages, 4 figures
Abstract:Time-series forecasting supports decisions in finance, en-ergy, transportation, public health, and industrial monitoring. Recent foundation models improve transfer across forecast-ing tasks, but many depend on centralized data and Trans-former attention, which restricts their use for long, high-di-mensional, and privacy-sensitive signals. This paper presents QuantFlow, a probabilistic forecasting framework that com-bines inverted sequence embedding, bidirectional Mamba state-space decoders, quantile regression, and federated learning. Each variable is embedded over the complete ob-servation window, processed in forward and reverse direc-tions, and projected to five conditional quantiles. TSMixup expands temporal diversity through Dirichlet-weighted inter-polation while preserving sequence structure. Experiments cover cryptocurrency, traffic, electricity, Electricity Trans-former Temperature, influenza, and weather data. QuantFlow obtains mean squared errors of 0.2834 on ETTm1 and 0.2218 on Weather, and a 20-client non-IID deployment retains use-ful accuracy after three communication rounds without cen-tralizing raw records. The results indicate that selective state-space modelling is a promising basis for scalable, uncer-tainty-aware, and privacy-conscious time-series prediction, while also revealing limitations on irregular epidemiological signals and long-horizon generalization.
[AI-218] Fine-Grained Computation Offload for Off-the-Shelf Servers in Tens of Lines
链接: https://arxiv.org/abs/2607.02630
作者: Bojie Li
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Operating Systems (cs.OS)
备注:
Abstract:Hardware accelerators now sit on the critical path of online serving. GPUs, FPGAs, and increasingly remote services such as hardware security modules, post-quantum KEMs, and inference servers. For fine-grained offloads (microseconds to a few milliseconds) the classic responses to the resulting stall both fail: a context switch costs as much as the offload, and a busy-wait burns the core. Overlapping the offload with other requests is the fix, and prior systems obtain it by adding concurrency: an async-framework rewrite, a new runtime or dataplane OS, or a hand-tuned point integration. We observe that the concurrency already exists: serving concurrent requests is suspending and resuming them, so every server ships the machinery overlap needs. Overlap is then a routing problem, not a rewrite problem: submit the offload to an executor, suspend the request with the server’s own deferred-response primitive, resume it on completion. Across ten off-the-shelf servers spanning every production concurrency model, this recipe takes 22-138 lines added, at most one modified, and recovers 1.2-5.4x on real hardware; the server’s concurrency model and the offload’s weight predict both numbers in advance, and the win is bounded by device throughput and the server’s own overlap capacity. At the limit, an LD_PRELOAD fiber runtime injects the reroute into an unmodified thread-per-connection binary (17.3x) within a characterized envelope. Rerouting suspends run-to-completion atomicity; a measured taxonomy confines the hazard to unlocked shared aggregates, and a transparent page-protection detector guards exactly those, validated on stock Redis. Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Operating Systems (cs.OS) Cite as: arXiv:2607.02630 [cs.DC] (or arXiv:2607.02630v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2607.02630 Focus to learn more arXiv-issued DOI via DataCite
[AI-219] he agent creates we validate: A Lightweight Framework for Agent ic Artifact Generation
链接: https://arxiv.org/abs/2607.02615
作者: Yaniv Melamed,Yoni Zukerman,Michal Shechter,Miri Weissler,Ashwin Patil,Hani Neuvirth-Telem
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Generating structured artifacts with Large Language Models - e.g. database queries, threat framework mappings, entity schemas - is relatively straightforward; however, making them reliable enough for production deployments presents challenges. We present a lightweight framework based on a core principle: LLMs generate, we validate. This reframing shifts responsibility from generation quality to validation rigor. The framework rests on three key attributes: First, test driven generation: when tests fail, the LLM receives indicative error messages that expose why the output failed, enabling the LLM to understand its mistakes and refine subsequent attempts. Second, deterministic and LLM-based tests: deterministic tests catch heuristics that can be programmatically verified (schema, syntax, cross-reference), while LLM-based tests evaluate nuanced semantic and delicate features that resist programmatic inspection (intent alignment, logical consistency, domain correctness). Third, expert-distilled judges: LLM-based tests are calibrated to distill and replicate human expert decision distribution, transforming manual human quality gates into scalable, reusable evaluation proxies that reflect professional-grade validation standards. We demonstrate the framework on three artifact types in the security domain - KQL query generation, MITRE ATT\CK mapping, and entity mapping - deployed in production at Microsoft Sentinel. We believe this framework can be applied beyond security to other artifact generation tasks, providing a path to reliable, high-quality outputs without sacrificing the efficiency gains of LLM generation.
[AI-220] Knowledge-Centric Information Systems
链接: https://arxiv.org/abs/2607.02609
作者: Mariano Garralda-Barrio
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Databases (cs.DB)
备注: Vision paper; 10 pages, 3 figures, 2 tables
Abstract:For decades, data engineering has developed mature architectural principles for integrating, governing, validating, cataloging, and serving organizational data. The rise of large language models does not eliminate these concerns; it exposes a broader version of them. Organizational knowledge is becoming executable infrastructure: systems increasingly retrieve it, assemble it, reason over it, and act on it. This paper argues that enterprise artificial intelligence (AI) systems suggest a transition toward an architectural discipline for representing, maintaining, governing, and operationally delivering organizational knowledge. We refer to this discipline as \emphknowledge architecture. We offer a conceptual model and taxonomy showing how classical data-engineering guarantees must be redefined when the managed unit shifts from records to knowledge artifacts: extract, transform, and load (ETL) becomes knowledge ingestion, change-data capture (CDC) becomes knowledge change detection, lineage becomes provenance, catalogs become knowledge catalogs, materialized views become knowledge views, and medallion architectures become raw–curated–operational knowledge layers. Emerging formats such as large language model (LLM) Wiki and the Open Knowledge Format (OKF) are treated as early evidence of this transition, not as its endpoint. The central claim is that knowledge architecture becomes useful when organizational knowledge ceases to be a passive information resource and becomes an operational asset used by humans, agents, workflows, and models to execute work.
[AI-221] Agent LTL: A Trace-Verification Framework for Measuring Enforcing and Training Procedural Compliance in Tool-Using LLM Agents
链接: https://arxiv.org/abs/2607.02599
作者: Laïla Elkoussy(LRE, EPITA),Julien Perez(LRE)
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
备注:
Abstract:Tool-using LLM agents are usually evaluated by final-answer correctness or LLM judges. Neither captures how an answer was produced. In safety-critical settings, the procedure itself is part of correctness. In this paper, we introduce AgentLTL, a language derived from First-Order Linear Temporal Logic (FO-LTL) that expresses procedural rules over agent traces. It yields a deterministic, judge-free compliance score. In this framework, a single specification drives two usages. The first is harnessing: the constraints score completed traces, or gate tool calls by checking each prefix online, before execution. The second is finetuning: the score serves as a dense reward. On a benchmark spanning ordering, branching, iteration, and grounding, block-and-warn harnessing improves compliance on five of seven models. Finetuning with the same reward yields +38 and +17.5 percentage point gains in accuracy and compliance on held-out patterns, including unseen tool-name aliases. These findings are consistent with the model acquiring procedural structure rather than memorizing surface tool names and procedures.
[AI-222] From Tensor Buffer to Distributed Memory Hierarchy: A Survey of KV Cache Management for LLM Serving
链接: https://arxiv.org/abs/2607.02574
作者: Jie Li,Tongyang Wang,Yong Chen
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:The key-value (KV) cache has become a first-order memory object in LLM serving rather than a temporary per-request tensor. This survey classifies more than thirty KV-management systems and frameworks using four axes: locality, lifetime, ownership, and substrate. The axes reveal five architectural archetypes – local-paged, disaggregated-pipeline, shared-store, memory-pool, and hybrid-tier. Once workload and hardware are fixed, ownership accounts for much of the remaining design variance among distributed systems. The survey also audits current evaluations and identifies seven missing KV-specific measurements, linking them to open problems in fault tolerance, isolation, tiered eviction, speculative decoding, MoE serving, and shared-cache semantics.
[AI-223] Not Every Sync Is Safe: Calibrated DiLoCo Scheduling for Shared AI Infrastructure
链接: https://arxiv.org/abs/2607.02544
作者: Maxwell Twelftree,David Lemphers,An-chi He,Yue Yang
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注:
Abstract:DiLoCo-style training reduces communication by letting learner islands train locally before occasional outer synchronization, making it attractive for fragmented industrial AI fleets where training shares hardware with latency-sensitive serving. The question for such fleets is when an outer merge is worth its system cost, and whether choosing \emphwhich windows to defer matters at all. Existing scheduling studies evaluate workload-aware policies against fixed-period baselines, but most omit the control that isolates timing from budget: matched random deferral, which inherits the controller’s synchronization budget but is not itself deployable. This omission is consequential: across controlled stress tests and real vLLM sidecar replays, matched random ties or beats every forecast-free policy we test, so gains reported against weaker baselines cannot be attributed to window choice. We fill this gap with Workload-Aware DiLoCo (WA-DiLoCo), a score-based controller that weighs learner progress against fleet pressure, and a calibration protocol that determines when matched random can be beaten, then demonstrate that it can. In the bursty regime where calibration exposes request-overlap structure, adding a one-step EWMA burst forecast to the online controller beats matched random in real vLLM sidecar replay, reducing SLO violations from 6.54% to 5.09% (8 of 10 seeds, p=0.021 ); offline Calibrated-WA, a non-deployable bound, shows the remaining headroom at 4.45% versus 6.26%. The deployable lesson remains the protocol: report real-sidecar effect-size transfer, a no-sync load match, and a matched-random envelope before claiming serving-SLO improvement.
[AI-224] he Hidden Water Geography of U.S. Hyperscale Data Centers in the AI Era
链接: https://arxiv.org/abs/2607.02531
作者: Gianluca Guidi,Francesca Dominici
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:Water use by data centers is routinely reported as a single footprint, but water is consumed through two physically distinct pathways: at the site for cooling and in the power system that generates electricity. We mapped both pathways for 472 U.S. hyperscale facilities by linking facility locations to electricity regions, hydrologic basins, and water-stress data. Under baseline assumptions, operational water consumption totals approximately 300 GL yr^-1 (range 205-451 across scenarios), with electricity-related water contributing three-quarters of the total. The two pathways produce different hotspot geographies: direct cooling burdens concentrate in stressed western and south-central basins, whereas electricity-related burdens concentrate in a few eastern grid regions with fossil-heavy supply. Just 3 of 24 hosting balancing authorities account for 59% of electricity-related water. Separating pathways identifies which decisions matter where: cooling design and water sourcing locally, electricity planning and procurement regionally
[AI-225] PEEK: Predictive Queue-Informed KV Cache Management for LLM Serving
链接: https://arxiv.org/abs/2607.02525
作者: Bing Xie,Zhipeng Wang,Masahiro Tanaka,Zheng Zhen
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注: 26 pages, 21 figures, 21 tables. Preprint, under review
Abstract:We present PEEK, a lightweight scheduling and eviction framework for both online (streaming) and offline (batch) LLM serving; this paper focuses on the online regime. PEEK maintains an incremental radix tree over the pending queue, exposing prefix-sharing clusters no existing engine surfaces. A low-overhead dual-walk matches the tree against the engine’s prefix cache to yield longest-prefix-match for every waiting request; PEEK then admits cluster pioneers first so siblings inherit the freshly cached prefix, a co-designed eviction hook protects blocks ancestral to queued demand, and a multi-lane stride scheduler bounds starvation. On SGLang and vLLM across five workloads up to 4 \times H100 (DP=2 over TP=2), PEEK delivers up to 3.0 \times /2.6 \times cache hit, 7.9 \times /7.1 \times TTFT, 6.7 \times /5.5 \times E2E, and 3.6 \times /4.5 \times throughput gains over each engine’s strongest stock baseline (SGLang/vLLM), while matching baselines within noise on workloads with no exploitable prefix structure. Wins hold as KV-cache pressure and inference parallelism scale. Comments: 26 pages, 21 figures, 21 tables. Preprint, under review Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.02525 [cs.DC] (or arXiv:2607.02525v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2607.02525 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Bing Xie [view email] [v1] Sun, 10 May 2026 04:48:04 UTC (311 KB) Full-text links: Access Paper: View a PDF of the paper titled PEEK: Predictive Queue-Informed KV Cache Management for LLM Serving, by Bing Xie and 3 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.DC prev | next new | recent | 2026-07 Change to browse by: cs cs.AI References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[AI-226] SWIFT: Spatio-temporal Wavelet Integrated Forecasting Framework for Workload Traces
链接: https://arxiv.org/abs/2607.02524
作者: Zeyuan Ding,Lingfeng Zheng,Dian Ding,Guangtao Xue
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注:
Abstract:Accurate cloud workload forecasting is pivotal for efficient resource management but remains challenging as workloads are highly volatile and prone to sudden bursts. Although wavelets preserve temporal locality, rigid fixed bases struggle with complex patterns and isolated processing neglects critical spatial dependencies. To address this, we propose SWIFT, a pure convolutional framework designed for high-efficiency workload forecasting. We introduce a Learnable Cascaded Wavelet Path that reformulates the traditional fixed wavelet bases into adaptive convolutional operators, enabling precise, data-driven feature peeling. Complementing this, our Multivariate Interaction Module sequentially models inter-variable spatial and intra-variable feature interactions to stabilize and refine noisy workload states. Extensive experiments demonstrate that SWIFT achieves SOTA accuracy with linear O(L) complexity, reducing prediction error by up to 31.04% while cutting latency by 79.74%.
[AI-227] AutoResearch: An Execution-Grounded Multi-Agent Framework for Reliable Research Workflow Automation
链接: https://arxiv.org/abs/2607.02520
作者: Rajesh Kumar,Waqar Ali,Junaid Ahmed,Abdullah Aman Khan,Shaoning Zeng
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Automated research agents increasingly generate code, retrieve literature, and draft scientific artifacts, but they often fail to verify whether generated experiments execute correctly or whether cited sources support generated claims. We present AutoResearch, an execution-grounded multi-agent framework for reliable research workflow automation. AutoResearch couples sandboxed Python/PyTorch execution, iterative code repair, citation verification, claim-support auditing, decision control, and structured \LaTeX artifact generation. The system treats runtime errors, citation-verification failures, and review-agent feedback as practical filtering signals for generated research artifacts. In controlled evaluations on HumanEval, MBPP, a SciCode subset, citation-validation tasks, claim-support auditing, and small end-to-end workflow stress tests, AutoResearch improves execution success, citation validity, local claim support, and workflow completion relative to directly comparable baselines. Code-oriented agents are reported separately as partial comparisons. AutoResearch is intended as a reliability-oriented research assistant, not as a fully autonomous scientist or a standalone manuscript-quality benchmark. Source Code: this https URL
[AI-228] GLM-5 Serving Parameter Tuning for OpenClaw: Single-Deployment MaaS Inference Optimization for Long-Context Agent Workloads
链接: https://arxiv.org/abs/2607.02518
作者: Minjie Hua,Ning Wang,Peijun Yang,Kai Wang,Shiguo Lian
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注: 10 pages, 3 figures, 2 tables; technical report
Abstract:OpenClaw requests are dominated by long, tool-augmented prefixes, including system prompts, conversation history, and tool outputs fed back into the context window. For this workload, with about 28k-30k input tokens and 500 output tokens per request, serving quality is governed by throughput, TTFT, and tail latency rather than short-prompt throughput alone. This report studies GLM-5 serving-parameter tuning within a MaaS multi-model inference optimization architecture. The scope is the Single-Node Optimization block of the inference-optimization layer, where chunked prefill, tensor parallelism (TP), pipeline parallelism (PP), and request concurrency are tuned for one GLM-5 serving deployment; in this report, “Single-Node Optimization” denotes the architecture block, while experiments run on a two-node, sixteen-GPU cluster. Within the tested space, the best configuration is chunked-prefill-size=3072, tp=4, pp-size=4, and max-running-requests=24. Compared with the conservative 2048/4/4/16 baseline, it increases request throughput from 0.43 to 0.48 req/s and total token throughput from 9029.64 to 9993.23 tok/s, while reducing average TTFT from 8.98 to 6.69 s and latency P90 from 40.23 to 32.64 s. Under the same hardware footprint, this corresponds to an estimated 10.4% lower serving cost per request and 9.6% lower cost per token. The results show that the optimum is workload-specific: larger chunk sizes and deeper queueing do not monotonically improve performance. We therefore recommend 3072 / tp4 / pp4 / max24 as the default OpenClaw deployment profile. Comments: 10 pages, 3 figures, 2 tables; technical report Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.02518 [cs.DC] (or arXiv:2607.02518v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2607.02518 Focus to learn more arXiv-issued DOI via DataCite
[AI-229] Interpretable Human-Label-Free Deep Learning for Real-Bogus Classification with Uncertainty Quantification
链接: https://arxiv.org/abs/2607.05393
作者: Raphaël Bonnet-Guerrini,Bruno Sanchez,Dominique Fouchez,Benjamin Racine,Maya Guy,Mariam Sabalbal,Manal Yassine,Vincenzo Piuri
类目: Instrumentation and Methods for Astrophysics (astro-ph.IM); Astrophysics of Galaxies (astro-ph.GA); High Energy Astrophysical Phenomena (astro-ph.HE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Submitted to Astronomy Astrophysics, revised after first referee report
Abstract:Time-domain surveys generate many transient candidates, making Real-Bogus classification a critical step in automated discovery pipelines. Reliable labels are costly, while community labels can be noisy and survey-dependent. We aim to develop a Real-Bogus classification framework that can be trained without human-labeled data using injected transients and bogus-dominated survey data, remains robust under strong class contamination, and provides calibrated uncertainty quantification. We combine simulated transient injections with a contaminated survey class and train a dual-network model using asymmetric co-teaching for classes with different label-noise levels. We evaluate performance on a benchmark subset and analyze the learned representation with latent-space visualization tools. For uncertainty quantification (UQ), we compare MC dropout and deep ensembles and propose a low-cost hybrid strategy that exploits the dual-network setting to improve calibration. We extend the evaluation to the light-curve domain to assess recovery of light-curve classes. The method achieves strong Real-Bogus performance on the labeled subset and remains stable under severe class contamination. It recovers transient light-curve classes with high fidelity, while single-source identification is limited by ambiguity in light-curve-derived labels. Our hybrid UQ approach achieves competitive calibration relative to more expensive ensemble baselines. Latent-space analyses indicate that uncertainty aligns with the decision boundary and reveal subclasses within the bogus population. Our results show that injection-driven, weakly supervised training can enable scalable and consistent Real-Bogus classification without human-labeled training data while providing calibrated uncertainties. The method is suited for transfer to forthcoming surveys by re-running the injection-based training pipeline.
[AI-230] Wavelet Scattering Transform for Interpretable Schizophrenia Biomarker Discovery and Classification from Resting-State EEG
链接: https://arxiv.org/abs/2607.05282
作者: Md. Taksimul Ahsan Tawhid,Nasif Ahmed Rafe,Alif Tahmid Priyom,K. M. Mustafizur Rahman
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Methodology (stat.ME)
备注: 15 pages, 11 figures
Abstract:Schizophrenia is a debilitating neuropsychiatric disorder characterized by profound cortical network dysregulation, for which objective, clinically translatable EEG based biomarkers remain underdeveloped. Existing automated classification pipelines rely predominantly on static power spectral density features inherently blind to amplitude modulation dynamics and cross-frequency coupling, phenomena central to schizophrenia pathophysiology, while adopting epoch level cross validation strategies that introduce temporal data leakage, artificially inflate reported performance. This study introduces a mathematically principled diagnostic framework integrating the multi-order Wavelet Scattering Transform(WST), strict Leave One Subject Out (LOSO) cross-validation, and SHAP explainability for simultaneous EEG classification and biomarker discovery. Hierarchical WST coefficients capturing multi-scale amplitude modulation structure were extracted from resting state multichannel EEG. Subject-level ANOVA with Benjamini Hochberg false discovery rate correction identified significant biomarkers, with Random Forest and SVM classifiers evaluated under strict LOSO cross validation and subject-level majority voting. Second-order scattering coefficients encoding cross frequency coupling dominated the discriminative biomarker set, with gamma-band features most prevalent, demonstrating that temporal amplitude modulation constitutes the primary electrophysiological signature of schizophrenia. Electrode P3 was identified as the single most discriminative site. Under rigorous subject independent evaluation, the Random Forest achieved 90.48% accuracy (AUC = 0.9339; sensitivity = 95.56%). The proposed WST framework establishes a rigorous, interpretable standard for EEG-driven psychiatric biomarker discovery that can also be applicable in the detection of schizophrenia subtypes in the future.
[AI-231] ProPS: Prompted Profile Synthesis for Natural Language-Conditioned Speaker Embedding Distributions
链接: https://arxiv.org/abs/2607.05276
作者: Thomas Thebaud,Junhyeok Lee,Laureano Moro-Velazquez,Jesus Villalba Lopez,Najim Dehak
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI)
备注:
Abstract:Speaker embeddings, or x-vectors, are widely used to represent speaker identity and speaker-related attributes, but existing embedding extractors are typically descriptive rather than generative: they map an observed speech segment to an x-vector, which is then used for downstream applications. We introduce ProPS, Prompted Profile Synthesis, a framework for generating distributions of speaker embeddings conditioned on natural language prompts such as “a thirties male speaker with an Indian accent”. ProPS converts human-written profile descriptions into sentence embeddings and uses a mixture density network trained on a large-scale dataset to predict a Gaussian mixture model in the x-vector space. The model is trained by maximizing the likelihood that real speaker embeddings match the requested profile, and its generated distributions are evaluated by negative log-likelihood on held-out x-vectors and by attribute classification accuracies on sampled synthetic x-vectors. Experiments show that ProPS produces profile-conditioned distributions and generates x-vectors that preserve requested speaker attributes such as age, gender, accent, and prosodic characteristics. This design enables controllable speaker-profile synthesis for speech generation systems like Text-To-Speech (TTS) or Voice Conversion (VC) while anchoring generated distributions in observed speaker-embedding structure.
[AI-232] AIFS-SUBS: Extending Data-Driven Forecasting to Sub-Seasonal Timescales
链接: https://arxiv.org/abs/2607.05100
作者: Jakob Schloer,Steffen Tietsche,Christopher D. Roberts,Lorenzo Zampieri,Simon Lang,Gert Mertes,Gareth Jones,Matthew Chantry,Frederic Vitart
类目: Atmospheric and Oceanic Physics (physics.ao-ph); Artificial Intelligence (cs.AI)
备注:
Abstract:Data-driven models now rival numerical weather prediction in the medium range, but extending them to sub-seasonal lead times raises challenges absent at shorter horizons. Errors accumulate over long autoregressive rollouts, systematic biases grow with lead time, and several years of data must be held out for independent verification, even though machine-learning models otherwise benefit from longer training records. To address these challenges, we adapt ECMWF’s AIFS-CRPS medium-range model. AIFS-SUBS adopts a 24h autoregressive time step to reduce error accumulation, adds stratospheric levels and top-of-atmosphere thermal radiation as predictors, and reserves 2007–2011 as an independent verification window. We evaluate two config-durations: AIFS-SUBS, fine-tuned on operational analyses, and AIFS-SUBS-ERA5, trained on ERA5 alone. Across weeks 2–6, AIFS-SUBS matches the operational Integrated Forecasting System (IFS) in probabilistic skill while reducing systematic biases. For the convective (OLR) component of the Madden–Julian Oscillation (MJO), AIFS-SUBS extends skilful forecasts (correlation 0.5) by eight days relative to the IFS, while matching or exceeding the IFS for the full multivariate RMM index. AIFS-SUBS also reproduces the observed MJO modulation of tropical cyclone activity comparably. Stratospheric skill is particularly strong with AIFS-SUBS reproducing sudden stratospheric warming (SSW) frequency and surface impact. In the AI Weather Quest, AIFS-SUBS-ERA5 attains a variable-averaged ranked probability skill score slightly ahead of the IFS at weeks 3 and 4. At inference, AIFS-SUBS uses about 200 times less energy than the IFS, opening the door to much larger real-time ensembles. AIFS-SUBS is ECMWF’s first machine-learning model targeted at sub-seasonal time-scales. Subjects: Atmospheric and Oceanic Physics (physics.ao-ph); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.05100 [physics.ao-ph] (or arXiv:2607.05100v1 [physics.ao-ph] for this version) https://doi.org/10.48550/arXiv.2607.05100 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-233] Joint Velocity Slope Diffusion Prior for Structurally Constrained Velocity Model Building
链接: https://arxiv.org/abs/2607.04982
作者: Francesco Brandolin,Tariq Alkhalifah
类目: Geophysics (physics.geo-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:High-resolution velocity models are crucial for reservoir characterization and subsurface delineation. However, the band limited nature of our surface recorded data limits resolution. Utilizing well measurements to enhance the resolution of our subsurface models is an important objective. To this end, we present a diffusion-guided framework for structurally preconditioned velocity-model reconstruction from sparse well-log information. The proposed approach combines plane-wave PDE regularization, structurally preconditioned inversion, and measurement-guided diffusion posterior sampling within a unified formulation. Local structural slopes estimated through plane-wave destruction are used both to propagate well information along geological dip directions and to guide the diffusion sampling process through a joint velocity–slope generative prior. Numerical experiments on the Volve synthetic model and the Viking Graben field dataset demonstrate that the proposed framework improves structural continuity, lateral consistency, and geological realism compared with conventional structurally preconditioned inversion approaches while maintaining computationally practical inference through DDIM sampling.
[AI-234] HamQASBench: A Hamiltonian-Informed Diagnostic Benchmark for Evaluating Quantum Architecture Search
链接: https://arxiv.org/abs/2607.04845
作者: Jiayang Niu,Akib Karim,Yan Wang,Jie Li,Ke Deng,Azadeh Alavi,Muhammad Usman,Yongli Ren
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI)
备注:
Abstract:Quantum Architecture Search (QAS) automates the design of parameterized quantum circuits for variational quantum algorithms, yet existing benchmarks organize instances by molecular identity or qubit count – criteria agnostic to Hamiltonian structure – and rely solely on energy accuracy, which cannot detect structural failures such as over-parameterization on near-product ground states. We introduce HamQASBench, a Hamiltonian-informed diagnostic benchmark organizing 11 molecules into five structural tiers via fingerprints derived from the Pauli operator basis, computational basis representation, and ground-state entanglement. A post-hoc critical-structure extraction procedure identifies minimal circuits consistent with each tier’s requirements, complementing energy-based evaluation with per-qubit entanglement analysis and pairwise state fidelity. Benchmarking five QAS methods across four paradigms reveals failure modes invisible to conventional metrics: over-parameterization in the minimalism regime, eigenstate commitment under degeneracy, a representation bottleneck in strongly correlated systems, topology-induced routing failure, and circuit search space growth as a scalability bottleneck.
[AI-235] Wasserstein Residuals: Learning Gradient Flows from Population Dynamics
链接: https://arxiv.org/abs/2607.04738
作者: Markus Heinonen,Yair Shenfeld,Ricardo Baptista,Daniel Waxman,Dmitry Batenkov,Tim Cooijmans,Eli Bingham
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Reconstructing population dynamics is a central problem in the physical and data sciences. Often, the dynamics are modeled as a Wasserstein gradient flow (WGF): a curve of distributions driven by an energy functional. Though there are multiple mathematical characterizations of a WGF, the dominant algorithmic approach relies on the Jordan–Kinderlehrer–Otto (JKO) scheme. JKO-based methods are inflexible to time discretisation and require solving costly optimal transport problems. We take a residual approach, enforcing the continuity equations via a non-negative loss function whose minimum is the WGF. Combined with a data-fitting divergence, this gives a single global objective. This perspective unifies several existing methods and leads to a new particle-based method, stitching, that is simulation-free and robust to large gaps between observations. We demonstrate that the stitching method achieves state-of-the-art performance across trajectory inference benchmarks. For code see this http URL.
[AI-236] Generative wave propagator
链接: https://arxiv.org/abs/2607.04440
作者: Shijun Cheng,Tariq Alkhalifah
类目: Geophysics (physics.geo-ph); Artificial Intelligence (cs.AI)
备注:
Abstract:Seismic wavefield simulation is fundamental to seismology, but conventional finite-difference (FD) methods remain limited by numerical dispersion and stability constraints, which often require dense spatial grids and small time steps and thereby severely limit the effectiveness of iterative inversion workflows. We introduce a conditional diffusion-based wavefield propagator that advances seismic wavefields recursively from one time step to the next. Instead of learning an unconditional data distribution of wavefield evolution, the model is conditioned by a short history of recent wavefield time steps (snapshots), the velocity model, and the wavefield time step index, allowing it to represent the conditional transition between adjacent physical states. By training the network to directly predict the clean next wavefield snapshot, this strong physical conditioning makes it possible to replace the iterative reverse diffusion process with a single network evaluation for each predicted snapshot. To improve stability over long recursive rollouts, we further introduce a causal time-weighted loss, in which adaptive weights, accumulated as exponential moving averages of per-snapshot training errors, emphasize training directions that are consistent with the forward propagation sequence and reduce the amplification of one-step prediction errors. Because the learned propagator is tied to the temporal spacing of the training snapshots rather than to the FD stability limit, it can advance the wavefield using a physical time step ten times larger than that required by the underlying solver. Experiments on the Overthrust, SEG/EAGE, and Marmousi models show that the proposed method accurately reproduces wavefield snapshots and shot gathers and achieves an end-to-end speedup of 2.17 x over a GPU-accelerated tenth-order staggered-grid FD implementation under matched hardware conditions.
[AI-237] Fixed-Confidence Best-Arm Identification for Causal Mediation Analysis
链接: https://arxiv.org/abs/2607.04315
作者: Harsh Shrivastava,Yuta Kawakami,Junpei Komiyama,Jin Tian
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:This paper studies the problem of identifying the treatment that maximizes the expected natural direct potential outcome (NDPO), which captures the potential outcome of an intervention while excluding the pathway transmitted through a mediator that researchers may wish to remove from evaluation. We first establish population-level identification of the expected NDPO in a causal bandit setting using observable interventional distributions. We then develop a fixed-confidence best-arm identification (BAI) algorithm based on the Track-and-Stop (TaS) framework, employing a cutting-set method to solve the resulting semi-infinite optimization problem. The proposed algorithm achieves sample-efficient identification with a high-probability correctness guarantee. We prove that it satisfies \delta -correctness and asymptotic optimality. Finally, we validate the approach through empirical evaluations on a large-scale real-world advertising dataset (IPinYou).
[AI-238] Self-Reference in Large Language Models : The Introspection Threshold for Recursive Self-Improvement
链接: https://arxiv.org/abs/2607.04277
作者: Jiang Zhang,Bing Yuan,Qian Zhang
类目: Physics and Society (physics.soc-ph); Artificial Intelligence (cs.AI)
备注: 21 pages, 4 figures, 1 table
Abstract:The pursuit of self-evolving AI raises a critical question: when is autonomous self-improvement sustainable rather than degenerative? Drawing an analogy to von Neumann’s complexity threshold for self-reproducing automata, we argue that sustainable recursive self-improvement in Large Language Models (LLMs) requires a functional analogue: introspection – the system’s capacity to simulate its own operations and target modifications. Grounded in Kleene’s Second Recursion Theorem, we demonstrate the theoretical existence of such introspective programs. However, an empirical review reveals that while current LLMs exhibit quasi-introspection (e.g., partial metacognition), they fall short of true introspection due to structural bottlenecks: a lack of complete self-access, the feedforward nature of the Transformer, and computational class constraints that prevent fixed-point iteration. We conclude by outlining architectural paths to cross this complexity threshold and discussing the associated safety implications.
[AI-239] NouveauVoice: Generating Novel Pseudo Speakers for Voice Anonymization
链接: https://arxiv.org/abs/2607.03985
作者: Meiying Melissa Chen,Anastasia Kuznetsova,Zhenyu Wang,Zhiyao Duan
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI)
备注:
Abstract:Advanced neural technologies in speech synthesis and voice conversion (VC) have introduced severe risks to personal privacy, necessitating robust Speaker Anonymization Systems (SAS). Existing SAS approaches modify voice characteristics in the hand-crafted feature space or speaker embedding space, often struggling to provide sufficient identity variance across generated voices. In this paper, we propose NouveauVoice, a novel pseudo-speaker generation framework based on a Hierarchical Deep Variational Autoencoder (NVAE). Integrated as a standalone plug-in module on top of state-of-the-art architectures (FACodec and CosyVoice2), our approach leverages tractable sampling and the Evidence Lower Bound (ELBO) objective to synthesize highly expressive pseudo-speaker embeddings with significantly enhanced speaker diversity. Evaluating our framework under a protocol similar to the VoicePrivacy Challenge alongside Maximum Mean Discrepancy (MMD) analysis, we demonstrate that NouveauVoice achieves strong identity concealment, yielding an Equal Error Rate (EER) exceeding 38% against an automatic speaker verification attacker model. Our system shows a reasonable trade-off between strict anonymity, rich pseudo-speaker diversity, and downstream speech utility, such as intelligibility and emotional expressiveness.
[AI-240] Probing Low-Level Acoustic Attribute Encoding in CLAP Audio Embeddings
链接: https://arxiv.org/abs/2607.03806
作者: Héctor Martel,Joe Hennessy-Priest,Taemin Cho
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
备注: Accepted to DAFx 2026
Abstract:Audio foundation models are widely adopted as general-purpose feature extractors, yet the internal structure of their learned representations remains insufficiently understood. In this work, we analyze CLAP audio embeddings through a probing framework, studying the encoding of three fundamental perceptual dimensions: reverberation (RT60), loudness (LUFS), and spectral content, measured via spectral centroid (SC) and relative pitch (RP). Probes of increasing complexity are trained to predict each attribute from frozen embeddings across five datasets spanning noise, speech, monophonic musical notes, and music mixtures. Our primary finding is that all of these attributes are reliably recoverable from the CLAP embedding space across the examined datasets. Within this global picture, two encoding regimes emerge: RT60, LUFS, and RP are approximately linearly encoded, while SC requires non-linear probes. Both regimes generalize across eight additional audio foundation models, with the notable exception that amplitude-invariant architectures discard loudness entirely by construction. The identified linear feature directions are geometrically consistent across datasets for RT60 and LUFS, while highly domain-specific for RP. Finally, we provide a qualitative demonstration of cross-modal consistency, showing that text embeddings of acoustic descriptors align geometrically with the identified RT60 feature direction.
[AI-241] An AI-Assisted Solution to the Signed BAR Conjecture: Uniqueness in the Harrison–Reiman Class and a Completely-mathcalS Class Obstruction
链接: https://arxiv.org/abs/2607.03639
作者: Yiping Lu,Youheng Zhu
类目: Probability (math.PR); Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI); Statistics Theory (math.ST)
备注:
Abstract:For a multidimensional reflected diffusion, determining whether the associated basic adjoint relationship (BAR) uniquely characterizes the stationary distribution is a basic uniqueness problem in the BAR approach. The problem has remained unresolved for more than 35 years since the introduction of the BAR approach. In this paper, we resolve the finite-signed uniqueness problem for stable Harrison–Reiman data with a nonsingular M -matrix reflection matrix. The proof uses pathwise differentiability of the reflected diffusion implies feasible directional differentiability of the probabilistic resolvent to show that, at boundary points, its one-sided initial-state derivative factors through the tangent projection and vanishes along active reflection directions. An interior one-sided convolution then yields smooth test functions whose oblique derivatives are uniformly bounded and converge pointwise to zero on each closed face. The interior signed measure is consequently invariant for the reflected semigroup. The proof was discovered with the assistance of ChatGPT 5.5 Pro and subsequently verified by the authors. We also show that the nonsingular M -matrix assumption is structural. In the larger completely- \mathcalS class, a nonsingular reflection matrix with a singular proper principal block admits boundary gauges supported on lower-dimensional strata. Under standard exponential ergodicity and a mild one-step regulator bound, these gauges produce nonzero zero-mass signed BAR tuples; indeed the zero-mass interior BAR coordinates contain an infinite-dimensional subspace. A four-parameter three-dimensional family, including an explicit rational example, verifies the obstruction. Thus the finite signed version of the Dai–Dieker question has a positive answer in the Harrison–Reiman M -matrix class and a negative answer in a natural completely- \mathcalS extension.
[AI-242] he S-ICDF Dataset: Sionna-Simulated Dynamic Interference Characterization and Direction Finding
链接: https://arxiv.org/abs/2607.03411
作者: Christian Wielenberg,Lucas Heublein,Jonathan Ott,Alexander Mattick,Nisha L. Raichur,Jonas Pirkl,Lukas Schelenz,Tobias Feigl,George Yammine,Christopher Mutschler,Felix Ott
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI)
备注: 6 pages, 9 figures
Abstract:Jamming and spoofing threaten wireless and satellite navigation by disrupting or manipulating radio frequency (RF) signals, undermining availability, integrity, and trust. Robust interference monitoring (i.e., detection, classification, characterization, and direction finding) is therefore essential to identify and localize anomalous signals. While machine learning (ML) promises improved performance in complex environments, its development and validation depend on large-scale datasets that capture realistic signal and channel variability. Collecting such data in the real world is difficult because intentional jamming is illegal and ground-truth attribution is confounded by propagation, hardware, and environmental effects. To address this gap, we create and publish S-ICDF, a large-scale indoor interference dataset generated with Sionna, a GPU-accelerated simulation library for physical-layer wireless communications. S-ICDF covers 102 interference configurations, including diverse antenna array patterns, bandwidths, and simulation settings such as noise level and reflection depth. We further provide baseline results by benchmarking S-ICDF with classical estimation and direction finding (DF) methods (MUSIC, ESPRIT, and CAPON) and with modern ML approaches. The dataset is publicly available at: this https URL
[AI-243] Self-Specializing Vision-Language Transmon Chip Calibration in a Physics-Grounded Environment
链接: https://arxiv.org/abs/2607.03193
作者: Animesh Tripathy,Aswanth Krishnan
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Calibrating a superconducting transmon chip is a sequential decision problem under noise, drift, and a finite budget: an expert must choose experiments, read ambiguous plots, judge fit quality, and revise stale beliefs as the chip drifts. We study whether a vision-language agent can close this loop and specialize itself to one physical device without weight updates, via three co-designed artifacts. The first is a physics-grounded simulation environment for transmon chips: calibration observables derive from circuit-quantized parameters via scqubits, with realistic flux-line distortion, wall-time-scaled and mid-scan drift, and gate leakage, concerns a toy simulator would omit; each tool call advances a modeled clock so drift accrues by wall time, not call count. The second is a vision-language agent that runs the loop end to end, calling tools, reading plots, maintaining a structured notebook, and submitting parameters without hidden truth, scored against hidden parameters and gate fidelities measured on the device. The third is gradient-free online adaptation: a reflector reads truth-free anomaly signatures from past attempts and grows a small, human-readable device note appended to the prompt, admitted by a paired-snapshot accept gate that isolates strategy improvement from drift. On a hard-tier chip under budget pressure, six iterations raised the worst-case CZ fidelity from 0.678 to 0.787 and cut its variance, reproducing at four-qubit scale; a single accepted note raised CZ fidelity from 0.678 to 0.913 on its paired snapshot. A planted-fault study confirms the note is causal, diagnosing a hardware fault truth-free, its principal value raising the failure floor and cutting variance. The agent, scoring, and reward transfer to real hardware via a measurement-backend swap; only the accept gate is a simulation affordance, reducing to a held-out-slice or repeat-and-average form.
[AI-244] COMET: Combinatorial Optimization for Multiplex Editing Targets Via Constraint-Preserving QAOA
链接: https://arxiv.org/abs/2607.02622
作者: Priyansh Singhal,Sumit Maheshwari,Piyush Joshi
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI)
备注:
Abstract:Multiplex CRISPR-Cas9 gene editing requires selecting one guide RNA per target gene subject to cross-gene interactions: a constrained combinatorial problem that can be formulated as a Quadratic Unconstrained Binary Optimization (QUBO) and solved via the Quantum Approximate Optimization Algorithm (QAOA). The one-hot per-gene constraint is conventionally enforced by adding quadratic penalty terms to the cost Hamiltonian, but penalty coefficient selection is heuristic and penalties amplify hardware noise. An alternative is to enforce the constraint structurally via the XY-mixer, which preserves feasibility by construction. We present COMET, a systematic comparison of penalty-based and XY-mixer QAOA on a three-gene, twelve-qubit multiplex editing instance targeting the immune-checkpoint genes PDCD1, LAG3, and HAVCR2. In simulation, the XY-mixer exceeds 95% probability of the optimum by QAOA depth p=3, while three penalty variants spanning an order of magnitude in penalty coefficient remain below 6% at every depth. On IBM’s ibm_kingston (Heron r2) processor, the XY-mixer’s simulator-hardware energy gap stays within |0.8| across all depths, while the worst-tuned penalty variant’s gap reaches +53.9. We provide an honest account of where the structural guarantee partially breaks under gate-level noise. The twelve-qubit instance is classically trivial; our contribution is a methodological comparison of constraint-enforcement strategies in a biologically motivated domain, with real-hardware validation.
[AI-245] DOSE-I: A Multimodal Biosignal Dataset of Procedural Sedation for Endoscopy – Technical Report
链接: https://arxiv.org/abs/2607.02570
作者: Jakob Garbe,Jan W. Kantelhardt,Katja Seeliger,Thomas Schmid
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI)
备注: Dataset can be accessed via zenodo DOI this https URL For citation use the primary academic reference: Garbe J et al. Towards predicting sedation depth in endoscopy with large clinically annotated EEG data of continuous Propofol sedation. In: P. Andreevetal (Eds.): AIME2026, LNAI 16749, p.1-6, Springer, 2026. this https URL
Abstract:In this document, we describe characteristics and technical details of the multimodal biosignal dataset DOSE-I of procedural sedation for endoscopy published on zenodo. The DOSE-I dataset includes 78.5 hours of recording in 171 records ranging from 6.7 to 70.8 minutes (mean: 27.5, SD: 11.6) of 281 endoscopic procedures. 1129 (median: 6 per record) transitions of consciousness and 7328 (median: 39 per record) individual sedation depth labels were recorded. In addition to clinically annotated biosignals, the DOSE-I dataset provides detailed static data about the respective study subject and metadata about the respective recordings. To further support future research, we provide details about artifact detection and preprocessed pEEG features, too. C code used for this preprocessing is provided separately via Github.
[AI-246] CRODA-ST: Single-Target Cross-Receiver Open-Set Radio Fingerprint Recognition
链接: https://arxiv.org/abs/2607.02567
作者: Fengchong Yao,Jianbing Li,Qing Liu,Kefeng Song,Haitao Li,Song Wang,Feixiang Wang
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI)
备注:
Abstract:Radio frequency fingerprint identification (RFFI) provides a physical-layer credential for Internet of Things devices, but open-set decisions become fragile when a threshold calibrated on a source receiver is transferred to a target receiver. Receiver shift can lower the confidence of known transmitters and cause false rejection; closed-set alignment can have the opposite effect by pulling unseen target transmitters into known regions and increasing false acceptance. This letter presents CRODA-ST, a structure-first adaptation framework for singlesource single-target cross-receiver open-set RFFI. Its two components target the bottlenecks behind unreliable source-calibrated rejection: Discriminative Structure Anchoring (DSA) restores target-receiver known-class references from limited labeled target enrollment samples, and Rejection-Oriented Alignment (ROA) reduces receiver-sensitive confidence fluctuations around the anchored structure. On the WiSig ManyTx dataset, CRODA-ST reaches 0.9092 known-class accuracy, 0.9692 AUROC, and 0.9580 OSCR. Score-sweep analysis further reduces FPR90 to 0.0469.
[AI-247] Neural-Network Inverse Design of SRF Cavities and Transmons for Bosonic Quantum Computation
链接: https://arxiv.org/abs/2607.02289
作者: Joseph Yaker,Jovan Markovic,Alessandro Reineri,Doga Murat Kurkcuoglu,Silvia Zorzetti
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); High Energy Physics - Experiment (hep-ex)
备注:
Abstract:Three-dimensional superconducting radio-frequency (SRF) cavities provide exceptionally long-lived electromagnetic modes and, when coupled to nonlinear elements such as transmon qubits, become promising architectures for bosonic quantum information processing. The inverse design of such systems, i.e., recovering device geometries that produce specified electromagnetic and coupling targets, is generally a one-to-many problem. The qubit-cavity coupling strength depends sensitively on both the transmon geometry and its position within the cavity’s electromagnetic field. As these systems scale up and their design parameter spaces grow, the cost of conventional iterative simulation becomes prohibitive. We present two deep neural network (DNN) approaches that address this inverse-design problem at complementary levels of the design stack. The first proposes SRF cavity geometries that produce target cavity observables. The second proposes transmon qubit designs that produce target qubit-cavity parameters – the coupling rate, qubit frequency, and anharmonicity (g, \nu_q, \alpha) . The recovered candidate designs match the targets to within \sim 5% (cavity) and \sim 2% (transmon), confirmed by end-to-end re-simulation. Both approaches map desired device behavior directly to candidate designs, a fast alternative to the iterative simulation studies usually required.
机器学习
[LG-0] abPack: Efficient Hyperparameter Ensembles for Tabular Deep Learning ICML2026
链接: https://arxiv.org/abs/2607.05380
作者: Yury Gorishniy,Akim Kotelnikov,Ivan Rubachev,Artem Babenko
类目: Machine Learning (cs.LG)
*备注: ICML 2026. Code: this https URL
Abstract:In deep learning for tabular data, efficient ensembles of multilayer perceptrons (MLPs) have recently emerged as effective and practical architectures. Existing methods of this kind use the same hyperparameters for all underlying MLPs, which requires hyperparameter tuning for achieving the best performance. In this work, we introduce TabPack, an efficient MLP ensemble with strong out-of-the-box performance and reduced reliance on traditional tuning. In a single run, TabPack samples and trains many MLPs with different hyperparameters efficiently in parallel and selects ensemble members on the fly during training. Thus, TabPack only requires specifying ranges from which to sample MLP hyperparameter rather than exact hyperparameter values, which naturally demands less precision for good performance. In experiments on medium-to-large public datasets, TabPack with default settings performs on par with extensively tuned prior methods, thus substantially reducing effort and compute resources needed to achieve competitive results on tabular tasks. Notably, running the default TabPack configuration on a modern MacBook took less time than tuning some baselines on an industry-grade GPU.
[LG-1] CompactionRL: Reinforcement Learning with Context Compaction for Long-Horizon Agents
链接: https://arxiv.org/abs/2607.05378
作者: Yujiang Li,Zhenyu Hou,Yi Jing,Jie Tang,Yuxiao Dong
类目: Machine Learning (cs.LG)
*备注:
Abstract:Long-horizon agentic LLMs are increasingly limited by finite context windows, as extended interaction trajectories can exceed the maximum context length before a task is completed. Context compaction offers a natural solution by summarizing previous interaction states and continuing the rollout under a compressed context, but incorporating compaction into reinforcement learning remains underexplored. We propose CompactionRL, a reinforcement learning strategy to train long-horizon agentic LLMs with context compaction. Our approach jointly optimizes task execution and summary generation with token-level loss normalization and cross-trajectory generalized advantage estimation. This design enables the LLM agents to learn from compacted long-horizon trajectories. We train CompactionRL on top of open models and observe consistent performance gains on agentic coding tasks. CompactionRL enables the open GLM-4.5-Air model (106B-A30B) to achieve Pass@1 scores of 66.8% on SWE-bench Verified and 24.5% on Terminal-Bench 2.0, with absolute gains of 7.0 and 3.1 points, respectively. Built upon GLM-4.7-Flash (30B-A3B), CompactionRL improves Pass@1 by 5.5 and 6.8 points, reaching 56.0% on SWE-bench Verified and 20.2% on Terminal-Bench 2.0, respectively. CompactionRL is thus deployed in the RL pipeline for training the open GLM-5.2 model (750B-A40B).
[LG-2] How Far is Too Far? Defining the Distance Threshold for Verification Siamese Networks
链接: https://arxiv.org/abs/2607.05329
作者: Heloísa Dias Viotto,Cauê Samonek,Lucas Garcia Pedroso,Marcos Sunye,André Abed Grégio,Paulo Lisboa de Almeida
类目: Machine Learning (cs.LG)
*备注:
Abstract:Siamese verification networks are widely used to compare items such as faces, cars, or signatures. In these scenarios, the network is trained to learn an embedding space in which similar objects are mapped closer together, while dissimilar objects are mapped further apart. Two objects are considered to belong to the same class (e.g., the same person in two different images) when the distance between their embeddings falls below a predefined threshold. Defining this threshold, however, is a non-trivial task and typically requires labeled data. In this work, we assume that the distribution of distances produced by a siamese verification network can be approximated by a bimodal function. Based on this assumption, we propose an unsupervised method to determine the verification threshold by identifying the minimum point between the two modes. The proposed approach does not require annotated samples, enabling the verification threshold to be updated directly in the deployment environment without the cost of manual labeling. We evaluate our method on four datasets: MNIST, CIFAR-10, LFW, and PKLot. The results indicate that the proposed approach achieves an average verification accuracy of 94%, comparable to the Equal Error Rate method, while eliminating the need for labeled data.
[LG-3] Biologically Informed Deep Neural Networks for Multi-Omic Integration Pathway Activity Inference and Risk Stratification in Cancer
链接: https://arxiv.org/abs/2607.05306
作者: Pedro Henrique da Costa Avelar,Le Ou-Yang,Min Wu,Sophia Tsoka
类目: Machine Learning (cs.LG)
*备注:
Abstract:Integrating complex, multi-omics data presents significant challenges. Existing approaches often face a trade-off between model interpretability and representational capacity, with most either relying on post-hoc interpretation or use linear models that may overlook complex interactions. We report Pathway Activity Autoencoders for the multi-omics setting, which embed prior knowledge via pathway-informed architectural constraints, fostering interpretability, while preserving representational power. Our multi-omic framework is applied in the context of breast cancer and is evaluated in survival prediction and subtype classification with results indicating a positive effect of integration. We conduct analysis of individual omics layer impact on end-task performance, revealing that gene, protein, and microRNA expression layers provide the strongest contribution. Repeatability studies indicate that, while dropout improves model robustness and consistency, excessive regularisation can reduce predictive performance. Finally, visualizations of the learned feature space illustrate the framework’s intrinsic transparency and clinical relevance. The results underscore the value of multi-omic integration and delineate the impact of individual omics layers, establishing practical guidelines for integration within our framework. Overall, our pathway activity autoencoder frameworks yield superior latent representations that are biologically meaningful and are directly translatable into clinically relevant insights.
[LG-4] Learning Only What Valid Adapters Can Express: Subspace-Constrained Adaptation Against Fine-Tuning Poisoning
链接: https://arxiv.org/abs/2607.05300
作者: Fabien Polly
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注: 10 pages, 7 figures, 2 tables. Code and data: this https URL
Abstract:Parameter-efficient fine-tuning still leaves a broad space of behavior-changing updates reachable, so a poisoned objective can be represented and optimized. We study an alternative: adaptation constrained to the subspace estimated from a trusted pool of existing task adapters. On flan-t5-large with 196 public LoRA adapters, we show that (1) the functionally relevant content of an adapter lies in a low-dimensional shared subspace, 30 to 38 percent of its weight norm being redundant under the evaluated task distributions; (2) gradient adaptation restricted to 128 coordinates on this subspace matches full LoRA fine-tuning on clean classification data, while under targeted label inversion LoRA collapses to 3-26 percent exact match and the constrained learner keeps 62-96 percent on the tasks the pool covers; (3) the constrained learner cannot fit corrupted data, its adaptation loss separating clean from garbage by two orders of magnitude (120x), an out-of-distribution signal without an extra detector; and (4) against an adaptive backdoor attacker who optimizes within the subspace, the attack is blocked (8 percent success versus 100 for LoRA) on the task where its target behavior is unlike anything in the pool, and only partially blocked (85 percent) when the target coincides with a common pool behavior. On these two tasks the outcome is consistent with how close the target is to the pool’s directions, which suggests but does not establish a pool-relative boundary. The mechanism trades peak plasticity for these properties: on tasks the pool covers poorly, unconstrained fine-tuning wins, and the protection assumes the pool itself is trusted. Code and data are public.
[LG-5] Advances in Neural Controlled Differential Equations
链接: https://arxiv.org/abs/2607.05280
作者: Benjamin Walker
类目: Machine Learning (cs.LG)
*备注: DPhil thesis, University of Oxford, 188 pages, 17 figures
Abstract:Many real-world systems evolve continuously, yet most machine learning models interpret time series as discrete sequences. Continuous-time approaches instead treat time series as samples from an underlying input path, a formulation that naturally accommodates irregularly sampled or oversampled data. Among these, Neural Controlled Differential Equations (NCDEs) are a maximally expressive class of models that parametrise a vector field using a neural network and evolve their hidden state by solving a dynamical system driven by the input path. NCDEs typically use a non-linear vector field, so their expressive power and continuous-time flexibility come at the cost of a forward pass that is both computationally expensive and inherently sequential, limiting their scalability and practical applicability. This thesis advances the training and scalability of NCDEs through three complementary contributions. First, building on neural rough differential equations, Log-NCDEs apply the Log-ODE method to efficiently approximate an NCDE’s solution during training, improving both computational speed and empirical performance. Second, Linear NCDEs replace the non-linear vector field with a linear one, enabling closed-form solutions and parallel-in-time computation without sacrificing theoretical expressivity. Third, Structured Linear NCDEs use structured linear vector fields to further enhance efficiency while maintaining theoretical expressiveness and empirical performance. Collectively, these methods reduce the time per training step for an NCDE by up to three orders of magnitude while achieving state-of-the-art performance across diverse time series benchmarks. Comments: DPhil thesis, University of Oxford, 188 pages, 17 figures Subjects: Machine Learning (cs.LG) Cite as: arXiv:2607.05280 [cs.LG] (or arXiv:2607.05280v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.05280 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-6] Untrusted Content Masking for Web Agents with Security Guarantees
链接: https://arxiv.org/abs/2607.05277
作者: Kristina Nikolić,Egor Zverev,Javier Rando,Matthew Jagielski,Edoardo Debenedetti,Florian Tramèr
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:Defenses that provide security guarantees against prompt injection attacks rely on strict isolation between trusted instructions and untrusted data. In text-based environments such as tool-use APIs, this separation arises naturally: agents can reason from interface definitions without ever processing untrusted content. Extending these guarantees to web agents faces a fundamental challenge: to perceive and interact with their environment, web agents must first observe the rendered page, which intermingles trusted content with untrusted content. This structural entanglement removes the trust boundary on which security guarantees depend, undermining provable defenses for web agents. In this paper, we present Untrusted Content Masking (UCM), a simple and effective approach that restores this boundary in web environments. We leverage a key structural insight: a webpage’s Document Object Model (DOM) encodes sufficient information to distinguish trusted from untrusted regions without reading their content. Our framework exploits this by redacting untrusted regions before they reach the agent and routing interaction through a sandboxed interface with strict privilege separation, thereby enabling agents to observe and interact with their environment while remaining isolated from adversarial content. The code is publicly available.
[LG-7] arget-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach
链接: https://arxiv.org/abs/2607.05271
作者: Qian Hu,Bin Fan,Yao Xiao,Zhicheng Lin,Meixin Xiong
类目: Machine Learning (cs.LG)
*备注:
Abstract:Physics-informed neural networks (PINNs) encounter ill-posed optimization, loss competition, and parameter compensation in partial differential equation (PDE) inverse problems. Transfer learning can reuse representations from source tasks, but direct fine-tuning may introduce negative transfer when dominant physical mechanisms, governing parameters, or observation noise differ between source and target domains: the model achieves low field error yet recovers incorrect target physical parameters. To mitigate, we propose Target-Guided Selective Reweighting PINN (TGSR-PINN), a target-evidence-driven representation correction method for PINN inverse transfer learning. TGSR-PINN transfers only the weights and biases from the source PINN, while target physical parameters are independently initialized; after a short target-adaptation phase, the method computes neuron target scores using first-order Taylor sensitivity and pre-activation variance on fixed scoring batches, and converts evidence associated with low-scoring neurons into continuous weak-adaptation signals via a Gaussian mixture model (GMM) with rank fallback. TGSR-PINN then applies selective soft decay to input weight rows and biases of low-scoring neurons instead of hard pruning or random resetting. In experiments, TGSR-PINN improves target parameter recovery while maintaining comparable field accuracy in the high-Péclet 2D advection-diffusion task and in the Allen–Cahn to Burgers cross-PDE-family transfer task; a 5%-noise reaction–diffusion case provides supplementary evidence under milder source-target mismatch. Ablation studies suggest that neuron target scoring, weak-adaptation signal estimation, layer protection, and selective soft decay jointly contribute to the benefits.
[LG-8] GeoFlow: Geo-Aware Modeling of Inter-Area Relationships in Origin-Destination Flow Prediction and Generation ICML2026
链接: https://arxiv.org/abs/2607.05257
作者: Zherui Huang,Guanjie Zheng,Hao Xue,Linghe Kong
类目: Machine Learning (cs.LG)
*备注: Accepted by ICML 2026
Abstract:Origin-destination (OD) flow modeling underpins urban planning and mobility analysis, but prevailing graph-based methods often neglect salient geographic attributes, limiting their ability to model long-range and multi-area dependencies. In this paper, we introduce GeoFlow, a novel framework that (i) augments area representations with geospatial attributes, including relative positions, k-hop and geodesic distances, (ii) employs a specialized geometric-intrinsic fusion encoder design that combines graph attention for intrinsic area signals with coordinate-aware encoders for global structure, and (iii) adopts an axial-global attention decoder to capture OD-specific competitive dependencies. For OD flow generation, GeoFlow is paired with flow matching models to produce more authentic and diverse mobility samples. Empirically, GeoFlow achieves superior performance in predictive accuracy, while substantially improving generative fidelity and diversity. Ablation and analytical studies confirm the contribution of each component. Code is available at this https URL.
[LG-9] FUSE: FK-Steered Multi-Modal Flow Matching for Efficient Simulation-Based Posterior Estimation ICML2026
链接: https://arxiv.org/abs/2607.05252
作者: Weichen Qin,Yufan Xie,Peihao Wang,Chia-Jui Chou,Minghui Du,Peng Xu,Ziren Luo,Yi Yang,Jingyi Yu,Bo Liang,Jiakai Zhang
类目: Machine Learning (cs.LG)
*备注: Accepted to the 43rd International Conference on Machine Learning (ICML 2026). 22 pages, 5 figures
Abstract:Simulation-Based Inference (SBI) is critical for scientific discovery, with generative models offering a promising path toward efficient inference. However, existing methods struggle with effective multimodal modeling. They often rely on brute-force fusion strategies that ignore the structural disparities between parameters and observations, thus limiting estimation fidelity. In this work, we introduce FUSE (Feynman-Kac steered mUlti-modal flow matching for efficient Simulation-based posterior Estimation). Unlike prior work, FUSE employs a dual-track architecture that preserves the distinct features of multimodal inputs while facilitating dynamic interaction. Additionally, we propose an FK-steered sampling strategy that leverages intermediate observation likelihoods to guide the generative trajectories, effectively improving the sample quality during inference. Our approach outperforms state-of-the-art baselines on standard SBI benchmarks, producing posteriors that closely match ground-truth MCMC. Furthermore, in a real-world exoplanet orbital estimation task, FUSE successfully resolves complex parameter degeneracies that challenge existing methods, highlighting its potential to accelerate complex scientific discoveries in astrophysics and beyond.
[LG-10] Video-based detection of cessation of breathing in pre-term infants using machine learning
链接: https://arxiv.org/abs/2607.05230
作者: Dineo Serame,Lionel Tarassenko,Mauricio Villarroel
类目: Machine Learning (cs.LG)
*备注: Paper submitted to Computer Methods and Programs in Biomedicine (CMPB)
Abstract:Pre-term infants are susceptible to potentially harmful apnoea-related cessations of breathing due to immature respiratory control. However, reliable respiratory monitoring in the neonatal intensive care unit (NICU) remains challenging because motion artefacts, sensor displacement, and skin fragility can compromise contact-based measurements. Non-contact video monitoring offers a complementary approach that does not depend on adhesive sensors while providing additional respiratory information. We investigated whether camera-based signals can detect apnoea-related cessation of breathing (COBE) and provide complementary information to routinely acquired physiological signals. Using video and clinical recordings from 30 pre-term infants, respiratory motion was extracted from dynamically tracked torso regions to generate camera-derived time-series signals. Camera-only models were trained using residual network (ResNet) architectures, while hybrid models combined video-derived signals with impedance pneumography (IP), ECG-derived respiration (EDR), and the PPG-derived respiratory envelope. Camera-only models achieved a balanced accuracy of 76.9%, demonstrating the feasibility of non-contact COBE detection. Combining video-derived features with IP improved balanced accuracy to 90.6%, outperforming either modality alone and indicating that video provides respiratory information beyond standard physiological signals. These findings show that video-derived signals contain clinically relevant respiratory features and enhance COBE detection when combined with conventional physiological signals. This supports non-contact video as a complementary modality for automated COBE detection and highlights its potential to improve the robustness of neonatal respiratory monitoring. Comments: Paper submitted to Computer Methods and Programs in Biomedicine (CMPB) Subjects: Machine Learning (cs.LG) ACMclasses: I.2.6 Cite as: arXiv:2607.05230 [cs.LG] (or arXiv:2607.05230v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.05230 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Dineo Serame DPhil [view email] [v1] Mon, 6 Jul 2026 15:42:21 UTC (6,193 KB)
[LG-11] FlatManifold: Robust Continual Learning under Severe Label Noise and Domain Shifts via Intrinsic Manifold Flattening
链接: https://arxiv.org/abs/2607.05201
作者: Rai Hisada,Kanji Tanaka
类目: Machine Learning (cs.LG)
*备注: 5 pages, technical report
Abstract:In non-stationary streaming environments, simultaneously adapting to complex, non-linear domain shifts via continual learning while mitigating the catastrophic effects of severe, uncalibrated label noise poses a fundamental mathematical challenge. In this paper, we propose \FlatManifold, a novel, streamlined robust continual learning framework that utilizes a Nyström manifold flattening map based on the kernel trick and projection onto an orthogonalized Reproducing Kernel Hilbert Space (RKHS). Unlike traditional methods that rely on complex, error-prone sample-filtering pipelines, the proposed approach exploits the intrinsic mathematical robustness of the flattened space itself. By mapping feature distributions onto a fixed orthogonal target topology with a ridge regularizer, the framework naturally smoothes and counteracts the influence of extreme label noise during the optimization process. Concurrently, catastrophic forgetting is prevented via a continual topology brake term that leverages the covariance matrix of past experiences. Extensive evaluation on real-world multi-session robotics datasets demonstrates that even under severe conditions featuring 40% symmetric label noise, \FlatManifold successfully mitigates gradient corruption. Under extreme cross-session domain shifts spanning various seasons and lighting conditions, the proposed framework establishes high generalization capabilities, significantly outperforming standard sequential optimization baselines and proving that structural linearization itself serves as a powerful mathematical barrier against distributed label corruption. Comments: 5 pages, technical report Subjects: Machine Learning (cs.LG) Cite as: arXiv:2607.05201 [cs.LG] (or arXiv:2607.05201v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.05201 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-12] Latent Programming Horizons in Coding Agents
链接: https://arxiv.org/abs/2607.05188
作者: André Silva,Han Tu,Martin Monperrus
类目: Machine Learning (cs.LG); Software Engineering (cs.SE)
*备注:
Abstract:A coding agent solving a software-engineering task spends dozens of steps reasoning, editing code, and running tests, yet little is known about what the underlying language model internally represents about the program it is working on. We show that the residual streams of language models under coding agents linearly encode properties of the evolving program: a logistic-regression probe on hidden states is able to decode whether the current code parses, passes its test suite, reduces the number of failing tests, and introduces regressions, reaching AUC up to 0.83 for correctness across two models and two benchmarks. Our second finding is more surprising: these representations run ahead of the agent’s own edits. Probes trained to predict the outcome of future edits (before they are materialized and written on disk) achieve performance above chance up to roughly 25 steps in advance. We call this the agent’s latent programming horizon. As a proof of external validity, we show that the probes transfer across benchmarks without retraining. Our positive results open calls for more research in mechanistic interpretability of coding agents.
[LG-13] SMART: A Machine Learning and Monte Carlo Framework for Rapid Analysis of Stochastic Transistor Aging and Process Variation in Digital Circuits
链接: https://arxiv.org/abs/2607.05187
作者: Arash Esshaghi,Siavash Es’haghi,Gholamreza Shahabadi,Alireza Moradi
类目: Machine Learning (cs.LG); Hardware Architecture (cs.AR)
*备注: Submitted to Engineering Reports, Under Review
Abstract:As CMOS technology scales into the deep nanometer regime, digital circuit reliability is increasingly threatened by the combined stochastic effects of Bias Temperature Instability (BTI) and Process Variation (PV). Traditional reliability analysis methods, which rely on computationally intensive simulations or extensive lookup tables, fail to scale efficiently for large designs, creating a critical bottleneck in design space exploration. To address this, we propose SMART, a novel framework that integrates Machine Learning (ML) with Monte Carlo simulation to enable rapid, high-fidelity reliability analysis. SMART employs Random Forest regression to predict gate delay distributions directly, bypassing time-consuming atomic model parameter extractions. Crucially, the model utilizes Bayesian Optimization for automated hyperparameter tuning, ensuring maximum predictive robustness across diverse libraries. Experimental validation on ISCAS85 benchmark circuits demonstrates that SMART achieves a 94.54% reduction in analysis time compared to state-of-the-art methods, while maintaining a remarkable average accuracy error of just 1.63%. By shifting computational complexity to an offline training phase, the proposed framework offers a scalable, accurate solution for designing resilient, reliability-aware digital systems.
[LG-14] Platonic Projection Structures: Operator-Induced Observability in Representation Learning
链接: https://arxiv.org/abs/2607.05175
作者: Kazuo Ishii,Bishnu Prasad Gautam,Jieling Wu,Javaid Saher
类目: Machine Learning (cs.LG)
*备注: 29 pages, 7 figures. Published in Entropy
Abstract:We characterize observability in representation learning through Platonic Projection Structures (PPS), an operator-theoretic framework for analyzing representation accessibility under partial observation. Rather than treating observable outputs as direct reflections of latent representations, PPS models observation through a self-adjoint positive semidefinite operator acting on a latent representation space. A system is represented as a triple (H, \Pi, O) , where H is a latent representation space, \Pi \succeq 0 is an observation operator, and O(v)=\langle v,\Pi v\rangle defines an induced scalar observable. Observability is characterized by the quotient geometry H/\ker(\Pi) , representing equivalence classes of latent states indistinguishable under observation. We show that quantum measurement and representation inference under linear observation models share this operator-theoretic structure while differing in the algebraic properties of their observation operators; the correspondence is structural rather than physical. Representation transfer and knowledge distillation can likewise be interpreted as approximate preservation of observable geometry through \Phi \Pi_T \approx \Pi_S \Phi . PPS also reveals a structural limitation of output-based interpretability: latent components in \ker(\Pi) are inaccessible from induced observables, imposing intrinsic constraints on attribution and explanation methods. Controlled empirical validations demonstrate kernel-invariant observability, projection-induced attribution gaps, and rank-controlled observable geometry in latent representation spaces. PPS thus provides an explicit characterization of observability through operator-induced quotient geometry and a unified perspective on representation accessibility, interpretability, and projection-mediated inference.
[LG-15] MeGA-MP: Metric Graph Advection Message Passing – A Physics-Informed Message Passing Operator for Advection-Dominated Metric Graphs
链接: https://arxiv.org/abs/2607.05167
作者: Janine Strotherm,Luca Hermes,André Artelt,Barbara Hammer
类目: Machine Learning (cs.LG)
*备注:
Abstract:Many real-world systems are organized as networks where spatio-temporal dynamics unfold along connections and not discretely between nodes. Examples include utility networks such as water distribution systems or gas networks, electrical grids, and traffic flow networks. Such systems are naturally modeled as metric graphs, where edges correspond to one-dimensional Euclidean subspaces connected at vertices. Metric graphs are independent of an underlying global Euclidean space, limiting direct application of typical PINNs and operator-learning methods. Especially transport dynamics like advection require a methodology able to capture antisymmetric and long-range dependencies on graphs, which is itself a challenge. We propose a novel physics-informed message passing operator that encodes linear advection on metric graphs as an inductive bias. In the purely advective setting, the operator provably recovers the exact dynamics up to a theoretically derived discretization error without any training. Combined with trainable components like MLPs, our message passing operator extends to realistic advection-reaction dynamics in water distribution systems, where we achieve superior performance compared to baselines and zero-shot generalization across different graph topologies.
[LG-16] Physiological Noise Augmentation Improves Non-Invasive Brain-to-Speech
链接: https://arxiv.org/abs/2607.05165
作者: Benjamin Ballyk,Teyun Kwon,Miran Özdogan,Oiwi Parker Jones
类目: Machine Learning (cs.LG)
*备注: 18 pages, 10 figures
Abstract:Non-invasive brain-to-speech decoding aims to restore communication to patients suffering from neurodegenerative disease, without the risks of neurosurgery. Existing MEG- and EEG-based methods, while scalable, continue to suffer from high word error rates driven by relatively low signal-to-noise ratios compared to invasive recordings. We propose physiological noise augmentation (PNA), a data augmentation method that explicitly trains decoders to become invariant to task-agnostic artifacts (e.g. ocular and cardiac activity). PNA draws inspiration from automatic speech recognition systems, where environmental noise (e.g. dogs barking, city traffic) is added to clean speech to improve robustness. Analogously, we decompose brain recordings into clean data and noise artifacts using independent component analysis (ICA), before scaling and remixing to generate biophysically realistic, label-preserving training examples. We show that PNA approximates anisotropic regularization, penalizing decoder sensitivity along artifact-dominated directions. On MegNIST, a 12k-trial imagined-digit MEG dataset, PNA with 10-trial averaging improves EEGNet decoding accuracy by 4.7 percentage points (absolute) over training on real data alone. Our results suggest that artifact-aware augmentation and trial averaging are complementary tools for improving robustness in non-invasive speech BCIs.
[LG-17] Choosing a parallel heterogeneous ensemble method for tabular classification
链接: https://arxiv.org/abs/2607.05103
作者: Vassili Maillet,Gustavo(Jesus)Angulo,Pierre Jouvelot
类目: Machine Learning (cs.LG)
*备注:
Abstract:Parallel ensemble methods were compared on 56 small-to-medium tabular classification tasks drawn from OpenML CC18. A set of ``best practice’’ recommendations on the use of ensemble methods was derived from these observations. It was later validated on 28 additional tasks using TabArena’s precomputed data, where the recommendation set significantly outperformed Single Best and matched or exceeded individual ensemble methods. Two key observations were made. First, Blending and Stacking are inconsistent, but their inconsistencies are independent and happen on different tasks. Second, while Hard Voting’s probabilistic classification is rather weak, a consequence of using vote proportions as posterior estimates, Robust Soft Voting’s probabilistic classification is particularly successful, especially in the multiclass case. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2607.05103 [cs.LG] (or arXiv:2607.05103v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.05103 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-18] Counterfactual Methods for Detecting Unfairness in Anti-Money Laundering Algorithms
链接: https://arxiv.org/abs/2607.05101
作者: Lea Multerer,Michele Inchingolo,David Kletz,Adrian Cosma,Alessandro Antonucci,Martina Gogova
类目: Machine Learning (cs.LG)
*备注:
Abstract:The application of machine learning-based predictive algorithms to Anti-Money Laundering (AML) has grown rapidly, driven by the vast volume of financial transaction data available to banks. These algorithms are typically trained not only on transactional data but also on sensitive client information, which may raise fairness concerns. Despite this, AML detection systems remain largely underexplored from a fairness perspective, even though deeper analytical methods based on counterfactuals are now available. Such techniques enable the decomposition of the direct and indirect effects of potentially sensitive features on model predictions, thereby supporting the evaluation of whether their influence is acceptable from a fairness perspective. Closing this gap, we consider the synthetic IBM AMLSim transaction dataset and construct additional features of the country of an account and its average behaviour. This improves the predictive performance of diverse machine learning models, ranging from baseline decision trees to state-of-the-art graph neural networks. We assess the potential unfairness associated with these features through a counterfactual, path-specific effect analysis. This reveals that fairness violations tend to be more pronounced for models whose predictive performance benefits the most from the extended features. Such a finding highlights a concrete instance of the trade-off between predictive accuracy and fairness in AML applications, thus underscoring the urgency of a systematic fairness analysis in such critical domains.
[LG-19] Functional Bilevel Optimization for Predictive Fairness
链接: https://arxiv.org/abs/2607.05098
作者: Ieva Petrulionyte,Julien Mairal,Michael Arbel
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:When sensitive attributes are continuous and high-dimensional - demographic score vectors, posteriors over attributes, age or income profiles - enforcing full statistical independence is often too restrictive, and existing relaxations rely on indirect dependence penalties or adversarial schemes that do not directly target the fairness-accuracy trade-off. We instead consider mean demographic parity through DPVar, the variance of the conditional-mean prediction given the sensitive attribute, and show that optimizing it yields a functional bilevel problem. We propose two algorithms for this problem: FBO, which uses a closed-form adjoint we derive for the squared-loss case to obtain an exact hypergradient, and ITD, which differentiates through unrolled inner steps and extends beyond squared loss. On synthetic data and a new semi-synthetic benchmark built from 60 tabular regression datasets, both methods achieve the lowest or near-lowest aggregate fairness-accuracy regret, and consistently match or outperform strong HSIC, adversarial, linear-dependence, and generalized-DP baselines.
[LG-20] FAST: A Holistic Framework for Optimizing Memory-I/O Computation and Sampling in Temporal GNN Training
链接: https://arxiv.org/abs/2607.05095
作者: Yushu Cai,Qingrui Zhu,Lei Liu,Kai Sheng,Hao Chen,Xin He
类目: Machine Learning (cs.LG)
*备注:
Abstract:Temporal Graph Neural Networks (TGNNs) are widely used for learning from dynamic graphs in applications such as recommendation, social network analysis, and traffic forecasting. However, scaling TGNN training to large dynamic graphs remains challenging due to three intertwined bottlenecks: memory I/O, irregular computation, and temporal neighbor sampling. Existing systems often optimize these stages in isolation, leaving substantial performance headroom on the table. We present FAST, a holistic framework that accelerates end-to-end TGNN training by jointly optimizing sampling, memory I/O, and computation. FAST introduces SlimCache, which exploits within-batch compression and cross-batch caching to reduce host-device data movement under limited GPU memory budgets. It further designs thread-efficient graph operators tailored to sparse temporal subgraphs, improving GPU cache locality and reducing the latency of aggregation and edge softmax. In addition, FAST employs a topology-aware sampling strategy that improves CPU cache locality and accelerates temporal neighbor sampling. Extensive experiments on real-world large dynamic graphs show that FAST achieves an average of 2.1x (up to 4.7x) speedup over state-of-the-art systems without sacrificing model accuracy.
[LG-21] Computing Monetary Risk Measures in Linear Time
链接: https://arxiv.org/abs/2607.05078
作者: Palash Agrawal,Gersi Doko,Maeve Burwell,Marek Petrik
类目: Machine Learning (cs.LG); Mathematical Software (cs.MS); Applications (stat.AP)
*备注:
Abstract:Monetary risk measures have gained popularity for expressing decision-makers’ risk aversion. Value-at-Risk (VaR) and Conditional-Value-at-Risk (CVaR), in particular, are used commonly for this purpose. This paper proposes new efficient algorithms to compute these risk measures for a discrete random variable in expected linear time with respect to the size of its domain. First, we propose a QuickVaR algorithm that computes the VaR of a discrete random variable. Then, we leverage QuickVaR to propose QuickDivergence, an algorithm for computing a class of \varphi -divergence risk measures, including the popular CVaR risk measure. The QuickVaR algorithm adapts the well-known Quickselect algorithm, while QuickDivergence builds on polymatroid optimization algorithms. Numerical results show that our new algorithms offer an order-of-magnitude speedup for large domains, and a library implementation of the algorithms is available at this https URL.
[LG-22] KVpop – Key-Value Cache Compression with Predictive Online Pruning
链接: https://arxiv.org/abs/2607.05061
作者: Lukas Hauzenberger,Niklas Schmidinger,Anamaria-Roberta Hartl,David Stap,Thomas Schmied,Sebastian Böck,Günter Klambauer,Sepp Hochreiter
类目: Machine Learning (cs.LG)
*备注:
Abstract:Key-value (KV) cache growth is a major bottleneck in autoregressive decoding, as memory and bandwidth scale linearly with context length. Existing KV eviction methods often rely on static heuristics or proxy scores, which poorly track future token utility and cause brittle eviction as relevance shifts. To address this, we introduce KVpop, which learns a fixed-budget KV eviction policy by directly supervising the keep-or-drop decision. The scorer is trained against a novel future-attention target, computed efficiently without materializing dense attention maps. We further introduce a delayed memory-based scorer that, uniquely among learned eviction methods, defers scoring for a fixed number of steps to exploit near-future context. On AIME and HMMT mathematical reasoning, KVpop retains 98% of full-attention performance on Qwen3-4B at 75% KV cache compression and 97% at 88% compression, consistently outperforming established eviction baselines. Qwen3-8B shows even stronger results, reaching near-full teacher performance. These results show that supervising eviction with future-attention signals cuts memory costs while maintaining quality.
[LG-23] CollabEval: Statistically Efficient Collaborative Model Evaluation via Matrix Completion
链接: https://arxiv.org/abs/2607.05046
作者: Adam Fisch,Daniel Deutsch,Joshua Maynez,Alekh Agarwal,Jonathan Berant,William Cohen,Amir Globerson,Jacob Eisenstein
类目: Machine Learning (cs.LG)
*备注:
Abstract:Evaluating generative AI models is a routine, but resource-intensive, process that is conducted over and over again during the course of model development. In this work, we propose Collaborative Evaluation (CollabEval), a simple, effective, and principled method for exploiting dependencies between historical runs of different models on the same tasks to improve statistical efficiency. Specifically, our approach treats model evaluation as a matrix completion problem over an M \times N matrix of evaluation scores, where M is the total number of models and N is the total number of evaluation prompts. We assume that a subset of these M models are targeted for evaluation. For these target models only a small fraction, p , of prompts has been annotated with evaluation scores. Leveraging recent results in prediction-powered inference, we build a low-rank approximation of the score matrix, and use the reconstructed values as control variates in a manner that guarantees unbiased estimates of the true evaluation metric mean, in addition to statistically valid confidence intervals. Empirically, across a wide range of datasets, models, and sparsity levels p , we find that CollabEval substantially reduces the mean confidence interval size, and the mean squared error of the point estimate, compared to baseline methods at the same annotation budget.
[LG-24] Uncertainty-aware damage identification in short-span bridges via physics-informed variational autoencoder
链接: https://arxiv.org/abs/2607.05025
作者: Ana Fernandez Navamuel,A. Javier Omella,Diego Zamora-Sanchez,David Pardo
类目: Machine Learning (cs.LG)
*备注:
Abstract:Vibration-based damage identification in civil infrastructure is a challenging, ill-posed inverse problem due to measurement noise, sparse sensor arrays, and environmental variability. While deep learning is powerful for system identification, deterministic approaches lack reliable uncertainty quantification and can yield physically inconsistent results. This work proposes a robust probabilistic Scientific Machine Learning (SciML) framework: a physics-informed Gaussian copula variational autoencoder (PI-GCVAE) for structural health monitoring (SHM). First, we eliminate the need for data-driven surrogates by embedding a differentiable numerical eigenvalue solver directly into the VAE architecture. This ensures that latent space samples satisfy the governing equations of structural dynamics, reducing the trainable parameter space and improving generalization. Second, we replace the conventional independence assumption of latent variables with a Gaussian copula. This model captures complex, physics-dependent spatial cross-correlations between adjacent structural elements, defining feasible solutions while accounting for inherent system variability and measurement errors. Third, compared with alternatives such as Gaussian mixtures, our copula-based VAE provides an efficient distributional model for high-dimensional, strongly correlated latent spaces. We validate the approach using a synthetic dataset of a simply supported bridge subjected to various damage scenarios and corrupted with stochastic Gaussian noise. Synthetic data enables exhaustive validation against ground-truth stiffness values unavailable in practice. Results demonstrate that the PI-GCVAE accurately recovers the true posterior distribution, achieving 77.2% coverage. The proposed framework provides a reliable, scalable tool for early-stage damage diagnosis in operating bridges.
[LG-25] Non-Convex Sparse Reinforcement Learning via Non-Monotone Inclusions
链接: https://arxiv.org/abs/2607.04990
作者: Kyohei Suzuki,onstantinos Slavakis
类目: Machine Learning (cs.LG)
*备注:
Abstract:This work delivers two key contributions: one to efficient feature selection in reinforcement learning (RL), the other to the theory of non-monotone inclusions. On the RL side, the estimation bias inherent in conventional regularization schemes is addressed by augmenting classical least-squares temporal-difference (LSTD) policy evaluation with the sparsity-inducing, non-convex projected minimax concave (PMC) penalty. Because the PMC penalty is weakly convex, the resulting fixed-point problem is no longer monotone; instead, it falls under a broader class of non-monotone inclusions involving the sum of a monotone Lipschitz operator and a hypomonotone operator. On the theory side, novel convergence conditions are developed for the forward-reflected-backward splitting (FRBS) method applied to this broader class of non-monotone inclusion problems. Under mild conditions, Lyapunov stability and the existence of a limit point of the sequence of FRBS iterates are established; alternatively, under the weak Minty variational inequality assumption, exact convergence is guaranteed. Numerical tests on benchmark datasets show that the proposed FRBS iterates, applied to the non-convexly regularized LSTD problem, substantially outperform state-of-the-art feature-selection methods, especially when many noisy features are present. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2607.04990 [cs.LG] (or arXiv:2607.04990v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.04990 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Kyohei Suzuki [view email] [v1] Mon, 6 Jul 2026 12:25:47 UTC (529 KB) Full-text links: Access Paper: View a PDF of the paper titled Non-Convex Sparse Reinforcement Learning via Non-Monotone Inclusions, by Kyohei Suzuki and onstantinos SlavakisView PDFHTML (experimental)TeX Source view license Current browse context: cs.LG prev | next new | recent | 2026-07 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?) 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(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[LG-26] Data-Driven Soft Labeling Scales DNA Read Classification to Whole-Body Cell-Type Deconvolution
链接: https://arxiv.org/abs/2607.04987
作者: Dmytro Rizdvanetskyi,Nathan Ross,Pavlo Lutsik
类目: Machine Learning (cs.LG); Genomics (q-bio.GN); Quantitative Methods (q-bio.QM)
*备注:
Abstract:Cell-type deconvolution, the task of estimating the proportions of constituent cell types in a heterogeneous biological sample, is a core problem in computational biology. Methods that rely on epigenetic marks such as DNA methylation typically operate on aggregated methylation estimates, discarding the pattern-level information carried by individual DNA reads. Existing read-level approaches that exploit this information are scarce, and all remain restricted to few-class settings; scaling them further is an open problem because, at scale, non-discriminative reads dominate and hard labels conflict with the many-to-many mapping between methylation patterns and cell types, preventing classifier convergence. To overcome this, we propose data-driven soft labels that estimate the conditional cell-type distribution for each read, and integrate this scheme into Syto, a new modular framework for read-level classification-based deconvolution. On a whole-body atlas of 39 human cell types, Syto reduces MSE by 2.56 \times over SoTA, with gains transferring to an out-of-distribution dataset spanning 16 tissues. Syto lays the foundation for modeling increasingly large cell-type panels, with improved applications in biology and healthcare. The proposed soft-labeling scheme is further translatable to any setting with a many-to-many signal-to-label mapping.
[LG-27] Geometry-Aware Bayesian Quantification via Compositional Data Analysis
链接: https://arxiv.org/abs/2607.04977
作者: Alejandro Moreo,Pablo González,Juan José del Coz
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Accurately estimating the unknown target label distribution is the critical first step for adapting to label shift. This task, widely known as quantification or class prevalence estimation, has recently seen significant advances through continuous KDE-based methods which model the density of multiclass classifier posteriors. Posterior vectors might be regarded as compositional data, since they lie on the probability simplex. However, existing KDE-based quantifiers typically rely on Euclidean Gaussian kernels, which ignore simplex geometry and incorrectly assign probability mass outside its boundaries. We introduce a geometry-aware KDE model for multiclass quantification based on log-ratio representations and Aitchison geometry, together with a shrinkage regularization that improves robustness near the simplex boundary. Combined with a maximum-likelihood interpretation of KDE-based quantification, we derive both point-estimation and Bayesian inference procedures for class prevalences. Experiments on 42 datasets across tabular, text, and image domains show that the proposed method is competitive with state-of-the-art quantifiers, often improving over standard KDE-based baselines, while also yielding strong results among Bayesian quantification methods.
[LG-28] Sensitivity Sampling with Predictions for k-Means Clustering KDD2026 ECML
链接: https://arxiv.org/abs/2607.04949
作者: Cristian Boldrin,Fabio Vandin
类目: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS)
*备注: ECML PKDD 2026
Abstract:We study the problem of k-means clustering on large datasets. The state-of-the-art for the problem is given by coresets-based approaches, which build small weighted summaries of the input and derive approximate solutions with rigorous quality guarantees from them. One of the most popular and advanced approaches to derive coresets for k-means is sensitivity sampling. However, sensitivity sampling requires to compute the importance of each input point with respect to the whole dataset over all possible choices of centers. Since the exact computation of such quantities is unfeasible, current approaches work by approximating the sensitivity values. Nevertheless, the runtime of such approaches is still impractical for large datasets. In this work, we propose to reduce the runtime of sensitivity-based approaches for k-means by leveraging predictions to approximate the importance of input points. We first formally prove that current theoretical results on coresets construction via sensitivity sampling hold for coarser approximations of sensitivities compared to the one required by existing approaches. This implies that even fairly noisy predictors can be leveraged for sensitivity-sampling approaches. We then propose a natural predictor, which applies to the common scenario where clustering is performed (over time) on a sequence of datasets from the same problem. We prove that when the datasets in the sequence come from the same (unknown) distribution, centers resulting in a low error on one dataset can be used as predictions for sensitivity sampling in subsequent datasets, with guarantees on their quality. We perform an extensive experimental evaluation showing that our approach significantly improves, in terms of clustering cost vs runtime, over uniform sampling and state-of-the-art sensitivity sampling approaches when applied to sequences of datasets. Comments: ECML PKDD 2026 Subjects: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS) Cite as: arXiv:2607.04949 [cs.LG] (or arXiv:2607.04949v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.04949 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-29] Lightweight ML-Based Automatic Sleep Staging Framework with Constrained CNN and Mamba for Small-Sample EEG Datasets
链接: https://arxiv.org/abs/2607.04934
作者: Zihao Wei,Yulin Gong,Yudan Lv
类目: Machine Learning (cs.LG)
*备注:
Abstract:Automatic sleep staging is a key technology for precise diagnosis and treatment of sleep disorders as well as long-term home sleep monitoring. Portable electroencephalogram (EEG) devices have become the focus of research due to their convenience in data collection. However, current methods still face three major challenges: large parameter sizes that easily lead to overfitting on small datasets, low accuracy in classifying difficult stages such as N1 and REM, unclear optimal training dataset size, and difficulty in deployment. This paper proposes GamSleepNet, a lightweight and low-latency automatic sleep staging framework for single-channel EEG. The framework features the FEB module, which combines improved Gabor kernels with learnable filters for feature extraction, uses the Mamba architecture to build a temporal classification network, introduces a novel contrastive loss and a two-stage training strategy, and experimentally validates the optimal dataset size for single-channel EEG sleep staging models. On the Sleepedf dataset, this model achieves an overall accuracy of 87.86 percent with only 30.86 thousand parameters, with all metrics reaching SOTA levels and significantly improving the identification accuracy of challenging sleep stages.
[LG-30] When Do Foundation Models Pay Off? A Break-Even Analysis of Pretrained Time Series Forecasters
链接: https://arxiv.org/abs/2607.04919
作者: Nicholas Tan Jerome,Frank Simon
类目: Machine Learning (cs.LG)
*备注:
Abstract:Deploying a time series foundation model requires GPU infrastructure, engineering overhead, and carries no guarantee of improvement over XGBoost. We provide the first systematic break-even analysis answering when this investment pays off. Across 30 benchmark datasets, we compare zero-shot and LoRA fine-tuned foundation models (Chronos, Moirai, Lag-Llama) against classical baselines (Naive, ETS, ARIMA, XGBoost) at six training set sizes from 2% to 100% of available data. Foundation models outperform classical methods at every evaluated training fraction on 15 of 30 datasets – GPU deployment is unconditionally justified on these regardless of data volume. On 6 datasets, classical methods surpass zero-shot foundation models with as little as 2% of training data (21-2,768 samples); on the remaining 9, break-even ranges from 24 to 8,361 samples. One robust deployment rule requires no model training: if n_train 700 and seasonality is non-negligible, use FM zero-shot and skip fine-tuning – this resolves 10 of 30 deployment decisions immediately. Contrary to common practice, LoRA fine-tuning can actively degrade performance on short series. We operationalise these findings as a two-step decision framework – compute dataset length and seasonality strength, run a brief 5-10% pilot only if needed – enabling practitioners to make the FM-versus-classical decision before committing to full infrastructure. Four dataset features motivate mechanistic hypotheses for the remaining cases, though reliable automated prediction at this benchmark scale remains an open problem. Code, benchmark, and decision tools are available at this https URL.
[LG-31] RL-Ballast: Ship Ballast Water Path Planning and Clog Prediction via Reinforcement Learning
链接: https://arxiv.org/abs/2607.04906
作者: Ming-Kuan Lin,Yi-Chung Lai,Ming-Hsin Chiang,Tsung-Wei Pan,Jung-Hua Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Under the Shipping 4.0 paradigm, autonomous and reduced-crew vessels require intelligent internal systems to maintain operational safety and structural stability. Ballast-water control is essential for ship trim and integrity, but conventional rule-based or manual approaches have limited adaptability to hydraulic anomalies such as valve failures and pipe blockages, and often depend on dense pressure or flow sensors for diagnosis. To address these limitations, this paper proposes RL-Ballast, a graph-based deep reinforcement learning framework for adaptive ballast-water path planning and sensor-frugal blockage candidate scoring. The valve-permutation problem is transformed into 54 feasible fluid-transfer routes generated using graph theory and depth-first search. The partially observable ballast environment is approximated with frame-stacked tank levels and action outcomes, allowing the agent to infer hidden blockage effects without explicitly modeling a high-dimensional POMDP. During deterministic inference, episode-level failed-action memory and dynamic action masking prevent repeated ineffective actions and support immediate rerouting. Failed transfer histories are further accumulated to rank suspicious valves or pipe segments without dense instrumentation. Monte Carlo simulations show that RL-Ballast completes all unexpected single-blockage scenarios and reduces average decision steps from 61.0 to 41.5 compared with a Dijkstra rule-based baseline. For diagnostic support, the failure-history scoring scheme achieves a 100% Top-3 hit rate, a 66.7% strict Top-1 hit rate, and an 83.3% Top-1 tie-hit rate under serially indistinguishable blockage conditions. These results suggest that RL-Ballast enables adaptive rerouting and maintenance-oriented blockage diagnosis under limited sensing conditions.
[LG-32] Active Learning on Adversarially Corrupted Graphs COLT2026
链接: https://arxiv.org/abs/2607.04869
作者: Marco Bressan,Nicolò Cesa-Bianchi,Tommaso d`Orsi,Emmanuel Esposito,Silvio Lattanzi
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 37 pages, presented at COLT 2026
Abstract:Motivated by real-world scenarios where malicious entities tamper with existing networks, we define a model where an adversary seeks to hide a set of \emphcorrupted vertices inside a graph G^* . To this end, the adversary can add edges between the corrupted vertices, as well as edges between the corrupted vertices and G^* , and its power is then measured by the size of the \emphneighborhood of the corrupted vertices in G^* . Our goal is to design an active learning algorithm that efficiently finds the subset of corrupted vertices using a small number of label queries. We devise an efficient algorithm that approximately recovers the corrupted vertices with a query complexity that depends polynomially on both the power of the adversary and the \emphvertex expansion of G^* , a fundamental measure of graph connectivity. At the heart of this result is a polynomial-time algorithm, obtained by carefully adapting sum-of-squares algorithms for approximating minimum expansion, that finds a set with small vertex expansion subject to cardinality constraints. To the best of our knowledge, this is the first time that the vertex expansion is shown to play a key role in determining the query complexity of active learning algorithms robust to structural adversarial attacks.
[LG-33] Enhancing the Forecasting Capability of Multi-Model Blending Algorithms for Extreme Precipitation via Joint Use of Station and Gridded Observations
链接: https://arxiv.org/abs/2607.04862
作者: Yu Wang,Yong Cao,Kan Dai,Yue Shen,Xiaoqing Zeng,Ruixia Zhao
类目: Machine Learning (cs.LG)
*备注: 4 tables, 5 figures
Abstract:Accurate extreme precipitation forecasting is critical for disaster mitigation but remains challenging for numerical weather prediction (NWP) models due to systemic intensity underestimation and spatial displacement. Traditional precipitation multi-model blending algorithms perform pixel-by-pixel blending on the forecast field based on weights, which may lead to the expansion of precipitation areas and the smoothing of extreme values. This study proposes an U-Net based two-stage framework: probability classification followed by value reconstruction, to blend forecasts from six major NWP models. A novel station-grid joint supervision mechanism is introduced by integrating observations from 2411 national meteorological stations in China into the loss function, simultaneously constraining spatial structures and peak intensities. Evaluations using independent samples from the 2025 flood season demonstrate that our model significantly outperforms both individual NWPs and current operational products. For rainstorms (=50 mm), the Threat Score (TS) improved by 38.4% compared to the best NWP. Notably, for extreme events (=100 mm) driven by extratropical cyclones and the subtropical high, the model successfully elevated the TS to above 0.1, transforming forecasts from having negligible reference value into those with certain operational utility. Furthermore, the model exhibits data-driven spatial correction capabilities, effectively realigning systematic rainbelt displacements with actual precipitation centers. The inclusion of station observations specifically enhanced the TS for rainstorms by 10.4% and effectively balanced the Bias. These results highlight the efficacy of multi-source joint supervision in enhancing the capture of extreme precipitation events.
[LG-34] Framework for Grouping Local Process Models
链接: https://arxiv.org/abs/2607.04856
作者: Viki Peeva,Wil M. P. van der Aalst
类目: Machine Learning (cs.LG)
*备注: 26 pages, 5 figures
Abstract:Local Process Models (LPMs) are an underexplored concept in process mining. LPMs describe patterns in event data considering sequence, choice, concurrency, and loop. In recent years, process mining has proved successful in the analysis and improvement of operational processes. More often than not, surprising findings are found when one does not consider the full process, making LPMs and their discovery highly valuable. However, similar to other pattern mining approaches, LPM discovery algorithms face the problems of model explosion and model repetition, i.e., the algorithms may create hundreds if not thousands of LPMs, and subsets of them are close in structure or behavior. Practically, no analyst would be able to comb through thousands of LPMs leading to using a sample of LPMs that are easily accessible. The current sentiment is that the top-scoring LPMs form the optimal sample to be presented. However, different applications should demand a different optimal sample. With this work, we show that if the goal of the mined LPMs is to understand a process, using the top-scoring LPMs as an optimal sample is a poor choice because of high repetition. We propose a framework for grouping LPMs and creating an optimal sample by taking one representative LPM for each group. We measure similarity between models via established process model similarity measures or by comparing the context in which an LPM appears. The context is formed using data attributes available in the underlying event logs. We demonstrate the usefulness of grouping on multiple event logs by comparing repetition and coverage between samples comprised of the top-scoring models and the representatives of discovered groups.
[LG-35] SleepBand: Single-Source Domain Generalization for Sleep Staging via Physiologically Structured Spectral Modeling
链接: https://arxiv.org/abs/2607.04851
作者: Zhi Lu,Yang Hu,Yan Chen
类目: Multimedia (cs.MM); Machine Learning (cs.LG)
*备注:
Abstract:Generalizing sleep staging models to unseen datasets is challenging, and typical domain generalization (DG) methods often rely on multiple source domains or domain labels that are rarely available in practice. We tackle the stricter and more practical setting of single-source domain generalization: training on a single labeled source dataset, without domain labels or access to target data. We present SleepBand, a physiology-guided framework that embeds oscillatory priors via a learnable Morlet filter bank and a structured integration-and-recalibration pipeline. This anchors representations to domain-invariant sleep rhythms (e.g., slow waves, spindles), reducing reliance on dataset-specific artefacts. On five public datasets, SleepBand achieves state-of-the-art SDG performance and remains competitive under leave-one-domain-out (multi-source) DG. Analyses show that the learned filters align with canonical neurophysiology and that robustness stems from focusing on narrowband, physiologically meaningful cues. Our results suggest that principled, physiology-aware inductive biases are a promising path for robust single-domain sleep staging. Code is available at this https URL
[LG-36] Representing and Detecting Label Ambiguity in IMU-Based Exercise Evaluation
链接: https://arxiv.org/abs/2607.04842
作者: Andreas Spilz,Heiko Oppel,Michael Munz
类目: Machine Learning (cs.LG)
*备注:
Abstract:Home-based physiotherapy is performed without supervision, which leads to incorrect execution and motivates systems that assess movement automatically from inertial measurement units (IMUs). Such systems assign each repetition to a category, yet a relevant share of repetitions falls near a class boundary, where even trained raters disagree. Classifiers trained with one-hot labels collapse these borderline repetitions onto a single class and discard this ambiguity. We address this with a method that automatically generates a label distribution per repetition without a large rater pool. We train a network to reproduce the full distribution with a Kullback-Leibler objective, the ambiguity approach, and compare it against a one-hot cross-entropy baseline on four IMU exercise datasets. From the network output we further determine whether a repetition is ambiguous and which classes are relevant to it. The ambiguity approach matched or exceeded the baseline classification on all four datasets, and detected ambiguity and the relevant classes more reliably. Representing the label distribution in the training target therefore adds information about ambiguity at no cost to classification.
[LG-37] Probably Correct Optimal Stable Matching under Two-Sided Uncertainty
链接: https://arxiv.org/abs/2607.04824
作者: Andreas Athanasopoulos,Anne-Marie George,Christos Dimitrakakis
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:We study a sequential learning problem for stable matchings in two-sided markets where preferences on both sides are initially unknown. We focus on a centralized setting where an algorithm matches agents at each time step and receives noisy rewards that reflect the preferences of the matched agents, following a semi-bandit feedback structure. We adopt a pure exploration perspective, aiming to efficiently identify the optimal stable matching with high probability. Our work extends prior results by handling \emphtwo-sided uncertainty and by exploiting \emphpartial preference information. A central ingredient is the notion of \textbfpervasive stable matching, which enables the identification of optimal stable matchings under partial preferences. We propose elimination-based algorithms whose stopping criteria exploit the structure of the learned partial preferences, and provide a refined sample-complexity analysis. Beyond pure exploration, we extend our approach to regret minimization and establish regret bounds with respect to the \emphoptimal stable matching that avoid dependence on the minimum reward gap \Delta_\min .
[LG-38] KinEMbed: Decoding Kinematics from Electromyography via Cross-Modal Contrastive Learning ICML2026 ALT
链接: https://arxiv.org/abs/2607.04820
作者: Sofia Gilardini,Chenfei Ma,Kianoush Nazarpour
类目: Machine Learning (cs.LG)
*备注: ICML 2026 Workshop on Structured Data for Health, Seoul, South Korea
Abstract:Decoding hand kinematics from surface electromyography (EMG) is a core challenge in wearable biosignal processing with clinical relevance for prosthetic control and motor rehabilitation. Most representation learning approaches for EMG focus on discrete gesture classification, and few focus on continuous regression. We present KinEMbed, a cross-modal contrastive learning framework for hand kinematics regression that jointly trains dual encoders – one for windowed EMG features and one for kinematic (joint angle) targets. The resulting embeddings inherit the geometric structure of the kinematic space without requiring kinematic signals at inference time. Evaluating on the NinaPro DB8 dataset that includes both able-bodied users and subjects with limb difference (N=11), KinEMbed outperforms PCA, PLS, autoencoder and contrastive (CEBRA) baselines on held-out sessions, with largest gains on the most challenging thumb degrees of articulation. We position this work as a first step toward contrastive representation learning for regression of hand kinematics from structured wearable biosignals.
[LG-39] Layer-Parallel Inference Reduces Encrypted Nonlinear Depth in Transformers
链接: https://arxiv.org/abs/2607.04819
作者: Ligong Han,Kai Xu,Hao Wang,Ruijiang Gao,Akash Srivastava
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注: Code is available at this https URL
Abstract:Fully homomorphic encryption (FHE) enables computation on encrypted data, but practical encrypted Transformer inference is bottlenecked by the sequential composition of many nonlinear blocks. We study whether Structured Newton Layer Parallelism (SNLP) can make this inter-layer composition more FHE-friendly: each Transformer block still requires polynomial approximations for operations such as softmax and RMSNorm, but SNLP reduces the layerwise sequential nonlinear depth from L stages to a small number of solver iterations plus linear structured corrections. Using a simulation framework based on Chebyshev polynomial approximations, we measure error accumulation under sequential versus SNLP inference across 8 models and 4 architecture families. On a 0.5B IDN-trained model, SNLP reduces symbolic bootstraps from 53 to 20 (2.65x) with only +1.2% perplexity degradation, while lowering error amplification (1.36x vs. 1.42x). Across all tested models, SNLP has lower amplification than sequential inference. Ablations show that softmax approximation dominates the error budget and CKKS arithmetic noise is negligible in our setting, suggesting that SNLP is complementary to block-level FHE-friendly operator design rather than a replacement for it.
[LG-40] Compressed Computation under L4 Loss is likely Computation in Superposition
链接: https://arxiv.org/abs/2607.04800
作者: Francisco Ferreira da Silva,Stefan Heimersheim
类目: Machine Learning (cs.LG)
*备注: 6 pages, 7 figures, 1 table + 2 pages, 5 figures, 1 table appendix
Abstract:Neural networks are thought to represent concepts as directions in their activation space, and superposition lets them encode more concepts than they have dimensions. It is natural to ask whether they can also compute more functions than they have neurons, i.e., perform computation in superposition. In this regime many functions of sparse inputs are evaluated by a layer with fewer neurons than there are functions to compute. Representation in superposition is by now fairly well understood, but computation in superposition is not, and there are few toy models of it arising through training rather than being hand designed. As a toy model of computation in superposition we study the compressed-computation setup: a single-hidden-layer ReLU network with 50 neurons that must compute the ReLU of each of 100 sparse input features. We show that training it under an L^4 loss (the mean fourth power of the error), rather than the usual L^2 , elicits a solution that appears to compute all features in superposition. We then reverse-engineer this solution. We find that the network assigns each feature a sparse binary codeword over neurons and decodes it with a pseudoinverse of the encoder. Given these codewords, a description with only three scalars recovers most of the network’s performance, and we validate it by building equivalent networks from hand-designed codes.
[LG-41] owards Personalized Differentially Private Learning for Decentralized Local Graphs
链接: https://arxiv.org/abs/2607.04777
作者: Longzhu He,Peng Tang,Chaozhuo Li,Jinhu Fu,Litian Zhang,Li Sun,Philip S. Yu,Sen Su
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注: IEEE TKDE 2026
Abstract:Graph-structured data is increasingly generated and stored in decentralized environments, such as social platforms, mobile applications, and edge networks, where users maintain control over their local graph data. However, collecting and analyzing such decentralized graph data for downstream learning tasks raises significant privacy concerns, as nodes and their attributes often contain sensitive personal information. Local Differential Privacy (LDP) has emerged as a promising solution for privacy-preserving data collection without relying on trusted servers. Nevertheless, existing LDP-based graph learning methods typically assume uniform privacy requirements across users, ignoring the heterogeneous and personalized privacy preferences commonly observed in real-world systems. This uniform treatment leads to inflexible noise injection at the data collection stage, resulting in substantial distortion of graph data and degraded utility in subsequent analysis. To address this limitation, we propose PPGNN, a personalized differentially private framework for decentralized graph data. PPGNN enables user-specific privacy budgets during local perturbation while preserving analytical utility. To handle heterogeneous privacy levels and noise distortion, we design a two-stage solution consisting of a Personalized Perturbation Mechanism (PPM) and a weighted calibration strategy, FlexProp. Extensive experiments on six real-world graph datasets demonstrate that PPGNN effectively balances personalized privacy protection and data utility in decentralized graph learning scenarios.
[LG-42] MARLIN: De Novo Molecular Structure Elucidation from Tandem Mass Spectra without a Ground-Truth Formula
链接: https://arxiv.org/abs/2607.04774
作者: Xujun Che,Xiuxia Du,Depeng Xu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Untargeted tandem mass spectrometry (MS/MS) detects thousands of small molecules per biological sample, yet most go unidentified because they are absent from spectral libraries. These uncharacterized metabolites and natural products are precisely the compounds that matter for drug discovery, biomarker research, and exposomics. Computational de novo structure elucidation could close this gap, but almost all state-of-the-art methods assume the ground-truth molecular formula is known, an oracle that does not exist for genuinely novel compounds and is itself predicted with substantial error. We present MARLIN, a de novo method that elucidates structures directly from a spectrum with no molecular formula at any stage. A self-supervised encoder predicts a molecular fingerprint from the raw peaks, and a block-diffusion language model generates candidate structures conditioned only on the fingerprint and the instrument-measured precursor mass. A provably safe mass-shell constraint keeps every candidate consistent with the measured mass without fixing the atom inventory, and candidates are accepted by exact parts-per-million mass agreement. A symmetric noise objective absorbs encoder error, and a candidate-diversity mechanism keeps the candidates from collapsing to a single structure. On the NPLIB1 benchmark, MARLIN is the strongest method evaluated without a ground-truth formula across exact-match accuracy, structural distance, and fingerprint similarity, and it recovers the correct molecular formula as a byproduct about as often as a dedicated predictor without ever using one. MARLIN enables reliable de novo structure elucidation in the realistic discovery regime where the molecular formula is unavailable.
[LG-43] Identifiability of Relational Queries in Multi-View Pretraining
链接: https://arxiv.org/abs/2607.04735
作者: Ratan Bahadur Thapa,Daniel Hernández
类目: Databases (cs.DB); Machine Learning (cs.LG)
*备注: Artifacts to be archived at DaRUS: this https URL
Abstract:When data sources are integrated through a shared interface, a downstream query may or may not be determined by what the interface exposes: two globally consistent worlds can agree on every shared attribute yet disagree on the query answer. This ambiguity is structural – a property of the interface design, not the data volume – and cannot be resolved by collecting more records or training a larger model. We formalize query identifiability for data integration under interface laws (functional dependencies that hold uniformly across all legal worlds rather than within a single instance) and prove three results. (i) A polynomial-time certificate (CheckCert) decides identifiability via attribute closure, and is exact on instances that expose any residual ambiguity (closure-separable). (ii) Non-identifiable queries face an irreducible 1/2 minimax error floor for any estimator using only interface evidence, bounding multi-view pretraining systems from below. (iii) A minimum-augmentation algorithm (Greedy-MinAug) finds the smallest set of interface additions to certify a query, reducing to Set Cover (logarithmic approximation). Experiments on synthetic benchmarks, real integration datasets spanning three domains (scholarly, product, restaurant), and schemas up to 10^3 attributes confirm CheckCert is exact, both algorithms run in single-digit milliseconds, and ML classifiers exhibit the predicted error floor and abrupt capability gains. Comments: Artifacts to be archived at DaRUS: this https URL Subjects: Databases (cs.DB); Machine Learning (cs.LG) MSC classes: 68P15, 68T05, 94A17 ACMclasses: H.2.5; H.2.4; F.2.2; H.2.1; I.2.6 Cite as: arXiv:2607.04735 [cs.DB] (or arXiv:2607.04735v1 [cs.DB] for this version) https://doi.org/10.48550/arXiv.2607.04735 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-44] F-ACVAE: A Federated Adaptive Conditional Variational Auto-Encoder for Privacy-Preserving Intrusion Detection in IoT Networks
链接: https://arxiv.org/abs/2607.04698
作者: Mohammad Ansarimehr,Somayeh Changiz,Ehsan Baghishani,Ali Mousavi
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注:
Abstract:The rapid proliferation of Internet of things (IoT) devices has significantly expanded the cyber-attack surface, necessitating robust and privacy-preserving intrusion detection systems (IDS). However, centralized learning approaches often suffer from severe performance degradation due to high-dimensional traffic data, extreme class imbalance, and highly non-independent and identically distributed (non-IID) data across heterogeneous edge devices. To address these challenges, this paper proposes F-ACVAE, a federated adaptive conditional variational autoencoder framework that enables collaborative model training across distributed IoT devices without sharing raw data. F-ACVAE incorporates selective parameter aggregation, where local encoders remain private while globally shared components are synchronized to preserve discriminative latent structures. To further enhance stability under extreme non-IID settings and feature distribution shifts, we introduce a novel constrained momentum Gaussian aggregation (CMGA) strategy that combines update clamping with momentum-based smoothing to mitigate client drift. Extensive experiments on the N-BaIoT dataset demonstrate that F-ACVAE achieves an average accuracy and macro F1-score of 99%, outperforming state-of-the-art baselines. Moreover, the selective aggregation mechanism reduces communication overhead by approximately 62%, making the framework particularly suitable for resource-constrained IoT environments. These results highlight the effectiveness of F-ACVAE in achieving high detection performance while ensuring privacy preservation and communication efficiency.
[LG-45] A Physics-Regulated Neural Framework for Learning 3D Grain Growth Dynamics
链接: https://arxiv.org/abs/2607.04680
作者: Zhihui Tian,Kang Yang,Michael Tonks,Amanda R. Krause,Joel B. Harley
类目: Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
*备注:
Abstract:Grain growth is governed by the reduction in grain boundary energy and exhibits well-established statistical scaling laws. Developing data-driven surrogates that preserve these physical invariants while remaining computationally scalable remains challenging, especially in 3D. We present 3D-PRIMME (Physics-Regulated Interpretable Machine Learning for Microstructure Evolution) for learning three-dimensional grain growth dynamics. The model is trained using only two consecutive time steps yet accurately reproduces the linear coarsening law and preserves topological statistics over extended time scales. Despite being trained on a 100^3 grid points with 512 grains, the learned evolution operator is applied to domains up to 1024^3 grid points with 550000 grains without retraining, maintaining consistent kinetics and grain topology across orders-of-magnitude increases in system size. These results demonstrate that 3D-PRIMME learns a scale-independent and temporally stable local evolution rule, enabling efficient and robust large-scale surrogate prediction of 3D microstructure evolution.
[LG-46] Reliability and Identifiability in Persona-Trained Monte Carlo: Variance Decomposition Stability Bounds and the Identifiability of Heterogeneous News Reaction
链接: https://arxiv.org/abs/2607.04627
作者: Salavat Ishbulatov
类目: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE)
*备注: 29 pages. Companion theory paper to arXiv:2606.29556 (framework specification, “Persona-Trained Monte Carlo: Estimating Market-Outcome Distributions via Swarms of Persona-Conditioned Neural Policy Bots in a Limit Order Book”). Theory-only: all proofs given in full, no simulation results reported
Abstract:Persona-Trained Monte Carlo (PTMC) estimates distributions of market-outcome functionals by repeatedly simulating limit-order-book interaction among K neural policy bots whose behavioral personas are drawn from a learned heterogeneity distribution \mathcalP . This paper develops the statistical theory that makes the word “reliable” precise for such estimators. We decompose estimator variance into a persona-draw component \sigma_P^2 and a within-run component \sigma_w^2 , give unbiased ANOVA estimators of both, and derive the variance-optimal allocation of a fixed compute budget between outer persona draws and inner replications. A coupling-based stability bound quantifies how misestimation of \mathcalP and error in the trained policy propagate into the estimand, yielding a three-term total-error budget whose terms are separately estimable; a uniform-in-horizon version holds under a Doeblin condition on the market chain. The main contribution is an identification theory for heterogeneous news reaction: under a fixed response nonlinearity, the aggregate impact curve A(z)=\mathbbE_Q[g(\eta z)] detects heterogeneous news sensitivity through a strict Jensen gap and identifies the distribution Q locally via odd moments and Hausdorff determinacy, with sharp failure when the response family is unknown. We provide \sqrtn -consistent estimators and a boundary-corrected test of homogeneous news reaction. Two separation theorems delimit when PTMC is provably preferable to homogeneous-population simulators and reduced-form forecasters, formalizing an irreducible Jensen bias floor and the Lucas critique as a minimax limit on intervention extrapolation. All proofs are given in full; guarantees are classified as unconditional (Monte Carlo convergence), conditional worst-case (the error budget), or open (the large- K mean-field limit). Comments: 29 pages. Companion theory paper to arXiv:2606.29556 (framework specification, “Persona-Trained Monte Carlo: Estimating Market-Outcome Distributions via Swarms of Persona-Conditioned Neural Policy Bots in a Limit Order Book”). Theory-only: all proofs given in full, no simulation results reported Subjects: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE) Cite as: arXiv:2607.04627 [cs.LG] (or arXiv:2607.04627v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.04627 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-47] Measuring What Matters: A Unified Evaluation Framework for GNN Explainability
链接: https://arxiv.org/abs/2607.04600
作者: Francesco Paolo Nerini,Mirko Zaffaroni,Paolo Baracco,Gabriele Ciravegna,Alan Perotti
类目: Machine Learning (cs.LG)
*备注:
Abstract:Graph eXplainable AI (G-XAI) is increasingly important for making Graph Neural Networks interpretable and accountable. While a growing number of explainers are available, choosing the right method and assessing the trustworthiness of its outputs remains unclear. Consistent evaluation practices and actionable guidance are still missing, hindering practical adoption. In this paper, we introduce a unified, quantitative benchmarking framework for G-XAI that requires no ground-truth assumptions. We formalize tabular explainability metrics for graph data, evaluating topological structure and node features as independent components. Our large-scale benchmarking study identifies explainers that consistently lie on the Pareto front across metric pairs and tasks, establishing robustly non-dominated solutions - while confirming that no single explainer achieves universal superiority. We distill our findings into actionable G-XAI usability guidelines to support Machine Learning practitioners in evaluating and deploying trustworthy GNN-based pipelines.
[LG-48] Minimum Block Width for Universal Approximation by Residual Neural Networks with Inner Width One
链接: https://arxiv.org/abs/2607.04597
作者: Qi Zhou,Xuan Zhou,Xiao-Song Yang
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:In this paper, we study the universal approximation property of residual neural networks, and obtain some new results. For input and output dimensions d_x and d_y , and LeakyReLU, ReLU, ReLU-like activation functions, the upper and lower bounds of the block width are established. To achieve L^p approximation (1\leq p +\infty) on any compact domain, we show that the exact minimum block width is \max\d_x,d_y\ when the inner width is 1. Furthermore, we show that residual neural networks with block width \min\d_x+d_y, \max\2d_x+1,d_y\ can achieve uniform approximation on any compact domain under the constraint that each residual branch has inner width 1. Besides, for any activation function family, we prove that residual neural networks with block width less than \max\d_x, d_y\ cannot approximate all target functions, both in the L^p sense and the uniform sense, regardless of inner width.
[LG-49] Score Distributions Not Cells: Evaluating Single-Cell Perturbations Under Class Overlap
链接: https://arxiv.org/abs/2607.04595
作者: Youssef Marrakchi,Davide D’Ascenzo,Sebastiano Cultrera di Montesano
类目: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM); Machine Learning (stat.ML)
*备注:
Abstract:Most classification problems assume the classes are roughly separable, so that an individual sample can usually be assigned to one class. Single-cell perturbation data violates this assumption: two perturbations can produce different populations of cells while overlapping so much that an individual cell could belong to either. Per-cell accuracy then measures this overlap rather than model quality. We see this on Tahoe-100M and the Virtual Cell Challenge, where a linear classifier, an MLP, and a Transformer all plateau near macro-F1 0.2-0.3 even though almost every pair of perturbations is statistically distinguishable. The fix is to score perturbations across the whole population rather than cell by cell. We average a classifier’s per-cell probability vectors over all cells of a perturbation to form a population profile, then rank candidate perturbations by this profile; we call the resulting score the Classifier Discrimination Score (CDS). Taking the top-ranked class recovers the winning perturbation. It needs no retraining, costs linear time in the number of cells, and recovers near-perfect identification from the same weak models. CDS differs from the pseudobulk-based Perturbation Discrimination Score (PDS) used in recent benchmarks only in where the average is taken, raw gene expression for PDS versus a learned discriminative space for CDS, and identifies the true perturbation more reliably on both datasets, with the gap widening as cells grow scarce. Because a metric that misranks the ground truth will misrank the models scored against it, per-cell accuracy and raw-pseudobulk scores should be used with caution when comparing perturbation models. Subjects: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM); Machine Learning (stat.ML) MSC classes: 68T07, 62H30, 92-08 ACMclasses: I.2.6; I.5.2; J.3 Cite as: arXiv:2607.04595 [cs.LG] (or arXiv:2607.04595v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.04595 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-50] Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning
链接: https://arxiv.org/abs/2607.04577
作者: Saurabhsingh Rajput,Tushar Sharma
类目: Machine Learning (cs.LG); Software Engineering (cs.SE)
*备注:
Abstract:Code models strictly prioritize functional correctness, leaving software energy efficiency as an unoptimized byproduct. Training models to generate energy-efficient code requires reproducible feedback at scale, which physical hardware measurement cannot reliably provide due to variance. In this paper, we replace hardware profiling with a deterministic architectural simulation harness to build Green Tea, a corpus of 3.5 million evaluations across 1,474 C++ problems. We train an energy-aware code model via supervised fine-tuning on energy-contrastive pairs, followed by closed-loop reinforcement learning (GRPO) using simulation-in-the-loop feedback. To rigorously evaluate deployment readiness, we introduce the Correctness-Adjusted Reduction in Energy Total (CARET), a metric that explicitly penalizes code that sacrifices functionality for efficiency. On 143 held-out problems, our simulation-in-the-loop pipeline achieves 12.63% CARET, nearly tripling the gain of fine-tuning alone, and successfully beats the energy efficiency of human-expert references on 58.4% of its valid outputs. Furthermore, our analysis exposes the IPC trap: standard throughput proxies like Instructions-Per-Cycle (IPC) actively misrank true energy efficiency on 67.8% of problems, proving the absolute necessity of direct energy simulation. By releasing our dataset and infrastructure, we bypass the 263,000 CPU-hours required for reproduction, structurally empowering the community to deploy inherently energy-efficient code generation models. Subjects: Machine Learning (cs.LG); Software Engineering (cs.SE) Cite as: arXiv:2607.04577 [cs.LG] (or arXiv:2607.04577v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.04577 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-51] ManifoldFlow: SPD-Relaxed Stiefel Layers with Learnable Singular Spectrum
链接: https://arxiv.org/abs/2607.04535
作者: Haiwen Yi,Xinyuan Song
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 39 pages
Abstract:Orthogonal and Stiefel layers give neural weights exact spectral control, but they also impose a strong modeling constraint: all represented singular values are fixed at one. Many settings that benefit from an orthonormal basis still need direction-dependent attenuation or amplification. We introduce ManifoldFlow, a minimal relaxation of a fixed-spectrum Stiefel layer that keeps the basis on the Stiefel manifold while learning a bounded positive spectrum through W = Q S^1/2, with Q^T Q = I and S positive definite. Since W^T W = S, the eigenvalues of S are exactly the squared singular values of the realized weight, making eigenvalue clipping a direct singular-value control mechanism. Across paired sequence, tabular, and image experiments, the learnable SPD spectrum improves the fixed-spectrum Stiefel counterpart in the reported settings where the Stiefel prior is useful, with the largest gains in recurrent language-model projections. Boundary cases in convolutional classifier heads clarify the intended scope: ManifoldFlow is not a universal dense-layer replacement, but a spectrum-learnable Stiefel relaxation for settings where an orthonormal basis is a useful prior. When the basis should be orthonormal, its spectrum need not be frozen. Code available at this https URL
[LG-52] Beyond travel mode: urban context shapes active mobilitys mental health effects over time
链接: https://arxiv.org/abs/2607.04520
作者: Shujuan Chen,Yue Li,Ying Jin
类目: Machine Learning (cs.LG)
*备注:
Abstract:Active mobility is widely promoted for sustainable and healthier living, but whether it translates into equitable mental health benefits across individuals and places over time remains unknown. Using causal machine learning and causal deep learning in 264168 UK adults, we find substantial inequalities in individualized effects of active mobility on anxiety, depression, and common mental disorders. These inequalities widen over time and are strongly structured by urban context. For example, anxiety risk at follow-up ranges from a 40.6% reduction to a 10.1% increase across individuals, versus a 10.4% reduction to a 0.1% increase at baseline. Benefits are greatest in greener, safer, less polluted, and less deprived neighborhood environments, with 81.8% of individuals experiencing above-average benefits and mean anxiety risk reduced by 26.4%, versus 10.4% of individuals and 7.4% reduction in the least supportive environments. Urban compact form further modifies these effects through nonlinear interactions with neighborhood environments, amplifying benefits only under supportive conditions. Despite these strong environmental gradients, genetic moderation is negligible. These findings suggest universal active mobility promotion could widen health inequalities if individual and contextual differences are not accounted for.
[LG-53] Knowledge-Informed Local Causal Discovery of Optimal Adjustment Sets
链接: https://arxiv.org/abs/2607.04447
作者: Seong Woo Ahn,Alessandro Leite,José Lucas De Melo Costa,Fabrice Popineau,Bich-Liên Doan,Arpad Rimmel
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Local causal discovery is a scalable alternative to global structure learning. However, it can struggle to identify valid adjustment sets in data-scarce settings because of finite-sample uncertainty, incomplete local neighborhoods, and unresolved Markov equivalence. Although many application domains provide structured background knowledge, its integration into local causal discovery remains limited. We propose b-LOAD, a knowledge-informed extension of the LOAD algorithm for local discovery of optimal adjustment sets. b-LOAD incorporates prior edge constraints directly into the local structure-learning procedure and uses Meek’s rules to expand the discovery frontier dynamically, yielding a knowledge-constrained partially directed graph over the relevant local subgraph. This strategy helps prevent structurally relevant nodes introduced by prior knowledge from being excluded by local search. We prove that, under sound background knowledge, the procedure monotonically refines the admissible equivalence class and can enlarge the set of identifiable causal queries, enabling recovery of optimal adjustment sets that are not identifiable from observational conditional-independence information alone. Empirically, b-LOAD improves downstream causal effect estimation relative to purely data-driven and standard knowledge-augmented baselines, particularly in data-scarce and structurally complex regimes. Results on real-world biological networks show that locally targeted prior knowledge provides the largest gains and remains beneficial under moderate structural noise. These findings position b-LOAD as a scalable approach for converting fragmented domain knowledge into more reliable causal-effect estimation.
[LG-54] Environmental Drivers of Respiratory Disease: A District Level Analysis
链接: https://arxiv.org/abs/2607.04416
作者: Rahim Iqbal,Asfi Ahamed,Izzath Nisfer,Shazan Shaheed,Muhammadu Ilham,Nathali Athukorala,Madara Mendis,Nisansa de Silva,Sandareka Wickramanayake
类目: Machine Learning (cs.LG)
*备注: 6 pages, 8 figures, 4 tables
Abstract:Sri Lanka has experienced a decade of progressive forest degradation and rising atmospheric pollution, yet district-level respiratory admissions have paradoxically declined, pointing to the confounding role of healthcare access. This study addresses that gap by constructing an 11-year (2014-2024) panel dataset across all 25 administrative districts, integrating satellite-derived vegetation indices, fire radiative power, pollutant concentrations (particulate matter (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2)), carbon flux metrics and population-normalized respiratory admission rates. Two temporally validated XGBoost models were created for annual district-level respiratory rate (R^2 = 0.937) and monthly PM2.5 concentration (R^2 = 0.976) with generalization validated in 21 out of 25 districts (Mean Absolute Percentage Error (MAPE) = 20%). Shapley Additive Explanations (SHAP) analysis established that cumulative air quality burden is the overwhelming driver of respiratory rate variance (80.1%), ahead of forest degradation (15.6%) and fire activity (4.3%). The Forest-Air-Health (FAH) Risk Index used these SHAP-derived weights to find the districts with the highest risk: Colombo (FAH = 0.802), Gampaha (0.708), and Kalutara (0.682). These findings present the inaugural evidence-based, district-level framework correlating environmental degradation with respiratory health in Sri Lanka, establishing a quantitative basis for focused public health and environmental policy.
[LG-55] Learning Task-Sufficient World Models by Synergizing Agent ic Exploration and Structured Modeling
链接: https://arxiv.org/abs/2607.04409
作者: Fan Feng,Yujia Zheng,Minghao Fu,Yongqiang Chen,Guangyi Chen,Kevin Murphy,Biwei Huang,Kun Zhang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Learning and planning in imagination using world models provides an effective paradigm for training agents for decision-making. However, existing approaches often rely on high-dimensional latent spaces or generic visual embeddings that retain many factors irrelevant to control, limiting efficiency and generalization across tasks. To this end, we study how agents can learn world models with representations that are task-specific, minimal, and sufficient for decision-making. We achieve this via a closed-loop synergy between the agent and the world model, in which structured world-model learning distills task-sufficient representations from informative interaction data. On the agent side, agents actively probe the environment to collect informative trajectories that expose task-relevant latent factors, guided by an adaptive curriculum. On the world-model side, we learn structured representations over observations to distill compact, task-sufficient latent states from the collected interaction data. This synergy enables the empirical recovery of task-sufficient latent representations that capture all control-relevant factors. Leveraging these representations, the resulting policies achieve improved sample efficiency and generalization, including generalization across skills, object-skill compositions, and previously unseen tasks on standard continuous-control and robotic-manipulation benchmarks.
[LG-56] NKI-Agent : Domain-Specific Fine-Tuning and Agent ic Tool Use for Neuron Kernel Generation ICML2026
链接: https://arxiv.org/abs/2607.04395
作者: Junjie Tang,Jun Huan,Hao Zhou,Yuhao Zhang,Lin Wang
类目: Machine Learning (cs.LG)
*备注: 7 pages. Accepted at DL4C @ ICML 2026 Workshop
Abstract:Recent agentic approaches to LLM-based kernel generation have achieved impressive results on CUDA. For emerging AI accelerators such as AWS Trainium and Inferentia, automated kernel generation and optimization remain largely unaddressed. Writing kernels for these chips via the Neuron Kernel Interface (NKI) is particularly challenging: developers must navigate a multi-engine architecture, tile-based programming, and explicit data movement across multi-level memory hierarchy. Moreover, no publicly-available training data, benchmarks, or tool-augmented agents exist for this domain. We introduce NKI-Agent, the first system combining domain-specific supervised fine-tuning (SFT) with a compile-verify-fix agent loop for NKI kernel generation. We adapt the existing CUDA-Agent framework to Neuron hardware, curate 6,000 NKI kernel generation tasks for training, and construct NKIBench, a 250-task benchmark across three difficulty levels. Evaluated on real Trn1 hardware, NKI-Agent with Claude Opus 4.8 and a rank-aware system prompt achieves a 77.3% pass rate on the 150-task NKIBench. We show that tool use is critical: Opus 4.8 scores 6% in single-shot mode without agent tools. On a 60-task subset, we show that an SFT-trained Qwen3-Coder-30B-A3B achieves 25.0% pass rate at 1/100th the cost, outperforming Claude Sonnet 4 (15.0%). We also report that Group Relative Policy Optimization (GRPO) with binary compilation reward fails to improve over SFT, providing guidance on reward design for RL-based kernel generation.
[LG-57] RL Forgets! Towards Continual Policy Optimization
链接: https://arxiv.org/abs/2607.04364
作者: Mao-Lin Luo,Zhe-Xu Wang,Zi-Hao Zhou,Bo Ye,Jian Zhao,Min-Ling Zhang,Tong Wei
类目: Machine Learning (cs.LG)
*备注:
Abstract:Continual post-training is becoming a central paradigm for adapting vision-language models to evolving tasks. Recent work has increasingly favored reinforcement learning over supervised fine-tuning, driven by the belief that reinforcement learning is inherently less prone to forgetting. However, the belief remains insufficiently validated, as existing evidence is largely drawn from outdated or homogeneous benchmarks. To revisit this assumption, we introduce MRCL, a Multimodal Reasoning Continual Learning benchmark built from diverse and recently released multimodal datasets. Experiments on MRCL show that reinforcement learning can still suffer from severe catastrophic forgetting during continual post-training. To address this challenge, we propose Continual Policy Optimization (CPO), a replay-free framework grounded in the prior-task behavioral KL objective. CPO uses a theoretically justified parameter-movement regularization to limit policy drift on previous tasks. Extensive experiments across multiple model scales demonstrate that CPO consistently reduces forgetting while preserving, and in some cases improving, pretrained model capabilities. On Qwen3-VL-8B, CPO reduces forgetting by 13.7% and improves pretrained capability by 7.0%. The implementation code is available at this https URL.
[LG-58] How Many Initial Points Does Bayesian Optimization Need? ICML2026
链接: https://arxiv.org/abs/2607.04356
作者: Mujin Cheon,James Odgers,Dong-Yeun Koh,Calvin Tsay
类目: Machine Learning (cs.LG)
*备注: 4 pages. Accepted at the ICML 2026 Workshop on Decision-Making from Offline Datasets to Online Adaptation
Abstract:Bayesian Optimization (BO) generally begins with an initialization phase: a batch of n_0 uninformed evaluations. The choice of n_0 remains largely heuristic, and we empirically observe that the total cost (random initial points plus BO iterations needed to find the global optimum) is U-shaped in n_0 , i.e., a practitioner wastes resources by selecting either too low or too high a value of n_0 . We find this tradeoff persists across MLE, Bayesian MCMC, and exact GP hyperparameters, as well as across acquisition functions. Toward the latter, Thompson Sampling appears an exception, with both total cost and simple regret essentially n_0 -agnostic, though higher in our experiments. We attribute this U-shape to the known boundary issue of variance-driven BO: BO burns early budget on corners of the hypercube before turning inward. We demonstrate this effect using a 3D BO trajectory where the exact hyperparameters are known. We conclude with practical recommendations: use multi-step lookahead BO where possible; otherwise use Thompson Sampling when n_0 cannot be tuned, and a generously large n_0 when it can.
[LG-59] Structure-Specific Representational Priors Causally Control the Grokking Delay
链接: https://arxiv.org/abs/2607.04333
作者: Gunner Levi Howe
类目: Machine Learning (cs.LG)
*备注: 13 pages, 6 figures. Code and artifacts: this https URL
Abstract:Grokking – generalization arriving long after training-set interpolation – can be accelerated by structure-agnostic interventions: gradient filtering, weight-norm clamping, geometric penalties on hidden representations. Whether the delay specifically measures the time to form task-structured representations has remained an observational claim. We test it causally by injecting representational priors of varying structural content into a one-layer transformer learning modular addition: a supervised-contrastive auxiliary loss whose positives encode (i) the task’s true equivalence structure ( (a+b) \bmod p ), (ii) a coherent-but-wrong sibling structure ( (a-b) \bmod p ), or (iii) a random partition, all with identical loss form, strength, class sizes, and geometry. Whether generalization occurs follows a clean gradation: true structure 22/30 runs; sibling structure, which needs the same periodic features but the wrong combination, 14/15; random partition, satisfiable only by memorization, 0/20 (Fisher exact p = 1.3 \times 10^-7 ). A weight-norm-matched control replaying each intervention’s norm trajectory onto plain cross-entropy generalizes in 0/15, collapsing into logit-scale saturation, ruling out the norm as mediator. Representation probes show structure formation precedes and predicts generalization in all 95 runs. Only the true structure also accelerates grokking, up to 2.75\times faster than baseline, but the acceleration is dose-dependent, bimodal across seeds, and a net wall-clock win only in its strongest cases given the contrastive term’s overhead. The grokking delay is, causally, the time to form the right representational structure, where “right” is decided at the level of features rather than labels: coherent-but-wrong structure leaves grokking intact, random structure abolishes it, and only the true structure hastens it.
[LG-60] On the effectiveness of reward functions in reinforcement learning for confidence calibration of large language models
链接: https://arxiv.org/abs/2607.04332
作者: Chee Heng Tan,Zhuoyi Lin,Mehul Motani,Wee Sun Lee
类目: Machine Learning (cs.LG)
*备注: 50 pages, 8 figures
Abstract:In this paper, we consider the setting where large language models (LLMs) are trained using reinforcement learning (RL) to simultaneously improve reasoning accuracy and verbalize its confidence. Our reward scheme uses two functions for rewarding confidence verbalized by the LLM: one when the LLM is correct and a different one when the LLM is incorrect. With a poorly designed reward scheme, the LLM may be incentivized to answer incorrectly so that it can be confident that its answer is indeed incorrect, a phenomenon that we call confidence reward hacking. We propose the concept of non-hackable confidence reward schemes and define a spectrum of such reward schemes for RL confidence calibration training in LLMs. We demonstrate that selective confidence reward hacking can occur in practical datasets with reward schemes that are not designed to be non-hackable. We also demonstrate that the reward scheme with the best calibration to accuracy tradeoff depends on the dataset and the application, and propose using the reward scheme as a hyperparameter to optimize the tradeoffs in accordance to what is important for the application. The code of our experiments is available in this https URL.
[LG-61] On Preserving Geometrical Invariance for Superpixel Image Classification using Graph Transformer
链接: https://arxiv.org/abs/2607.04262
作者: Sarabeshwar Balaji,Shubham Mohanty,Akash Anil
类目: Machine Learning (cs.LG)
*备注:
Abstract:Convolutional Neural Network (CNN) and Vision Transformer (ViT) for image classification exploit a dense grid of pixels containing redundant information. Consequently, for a larger image dataset, CNNs and ViTs face deployability challenges due to high computational complexity. Representing images as graphs of superpixels offers an efficient alternative that preserves key information while eliminating pixel-level redundancy. Graph Neural Networks (GNNs) have been utilized on such graphs to perform image classification. However, GNNs are known to struggle with capturing long-range dependencies which is important in the domain of image classification. Furthermore, a majority of these superpixel-based image classification approaches do not explicitly preserve translation/rotation invariance. Nevertheless, preserving translation/rotation invariance is important for robust image classification. Thus, this paper proposes SuperGT, a Graph Transformer-based framework for image classification, which captures the long range dependencies, along with a pre-processing scheme that preserves translation/rotation invariance. We evaluate SuperGT on CIFAR-10 dataset and observe that it performs significantly better than many baselines. Furthermore, we note that the overall performance of SuperGT is comparable to the previous state-of-the-art model, namely, ShapeGNN, without relying on coordinates of the boundary points of each superpixel required by ShapeGNN.
[LG-62] Air-Plan: Query-Optimized Topology Selection for Over-the-Air Decentralized Federated Learning
链接: https://arxiv.org/abs/2607.04254
作者: Kaushal Attaluri,Rebeca P. Diaz-Redondo,Manuel Fernandez Veiga
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注: 15 pages, 11 figures 4 tables
Abstract:Over-the-air (OTA) aggregation exploits the superposition property of wireless multiple-access channels to aggregate model updates from multiple devices within a single transmission slot, significantly reducing communication latency. While OTA computation has been extensively studied for centralized federated learning (FL), its integration with decentralized federated learning (DFL) remains largely unexplored, and principled communication topology selection is absent from existing work. We present AIRPLAN, a query-optimized topology selection framework for Over-the-Air Decentralized Federated Learning (OTA-DFL). AIRPLAN establishes a formal equivalence between OTA-DFL and distributed query processing, enabling topology selection to be formulated as a cost-based query optimization problem. Using privacy-preserving Count-Min Sketch statistics, AIRPLAN estimates workload characteristics, evaluates a graph-aware cost model across candidate topologies, and selects the communication graph that minimizes training cost while satisfying a target accuracy SLA. Experiments across five graph families, three vision benchmarks, four client scales, and multiple SNR settings show that AIRPLAN matches the oracle-optimal topology in 91.4% of workloads while introducing less than 1.8% overhead. We further derive theoretical error bounds for topology-aware sparsification, demonstrating that well-connected topologies better tolerate aggressive compression. AIRPLAN introduces a systems-oriented perspective that bridges wireless federated learning and distributed query optimization. Comments: 15 pages, 11 figures 4 tables Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG) Cite as: arXiv:2607.04254 [cs.DC] (or arXiv:2607.04254v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2607.04254 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-63] Quantize the Target Quantize the Drafter: Efficient Inference with Qwen 3.5-4B ICML2026
链接: https://arxiv.org/abs/2607.04244
作者: Jaeyeon Kim,Jewon Lee,Bo-Kyeong Kim
类目: Machine Learning (cs.LG)
*备注: All authors contributed equally. Our submission to Efficient Qwen Competition, ICML 2026 Workshop on AdaptFM
Abstract:This report describes our approach to the Efficient Qwen Competition, where the goal is to enable low-latency serving of Qwen3.5-4B on a resource-constrained NVIDIA A10G GPU. Our system combines a quantized target model with speculative decoding. To recover accuracy, we apply quantization-aware distillation to the target model while retaining the original quantization grid. To speed up decoding, a block-diffusion drafter specialized for the quantized target model is trained using a two-stage procedure: first learning from the high-precision target and then adapting to the low-precision target. Because the drafter is invoked at every speculative decoding step, we further reduce its overhead with quantization and sliding-window attention, preserving draft-token acceptance while improving long-context decoding latency. As a result, our submission achieves a 6.978 \times average speedup over the baseline while satisfying the required quality thresholds, ranking 3rd overall. We hope these results provide useful insights for practical LLM inference. The code and resources are available at this https URL
[LG-64] Channel-Adaptive Robust Aggregation for Over-the-Air Federated Learning in Heterogeneous Networks
链接: https://arxiv.org/abs/2607.04218
作者: Zubaida Fatima,Zubair Shaban,Yusuf Jamal,Nazreen Shah,Ranjitha Prasad,B. N. Bharath
类目: Machine Learning (cs.LG)
*备注:
Abstract:The growing demand for privacy-preserving, data-intensive applications such as IoT, augmented reality, and autonomous systems positions Federated Learning (FL) as a key enabler in 6G networks. Over-the-Air FL (OTA-FL) leverages the superposition property of the wireless multiple access channel for efficient aggregation via simultaneous transmissions. Existing methods rely on fixed aggregation schedules and do not jointly address noise, fading, and client heterogeneity. We propose CHARGE-FL (CHannel-Adaptive Robust agGrEgation), a framework that adaptively schedules aggregation based on channel dynamics and application readiness. By combining a tailored optimization strategy with a dual-purpose precoding mechanism, CHARGE-FL mitigates channel distortion and bias from partial updates, achieving superior accuracy, stability, and convergence under realistic wireless conditions. Empirical results under realistic wireless conditions show that CHARGE-FL significantly improves accuracy, stability, and convergence over state-of-the-art OTA-FL methods, particularly in straggler-prone and noisy scenarios.
[LG-65] Sangam: Efficiently Serving Diffusion LLM s with the AR Stack
链接: https://arxiv.org/abs/2607.04206
作者: Nitin Kedia,Saurabh Agarwal,Myungjin Lee,Aditya Akella
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注:
Abstract:Diffusion language models (dLLMs) generate text by iteratively denoising a masked response and can commit multiple output positions per model invocation. Their bidirectional attention prevents exact autoregressive-style KV caching, since committing one position shifts the KV activations of all others. Approximate caching techniques such as Fast-dLLM and dKV-Cache refresh KV activations repeatedly and reuse them across intervening decodes, inducing a repeated prefill/decode structure. This makes AR serving mechanisms relevant to dLLMs, but not directly applicable. dLLM decodes are block-sized rather than token-sized, prefills recur, and bidirectional attention precludes the chunked prefill mechanism used for stall-free colocated serving. We present Sangam, a serving system for cached dLLM inference. Sangam introduces a deficit token-budget scheduler that admits in-flight decodes first, admits whole indivisible prefills only when the accumulated token budget allows, and carries unused budget forward. This achieves amortized stall-free scheduling. Disaggregated serving avoids prefill-decode interference but suffers from prefill/decode resource partitioning problem. Sangam adopts a hybrid serving strategy, overflowing prefills onto decode workers to relieve prefill under-provisioning, and uses the same deficit-budget scheduler to protect those workers’ decodes from the overflow. We show that like AR serving, dLLM serving design space is governed by prefill-decode interference and prefill/decode partitioning. Colocated serving is most effective on decode-heavy workloads, cutting mean latency by 9-20% over hybrid execution on LLaDA-8B ShareGPT; while hybrid execution is most effective on prefill-heavy workloads, cutting mean latency by 8-20% over colocated execution on Dream-7B arXiv. Sangam is available at this https URL.
[LG-66] Exploring Convolutional Neural Processes for Weather Downscaling
链接: https://arxiv.org/abs/2607.04190
作者: Francisco Passos
类目: Machine Learning (cs.LG)
*备注:
Abstract:Global reanalysis products such as ERA5-Land provide spatially complete weather fields but at resolutions too coarse for local applications, particularly in mountainous regions where temperature can vary by several degrees over short distances. This project investigates Convolutional Conditional Neural Processes (ConvCNPs) for statistical downscaling of daily maximum temperature from the ~11km resolution ERA5-Land grid to ~1km resolution over Switzerland, building upon the architecture of Vaughan et al. (2022) and adapting it to the topographically complex Swiss domain with high-resolution elevation features from the swisstopo DHM25. The best model, trained on ten years of data (2014-2023) with five-fold temporal cross-validation, achieves a mean absolute error of 1.31 Celsius and a CRPS-based skill score of 0.524 relative to bilinear interpolation, reducing the expected prediction error by more than half. An ablation study reveals that the elevation MLP is the indispensable component - without it, the model diverges entirely - while explicit seasonal features and Topographic Position Index provide secondary benefits. Under sparse on-grid input the model degrades gracefully, maintaining positive skill down to approximately 10% of the input grid; however, zero-shot deployment on off-grid station observations does not achieve positive skill at any density tested. All configurations exhibit severely overconfident uncertainty estimates, a structural limitation of the Gaussian likelihood training objective. These results demonstrate that ConvCNPs are a viable and effective approach to climate downscaling in complex terrain, and identify uncertainty calibration and native support for non-gridded input as the key challenges for operational deployment.
[LG-67] SpecGradFilter: A Spectral Gradient Filtering Framework for Taming Federated Heterogeneity
链接: https://arxiv.org/abs/2607.04189
作者: Liyang Yuan,Yibo Yang,Dandan Guo,Peter Richtarik,Zhouchen Lin
类目: Machine Learning (cs.LG)
*备注:
Abstract:Federated Learning (FL) is fundamentally challenged by statistical heterogeneity, where non-identically distributed (non-IID) data induces client drift that severely hampers global convergence. While existing approaches attempt to mitigate this drift through spatial-domain gradient correction or regularization, they overlook the intrinsic spectral structure of optimization signals. In this work, we revisit client drift from a novel frequency-domain perspective and uncover a critical Spectral Bias of Drift: inter-client gradient divergence is predominantly concentrated in low-frequency components which encode client-specific distributional shifts, while high-frequency components representing fine-grained features remain relatively consistent. Motivated by this, we propose SpecGradFilter, a unified Spectral Gradient Filtering Framework that tames heterogeneity by suppressing discordant low-frequency signals. Crucially, we demonstrate that SpecGradFilter is a generalizable principle, effective not only via precise FFT-based truncation but also through spatial approximations like Gaussian detrending. Extensive experiments on benchmarks such as CIFAR-10/100 and Tiny-ImageNet demonstrate that SpecGradFilter significantly performs better performance in highly Non-IID settings with negligible communication overhead, establishing a new paradigm for robust federated optimization.
[LG-68] Physics-Informed Graph Learning with Uncertainty Awareness for Open-Set Domain Generalization in Fault Diagnosis
链接: https://arxiv.org/abs/2607.04188
作者: Jinfeng Zhu,Shiyu Long,Ye Yuan
类目: Machine Learning (cs.LG)
*备注:
Abstract:Intelligent industrial maintenance critically relies on reliable fault diagnosis of rotating machinery. However, it faces formidable challenges from unknown fault types and domain shifts induced by varying operating conditions, which is formally formulated as the open-set domain generalization (OSDG) problem. Existing methods are mainly data-driven, thereby overlooking the cascaded propagation of uncertainty across feature extraction, topological learning, and decision-making this http URL tackle this challenge, we propose PGU-OD, a novel Physics-Informed Graph Learning framework with Uncertainty Awareness for Open-set Domain generalization. First, it designs a physics-informed spectral attention module to extract condition-robust fault features, thereby suppressing perceptual uncertainty caused by frequency shifts. Further, it constructs an uncertainty aware adaptive graph learning mechanism to dynamically adjust the edge weights of the sample graph guided by class-scale Gaussian distribution parameters, which mitigates the structural propagation of uncertainty. Finally, a Gaussian-distribution-based adaptive boundary loss function and a dual-criteria open-set inference strategy are developed to optimize decision boundaries and reliably reject unknown faults. Extensive experimental evaluations on two public and widely used rotating machinery fault datasets demonstrate that the proposed PGU-OD outperforms state-of-the-art baselines in both known fault classification and unknown fault rejection under domain shifts.
[LG-69] XS-VLA: Coupling Coarse-grained Spatial Distillation with Latent Flow Matching for Lightweight Robotic Control
链接: https://arxiv.org/abs/2607.04171
作者: Lei Iok Tong,Qingchen Xie,Wei Huang,Ying Jie Yap,Yujie Zhang,Qianzhi Li,Xiaolong Liu,Zhidong Deng
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: Preprint
Abstract:Large Vision-Language Models (LVLMs) have shown strong multimodal understanding and spatial grounding, but their computational cost limits real-time robotic control. In contrast, lightweight models are suitable for edge deployment but often suffer from “spatial blindness”, namely weak native spatial prediction ability. Training Vision-Language-Action (VLA) models on mixed human demonstrations can also degrade policy performance due to highly diverse behaviors. To address these limitations, we propose XS-VLA, a two-stage framework for efficient and spatially grounded robotic manipulation. First, we distill spatial semantic knowledge from Qwen3-VL-4B into the SmolVLM2-0.25B backbone by fine-tuning on curated coarse-grained spatial descriptions, turning the lightweight model into a spatially grounded engine. Second, we use this enhanced backbone to condition a Latent Flow Matching policy. Unlike deterministic controllers, our policy combines a Conditional Variational Autoencoder (CVAE) with Flow Matching dynamics to model complex multimodal action distributions. On the LIBERO benchmark, XS-VLA achieves state-of-the-art performance among models with fewer than 0.5B parameters. It improves average success rates by up to 7.2 percent, including a 23 percent gain on LIBERO-Long, over the SmolVLA 0.25B baseline, and outperforms the larger 2.2B vanilla SmolVLA. Ablations show that spatial tuning and generative latent flow control substantially improve lightweight VLA performance, delivering a 3.2 times speedup in mission execution over the previous lightweight flow matching policy.
[LG-70] FedFFT: Taming Client Drift in Federated SAM via Spectral Perturbation Filtering
链接: https://arxiv.org/abs/2607.04170
作者: Liyang Yuan,Yibo Yang,Dandan Guo
类目: Machine Learning (cs.LG)
*备注:
Abstract:Federated Learning (FL) enables decentralized training without data sharing, but suffers from statistical heterogeneity across clients, leading to client drift, poor generalization, and sharp minima compared to centralized training. Sharpness-Aware Minimization (SAM) has emerged as a promising approach to improve generalization, yet its application in federated learning still suffers from divergence problems, since perturbations are computed locally and reflect client-specific loss geometries. To better understand this issue, we provide experimental evidence from a new perspective, the frequency domain, for SAM perturbations in federated settings, revealing that inter-client perturbation inconsistencies are predominantly concentrated in the low-frequency spectrum. Motivated by this insight, we propose Federated learning with Frequency-domain Filtering of SAM perturbations (FedFFT). It is a lightweight and plug-and-play method that filters out low-frequency components of SAM perturbations without requiring additional communication, thereby suppressing inconsistent components in client updates while preserving consistent learning signals. Extensive experiments across multiple benchmarks and diverse backbones demonstrate that FedFFT consistently outperforms SAM-based FL methods, particularly under severe non-IID distributions. These results highlight the effectiveness, scalability, and general applicability of our frequency-domain perspective for sharpness-aware federated optimization.
[LG-71] Geometry of Ordinal Representations in Language Models
链接: https://arxiv.org/abs/2607.04167
作者: Saksham Bassi,Sharvi Tomar
类目: Machine Learning (cs.LG)
*备注:
Abstract:Recent work showed that language models represent character counts on curved 1D manifolds, with attention heads performing geometric transformations to enable computation. We test whether this generalizes across four ordinal tasks (bracket depth, indentation, table position, numeric magnitude) in Gemma-2-2B, Gemma-2-9B, and Qwen3-4B. We find that 1D manifolds with place-cell feature tiling emerge for tasks where the ordinal variable is locally computable from token identity, while tasks requiring cross-position integration or semantic extraction produce higher-dimensional or incoherent representations. Geometric computation is architecture-dependent: Qwen3-4B shows substantially stronger twisting than Gemma models for indentation, and its twisters preserve ordinal order, unlike its numeric twisters. Activation patching confirms that the identified manifold subspaces concentrate task-relevant information, with manifold-direction ablation causing dramatically larger probe accuracy drops than random-direction controls.
[LG-72] ACE: Agent ic Control for Embodied Manipulation via Zero-shot Workflow Reasoning
链接: https://arxiv.org/abs/2607.04162
作者: Iok Tong Lei,QianZhi Li,Ying Jie Yap,Yujie Zhang,Rui Zhong,Haichao Gui,Xiaolong Liu,Zhidong Deng
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: Preprint
Abstract:Open-ended tabletop manipulation requires agents to not only understand natural language but also adapt to dynamic environments and execution failures. We present ACE (Agentic Control for Embodied Manipulation), a zero-shot workflow reasoning framework for tabletop pick-and-place from natural language. Rather than relying on direct low-level action mapping, ACE combines agentic workflow reasoning with two robot-facing executable skills: a visual grounding interface and a reusable pick-and-place primitive. To bridge semantic reasoning and physical control, the active sub-goal is grounded into a mask-mediated vision-action interface. This unified mask specifies the target object and destination, is tracked over time, exposed for human verification, and ultimately passed to a task-agnostic downstream policy for execution. Crucially, ACE operates in a closed loop supported by a multi-timescale memory. After an action is executed, the system automatically verifies whether the intended sub-goal succeeded, using the outcome to advance, retry, repair, or replan. This enables online adaptation to user corrections, scene changes, and physical failures. We evaluate ACE on logically complex, long-horizon tasks, including zero-shot multi-step equation formation with number cubes and constraint-based object retrieval. ACE demonstrates task-level zero-shot generalization on novel semantic constraints and randomized tabletop scenes without task-specific retraining. Specifically, while standard end-to-end baselines struggle to complete these logically demanding tasks, ACE achieves a 50% success rate in equation formation and a 70% success rate in constraint retrieval. This contrast demonstrates that explicit workflow reasoning and mask-mediated control offer a robust, practical route toward adaptable robotic manipulation.
[LG-73] Masked Generative-Contrastive Representation Learning for Cross-Dataset EEG-Based Emotion Recognition
链接: https://arxiv.org/abs/2607.04139
作者: Huqin Weng,Jiayang Huang,Yimin Wen,Jie Du,Chi-Man Vong,Chuangquan Chen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Self-supervised learning (SSL) shows strong potential for cross-dataset transfer by improving feature representation and generalization. However, its application to EEG-based emotion recognition remains largely unexplored. Existing SSL methods struggle to capture the intricate spatiotemporal dependencies of EEG signals under varying channel configurations, extract fine-grained representations resilient to noise, and derive global features that generalize well across subjects. To address these challenges, we propose Masked Generative-Contrastive Representation Learning (MGCRL), a novel SSL framework specifically designed for EEG-based emotion recognition. Built upon a region-aware spatiotemporal encoder, MGCRL integrates generative and contrastive learning to achieve both fine-grained and global discriminative representations for cross-dataset generalization. MGCRL introduces three key designs: 1) a spatiotemporal encoder that incorporates region-based graph convolution to capture localized spatial and functional relationships, enhancing region-specific feature learning and mitigating the impact of varying EEG channel configurations across datasets; 2) a generative learning mechanism based on the joint embedding predictive architecture (JEPA) that utilizes masked features to capture noise robustness fine-grained representations, improving the model’s capability to characterize subtle emotional states; and 3) a contrastive learning strategy that leverages masked and original features to learn temporally stable and cross-subject-invariant representations across the same stimuli, boosting emotion discrimination and cross-subject generalization. Under these designs, MGCRL exhibits remarkable ability to learn universal representation. Extensive experiments involving pretraining on the large FACED dataset and fine-tuning on multiple SEED-series datasets demonstrate the effectiveness of MGCRL.
[LG-74] MDL Meets Latent Confounders: LNML-based Causal Discovery ECML-PKDD2026
链接: https://arxiv.org/abs/2607.04133
作者: Zhongyi Que,Shin Matsushima,Kenji Yamanishi
类目: Machine Learning (cs.LG)
*备注: Accepted at ECML-PKDD 2026
Abstract:Causal discovery with nonlinear mechanisms and latent confounders remains challenging. Existing methods often rely on either linear assumptions or causal sufficiency, limiting their applicability. We propose an MDL-based causal discovery framework that explicitly accounts for latent confounders while allowing flexible nonlinear mechanisms by minimizing the luckiness normalized maximum likelihood (LNML) code-length. The causal relationship between each variable pair is determined by selecting the shortest code-length of the causal model, and we introduce the notion of \Delta -pseudo-collinearity to identify dependencies induced by latent confounders. Based on these ideas, we develop a greedy algorithm, termed Pseudo-Collinearity Guided Causal Discovery (PCG-CD). Experiments on synthetic and real-world datasets demonstrate that the proposed method accurately recovers directed causal relationships and effectively detects latent confounders.
[LG-75] CertMix: Certified Data-Efficient Metamaterial Design by Affine Mixing of Aligned Neural-Implicit Weight Spaces
链接: https://arxiv.org/abs/2607.04123
作者: Yifan Wang
类目: Machine Learning (cs.LG)
*备注: 6 pages, 6 figures
Abstract:Inverse design of mechanical metamaterials seeks a periodic unit cell whose homogenized elastic properties meet a prescribed target, but current learning-based methods are data-hungry, mostly interpolative, and provide no guarantee that the generated design satisfies the specification. We introduce CertMix, a data-efficient framework that represents each exemplar unit cell as a small periodic neural implicit field, specifically a SIREN signed-distance decoder overfit from a shared anchor, so that exemplar weight vectors become aligned and directly comparable. The key observation is that, in this aligned weight space, the homogenized elasticity tensor is approximately linear in the mixing coefficients. Targeted design therefore reduces to a small constrained affine-mixing problem solved with a differentiable periodic homogenizer in the loop. Negative coefficients enable extrapolation beyond the exemplar range, a linearity-mismatch trust region keeps blends valid, and split-conformal calibration converts the mismatch signal into a distribution-free certificate on achieved-property error. From as few as 50 exemplars, CertMix attains a scaled property error of 10^-4 , roughly two to three orders of magnitude below conditional generative baselines trained on 1000 cells. It remains accurate far outside the exemplar range, is 57\times faster than per-target topology optimization while avoiding checkerboards and enclosed voids, and extends to spatially graded fields, 3D triply periodic surfaces, and a certified running-shoe midsole application.
[LG-76] Asymptotic-Preserving A Posteriori Analysis of Diffusion and Flow-Matching Samplers
链接: https://arxiv.org/abs/2607.04113
作者: Shiheng Zhang
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注:
Abstract:Diffusion and flow-matching samplers integrate a learned probability-flow ODE from a large noise scale down to a small terminal floor \sigma_\min , at which the score is stiff and the flow develops a boundary layer. We treat \sigma_\min as a singular-perturbation parameter and determine which fixed-step samplers are asymptotic-preserving (AP), that is, stable and uniformly accurate as \sigma_\min\to0 , casting the criteria as an a posteriori audit: residual functionals with \sigma_\min -uniform coefficients, computable on a pretrained checkpoint without ground-truth scores or exact trajectories. On the terminal layer, Euler in the \sigma -clock, the deterministic DDIM update, is the unique layer-exact discretization up to affine reparameterization, with rectified flow its flow-matching counterpart; the \lambda -clock is stable only for steps h\le h_\star=1+W(1/e) , and the uniform- \sigma^2 heat clock stalls a \sigma_\min -independent distance from the data. On two solvable models (rank-deficient Gaussian, symmetric two-point mixture), deterministic samplers remain first-order uniformly accurate with no \log(1/\sigma_\min) factor, even across a symmetric posterior-switching interface whose distributional budget is a universal constant; the logarithm is charged entirely to the Itô term of stochastic samplers, whose path-KL scales as \Lambda^2/N against the ODE’s O(\Lambda^2/N^2) budget, with \Lambda=\log(\sigma_\max/\sigma_\min) . On the EDM CIFAR-10 checkpoint, spectra measured once predict held-out residual budgets across step count, schedule, and noise level against pre-specified gates with no per-configuration refitting, and calibrate the Itô coefficient at M_1=1.00\pm0.01 . The clock decides stability; the noise, not the geometry, charges the logarithm.
[LG-77] Dictionaries Not Darwin: Set-Level Selection Beats LLM Evolution in Scientific Equation Discovery
链接: https://arxiv.org/abs/2607.04108
作者: Pan Li
类目: Machine Learning (cs.LG)
*备注:
Abstract:Large language models are increasingly used as evolutionary engines for scientific discovery: generate candidates, select winners, feed them back as parents, and repeat. We audit whether this loop actually compounds discovery in scientific equation discovery, a setting where finite samples make structure underdetermined and interpolation easy. Under matched LLM-call budgets, parent-conditioned evolution is indistinguishable from fresh independent sampling: median OOD NMSE is 0.045 vs. 0.049, instructed multi-parent crossover is worse, final success is predicted by initial proposal quality, and multiple iteration schemes fail to add solved problems. Operationally, the loop reduces to what it produces: a dictionary of candidate terms. We turn that diagnosis into PTB-Search, a one-generation method for componentized scientific discovery. PTB-Search samples independent LLM proposals once, extracts reusable terms into a per-problem dictionary, and performs train-only set-level sparse selection with least-squares coefficients. Its central principle is that underdetermined data identifies the joint behavior of term sets, not reliable per-term credit. On identical dictionaries and zero additional LLM calls, set-level selectors solve 165–169 of 717 cells, while single-term reductions solve only 74–78. On the official 239-problem LLM-SRBench split, PTB-Search reaches 73.2% Acc0.1 with Llama-3.1-8B and 77.0% with a single-seed DeepSeek-V4 anchor, versus 49.2% for the best reported baseline, using one tenth of the standardized call budget. A program-domain stress test gives a scoped boundary: generation count remains unreliable, while retained external state can help in harder non-linear spaces. Across these results, LLMs are best understood as material suppliers; discovery is carried by external set-level selection over reusable components.
[LG-78] arget-Aware Interaction-Guided Reinforcement Learning for Black-Box Node Injection Attacks on Graph Neural Networks
链接: https://arxiv.org/abs/2607.04091
作者: Yi Lan,Ye Yuan
类目: Machine Learning (cs.LG)
*备注:
Abstract:Graph Neural Networks (GNNs) have achieved remarkable performance in graph representation learning, yet their inherent vulnerability to adversarial attacks poses severe security risks. Especially, black-box node injection attacks have become a major threat to GNNs since they inject malicious nodes without altering the original graph topology. However, they typically decouple the generation of malicious node features and edge connections, thereby resulting in suboptimal attack efficacy under stringent budgets. To address this critical issue, this study proposes a novel Target-aware Interaction-guided Reinforcement learning for Black-box node injection Attacks on GNNs (TIRBA), which formulates the attack as a Markov Decision Process and jointly optimizes node feature generation and edge construction in a heterogeneous action space. Firstly, TIRBA designs a target-aware interaction encoder to fuse information of node features and edges. Further, it introduces a class-center guidance mechanism to utilize prior class distribution information, thereby guiding efficient exploration of the high-dimensional feature space. Finally, a topology difference-aware state value evaluation is adopted to explicitly capture local structural anomalies caused by injected nodes, thereby stabilizing the reinforcement learning training process. Experimental results demonstrate that the proposed TIRBA significantly outperforms state-of-the-art black-box node injection attack methods.
[LG-79] A Unified Framework for In-Context Learning with Causal and Masked Language Models
链接: https://arxiv.org/abs/2607.04081
作者: Chenrui Liu,Chuanlong Xie,Falong Tan,Yicheng Zeng,Lixing Zhu
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:In-context learning (ICL) has emerged as a central capability of pretrained language models, yet its theoretical analysis has focused primarily on causal language models trained by left-to-right autoregressive prediction, such as GPT-style models. Masked language models instead recover masked tokens from bidirectional context, and their role in ICL remains less understood. We develop a statistical learning framework that represents the context examples by their empirical measure and models prediction as a function of the context and the query. This formulation places autoregressive and masked pretraining objectives within a common excess-risk analysis. Under Wasserstein-type regularity conditions, we relate pretraining with T tasks and N samples per task to k-shot excess risk at inference, obtaining same-order upper bounds for masked and autoregressive objectives. We also study task-distribution shift, where pretraining tasks are sampled from P and inference tasks from Q; the resulting bound contains an additional term controlled by the lifted Wasserstein distance between P and Q. The bounds further imply an order-optimal allocation under a fixed pretraining data budget and refined rates under intrinsic low-dimensional structure. Experiments on controlled function-learning tasks show that the Masked Pair Encoder (MPE) can achieve performance comparable to GPT-2-style causal Transformers, suggesting that ICL behavior is not specific to causal language models.
[LG-80] Directional Curvature from Armijo Backtracking: A Low-Cost Sharpness Probe and a Calibration-Free Learning-Rate Safeguard for Adam
链接: https://arxiv.org/abs/2607.03998
作者: Ashmitha R,Jörg Frochte
类目: Machine Learning (cs.LG)
*备注: 27 pages, 6 figures
Abstract:The local sharpness of the loss, the top Hessian eigenvalue \lambda_1 , determines the largest stable gradient step, but measuring it normally requires Lanczos or Hessian-vector iterations. We observe that a single Armijo backtracking line search already carries this information at the cost of a few forward passes: the accepted step \alpha brackets the \emphdirectional curvature q = g^\top H g/|g|^2 within the multiplicative band set by the backtracking factor. Across CIFAR-10, Fashion-MNIST and Imagenette, \log\alpha tracks \log\lambda_1 at Pearson -0.91 to -0.95 , giving a low-cost online Edge-of-Stability reading. Used once at initialisation, this measurement yields a learning-rate cap (a safeguard, not a faster optimiser) that makes Adam robust to a too-large initial learning rate across more than three orders of magnitude ( 10^-3 to 3.0 ), at about one percent overhead, and it is a no-op when the chosen rate is already safe. One probe is enough: periodic in-training probing adds no robust benefit. The raw-gradient probe exposes the mechanism but needs a safety factor calibrated to the architecture by a one-minute divergence sweep. Probing along Adam’s own update direction removes this calibration: a single fixed safety factor \kappa = 2 avoids divergence on all nine architectures we test and across the full learning-rate grids of all four benchmarks, and the recipe transfers to AdamW unchanged.
[LG-81] MANCE: Manifold Aware Concept Erasure
链接: https://arxiv.org/abs/2607.03973
作者: Matan Avitan,Yoav Goldberg,Yanai Elazar
类目: Machine Learning (cs.LG)
*备注:
Abstract:Concept erasure aims to remove a target concept from a representation while preserving the other information encoded in it. This is difficult because representations encode many concepts that are often correlated with the erasure target, so removing the target risks damaging them. We propose the Manifold Constraint Hypothesis (MCH): if natural representations concentrate on a structured, lower-dimensional manifold, then interventions should be constrained to that manifold and better preserve other information encoded in the representation during interventions. We instantiate MCH in a new concept erasure method: MANifold aware Concept Erasure (MANCE). MANCE performs iterative updates to the representations using signals from a classifier that predicts a target concept. We estimate the manifold using representations obtained from natural inputs, and then we project the concept removal update to the estimated manifold. We perform extensive evaluation on 119 settings spanning text and vision, including 13 language models, three NLP concepts, and 40 CelebA-CLIP attributes. Employing MANCE on top of previous methods shows consistent improved leakage results. We also introduce MANCE+ and MANCE++, which prepend a closed-form erasure algorithm before employing MANCE, achieving better leakage–surgicality tradeoffs relative to matched full-space updates. MANCE++, our best method, achieves state-of-the-art results on nonlinear concept erasure. These results support MCH in the erasure setting: interventions should be constrained to the natural representation manifold.
[LG-82] NeuroOnline: Bridging Pretraining and Online Adaptation for EEG Foundation Models
链接: https://arxiv.org/abs/2607.03925
作者: Weibin Li,Wendu Li,Yushan You,Chen Wei,Quanying Liu
类目: Machine Learning (cs.LG)
*备注:
Abstract:EEG foundation models have shown strong potential in learning generalized representations across subjects and tasks. However, most existing approaches follow a pretraining-static deployment paradigm, which suffers from two key limitations: (1) misalignment between pretraining objectives and downstream tasks, and (2) limited adaptability to distribution shifts in online settings. We propose Online Neural Adaptation (NeuroOnline), a unified framework that enables continuous adaptation in online scenarios. NeuroOnline integrates two complementary mechanisms: (1) multi-view consistency learning, which enforces cross-view alignment to promote consistent and task-relevant representations, and (2) context-aware representation modulation, which leverages a learnable context prompt with cross-attention to dynamically adapt representations to evolving data distributions. Together, these mechanisms unify representation alignment and dynamic adaptation. Experiments on multiple EEG benchmarks show that NeuroOnline consistently outperforms strong baselines in online settings, achieving better performance under distribution shifts. Ablation and sensitivity studies further validate the necessity of each component and the effectiveness of the overall design.
[LG-83] ransformers with Physics-Informed Encodings and Simulation-Based Inference for Robust Detection of Eccentric Binary Black Holes in Pulsar Timing Array Data
链接: https://arxiv.org/abs/2607.03904
作者: Subhajit Dandapat,Alvin J. K. Chua
类目: Machine Learning (cs.LG); Instrumentation and Methods for Astrophysics (astro-ph.IM); General Relativity and Quantum Cosmology (gr-qc)
*备注: 24 pages, 7 figures, 4 tables
Abstract:Pulsar timing arrays (PTAs) provide a unique window into nanohertz gravitational waves (GWs), but extracting astrophysical parameters from noisy, long-baseline timing residuals remains computationally challenging with traditional Bayesian techniques due to the high dimensionality of the parameter space, complex and correlated noise models, and the cost of repeated likelihood evaluations. We introduce a Transformer with a physics-informed positional-encoding framework for the efficient inference of eccentric binary black holes in relativistic orbits from PTA data. Our approach embeds analytical GW phase evolution directly into the model through structured positional encodings, enabling the network to learn physically meaningful representations from raw PTA timing residuals. We then use generative models, including discrete and continuous conditional normalizing flows, to infer posterior distributions within a simulation-based inference framework. Across a range of signal-to-noise ratios, the proposed method achieves improved accuracy, sharper posteriors, and faster inference compared to physics-agnostic baselines. While presented for deterministic white-noise signals, the modular framework readily generalizes to realistic PTA analyses incorporating red noise and additional components. This work highlights the potential of physics-aware deep learning models as scalable alternatives to conventional inference pipelines for next-generation PTA datasets.
[LG-84] CDCP: Conditional Diffusion Model with Contextual Prompts for Multi-task Offline Safe Reinforcement Learning
链接: https://arxiv.org/abs/2607.03903
作者: Jiayi Guan,Tianle Zhang,Li Shen,Ruiqi Zhang,Ao Zhou,Lusong Li,Guai Chen,Mengjie Li,Alois Knoll,Xiaodong He,Changjun Jiang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Multi-task offline safe reinforcement learning (RL) promises to learn a shared optimal safe policy from offline data across multiple tasks. This paradigm provides an effective means for the widespread application of RL in multi-task scenarios with high risk and interaction costs. However, the triple challenges of multi-tasking, safety constraints, and out-of-distribution (OOD) actions pose a significant hurdle for existing methods to ensure safety while maximizing reward returns. In this work, we propose a Conditional Diffusion model with Contextual Prompts (CDCP) to address these challenges. Concretely, we first rethink the requirements and challenges in current multi-task decision-making and control scenarios and establish the objectives of multi-task offline safe RL. Subsequently, we transform the multi-task constrained optimization problem into a conditional generation problem using the diffusion model. Based on this, we design a classifier-free guided cost-constraint strategy to provide flexible cost constraints and eliminate extrapolation errors from OOD actions via supervised learning. Additionally, we introduce a novel contextual prompting method to enhance multi-task representation accuracy and adaptability to unseen tasks. A gradient loss synchronization strategy is also introduced to eliminate gradient interference, improving training stability. Finally, extensive experiments demonstrate that the CDCP algorithm exhibits higher performance and safety in multi-task scenarios than the current state-of-the-art baseline methods. It meets different cost constraints without further training, providing a more flexible cost-constraint solution for the multi-task safe RL.
[LG-85] AdaptiveSD A Stability-Aware Runtime-Adaptive Speculative Decoding Framework with Multi-Policy Orchestration for CPU-Constrained LLM Inference
链接: https://arxiv.org/abs/2607.03876
作者: Sadra Saremi
类目: Machine Learning (cs.LG)
*备注:
Abstract:With the rise of small quantized GGUF-based language models and their increasing use for on-device inference tasks, we have seen the growing need for an approach capable of reliably delivering these models at scale even under severe memory bandwidth constraints such as those imposed by pure CPU implementations. Fixed-depth speculative decoding has emerged as one promising technique, but in practice, it often leads to performance degradation due to either bandwidth saturation, instability, or even catastrophic resource exhaustion resulting in system failure. To overcome this problem, we introduce AdaptiveSD, a fully runtime-adaptive speculative decoding framework aimed at ensuring robust, reliable execution across the spectrum of model types and workloads. Our solution consists of four tightly-coupled components working together in a continuous feedback loop: a Runtime Monitoring Engine tracking multiple signals relevant to ongoing computation, an Adaptive Draft Controller enforcing an eleven rule policy hierarchy prioritizing system resource preservation over raw draft count, a Dynamic Policy Engine employing a suite of heuristic and reinforcement learning techniques to dynamically modify policies depending upon workload behavior, and finally, a KV Cache Coordination Layer managing cache states with fine-grained control through INT8 shadow buffers and position aware evictions. While conventional approaches focus solely on maximizing throughput, we instead assess the effectiveness of our approach based on several key metrics including wasted drafted compute and inter-token latency dispersion alongside standard measures of speculative efficiency.
[LG-86] A Gradient Flow Perspective on Minimum MMD Estimation
链接: https://arxiv.org/abs/2607.03871
作者: Sophia Seulkee Kang,Louis Sharrock,Xiaoyuan Cheng,François-Xavier Briol,Zonghao Chen
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Minimum maximum mean discrepancy (MMD) estimation has emerged as a robust and likelihood-free alternative to maximum likelihood estimation for parameter estimation. Yet, despite its practical success, the associated optimization problem remains poorly understood, with theoretical guarantees for existing algorithms hinging on convexity assumptions that rarely hold in practice. We address this gap by proposing a preconditioned gradient descent (PGD) scheme, establishing its asymptotic \emphglobal convergence under explicit gradient-dominance and projection-residual conditions. Our approach is inspired by recent progress on MMD gradient flows, a nonparametric descent scheme on the space of probability measures. We provide extensive empirical evidence that our PGD scheme outperforms standard gradient descent across a range of challenging parameter estimation and composite hypothesis testing problems.
[LG-87] A Unified Framework for Quantized and Continuous Strong Lottery Tickets
链接: https://arxiv.org/abs/2607.03860
作者: Aakash Kumar,Emanuele Natale
类目: Machine Learning (cs.LG)
*备注:
Abstract:The Strong Lottery Ticket Hypothesis (SLTH) asserts that sufficiently overparameterized, randomly initialized neural networks contain sparse subnetworks that, even without any training, can match the performance of a small trained network on a given dataset. A key mathematical tool in the theoretical study of SLTH has been the Random Subset Sum Problem (RSSP). The SLTH has recently been extended to the quantized setting, where the network weights are sampled from a discrete set rather than from a continuous interval. These new results are however far from those in arbitrary-precision setting in several ways. In this work, we provide an analysis of the RSSP in the discrete setting, and use it to derive tight SLTH guarantees in the quantized case. Our analysis obtain tight bounds on the failure probability of finding a strong lottery ticket in the quantized regime, providing an exponential improvement over previous results. Most importantly, it unifies the literature by showing that both approximate representations in the continuous setting and exact representations in quantized settings naturally emerge as limiting cases of our results. This perspective not only sharpens existing bounds but also provides a cohesive framework that simultaneously handles approximation and rounding errors.
[LG-88] NeSy-CSA: A Neuro-Symbolic Framework for Open-Ended Critical Scenario Attribution
链接: https://arxiv.org/abs/2607.03847
作者: Qitong Chu,Xunjie He,Chen Deng,Huaxin Pei,Yufeng Yue
类目: Machine Learning (cs.LG)
*备注:
Abstract:Understanding why discovered scenarios become critical in scenario-based testing is essential for effectively leveraging them in decision-making systems. Reasoning about such criticality can be formulated as an attribution problem. However, across different decision-making tasks, the causes of criticality may involve diverse state variables, interaction patterns, and failure mechanisms, making attribution an inherently open-ended problem beyond predefined explanation spaces. Existing attribution methods still struggle to balance open-ended reasoning flexibility with the interpretability and traceability required for critical scenario reasoning. To address this limitation, we propose NeSy-CSA, a neuro-symbolic framework that transforms open-ended critical scenario attribution from unconstrained explanation generation into structured and traceable reasoning. NeSy-CSA narrows the attribution space by selecting relevant factors, makes the reasoning process traceable through a dependency-aware evidence graph, and executes symbolic reasoning procedures derived from atomic operations, coordinated with evidence-constrained neural inference to support flexible open-ended attribution. We further introduce a process-level and result-level assessment module to evaluate the structural validity of the attribution process and the behavioral effectiveness of the attribution results under controlled interventions. Experiments across four decision-making environments show that NeSy-CSA improves two intervention-based measures of attribution effectiveness by 18.32% and 13.67% over LLM-based baselines. These results demonstrate its potential to transform discovered critical scenarios into reusable knowledge for subsequent testing and safety analysis.
[LG-89] Adversarial LassoNet: Robust Feature Selection via Stability-Driven Sparse Learning
链接: https://arxiv.org/abs/2607.03839
作者: Zhen Huang,Peicheng Xu,Junbiao Pang,Yulong Zheng
类目: Machine Learning (cs.LG)
*备注:
Abstract:Sparse feature selection is critical for high-dimensional machine learning, yet traditional \ell_1 -regularized methods are often brittle under observational noise and spurious correlations, leading to unstable feature supports and degraded generalization. Although adversarial training has been widely used to improve model robustness, its interaction with hierarchical sparse feature selection remains underexplored. In this work, we propose Adversarial LassoNet (AdLNet), a stability-driven sparse feature selection framework that integrates input-space adversarial perturbations with the hierarchical sparsity mechanism of LassoNet. We derive a tractable first-order adversarial approximation under local smoothness assumptions and provide an NTK-inspired spectral analysis to characterize how perturbation-driven training can reduce gradient concentration. Experiments on high-dimensional SERS data, six public benchmark datasets, and ColoredMNIST show that AdLNet maintains competitive sparse-selection performance while improving out-of-distribution robustness by 4.4% and feature support reproducibility by 6.3% under nearly matched support sparsity on ColoredMNIST. On the high-dimensional lung cancer screening dataset, AdLNet achieves a 5.3% test accuracy gain and a 6.0% AUC improvement over vanilla LassoNet. Code and dataset are available at this https URL.
[LG-90] Weave: Verified Netlist-to-Schematic Conversion via Layered Graph Layout
链接: https://arxiv.org/abs/2607.03835
作者: Senol Gulgonul
类目: Hardware Architecture (cs.AR); Machine Learning (cs.LG)
*备注: 6 pages, 5 figures, 2 tables. Tool: this https URL Code and benchmark: this https URL
Abstract:Converting a SPICE netlist into a human-readable schematic is a longstanding problem in electronic design automation: simulators and machine-learning pipelines readily produce netlists, but designers reason about circuits through diagrams. Recent learning-based approaches translate netlists into schematics probabilistically, yet they provide no guarantee that the generated drawing preserves the original connectivity, and their accuracy degrades sharply as circuits grow. We present Weave, a deterministic converter that turns a SPICE netlist into an LTspice .asc schematic using a layered (Sugiyama-style) graph layout, and that certifies every output by a round-trip connectivity check: the generated schematic is re-parsed into a netlist and compared, net for net, against the input. A result is reported as correct only when the two partitions are identical, giving a binary correctness certificate rather than a similarity score. Weave runs entirely client-side as a single dependency-free file and embeds a pin table for 5093 LTspice symbols. On the identical public Circuits-LTSpice test set used by the state-of-the-art LLM converter Schemato (117 circuits, netlisted with LTspice itself), Weave achieves 100% compilation and 100% round-trip-verified connectivity equivalence, compared with Schemato’s reported 76% compilation and a graph-edit-distance similarity of 0.35; notably, 73% of that set exceeds the five-component threshold beyond which Schemato reports losing connectivity accuracy. On a larger and harder corpus, the 3460 netlistable circuits of the official Analog Devices LTspice demo collection, Weave verifies exact connectivity for 88.4% of circuits, with the remaining failures concentrated in a single, well-characterized class of dense multi-pin power modules.
[LG-91] Stable Global Weighting of Flow Mixtures using Simplex Exponential Moving Averag e
链接: https://arxiv.org/abs/2607.03809
作者: Benjamin Wiriyapong,Oktay Karakus,Can Eyupoglu,Kirill Sidorov
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Normalising flows provide a powerful variational family for approximate inference, yet individual architectures often fail to generalise across heterogeneous posterior geometries. We revisit mixture-based flow formulations and introduce \emphAMF\mbox-VI\mbox-sEMA, a two-stage framework featuring a \emphstable global weighting mechanism based on a \emphSimplex Exponential Moving Average (sEMA) update. In Stage~1, a heterogeneous set of experts (\textscRealNVP, \textscMAF, \textscRBIG) are trained independently to specialise in distinct structural regimes. In Stage~2, expert parameters are frozen and global mixture weights are learned through a temperature-controlled softmax of average log-likelihoods, followed by a smooth EMA update on the probability simplex. This design produces a tractable, data-agnostic gating mechanism (without per-sample gating or gradient backpropagation through weights) that adaptively reallocates capacity while avoiding component collapse. We evaluate the framework on ten posterior benchmarks: six canonical 2D synthetic families (Banana, X-Shaped, Bimodal, Multimodal, Two-moons, Rings) and four real/low-dimensional Bayesian targets (BLR, BPR, Weibull, Real-GMM2), with stronger baselines (\textscNICE, \textscResFlow, and EM-Mixing). Comprehensive evaluation covers NLL, KL divergence, Wasserstein-2 distance, and MMD, together with diagnostics of mixture dynamics, hyperparameter sensitivity, and cross-seed robustness. Empirically, \emphAMF\mbox-VI\mbox-sEMA achieves consistent NLL improvements over its predecessor \emphAMF\mbox-VI and avoids the catastrophic transport failures of single-flow baselines, while maintaining stable weight trajectories ( N_\mathrmeff1.4 on all datasets) with minimal computational overhead.
[LG-92] SP with Predictions: Heatmap to Tour with Provable Guarantees
链接: https://arxiv.org/abs/2607.03791
作者: Marek Eliáš,Fabrizio Grandoni,Adam Polak,Eleonora Vercesi
类目: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)
*备注:
Abstract:The Traveling Salesperson Problem (TSP) has long served as a benchmark for evaluating the strength of optimization techniques in the classical theory of algorithms. In recent efforts to apply ML to algorithmic problems, TSP has also become a natural testbed for the development of ML-based techniques. A common approach is to train a neural network to output a heatmap estimating the likelihood of each edge to be part of the optimal tour; however, converting such a heatmap into an actual tour remains a non-trivial and often computationally intensive step. In this work, we propose algorithms for transforming heatmaps into tours with theoretical guarantees linking the achieved approximation ratio to the quality of the provided heatmap. In the spirit of algorithms with predictions, our results can be described as (1+2\frac\eta\mathrmOPT) -approximation algorithms, where \eta denotes the L1 distance between the prediction (heatmap) and an optimal solution (tour). Since the previous works lack such explicit guarantees, we compare our approach against them experimentally.
[LG-93] nsor-Train Joint Modeling for Few-Step Discrete Diffusion
链接: https://arxiv.org/abs/2607.03788
作者: Byoungkwon Kim,Minhyuk Sung
类目: Machine Learning (cs.LG)
*备注:
Abstract:Discrete diffusion promises orders-of-magnitude faster generation than autoregressive (AR) models for sequential discrete data, yet its full potential of few-step generation has remained out of reach due to a fundamental structural limitation. The conditional-independence assumption underlying current discrete diffusion models introduces a systematic parallelization bias that compounds with the number of tokens unmasked per step, becoming severe in the few-step regime that fast generation requires. We address this with the first framework for explicit joint distribution modeling in discrete diffusion via tensor decomposition, which represents the conditional clean distribution as a low-rank tensor with controllable expressivity. The framework supports both Canonical Polyadic (CPD) and Tensor-Train (TTD) decompositions, and we identify a structural bias of TTD toward dependencies between nearby tokens, formalized through Oseledets’ theorem relating TT-rank to unfolding-matrix rank, which is well-suited to sequential data such as natural language and line notations for molecular data. To enable efficient generation, we present an iterative marginal inference procedure with specialization for predetermined position schedules. Our framework integrates into pretrained MDMs through lightweight fine-tuning, yielding substantial improvements in few-step generation at a fraction of the cost of training from scratch.
[LG-94] Conservative Subject Invariant EMG-based Gesture Recognition
链接: https://arxiv.org/abs/2607.03783
作者: Hamed Rafiei,Ali Mousavi
类目: Machine Learning (cs.LG)
*备注:
Abstract:Cross-subject generalization remains a fundamental challenge in surface electromyography (sEMG)-based gesture recognition. Although deep learning methods have improved within-subject performance, they often rely on subject-specific data and struggle to balance invariance and discriminability. In this work, we propose a conservative multi-objective learning framework for subject-invariant sEMG gesture recognition. The proposed model adopts a multi-head architecture that jointly optimizes gesture classification, adversarial subject confusion through gradient reversal, and triplet-based metric learning to encourage discriminative and subject-invariant representations. To improve optimization stability, a Lipschitz-inspired adaptive weighting mechanism is introduced to dynamically balance the auxiliary objectives according to their relative magnitudes during training. The proposed method is evaluated on two benchmark datasets: UCI EMG (36 subjects, 6 gestures) and NinaPro DB5 (10 subjects, 10 gestures). On the UCI EMG dataset, the method achieves 84.48% accuracy compared to 78.2% reported by state-of-the-art methods. On NinaPro DB5, it achieves 61.44% accuracy versus 41.30%, corresponding to a 49% relative improvement. In addition, the proposed framework reduces cross-subject prediction variance and produces more structured latent representations. These results indicate that jointly enforcing invariance and discriminability through adaptive multi-objective optimization leads to more stable training and improved cross-subject generalization in sEMG-based gesture recognition systems.
[LG-95] SAVER: Stochastic Adaptive Variance-Driven Exploration and Reconstruction for Low-Dose Computed Tomography
链接: https://arxiv.org/abs/2607.03761
作者: Shunta Nonaga,Koji Tabata,Junya Honda,Hiroyuki Kudo,Wataru Yashiro,Tamiki Komatsuzaki
类目: Machine Learning (cs.LG)
*备注:
Abstract:Computed Tomography (CT) is indispensable in clinical diagnostics, yet minimizing radiation dose without compromising image quality remains a critical challenge. Conventional low-dose protocols often rely on fixed, uniform angular sampling, independent of the underlying structural complexity of organs of individual patients. We propose ``Stochastic Adaptive Variance-Driven Exploration and Reconstruction’’ (SAVER), an adaptive data acquisition framework that selects projection angles in real-time based on the statistical variance of acquired data. Utilizing a Softmax-based stochastic scheduling scheme with simulated annealing, SAVER prioritizes directions with high structural information while maintaining necessary exploration. Numerical experiments across 8 diverse phantoms demonstrate that SAVER achieves consistently higher reconstruction fidelity than conventional random sampling, particularly for objects with high structural anisotropy. Furthermore, the proposed method exhibits robust performance under significant measurement noise. By dynamically reallocating radiation dose to the most informative projections, SAVER provides a mathematically-grounded approach to maximize diagnostic quality per unit of radiation dose, marking a shift toward sample-dependent, data-driven CT acquisition.
[LG-96] SABLE: An NDA-Safe Closed-Loop LLM Framework for Analog Circuit Optimization in Industrial EDA Flows
链接: https://arxiv.org/abs/2607.03701
作者: Xunqi Li,Chris H. Kim
类目: Hardware Architecture (cs.AR); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:Large language models (LLMs) can propose circuit-optimization decisions, but industrial analog flows cannot expose foundry PDK content, proprietary schematics, absolute simulation paths, or license-bound tool state to a cloud endpoint. We present SABLE (Safe Analog Boundary for LLM-driven EDA), an NDA-safe closed-loop framework that lets LLMs optimize analog circuits through Cadence Virtuoso, Maestro, and Spectre while returning only scrubbed topology intent, numeric metrics, operating-point summaries, and scoped writeback status. “NDA-safe” denotes enforcement under a stated curious-but-passive cloud-provider threat model, not a formal non-interference proof. The framework combines an explicit threat model, a whitelist of 28 scoped SKILL entry points, PDK/path/model scrubbing on every return path, structured Maestro setup and writeback, a strict JSON action contract with six machine-checked stop conditions, and best-so-far state preservation. We evaluate eleven LLM checkpoints from the same documented reset state on two real closed-loop tasks, both run as process-voltage-temperature (PVT) sign-offs across three corners: a 20 GHz LC-VCO tuning-curve task and a two-stage op-amp task. On the LC-VCO task 7 of 11 models pass; on the harder op-amp task, where every metric must hold at the worst corner and a phase-margin gate rejects unstable high-gain points, 4 of 11 pass within a 15-iteration budget. Feedback-path ablations show that removing individual sanitized channels either silently weakens the specification or degrades the search. Model quality differs sharply once the loop requires tool discipline, bias reasoning, and specification repair, yet an NDA-safe boundary still provides enough sanitized feedback for successful analog circuit optimization.
[LG-97] Social Networks of LLM Agents
链接: https://arxiv.org/abs/2607.03695
作者: Kaixuan Liu,Guojun Xiong,Weinan Zhang,Shengpu Tang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Large language model (LLM) agents are increasingly deployed in interacting populations, raising the question of what such populations come to believe collectively. Whether a population aggregates genuine knowledge or collapses into a false consensus directly affects how much such systems can be trusted. Classical social-network models assume that the network itself determines how beliefs combine. This assumption breaks down for LLM agents, whose limited attention takes in only part of what they are exposed to, so these models overstate how much information a population actually pools and cannot tell genuine consensus from herding. We introduce SNLA, a framework that models how much each agent actually influences others, rather than merely how the network connects them. This influence depends on each agent’s position in the network and on how sharply attention focuses. Theoretically, we show on a tractable proxy that narrow attention causes herding, where the effective sample size stays bounded regardless of population size, while wide attention recovers wisdom-of-crowds behavior only when the exposure graph is undirected and degree-regular. Empirically, a controlled testbed validates these predictions directly, and the herding-wisdom transition reproduces on operator-controlled variants of three multi-agent LLM benchmarks.
[LG-98] PIEFS: Physics-Informed Eigenfunction Features with Learnable Scaling
链接: https://arxiv.org/abs/2607.03692
作者: Varvara Nazarenkko,Timur Lidzhiev,Alexander Tarakanov
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA); Statistics Theory (math.ST)
*备注: Comments are welcome
Abstract:Spectral methods are widely used to construct representations from the geometry of data, but they often rely on a fixed kernel, graph Laplacian, or manually selected feature scaling. We propose Physics-Informed Eigenfunction Features with Learnable Scaling (PIEFS), a supervised neural representation-learning framework with a spectral inductive bias, based on a modified Dirichlet energy. In PIEFS, scalar coordinate maps are trained under empirical Gram orthogonality, a supervised linear readout, and a Dirichlet penalty in which the input gradient is transformed by a learnable metric A(x)=\Lambda(x)U(x) . The diagonal factor \Lambda(x) controls anisotropic scaling, while the orthogonal factor U(x) is parameterized by a structured product of Givens rotations. This construction yields task-adaptive Dirichlet-regularized coordinates rather than eigenfunctions of a fixed supervision-independent operator. Experiments on synthetic, tabular, and image-based benchmarks study the effect of identity, diagonal, and rotation-scaling metrics, and compare the resulting coordinates with classical baselines and NeuralEF. The results support PIEFS as a compact supervised spectral representation method and identify optimization stability, validation on explicit operator eigenproblems, and richer metric parameterizations as the main directions for future work.
[LG-99] Validation-Induced Shapley Shifts: How Validation Structure Distorts Data Valuation
链接: https://arxiv.org/abs/2607.03675
作者: Yinan Shen,Ziao Yang,Hongfu Liu
类目: Machine Learning (cs.LG)
*备注: 12 pages, 8 figures, 1 table
Abstract:Shapley values are widely used to attribute value to training data based on their marginal contribution to performance on a validation set. Existing practice often assumes these values are stable once the training data and model are fixed. In this work, we uncover a systematic vulnerability: even modest changes to the validation set, such as introducing noises, cause directional shifts in Shapley distributions. As noises are added, Shapley values of training samples compress toward zero. We trace this to a noise-induced neighborhood reshuffling effect: perturbations alter the local rank order between validation and training samples, flattening the valuation landscape. Using the KNN-Shapley framework, we show through synthetic and real data that these shifts are consistent and reproducible. Our findings challenge the assumption of Shapley stability and reveal a new axis of fragility in data valuation. We propose normalization and boundary-aware validation strategies to mitigate these distortions and enable more robust, interpretable valuation in machine learning marketplaces.
[LG-100] A Structural Interpretation of GELU and Threshold-Transmission Activations via the First-Order Loss Function
链接: https://arxiv.org/abs/2607.03664
作者: Roberto Rossi
类目: Machine Learning (cs.LG); Optimization and Control (math.OC); Machine Learning (stat.ML)
*备注: 16 pages, 8 figures, 7 tables
Abstract:The Gaussian Error Linear Unit is usually motivated as the expected output of an input-dependent stochastic Bernoulli gate. This work gives a complementary interpretation based on the Gaussian complementary first-order loss function: GELU is the signal-transmission term of the expected surplus of a hard linear gate with a Gaussian random threshold. This view separates loss accounting from forward signal transmission and generalises to a threshold-transmission family that includes ReLU, GELU, SiLU/Swish, and hard swish as special cases. The uniform-threshold case recovers a hard-swish-like compact piecewise-polynomial gate with an explicit threshold-width parameter, yielding fixed- and learned-width variants. Controlled experiments on compact vision and language models show that calibrated or learned uniform-threshold gates are consistently competitive with GELU, ReLU, and SiLU/Swish, improve over them in most tested settings, and use the finite transition region nontrivially.
[LG-101] hreatVisionAI: A Hybrid CNN-ViT Framework for Image-Based Malware Classification
链接: https://arxiv.org/abs/2607.03653
作者: Allyson Taylor,Prashanth BusiReddyGari
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: Proceedings of the IEEE AIIoT World Congress Conference, 2026
Abstract:Traditional malware detection methods struggle to generalize to obfuscated or previously unseen threats. This paper introduces ThreatVisionAI, a hybrid malware family classification framework that integrates a raw-image CNN, a wavelet-based CNN, and a Vision Transformer (ViT) to capture complementary spatial, frequency-domain, and global relational features in malware images. The wavelet-based CNN captures multi-scale frequency information that helps distinguish closely related families, while the ViT branch models long-range dependencies across the image. Evaluated on the Malimg dataset, ThreatVisionAI achieves 98.01% accuracy and a weighted F1 score of 0.9742, with wavelet-domain features providing measurable gains on minority and visually similar families. These results confirm that frequency-aware and transformer-based representations improve image-based malware family classification.
[LG-102] LLM -Guided Transportation Hub Capacity Planning with Textual Business Inputs
链接: https://arxiv.org/abs/2607.03651
作者: Xiaoyue Liu,Zheng Dong
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:While traditional hub capacity planning models optimize effectively for quantitative inputs, they often fail to digest qualitative business context. We propose a novel framework where a large language model (LLM) agent iteratively proposes hub capacity decisions guided by natural-language business context descriptions. The key mechanism is a chain-of-thought reasoning protocol: the LLM constructs a structured decision table that maps each contextual item to specific capacity adjustments based on the implied direction and magnitude of changes. The new capacity decision is then validated through a feedback loop with an optimization model, which provides routing-based performance metrics to guide the agent’s selection. On a real-world 13-hub freight network in the southeastern US, our framework achieves a 2.8% optimality gap relative to the hidden ground-truth, a significant improvement over the 11.0% gap produced by the traditional optimization model without textual business inputs. This demonstrates that LLMs can serve as a contextual bridge, integrating qualitative business insights into Operations Research workflows.
[LG-103] Reflected Schrödinger Bridge Matching
链接: https://arxiv.org/abs/2607.03626
作者: Marcus Häggbom,Viktor Nilsson,Pierre Nyquist,Joakim andén
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Recent advances in generative modeling have enabled the efficient computation of Schrödinger bridges (SB) in high-dimensional settings by leveraging partially simulation-free training methods inspired by flow matching. However, these have not covered SBs with reflecting dynamics, a useful model choice with built-in guarantees that generated samples stay in the data domain. Existing alternatives for reflected SBs instead rely on more complex training based on forward–backward SDE theory, requiring expensive higher-order derivatives and sampling entire paths during training. In this article, we introduce a partially simulation-free framework that allows reflected SBs to be trained similarly to flow matching, using a new sampling method and regression target. We demonstrate our results by coupling pairs of well-known high-dimensional image datasets. Using reflected dynamics incurs negligible additional wall-clock time during both training and inference while maintaining or slightly improving generative performance.
[LG-104] Implicit Bias of SGD in Multivariate ReLU Networks: Effective Width Collapse
链接: https://arxiv.org/abs/2607.03613
作者: Shuang Liang,Tom Jacobs,Guido Montúfar
类目: Machine Learning (cs.LG)
*备注:
Abstract:We study the implicit bias of noisy stochastic gradient descent in training wide two-layer ReLU networks for multivariate regression. In a mean-field regime, the training dynamics are approximated by a Wasserstein gradient flow that converges to a unique stationary measure. We characterize the structure of this stationary measure and the predictor it represents. We show that, despite the network being infinitely overparameterized, the learned predictor admits an effectively finite representation: the input weights and biases align along finitely many directions, leading to an effective width collapse. In particular, the solution function is continuous piecewise affine, with affine regions determined by the cells of a finite hyperplane arrangement. The number of learned directions, and hence hyperplanes, is bounded above by 2\mathcalP-1 , where \mathcalP denotes the number of linear dichotomies realizable on the training inputs. We further establish a non-redundancy property of the learned representation by proving that each learned direction induces a unique ternary activation pattern on the training data. Consequently, the complexity of the learned predictor is governed by the combinatorial geometry of the training data.
[LG-105] Graph Classification via Network Usable Information: From Representation Evaluation to Structure Selection
链接: https://arxiv.org/abs/2607.03587
作者: Abdullah Shaik,Anwar Said
类目: Machine Learning (cs.LG)
*备注: 7 PAGES
Abstract:We propose NetinfoGC, a framework for graph classification that extends the Network Usable Information (NUI) paradigm to graph-level learning. Unlike conventional graph neural network approaches that rely on end-to-end training of black-box embeddings, NetinfoGC constructs a family of permutation-invariant graph representations derived from propagation-based mechanisms and classical structural descriptors, including graph centrality measures. To evaluate representation quality, we introduce a training-free NUI estimation procedure based on clustering consistency with ground-truth labels, providing a proxy for task-relevant information without supervised learning. We further exploit the same representations using sparse-group LASSO regularization, enabling automatic selection of informative structural descriptors while suppressing redundant ones. Experiments on benchmark datasets show that classical centrality measures are highly competitive with learned propagation-based representations, and in several cases yield superior performance. Moreover, we observe a strong correlation between estimated NUI and downstream classification accuracy, validating NUI as an effective measure of representation utility. Overall, NetinfoGC provides a unified and interpretable framework for evaluating and exploiting graph representations without requiring end-to-end neural training. Comments: 7 PAGES Subjects: Machine Learning (cs.LG) Cite as: arXiv:2607.03587 [cs.LG] (or arXiv:2607.03587v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.03587 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Anwar Said [view email] [v1] Fri, 3 Jul 2026 20:11:34 UTC (12 KB)
[LG-106] Modular Foundation Models for Time-Series Perception in Digital Twins
链接: https://arxiv.org/abs/2607.03585
作者: Quang Hung Pham,Ryad Zemouri,Martin Gagnon,Luc Vouligny
类目: Machine Learning (cs.LG)
*备注:
Abstract:Engineering Digital Twins and Prognostics and Health Management (PHM) systems rely on robust perception modules to extract actionable information from heterogeneous and non-stationary time-series data. However, most existing approaches remain task-specific, data-hungry, and difficult to integrate into scalable monitoring and decision-making pipelines. Moreover, purely data-driven models often lack robustness and transferability across varying operating conditions. To address these challenges, this paper proposes a modular foundation model for time-series perception based on a collection of pretrained representation encoders. The framework leverages self-supervised learning on heterogeneous datasets to learn transferable and task-agnostic representations, which can be reused across multiple PHM tasks. A gating mechanism is introduced to dynamically select relevant encoders for a given target dataset, enabling conditional computation and adaptive model composition. The selected representations are projected into a shared latent space and aggregated using a Transformer-based self-attention module that explicitly models cross-encoder interactions. The resulting architecture supports multiple downstream tasks, including imputation, long-term forecasting, and few-shot learning, through lightweight task-specific heads, while keeping pretrained encoders frozen during adaptation. Extensive ablation studies demonstrate the complementary roles of self-supervised pretraining, encoder selection, representation alignment, and adaptive aggregation. Experimental results on the ETT benchmark show competitive performance across tasks, while a real-world industrial case study on virtual sensing for hydro-generator rotor temperature highlights the practical relevance of the approach.
[LG-107] WeightCLIP: Aligning Datasets and Models for Weight Space Learning ICML2026
链接: https://arxiv.org/abs/2607.03551
作者: Aron Asefaw,Konstantinos Tzevelekakis,Damian Falk,Léo Meynent,Damian Borth
类目: Machine Learning (cs.LG)
*备注: Accepted at ICML 2026
Abstract:Weight space learning aims to learn representations of neural network (NN) weights, enabling different downstream tasks. Existing approaches show promising performance, but lacking a way to shape these weight-space representations using information about the datasets the models were trained on, thus limiting downstream applications. We propose WeightCLIP, a method for learning a dataset-aligned latent space for neural networks, where datasets information is induced during training. The NNs are encoded as latent representations using an autoencoder, while dataset samples are encoded using a dataset encoder. The two representations are aligned using a contrastive objective, effectively reshaping the weight-space representations according to the datasets. We demonstrate that such representations can be used for different downstream tasks, including mapping dataset information to a weight-space representation that decode to strong models. In addition, we introduce a latent refinement process for generating models that outperforms standard fine-tuning. Overall, our results demonstrate that explicitly incorporating dataset information improves what can be achieved with weight-space representations across retrieval, generation, and refinement. Code will be available at this https URL.
[LG-108] Co-Adaptive Multi-Task LoRA: Transfer-Aware Label-Free Control of Domain Participation
链接: https://arxiv.org/abs/2607.03522
作者: Wei Zhang,Lin Tang,Ming Zhao,Yuxuan Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Fine-tuning a single low-rank adapter on many domains at once is multi-task learning: the domains must be co-learned, and how they share the adapter decides whether they help or hurt one another. Most efficient fine-tuning pipelines ignore this and train on a fixed, uniform mixture, leaving two coupled questions unanswered: how much should each domain participate, and which domains should be co-trained given that some transfer positively and others interfere? We show that both answers can be read off cheaply and without labels. A forward pass of the current shared adapter over a small unlabeled probe yields, per domain, a competence signal whose level tracks remaining headroom and whose trajectory tracks learning speed; the drift of these probe representations yields a signed cross-domain affinity that predicts pairwise transfer. We fold both into CoDA, a co-adaptive controller that solves a small entropy-regularized quadratic program on the simplex to set each domain’s participation – jointly its loss weight and its share of the sampled data – rewarding high-headroom, still-learning, mutually synergistic domains and damping interfering ones. The controller is forward-only, adds no trainable parameters, and wraps any multi-task LoRA pipeline. Across five heterogeneous domains and two backbones, CoDA improves the average over uniform mixing, learned mixtures, gradient-surgery multi-task optimizers, and online data selection while using half the data, and lowers cross-domain gradient conflict. We prove that the competence signal tracks domain risk, that the participation program has a unique fixed point reached by a contraction, and that its solution performs transfer-aware water-filling; analysis, ablations, and controls corroborate each claim.
[LG-109] On the Convergence of Adam Revisited
链接: https://arxiv.org/abs/2607.03519
作者: Steven Heilman,Sampad Mohanty
类目: Machine Learning (cs.LG); Optimization and Control (math.OC); Machine Learning (stat.ML)
*备注: 26 pages
Abstract:We show that projected Adam for online optimization with arbitrary moment decay parameters \beta_1,\beta_2\in[0,1) can have average regret bounded away from zero. A similar result of Reddi-Kale-Kumar from 2018 required \beta_1\sqrt\beta_2 . Similar to their result, we use a three-periodic sequence of linear functions on [-1,1] with slopes c,-1,-1 , though we use c slightly larger than 2 . This nonzero average regret result extends to Adam variants such as AdamW, RMSProp, NAdam, Adan, AdaMax, Muon, and to an i.i.d. variant of the three-periodic sequence of slopes for Adam.
[LG-110] A Near-Linear-Time Solver for Graph p-Laplacian Semi-Supervised Learning via Continuation in p
链接: https://arxiv.org/abs/2607.03503
作者: Oren E. Livne
类目: Machine Learning (cs.LG)
*备注: 17 pages, 3 figures, 5 tables. Submitted to SIAM Journal on Scientific Computing
Abstract:Graph-based semi-supervised learning (SSL) propagates a few labels over a similarity graph by minimizing a Dirichlet-type energy. The standard quadratic ( p=2 ) energy reduces to a single graph-Laplacian solve, but it degenerates exactly where SSL is most useful when labels are scarce: gathering more unlabeled data drives the p=2 estimate to a near-constant function whenever d\ge2 (Nadler-Srebro-Zhou). Well-posedness requires the nonlinear p -Laplacian energy with pd . Existing solvers reduce this to a sequence of weighted Laplacian solves, but their reference implementations use a direct sparse factorization or ichol-preconditioned CG instead. Plugging a near-linear Laplacian solver is not straightforward: at large p the conductance weights degenerate near flat-gradient edges, making the system nearly singular and causing stagnation without a damped outer iteration. We close this gap. Recasting p -Laplacian SSL as a source-form nonlinear Laplacian flow B\rho_p(B^\top x)=b and solving by damped chord-Newton continuation in p , every linearized system stays well-conditioned and can be delegated to a near-linear Laplacian engine. On size-scaled graph families the wall-clock is empirically m^0.96 - m^1.02 per family (approximate Cholesky default), and a pooled fit across 228 SuiteSparse graphs gives m^1.19 vs.\ m^1.45 for direct factorization; the solver handles a 6.8\times10^7 -edge social network in minutes. Memory is the binding constraint: Cholesky fill reaches 10 - 280\times the graph nonzeros vs.\ our O(m) hierarchy. Against the released FCL solver we are 1.5 - 14\times faster at matched accuracy. On MNIST 10 -NN, p=3 scores 64% at one label per class vs.\ 36% for p=2 . Code: this https URL.
[LG-111] ADP: Adversarial Dynamics Priors for Physically Grounded Humanoid Locomotion
链接: https://arxiv.org/abs/2607.03454
作者: Seokju Lee,Jeongtae Lee,Jeonghyeok Lim,Jeonguk Kang,Byungwook Lee,Seungho Han,Keun Ha Choi,Dongil Park,Kyung-Soo Kim
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: 8 pages, 6 figures
Abstract:In this paper, we propose Adversarial Dynamics Priors (ADP) for perturbation-resilient humanoid locomotion control. Existing motion prior-based methods induce natural motion styles by imitating kinematic motion features, but they do not directly regularize dynamics features, such as CoM motion, centroidal momentum, contact forces, and contact states. To address this limitation, we replace kinematic motion-style feature with selected dynamics features extracted from locomotion trajectories as the target of adversarial this http URL this end, we use trajectory optimization to construct a reference dataset and train a discriminator to evaluate whether policy-induced temporal windows are consistent with the resulting reference this http URL explicit motion tracking, ADP encourages policy rollouts to remain close to the reference support, even after perturbations. Experimental results show that, compared with AMP, the strongest baseline in our evaluation, ADP improves the 80% -success impulse threshold ( J_80 ) by 16.7% , while reducing direction-averaged recovery time and velocity tracking error by 47.9% and 35.4% , respectively.
[LG-112] How Much of the Routing Gap Is Real? Decomposing the Router-to-Oracle Gap into Reproducible Specialist Advantage and Single-Draw Label Noise ACL KR
链接: https://arxiv.org/abs/2607.03436
作者: Teng-Ruei Chen
类目: Machine Learning (cs.LG)
*备注: 28 pages, 9 figures. Code and data: this https URL (v1.0)
Abstract:Routing among large language models (LLMs) promises better quality at lower cost, motivated by the reported gap between learned routers and a per-instance oracle. But that oracle is computed from a single correctness label per (query, model), so under stochastic decoding it is one Bernoulli draw, not a reproducible property. We recast the question structurally: the expected per-instance oracle decomposes as O^\exp=O^\mathrmrepro+\Delta , into reproducible single-commit headroom O^\mathrmrepro and a non-negative single-commit selection floor \Delta . Our main result is a recoverability asymmetry: this floor is closed by no single-commit router, yet is recovered by test-time sampling – best-of- K on the committed model, at the oracle’s own budget, dominates the independent-pool single-draw oracle. The cap needs no cross-model independence; we prove it with the exact decomposition and noise-share bounds that shrink as the budget grows. The procedure adds no new router, only resampling. The floor’s magnitude is a prospective, conservative localization, not an audit: our primary target LLMRouterBench (33 models, 391,645 instances) defines its oracle as a per-query union over single T=0.2 generations – by construction a union of stochastic single draws. Since O^\mathrmrepro is non-identifiable from the released k=1 matrix, we estimate the noise share by fresh k\ge20 resampling under one-sided, dependence- and guessing-floor-corrected bounds, recasting ‘model-recall failure’ as thin-support union inflation. On a controlled open-model re-generation, single-draw noise is a substantial minority of the gap – larger on an unsaturated benchmark, approaching half on the hardest queries where no model is reliable – while the majority remains recoverable specialist advantage. We release a multi-sample oracle evaluation protocol for routing benchmarks.
[LG-113] Scalable Differentially Private Data Compression via Diffusion and Stochastic Codes ICML2026
链接: https://arxiv.org/abs/2607.03392
作者: Gergely Flamich,Oykü Sıla Güner,Yanxiao Liu,Deniz Gündüz
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: To appear in ICML 2026 Workshop on Structured Probabilistic Inference Generative Modeling
Abstract:The ever-increasing collection of personal data has created mounting pressure to develop technologies that protect sensitive aspects of individual identity. Differential privacy (DP) provides a principled framework with strong formal guarantees and has already achieved practical success. However, releasing high-dimensional data, such as images, has remained elusive: releasing uncompressed privatized data requires significant storage. At the same time, no effective data compression scheme exists that can compress high-resolution data with privacy guarantees. We address this challenge with DP-DiPP, a compression pipeline that combines stochastic codes with diffusion models. DP-DiPP is highly flexible: the practitioner has direct control over the compression rate-privacy-utility tradeoff. As the theoretical backbone, we extend the Poisson private representation (PPR) to encode the outputs of privacy mechanisms. We then combine it with DiffC, a diffusion-based lossy data compression method, to obtain a differentially private image compressor. Our experiments on privatized image classification on CIFAR-10 demonstrate that DP-DiPP significantly outperforms the baseline, achieving a 10-30 times better compression while retaining comparable privacy guarantees and utility. Comments: To appear in ICML 2026 Workshop on Structured Probabilistic Inference Generative Modeling Subjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG) Cite as: arXiv:2607.03392 [cs.CR] (or arXiv:2607.03392v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2607.03392 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-114] he Multiscale Single-Index Model: A Stylized Model for Hierarchical Feature Learning
链接: https://arxiv.org/abs/2607.03347
作者: Joan Bruna
类目: Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注: 91 pages
Abstract:We consider the Multiscale Single-Index Model (MSIM), first introduced in \citeoymak2021learning, as a stylized model for hierarchical learning with \emphscale separation. Each layer extracts a shared single-index feature at one physical scale and passes it to the next, thus defining a tractable setting in which to study how deep architectures learn multiscale representations. Under non-degeneracy and delocalization assumptions on the link function and planted features respectively, for fixed depth K and local scale d , the first Wiener chaos of the target behaves as a perturbed spiked tensor, where the perturbation of order d^-1/2 comes from the non-linearity – revealing the MSIM as a natural non-linear analogue of the Tensor PCA model \citemontanari2014statistical. While this perturbative picture is sufficient to enable efficient spectral recovery based on Tensor unfolding (as already observed in \citeoymak2021learning), it is not precise enough for the analysis of backpropagation gradient-based methods. In this work, we address this limitation by performing a fine-grained analysis of the Wiener chaos using Edgeworth expansions. In the first chaos, this gives a finite-rank hierarchy at scales d^-q/2 . In higher chaoses, balanced flattenings exhibit staircase singular-value plateaus of size d^-\rho/2 and multiplicity d^\rho under a natural higher-chaos non-cancellation condition. Using this higher-chaos structure, and under an additional slow Hermite-energy tail condition, we first establish shallow-network approximation lower bounds, quantifying the benefit of depth in this model. Next, and most importantly, we prove that online SGD on the correlation objective, where all layers evolve in the same timescale, achieves 1 - o_d(1) recovery with n = \widetildeO( d^K-1) samples, recovering the same sample complexity as in the linear counterpart.
[LG-115] CSympNet-ID: conformal-symplectic map learning for linearly damped Hamiltonian systems
链接: https://arxiv.org/abs/2607.03339
作者: Jiale Gong(1),Pengzhan Jin(2),Dongyang Kuang(1),Lu Li(1),Yifa Tang(3) ((1) School of Mathematics (Zhuhai), Sun Yat-sen University, Zhuhai, China, (2) National Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing, China, (3) State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China)
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA); Computational Physics (physics.comp-ph)
*备注: 24 pages, 7 figures
Abstract:Learning dissipative dynamics from discrete observations is essential for reliable long-horizon prediction and physically meaningful parameter identification. For linearly damped Hamiltonian systems, the exact flow is generally not symplectic but conformally symplectic, contracting the canonical symplectic form by a scalar factor that reflects the net dissipation. We propose Conformal Symplectic Networks with damping identification (CSympNet-ID), a discrete-time map-learning framework that learns the one-step flow map directly from snapshot pairs while enforcing exact discrete conformal symplecticity by construction, without penalty terms or projection. The architecture composes an exact symplectic neural core with explicit diagonal scaling layers whose factors are parameterized exponentially by a scalar damping-rate parameter, thereby guaranteeing positivity and interpretability of the learned dissipation factor. We establish a scaling-conjugacy factorization for conformal symplectic maps and derive a pointwise-in-step density result for CSympNet-ID. We evaluate an irregular-step damped oscillator, a damped spring-mass chain, a damped nonlinear cubic oscillator, and additional high-dimensional extensions. CSympNet-ID gives the most favorable overall results among the compared models in the reported experiments, particularly in data-scarce regimes, target contraction-law recovery, and high-dimensional tests where unstructured baselines degrade rapidly.
[LG-116] Statistically Meaningful Geometry (SMG) Beyond the Euclidean Paradigm with Application to Generative AI
链接: https://arxiv.org/abs/2607.03329
作者: Bing Cheng,Yi-Shuai Niu,Howell Tong,Shing-Tung Yau
类目: Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:Conventional uniform convergence bounds and empirical risk minimization break down in massive over-parameterized models, such as large language transformers and biological sequence networks. With near-infinite unconstrained internal degrees of freedom, their optimization landscapes develop flat vertical gauge valleys, rendering classical generalization metrics vacuous and inducing severe pathologies, specifically generative hallucination and catastrophic forgetting. We introduce the Statistically Meaningful Geometry (SMG) framework, an information-geometric paradigm lifting deterministic parametric models into infinite-dimensional non-parametric Orlicz statistical manifolds. Modeling the total state space as a differential fiber bundle ( \mathcalM, \mathcalB, \pi, \mathcalV, \mathcalH, \omega ), we establish a Two-Fold Inference Paradigm. We formalize an Ehresmann connection 1-form \omega as a dynamic geometric filter that strips away vertical gauge noise (Structural Internal Directions, or SID) and isolates learning trajectories along the strictly non-degenerate horizontal distribution (Statistical Variational Directions, or SVD \chi ). We prove that under connection-filtered pre-training, out-of-distribution predictive variance is strictly upper-bounded by the finite diameter of the identifiable quotient base manifold \mathcalB , establishing a hard geometric containment of generative hallucinations. By projecting downstream updates onto the orthogonal complement of the historical horizontal carriage, we formalize the SMG Sequential Adaptation Flow, proving the total non-asymptotic elimination of catastrophic forgetting. SMG replaces empirical fine-tuning heuristics with coordinate-free topological constraints, bridging advanced differential geometry with structural reliability in AI.
[LG-117] Adaptive Loss Balancing for Multi-Task Bioacoustic Classification of Bird Species and Call Types
链接: https://arxiv.org/abs/2607.03304
作者: Paria Vali Zadeh,Sven Tomforde
类目: ound (cs.SD); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS); Quantitative Methods (q-bio.QM)
*备注: 30 pages, 4 figures, 4 tables. Submitted to Lecture Notes in Artificial Intelligence (LNAI). Extended version of the ICAART 2026 paper “BirdCallNet: Joint Species and Call-Type Classification.”
Abstract:Reliable analysis of bird vocalisations in passive acoustic monitoring requires models handling multiple, imbalanced annotation targets. We extend BirdCallNet for joint species and call-type classification on the long-tailed WiWa dataset and investigate how task-loss balancing interacts with pretrained representations and adaptation depth. We evaluate four bird-domain encoders, ConvNeXtBS, EAT, BirdMAE, and ProtoCLR, with separate species and call-type heads under linear probing, attentive probing, and full fine-tuning. A manually tuned fixed objective is compared with homoscedastic uncertainty weighting and Dynamic Weight Averaging across all three adaptation regimes, while GradNorm is evaluated only under full fine-tuning. Results indicate that the factorised multi-task formulation yields the most consistent improvements over the combined single-task baseline for call-type recognition, while its effect on species recognition depends on the adaptation regime. Full fine-tuning is not consistently optimal: ConvNeXtBS achieves the highest mean species performance under linear probing, whereas BirdMAE provides the strongest call-type performance under attentive probing. Adaptive weighting benefits species recognition more consistently than call-type recognition. Uncertainty weighting is particularly effective for species recognition under attentive probing, whereas Dynamic Weight Averaging is generally stronger for the same task under full fine-tuning. GradNorm achieves competitive call-type performance for selected backbones but consistently underperforms other weighting strategies for species recognition and incurs higher computational and memory costs. Overall, the preferred loss-balancing strategy depends on the backbone, adaptation regime, and target task, while frozen-backbone adaptation can provide a more favourable performance-efficiency trade-off than end-to-end fine-tuning. Comments: 30 pages, 4 figures, 4 tables. Submitted to Lecture Notes in Artificial Intelligence (LNAI). Extended version of the ICAART 2026 paper “BirdCallNet: Joint Species and Call-Type Classification.” Subjects: Sound (cs.SD); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS); Quantitative Methods (q-bio.QM) Cite as: arXiv:2607.03304 [cs.SD] (or arXiv:2607.03304v1 [cs.SD] for this version) https://doi.org/10.48550/arXiv.2607.03304 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-118] From Global to Local: Efficient Regional Weather Downscaling with Global Weather Foundation Model
链接: https://arxiv.org/abs/2607.03279
作者: Wiktor Kamzela,Jakub Kubiak,Adam Dobosz,Jędrzej Miczke,Anatol Kaczmarek,Piotr Wyrwiński,Wojciech Stefaniak,Wojciech Kotłowski
类目: Machine Learning (cs.LG)
*备注:
Abstract:Accurate regional weather prediction requires resolving fine-scale structure while remaining consistent with global dynamics. Traditional limited area models rely on computationally expensive simulations, while many learning-based approaches frame the problem as super-resolution, overlooking statistical and physical mismatches across scales. We propose a foundation-model-driven downscaling framework that learns regional refinements of global forecasts by augmenting a pretrained weather model backbone with lightweight, multi-scale prediction heads operating directly in its latent space. Despite being trained on substantially coarser inputs, the pretrained backbone supports regional adaptation at resolutions corresponding to a two-order-of-magnitude increase in grid-cell resolution, without the need for retraining. The proposed approach uses regional numerical simulations as training targets and is evaluated not only against gridded datasets but also against ground-based weather station observations, enabling analysis of systematic biases between global reanalysis, regional simulations, and in-situ weather station observations. Our experiments show improved accuracy in comparison to NWP on most of the metrics at the fraction of computational cost. Moreover, we observe that building on a latent space of globally pre-trained weather foundation model offers better downscaling capabilities than the standard image-based super-resolution approaches.
[LG-119] PhenoNEST: A Neuro-Symbolic Framework for Ontology-Aware Multimodal Plant Phenotyping and Trait Discovery
链接: https://arxiv.org/abs/2607.03245
作者: Jayant Ghadge,Soumyashree Kar,Surya S. Durbha
类目: Machine Learning (cs.LG)
*备注:
Abstract:High-throughput plant phenotyping generates valuable data that often remains trapped in unstructured text and isolated RGB images. To bridge this semantic gap, we propose a framework for constructing a multimodal granular Knowledge Graph (KG) to monitor genotype-phenotype interactions across time and experiments. In this work, we focus on wheat Triticum aestivum as a representative target crop to validate our methodology across complex canopy environments. Our pipeline first distills noisy field notes to extract entities and relations, dynamically constructing the KG by converting unique instances into hierarchical class entities via RDF-typing. These graph nodes are then aligned with standardized ontologies (PO, RO, WTO) using PlantDeBERTa. To visually ground the constructed graph, a Vision-Language Model paired with a wheat-segmentation ViT generates attention-based softmaps, linking specific KG entities directly to image pixels. We introduce a central observation node Plant_Obs_Id to connect these multimodal subgraphs temporally. Evaluated on 500 curated WisWheat samples using Pointing Game accuracy, Visual Word Sense Disambiguation (VWSD), and rank-based metrics, our neuro-symbolic approach successfully maps complex field observations to a structured graph. This enables automated field note auditing, temporal stress monitoring, and precise spatial trait localization for wheat breeders.
[LG-120] Joint distribution of upstream runoff governs downstream river-discharge prediction uncertainty in distributed ML models
链接: https://arxiv.org/abs/2607.03217
作者: Karan Ruparell,Tristan Hascoet,Takemasa Miyoshi,Kieran M. R. Hunt,Hannah L. Cloke,Christel Prudhomme,Florian Pappenberger
类目: Machine Learning (cs.LG); Applications (stat.AP)
*备注:
Abstract:Uncertainty quantification of hydrological predictions is necessary to inform operational decisions. Recent generative machine-learning methods have advanced probabilistic streamflow prediction, but have remained confined to lumped models that predict a basin outlet directly. At the same time, deterministic LSTM runoff models are increasingly applied at grid or catchment scale and routed through river networks to produce spatially continuous, physically consistent discharge fields. This technical note argues that moving probabilistic prediction from lumped to distributed models introduces a specific new requirement: the joint distribution of upstream runoff generation must be sampled jointly. In lumped inference, the model predicts the outlet distribution directly and can modulate spread from basin attributes. In distributed inference, downstream discharge is obtained by routing many upstream runoff predictions, so independent local sampling averages uncertainty away. Using Japan as a case study, we train two probabilistic basin-scale runoff LSTMs and route their runoff through a Hayami routing scheme. Randomly matching upstream ensemble members produces severely under-dispersed downstream ensembles, whereas a simple quantile matching strategy restores much of the spread of the direct basin-scale reference. The shift from lumped to distributed probabilistic hydrology therefore requires explicit attention to the spatial joint structure of runoff uncertainty.
[LG-121] OpFlow: Learning Opportunity-Conditioned Choice Potentials for Robust OD Flow Prediction
链接: https://arxiv.org/abs/2607.03200
作者: Changjian Liu,Yong Gao,Yuqing Wang,Leyi Su,Honglei Guo,Zhiyang Wang,Xiaoyu Wang,Fan Zhang
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Origin-destination (OD) flow prediction is central to urban analytics, yet deep models trained on raw counts remain vulnerable to distribution shift. The core problem is that raw count supervision cannot distinguish transferable choice mechanisms from environment-specific shortcuts. Raw OD count mixes two objects: how much demand an origin produces and how that demand is allocated across destinations. We argue that the transferable object is the exposure-to-choice law that maps spatial conditions to relative destination preferences. We propose OpFlow, a mechanism-constrained framework that learns row-centered choice potentials and reconstructs flows by combining the induced allocation with a separately calibrated origin scale. Under distribution shift, spatial exposures and the induced allocations are allowed to vary; what transfers is the conditional map from exposure states to relative choice potentials. Theoretically, we characterize the identifiable row-centered potential and show that classical spatial interaction laws are restricted log-potential cases. Controlled synthetic shifts and a real-world experiment show OpFlow improves robustness under environment shifts.
[LG-122] Reduced-Order Models: The Mother of World Models
链接: https://arxiv.org/abs/2607.03198
作者: Rajat Ghosh
类目: Machine Learning (cs.LG); Mathematical Physics (math-ph)
*备注:
Abstract:World models – compressed latent representations of an environment that support action-conditioned prediction and planning – are typically presented as a product of modern self-supervised learning. This paper argues that the functional anatomy of a world model was independently developed, deployed, and formally analyzed decades earlier in the model-order-reduction (MOR) and control literature, under different names and for a different purpose: the real-time operation of physical systems. We trace the anatomy across three communities. Low-dimensional models of turbulence built on proper orthogonal decomposition (POD) supplied latent dynamics learned from data of a chaotic environment; eigenface methods in early computer vision supplied the encoder-decoder half, including a primitive runtime validity check; and measurement-based POD frameworks for facility thermal control assembled the complete loop – POD coefficients as latent state, parametric dependence on actuator setpoints as action conditioning, modal reconstruction as decoding, and, critically, a priori analytical error bounds as a verification layer that certified when the model’s predictions could be trusted in closed loop. We then examine what each tradition possesses that the other lacks: MOR contributes verification, physical grounding, and extreme data efficiency; learned world models contribute nonlinear representation, transferability, and horizon. We argue that the outstanding obstacle to deploying world models in systems that cannot fail – power, thermal, process control – is not predictive fidelity but verifiability, and we outline a research agenda for physics-grounded, verifiable world models that unifies the two lineages.
[LG-123] Understanding electricity consumption behaviour through Inverse Reinforcement Learning
链接: https://arxiv.org/abs/2607.03176
作者: Enrico Cofler,Carlos Rodriguez-Pardo,Matteo Giuliani,Andrea Castelletti,Massimo Tavoni
类目: Machine Learning (cs.LG)
*备注:
Abstract:Understanding how households consume electricity in response to socioeconomic and climatic drivers is important for decision-makers designing energy policies in a changing climate and under geopolitical tensions. Consumers respond differently to thermal stress depending on income, consumption habits and the surrounding built environment, a nonlinear behaviour that most approaches oversimplify. In this study, households are treated as agents interacting with complex environments, and Inverse Reinforcement Learning is used to represent their consumption behaviour as model implied reward functions. Specifically, we observe how these reward functions change when households undergo socioeconomic and climatic shocks. The framework is tested on different clusters of electricity consumption profiles in Italy. Clusters’ reward functions are retrieved and used to understand how cooling behaviour changes from summer 2021 to summer 2022 and 2023, before, during and after the energy crisis and a heatwave. We find that these shocks reshaped cooling behaviour heterogeneously across consumer groups, in directions conditioned by their prior habits and built environment. Across the 2021 to 2023 summers, we identify a spectrum of responses: transient adjustments that receded as the shocks eased, durable shifts persisting into 2023, and consumers exhibiting negligible change. At the intradaily scale, groups comparable in socioeconomic and environmental context but differing in their daily timing of consumption responded distinctly, identifying time of use as a separate dimension of behavioural heterogeneity. Energy policies and demand-response schemes should therefore account not only for who consumers are and where they live, but for when they consume and whether their response to a shock persists.
[LG-124] Sample-Efficient Pareto Front Modeling for Energy-Aware Reinforcement Learning Using Bayesian Optimization
链接: https://arxiv.org/abs/2607.03140
作者: Georg Schäfer,Jakob Rehrl,Stefan Huber,Simon Hirlaender
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注: Accepted at Eurocast 2026
Abstract:Industrial automation increasingly demands control strategies that balance operational performance with strict energy efficiency requirements. A common approach to solving this multi-objective problem, particularly within the framework of reinforcement learning (RL), is to formulate a single, scalar reward function that linearly combines the competing objectives. However, the manual weighting of these different objectives is heavily reliant on domain intuition, incredibly time-consuming, prone to human bias, and frequently fails to uncover optimal trade-off solutions. This work addresses the critical challenge of automating the weight selection process to systematically and efficiently discover the Pareto front of optimal trade-off policies. We formulate the weight selection process as a multi-objective Bayesian optimization (MOBO) problem and evaluate its sample efficiency against a standard uniform grid search baseline. Using a physical Quanser Aero 2 testbed configured for 1-DoF pitch control, our results demonstrate that the MOBO approach, utilizing the expected hypervolume improvement (qEHVI) acquisition function, consistently outperforms uniform grid sampling. MOBO achieves superior hypervolume and maximum spread, successfully identifying high-quality, diverse trade-off policies with a reduced evaluation budget, thereby enabling highly efficient energy-aware control in complex mechatronic systems.
[LG-125] Anticipatory Reinforcement Learning for Trajectory Tracking
链接: https://arxiv.org/abs/2607.03132
作者: Georg Schäfer,Jakob Rehrl,Stefan Huber,Simon Hirlaender
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注: Accepted at DEXA AI4IP 2026
Abstract:Deep reinforcement learning (DRL) in industrial control often suffers from lag and overshoot due to purely reactive control based on the current tracking error. To achieve anticipatory control without high computational overhead, we introduce a predictive formulation that augments the DRL state space with target velocities and future reference horizons. Evaluating eight configurations using proximal policy optimization (PPO) on a 1-degree-of-freedom (1-DoF) helicopter testbed, simulation results showed a 9-fold error reduction, lowering the mean absolute deviation from 2.73° to 0.31°. However, zero-shot transfer to physical hardware revealed a sim-to-real gap. Interestingly, a simpler configuration using a single, further look-ahead horizon matched the real-world top performance of the most complex model (1.11°). Overall, evaluating various combinations of prediction horizons and target velocities demonstrated that highly granular predictive data is not necessarily required for physical transfer.
[LG-126] Integrating Physics-Informed Neural Networks for Safe Reinforcement Learning in a 1-DoF Helicopter System
链接: https://arxiv.org/abs/2607.03125
作者: Georg Schäfer,Jakob Rehrl,Stefan Huber
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注: Accepted at DEXA AI4IP 2026
Abstract:Deep reinforcement learning (DRL) offers powerful control for industrial cyber-physical systems (ICPSs), but its “black-box” exploration risks violating strict hardware safety limits. Typically, these constraints are managed through complex reward shaping. In this work-in-progress paper, we embed a differentiable physics model directly into the proximal policy optimization (PPO) actor loss function. By simulating short-horizon future trajectories during training, the policy is penalized for anticipated safety violations independent of the task-reward signal. Evaluated on a simulated 1-degree-of-freedom helicopter testbed with strict pitch constraints, our physics-informed soft regularizations substantially reduce constraint violations while maintaining reliable target tracking.
[LG-127] Dimension Reduction for Curves: Simplified and Generalized
链接: https://arxiv.org/abs/2607.03112
作者: Matthijs Ebbens,Jie Lu,Alexander Munteanu
类目: Data Structures and Algorithms (cs.DS); Computational Geometry (cs.CG); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: ESA 2026
Abstract:We revisit random projections for reducing the dimension of high-dimensional polygonal curves. Drawing from the toolbox of randomized linear algebra, we give a considerably simplified proof of the known O(\varepsilon^-2\log(nm)) bound on the target dimension of a random projection that preserves the continuous Fréchet distance of polygonal curves up to a factor (1\pm\varepsilon) . Our proof is based on the concept of sparse oblivious subspace embeddings. While previous techniques were limited to the case of the Fréchet distance, our techniques are fairly general and extend to all possible distance measures that involve the maximum, a sum or an integral over Euclidean distances between pairs of points on both input curves. We define a generalized dissimilarity measure for curves that includes several popular measures such as Fréchet, q -DTW, Hausdorff, etc. as special cases and show that the same dimension reduction technique works for this generalized dissimilarity measure. Finally, we apply the same framework for dimension reduction to piecewise linear surfaces, after extending the distance measure suitably to such surfaces.
[LG-128] Heterogeneous Graph Condensation via Role-Aware Clustering
链接: https://arxiv.org/abs/2607.03097
作者: Fuyan Ou,Yulin Hu,Ye Yuan
类目: Machine Learning (cs.LG)
*备注: 10 pages, 5 figures
Abstract:Heterogeneous Graph Neural Networks (HGNNs) have exhibited remarkable efficacy in modeling complex systems with multiple types of nodes and relations, yet their training on large-scale heterogeneous graphs remains computationally prohibitive. Although graph condensation methods can effectively improve learning efficiency on large-scale graphs, existing condensation processes are mainly designed for homogeneous graphs and typically rely on computationally expensive gradient matching or bilevel optimization paradigms, rendering them impractical for heterogeneous settings. To address these limitations, we propose HGC-RC, a simple yet effective role-aware heterogeneous graph condensation framework. Specifically, HGC-RC first extracts semantically enhanced node embeddings via lightweight propagation. It then introduces a role-aware hybrid clustering strategy consisting of class-partitioned clustering for labeled target nodes to preserve class distributions and unsupervised type-wise clustering for non-target nodes to retain critical cross-type connectivity. Finally, a compact heterogeneous graph is efficiently reconstructed based on the resulting cluster assignments. Extensive experiments demonstrate that HGC-RC outperforms state-of-the-art baselines, offering a practical pathway to accelerate HGNN training on large-scale heterogeneous graphs without sacrificing task performance
[LG-129] Stacked LoRA for Subject-Adaptive EEG Foundation Models in Motor Imagery Decoding
链接: https://arxiv.org/abs/2607.03094
作者: Aymen Sarhane,Fouad Lbakali,Mouad Souissi,Jonathan Lys,Giulia Lioi
类目: Machine Learning (cs.LG)
*备注: 10 pages, 1 figure, 2 tables. Accepted at the 2026 IEEE World Congress on Computational Intelligence (WCCI 2026), Maastricht, Netherlands
Abstract:Electroencephalography (EEG) decoding for brain-computer interfaces (BCIs) faces a major challenge: substantial inter-subject variability limits effective cross-subject generalization. Consequently, practical systems still rely largely on subject-specific models trained from scratch and requiring individual recalibration. EEG foundation models have recently emerged as a promising alternative; however, even large pretrained models cannot simply be used as fixed feature extractors and still require additional adaptation before they can be reliably applied to downstream tasks. In this work, we address this challenge through targeted adaptation strategies. Building on recent EEG foundation models such as REVE, LaBraM, and LUNA, we examine the impact of different low-rank adaptation strategies on motor imagery classification. We propose a framework that structurally decouples subject-invariant knowledge from subject-specific neural signatures: the low-rank update at each adapted layer is split into a Global adapter, trained jointly across all subjects, and Subject-Specific adapters, each absorbing individual variability. To assess the contribution of each path, we compare three adaptation strategies: (i) subject-specific LoRA (ii) global LoRA and (iii) stacked LoRA, combining both Global and Subject Specific adapters. Experiments on BCI Competition IV-2a, PhysioNet Motor Imagery, and the clinical Zuo2025 benchmark show that Stacked LoRA effectively mitigates inter-subject variability, achieving the best accuracy in the large majority of backbone and dataset combinations. Our analysis further reveals that the optimal balance between the global and subject-specific paths depends on the target population: a shared adapter is sufficient for large, diverse cohorts, whereas subject-specific adaptation is decisive under the high inter-session variability of clinical recordings.
[LG-130] SHiPPO: Recurrent Memory with Transported Polynomial Projections
链接: https://arxiv.org/abs/2607.03055
作者: Tomoya Mizuguchi,Bum Jun Kim
类目: Machine Learning (cs.LG)
*备注: 45 pages, 2 figures
Abstract:HiPPO gives recurrent states memory semantics as coefficients of online polynomial projections, but in fixed channel coordinates. Modern selective SSMs, by contrast, rely on token-dependent control and channel interaction. We introduce SHiPPO (Sylvester HiPPO), a transported projection-memory prior that lifts HiPPO coefficient memories into a moving channel frame. For any fixed or realized right-transport path, SHiPPO transports the approximation family and channel metric together; conditional on that path, the state is ordinary HiPPO in a tied moving frame and follows Sylvester coefficient dynamics, preserving the left online-memory operator while adding right-action transport. For selective-SSM execution, we derive a restricted group-local realization with controller-compatible right actions, exponential-adjusted updates, exact block-affine scan, and recurrent decoding. We also give a simultaneous-reducibility criterion identifying when right transports collapse to static mixing plus independent scalar or blockwise banks. Controlled diagnostics show that larger current-token write rank improves ordinary prediction error but cannot recover order-sensitive changes to already-written memory; transported-memory variants recover this signal, which disappears when the transport pathway is removed. A finite-field associative-recall diagnostic with interleaved bindings, operations, and queries provides complementary autoregressive evidence while leaving the preferred right-action realization open. Taken together, these results support SHiPPO as a mechanistically grounded transported-memory prior, with evidence focused on memory mechanisms rather than broad sequence-modeling dominance.
[LG-131] Out-of-distribution Neural Inference in Dynamical Ising Models
链接: https://arxiv.org/abs/2607.03039
作者: Yuan-Bin Zhu,Shuang Qiao,Shi-Ju Ran
类目: Machine Learning (cs.LG); Statistical Mechanics (cond-mat.stat-mech)
*备注:
Abstract:Neural networks are increasingly used to infer hidden physical structure from dynamical observations, yet it remains unclear whether their out-of-distribution performance reflects transferable physical rule learning. We address this question in a controlled inverse problem: reconstructing interaction graphs of a kinetic Ising model from Glauber magnetization trajectories. Across convolutional, graph, Transformer, and hybrid architectures, we find that data-driven training produces distinct and reproducible statistical strategies under topology and temperature shifts. Edge-population diagnostics reveal that Transformer-based models tend to preserve the link density of the training ensemble, whereas convolutional models can collapse toward sparse- or no-link predictions that appear out-of-distribution stable by exploiting the majority no-link class. Thus, high in-distribution accuracy and apparent out-of-distribution robustness do not necessarily imply a learned dynamics-to-structure rule. Instead, neural reconstruction can be governed by architecture-dependent statistical priors. Our results identify a concrete failure mode of standard data-driven learning in physical inverse problems and motivate rule-guided principles for machine-learning-assisted scientific discovery.
[LG-132] Do ECG Foundation Models Transfer to Rare Cardiac Diseases? Evidence from Brugada Syndrome Detection
链接: https://arxiv.org/abs/2607.03009
作者: Beatrice Zanchi,Giuliana Monachino,Alvise Dei Rossi,Luigi Fiorillo,Georgia Sarquella-Brugada,Giulio Conte,Francesca Dalia Faraci
类目: Machine Learning (cs.LG)
*备注:
Abstract:Background: Foundation models (FMs) trained on large-scale unlabeled physiological data have emerged as a promising paradigm for medical artificial intelligence. Their ability to capture clinically meaningful, transferable representations for rare diseases remains largely unproven. This study investigates whether FM pre-training provides genuine clinical generalization benefits beyond improved optimization for rare electrocardiographic (ECG) phenotypes. Methods: We systematically evaluated nine publicly available ECG FMs for Brugada syndrome detection on the BrSwiss cohort (294 patients, 87 cases) and the independent external HUCA cohort (363 patients, 76 cases), under three strategies (from-scratch training, linear probing, full fine-tuning) across several configurations, including a 3% data ablation and zero-shot cross-site transfers. Results: Pre-training was necessary for high-capacity architectures unable to converge from scratch (AUC gain up to 0.411, p 0.05), but gave no significant gain for compact architectures already converged on labeled data alone. On full BrSwiss, the best fine-tuned FM (ECG-CPC, AUC = 0.962) only marginally exceeded the strongest supervised baseline (ECG-CPC from scratch, AUC = 0.932; p = 0.091). At matched training-set size, the data-efficiency advantage on BrSwiss-3% (AUC gain = 0.055, p 0.01) did not replicate on HUCA. Under zero-shot cross-site transfer, FM-based pipelines did not generalize better than supervised baselines, all approaching chance-level performance. Conclusion: For Brugada syndrome detection, FM pre-training is mechanical rather than semantic, providing optimization stability rather than transferable clinical knowledge. These findings challenge the assumption that large-scale pre-training inherently encodes clinically meaningful representations, highlighting the central role of model architecture and data-domain alignment.
[LG-133] ransfer Learning in High-dimensional Ising Models ICML2026
链接: https://arxiv.org/abs/2607.03005
作者: Joonho Kim,Seyoung Park
类目: Machine Learning (cs.LG); Methodology (stat.ME); Machine Learning (stat.ML)
*备注: Published as a conference paper at ICML 2026
Abstract:In high-dimensional Ising model estimation, target sample sizes are often limited, and effectively using auxiliary binary datasets of unknown relevance remains challenging. To address this, we propose Trans-Ising, a transfer learning method that combines a loss-based source screening rule with a two-stage estimation procedure. The method first identifies informative auxiliary sources using held-out target pseudolikelihood to prevent negative transfer. It then computes an initial estimator via pooled nodewise \ell_1 -regularized logistic regression, followed by a target-only correction step using a folded-concave penalty. Theoretically, we establish fixed-node \ell_2 and \ell_1 error bounds, exact graph selection consistency, and the conditional consistency of the screening rule. Through extensive simulations and real-data analyses, we demonstrate that Trans-Ising achieves lower estimation errors than both target-only estimation and naive data pooling.
[LG-134] MABLE: Masked Autoencoding with Bi-Lipschitz Decoding for Embeddings and Graph Metric Learning
链接: https://arxiv.org/abs/2607.02990
作者: Yaniv Shulman,Shaghayegh Akbarpour,Jack B. Muir
类目: Machine Learning (cs.LG); Geophysics (physics.geo-ph)
*备注:
Abstract:We propose MABLE (Masked Autoencoding with Bi-Lipschitz Decoding for Embeddings and Graph Metric Learning), a self-supervised framework for learning node and graph embeddings from large, heterogeneous graphs, demonstrated here on geospatial mineral-exploration data. MABLE combines masked reconstruction with fixed cosine-similarity losses that align matched augmented views while keeping unpaired embeddings well spread. A bi-Lipschitz feature decoder ties a low-dimensional reconstruction component of each node embedding to feature similarity, while matched-node consistency shapes the remaining context used by graph pooling. Lipschitz-controlled pooling helps stabilize graph-level representations under perturbations of retained node embeddings, while augmentation alignment trains robustness to masking, node dropping, and sampling variation. Across local copper and regional Arabian Shield studies, MABLE embeddings provide complementary downstream signal and produce coherent embedding-derived layers for hypothesis generation without learned discriminators or hard-negative selection.
[LG-135] Rank-Order N-of-M Codes for Sparse Distributed Memory: Disentangling Representation and Learning Effects in Noise Robustness Against Contemporary Neuromorphic Architectures
链接: https://arxiv.org/abs/2607.02967
作者: Joy Bose
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注: 14 pages, 8 tables, 2 figures. Revisits rank-order sparse distributed memory for continual learning and memory-augmented AI. Includes comparisons with CALM and SpikingMamba, statistical evaluation, and real embedding experiments
Abstract:Large language models remain limited as continual learning systems, motivating renewed interest in Sparse Distributed Memory (SDM) as an explicit online episodic memory. CALM (Nechesov and Ruponen, 2025) identifies its threshold-binary encoder as an open design question. This paper evaluates rank-order N-of-M encoding (Furber et al., 2007) as an alternative. We make three contributions. First, a faithful reimplementation validates the published architecture by confirming exact equivalence between WheelSDM and RankOrderSDM (cosine similarity 1.0000 across 10 seeds) and reproducing the documented divergence of RDLIF neurons under interference. Second, multi-seed capacity experiments show RankOrderSDM outperforming StandardSDM by 13.4 percentage points at saturation in the scaled configuration and by 0.8 percentage points at the published architecture scale. Third, BER robustness experiments disentangle representation and learning effects, showing that the large robustness gain arises primarily from the interaction of rank-order encoding with MAX-Hebbian learning, while the encoder alone provides only a small advantage under matched learning conditions. Experiments on GloVe-100 embeddings confirm this small but consistent encoding benefit on real structured data, whereas sentence embeddings exhibit a ceiling effect at low memory load. A secondary analysis shows that idealized rank-order encoding requires half the component-level encoding energy of SpikingMamba’s SI-LIF neurons at four-bit precision, although decoder costs dominate overall system energy. These results identify which components of the original rank-order SDM architecture provide measurable benefits for contemporary memory-augmented AI systems, offering practical guidance for architectures such as CALM.
[LG-136] Missingness as Signal: Channel-Independent Spectrogram Learning for Clinical Time Series Prediction
链接: https://arxiv.org/abs/2607.02938
作者: Soyeon Park,Charmgil Hong
类目: Machine Learning (cs.LG)
*备注:
Abstract:Clinical time series prediction in intensive care units remains challenging due to heterogeneous physiological variables and informative missingness. The presence or absence of a measurement can reflect clinical decisions and patient severity, and thus missingness can serve as a predictive signal rather than a simple data artifact. This work presents CISM, a Channel-Independent Spectrogram framework with a Missingness stream for clinical multivariate time series prediction. CISM converts each clinical variable into a variable-wise time-frequency spectrogram, preserves variable identity through variable-aligned encoding, and aligns an explicit missingness stream with the spectrogram representation. Experiments on an in-hospital mortality task derived from MIMIC-IV show that CISM achieves the highest mean AUROC (0.7225), AUPRC (0.3308), and F1 (0.3808) among the compared time series, missingness-aware, vision, and time-frequency baselines. Ablation studies further show that observation patterns provide a meaningful informative signal. Pixel-level mask injection improves performance over plain spectrogram inputs and recovers much of this predictive value. The aligned missingness stream contributes a further, complementary gain in both AUROC and AUPRC. These results highlight the importance of modeling observation patterns as structured signals in clinical time series prediction.
[LG-137] In-span learning: adapting reduced-order models using their own predictions
链接: https://arxiv.org/abs/2607.02937
作者: Amirpasha Hedayat,Laura Balzano,Karthik Duraisamy
类目: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE); Numerical Analysis (math.NA)
*备注:
Abstract:Reduced-order models compress high-dimensional dynamics into low-dimensional representations that can be evaluated rapidly, but they lose accuracy when online dynamics drift beyond the training data. Adaptive methods address this by updating the subspace online with external, out-of-span information, such as full-order corrections or sensor snapshots. We discovered that a complementary and previously unexploited in-span adaptation channel exists within the current reduced subspace. By streaming the model’s own predictions through an incremental singular-value decomposition with forgetting, we obtain a trajectory-informed spectral preconditioner, in which the subspace is unchanged but the basis is reweighted and realigned toward the modes visited by the dynamics. This enables the model to absorb future out-of-span corrections more effectively. We expose aspects of this mechanism on a three-dimensional spiral and confirm it on viscous Burgers and Fisher-KPP dynamics. We also discuss how in-span learning can be viewed as a dynamical-systems analogue of in-context learning. More broadly, in-span learning suggests a new principle for computational science, revealing that model-generated trajectories contain more usable information than previously recognized.
[LG-138] CoFEND: A Cross-Modal Fusion End-to-End Network for Cold-Start Drug-Drug Interaction Prediction KDD2026
链接: https://arxiv.org/abs/2607.02928
作者: Di Wu,Hongyi Sun,Haichao Xu,Jia Chen,Zhong Chen,Jie Yang
类目: Machine Learning (cs.LG)
*备注: 11 pages, 2 figures, accepted by KDD 2026
Abstract:Cold-start drug-drug interaction (DDI) prediction for new drugs is critical for minimizing unexpected adverse drug reactions. The key challenge is to capture similarity between new and known drugs. However, such similarity is closely associated with complex relationships and mechanisms among drugs, enzymes, transporters, molecular structures, and other biomedical entities. Existing methods have three limitations in capturing such similarity: (1) only partial relationships and mechanisms are considered, which overlooks cross-modal information and yields incomplete or biased similarity modeling; (2) similarity computation between new and known drugs is conducted separately across modalities and performed offline for cold-start DDI prediction, leading to misalignment between similarity computation and DDI prediction; and (3) existing interpretability analyses are typically single-modality and focus primarily on key determinants of the perpetrator drug, while the underlying causes of susceptibility for the victim drug are seldom investigated. To address these issues, this paper proposes a novel Cross-Modal-Fused End-to-End Learning Network (CMF-ELN) with three components. First, diverse multimodal information is leveraged to construct four types of drug-centered knowledge graphs, enabling comprehensive similarity modeling under reconstruction-based supervision. Second, a four-channel graph autoencoder is designed to fuse cross-modal similarity within an end-to-end learning framework. Finally, a two-stage interpretability scheme is devised to precisely localize key factors for both perpetrator and victim drugs. Extensive experiments on two real datasets demonstrate that CMF-ELN achieves significantly higher prediction accuracy and more comprehensive interpretability of mechanisms than its peers.
[LG-139] Open Problem: Is Interaction Necessary for Order-Optimal 1-bit Mean Estimation? COLT2026
链接: https://arxiv.org/abs/2607.02896
作者: Ivan Lau,Jonathan Scarlett
类目: Information Theory (cs.IT); Machine Learning (cs.LG); Statistics Theory (math.ST); Machine Learning (stat.ML)
*备注: COLT 2026 Open Problem
Abstract:We ask whether interaction is necessary for order-optimal 1-bit mean estimation over nonparametric finite-moment classes. Adaptive threshold-query protocols achieve the order-optimal 1-bit minimax rate, and the same rate is attainable with general 1-bit queries using only one adaptive transition (i.e., two stages of querying). In the non-adaptive setting, threshold and interval queries are known to be highly suboptimal, but the case of arbitrary non-adaptive quantizers remains unresolved. Can such quantizers match the adaptive rate, yielding an optimal one-shot protocol? Or is the known two-stage estimator stage-optimal, with a single adaptive transition being necessary and sufficient?
[LG-140] Selectivity Estimation for Linear Queries via Online Learning
链接: https://arxiv.org/abs/2607.02895
作者: Fangzhu Shen,Debmalya Panigrahi,Sudeepa Roy
类目: Databases (cs.DB); Machine Learning (cs.LG)
*备注: 29 pages
Abstract:Learning-based approaches for selectivity estimation in databases have gained significant traction in recent years. However, theoretical studies of these learning-based approaches are essentially limited to fixed query distributions on static databases. In practice, both the underlying database and the query workload can dynamically change over time. In this work, we propose an algorithmic framework for learning selectivity of queries in this more general dynamic setup. Inspired by online learning, we measure the performance of the learning algorithm in this setting by its regret, which compares the cumulative loss incurred by the learning algorithm to that of the best fixed strategy. We establish upper and lower bounds on regret for histogram-based linear queries, such as point, range, and subset selection queries, under standard loss functions, in both static and dynamic database settings.
[LG-141] Dynamic Regret for Non-Stationary Linear Bandits via Misspecification Reductions
链接: https://arxiv.org/abs/2607.02891
作者: Zihao Hu,Yuan Yao,Jiheng Zhang,Zhengyuan Zhou
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Many online decision-making problems involve both round-specific feasible actions and drifting reward models: eligible ad impressions, feasible prices, and available treatments can change over time, while user preferences, demand curves, and patient responses may evolve. Motivated by these applications, we study non-stationary linear bandits with round-specific feasible decision sets. Existing methods that obtain the optimal (\widetilde O(T^2/3P_T^1/3)) dependence, where (P_T) is the path length of the reward-parameter sequence, impose an orthogonal-structure assumption on round-specific decision sets, which can be restrictive in contextual applications. We address this gap through a unified misspecification-reduction viewpoint: after partitioning the horizon into blocks, we relate each block’s dynamic regret to regret against a fixed-parameter linear bandit benchmark, with the within-block parameter drift entering as bounded misspecification. Restarting algorithms with misspecification-dependent regret guarantees then yields the optimal (T^2/3P_T^1/3) dynamic-regret dependence for both linear bandits with general compact decision sets and (K)-armed contextual linear bandits.
[LG-142] Poisson-Gamma Modeling of Inter-Relational Dependencies in Dynamic Knowledge Graphs UAI2026
链接: https://arxiv.org/abs/2607.02872
作者: Nan Fang,Yijun Wang,Hao Liao,Sikun Yang
类目: Machine Learning (cs.LG)
*备注: Accepted at the 42nd Conference on Uncertainty in Artificial Intelligence (UAI 2026). 20 pages, 7 figures, 4 tables
Abstract:Dynamic knowledge graphs are ubiquitous in today’s AI applications, as we represent molecular structures, social relationships, and language information using these graph models. As knowledge graphs evolve over time and are often noisy and incomplete, modeling their temporal and relational dependencies becomes crucial for downstream tasks. To address these challenges, this paper proposes PGRE (Poisson-Gamma Relational Evolution), a probabilistic model for modeling inter-relational dependencies in dynamic knowledge graphs. PGRE represents multi-relational temporal links via a Poisson-Bernoulli formulation. It introduces Gamma-distributed latent variables to capture entity-factor associations and cross-relation dependencies mediated by shared latent communities. A Gamma Markov process further models the temporal evolution of these latent variables, enabling principled characterization of relational dynamics. Experiments on benchmark datasets show that PGRE achieves competitive performance in link prediction, particularly in sparse settings, while revealing meaningful relational evolution patterns in dynamic knowledge graphs.
[LG-143] Reward Granularity in RLVR: Comparing Process and Outcome Reward Structures for Mathematical Reasoning in Small Language Models
链接: https://arxiv.org/abs/2607.02869
作者: Anagha Radhakrishna Palandye,Rebecca Glick,Osheen Kaul
类目: Machine Learning (cs.LG)
*备注: 8 pages, 4 figures
Abstract:Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for improving mathematical reasoning in language models. Yet most RLVR work rewards only the final answer (outcome-based rewards), leaving the impact of step-level process supervision (process rewards) underexplored especially for small models that lack the capacity to self-correct under sparse feedback. We systematically compare five reward conditions applied to Qwen2.5-0.5B fine-tuned with Group Relative Policy Optimization (GRPO) on GSM8K: a no-RL baseline, process-only, outcome-only, and three hybrid weightings ( \lambda \in \0.9, 0.5, 0.1\ process weight). Process-only supervision achieves 63.73% test accuracy versus 53.75% for outcome-only, a nearly 10-percentage point gap while yielding reasoning traces with higher step validity and lower deviation from ground-truth chain length. Hybrid rewards generally correlate positively with process weight, with one notable anomaly: the low-process / high-outcome configuration ( \lambda=0.1 ) underperforms pure outcome supervision, suggesting conflicting optimization signals. Error analysis using GPT-4o as a judge reveals distinct failure mode distributions: process models generate structurally inconsistent but arithmetically grounded traces, while outcome models produce concise but derivation-error-prone chains. Our results demonstrate that reward granularity is a first-order design decision for RLVR, with process-level supervision substantially improving both accuracy and trace fidelity in small language models.
[LG-144] rading Confidence: Comprehensive Uncertainty Estimation in Algorithmic Trading
链接: https://arxiv.org/abs/2607.02864
作者: Lin Li,Li Rong Wang,Hsuan Fu,Xiuyi Fan
类目: Machine Learning (cs.LG)
*备注: Accepted at the Pacific Asia Conference on Information Systems (PACIS 2025). 17 pages, 5 figures, 2 tables
Abstract:Reinforcement Learning (RL) has emerged as a powerful approach in financial trading, enabling agents to learn optimal strategies through direct market interaction. However, financial markets are highly uncertain, with price fluctuations driven by stochastic volatility, model limitations, and regime shifts. Traditional RL models struggle in dynamic environments, often failing to adapt to sudden market disruptions, leading to suboptimal trading decisions. To address this challenge, we propose an uncertainty-aware RL framework that integrates distributional, epistemic, and aleatoric uncertainty estimations. Our approach enhances uncertainty estimation using SHAP-weighted reconstruction uncertainty, MC Dropout, and an LSTM-based technical indicator consensus mechanism. Experimental results on five major U.S. stock indices demonstrate that RL agents equipped with uncertainty estimation significantly outperform traditional models in return and risk management. This study advances uncertainty estimation in RL-based financial trading, with future research extending its application to other asset classes and alternative RL architectures for greater adaptability.
[LG-145] EvoOtter: Evolutionary Reproduction Test Generator
链接: https://arxiv.org/abs/2607.02854
作者: Toufique Ahmed,Jatin Ganhotra,Avraham Shinnar,Martin Hirzel
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG)
*备注:
Abstract:Before fixing an issue, it is useful to first reproduce it by generating a bug reproduction test (BRT). However, generating a BRT is itself a challenging task, because issue descriptions tend to be informal, making it difficult to determine whether a candidate BRT indeed fails for the reason in the issue. Prior work has attempted to tackle this problem via inference scaling, using large language models to generate many BRTs and patches, then using execution feedback to select and improve them. Unfortunately, this is expensive and the feedback is unreliable. This paper explores evolutionary programming for BRT generation to sharpen the feedback, while enhancing evolutionary programming to keep costs in check. Our new approach, EvoOtter, controls test execution costs via successive halving. Furthermore, it controls LLM costs via batched crossover for an entire generation in a single LLM call, as well as via rule-based code mutations, with a new fitness score tailored for BRTs. As a result, EvoOtter generates state-of-the-art quality BRTs at the fraction of the cost of prior inference-scaling approaches to this problem. More broadly, this paper points at how to efficiently and effectively combine evolutionary programming with large language models for software engineering.
[LG-146] Labeled-Data-Free Meta-Learning: Efficient Task Generation Using Pre-trained Models and Unlabeled Data
链接: https://arxiv.org/abs/2607.02850
作者: Lei Sun,Yusuke Tanaka,Tomoharu Iwata
类目: Machine Learning (cs.LG)
*备注:
Abstract:Meta-learning without labeled data is crucial for real-world applications, where obtaining labeled datasets can be expensive or restricted due to privacy concerns. Data-Free Meta-Learning (DFML) addresses this challenge by leveraging pre-trained models without access to training data. However, existing DFML methods rely on model inversion to generate training data, a process that is generally difficult and computationally expensive due to the need to generate high-dimensional data matching the original distribution. To address this limitation, we propose a novel meta-learning setting that avoids model inversion by jointly leveraging pre-trained models and unlabeled data. Our method generates meta-training tasks by assigning soft labels from pre-trained models to unlabeled data. Since the quality of these tasks can vary, we introduce a task-weighting mechanism based on task confidence and class distribution balance to ensure effective meta-learning. Extensive experiments demonstrate that our approach substantially reduces computational cost and improves generalization, achieving up to 104-fold speedup and 8.4 percent to 36.4 percent improvements in few-shot classification accuracy compared to state-of-the-art DFML methods.
[LG-147] On the Design Space of Discrete Diffusion Online Adaptation for Molecular Optimization
链接: https://arxiv.org/abs/2607.02834
作者: Trevor Chen,Ariel Dai,Jason Yang,Riccardo De Santi,Daniel Khalil,Wenda Chu,Nate Gruver,Pranav Murugan,Alexander F. G. Goldberg,Maruan Al-Shedivat,Yisong Yue
类目: Machine Learning (cs.LG)
*备注: Submitted to The Fortieth Annual Conference on Neural Information Processing Systems
Abstract:Molecular optimization often starts from a pretrained generative model that captures a broad prior over valid molecular structures. At test time, however, the goal is not to sample from this prior, but to use a limited oracle budget to shift generation toward task-specific high-reward molecules. We study this adaptation problem for discrete diffusion models. Each online round couples several choices. The loop must decide which candidates to evaluate, how rewards become model updates, which feedback to reuse, and how far to move beyond the pretrained prior. These choices have mostly been studied in isolation, leaving open whether they complement one another, become redundant, or interfere inside a full online adaptation loop. We conduct controlled studies across six small-molecule binding-affinity tasks and three protein-fitness tasks. We find that acquisition, reward shaping, and model debiasing provide complementary routes to higher reward, especially for small molecules. Replay further stabilizes learning, while validity penalties keep small-molecule exploration on the valid molecular manifold. Together, these findings point to a practical recipe for feedback-efficient molecular optimization: online fine-tuning with acquisition, reward shaping, debiasing, replay, and validity control. This recipe outperforms offline fine-tuning and inference-time search baselines under matched oracle-call budgets and GPU-hour accounting. The gains are largest when high-reward candidates require larger shifts from the pretrained prior.
[LG-148] Less Tokens Better Forecasts: Sparse Residual Routing for Efficient Weather Prediction ECCV2026
链接: https://arxiv.org/abs/2607.02829
作者: Janet Wang,Yunbei Zhang,Lin Zhao,Xi Xiao,Jihun Hamm,Xiao Wang
类目: Machine Learning (cs.LG)
*备注: ECCV 2026
Abstract:Existing ViT-based weather forecasting models apply uniform computation across all spatial tokens, even though nearby atmospheric grid points often contain similar values and large regions evolve smoothly over time. This makes much of the intermediate per-token computation redundant. Standard token-efficiency methods, such as pruning or merging, reduce cost by removing or fusing tokens. However, weather forecasting is a spatiotemporal dense prediction problem in which a history of atmospheric states must be mapped to future values on the original latitude-longitude grid. Thus, every grid cell must retain a physically meaningful representation, especially under autoregressive rollout. We introduce Sparse-Reslim, a parameter-free plug-in routing module that makes sparse token processing compatible with this fixed-grid requirement. Sparse-Reslim routes only 25% of spatial tokens through the expensive middle transformer blocks and treats those blocks as residual updates: it computes the change produced for the routed tokens and scatters only this delta back to the full sequence. Unselected tokens keep their pre-routing representations exactly, so no grid cell is dropped or replaced by a mask token, and no fusion layer or additional parameters are introduced. Across ERA5 resolutions up to the operational 0.25\textdegree standard and two model families, a deterministic Transformer and a diffusion model, Sparse-Reslim improves forecast accuracy on every evaluated variable while substantially reducing cost: training is about 2.5x faster in the main settings and reaches 3.18x speedup at 0.25\textdegree, with over 2.2x lower peak memory. A controlled decomposition shows that the accuracy gain comes primarily from sparse routing itself, while random token selection provides an additional regularization benefit without selector overhead.
[LG-149] Induction Heads Interpolate N-Grams ICML2026
链接: https://arxiv.org/abs/2607.02800
作者: Francesco D’Angelo,Oguz Kaan Yuksel,Swathi Shree Narashiman,Nicolas Flammarion
类目: Machine Learning (cs.LG)
*备注: Published as a conference paper at ICML 2026. OpenReview: this https URL
Abstract:Induction heads are attention circuits believed to underlie in-context learning in transformers, yet a precise characterization of the estimators they implement remains elusive. We study transformers trained on order- k Markov chains and identify two complementary smoothing mechanisms. First, at finite attention-weight scale, the circuit implements a soft context-matching estimator: it aggregates contributions from exact and partial context matches, weighted exponentially by their overlap, and induces a data-dependent interpolation across context orders analogous to Jelinek-Mercer smoothing. Second, a beginning-of-sequence (BOS) token induces additive pseudo-counts, recovering Dirichlet-style smoothing. We construct a disentangled transformer implementing both mechanisms and show that trained transformers recover the predicted attention patterns. Across settings where pseudo-count smoothing is optimal or lower-order contexts provide structured evidence, trained transformers match or outperform classical count-based baselines. Our results bridge mechanistic interpretability of induction heads with classical statistical smoothing, revealing that transformers learn to regularize in-context estimation rather than simply count.
[LG-150] Weighted Conformal Prediction for Lab-to-Track Thermal Transfer in EV Motorsport Powertrains
链接: https://arxiv.org/abs/2607.02722
作者: Varshith Roy Kotla
类目: Machine Learning (cs.LG)
*备注: 8 pages, 3 tables, github like for the codes and dataset: this https URL
Abstract:Predicting thermal volatility in high-performance EV powertrains is difficult as internal temperatures are rarely observable outside the lab, and models calibrated on lab drive cycles fail when deployed against real-world loads. We study this lab-to-track transfer problem using conformal prediction, offering distribution-free uncertainty bounds. We implement Ensemble Batch Prediction Intervals (EnbPI; Xu Xie, 2021), a leave-one-out bootstrap-ensemble conformal method for autocorrelated time series, and calibrate it on real CALCE lithium-ion cycler data (A123 SP20 cells, FUDS profile). We evaluate it under a genuine, measured covariate shift: a second real CALCE test condition (US06 Highway Driving Schedule at 45°C). The unweighted EnbPI bound, achieving its nominal 95% coverage in-distribution (measured: 95.00%), degrades to 70.13% empirical coverage under this real shift. We introduce a weighted EnbPI procedure combining EnbPI’s ensemble residuals with density-ratio weighting (Tibshirani et al., 2019), estimating the density ratio via a probabilistic domain classifier. This recovers coverage to 72.42%, a modest, honestly-reported improvement, not a complete fix. We additionally apply the calibrated model to real 2023 Formula 1 telemetry (Monza and Silverstone, driver VER) as an unsupervised out-of-distribution diagnostic. Because no internal thermal channel exists in public trackside telemetry, we report only unsupervised flag rates (65.6% at Monza, 58.0% at Silverstone, well above the 5% in-distribution base rate) and note inconsistent associations between flags and braking/DRS zones. We conclude that conformal domain adaptation is a promising but only partially solved tool for this problem, detailing exactly where it falls short.
[LG-151] LiNO: Lifting based multiresolution neural operator
链接: https://arxiv.org/abs/2607.02715
作者: Himanshu Pandey,Subham Patel,Ratikanta Behera
类目: Machine Learning (cs.LG)
*备注:
Abstract:Recently, neural operators have shown promising outcomes for learning solution operators of differential equations directly from data. This framework learns a functional mapping from the parameter field to the solution field, enabling the prediction of an entire class of solutions rather than a specific instance. However, existing operators often struggle to capture both global dynamics and fine-scale structure simultaneously. To design an effective operator capable of representing multiscale features, a hierarchical multiscale decomposition framework is required. In this study, we develop the Lifting Neural Operator (LiNO), a multiresolution operator built on the second-generation wavelet lifting scheme. LiNO learns a multiresolution decomposition directly from data by parameterizing the lifting transform. This lifting transformation is adaptive to the underlying solution function and exactly invertible by construction, enabling information-preserving multiscale operator learning. In the lifted multiresolution space, the operator evolves coarse and directional detail coefficients separately, resulting in scale-aware modeling of the underlying physics. We evaluate LiNO on several benchmarks, including Darcy flow, the Poisson equation, the Allen-Cahn equation, the compressible Navier-Stokes equation, and the Gray-Scott reaction-diffusion system. Together, these benchmarks cover a wide range of physical behaviors, including multiscale phenomena, transport-dominated dynamics, and chaotic systems. LiNO demonstrates strong performance on these challenging benchmarks compared with state-of-the-art neural operators. These results suggest that adaptive multiresolution operators provide a promising direction for scientific machine learning.
[LG-152] RES-DARE: Failure-Aware Expert Adaptation and Rollback-Safe Self-Repair for Intrusion Detection
链接: https://arxiv.org/abs/2607.02687
作者: Rahil Aftab,Anyash Prasad,Soumya Mazumdar,Vineet Kumar Rakesh,Tapas Samanta
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:Intrusion detection systems are often trained under static benchmark conditions, although deployed network environments are affected by traffic drift, sensor noise, changing workloads, and evolving attack behaviour. Under such distribution shifts, static detectors may produce confident but incorrect predictions, leading to silent and unsafe failure modes. In this paper, RES-DARE (Recursive Evolving Specialists-Digital Adaptive Reasoning Engine) is proposed as a failure-aware continual intrusion detection framework with rollback-safe self-repair. Difficult, uncertain, and misclassified samples are treated as failure signals for expert specialisation rather than being discarded as noise. A supervised contrastive encoder, two-pass expert router, failure-buffer mechanism, HDBSCAN-based failure-region discovery, and trust-risk monitor are integrated to support adaptive IDS behaviour. AEHM-v2 is introduced as a rollback-safe repair mechanism, where candidate adaptations are provisionally activated and committed only when macro-F1 is preserved or improved while trust risk remains stable. Otherwise, the system is rolled back to its last validated state. RES-DARE is evaluated on CICIDS2017, UNSW-NB15, and TON_IoT, achieving macro-F1 scores of 0.9850, 0.9736, and 0.9691, respectively. Under Gaussian feature corruption at strength 0.10, RES-DARE retains an Attack-F1 of 0.7920 on CICIDS2017 and achieves near-zero catastrophic forgetting with F = 0.0015. The results show that RES-DARE improves robustness, warning capability, and deployment safety under degraded conditions.
[LG-153] A Granularity-Aware EEG Feature Framework for Psychopathology Dimension Prediction
链接: https://arxiv.org/abs/2607.02670
作者: Haofan Cheng,Jingjing Hu,Jingrong Pei,Shuaiqi Fu,Meilun Shen,Shuai Fang,Meng Wang,Dan Guo,Jie Zhang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Electroencephalography (EEG) offers a noninvasive approach for examining neurophysiological correlates of dimensional psychopathology, yet systematic evidence across EEG paradigms and feature granularities remains limited. Here, we develop a granularity-aware EEG feature pipeline that organizes multi-scale descriptors into global, regional, and channel levels. Using the Healthy Brain Network (HBN) cohort, we evaluate the prediction of four psychopathology dimensions: p-factor, internalizing, externalizing, and attention problems, across four EEG paradigms. Given the heterogeneity of pediatric psychopathology and the moderate reliability of questionnaire-derived scores, this setting represents a challenging feasibility test rather than a clinical screening scenario. Tree-based models and granularity-balanced feature selection showed promising improvements over conventional approaches in selected conditions, although effect sizes remained modest. Visualization of selected markers revealed dimension-specific spatial and spectral patterns that were broadly aligned with existing neurophysiological knowledge. An exploratory cross-dataset sanity check on the independent PEARL cohort suggested that the proposed selection principle remains technically feasible under protocol shifts, without claiming cross-dataset generalizability. Overall, multi-scale EEG features contain weak but detectable signals related to dimensional psychopathology, and granularity-aware selection may serve as a useful feature-reduction strategy for future EEG-based phenotyping studies.
[LG-154] Schedulable Job-Level Dependencies for Cause-Effect Chains via Graph Neural Networks
链接: https://arxiv.org/abs/2607.02624
作者: Silviu S. Craciunas,Christian Hakert,Jian-Jia Chen,Zdeněk Hanzálek,Paul Pop
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注:
Abstract:Modern automotive software architectures comprise large sets of mixed-criticality functions executing on shared multi-core platforms with strict real-time and end-to-end timing requirements. Sensor-to-actuator data propagation in such systems is typically expressed via cause-effect chains with worst-case data-age budgets. Job-level dependencies (JLDs) have been introduced to provide a schedule-agnostic mechanism for bounding the data age independently of the underlying scheduler. The state-of-the-art methods for synthesizing JLDs, however, do not check whether the produced JLDs are enforceable under a concrete scheduling policy or jointly schedulable at the system level. In this paper we propose the first machine-learning-based JLD synthesis method, built around a two-level Graph Neural Network with temperature-controlled sampling that learns the structural patterns connecting cause-effect chain configurations to their JLD solutions. Since learned outputs may not be correct by construction, we embed the GNN in a novel Generate-and-Verify architecture in which a safe DP data-age checker, together with a per-chain EDF feasibility checker and a system-level demand-bound test, accept or reject each candidate. We show that the ML-based generator substantially outperforms the original greedy heuristic while achieving orders-of-magnitude lower synthesis time, demonstrating that learned structural priors can effectively replace exponential propagation-tree enumeration on this class of real-time scheduling problems.
[LG-155] Evaluating Time Series Foundation Models for Electricity Price Forecasting: Contamination Risk Distributional Shifts and Covariate Dependence
链接: https://arxiv.org/abs/2607.02623
作者: Zhenghua Pan,Ahmed Aziz Ezzat
类目: Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注:
Abstract:Time series foundation models (TSFMs) have shown strong zero-shot forecasting performance, but their generalization in covariate-driven, non-stationary settings is underexplored. Electricity price forecasting (EPF) presents a challenging testbed due to complex temporal dependencies, distributional shifts, and strong reliance on structural and contextual information. We propose a two-dataset-benchmarking framework for EPF to mitigate contamination risk and enable fair evaluation of TSFMs. We examine key aspects of EPF including point and probabilistic forecasting performance, tail behavior, price spikes, and comparisons against domain-specific methods. We find that TSFMs are highly competitive and often outperform general-purpose baselines. Yet, their performance depends critically on covariate support, and they do not consistently surpass domain-specific methods tailored to EPF. Interestingly, simple ensembles of TSFMs and domain-specific methods appear to have significant potential, suggesting that the two approaches capture complementary predictive information.
[LG-156] owards transferable lightweight neuromorphic computing through a model-free temporal-switch framework
链接: https://arxiv.org/abs/2607.02608
作者: Zefeng Zhang,Chao Li,Siyao Chen,Pei Chen,Bo-Wei Qin,Xumeng Zhang,Wei Lin,Qi Liu
类目: Hardware Architecture (cs.AR); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注:
Abstract:Lightweight neuromorphic computing offers a promising route to efficient AI, with particular benefits for resource-constrained edge deployments. However, its scalable deployment that can reliably transfer the expected performance has long been hindered by device-to-device variations, which necessitate costly and repeated re-training on new copies and undermine the practical advantages. To address this issue, we introduce a model-free temporal-switch (TS) framework to improve the direct transfer performance, without post-training calibration or adjustment. The TS framework provides a methodology to incorporate a broader spectrum of devices in the training process. In the validation using memristor-based reservoir computing, it enables high performance on unseen devices with a directly transferred readout. It achieves improved prediction in the representative Mackey–Glass benchmark, and the accuracy of 92.4% in spoken digit classification. Its efficacy is validated across different memristor families and RC configurations. Theoretical analysis not only reveals the general computational mechanism underlying its efficacy, but also underlines its potential applicability to other physical platforms.
[LG-157] Scaling Weisfeiler-Leman Expressiveness Analysis to Massive Graphs with GPUs
链接: https://arxiv.org/abs/2607.02603
作者: Filippo Biondi,Mirco Tribastone,Max Tschaikowski
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注:
Abstract:The stable coloring of the Weisfeiler-Leman (1-WL) test is a cornerstone of Graph Neural Networks because it provides an upper bound to the expressive power of message-passing architectures. Unfortunately, computing it presents two fundamental bottlenecks. First, classic algorithms are inherently sequential and cannot exploit modern massively parallel hardware. Second, these are \emphglobal algorithms, i.e., they require availability in memory of the full graph, severely limiting applicability to real-world instances. We leverage a linear-algebraic interpretation of 1-WL stable coloring and introduce two key contributions: (i)~a randomized refinement algorithm with tight probabilistic guarantees and (ii)~a correctness-preserving batching scheme that decomposes the graph into independently processable subgraphs while provably returning a stable coloring of the original graph. This approach maps directly to GPU-efficient primitives. In numerical experiments, our CUDA implementation delivers speedups up to two orders of magnitude over classical CPU-based partition refinement and, for the first time, successfully computes stable colorings on web-scale graphs with over 30 billion edges, where CPU baselines time out or fail.
[LG-158] he Moving Target: A Longitudinal Audit of Trustworthiness Drift Across Twelve Checkpoints of Open-Source Chat LLM s ICML2026
链接: https://arxiv.org/abs/2607.02587
作者: Zhichao Fan,Yanhang Li,Zexin Zhuang,Xian Sun,Yingshuo Wang
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG)
*备注: 22 pages, 2 figures; accepted as a poster at the ICML 2026 TAIGA Workshop
Abstract:Model cards quote trust-benchmark scores without recording when they were measured, and the same number is routinely carried across successive checkpoints of one release line as if the model behind it had not shifted. We test whether it has shifted by auditing four open-source release lines, Yi, Qwen, Mistral, and Gemma, at three successive generations each, on a fixed basket of trust benchmarks under multiple prompt templates. Mean absolute adjacent-generation drift lands well above an independence-based no-drift reference null, and the gap persists when we drop a benchmark, drop a release line, or switch to strict scoring. We therefore conclude that a trust score attached to a release line should not be carried forward to the next checkpoint without remeasurement; it should instead be reported as a checkpoint-bound, dated artefact, which we package as a longitudinal model card. Closed APIs, larger models, canonical benchmark protocols, and fixed month-cadence rules lie outside the audited scope and require their own evaluation.
[LG-159] Auditing the Audit: Five Failure Modes in Benchmark-Validity Audits ICML2026
链接: https://arxiv.org/abs/2607.02586
作者: Yanhang Li,Zhichao Fan,Zexin Zhuang
类目: Machine Learning (cs.LG); Software Engineering (cs.SE)
*备注: 18 pages, 4 figures. Accepted at the TAIGR Workshop at ICML 2026
Abstract:Governance frameworks ask AI providers and auditors for documented evaluation evidence, and perturbation-based construct-validity audits are a common form of that evidence. We argue the audits are themselves fragile: their conclusions can be silently manufactured by implementation details that readers cannot see in the reported numbers. We name five classes of pipeline failure and demonstrate each in a self-audit over safety benchmarks and open-weight instruction-tuned models. Under a unified six-point due-diligence gate, every cell lands in a non-confirmatory bucket, and no cell reaches confirmatory. The evidence here is a single two-model, five-benchmark case study, and F1–F5 is an illustrative, deliberately non-exhaustive starting taxonomy – not a comprehensive partition of audit failures. We position the gate as a withholding and disclosure protocol for assurance-grade evidence, supplementary to (not a replacement for) classical construct-validity evidence, and not as a route to benchmark-validity verdicts.
[LG-160] MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems
链接: https://arxiv.org/abs/2607.02558
作者: Vijay Janapa Reddi
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注:
Abstract:As machine learning shifts from laboratory curiosity to critical infrastructure, the systems that sustain it span an extraordinary range, from sub-milliwatt microcontrollers to multi-gigawatt datacenter fleets. Reasoning across this range is hard: empirical profiling requires the target hardware in hand, while cycle-accurate simulation costs hours per configuration, leaving no tool for rapid, full-stack architectural reasoning. We present MLSYSIM (Machine Learning Systems Infrastructure Modeling), a first-principles analytical framework that formalizes the “physics of systems” into a dimensionally-strict Python engine. MLSysim is built on a demand-supply abstraction that decouples computational demand from silicon supply and environmental context, and it enforces unit integrity at runtime so the silent conversion errors that plague ad-hoc modeling cannot occur. Every input is drawn from a typed, provenance-tracked registry, so no number enters an analysis without a documented source. On this engine we codify a taxonomy of 22 “Systems Walls” resolved by 28 composable models and solvers, enabling sub-second design-space exploration that identifies binding constraints and synthesizes ideal hardware specifications across the entire ML systems lifecycle.
[LG-161] -Level Activation Overlap for Efficient LLM Inference
链接: https://arxiv.org/abs/2607.02521
作者: Abhinav Jangda,Tyler Sorensen,Sebastian Burckhardt,Jianlan YE,Chaoyin Li,Atul Gupta
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Programming Languages (cs.PL)
*备注:
Abstract:SwiGLU is the dominant MLP activation in modern large language models, yet its intermediate tensor materialization costs 9-37% of MLP execution time. We present two complementary CUTLASS-based SM90 kernels that fuse SwiGLU into GeMM at the tile level. Kernel-1 overlaps Swish computation on the Gate accumulator with Up-tile loading using the Pingpong warp-specialized schedule; Kernel-2 interleaves SwiGLU with tile stores via a custom Epilogue Visitor Tree. Evaluated on Qwen-2.5 models (0.5B-72B) on NVIDIA H100, our kernels achieve up to 2.47x speedup over PyTorch, shifting workloads from memory-bound to compute-bound and reaching 79.5% peak BF16 utilization. We demonstrate that this http URL cannot replicate this fusion (3-7x slower than our kernels), validating the need for hand-crafted tile-level design. Our fused kernels are also numerically superior, achieving zero mismatches compared to 4.5-11% for cuBLAS.
[LG-162] Fitted Occupancy-Ratio Evaluation without Bellm an Completeness
链接: https://arxiv.org/abs/2607.05375
作者: Lars van der Laan,Nathan Kallus
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Occupancy ratios correct distribution shift in offline reinforcement learning and are central to off-policy evaluation. Existing primal-dual and minimax methods typically estimate these ratios by enforcing occupancy-balance moments over a critic class. We propose fitted occupancy-ratio evaluation (FORE), a fitted fixed-point method that characterizes the discounted occupancy ratio through an adjoint Bellman recursion. At each iteration, FORE solves a single-level density-ratio objective on one-step-transition data, thereby projecting the adjoint Bellman image onto a log-ratio class in Kullback–Leibler (KL) divergence. Unlike analyses of fitted Q-evaluation, which typically require value-function realizability together with Bellman completeness or projected-operator stability, our central approximation condition is just realizability of the discounted occupancy ratio itself. Under this condition, the population KL-projected recursion contracts in relative entropy toward the true ratio by virtue of the adjoint Bellman operator being a KL-contraction. For the empirical recursion, we establish finite-sample regret bounds that yield convergence in KL up to log-ratio approximation error and a statistical error governed by the complexity of the ratio hypothesis class. The fitted ratio supports direct value estimation by reward reweighting, occupancy-weighted fitted Q-evaluation, and doubly robust estimation that combines the fitted ratio with a fitted Q-function. Together, these results identify discounted occupancy-ratio realizability as a sufficient condition for offline policy evaluation without any completeness assumptions.
[LG-163] Quantum Spectral Anomaly Detection
链接: https://arxiv.org/abs/2607.05307
作者: Yewei Yuan,Michele Minervini,Mark M. Wilde,Nana Liu
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注:
Abstract:A core task in quantum anomaly detection is to compute an anomaly score that quantifies how strongly a test quantum state deviates from a given quantum dataset assumed to be normal. Classically, principal component analysis (PCA) for centered data computes the anomaly score by evaluating the test sample relative to the subspace spanned by the selected leading eigenvectors. However, for quantum data that lack a standard centering, explicitly recovering principal eigenvectors, constructing full Gram matrices, or loading quantum-random-access-memory-style data can be more costly than estimating the anomaly score itself. To avoid these costs, we propose Quantum Spectral Anomaly Detection (QSPADE), which computes PCA-like anomaly scores directly from the spectrum of the average state of the normal dataset. By replacing hard PCA rank selection with a smooth, temperature-controlled spectral threshold, QSPADE makes near-threshold spectral components contribute partially to the anomaly score. This makes the score vary continuously rather than jump when a borderline component is included or excluded, and makes it less sensitive to noise or arbitrary hard cutoffs near the threshold. In the zero-temperature limit, QSPADE recovers the hard-projector PCA score. The proposed measurement-based quantum detector can be calibrated with a sample complexity independent of the data dimension. Numerical simulations show that QSPADE behaves like kernel-PCA on encoded classical data and detects changes across a transverse-field Ising transition without predefined order parameters. Consequently, QSPADE gives an efficient framework for both quantum-kernel anomaly detection on encoded classical data and the monitoring of quantum-native systems where diagnostic observables are unknown.
[LG-164] Routing Anonymity and Identifiability of Noisy Quantum Hardware
链接: https://arxiv.org/abs/2607.05281
作者: Ben Priestley,Mina Doosti
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注: 22+30 pages, 6 figures
Abstract:Present-day quantum computing is cloud-based, where a user submits a circuit to a service provider’s proprietary backend hardware. While providers may wish to hide implementation details, scheduling choices, or even which physical device was used, noisy finite-shot outputs can carry backend-specific fingerprints: information imprinted in the classical output distribution that can reveal the backend identity. So far, such fingerprints have mostly been studied from a benchmarking perspective, with limited attention to privacy considerations for users and providers. This work develops the first formal framework for backend identifiability and its privacy implications. We introduce a backend-identifiability game and use it to formalise routing anonymity as a security notion for quantum cloud services. We show that backend identifiability is a hypothesis-testing problem and prove that, under passive i.i.d. access to a single backend, routing anonymity decays exponentially at the Chernoff rate. We also establish a utility-anonymity trade-off, imposing fundamental limits on how much backend-specific information can be removed from classical outputs without degrading their usefulness. In addition, we observe that, for noisy quantum hardware, identifying fingerprints are inherently an intermediate-depth phenomenon, and establish a depth principle using Pauli-transfer-matrix tools. We complement the theory with experiments on Amazon Braket on AWS, using ion-trap and superconducting quantum processors. We observe 87-90% classification between superconducting backends and 96-100% classification across physical platforms, and find that identifiability can survive natural forms of post-processing. Overall, these results establish routing anonymity as a distinct security requirement for quantum cloud computing, and provide a framework for quantifying and controlling the utility-anonymity trade-off.
[LG-165] msPCA: An R Package for Sparse PCA with Multiple Components
链接: https://arxiv.org/abs/2607.05229
作者: Ryan Cory-Wright,Jean Pauphilet
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:We present msPCA: an open-source R package for sparse principal component analysis with multiple components. It implements an alternating maximization algorithm to generate a set of sparse loading vectors that collectively explain a large fraction of the variance in a dataset, while remaining non-redundant. The algorithm supports two definitions of non-redundancy: either orthogonality of the loading vectors or zero pairwise correlation between principal components (PCs). In the reported benchmarks, msPCA solves sparse PCA problems with thousands of features, achieving competitive runtimes while producing sparse components with controlled feasibility violations and a high fraction of variance explained.
[LG-166] Geometric Causal Models
链接: https://arxiv.org/abs/2607.05153
作者: Eli N. Weinstein,David M. Blei
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Biomolecules (q-bio.BM)
*备注:
Abstract:Scientists often seek to draw causal inferences from structured data that is not independently and identically distributed, such as spatial data, network data, or molecular data. We develop geometric causal models (GCMs), a framework for causal inference from dependent data that exploits underlying symmetries of the data generating process. For example, in spatial data, we consider processes that are symmetric under translations, or in graph data, symmetric under permutations of the nodes. We show how symmetries, formalized with group theory, can enable causal identification and estimation. We deploy ergodic theory for amenable groups to establish identification, and combine geometric deep learning with scalable Bayesian inference for estimation. We recover i.i.d. causal models and do-calculus when the data is a sequence and the symmetry is permutation equivariance, and find novel types of causal models when we use alternate structures and symmetries. As an example, we construct a causal model that satisfies the symmetries of DNA. This GCM enables new estimators for the effects of genetic variation, combining deep functional genomics models to describe outcomes and DNA language models to describe propensities. We illustrate on semisynthetic data.
[LG-167] Physically-Relevant Information Learning in High-Dimensional Time-Derivatives Spaces
链接: https://arxiv.org/abs/2607.05127
作者: Domiziano Doria,Matteo Becchi,Giovanni M. Pavan
类目: Chemical Physics (physics.chem-ph); Machine Learning (cs.LG)
*备注:
Abstract:Understanding the physics of many-body complex dynamical systems is typically non-trivial. High-dimensional analysis approaches are often deemed necessary to prevent losing important information. Typically, these use order parameters or descriptors capturing information related to, e.g., relative positions, symmetries, etc., of the units in the studied system. However, in many cases, gaining information related to the relative positions (or velocities) of the constitutive units alone may be insufficient, and to reach a more complete physical knowledge, one should ideally learn and correlate with each other both structure and dynamics. Here we demonstrate how to efficiently achieve such a goal by building and navigating high-dimensional Time-Derivatives (TiDe) space. A TiDe space can be easily generated for virtually any type of system/phenomenon under study from the time-series data collected along its observation over time. Each TiDe’s dimension corresponds to a growing-order time-derivative of the extracted data, thus containing information related to different types of physical phenomena/events that can be easily extracted via unsupervised approaches. We demonstrate how, by definition, TiDes can be directly analyzed without a need for prior dimensionality reduction, providing results that are intrinsically intuitive to interpret. We show the potential of the method by analyzing two prototypical example datasets extracted from molecular dynamics simulations or experimental tracking of different complex dynamical systems. Our results demonstrate how efficiently one can navigate and learn in such information-rich TiDe spaces, which provide robust general frameworks for data analysis and for studying complex dynamical systems from the data collected along their observation over time.
[LG-168] Canonical quantization of neurons
链接: https://arxiv.org/abs/2607.05000
作者: Alexander He,Nana Liu,Mark M. Wilde
类目: Quantum Physics (quant-ph); Statistical Mechanics (cond-mat.stat-mech); Machine Learning (cs.LG)
*备注: 6 pages, 3 figures, companion paper available at arXiv:2605.24386
Abstract:Canonical quantization provides a systematic procedure for constructing quantum models from classical Hamiltonians. Here, we apply this principle to a fundamental computational primitive of machine learning: the neuron. Specifically, by viewing a neuron as a composition of an energy function and an activation function, we quantize this model by replacing the energy function with a quantum Hamiltonian and applying the activation function to it through matrix functional calculus. This results in an activation observable that can be measured on an input quantum state. We investigate the use of these quantized neurons for function approximation, where the objective is to learn an unknown observable from labeled quantum data. For this purpose, we develop hybrid quantum-classical algorithms for training and evaluation, including procedures for measuring the activation observable and estimating gradients of the squared loss error. Our algorithms for gradient estimation rely on basic primitives like classical random sampling, the Hadamard test, and Hamiltonian simulation, and those for measuring an activation observable rely on quantum algorithms known as the power of one qumode and Schroedingerization. Numerical experiments demonstrate that our quantized neurons exhibit enhanced expressive capabilities relative to corresponding classical neurons on representative learning tasks. Our work establishes canonical quantization as a principled framework for constructing quantum machine learning primitives and provides a foundation for developing neural architectures tailored to quantum data.
[LG-169] Context-Constrained Transfer Learning for Tabular Foundation Models via Data Distillation
链接: https://arxiv.org/abs/2607.04809
作者: Yijun Lin,Sai Li
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Tabular Foundation Models (TFMs) have demonstrated strong empirical performance as black-box inference engines through in-context learning. However, their use in transfer learning is limited by two obstacles: strict context-size constraints and sensitivity to distribution shifts between source and target tasks. Directly pooling heterogeneous source data can therefore lead to negative transfer. To address these challenges, we propose Context-Constrained Transfer Learning via ANchoring and DIstillation (TL-ANDI), a posterior-aware distillation framework for TFMs. TL-ANDI constructs a compact source context by solving a budget-constrained optimal transport problem whose cost jointly measures target covariate coverage and posterior compatibility. The selected anchor samples are then equipped with locally distilled labels and combined with a residual calibration step using target data.
[LG-170] Non-Asymptotic Error Bounds for SMC with Biased Proposals: Application to Conditional Diffusion Sampling
链接: https://arxiv.org/abs/2607.04780
作者: Stanislas Strasman(SU, LPSM (UMR_8001)),Gabriel Victorino Cardoso,Sylvain Le Corff(LPSM (UMR_8001), SU),Vincent Lemaire(LPSM (UMR_8001), SU),Antonio Ocello
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Sequential Monte Carlo (SMC) methods are a natural tool for post-hoc conditioning of pretrained generative models, but in many applications the mutation kernels used by the particle system are biased approximations of an ideal Feynman–Kac flow. This paper develops a non-asymptotic error analysis for such SMC samplers. Under forward-smoothing forgetting conditions, we decompose the total error into a kernel bias, measuring the effect of replacing the ideal transition kernels by approximate ones, and a finite-particle Monte Carlo error. Our approach relies on extending local Doeblin-type conditions and Lyapunov drift arguments for Markov kernels to conditional distributions, thereby enabling a principled control of the bias. We then instantiate this general framework for conditional sampling with score-based diffusion models, and derive the first non-asymptotic error bound that jointly controls initialization error, time discretization, and score approximation in the reverse diffusion dynamics as well as finite-particle Monte Carlo error.
[LG-171] Non-asymptotic Convergence of Stochastic Gradient Descent in Score-based Generative Models
链接: https://arxiv.org/abs/2607.04775
作者: Stanislas Strasman(SU, LPSM (UMR_8001)),Sobihan Surendran(SU, LPSM (UMR_8001)),Sylvain Le Corff(SU, LPSM (UMR_8001))
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Score-based Generative Models (SGMs) have achieved impressive performance in data generation across a wide range of applications. While the statistical properties of their sampling procedures are increasingly well understood, the optimization dynamics underlying their training remain less explored. SGMs are typically trained by minimizing a weighted denoising scorematching objective, yet optimization guarantees with stochastic gradients remain limited. In this work, we study Stochastic Gradient Descent (SGD) for SGMs, contributing results in two complementary regimes. First, for general score parameterizations, we establish a non-convex convergence rate for SGD on the weighted denoising score-matching objective, with explicit dependence on the schedule-dependent weighting factors. Second, for overparameterized two-layer ReLU networks, we develop a Neural Tangent Kernel analysis tailored to diffusion training with stochastic gradients, yielding score-approximation error bounds along the SGD trajectory. Finally, our analysis quantifies the role of the reweighting factor in the score approximation error, providing theoretical guidance for weighting choices used in practice.
[LG-172] Decomposition for Bayesian Networks: Local and Parallel Inference
链接: https://arxiv.org/abs/2607.04650
作者: Pei Heng,Xinyi Hu,Yi Sun
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 13 pages, 5 figures,Code available at this https URL
Abstract:Probabilistic inference in high-dimensional Bayesian networks is difficult because exact manipulation of the joint distribution scales exponentially with network size. We propose a decomposition framework based on directed convex subgraphs and introduce a minimal d-decomposition tree. Together, they provide a principled alternative to classical junction-tree constructions. The proposed framework represents the joint distribution by lower-dimensional sub-models that can be learned and stored separately. This decomposition reduces computational cost and naturally enables parallel computation. Based on a minimal d-decomposition tree, we further develop two parallel algorithms for parameter estimation and probabilistic inference. Experiments show that the proposed method substantially improves computational efficiency over junction-tree methods while maintaining inference accuracy, especially for low-dimensional queries.
[LG-173] Integrating Neural Encoders in Bayesian Generalized Linear Mixed Models for Multimodal Data
链接: https://arxiv.org/abs/2607.04647
作者: Yuankang Zhao,Youngsoo Baek,Felipe A. Medeiros,Samuel Berchuck,Matthew M. Engelhard
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Computation (stat.CO)
*备注: 21 pages, 5 figures
Abstract:Scalable Bayesian inference for generalized linear mixed models (GLMMs) provides uncertainty-aware analysis of correlated longitudinal data, but existing scalable approaches largely assume low-dimensional tabular predictors and do not directly accommodate high-dimensional modalities such as images and text. We address this limitation by learning one or more modality-specific neural encoders jointly with a GLMM objective, then performing variance-corrected stochasticgradient MCMC for the GLMM parameters conditional on the learned representation. This conditional-Bayes design combines supervised representation learning with posterior uncertainty quantification for population-level effects, subjectspecific heterogeneity, and modality-level random slopes. The resulting model preserves interpretable fixed and random effects for structured covariates and learned modalities while scaling gracefully to large longitudinal datasets. In simulation studies, our method recovers posterior means and variance estimates from full-data MCMC benchmarks after covariance correction. We further evaluate uncertainty through parameter-level interval coverage in simulations and predictive calibration on held-out data. Applications to glaucoma progression and adolescent mental health demonstrate that the framework allows nuanced assessment of the relative importance of each modality on both individual and population levels without sacrificing predictive performance.
[LG-174] Breaking the One-Dimensional Expressibility-Trainability Tradeoff
链接: https://arxiv.org/abs/2607.04598
作者: Kyoungho Cho,Yu-Seong Jeon,Jinhyoung Lee,Jeongho Bang
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注: 35 pages (Main Text + Supplemental Material)
Abstract:Expressive parameterized quantum circuits (PQCs) are often designed under a dilemma: the growth of expressibility and entangling power (EP) that improves Hilbert-space coverage is also expected to randomize an ansatz and activate barren-plateau (BP) conditions. We show that this dilemma is not a one-dimensional tradeoff. The usual picture collapses three inequivalent objects – parameter-ensemble coverage, fixed-circuit entangling response, and local gradient moments – into one scalar narrative. For a fixed circuit probed by Haar-product inputs, EP is a global two-copy mean of the output-entanglement distribution, whereas entangling-power deviation (EPD) is a global four-copy fluctuation descriptor. Gradient variance, however, is a local two-copy contraction selected by a parameter light cone and a cost observable. This moment hierarchy yields an analytic separation: equal EP need not imply equal trainability, as witnessed by equal-EP circuits with different EPDs and different gradient variances. These separations turn EP and EPD into a two-dial design rule for PQC ansatzes: EP measures how far the circuit has moved along the coverage dial, while EPD monitors whether input-dependent variability remains. We find that ansatz routes can reach high, Haar-like coverage before EPD and gradient variance collapse, showing that coverage and BP activation are distinct crossover events. The EP/EPD framework thus breaks the apparent one-dimensional expressibility-trainability tradeoff into a practical design rule: search for highly expressive PQCs in the window where coverage is high but BP-like homogenization has not yet erased trainable structure.
[LG-175] Causal ASCEND: Scalable Two-tier Causal Discovery on High Dimensional Multi-omics Data
链接: https://arxiv.org/abs/2607.04527
作者: Stephen Asiedu,David Watson
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Genomics (q-bio.GN)
*备注: Main material: 8 pages + supplementary material. 16 pages in all
Abstract:Biological systems exhibit a hierarchical structure, characterised by directed flow from upstream regulators to downstream effects. Although this ordering provides a natural scaffold for causal inference, most causal discovery and GRN methods either ignore the tiered organisation or condition on all upstream variables, which becomes infeasible for high-dimensional omics data. We present ASCEND (Ancestral Scalable Causal discovEry via iNherited Descent), a constraint-based framework that leverages known two-tiered structure to enable genome-scale causal discovery. ASCEND introduces a divide-and-conquer strategy that maintains dynamically updated ancestral conditioning sets for each downstream variable, dramatically reducing the number of conditional independence tests required, and achieves polynomial-time complexity where traditional approaches face exponential blow-up. Through extensive simulations and real biological data, we demonstrate that ASCEND accurately recovers ancestral relationships, scales properly and much faster, and outperforms existing gene regulatory network inference methods in both causal precision and computational efficiency. The algorithm’s ability to resolve directionality makes it particularly suited for integrating multi-omic data where upstream regulators (e.g., SNPs, methylation sites) and downstream responses (e.g., gene expression) are measured jointly.
[LG-176] Boundary-layer asymptotics for Gaussian-smoothed singular measures
链接: https://arxiv.org/abs/2607.04514
作者: Nicolas Brosse,Arnak S. Dalalyan
类目: Probability (math.PR); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
Abstract:We study the small-noise asymptotics of Euclidean heat regularizations of probability measures supported on manifolds with corners. Near a boundary or corner stratum, the relevant regime is a conical boundary layer in which the observation point approaches the stratum at the same scale as the Gaussian smoothing parameter. After rescaling this layer, the support is replaced to leading order by its inward tangent cone. We prove a two-term expansion for the heat-regularized density in this regime. The leading coefficient is the Gaussian mass of the linearized cone, weighted by the density on the support and by the adapted corner Jacobian; the first correction records the variation of the density, the Jacobian, and the quadratic geometry of the embedding. A localization argument then yields the corresponding expansion for the full heat regularization, with the nonlocal contribution exponentially small. From this density expansion we derive logarithmic asymptotics and uniform expansions for the score, the log-Hessian, and the scale derivative of the score. These formulas show how lower-dimensional support, boundary faces, corners, and curvature are encoded in the singular differential structure of small-noise Gaussian regularizations.
[LG-177] Constrained Flow Matching via Lagrangian Dual Flows
链接: https://arxiv.org/abs/2607.04513
作者: Vince Kurtz,Alexander Davydov
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:
Abstract:Flow matching is a powerful tool for generative modeling, but emerging applications in robotics, planning, and physics require inference-time constraints on generated outputs. Such constraints are often complex and highly nonlinear. As a result, methods designed for linear constraints like image inpainting are rarely sufficient, and projection or optimization-based alternatives can be prohibitively expensive. In this paper, we introduce Lagrangian Dual Flows, a new family of constrained generation techniques based on Lagrangian dual dynamics. By simply flowing a dual co-state alongside generated samples, we can guarantee nonlinear constraint satisfaction without expensive optimization subproblems, pseudoinverses, or projection steps during the denoising process. The resulting constrained generation algorithms are simple, effective, and open new theoretical connections between flow matching and primal-dual methods in numerical optimization.
[LG-178] Weakly Guided and Autoregressive Beamformer Parameterization for Generalizable Moving Speaker Extraction in Higher-Order Ambisonics
链接: https://arxiv.org/abs/2607.04471
作者: Jakob Kienegger,Tal Peer,Sina Khanagha,Timo Gerkmann
类目: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD)
*备注: Accepted at the International Workshop on Acoustic Signal Enhancement (IWAENC) 2026
Abstract:Linear spatial filters (beamformers) enable robust, generalizable and interpretable speech enhancement with performance guarantees under ideal parameterization. Modern beamformers are often parameterized by deep neural networks, whose performance degrades in dynamic scenarios with multiple moving speakers of unknown directions. We propose a data-driven beamforming pipeline, which only requires an estimate of the target’s initial direction. Building on a higher-order ambisonics representation, we show that neural temporal-spectral processing can be decoupled from linear spatial processing, and thereby achieve generalizable and array-agnostic enhancement. By incorporating autoregression into a frame-wise causal framework, we maintain consistent performance throughout fast speaker motion and long recordings. Evaluation on synthetic data demonstrates robust enhancement under challenging conditions with closely spaced and crossing speakers. Real-world recordings in a dynamic office meeting scenario complement these findings and show generalizability across varying ambisonics orders.
[LG-179] ghtening the Score Matching Gap for Diffusion Models
链接: https://arxiv.org/abs/2607.04442
作者: Benjamin Dupuis,Tyler Farghly,Maxime Haddouche,Alain Durmus,Umut Simsekli
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Diffusion models (DMs) are a state-of-the-art generative method to approximately sample from an unknown distribution. Their training and evaluation primarily rely on an Evidence Lower Bound (ELBO), which relates the Kullback-Leibler (KL) divergence of model samples to the score matching loss along the path, which serves as a tractable surrogate. The difference between sample quality and the score matching loss produced by this bound leads to the \emphscore matching gap, which is known to be tight in the worst-case but not descriptive of sample quality in general. In this work, we provide a theoretical analysis of this gap, developing tighter bounds for three metrics: KL divergence, reverse KL divergence, and Wasserstein distance, effectively exploiting the regularity of the class of score estimators. Our results suggest that the quality of the score approximation has more impact on closing the score matching gap for low noise scales. To obtain these bounds, our key technical insight is to exploit the contraction properties of the backward processes. In particular, we rely on entropy flows, logarithmic Sobolev inequalities and reflection couplings, rigorously linking the ergodicity of the Langevin diffusion to the score matching gap problem.
[LG-180] Quadrature-Aware Complex-Linear Neural Operator for Boundary-to-Field Prediction in Resonant Acoustics
链接: https://arxiv.org/abs/2607.04407
作者: Muhammad Idrees Khan,Hua-Dong Yao
类目: Fluid Dynamics (physics.flu-dyn); Machine Learning (cs.LG)
*备注: 27 pages, 10 figures
Abstract:Repeated prediction of acoustic fields from spatially distributed boundary excitation is computationally expensive when each source realization requires a new wave simulation. This work introduces a quadrature-aware complex-linear boundary operator (CLBO) that maps complex normal velocity on a vibrating surface to complex pressure at receiver locations. The model couples learned source and receiver basis functions through an explicit complex surface-quadrature contraction, so the boundary excitation enters linearly by construction. This preserves complex superposition, homogeneity, and zero response to zero excitation, while representing the source through coordinates, normals, and quadrature weights rather than a fixed flattened input vector. Reference data were generated using a verified three-dimensional multiple-relaxation-time (MRT) lattice Boltzmann solver and stored in a solver-agnostic boundary-to-field format. CLBO was compared with a fixed-sensor complex DeepONet under matched case splits and optimization settings, with additional tests of structural consistency, receiver-coordinate interpolation, source discretization, source-family holdout, label efficiency, physics-informed ablations, unseen source mixtures, and computational cost. Across five training seeds, CLBO achieved a mean complex relative field error of 0.184 +/- 0.00771, compared with 0.367 +/- 0.00742 for DeepONet. Its measured source-superposition error was 1.31 x 10^-7, and its mean error on newly simulated mixed-source cases was 0.237, compared with 0.415 for DeepONet. Inference was 1.83 x 10^4 faster than the reference calculation for the reported query size. These results show that enforcing the known complex-linear boundary-to-field structure improves physical consistency and generalization under distributed acoustic excitation.
[LG-181] Optimal Mixture-of-Experts Model Averag ing for Conditional Generative Models
链接: https://arxiv.org/abs/2607.04360
作者: Shijin Gong,Baihua He,Xinyu Zhang
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 32 pages, 1 figures
Abstract:Conditional generative models have emerged as powerful tools for sampling from target conditional distributions, driving substantial advances across a wide range of scientific and applied domains. As these models proliferate, practitioners often face multiple plausible generators whose performance can vary with the task, data, or input condition. We propose an optimal model averaging framework for conditional generative models, allowing candidate generators to be combined even when they are accessible only through conditional samples without tractable densities. Specifically, we use a sample-based maximum mean discrepancy between conditional distributions, which first leads to a static model averaging method, StaticMA, assigning fixed weights to different candidates. In addition, we develop MoEMA (mixture-of-experts model averaging), an input-adaptive method that parameterizes covariate-dependent weights through a softmax neural-network gate. We establish in-sample and out-of-sample asymptotic optimality for the proposed methods, together with consistency of the estimated adaptive weight function under regularity conditions. The framework applies directly to Euclidean responses and extends to unstructured data by combining our formulation with fixed representation maps. Across a broad set of simulations and real-data studies spanning tabular, image, and text modalities, MoEMA generally improves over competing baselines, demonstrating the effectiveness of our proposed methods.
[LG-182] Deep Learning for Dynamic Programming with Recursive Utility
链接: https://arxiv.org/abs/2607.04278
作者: Xianhua Peng,Wu Guo
类目: Computational Finance (q-fin.CP); Machine Learning (cs.LG); Econometrics (econ.EM); Optimization and Control (math.OC); Machine Learning (stat.ML)
*备注: 93 pages, 44 figures
Abstract:We propose the first deep learning algorithm, the Certainty Equivalent Learning (CEL) algorithm, for solving high-dimensional discrete-time dynamic programming problems with recursive utility. Dynamic programming with recursive utility is numerically challenging because the recursive utility does not have an explicit representation and the Bellman equation contains a certainty equivalent that is difficult to evaluate. The CEL algorithm learns this certainty-equivalent value directly with neural networks and jointly approximates value functions, policy functions, and certainty-equivalent functions. The CEL algorithm is mesh-free and simulation-based, allowing high-dimensional state and control spaces, and does not rely on Euler equations, first-order conditions, or differentiability of the state transition function. The CEL algorithm also works for dynamic programming problems with expected utility as expected utility is a special case of recursive utility. We apply the CEL to discounted linear exponential quadratic Gaussian control, small-noise robust control, Epstein-Zin DSGE, and multivariate strategic asset allocation problems. Compared with closed-form and VFI-based benchmarks, the CEL delivers accurate value and policy approximations, remains effective in high-dimensional problems, achieves accuracy comparable to VFI in the small-noise robust-control case, and produces out-of-sample Bellman errors and Euler or first-order residuals that are in the range from 1.0e-4 to 1.0e-3 for most problems.
[LG-183] Robust Bayes-Assisted Conformal Prediction AISTATS2026
链接: https://arxiv.org/abs/2607.04236
作者: Kianoosh Ashouritaklimi,Stefano Cortinovis,François Caron
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: Accepted to AISTATS 2026. 44 pages, 8 figures, 7 tables
Abstract:Bayes-assisted conformal prediction combines the strengths of Bayesian modelling with exact, distribution-free frequentist coverage guarantees. Although conformal validity is preserved even when the Bayesian working model (BWM) is misspecified, the size of the resulting prediction sets can degrade substantially when the prior is poorly aligned with the observed data. We address this limitation by introducing RoBAS (Robust Bayes-Assisted Shrinkage): a Bayes-assisted framework for constructing robust nonconformity scores, with two instantiations: one induced by a heavy-tailed BWM, and a closed-form empirical Bayes shrinkage score. The resulting scores adapt to the quality of the working information encoded in the prior: when this information is reliable, they exploit it to produce efficient prediction sets; when it is weak or inaccurate, they revert to the Distance-To-Average (DTA) score, a robust non-informative baseline. We evaluate the proposed scores on tabular and image regression tasks where the training distribution may differ from the calibration and test distributions, while the calibration and test data themselves remain exchangeable. We find that they are competitive with widely used scores in the absence of such shift, while substantially reducing interval widths in shifted settings.
[LG-184] Unified convergence analysis for gradient descent optimization methods in the training of deep neural networks
链接: https://arxiv.org/abs/2607.04233
作者: Shokhrukh Ibragimov,Arnulf Jentzen
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注: 51 pages
Abstract:Gradient based optimization methods are nowadays the methods of choice for training deep neural networks (DNNs) in artificial intelligence (AI) systems. In practically relevant DNN training problems, one does usually not apply the standard gradient descent (GD) optimization method but instead one employs suitable sophisticated GD optimization methods, which incorporate adaptivity and/or acceleration techniques, such as the famous Adam optimizer. It is a key contribution of this work to provide a general unified convergence analysis for GD optimization methods in the training of DNNs with analytic activations such as the softplus and the popular Gaussian error linear unit (GeLU) activation. Our general unified convergence result applies to a large class of gradient based optimization methods such as the standard GD, the momentum, the Nesterov accelerated gradient (NAG), the RMSprop, the Adam, the Adamax, the Nadam, the Nadamax, the Adan, the AdaBelief, the AMSGrad, and the Yogi optimizers. Our analysis employs the theory of Kurdyka-Łojasiewicz (KL) inequalities to establish convergence to critical points in the training of DNNs. To the best of our knowledge, the generality of our convergence analysis is also just in the special situation of the Adam optimizer a new contribution to the literature on the analysis of AI optimization algorithms.
[LG-185] AI-RAN on NPUs: Baseband Processing Without Baseband Chips
链接: https://arxiv.org/abs/2607.04224
作者: Shilong Zhang,Luping Xiang,Jienan Chen,Kun Yang
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注: 8 pages, 4 figures. Submitted to npj Wireless Technology
Abstract:AI-RAN aims to unify artificial intelligence and radio access network workloads on a shared compute substrate. While this paradigm has so far been demonstrated primarily on Graphics Processing Units (GPUs), it remains unclear whether Neural Processing Units (NPUs), which are AI accelerators optimized for inference, can also support wireless baseband processing. Here, we provide the first affirmative answer by resolving the fundamental mismatch between baseband workloads and NPU architecture. A computational isomorphism exists: matrix and vector engines NPUs dedicate to inference inherently cover physical-layer operations. Yet NPU architectures are natively shaped for dense-tensor AI inference, not baseband. This architectural mismatch surfaces as opposing optimization objectives: traditional baseband minimizes arithmetic operations, whereas NPU performance demands maximizing engine utilization. We close this gap by reconstructing communication algorithms onto AI compute primitives, prioritizing engine utilization over arithmetic count. We validate this with a complete OFDM transceiver on an Ascend 310B1 edge NPU, demonstrating end-to-end over-the-air transmission via USRP X300 at 3.0 GHz.
[LG-186] Governing Generative AI Across Financial Institutions: An SR 26-2-Compatible Framework for Generative AI Risk Control
链接: https://arxiv.org/abs/2607.04103
作者: Yiqing Wang,Yixin Kang,Luyun Lin,Siqi Mao
类目: Risk Management (q-fin.RM); Machine Learning (cs.LG)
*备注: 15 pages, 1 figure
Abstract:The release of SR 26-2 marks a significant modernization of U.S. model risk management by replacing SR 11-7 with a more risk-based and materiality-sensitive supervisory framework. However, generative and agentic AI are excluded, creating an important governance challenge for banking organizations and other financial institutions. Although generative AI may not directly estimate credit risk or make underwriting decisions, its outputs can materially affect the surrounding control environment through monitoring interpretation, policy analysis, or adverse-action language drafting. These uses may influence how regulated financial decisions are explained, challenged, documented, and governed. This paper proposes the Generative AI Control Framework (GAICF), an SR 26-2-compatible governance framework for generative AI-enabled financial workflows. The framework translates core model risk management principles into a layered control structure for generative AI applications that operate outside the formal model boundary but remain embedded within regulated banking processes. GAICF provides a practical approach for financial institutions seeking to align emerging generative AI governance practices with the risk-based supervisory expectations reflected in SR 26-2. Comments: 15 pages, 1 figure Subjects: Risk Management (q-fin.RM); Machine Learning (cs.LG) Cite as: arXiv:2607.04103 [q-fin.RM] (or arXiv:2607.04103v1 [q-fin.RM] for this version) https://doi.org/10.48550/arXiv.2607.04103 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-187] Fast Parallel Query-Efficient Binary Classification COLT2026
链接: https://arxiv.org/abs/2607.04062
作者: Ishani Karmarkar,Liam O’Carroll,Aaron Sidford
类目: Optimization and Control (math.OC); Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)
*备注: COLT 2026
Abstract:We study the fundamental classification problem of computing a separating hyperplane for a binary-labeled dataset of size n with normalized d -dimensional features. Letting \Phi \in \mathbbR^n \times d denote the feature matrix and \gamma the margin of the maximum-margin separating hyperplane, we present a randomized algorithm that solves this problem in \tildeO(\gamma^-2/3, \operatornamennz(\Phi) + \gamma^-2(\omega+1)/3) -sequential running time (work), \tildeO(\gamma^-2/3) -parallel (computational) depth, and accesses \Phi only through \tildeO(\gamma^-2/3) -matrix-vector queries (matvecs). We also present a second, faster randomized algorithm with a \tildeO(\gamma^-2/3, \operatornamennz(\Phi) + \gamma^-2) -sequential running time that uses \tildeO(\gamma^-2/3) -matvecs to \Phi , but achieves only \tildeO(\gamma^-4/3) -parallel depth. Both algorithms match the near-optimal deterministic matvec complexity recently established by Kornowski and Shamir [2025], Karmarkar et al. [2026] and achieve improved sequential runtime and parallel depth, albeit at the expense of using randomness.
[LG-188] A Policy Decomposition Framework for Dynamic Order Fulfillm ent Operations
链接: https://arxiv.org/abs/2607.04056
作者: Gal Neria,Michal Tzur,Marlin W. Ulmer
类目: Dynamical Systems (math.DS); Machine Learning (cs.LG); Combinatorics (math.CO); Optimization and Control (math.OC)
*备注:
Abstract:Modern supply chains span diverse operational environments, ranging from e-commerce distribution networks to customized production-to-order manufacturing lines. Across these settings, operational efficiency depends on coordinating two highly interdependent stages: order preparation and downstream delivery. Although these stages are traditionally managed in isolation, real-world fulfillment systems must satisfy stringent delivery expectations under dynamic stochastic order arrivals. To bridge this gap, we introduce the Dynamic Order Fulfillment Problem (DOFP), a new problem class unifying logistical challenges previously studied separately. We model DOFP as a Markov decision process whose state and decision spaces are partitioned into preparation and delivery sub-spaces, linked by synchronization constraints. While recent approaches attempt to optimize both fulfillment stages simultaneously over myopic rolling horizons, our framework isolates and optimizes the downstream delivery policy, treating preparation strictly as a state-level constraint filter. To solve this, we develop the Decomposition-Driven Framework with Value Function Approximation (DDF-VFA), which utilizes a novel policy-level decomposition. This design partitions the search into a delivery-stage master problem and a preparation-stage compatibility subproblem, iteratively refined via feedback loops. DDF-VFA executes this strategy by combining a large-neighborhood search over partial delivery decisions with a neural-network value function approximation for the cost-to-go. Numerical illustrations on two example variants using real-world datasets show that DDF-VFA consistently outperforms benchmarks that optimize the two stages independently or jointly without decomposition. Finally, the framework naturally scales to accommodate additional real-world complexities such as batched or multi-stage preparation.
[LG-189] Significance-First Splitting: Aligning Treatment Heterogeneity Detection with Honest Estimation
链接: https://arxiv.org/abs/2607.03999
作者: Pantelis Z. Hadjipantelis,Josephine Chiang,Karthik Nagesh
类目: Methodology (stat.ME); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Estimating heterogeneous treatment effects (CATE) requires simultaneously detecting effect modification and quantifying estimation uncertainty. Existing tree-based methods make an uneasy trade-off: significance-based approaches (Radcliffe and Surry 2011) identify subgroup interactions directly but lack valid inference; honest causal trees (Athey and Imbens 2016) deliver nominal confidence interval coverage but use outcome-agnostic splitting criteria that sacrifice interaction sensitivity. We introduce a hybrid algorithm that fuses significance-based splitting with honest sample-splitting and cross-validation. Our splitting criterion uses the squared t -statistic for the treatment \times side interaction ( t^2 ), which is shown to be directly aligned with the honest \textEMSE_\tau criterion when the interaction is strong. Post-hoc honest cross-validation selects the cost-complexity penalty, giving a single principled estimator with nominal CI coverage at the leaf level. For forests, we retain bootstrap count vectors to enable an infinitesimal jackknife (IJ) variance estimate of Monte-Carlo convergence rather than formal pointwise inference. On the three synthetic designs from (Athey and Imbens 2016) the single tree achieves approximately 90% leaf-average CI coverage at the 90% nominal level across all three designs (200 replications each); on the Criteo and Starbucks uplift datasets we match Qini coefficient performance of S- and T-learner baselines. An open-source Python package with reproducible seeds, sklearn-compatible API, and full test coverage accompanies this work (this https URL).
[LG-190] Smooth %MinMax: A Differentiable Relaxation for Codon Harmonization
链接: https://arxiv.org/abs/2607.03881
作者: Yoonho Jeong,Hyunwoo Choi,Ryan Fernandez Medina Hariri,Eok Kyun Lee,Seung Seo Lee,Insung S. Choi
类目: Quantitative Methods (q-bio.QM); Machine Learning (cs.LG)
*备注: 17 pages, 2 figures
Abstract:Codon harmonization aims to adapt the coding sequences for heterologous expression while preserving the native-like patterns of frequent and rare codons that may influence local translation dynamics and co-translational protein folding. However, widely used harmonization metrics, such as % MinMax, are defined on discrete codon sequences and are, therefore, not readily compatible with gradient-based neural codon design. Here, we introduce Smooth % MinMax, denoted as %\rm MinMax_(s) , a differentiable relaxation of the conventional hard % MinMax metric, denoted as %\rm MinMax_(h) . %\rm MinMax_(s) replaces the discrete codon-usage values with probability-weighted synonymous-codon usage values and replaces the hard % Max/ % Min branch with a sigmoid-gated interpolation. This formulation preserves the signed interpretation of %\rm MinMax_(h) , while enabling optimization with respect to the synonymous-codon probabilities and learnable parameters. In human-to-Escherichia coli codon harmonization experiments, %\rm MinMax_(s) closely approximates %\rm MinMax_(h) and supports gradient-based profile matching in synonymous-codon probability space. These results suggest %\rm MinMax_(s) as a practical bridge between profile-based codon harmonization and neural synonymous-sequence design.
[LG-191] A simplex-based measure of symmetry
链接: https://arxiv.org/abs/2607.03815
作者: Egor Bakaev,Amir Yehudayoff
类目: Metric Geometry (math.MG); Machine Learning (cs.LG)
*备注:
Abstract:For compact convex sets L,K \subset \mathbbR^n , denote by \lambda_K(L) the smallest size of a homothet of K that contains L . We define a measure of symmetry based on the n -simplex \Delta = \Delta^n \subset \mathbbR^n as the ratio [ \rho_\Delta(L):=\frac\lambda_-\Delta(L)\lambda_\Delta(L). ] We study this measure and deduce the following results: (1) The classical Minkowski measure of symmetry m^(L) can be defined as an affine-invariant version of \rho_\Delta(L) . (2) We improve the stability analysis for the Minkowski measure of symmetry; if m^(L)\ge n-\varepsilon then L is \tfrac11-\varepsilon -close to \Delta in the Banach–Mazur distance. (3) We obtain a novel characterization of simplices as the only convex bodies K for which the function L \mapsto \lambda_K(L) is additive (a property we term ``outer additivity’'). (4) Motivated by the expressivity of ReLU neural networks, we study the depth complexity of polytopes in \mathbbR^n under the two operations: Minkowski sum and convex hull of a union. We prove the sharp bound \rho_\Delta§ \leq 2^d -1 for every polytope P of depth complexity d . In other words, simplices cannot be approximated by low-depth polytopes. Subjects: Metric Geometry (math.MG); Machine Learning (cs.LG) Cite as: arXiv:2607.03815 [math.MG] (or arXiv:2607.03815v1 [math.MG] for this version) https://doi.org/10.48550/arXiv.2607.03815 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Egor Bakaev [view email] [v1] Sat, 4 Jul 2026 10:50:29 UTC (84 KB) Full-text links: Access Paper: View a PDF of the paper titled A simplex-based measure of symmetry, by Egor Bakaev and 1 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: math.MG prev | next new | recent | 2026-07 Change to browse by: cs cs.LG math References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[LG-192] LRX-PINN: A Layer-Resolving XNet Physics-Informed Neural Network with Integrated Cauchy Activations for Convection-Dominated Problems
链接: https://arxiv.org/abs/2607.03682
作者: Zihao Guo,Xin Li,Zhihong Xia
类目: Analysis of PDEs (math.AP); Machine Learning (cs.LG)
*备注: 30 pages; 16 figures
Abstract:Convection-dominated convection-diffusion problems often develop thin layers, where the solution has sharp transition profiles and its derivatives are highly localized. This creates a structural mismatch for standard physics-informed neural networks (PINNs), whose trial spaces are not designed to match the value–derivative structure of such layers. We propose a Layer-Resolving XNet Physics-Informed Neural Network (LRX-PINN) based on integrated Cauchy activations. The proposed basis is transition-type at the solution level, while its derivative recovers a localized Cauchy kernel. We show that this structure matches the scaling of convection-dominated layers, inherits the Cauchy approximation mechanism at the derivative-profile level, and identifies (d/|w|) as the effective physical width of a ridge neuron. For analytic layer profiles, this yields derivative-stable exponential approximation in the stretched coordinate and a layer-scaled estimate for the strong residual of the singularly perturbed operator. Numerical experiments on several convection-dominated benchmarks show that LRX-PINN achieves higher accuracy than PIKAN and Fourier-feature PINNs while using less than (30%) of their trainable parameters. On more challenging benchmarks, embedding the proposed representation into hp-VPINN-based frameworks further improves the best results obtained by existing hp-VPINN-based baselines without changing their original loss functionals or stabilization strategies. These results show that neural representations aligned with layer structure provide a compact and effective approach for convection-dominated problems.
[LG-193] Diffusion learning reveals viable parameter manifolds and compensation geometry in biological dynamical systems
链接: https://arxiv.org/abs/2607.03671
作者: Ruilin Zhang,Louis Tao,Zhuo-Cheng Xiao
类目: Quantitative Methods (q-bio.QM); Machine Learning (cs.LG); Chaotic Dynamics (nlin.CD); Neurons and Cognition (q-bio.NC)
*备注: 25 pages, 7 figures
Abstract:Models of complex systems often have many parameters, yet are constrained by far fewer experimentally accessible observables: similar activity can emerge from coordinated parameter changes. We formalize these compatible parameter sets as \emphviable parameter manifolds: the inverse images of a system’s target dynamical behaviors under a parameter-to-feature map. The relevant codimension is not the number of reported features, but the effective rank of that map at the target scale. Co-varying features lower the codimension, while poor conditioning, high curvature, or regime mixing degrade learnability. We train conditional score-based diffusion models on simulated parameter–feature pairs and use them as amortized samplers of prior-weighted viable sets. In the Lorenz system, scalar trajectory statistics generate thin viable sheets, and two-feature conditioning localizes a transition-adjacent corridor. In the Izhikevich neuron model, four firing descriptors lie close to a nearly two-dimensional family of features, and the learned inverse images reveal distinct regular and irregular compensation geometries. In a recent ODE reduction of finite spiking networks, the same framework reveals excitatory–inhibitory compensation, timescale–coupling tradeoffs, and input-dependent viable manifolds across 4–12 parameter dimensions. In this view, robustness, compensation, and hidden parameter dependencies are organized as inverse geometry, with diffusion models providing practical tools for sampling, visualizing, and interrogating that geometry.
[LG-194] Sequential Correlations Change In-Context Learning: Effective Context Length and Architectural Mismatch
链接: https://arxiv.org/abs/2607.03660
作者: Mary Letey,Yue M. Lu,Cengiz Pehlevan,Jacob Zavatone-Veth
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Computation (stat.CO)
*备注:
Abstract:Modern sequence models have a striking capacity for in-context learning (ICL); they can perform new tasks based only on examples given in the prompt. Understanding how this ability emerges requires theory that captures important properties of natural data. Linear regression has served as a useful sandbox for ICL theory, but existing work has largely focused on prompts with independent examples. In this work, we extend this setting to sequentially correlated data, a basic feature of real sequences. We present a solvable model based on linear attention and test our predictions on realistic transformer architectures. We identify two distinct effects: First, when the query token is independent of the context, within-context correlations induce an effective context length: correlated prompts behave like shorter i.i.d. prompts. Second, when the query is also correlated with its context, test error is reduced, particularly for softmax attention when compared to linear attention. These results suggest that correlated prompts alter not only the effective sample size of in-context learning, but also which attention architectures are best matched to the task.
[LG-195] Missing Data Imputation under Manifold Hypothesis
链接: https://arxiv.org/abs/2607.03641
作者: Zelong Bi,Amuchechukwu Ibenegbu,Sarat Moka
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:The manifold hypothesis posits that high-dimensional data are concentrated near a low-dimensional embedded manifold. Recent advances in mixture variational autoencoders (VAEs) provide a powerful tool for extracting such underlying structure in a faithful manner. The resulting geometric structure naturally introduces local and global relationships among variables, thereby providing a systematic way of imputing missing data. We propose a model-based imputation method that enables sampling from ( p(\bmx_\mathrmmis \mid \bmx_\mathrmobs) ) via a sampling-importance-resampling (SIR) procedure, which can be further augmented with a joint diffusion model in the latent space. Our method imputes missing data while respecting the underlying geometry, achieves competitive performance compared to state-of-the-art procedures, quantifies uncertainty in the imputations, and is model-based, thereby enabling on-the-fly imputation without rerunning the entire procedure.
[LG-196] AquaGen: Scaling generative models to molecular dynamics precision on thousands of atoms
链接: https://arxiv.org/abs/2607.03513
作者: Emmanuel Bengio,Sanjeev Raja,Yui Tik Pang,Kerstin Klaeser,Cristian Gabellini,Nikhil Shenoy,Francesco Di Giovanni,Prudencio Tossou
类目: Chemical Physics (physics.chem-ph); Machine Learning (cs.LG)
*备注: 22 pages
Abstract:We present AquaGen, the first all-atom, explicit solvent, periodic-boundary-condition-aware generative model that produces molecular configurations from the Boltzmann distribution at a fraction of the cost of molecular dynamics (MD). This is in contrast with existing generative models that remove degrees of freedom by operating on coarse-grained, vacuum, or implicit solvent systems. Operating at this resolution allows for post-processing through force field energy evaluations and MD simulations, and enables the prediction of relevant properties in a gray-box manner (as ensemble averages of potential energy evaluations over generated samples). We demonstrate the utility of this paradigm on absolute hydration free energy (AHFE), producing estimates 4-10x faster and with comparable accuracy to standard GPU-based MD. By generating uncorrelated samples from alchemical Boltzmann distributions, we create more accurate, interpretable, and refinable ensemble predictions with calibrated uncertainty estimates, unlike regression methods which are entirely black-box predictors. Our approach also yields predictable benefits from increasing train- and test-time compute, realized by scaling model size and generating more samples, respectively. We believe that this approach demonstrates the utility of high-resolution ensemble generation for free energy estimation, with future potential to replace MD in tasks such as the prediction of lipophilicity, membrane permeability, or absolute binding free energy (ABFE) – whose grounding and interpretability may be critical for the development of new drugs and materials.
[LG-197] A Hierarchy of Policy Learning Problems
链接: https://arxiv.org/abs/2607.03385
作者: Hamsa Bastani,Osbert Bastani,Shihan Chen
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Policy learning has received substantial attention with the goal of learning policies from observational data for decision-making. A majority of work in this space has focused on developing algorithms for computing policies that minimize regret compared to the optimal policy. However, in many practical settings, there is insufficient data to obtain low regret. As a result, recent work has shifted attention to alternative objectives, most notably, studying whether it is possible to learn an improving policy that statistically significantly outperforms baseline policies. We argue that there is substantial merit in studying a broader range of policy learning problems. When there is insufficient data to learn an improving policy, there may still be useful questions that can be answered. To this end, we provide a mathematical framework for studying the relationships between policy learning problems. We formalize three problems within our framework: beyond the optimal policy problem and the improving policy problem, we also propose the policy existence problem, which aims to determine if an improving policy exists. Within our framework, we show that the policy existence problem reduces to the improving policy problem, which in turn reduces to the optimal policy problem; these reductions prove that each problem is at least as easy as the next one (in sample complexity). A key question remains: is this hardness strict? We provide partial answers. First, the gap between the optimal policy and improving policy problems is strict. For the improving policy and policy existence problems, we prove that a sublinear polynomial gap exists under natural conditions on improving policy learning algorithms. Thus, we may be able to answer questions about the existence of an improving policy even when we cannot find one. These results highlight the value in studying a broader range of policy learning problems.
[LG-198] CaSPECT: Discovering Causally Homogeneous Subgroups via Directed Spectral Clustering
链接: https://arxiv.org/abs/2607.03364
作者: Arghya Pratihar,Shinjon Chakraborty,Swagatam Das
类目: Methodology (stat.ME); Machine Learning (cs.LG)
*备注:
Abstract:We propose \textbfCaSPECT, a causal spectral clustering framework for discovering causally homogeneous subgroups from observational data. Rather than clustering in covariate space, CaSPECT defines similarity through the topology of a learned directed acyclic graph (DAG); a bootstrap-stabilised PC algorithm recovers the causal skeleton; a novel \emphOrientation Validation Score (OVS) combines PC bootstrap evidence with DirectLiNGAM to orient edges robustly; directed edges are weighted by backdoor-identified average treatment effects estimated via OLS or double machine learning. Chung’s directed Laplacian provides a spectral embedding in which individuals close together share the same causal propagation pathways. We establish almost-sure consistency of the full pipeline and validate the method through a controlled simulation study and on LaLonde CPS1, IHDP, and 401(k) datasets, where CaSPECT recovers a positive and statistically significant treatment effect within the causally comparable subpopulation and corrects for severe confounding without requiring a pre-specified propensity score model.
[LG-199] Complexity of Normalized Persistence Problems for Topological Data Analysis and Local Hamiltonians
链接: https://arxiv.org/abs/2607.03278
作者: Dominic Lowe,M.S. Kim,Roberto Bondesan,Ryu Hayakawa
类目: Quantum Physics (quant-ph); Computational Complexity (cs.CC); Machine Learning (cs.LG)
*备注: 61 pages, 4 figures
Abstract:Topological data analysis (TDA) is a machine learning technique that uses topology to extract patterns from data and has shown the potential to exhibit quantum advantage. A key concept in TDA is persistent homology, which measures the robustness of topological information at different lengthscales. In this paper, we introduce and study the problem of normalized persistence, a practically motivated and easily interpretable version of persistent homology that counts the fraction of holes that persist at different lengthscales. We prove that a variant of normalized persistence is \mathsfDQC_1 -hard and contained in \mathsfBQP , giving evidence of an exponential quantum speedup for TDA under the standard assumption that \mathsfDQC_1 \not\subseteq \mathsfBPP . These are the first \mathsfDQC_1 -hardness results that are directly applicable to TDA instances. We also find a close connection between normalized persistence and the complexity of estimating spectral quantities in the low-energy subspace of local Hamiltonians. We study a family of such problems, including a low-energy normalized subtrace and spectral density. We show that these are \mathsfDQC_1 -hard for O(1) -local Hamiltonians, strengthening previous results that required log-local interactions. We also introduce a variant of \mathsfDQC_1 with perfect completeness ( \mathsfSDQC_1 ) to characterize the hardness of problems normalized by an exact kernel. This includes normalized persistence for O(1) -local Hamiltonians, which we show is \mathsfSDQC_1 -hard.
[LG-200] Deriving Benchmarking Datasets from Long-Form Recordings: Challenges and Opportunities
链接: https://arxiv.org/abs/2607.03201
作者: Kaveri K. Sheth,Lawrence Borst,Tarek Kunze,Marvin Lavechin,Okko Räsänen,Sho Tsuji,Loann Peurey,Alix Bourrée,Alejandrina Cristia
类目: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD)
*备注:
Abstract:Long-form recordings (LFRs) of child-centered audio are ecologically valid sources for studying early language development, but three problems limit their use. First, LFR corpora are collected across sites with heterogeneous formats and consent structures, making cross-corpus use non-trivial. Second, without standardized benchmarks, assessing whether tools generalize across languages and conditions is hard. Third, ML workflows rarely respect privacy constraints governing sensitive child speech. This paper presents a framework addressing all three: a standardized collection of 27 child-centered datasets built with open-source tools (S1); a replicable pipeline for four speech-processing benchmarks (S2); and ELSI, a role-based ecosystem embedding ethical governance into the ML workflow (S3). We demonstrate the framework via a voice type classification case study and show the three solutions are mutually dependent.
[LG-201] Quantum Kolmogorov–Arnold representation theorem for continuous unitary-valued maps
链接: https://arxiv.org/abs/2607.03187
作者: Sviatoslav V. Dzhenzher
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG); Functional Analysis (math.FA)
*备注: 10 pages, no figures
Abstract:The classical Kolmogorov–Arnold representation theorem states that any continuous multivariate function can be exactly decomposed into a finite composition of univariate continuous functions and addition operations. This foundational result has recently inspired the development of Kolmogorov–Arnold Networks (KANs) in classical machine learning, as well as their extensions into the quantum domain (QKANs). In this paper, we establish two quantum analogues of the Kolmogorov–Arnold representation theorem for continuous unitary-valued maps of several variables within an open 1 -neighbourhood of the identity matrix (O_1(\mathbfI) \subset \mathcalU(n)). First, we prove a representation theorem that yields an exact additive decomposition inside the matrix exponent of anti-Hermitian-valued maps. Second, due to the non-commutative nature of quantum operators, we derive a factorised version expressing the target unitary map as a finite sequential product of univariate matrix exponentials. Finally, we provide a concrete topological counterexample based on the lifting property of (\mathcalSU(2)) to demonstrate that these local representation theorems cannot be globally extended to the entire unitary group (\mathcalU(n)) without encountering fundamental structural obstructions. Comments: 10 pages, no figures Subjects: Quantum Physics (quant-ph); Machine Learning (cs.LG); Functional Analysis (math.FA) Cite as: arXiv:2607.03187 [quant-ph] (or arXiv:2607.03187v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2607.03187 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Sviatoslav Dzhenzher [view email] [v1] Fri, 3 Jul 2026 10:46:32 UTC (27 KB)
[LG-202] Entropy Regularization Improves Policy Robustness in Continuous-Time Reinforcement Learning
链接: https://arxiv.org/abs/2607.03168
作者: Jialun Cao,Fernando Acero,David Šiška,Yufei Zhang
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:
Abstract:Entropy regularization is widely used in continuous-time reinforcement learning (RL) to reduce sensitivity to environmental perturbations, yet its robustness benefits lack a rigorous theoretical foundation. This paper establishes the first robustness guarantees for entropy-regularized continuous-time Markov decision processes. We show that maximizing an entropy-regularized objective yields a lower bound on a worst-case robust RL problem with joint reward and transition perturbations. We analytically characterize the induced robust sets and prove that they expand monotonically with the regularization strength, justifying the empirical observation that stronger entropy improves robustness. In contrast to prior discrete-time analyses, our results remove the intractable state-distribution entropy term and provide guarantees invariant to action frequency. Experiments on queueing network control and market making confirm our theory, showing that entropy-regularized policies outperform greedy and \epsilon -greedy baselines under dynamics perturbations.
[LG-203] Denoised Conformal Alignment for Reliable Selection of Conditional Averag e Treatment Effect Predictions
链接: https://arxiv.org/abs/2607.03161
作者: Xinyun Lu,Haoang Chi,Zhiheng Zhang
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 53 pages, 29 figures
Abstract:In selective deployment, practitioners act only on a model-chosen subset of individuals based on predicted conditional average treatment effects, but marginal conformal guarantees need not control reliability on that selected subset. We study reliable selection for black-box CATE predictors: selecting candidates whose CATE errors are below a tolerance while controlling the false discovery rate (FDR). Since CATE errors are unobservable, we construct doubly robust proxy errors from pseudo-outcomes; however, naive proxies can lose power under heteroskedasticity because variance overwhelms the reliability signal. We propose Denoised Conformal Alignment, which subtracts an estimated conditional variance component and combines conformal calibration with Benjamini–Hochberg selection. Our analysis shows that validity is governed by stability of proxy/oracle threshold labels, rather than pointwise perfection of the variance estimator. Experiments show substantially improved power while maintaining FDR control across challenging settings.
[LG-204] An Intervention-Based Framework for Shortcut Diagnosis in Spoofing Countermeasures
链接: https://arxiv.org/abs/2607.03150
作者: Santiago Rubio,Pilar Bello,Dayana Ribas,Antonio Miguel,Eduardo Lleida,Alfonso Ortega
类目: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG)
*备注: Accepted at Odyssey 2026: The Speaker and Language Recognition Workshop
Abstract:While deepfake audio detection systems achieve high performance in controlled benchmarks, their reliability often diminishes in the wild. Prior work shows that dataset-specific artifacts contribute to this gap. Yet, systematic tools to identify which acoustic properties a model exploits as shortcuts remain limited. We propose an intervention-based diagnostic framework, grounded in a directed graphical model, that formally distinguishes confound-driven shortcut dependencies from legitimate domain shift. We operationalise this through controlled acoustic perturbations targeting non-speech structure, spectral content, and signal energy, complemented by corpus-level distributional analysis. Evaluating XLS-R-300M with RawGAT-ST across ASVspoof challenges datasets, we quantify model sensitivity to specific intervention types. Results reveal that non-speech interventions produce the largest performance shifts, confirming non-speech intervals as a dominant shortcut.
[LG-205] Open-Set Source Tracing as Compositional Factors via Structured Prototypes
链接: https://arxiv.org/abs/2607.03134
作者: Santiago Rubio,Antonio Almudévar,Antonio Miguel,Eduardo Lleida,Alfonso Ortega
类目: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG)
*备注: Submitted to IEEE Spoken Language Technology Workshop (SLT) 2026
Abstract:Recent research expands beyond binary anti-spoofing with the emergence of Source Tracing, the task of identifying the specific generative origins of synthetic speech. However, current research often equates a “source” with its generative architecture. We propose redefining a source as a compositional tuple of Architecture, Training Data, and other training factors affecting the generated speech. We propose a framework using Structured Orthonormal Prototypes to minimize class overlap and intra-class variance. Our Subspace Partitioning strategy splits the embedding into architecture and data subspaces, while a residual subspace captures stochastic variability, enabling “compositional generalization” for novel factor combinations. This approach improves performance for partially seen sources and maintains robustness in fully open-set scenarios. MLAAD evaluations for Few-Shot open-set Identification show our approach significantly outperforms angular-margin baselines.
[LG-206] Graph Neural Networks for the Graphical Bootstrap
链接: https://arxiv.org/abs/2607.03109
作者: Rigers Aliaj,Gabriele Dian,Reza Doobary,Paul Heslop
类目: High Energy Physics - Theory (hep-th); Machine Learning (cs.LG)
*备注:
Abstract:We study a graph classification problem involving over 20 million graphs, arising from high-order perturbative computations of correlators in planar \mathcalN=4 super-Yang–Mills, a model closely related to the theory of the strong nuclear force. We benchmark graph neural networks, including graph transformers, achieving robust generalization to larger graphs with up to 99.996% ROC AUC. Then, we analyze how the models can be used to gain a computational speedup compared to the traditional graphical bootstrap algorithm, through shrinking the redundant data by up to 85.5% at the level of denominator graphs. Finally, we study the embeddings of the models to investigate their interpretability.
[LG-207] CodeJeNN: A simple C neural network generator for physics applications
链接: https://arxiv.org/abs/2607.02746
作者: Jay Arcities,Pavel Popov,Eric J Ching,Kamal Viswanath,Ryan F Johnson
类目: Computational Physics (physics.comp-ph); Machine Learning (cs.LG)
*备注:
Abstract:Machine learning has shown speedups for numerical methods in physics applications, but integrating Python-based libraries into high-performance C++ solvers creates performance bottlenecks. We present CodeJeNN, which bridges this gap by auto-generating self-contained C++ code from trained Keras models for inference. This eliminates external dependencies through minimal inlined functions, allowing seamless integration into existing frameworks. We describe the Keras-to-C++ workflow, supported architectures, and limitations. CodeJeNN is demonstrated through inference benchmarks against Keras in eager and JIT modes and a CFD test case modeling viscosity in a hydrogen-air mixing layer, showing speedups without sacrificing accuracy.
[LG-208] Optimality-Informed Neural Networks for Lunar Landing Trajectory Optimization
链接: https://arxiv.org/abs/2607.02741
作者: Zhenbo Wang
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注:
Abstract:This paper develops an Optimality-Informed Neural Network (OINN) approach for the energy-optimal, free-final-time powered descent of a lunar lander from any initial position, velocity, and mass within a bounded operating envelope to a fixed landing site with zero terminal velocity. Building on a recent framework that jointly embeds Pontryagin’s minimum principle and the Hamilton-Jacobi-Bellman equation for general nonlinear optimal control, the proposed OINN approach specializes that idea to a lunar landing problem with free time of flight and fixed terminal state. Every boundary and transversality condition is hard-encoded into the network architecture by construction, the closed-form Pontryagin-optimal thrust magnitude and direction law is substituted directly rather than learned, and the remaining state, costate, and an auxiliary value-function output are trained against a physics-residual loss formed entirely from the necessary conditions of optimality, with no precomputed optimal trajectories required. A preliminary theoretical analysis is explored, establishing a stochastic-optimization stationarity guarantee for the offline training procedure, an explicit bound translating the achieved training residual into bounds on touchdown position, touchdown velocity, and flight-time error, and a fixed, input-independent onboard computational and memory cost suitable for real-time deployment. Numerical simulations evaluate the trained policy, with no retraining, against an independently solved indirect-method boundary-value problem at six representative initial states spanning the operating envelope and against eighty additional Monte Carlo simulation runs, demonstrating close agreement with the indirect-method solution and consistently small dynamics and transversality residuals throughout the envelope.
[LG-209] Contaminated Multi-task Learning with Heterogeneity: Fundamental Limits and Optimal Algorithms
链接: https://arxiv.org/abs/2607.02681
作者: Ye Tian,Mengchu Li,Marco Avella Medina
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME)
*备注: 91 pages, 1 figure, 10 tables
Abstract:Integrating information across related tasks can improve estimation and prediction in transfer, multi-task, and federated learning, but contamination and heterogeneity make robust borrowing challenging. We study a contaminated multi-task empirical risk minimization (ERM) framework in which an \epsilon fraction of K tasks, each with sample size n , may be arbitrarily contaminated while the remaining tasks are heterogeneous. Our goal is to estimate both the global minimizer of the average risk and the clean task-specific minimizers, thereby combining robustness and personalization. In the Gaussian mean model, we show that several common paradigms, including adaptive and robust regularization around a shared center, global matrix regularization, decomposition-based regularization, and score-based outlier-task detection, all suffer from a worst-case contamination error of order \epsilon\sqrtd/n , which is suboptimal compared to the lower bound \epsilon/\sqrtn . This identifies a dimension-dependent barrier for these approaches. We then establish minimax lower bounds for a general heterogeneous ERM setting and propose a computationally efficient filtering-based robust multi-task gradient descent method. Under local strong convexity, smoothness, and sub-Gaussian gradient assumptions, the proposed method attains high-probability upper bounds matching the minimax rates up to logarithmic factors over a broad regime. In particular, it removes the extra \sqrtd contamination dependence of many regularization-based methods and score-based outlier detection, while achieving personalization to local tasks under strong heterogeneity. Simulations and a real-data analysis demonstrate strong robustness and personalization relative to a broad range of benchmark methods.
[LG-210] Benign Overfitting Does Not Occur in Diffusion Models
链接: https://arxiv.org/abs/2607.02671
作者: Tyler Farghly,Benjamin Dupuis,Alain Durmus,Umut Simsekli
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Benign overfitting and double descent have come to shape our understanding of generalization in deep learning, establishing that overfitting is not only compatible with good generalization but can actively benefit it. Diffusion models share much of the machinery of standard deep learning, so it is natural to assume that they also exhibit these properties. In this work, we show that this assumption is largely incorrect. We first establish fundamental impossibility results showing that, unless the sample size grows exponentially with the data dimension, overfitting and good generalization cannot occur simultaneously. Consequently, the population loss follows a classical U-shaped curve in model complexity rather than exhibiting double descent. Analyzing a simplified setting, we identify a key difference between regression and score matching: regression benefits from an alignment between the target and the empirical covariance; score matching admits no such alignment, leaving overfitting irreparably harmful. We further identify implicit regularization stemming from time-smoothness of the score and early stopping during training as mechanisms that prevent such overfitting and verify our findings with high-dimensional image generation experiments. Our results reveal that generalization in diffusion models is governed by mechanisms distinct from those of traditional regression, motivating the development of new theory.
[LG-211] CORA: Per-Slice Coherent Orthogonal Rotation for SVD-based Low-Rank Adaptation
链接: https://arxiv.org/abs/2607.02576
作者: Pengcheng Wang,Ziran Liu,Wei Wang,Wei Jiang
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 13 pages, 1 figure
Abstract:Parameter-Efficient Fine-Tuning (PEFT) commonly adapts pretrained weights through low-rank updates, and recent methods further exploit the singular value decomposition (SVD) of the base weight for initialization or subspace selection. However, these methods do not explicitly preserve the coupled geometry between the pretrained left and right singular bases. Motivated by recent minimum-perturbation theory, which shows that stable finetuning follows a coherent SVD rotation in which a single orthogonal Q acts on both the left singular basis U_0 and the right singular basis V_0 , we prove a per-slice analogue: each row slice of W_0 can be adapted by a shared orthogonal rotation Q_i on its left basis U_i and right basis V_i together with a diagonal spectrum shift. We implement this form as CORA (Coherent Orthogonal Rotation Adaptation), which applies per-slice orthogonal rotations and a per-layer diagonal scale to the rank- r SVD truncation of W_0 . CORA uses \tfrac12m(r-1) trainable parameters per linear layer, about 4\times fewer than LoRA at the same rank. CORA outperforms LoRA, DoRA, PiSSA, and MiLoRA on commonsense reasoning and code generation while using about 8\times fewer parameters.
[LG-212] ransformer-based Multisensor Data Fusion of Ultrasonic Guided Wave and FBG-based Strain Measurements for Multitask Aerospace Structural Health Monitoring
链接: https://arxiv.org/abs/2607.02545
作者: Xin Yang,Morteza Moradi,Tongtong Yan,Jinbo Du,Yunlai Liao,Dimitrios Zarouchas,Dimitrios Chronopoulos
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注:
Abstract:Structural health monitoring (SHM) has emerged as an essential tool for ensuring the integrity and reliability of critical engineering structures, particularly in aerospace applications. Since each sensing technology has its limitations, the fusion of different modalities enables capturing a more complete picture of inhomogeneous materials, like composites. However, effective multisensor data fusion in SHM is often hindered by heterogeneous sensing modalities that operate at disparate sampling frequencies and acquisition intervals. To address these challenges, this paper proposes a Transformer-based data fusion framework that integrates multisensor data streams from piezoelectric transducer (PZT) capturing ultrasonic guided wave signals and fiber Bragg grating (FBG) sensors for strain measurements. By incorporating an attention-mechanism visualization, the proposed framework enables transparent, multitask learning for both health indicator (HI) prediction and damage localization. The framework was experimentally validated using aircraft composite structures subjected to compression-compression fatigue cyclic loading. For HI prediction, the framework consistently achieved a mean absolute error (MAE) and root mean squared error (RMSE) below 0.1, representing a nearly 60% performance improvement over single-sensor approaches (PZT or FBG alone) and baseline deep learning models. For damage localization, the model demonstrated the highest accuracy, maintaining an MAE and RMSE below 0.0465 and 0.1571, respectively. These results demonstrate that the proposed Transformer-based data fusion framework significantly outperforms single-source models and state-of-the-art deep learning models in both HI prediction and damage localization accuracy.
[LG-213] Physics-Informed Domain-Invariant Feature Learning with Autoencoder-Driven Gaussian Clustering for Robust Non-line-of-Sight Scenarios
链接: https://arxiv.org/abs/2607.02537
作者: Nisha L. Raichur,Lucas Heublein,Dominik Seuß,Frank Deinzer,Felix Ott
类目: ignal Processing (eess.SP); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 7 pages, 8 figures
Abstract:Jamming and spoofing pose significant threats to wireless and satellite navigation by disrupting radio-frequency (RF) signals and compromising availability and integrity. Robust RF interference direction finding through angle-of-arrival (AoA) estimation is therefore essential for detecting and localizing anomalous signals. Although data-driven methods perform well under line-of-sight (LoS) conditions, their performance degrades in practical environments due to non-line-of-sight (NLoS) multipath propagation. In this work, we propose a hybrid learning framework that incorporates physics-informed constraints into deep neural networks to improve the robustness of AoA estimation. A neural network is trained to estimate the azimuth and elevation of incoming signals received by a four-element antenna array, while a physics-informed loss enforces consistency between the predicted angles and inter-antenna phase differences under a plane-wave model. We further introduce a latent-space classifier to distinguish LoS from NLoS samples. Since inter-antenna phase differences under LoS propagation exhibit domain-invariant structure across environments, the physics-based loss is applied only to LoS samples, promoting physically consistent and domain-invariant representations without over-constraining the model in NLoS scenarios. In addition, domain-incremental learning (DIL) across NLoS environments with varying scatterer distributions improves cross-domain generalization. Evaluations on real-world datasets show that the proposed method reduces AoA estimation error by up to 6° in low-exemplar settings compared with DIL baselines.
[LG-214] Inverse Design of Metainterfaces for Static Friction Control: Beyond the Hertzian Limit
链接: https://arxiv.org/abs/2605.11012
作者: Jacopo Bilotto,Arnav Singhal,Joaquin Garcia-Suarez,Gaëtan Cortes,Lucas Fourel,Jean-François Molinari
类目: oft Condensed Matter (cond-mat.soft); Machine Learning (cs.LG)
*备注: 19 pages, 8 figures
Abstract:Programming the static friction of mechanical interfaces is critical for soft robotics, haptics, and precision gripping. Static friction is governed by the real contact area, and standard rough surfaces exhibit a linear area-load scaling inherent to classical Archard and Greenwood-Williamson models, severely restricting their functional range. Here, we propose a framework for the inverse design of tribological metainterfaces engineered for programmable contact behaviors. By utilizing general axisymmetric asperities, we unlock nonlinear macroscopic responses unattainable by standard Hertzian contacts. To solve the inverse problem, we embed a fully differentiable contact mechanics engine within a neural network and a quadratic optimizer. We leverage regularized physical gradients to automatically discover non-standard topographies that reproduce complex target friction laws, with only a few asperities in unit cells. The predicted designs are strictly validated against high-fidelity Boundary Element Method (BEM) simulations. This framework bridges data-driven optimization and rigorous physics, offering a scale-invariant pathway for discovering functional tribological surfaces.
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