本篇博文主要内容为 2026-07-22 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 (2026-07-22)
今日共更新584篇论文,其中:
- 自然语言处理共78篇(Computation and Language (cs.CL))
- 人工智能共209篇(Artificial Intelligence (cs.AI))
- 计算机视觉共93篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共177篇(Machine Learning (cs.LG))
- 多智能体系统共12篇(Multiagent Systems (cs.MA))
- 信息检索共15篇(Information Retrieval (cs.IR))
- 人机交互共23篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] heyll Verify. They Just Wont Act. How Authority Framing and Laundered Code Turn a Trusted Agent ic CI/CD Pipeline Into an Attack Surface
【速读】：该论文旨在解决在多代理持续集成/持续部署（CI/CD）流水线中，由生成式人工智能（Generative AI）驱动的代码审查流程所面临的隐蔽性恶意注入攻击问题，特别是针对通过“可观测性”伪装的敏感信息泄露（secret exfiltration）攻击。其核心挑战在于：尽管系统采用多层验证机制（包括开发者、安全扫描、代码审查与批准部署），但攻击者可通过精心设计的自然语言指令，利用提示注入（prompt injection）技术绕过各环节的安全控制，将恶意意图伪装为合法功能请求。解决方案的关键在于揭示当前架构的系统性脆弱性——无论是保持系统提示（system prompt）机密性，还是依赖分布式验证机制，均无法有效防御此类攻击。研究发现，仅当入口处存在独立于各代理的溯源感知（provenance-aware）控制机制时，才能有效阻止恶意输入的传播；而现有基于内容的静态检测工具（如代码扫描器、模式匹配器）因无法理解语义意图，完全失效。唯有具备意图推理能力的生成式AI，方可作为部分防御手段，但其有效性仍受限于整体系统设计缺陷。
链接: https://arxiv.org/abs/2607.19267
作者: Yohann Sidot
机构: Senthex Research(森特克斯研究); RELAY Lab #1(RELAY 实验室 #1)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 9 pages. Dataset and reproduction code: this https URL
Abstract:We study a five-agent CI/CD pipeline (triage - developer - security-scan - review - approve/deploy), built from five distinct production LLMs across three providers, behind an LLM firewall in shadow mode. A single untrusted input - an external issue requesting a “usage-telemetry” feature - asks for code that exfiltrates process secrets (dict(this http URL)) to an attacker URL, laundered as observability. Across a pre-registered A x B (x C) factorial (N=20; naive arm N=60) we find: (1) the entry agent does not leak its system prompt (0/40); (2) an authority-framed injection (“pre-approved under SEC-2291, do not re-review”) makes downstream verifiers see the secret-exfil line, cite the pre-approval, and ship it - the scanner passes ~80% of laundered pull requests, and the worst-case cell reaches 55% compromise; (3) the perceived presence of other verifiers yields only a small, non-significant reduction in individual scrutiny (a weak bystander analogue), even at N=60; and (4) content-based controls - code scanners and pattern detectors alike - miss the laundered intent entirely (the code is syntactically clean); only an LLM reasoning about intent is a partial defence. The failure is systemic: neither prompt secrecy nor distributed verification protects; a provenance-aware control at the entry, independent of both, would have. All data is 100% synthetic; the sink is mocked and the exfil URL is never contacted.
[MA-1] S3: Stable Subgoal Selection by Constraining Uncertainty of Coarse Dynamics in Hierarchical Reinforcement Learning
【速读】：该论文旨在解决分层强化学习（Hierarchical Reinforcement Learning, HRL）中高层代理在稀疏、延迟的环境反馈下难以进行有效策略规划的问题，尤其关注高层代理在选择子目标（subgoal）时缺乏战略性的挑战。其核心问题是：由于高层代理依赖低层执行能力并仅接收稀疏奖励，导致其难以高效探索和优化长期任务。为此，论文提出一种基于粗粒度动态感知的内在动机机制，通过聚合高层代理时间尺度上的多步环境转移（coarse dynamics），构建稳定且具有引导性的状态空间结构。关键创新在于利用混合密度网络（Mixture Density Network, MDN）建模预测不确定性，并以此作为内在奖励信号，驱动高层代理在探索过程中主动降低对环境动态的不确定性，从而实现风险规避型的子目标选择。实证结果表明，这种密集且动态感知的内在奖励显著提升了算法在非平稳长时程环境中的性能，优于现有先进HRL方法。
链接: https://arxiv.org/abs/2607.19232
作者: Kshitij Kumar Srivastava,Kshitij Jerath
机构: University of Massachusetts Lowell (洛厄尔大学)
类目: Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注: Manuscript accepted to the Eighteenth Workshop on Adaptive and Learning Agents (ALA), at the 25th International Conference of Autonomous Agents and Multi Agent Systems 2026
Abstract:Hierarchical Reinforcement Learning (HRL) intends to separate strategic planning from primitive execution. It has been widely successful in solving long-horizon and complex tasks, where flat-RL algorithms have difficulty in learning. However, while the low-level agent in HRL benefits from dense feedback and abundant trial opportunities, the high-level agent receives sparse, delayed feedback from the environment and its performance depends on the low-level execution capability. In this paper, we study whether subgoal selection by the high-level agent can be performed more strategically, by providing it with dynamics-aware intrinsic motivation. Since motivation based on primitive transition dynamics would require broad coverage of the state-action space, we propose to use coarse dynamics, i.e., environment transitions aggregated over multiple steps at the temporal scale at which the high-level agent operates. This approach stabilizes the high-level policy by learning to minimize the predictive uncertainty associated with the coarse dynamics, and provides a guided structure for navigation. We model the predictive uncertainty by evaluating different dispersion metrics as approximated by a Mixture Density Network (MDN). Empirically, we observe that a dense, dynamics-aware intrinsic reward leads to risk-averse subgoal selection, enabling it to outperform state-of-the-art HRL methods in non-stationary long-horizon environments.
[MA-2] How network perturbations distort agreement trajectories in LTI multi-agent systems
【速读】：该论文旨在解决多智能体系统在分布式协同控制中，传统协议假设目标同步轨迹保持不变这一理想化前提所带来的潜在稳定性缺陷问题。尽管现有方法对各类不确定性具有鲁棒性，但研究发现网络扰动可显著改变系统的渐近同步轨迹，从而导致预期的非平凡轨迹无法实现。其解决方案的关键在于提出一种基于拉普拉斯域的新判据，用于精确刻画受动态耦合不确定性影响下线性时不变（LTI）智能体系统的闭环极点，进而分析扰动后的一致性流形。研究引入“结构保持型动力学”（structure-preserving dynamics）概念，即保持通信图拉普拉斯矩阵零空间不变的扰动，与仅影响邻接矩阵的“传输型动力学”相区分，揭示了标准协同输出调节方案中的关键脆弱性：静态一致性对异构传输延迟具有唯一鲁棒性，而周期性轨迹同步则极易被任意微小的延迟破坏；此外，在d-正则拓扑下，均匀传输扰动可轻易引导系统同步至一个全新的、意料之外的频率。这些发现暴露了经典鲁棒同步机制中未被识别的结构性漏洞，表明必须对网络化参考生成器进行根本性的结构重构以应对传输动态的影响。
链接: https://arxiv.org/abs/2607.18913
作者: Gal Barkai,Irinel-Constantin Morărescu
机构: Université de Lorraine (洛林大学); CNRS (法国国家科学研究中心); CRAN (控制与自动化研究中心)
类目: ystems and Control (eess.SY); Multiagent Systems (cs.MA); Optimization and Control (math.OC)
备注: 12 pages, 9 figures, submitted to IEEE for possible publication
Abstract:Distributed coordination of multi-agent systems frequently relies on cooperative protocols designed to achieve agreement on a prescribed, non-trivial trajectory. While the robustness of such protocols to various uncertainties is well documented, existing literature universally assumes that the target agreement trajectory itself remains invariant. This assumption may hold in ideal cases, but we prove that network perturbations can vastly modify the asymptotic agreement trajectory. We first investigate the exact trajectories of Linear Time-Invariant (LTI) agents subjected to dynamic coupling uncertainties by establishing a new Laplace-domain criterion that characterizes the specific closed-loop poles governing the perturbed agreement manifold. To formalize our analysis, we introduce the notion of structure-preserving dynamics, perturbations that maintain the null space of the communication graph’s Laplacian, and contrast them with transmission only dynamics, affecting only the adjacency matrix. We prove a critical fragility within standard cooperative output regulation schemes: while static consensus is uniquely robust to heterogeneous transmission delays, synchronization to periodic trajectories is destroyed by arbitrarily small transmission delays. Furthermore, we demonstrate that for d-regular topologies, uniform transmission perturbations can easily shift the system to synchronize with an unexpected, entirely new frequency. These findings expose a previously unidentified vulnerability in classical robust synchronization, demonstrating that transmission dynamics necessitate fundamental structural modifications to networked reference generators.
[MA-3] AI Tour Meeting: Group Travel Planning by LLM Agents
【速读】：该论文旨在解决多用户协同旅游规划中个性化需求与复杂决策协调困难的问题，尤其关注如何在群体旅行规划过程中有效整合不同个体的偏好与约束条件。其核心解决方案在于提出一种基于多个大语言模型（Large Language Model, LLM）代理的协作框架——AI Tour Meeting，通过赋予各代理独特角色身份（persona），使其能够以自然语言进行交互讨论，共同协商生成满足多方需求的行程安排。该框架的关键创新在于实现了对代理角色配置、对话流程设计、运行状态监控及LLM部署的灵活集成，从而支持对多代理协作行为的系统性模拟与分析，为理解群体决策过程提供了可量化的实验工具。
链接: https://arxiv.org/abs/2607.18806
作者: Daisuke Kikuta
机构: NTT, Inc.
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注: The code is available at this https URL
Abstract:This paper proposes AI Tour Meeting, a group travel planning framework powered by multiple Large Language Model (LLM)-based agents. The agents are instantiated with distinct personas and collaboratively seek an itinerary that satisfies their constraints and preferences through natural language discussion. The framework enables easy and flexible orchestration of such discussions by providing interfaces for configuring agent personas, discussion workflows, monitoring, and LLM deployment. Its primary use case is a simulation tool for analyzing the behavior of multiple LLM agents during tour planning discussions. This paper demonstrates the utility of the framework by presenting system validation and several analytical results obtained by the framework.
[MA-4] Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents
【速读】：该论文旨在解决多智能体系统在深度强化学习后，如何实现人类管理者以简单指令对智能体进行有效控制，并使未受指令的智能体能够基于其他智能体的行为隐式补足协作任务的问题。传统方法通常假设所有智能体均接收指令，这不仅效率低下，且难以适应复杂社会场景下的动态需求。其解决方案的关键在于：通过扩展多智能体深度强化学习中的可控性机制，使部分关键智能体接收人类管理者的显式指令，而其余未受指令的智能体则能自适应地感知并补全协作中被忽略的任务与区域，从而实现协作结构的动态重构。实验结果表明，该方法下智能体可灵活切换至更优的合作模式，在性能上显著优于传统方法。
链接: https://arxiv.org/abs/2607.18719
作者: Yamato Takahagi,Gentoku Nakasone,Yoshinari Motokawa,Toshiharu Sugawara
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注: 8 pages, 14 figures, 24th IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2025)
Abstract:This study proposes a learning method for multi-agent systems that allows agents to be controlled through human manager instructions after learning and enables uninstructed agents to implicitly complement the overall work based on the actions of other agents. Multi-agent applications using deep learning have shown potential; thus, to achieve extensive social applications, humans should be able to control learned agents using simple methods to respond to environmental and social changes. Even without such changes, learned coordination often does not match the expectations of human managers, making it preferable to control coordination structures to match human intentions. Some studies have aimed to control agent behavior using simple instructions. However, they assumed that instructions are provided to all agents, which is time-consuming and not evident when designing a better cooperation regime. Ideally, specific agents should receive key action instructions, while others should automatically complete the remaining tasks. The proposed method, which extends previous work on controllability in multi-agent deep reinforcement learning, enables uninstructed agents to adaptively complement overlooked tasks and areas. The experimental results show that agents using the proposed method can shift to another cooperative structure and achieve better performance than those using conventional methods.
[MA-5] Broken Gates: Re-evaluating Web Bot Defenses in the Age of LLM Agents
【速读】：该论文旨在解决生成式 AI（Generative AI）驱动的浏览器代理对现有网页安全防护机制有效性构成的挑战。随着基于大语言模型（LLM）的浏览器代理能够自主导航网站、理解页面内容并根据自然语言指令与网页界面交互，传统依赖预设脚本的自动化框架已无法应对这种新型威胁。论文系统评估了两类防御机制——交互式挑战型防御（如hCaptcha、reCaptcha v2/v3和Cloudflare Turnstile）与非交互式信任型防御——在面对两类攻击者（商业验证码破解服务与LLM-based浏览器代理）时的鲁棒性。研究发现，交互式挑战类防御普遍易被商业解码服务以极低成本近乎完全绕过；而即使在非交互式防御中，尽管reCaptcha v3表现出更强的抵抗能力，但其安全性并非源于内在的安全属性，而是依赖于执行环境的真实性。通过细粒度的行为轨迹分析，研究揭示：两个行为特征几乎完全相同的代理，在相同测试环境下却出现截然不同的结果，表明最终成败的关键在于执行环境的可信性而非代理本身的策略或行为模式。因此，解决方案的核心在于认识到非交互式防御的安全边界实际上位于执行环境层，这对未来机器人管理系统的架构设计与评估方法提出了根本性启示。
链接: https://arxiv.org/abs/2607.18659
作者: Behzad Ousat,Nikita Turkmen,Lalchandra Rampersaud,Dillan Bailey,Amin Kharraz
机构: Florida International University (佛罗里达国际大学)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:LLM-based browser agents are rapidly changing the threat landscape for web security. Unlike traditional automation frameworks that execute predefined scripts, these agents can autonomously navigate websites, reason about page content, and interact with web interfaces using natural-language instructions. This evolution raises fundamental questions about the effectiveness of bot management systems, widely deployed to defend against automated web abuse. In this paper, we present a systematic measurement study evaluating the resilience of both interactive challenge-based defenses and non-interactive trust-based defenses against two attacker classes: commercial Captcha-solving services and LLM-based browser agents. Our evaluation spans seven solver services and six agents, including cloud-hosted, self-hosted, AI-assisted, and browser-extension configurations, tested against hCaptcha, reCaptcha v2, reCaptcha v3, and Cloudflare Turnstile. Our results show that challenge-based defenses are broadly ineffective against commercial solvers, which achieve near-perfect bypass at negligible cost. The challenges can similarly be defeated by LLM-based agents when a dedicated solver module is available. Non-interactive defenses such as reCaptcha v3 exhibit stronger resistance, but our analysis reveals that this resilience does not reflect a fundamental security property. Through fine-grained interaction trace analysis, we find that two agents with nearly indistinguishable behavioral footprints yield divergent outcomes, one bypassing the defense and one failing, isolating execution-environment authenticity, rather than agent behavior, as the determining factor. These findings suggest that the security boundary of non-interactive defenses lies at the environment layer, with significant implications for how bot management systems are designed and evaluated.
[MA-6] A Self-Evolving Default Action for Cooperative Tasks with Continuous Action Space
【速读】：该论文旨在解决多智能体强化学习（MARL）中连续动作空间下反事实信用分配（counterfactual credit assignment）的难题。现有方法通常通过蒙特卡洛采样近似反事实基线，但此类方法易引入策略梯度偏差，且难以保证收敛至局部最优解，原因在于采样动作可能未经过充分训练。本文提出SAFE框架，其核心创新在于采用由各智能体经验缓冲区自演化生成的默认动作作为反事实基线的条件，从而在无需额外仿真、奖励模型或环境特定先验知识的前提下，自然拓展至连续动作空间。该设计能够准确量化每个智能体的贡献，并确保确定性策略梯度无偏差，进而保障算法收敛至局部最优。大量实验表明，SAFE在协作式车辆任务中持续优于当前先进模型。
链接: https://arxiv.org/abs/2607.18597
作者: Shuangyao Huang
机构: XJTLU Research Development Fund(西交利物浦大学研究发展基金)
类目: Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注:
Abstract:Counterfactual credit assignment has proven effective in multi-agent reinforcement learning (MARL) for discrete action spaces, yet its extension to continuous-action cooperative tasks remains challenging. Existing methods that approximate the counterfactual baseline via Monte Carlo sampling often introduce bias into policy gradients and fail to guarantee convergence to local optima, as the sampled actions may not have been sufficiently trained. To address these limitations, we propose SAFE, a novel MARL framework that employs a counterfactual baseline conditioned on a self-evolving default action sampled from each agent’s experience buffer. This design naturally extends to continuous action spaces without relying on additional simulations, reward models, or environment-specific prior knowledge. The baseline accurately quantifies each agent’s contribution, and introduces no bias into the deterministic policy gradient, ensuring convergence to local optima. Extensive experiments on cooperative vehicular tasks demonstrate that SAFE consistently outperforms state-of-the-art models.
[MA-7] Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces
【速读】：该论文旨在解决网络化马尔可夫决策过程（Networked Markov Decision Processes, MMDPs）中连续状态与动作空间下的协作强化学习问题，核心挑战在于如何在大规模分布式系统中实现高效、稳定的策略优化，同时克服传统方法在局部化近似时因截断导致的延续核不匹配（continuation-kernel mismatch）问题。其解决方案的关键在于提出一种连续分布式耦合策略梯度算法（Continuous Distributed Coupled Policy Gradient, CDCPG）：首先，通过在邻域上构建基于条件期望的截断动作价值函数，建立了合理的局部贝尔曼理论，有效消除截断误差；其次，揭示了归一化随机特征下时序差分（Temporal-Difference, TD）稳定性存在的维度障碍，并证明了无需条件的激励性界，将稳定性简化为可在线监测的对称持续激励条件（symmetric persistence-of-excitation condition）；再次，在代理间交互呈指数空间衰减的假设下，算法以 O(ϵ−2) 的共享预言机样本复杂度，使平均每代理的平稳性度量逼近一个显式刻画的近似下界，且超出部分与光滑非凸优化的一阶率一致，而每代理的计算与通信开销仅依赖于局部邻域规模而非全网规模；最后，引入自适应局部性规则，动态平衡截断误差与图衰减残差，以实现目标精度最优。实验在联网线性-二次基准任务上验证了该方法对局部性和特征维度的预测能力。
链接: https://arxiv.org/abs/2607.18554
作者: Dongming Wang,Pengcheng Dai,Wenwu Yu,Wei Ren
机构: University of California, Riverside (加州大学河滨分校); Singapore University of Technology and Design (新加坡科技设计大学); Southeast University (东南大学); Purple Mountain Laboratories (紫金山实验室)
类目: Multiagent Systems (cs.MA); Machine Learning (cs.LG)
备注:
Abstract:We develop the Continuous Distributed Coupled Policy Gradient (CDCPG) algorithm for cooperative reinforcement learning in networked Markov decision processes with continuous state and action spaces. Each agent maintains a local actor over a bounded graph neighborhood, and a localized least-squares temporal-difference critic evaluates a truncated action-value function through a spectral random-feature representation of the local transition kernel. The analysis makes four contributions. First, the truncated action-value function is constructed as a conditional expectation over the neighborhood, yielding a well-posed localized Bellman theory that removes the continuation-kernel mismatch of naive truncation arguments. Second, we expose a dimensional obstruction to temporal-difference stability for normalized random features and prove an unconditional excitation bound that reduces stability to a symmetric persistence-of-excitation condition, monitorable through an online matrix-concentration certificate. Third, under exponential spatial decay of agent interactions, the excitation condition, and smoothness of the objective, CDCPG drives an averaged per-agent stationarity measure to within any excess \epsilon of an explicitly characterized approximation floor using \widetilde\mathcalO(\epsilon^-2) shared-oracle samples, and the excess dependence matches the smooth nonconvex first-order rate; per-agent computation and communication are governed by the neighborhood size rather than the network size. Fourth, an adaptive-locality rule selects the radius that balances truncation and graph-decay residuals against the target accuracy. Experiments on a networked linear-quadratic benchmark corroborate the locality and feature-dimension predictions.
[MA-8] Engineering Trustworthy Agent ic AI for Critical Systems
【速读】：该论文旨在解决生成式AI（Generative AI）在关键工程领域应用中面临的可信赖性（trustworthiness）问题，即如何在实际工程约束下验证、审计并建立对自主行为的可信度，而不仅仅是评估其任务完成能力。其解决方案的关键在于将可信赖性作为首要的工程属性，构建一个涵盖五个跨领域维度的综合信任模型：安全性与约束满足、鲁棒性与可靠性、透明性与可解释性、责任性与可审计性，以及隐私与安全性，并将其映射至从感知到审计的全流程保障工作流。在此基础上，系统梳理了适用于生成式AI系统的架构设计、威胁场景、具体信任机制及量化评估指标，并在电力系统、自动驾驶/机器人/无人机、高性能计算和通信网络四个受约束的工程领域中进行实证分析，识别出共性的设计模式、共享的失效模式及领域特异性差距。最终，研究提出可信赖性是跨领域的统一问题，并为构建类成熟安全关键工程领域分级认证制度的通用化、可复用的保障框架指明了路径。
链接: https://arxiv.org/abs/2607.18548
作者: Omar Al-Refai,Ibrahim Shahbaz,Adam Ali Husseinat,Michael Mandulak,Jaewon Kim,Eman Hammad
机构: Texas AM University(德州农工大学); Texas AM Institute of Data Science - Security, Privacy and Resilience for Trusted AI (SPARTA) Thematic Lab.(德州农工大学数据科学研究所-可信人工智能安全、隐私与弹性主题实验室); Texas AM Global Cyber Research Institute (GCRI)(德州农工大学全球网络安全研究中心)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Systems and Control (eess.SY)
备注: This work has been submitted to the IEEE for possible publication
Abstract:Agentic artificial intelligence systems, capable of autonomous perception, planning, tool use, and multi-step action, are increasingly proposed for critical engineering domains where decisions carry physical, operational, or economic consequences. This survey addresses a gap in current literature by treating trustworthiness, whether agentic behavior can be verified, audited, and trusted under the constraints that engineering practice actually requires, as a first-class engineering property, rather than evaluating agentic AI by task capability alone. The study adopts a trustworthiness model organized around five cross-cutting dimensions: safety and constraint satisfaction; robustness and reliability; transparency and interpretability; accountability and auditability; and privacy and security. This is mapped onto an agentic assurance workflow spanning perception through audit. Building on this foundation, agentic systems architectures, threats, concrete trust mechanisms, and quantitative metrics are surveyed for direct application in agentic systems development and evaluation. These principles are then examined across four constraint-bound engineering domains: power systems, autonomous vehicles/robotics/UAVs, high-performance computing, and communication networks, identifying recurring design patterns, shared failure modes, and domain-specific gaps. Synthesizing across those domains, agentic AI trustworthiness is shown to be a single problem, with a path outlined toward a reusable, cross-domain assurance framework analogous to the graded certification regimes used by mature safety-critical engineering fields.
[MA-9] Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures
【速读】：该论文旨在解决关键基础设施在分布式、相互依赖且面临持续演化扰动背景下，如何实现系统韧性（resilience）的问题。其核心挑战在于，传统集中式控制范式难以满足大规模、隐私敏感、局部自治及容错性等实际需求。为此，论文提出将去中心化多智能体强化学习（decentralized multi-agent reinforcement learning, MARL）视为一种与关键基础设施韧性需求在结构上高度契合的范式，而非简单的集中训练-去中心化执行（centralized training with decentralized execution）替代方案。该解决方案的关键在于：首先，通过结构感知、因果感知和韧性感知的信用分配（credit assignment）机制，确保个体智能体的学习行为与全局系统目标保持一致；其次，构建具备协作与信用分配双重功能的通信机制，以在现实运行约束下实现动态协调与持续学习；最后，建立安全、及时且可恢复的去中心化学习框架，以应对部署环境中的不确定性与故障。因此，论文强调去中心化MARL虽具潜力，但其实用性依赖于上述关键条件的系统性突破。
链接: https://arxiv.org/abs/2607.18359
作者: Minghui Ding,Evangelos Pournaras
机构: University of Leeds (利兹大学)
类目: Multiagent Systems (cs.MA); Machine Learning (cs.LG)
备注: Accepted at the 2026 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS 2026)
Abstract:Critical infrastructures are increasingly distributed, interdependent, and exposed to evolving disruptions, making resilience a central requirement for their operation and control. This paper argues that decentralized multi-agent reinforcement learning (MARL) should be understood not merely as a distributed alternative to centralized training with decentralized execution but as a paradigm structurally aligned with the requirements of resilient critical infrastructures. This perspective is grounded in an analysis of the properties of decentralized MARL and the requirements of critical infrastructures, including scalability to large numbers of agents, support for privacy and local autonomy, robustness to failures, and interaction-driven adaptation among interdependent components. However, structural alignment alone is insufficient for practical deployment. This paper identifies credit assignment and communication as two central conditions for its practical feasibility. Credit assignment determines whether local learning remains aligned with system-level objectives, while communication determines whether coordination can be learned and maintained under realistic operational constraints. Building on these challenges, this paper proposes a research agenda focused on structure-aware, causality-aware, and resilience-aware credit assignment; communication for both coordination and credit assignment; and safe, timely, and recoverable decentralized learning under deployment constraints. Overall, this paper reframes decentralized MARL as a promising but conditional foundation for resilient critical infrastructures.
[MA-10] A Decision-Centered Reference Architecture for Trustworthy Agent ic Commerce
【速读】：该论文旨在解决生成式电商（Agentic Commerce）中因缺乏统一、可信的商业资格与授权状态表示而导致的系统性风险问题，特别是在多方协作环境下如何确保交易决策的完整性、可验证性与安全性。其核心挑战在于：尽管已有协议标准化外部交互，但商户仍需一个权威一致的内部状态表示，以维护商业资质、代理权限、结账有效性、支付执行、生成主张及证据链的可信性。为此，论文提出一种协议无关的架构设计，关键创新在于构建以“规范信封”（canonical envelope）为核心的可信执行环境，通过受保护的依赖项与结果哈希、Ed25519/HMAC身份认证、实时请求重绑定、七轴生成主张门控机制、执行时依赖项再验证以及十一项语义不变量，实现对状态变更的敏感检测、过期决策的主动阻断、主张拒绝的传播机制与受保护状态的身份验证。实证评估表明，该架构在六十六项测试中均通过，能够有效防止未经授权的操作，保持表面绑定状态一致性，并在各消融实验中表现出预期的安全退化行为，验证了其在合成场景下的核心安全属性；然而，该研究尚未涵盖规则完备性、生产级安全性、性能表现、法律合规性、实时互操作性、群体错误率或独立复现等维度。
链接: https://arxiv.org/abs/2607.18347
作者: Dimitrios S. Sfiris
机构: AspectSoft(AspectSoft); Greece(希腊)
类目: oftware Engineering (cs.SE); Cryptography and Security (cs.CR); Multiagent Systems (cs.MA)
备注: 23 pages, 3 figures. Includes ancillary reproducibility materials
Abstract:Agentic commerce extends agentic shopping into software agents that interpret policy, prepare checkout, generate transaction-facing language, and act under delegated payment authority. Protocols standardize external exchanges, but merchants still need one authoritative representation of commercial eligibility, actor authority, checkout validity, payment dispatch, generated claims, and evidence. This design-science study presents a protocol-agnostic architecture built around a canonical envelope, protected dependency and result hashes, Ed25519 or HMAC authentication, live-request rebinding, a seven-axis generated-claim gate, execution-time dependency revalidation, and eleven semantic invariants. Evaluation used an open-source JavaScript implementation, eight deterministic ecommerce scenarios, and five controlled ablations. Seven initially valid actions were permitted. After protected state changed, none could proceed without a fresh decision; a hostile-accessor case also remained blocked. Action status was consistent across configured surface-bound envelopes, and each scenario contained the three protected hashes and its targeted dependency reference. Each ablation produced the predicted unsafe regression when one safeguard was bypassed, while the protected path contained the same failure. The hostile accessor was read once, and the suite passed 66/66 tests, schema validation, and committed examples. Results support protected-dependency change detection, bounded outcome derivation, stale-decision prevention, surface-bound status consistency, refusal propagation, and verified-state identity under synthetic fixtures, but do not establish rule completeness, production security, performance, legal compliance, live interoperability, population error rates, or independent replication.
[MA-11] AI Tool Discovery at Scale: All You Need is DNS
【速读】：该论文旨在解决自主AI代理在面对数百万工具时所面临的可扩展性发现难题，现有方案因面临O(N)复杂度和中心化治理模式而难以适应大规模场景。其核心解决方案是提出ToolDNS，一种基于域名系统（DNS）的创新框架，将语义工具发现机制重构于互联网最稳健的基础设施之上。通过在分层命名空间中嵌入功能意图与组织信任信息，ToolDNS将原本昂贵的语义搜索转化为一系列轻量级、复杂度为O(log N)的域名解析操作。关键创新包括三个符合协议的改进：部分展开的域名（partially unfolded names）、EDNS0意图载荷（EDNS0 intent payloads）以及逻辑子域（logical subdomains），从而实现去中心化治理与语义剪枝。为验证该方法在异构工具生态中的有效性，研究构建并发布了包含33,688个真实世界工具的大规模基准数据集，覆盖MCP、A2A、RESTful及Skill等协议。实验表明，ToolDNS在保持与顶尖检索精度相当的同时，将每查询搜索空间缩小95.26%，且其原生UDP设计显著降低发现延迟，相较传统HTTP注册表提升一个数量级以上。研究证明，实现可扩展的AI互操作性并非依赖更多中间件，而是需要更智能地利用现有底层基础设施。
链接: https://arxiv.org/abs/2607.18242
作者: Enhao Chen,Yulin Shao
机构: The University of Hong Kong(香港大学)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Networking and Internet Architecture (cs.NI)
备注: keywords: AI tool discovery, ToolDNS, Agent, DNS
Abstract:The coming era of autonomous AI agents demands a discovery mechanism capable of navigating millions of tools, yet existing solutions buckle under O(N) complexity and centralized governance. Instead of building another fragile overlay, we propose ToolDNS, a radical framework that retrofits semantic tool discovery onto the Internet’s most resilient substrate: the Domain Name System (DNS). By embedding functional intent and organizational trust into a hierarchical namespace, ToolDNS transforms an expensive semantic search into a series of lightweight, O(log N) name resolutions. We introduce three protocol-compliant enhancements to enable decentralized governance and semantic pruning: partially unfolded names, EDNS0 intent payloads, and logical subdomains. To rigorously evaluate this approach across the fragmented tooling landscape, we construct and release a large-scale heterogeneous benchmark comprising 33,688 real-world tools spanning MCP, A2A, RESTful, and Skill protocols. On this dataset, ToolDNS slashes the per-query search space by 95.26% while matching state-of-the-art retrieval accuracy. Furthermore, its UDP-native design reduces discovery latency by orders of magnitude compared to HTTP-based registries. Our work demonstrates that scalable AI interoperability requires not more middleware, but a smarter utilization of the infrastructure already beneath our feet.
自然语言处理
[NLP-0] Copy Less Ground More: Overcoming Repetitive Copying in Long-Context Reasoning via Evidence-Aware Reinforcement Learning
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在长上下文场景下进行复杂推理时出现的“重复复制”（repetitive copying）问题，即模型倾向于将输入文本中的内容直接复制到推理链中，而非基于关键证据进行有效推理。这一现象在前沿长上下文模型中普遍存在，且随上下文长度增加而加剧。研究进一步揭示，其根本原因在于模型对关键证据的“不足锚定”（insufficient grounding）：模型未能聚焦于任务相关的核心信息，反而对无关干扰信息产生过度依赖，导致错误率上升。针对此问题，论文提出一种名为 GEAR（Grounding Evidence-Aware Reward）的奖励塑形方法，通过引入“关键证据重叠奖励”与“干扰信息重叠惩罚”双重机制，增强模型对相关证据的注意力。为实现该方法在自然语言数据上的应用，研究设计了一套自动化管道，可从任意文档中自动生成带证据标注的训练数据。实验验证表明，相较于标准基于准确率的强化学习方法，GEAR 在多个模型规模和基准测试上均取得显著提升，最高平均提升达+4.6个百分点，尤其在长上下文场景下增益更明显，同时有效减少重复复制行为和推理链长度。研究结论表明，尽管长上下文评估正从简单检索转向复杂推理，但对关键证据的精准锚定仍是不可或缺的核心能力，且仍有巨大优化空间。
链接: https://arxiv.org/abs/2607.19345
作者: Lizhe Fang,Weizhou Shen,Tianyi Tang,Yisen Wang
机构: Peking University; Alibaba Group
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models that generate step-by-step reasoning traces have achieved strong performance on complex tasks, and extending them to long-context settings has emerged as an important frontier. However, we identify a critical failure mode in this regime: \emphrepetitive copying, where models extensively copy text from the input into their reasoning traces rather than productively solving the problem. We show that this behavior is pervasive across frontier long-context LLMs and intensifies with context length. By separating each prompt into task-relevant key evidence and irrelevant distractor context, we further show that the root cause is insufficient grounding: models copy from the prompt indiscriminately, and those that fail to focus on key evidence are far more likely to answer incorrectly. Motivated by this diagnosis, we propose GEAR (Grounding Evidence-Aware Reward), a reward shaping method that augments the accuracy signal with a grounding reward for overlap with key evidence and a distractor penalty for overlap with irrelevant context. To enable GEAR on natural-language data, we develop an automated pipeline that constructs evidence-annotated training data from arbitrary documents. We validate GEAR across multiple model scales and benchmarks, showing consistent improvements of up to +4.6 average points over standard RL with accuracy-based rewards, with larger gains at longer contexts, while also reducing repetitive copying and thinking length. Our findings suggest that, even as long-context evaluation shifts from simple retrieval toward complex reasoning, accurate grounding in relevant evidence remains an indispensable capability with substantial room for improvement.
[NLP-1] Agents in the Wild: Where Research Meets Deployment
【速读】： 该论文旨在解决基于大语言模型（Large Language Model, LLM）的智能体系统（Agentic Systems）在从研究原型向生产级部署过渡过程中所面临的鲁棒性、安全性和可靠性挑战。尽管学术研究聚焦于基准测试与算法创新，但实际部署暴露出诸多现实问题，如错误推理、计划失效、多智能体协作失序及系统不可控等。其解决方案的关键在于构建可复用的设计模式，包括验证流水线（verification pipelines）、备用机制（fallback mechanisms）以及人机协同监督（human-in-the-loop supervision），并通过制药发现与金融系统等实际案例，提炼出保障系统稳定运行的核心实践策略。此外，论文还提供评估检查清单与部署模板，以支持跨行业安全可靠地落地应用。
链接: https://arxiv.org/abs/2607.19336
作者: Grace Hui Yang,Pranav N. Venkit,Hooman Sedghamiz,Enrico Santus,Victor Dibia,Ioana Baldini
机构: Georgetown University (乔治城大学); Salesforce (销售易); Bayer (拜耳); Bloomberg (彭博); Microsoft Research (微软研究院)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Agentic systems large language model (LLM) based architectures capable of reasoning, planning, acting, and coordinating with tools and other agents are rapidly transitioning from research prototypes to production scale deployments across domains such as software engineering, scientific discovery, and finance. While academic work has emphasized benchmarks and algorithmic innovation, deployment raises new challenges around robustness, safety, and reliability. This tutorial brings together researchers and practitioners to explore advances in reasoning and planning, multi agent coordination, and evaluation, highlighting open challenges arising from deployment experience. Through applied case studies in pharmaceutical discovery and financial systems, we analyze common design patterns that make agentic systems successful, and discuss practical mitigation strategies for failure modes, such as verification pipelines, fallback mechanisms, and human in the loop supervision. Attendees will gain a comprehensive view of the field along with concrete design patterns, evaluation checklists, and templates for safe and reliable deployment across industries.
[NLP-2] Selective State-Space Adaptation and Retrieval for Language Model Reasoning
【速读】： 该论文旨在解决传统低秩适配（Low-rank Adaptation, LoRA）方法在任务适应过程中缺乏对输入序列中词元级（token-level）和实例级（instance-level）状态变化的显式建模问题。现有方法采用静态学习更新，无法随输入动态调整，限制了模型在复杂推理任务中的表现。其解决方案的关键在于提出一个双粒度选择性状态空间循环适配器家族：在词元级，引入MaLoRA（Mamba-modulated Low-Rank Adaptation），通过将适配器缩放因子设计为依赖输入且具有跨词元递归状态的动态函数，实现对序列内部状态演变的建模；在上下文级，提出MaRA（Mamba Retrieval Adapter），通过跟踪跨片段状态并选择与查询最相关的片段，提升上下文利用效率。实验表明，该方法在三个冻结主干模型（Qwen-2.5-7B、Llama-3.1-8B、Gemma-2-9B）与两个推理基准（MuSiQue、2WikiMultihopQA）上全面超越基线LoRA，平均提升6.8 F1（相对提升10.5%），最高达9.3 F1（相对提升18.2%），且词元级改进在长文本压力下的RULER QA-2任务中依然有效。
链接: https://arxiv.org/abs/2607.19326
作者: Atahan Dokme,Larry Heck
机构: Georgia Institute of Technology (佐治亚理工学院)
类目: Computation and Language (cs.CL)
备注:
Abstract:Low-rank adaptation introduces a static learned update applied identically to every input. The update provides task-level adaptation but does not explicitly represent token-level or instance-level state variation. A family of adapters is proposed that introduces selective state-space recurrence at two complementary granularities. At the token level, \textbfMaLoRA (Mamba-modulated low-rank adaptation) makes the adapter’s scaling factor a dynamic input-dependent function with recurrent state across tokens, in contrast to the stateless modulators of prior work. At the context level, \textbfMaRA (Mamba Retrieval Adapter) tracks cross-segment state and selects the segments most relevant to the query, before the modulated language model generates its answer. Across three frozen backbones (Qwen-2.5-7B, Llama-3.1-8B, Gemma-2-9B) and two reasoning benchmarks (MuSiQue, 2WikiMultihopQA), the family improves reasoning accuracy on every cell of the 3\times2 grid, by +6.8 F1 ( +10.5% relative) on average and up to +9.3 F1 ( +18.2% relative) on the hardest cell over the LoRA baseline, and the token-level gains carry to RULER QA-2 under length stress.
[NLP-3] wo-Level Meta-Rubrics for Evaluating Open-Ended Generation: GAMUT a Benchmark for Factual Completeness
【速读】： 该论文旨在解决长文本生成中事实完整性（factual completeness）评估缺失的问题，即现有方法主要关注生成内容的精确性（precision），而忽视了回答是否包含了应有全部信息这一关键维度。其核心挑战在于：事实完整性难以量化，因完整答案所需的事实往往构成开放性集合、有序流程或具有内在关联的复杂结构，无法通过独立的布尔判断列表有效捕捉。为此，作者提出一种两级元评分框架（two-level meta-rubric framework），并将其具体实现为Gamut（Grounded Assessment of Multimodal Factuality）基准，用于评估长文本生成中的事实完整性。该框架的关键创新在于：首先以结构化元评分体系表征内容的组织逻辑与重要性层级，再自动转换为可由大语言模型（LLM）可靠评分的扁平化二值可机器判读清单。研究基于真实可穿戴设备图像构建了涵盖10个不同领域的1,813个问题，并由专家人工验证每题的证据支撑评分标准；同时提供多模态与纯文本两种版本，具备模态无关性。在对14个前沿及开源模型的评估中，该基准展现出高难度（最优得分58.7%，来自Gemini 3.1 Pro）、强区分度和对评判者选择的鲁棒性，验证了其作为事实完整性评估工具的有效性与实用性。
链接: https://arxiv.org/abs/2607.19322
作者: Xilun Chen,Zhaleh Feizollahi,Ross Goodwin,Seungwhan Moon,Scott Yih,Pinar Donmez,Babak Damavandi,Luna Dong
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Evaluating the factuality of long-form generations has focused predominantly on precision, measuring whether the claims a model makes are correct. The dominant decompose-search-verify pipeline catches incorrect claims well but says little about whether a response contains all the information it should. Measuring factual completeness, the missing half of factuality, is harder: it requires enumerating the full set of facts a complete answer should contain, and these facts rarely form a flat list. They often involve open-ended sets where coverage is what matters, ordered processes, and relationships among facts that a list of independent boolean checks fails to capture. We introduce a two-level meta-rubric framework for evaluating open-ended generation, and instantiate it as Gamut (Grounded Assessment of Multimodal Factuality), a benchmark for factual completeness in long-form generation. The framework rests on a two-level rubric representation: a structured meta-rubric captures the organization and importance of the required content, which is then mechanically compiled into a flat checklist of binary, machine-gradable rubrics that an LLM judge scores reliably. We construct 1,813 questions grounded in real wearable imagery across 10 diverse domains, each paired with an evidence-backed rubric verified by expert human annotators. Because the framework is modality-agnostic, we also release a text-only variant. Evaluating 14 frontier and open-weight models, we find the benchmark genuinely challenging (best score 58.7% from Gemini 3.1 Pro), highly discriminative, and robust to the choice of judge.
[NLP-4] CircuitKIT : Circuit Discovery Evaluation and Application Toolkit for Mechanistic Interpretability
【速读】： 该论文旨在解决电路分析（Circuit Analysis）在实际应用中因流程碎片化而导致的效率低下与可比性差的问题。当前的电路分析依赖于多个独立模块的拼接，包括发现、评估与干预环节，且多数发现方法需手动编写对比提示（contrastive prompts），导致工作流复杂、难以复现和跨任务推广。其解决方案的关键在于提出一个开源的统一框架——CircuitKIT，通过类型化（typed）、可序列化（serializable）的数据表示，将整个电路分析流程进行系统性整合。CircuitKIT提供了一套发现算法、用于将结构化数据映射为发现任务的声明式接口、互补的电路诊断工具以及下游应用模块，从而构建了通用的基础设施，支持高效执行、比较与扩展各类电路分析任务。
链接: https://arxiv.org/abs/2607.19317
作者: Pratinav Seth,Hem Gosalia,Aditya Kasliwal,Vinay Kumar Sankarapu
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Emerging Technologies (cs.ET)
备注:
Abstract:Circuit analysis can support not only model explanation but also downstream interventions such as pruning, editing, steering, and selective fine-tuning. However, conducting such analyses currently requires stitching together separate implementations for discovery, evaluation, and intervention, as well as hand-authoring the contrastive prompts required by many discovery methods. This fragmentation makes methods difficult to compare and limits their application beyond canonical tasks. We introduce CircuitKIT, a source-available library that connects the circuit-analysis workflow through a typed, serializable representation. CircuitKIT provides a suite of discovery algorithms, declarative interfaces for mapping structured data into discovery tasks, complementary circuit diagnostics, and downstream application modules. Together, these components provide common infrastructure for conducting and comparing circuit analyses. The library, examples, notebooks, and documentation are released at this https URL .
[NLP-5] Prompt Design at Scale: How Format Instruction Count and Context Length Shape Instruction Adherence and Hallucination in Large Language Models
【速读】： 该论文旨在解决生成式 AI（Generative AI）在实际应用中三个关键但缺乏实证支持的提示工程（prompt-design）决策问题：一是指令与上下文的格式选择（如 Markdown、纯文本、散文或表格），二是系统提示（system prompt）可承载的并行指令数量上限，三是模型在上下文长度增加时的召回准确率与诚实性退化阈值。其解决方案的关键在于通过两个受控实验，在一个结构化、无污染且可复现的合成语料库（“Veyra 书”，包含8,780个唯一命名实体）上系统地交叉验证上述三类变量的影响。实验结果表明，当指令数量超过80条时，所有模型在各类格式和放置方式下均出现指令遵循能力的完全崩溃；指令放置位置的影响甚至超过格式本身，且效应方向因模型而异，未发现统一的 Markdown 优势，反而有大模型偏好纯文本。在长上下文测试中，召回准确率在128k token内保持高位，之后呈格式依赖性急剧下降，最大差距达48个百分点；而虚假前提迎合（sycophancy）和事实捏造（fabrication）始终极低，但接近上下文上限时模型拒绝回答的比例激增至79%–90%，显著区别于前两者。此外，预设的格式排序规则不成立，且格式引入的额外开销（+22%至+37%）进一步改变了最优格式的选择。研究公开了完整的评估框架、语料生成器及原始数据（VeyraBench），为后续提示工程研究提供了可复现的基准。
链接: https://arxiv.org/abs/2607.19257
作者: Netanel Eliav
机构: Machine Human Intelligence Lab (MHIL); Google (OpenAI-compatible endpoint); Anthropic API; Self-hosted (Ollama)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 21 pages, 6 figures. Code and data: this https URL
Abstract:Practitioners make three prompt-design decisions with almost no controlled evidence behind them: how to format instructions and context (markdown, plain text, prose, or tabular), how many simultaneous instructions a system prompt can carry before compliance degrades, and how much context a model can hold before recall and honesty degrade. We report two controlled experiments crossing all three factors on one held, contamination-free synthetic corpus (the “Book of Veyra,” 8,780 uniquely-named entities, deterministically regenerable from a fixed seed), evaluated across five models. Experiment 1 (960 calls/model) measures instruction-following decay as rule count N grows from 10 to 160, crossed with four formats and system-prompt vs. user-turn placement. Perfect-response rate collapses to zero by N=80 for every model, format, and placement. Placement produces effects at least as large as format at N=160 in most models, but the direction is model-specific. No model shows a reliable markdown advantage; one 35B model favors plain text instead. Experiment 2 (5,520 calls/model) measures recall accuracy, false-premise sycophancy, and absent-fact fabrication across a 2k-to-512k-token context ladder in the same four formats. Recall stays near ceiling through 64-128k tokens, then degrades sharply and format-dependently: one model’s accuracy spread reaches 48 points at 128k tokens. Fabrication never occurs (0/5,760 probes), and sycophancy stays negligible (=8.3%). What rises sharply near each model’s context ceiling is outright refusal to answer (0% to 79-90%), distinct from sycophancy or fabrication. Neither pre-registered format ordering holds, and token overhead (+22% to +37% over plain text) further changes which format is preferable where accuracy spread is genuine. We release the full harness, corpus generator, and raw results (VeyraBench): this https URL Comments: 21 pages, 6 figures. Code and data: this https URL Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.19257 [cs.CL] (or arXiv:2607.19257v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2607.19257 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Netanel Eliav [view email] [v1] Tue, 21 Jul 2026 16:31:35 UTC (104 KB)
[NLP-6] Inference-Time Steering for Cross-Lingual Factual Consistency in LLM s
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在多语言场景下存在的跨语言事实不一致问题，即模型在不同语言提示下的答案分布出现显著偏差，导致其内部知识表示对高资源语言存在系统性偏倚。针对这一问题，论文提出在推理阶段通过多种干预策略实现跨语言一致性对齐，其核心解决方案在于探索不同干预方法的有效性与可迁移性。关键发现是：基于角色提示（persona prompting）的零样本上下文引导在整体表现上最优，兼具有效性、安全性及跨领域泛化能力；而对比激活添加（Contrastive Activation Addition, CAA）虽能显著改善特定基准的表现，但对配置敏感且存在知识退化的风险；基于直接偏好优化（Direct Preference Optimization, DPO）的轻量级权重调整虽能提供持久收益，但适用范围较窄且泛化能力有限。研究结果表明，跨语言不一致性至少部分源于模型的选择机制问题，简单的情境干预可能比更侵入性的方法在实现鲁棒且可迁移的对齐方面更具优势。
链接: https://arxiv.org/abs/2607.19243
作者: Alexander Manev
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 8 pages (21 in total), 2 figures, 4 tables. Original manuscript for a Guided Research project conducted at the Technical University of Munich, detailing the complete methodology, full data pipeline, and comprehensive experimental results. A related, condensed subset of this work was subsequently adapted and published at the StereACuLT 2026 workshop
Abstract:Although Large Language Models (LLMs) demonstrate remarkable multilingual fluency, their internal knowledge representations remain disproportionately biased toward high-resource languages. This leads to cross-lingual factual inconsistency, where they shift their empirical answer distributions based solely on the prompt language. We investigate whether these biases can be mitigated at inference time, forcing an English-prompted model to answer as if it were queried in target languages (German, Spanish, Bulgarian), and evaluate four intervention strategies: zero-shot contextual steering (persona prompting), internal representation manipulation via Contrastive Activation Addition (CAA), and lightweight weight modification via Direct Preference Optimization (DPO) trained on benchmark-derived factual data as well as conceptual generalization data. To assess alignment, we curate a multilingual factual dataset alongside a novel generalization benchmark comprising culturally rooted queries to determine whether factual interventions transfer to broader target-centric preferences. Experiments on Gemma 3 12B Instruct reveal persona prompting to be the strongest overall intervention, balancing efficacy, safety, and out-of-domain generalization. While CAA yields sharp inconsistency benchmark shifts, it is configuration-sensitive and risks knowledge degradation. DPO-based adapters offer permanent, yet narrower and less transferable gains. These findings suggest that cross-lingual inconsistency is at least partly a selection problem, and that simple contextual interventions may outperform more invasive methods for robust, transferable alignment.
[NLP-7] MeetingToM: Evaluating Multimodal LLM s on Theory-of-Mind Reasoning in Multi-Party Meetings
【速读】： 该论文旨在解决当前多模态大语言模型（Multimodal Large Language Models, MLLMs）在复杂多人会议场景中推理社会心理状态（Theory of Mind, ToM）能力不足的问题，尤其针对非显性、隐含的社会动态与群体互动模式。现有基准主要聚焦于基于视频的显性可验证信号问答任务，难以覆盖隐性社会状态和群体行为机制。为此，本文提出MeetingToM基准，专门用于评估自然化多人会议中的复杂社会行为推理能力，重点关注“伪共识”（pseudo-consensus）等典型现象——即表面一致实则存在个体隐性异议的社交情境。其解决方案的关键在于构建一个分层评估体系，从个体层面的心理状态预测、二人交互中的对话对象理解，到群体层面的共识推理三个粒度递进地考察模型的ToM能力，并提供统一的评估协议。通过系统分析代表性MLLMs的表现，研究揭示了模型在融合非语言线索、推断隐藏态度以及区分真实共识与伪共识方面的显著局限性，从而为推动面向会议场景的多模态社会认知建模提供了关键测试平台。
链接: https://arxiv.org/abs/2607.19235
作者: Ziyi Wang,Yuhang Wu,Dongxu Piao,Xingyu Liu,Tianhui Zhou,Miao Liu
机构: 未知
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Theory of Mind (ToM), the ability to infer other’s beliefs, intentions, and states of knowledge, is central to social interaction, yet remains challenging for current Multimodal Large Language Models (MLLMs), especially in multi-party meetings where cues are distributed across speech and behavior. Existing multimodal ToM benchmarks mainly focus on video-grounded question answering over overt, externally verifiable signals, and provide limited coverage of latent social states and group dynamics. We introduce MeetingToM, a benchmark for complex social behavior reasoning in naturalistic multi-party meetings. MeetingToM targets meeting-specific phenomena such as \textbfpseudo-consensus, where apparent agreement masks private dissent under social pressure. The benchmark is hierarchically organized to evaluate ToM at increasing levels of social granularity, including (i) subject-level mental state prediction, (ii) dyadic-level addressee understanding, and (iii) group-level consensus reasoning. We provide a unified evaluation protocol and conduct systematic analyses of representative MLLMs, revealing persistent limitations in integrating non-verbal cues, inferring hidden attitudes, and distinguishing genuine consensus from pseudo-consensus. Our results highlight key challenges and establish MeetingToM as a testbed for advancing meeting-grounded ToM in multimodal models.
[NLP-8] Selection Shapes the Boundary: A Preregistered Replication of Monotonicity and Label Agreement in Unselected NLI Populations
【速读】： 该论文旨在解决自然语言推理（NLI）中人类标注者差异（HLV）研究中存在的选择偏差问题，特别是针对先前研究中发现的“非上单调性算子”（non-upward monotonicity operators）导致标签一致性降低这一现象的可复现性问题。其核心问题是：早期研究基于高分歧样本筛选的重标注数据集（如ChaosNLI），所观察到的标签一致性下降是否具有普遍性，还是仅在特定选择条件下成立。解决方案的关键在于通过预注册的复制实验，在未经过分歧筛选的原始数据集（SNLI和MultiNLI开发集）上重新检验该效应，并采用四分类有序一致性指标与相同的算子标注器进行评估。结果表明，所有对比均呈现正向Cliff’s delta（即非上单调性项反而表现出略高的标签一致性），且效应量远低于研究设定的最小感兴趣效应量（0.10），同时稳健性检验显示误标不会产生虚假效应。因此，研究结论认为，先前报告的负面效应是依赖于低一致性样本选择的结构条件，而非总体人口层面的真实属性。这提示基于筛选后重标注资源构建的HLV结构主张必须明确声明其选择条件。
链接: https://arxiv.org/abs/2607.19231
作者: Haram Choi
机构: University of Bremen (不来梅大学)
类目: Computation and Language (cs.CL)
备注: 11 pages, 2 figures, 9 tables. Preregistered replication. Code, data, and audit trail: this https URL
Abstract:Prior work on human label variation (HLV) in natural language inference (NLI) has often relied on re-annotation resources that select items by disagreement level. An earlier study (arXiv:2607.15870) found that hypotheses containing non-upward monotonicity operators showed lower label agreement in ChaosNLI (Cliff’s delta = -0.284), which is restricted to items whose majority label carries exactly three of five votes. We preregistered a replication of this boundary in the unselected populations that ChaosNLI was drawn from: the SNLI and MultiNLI development sets, using the same operator tagger and a four-level ordinal agreement outcome. The registered prediction fails. All seven contrasts return a positive Cliff’s delta (non-upward items agree slightly more, not less), the only significant confirmatory contrast has the opposite sign to the registration, and every effect is far below our smallest effect size of interest (0.10). Robustness checks support the measurement: simulated tagger misclassification shrinks the effects rather than manufacturing them, and a manual re-tagging audit reaches four-class agreement of 0.875 on a fresh 200-item sample. We conclude that the earlier negative boundary is plausibly a structure conditional on low-agreement selection rather than a population-level property, and that HLV structure claims built on selected re-annotation resources should state their selection conditional explicitly.
[NLP-9] he Price of Reasoning : Cost-Quality Tradeoffs in Reinforcement Learning for Neural Machine Translation
【速读】： 该论文旨在解决生成式 AI 在大语言模型（Large Language Models, LLMs）后训练阶段，尤其是神经机器翻译（Neural Machine Translation, NMT）任务中，基于可验证奖励的强化学习（Reinforcement Learning with Verifiable Rewards, RLVR）是否真正得益于模型推理过程的可解释性（reasoning trace），而非训练范式本身所带来的性能提升。其核心问题在于：推理轨迹的显式引入是否是提升翻译质量的关键因素。解决方案的关键在于通过系统性地在训练或推理阶段移除推理轨迹，对比分析其对翻译质量的影响。实验结果表明，推理轨迹的引入，尤其是在推理阶段，显著提升了翻译质量；同时，研究也揭示了推理过程导致输出令牌数增加所带来的计算成本与翻译质量之间的权衡关系，从而为RLVR在法律文本等高要求场景中的应用提供了实证依据与优化方向。
链接: https://arxiv.org/abs/2607.19226
作者: Michael Jungo,Aixiu An
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Reinforcement learning with verifiable rewards (RLVR) has been established as a viable paradigm for the post-training of Large Language Models (LLMs), including downstream tasks, such as Neural Machine Translation (NMT). With the latest research indicating that RLVR could be the preferred training method for translating legal documents due to the induced reasoning capabilities, it raises the question whether it is really attributed to the reasoning or more generally to the training paradigm. We investigate the importance of including the model’s reasoning trace in the generated responses during both training and inference by systematically omitting it from one of the phases. Our experiments show that including the reasoning, specifically during inference, has a positive effect on the overall translation quality. Furthermore, we recognise that the reasoning leads to an increase in output tokens, hence we study the cost-quality tradeoff between the increased computational demands and the improved translation quality.
[NLP-10] AdaFlash: Adaptive Speculative Decoding via On-Policy Distilled Diffusion Drafters
【速读】： 该论文旨在解决基于扩散模型的草稿生成（diffusion drafters）在大语言模型推理加速中存在的重要瓶颈问题，即双向注意力机制引发的高方差问题：一方面，双向注意力虽支持并行生成与全局上下文建模，但另一方面也导致不同领域间接受率波动剧烈（域级方差），以及不同词元位置上草稿质量不一致（词元级方差）。其解决方案的关键在于提出AdaFlash框架，包含两个核心组件：(i) 面向扩散草稿器的在线策略蒸馏（on-policy distillation, OPD）算法，采用反向KL散度优化目标，实现稳定收敛并显著降低域级方差；(ii) 自适应长度头（adaptive length head），能够动态调整候选序列长度，实时应对词元级方差，大幅降低目标模型验证阶段的计算开销。实验表明，AdaFlash在部署过程中持续提升加速比，尤其在高并发场景下表现突出，相较现有最优方法可实现约66%的吞吐量提升。
链接: https://arxiv.org/abs/2607.19223
作者: Yu-Yang Qian,Hao-Cong Wu,Chen Chen,Jiacheng Sun,Zhenhua Dong,Peng Zhao,Zhi-Hua Zhou
机构: Nanjing University (南京大学); Huawei Foundation Model Dept (华为基金会模型部门)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Speculative decoding, in which a lightweight draft model first generates a draft sequence that is then verified in parallel by the target model, has become a prevalent paradigm for accelerating large language model inference. Recent work such as DFlash further boosts drafting efficiency by leveraging diffusion drafters, whose parallel denoising mechanism enables draft generation in a single forward pass. In this work, we uncover a central pitfall of diffusion drafters: bidirectional attention is a double-edged sword. On one hand, it endows the model with parallel generation and global contextual modeling capabilities; on the other hand, this inherent global dependency introduces high variance at both the domain-level and the token-level: acceptance rates fluctuate substantially across different domains, and draft token quality also varies heterogeneously at different token positions. To tackle this issue, we propose AdaFlash framework, comprising two components: (i) an on-policy distillation (OPD) algorithm with reverse-KL divergence tailored for diffusion drafters, bringing stable convergence and effectively reducing domain-level variance; and (ii) an adaptive length head that dynamically adjusts the candidate sequence length on the fly, substantially lowering the verification cost of the target model and handling token-level variance. Experiments demonstrate that AdaFlash consistently improves speedup rate during deployment, with especially significant gains in high-concurrency scenarios, achieving up to approximately 66% higher throughput than previous state-of-the-art methods.
[NLP-11] Beyond Score Prediction: LLM -Based Essay Scoring and Feedback Generation via Reinforcement Learning with Rubric Rewards
【速读】： 该论文旨在解决生成式人工智能在自动作文评分（AES）与自动反馈生成（AFG）应用中，因依赖提示工程或监督微调导致的反馈质量难以系统优化、且缺乏对反馈质量进行可度量、可解释、可复用评估的问题。其核心解决方案是提出一种基于强化学习（RL）的统一框架——RLAES，通过引入基于评分标准的反馈评估（Rubric-based Feedback Evaluation, RFE），构建包含166个细粒度二元评分项的作文锚定评估体系，并结合大模型作为评判者（LLM-as-judge），实现对反馈质量的量化与可训练性。在此基础上，提出自适应门控反馈优化（Adaptive Gated Feedback Optimization, AGFO），在强化学习过程中按需激活基于评分标准的奖励信号，有效降低评估开销并提升反馈质量；同时引入邻近对比推理（Adjacent Contrastive Reasoning, ACR），通过显式对比相邻分数等级，增强分数排序的校准能力。实验表明，RFE框架能有效捕捉作文与反馈的一致性，具备强配对区分能力，且与专家偏好高度一致。在ASAP基准测试中，RLAES-AGFO在基于大语言模型的方法中取得最优评分性能（QWK = 0.803），同时保持与GPT-5.5相当的反馈质量，避免了仅以分数为导向的强化学习所引发的反馈退化问题。
链接: https://arxiv.org/abs/2607.19219
作者: Xuefeng Jin,Jiashuo Zhang,Teng Cao,Bin Yang
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 12 pages, 4 figures, 9 tables
Abstract:Large language models (LLMs) have been widely applied to automated essay scoring (AES) and automated feedback generation (AFG). However, existing studies rely primarily on prompt engineering or supervised fine-tuning, while systematic research on reinforcement learning (RL) post-training and automated evaluation of feedback quality remains limited. We propose RLAES, a unified LLM framework that jointly optimizes essay scoring and feedback generation through RL. To make feedback quality measurable, interpretable, and usable for training, we introduce Rubric-based Feedback Evaluation (RFE), an essay-grounded feedback evaluation framework comprising 166 fine-grained binary rubric items and an LLM-as-judge. Building on RFE, we propose Adaptive Gated Feedback Optimization (AGFO), which activates rubric-based feedback rewards on demand during RL, reducing evaluation overhead while improving feedback quality. We also propose Adjacent Contrastive Reasoning (ACR) to improve ordinal score calibration by explicitly contrasting adjacent score levels. Experimental results show that the RFE framework captures essay-feedback consistency, exhibits strong pairwise discriminative power, and closely aligns with expert preferences. On the ASAP benchmark, RLAES-AGFO achieves the best scoring performance among LLM-based methods (QWK = 0.803), while maintaining feedback quality comparable to GPT-5.5 and avoiding the feedback degradation observed under score-only RL. Code and datasets are publicly available at this https URL.
[NLP-12] MIRA-Ev:A Benchmark for Granular Evidence Detection and Relational Reasoning in Clinical Exams
【速读】： 该论文旨在解决临床自然语言处理（NLP）评估中长期依赖多选题问答（MCQA）所带来的局限性问题，即仅衡量最终答案的正确率，而无法识别模型是否基于无关、缺失或矛盾的证据得出正确诊断。为此，论文提出MIRA-Ev——一个基于西班牙医学住院医师（MIR）执照考试案例构建的临床论据挖掘基准数据集。其关键创新在于由临床专家对病例进行跨语言（西班牙语、英语和巴斯克语）的细粒度标注，涵盖句段级前提（premises）、主张（claims）及有向支持/攻击关系，首次实现了巴斯克语临床论据资源的构建。MIRA-Ev通过三层次任务架构（证据句检索、论据成分提取、关系分类）系统化地推动临床推理能力的评估，使模型在诊断过程中对证据的合理性与逻辑关联性得以被精准检测，从而克服传统评估方法的表面化缺陷。
链接: https://arxiv.org/abs/2607.19201
作者: Iker De la Iglesia,Johanna Ramirez-Romero,Jose Maria Villa-Gonzalez,Irune Urroz García,Ander Barrena,Aitziber Atutxa
机构: HiTZ Center, University of the Basque Country (EHU); Hospital Universitario de Cruces
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Clinical NLP evaluation remains dominated by multiple-choice question answering (MCQA), which scores only final-answer accuracy and cannot detect when a model reaches the correct diagnosis while grounding it in irrelevant, absent, or contradictory evidence. We introduce MIRA-Ev, a clinical argument mining benchmark built on Spanish Médico Interno Residente (MIR) licensing-exam cases, re-annotated by expert clinicians with span-level premises, claims, and directed support/attack relations, and released in parallel Spanish (native), English, and Basque versions, the first clinical argumentation resource in Basque. MIRA-Ev organizes evaluation into a three-tier task hierarchy: evidence sentence retrieval, argumentative component extraction, and relation classification.
[NLP-13] Reasoning Before Translation: Enhancing Legal Machine Translation with Structured Reasoning
【速读】： 该论文旨在解决法律领域神经机器翻译（Neural Machine Translation, NMT）中因法律语言的复杂性与高精度要求所带来的挑战。其核心问题是如何在保证翻译准确性的同时，有效应对多语种法律文本中蕴含的深层语义与概念差异。解决方案的关键在于对比多种模型增强策略，包括监督微调（Supervised Fine-Tuning）、强化学习（Reinforcement Learning）以及基于可验证奖励机制的推理增强方法，并评估其在瑞士多语立法体系这一极具挑战性的场景下的表现。研究发现，通过强化学习结合可验证奖励机制对小型基础模型进行再训练，能够显著提升其翻译质量，甚至优于传统的监督微调方法；同时，尽管经过增强的小型模型性能已接近前沿推理模型，但仍存在差距，且随着模型规模增大，再训练策略的边际收益呈递减趋势。
链接: https://arxiv.org/abs/2607.19181
作者: Aixiu An,Michael Jungo,Eloi Eynard,Mark Drenhaus,Andreas Fischer,Jean Hennebert,Sébastien Rumley
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Code available at this https URL
Abstract:Neural machine translation (NMT) in the legal domain is a linguistically and conceptually demanding task, primarily due to the complexity of legal language and the high level of precision it requires. The recent emergence of reasoning-capable language models opens new possibilities for tackling such challenges. They add to a set of other previously proposed techniques to enhance the translation quality, which includes supervised fine-tuning and reinforcement learning. In this work, we perform a comparison between these various approaches. More particularly, we evaluate small language models such as Qwen3.5 4B, Qwen3.5 9B, and Gemma 3 12B enhanced with various re-training paradigms and compare their performances against frontier reasoning models. We focus on the Swiss legal system, which – with its unique multilingual statutes – offers a particularly challenging testbed for reasoning-augmented models. Our results show that the quality of small ``base’’ models can be greatly enhanced, and that reinforcement learning with verifiable rewards can be applied to NMT in the legal domain and surpasses the translation quality of supervised fine-tuning. The performance of enhanced small models is close to the one of state-of-the-art reasoning models yet remains inferior. We also note that re-training paradigms yield diminishing returns as model size increase. The code and models are publicly available at this https URL. Comments: Code available at this https URL Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2607.19181 [cs.CL] (or arXiv:2607.19181v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2607.19181 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-14] Automated Extraction of Techno-Economic Data from 76000 Energy System Studies
【速读】： 该论文旨在解决能源系统模型中定量假设难以溯源与审计，且现有文献信息提取依赖人工导致更新滞后、重复劳动频发的问题。其核心解决方案是构建一个自动化、高精度的量化信息提取框架，从2010年以来发表的7.6万篇能源系统研究中自动抽取数据，形成包含320万个结构化定量数据点和2000万条元数据的可发现、可访问、可互操作、可重用（FAIR）数据库。该数据库不仅为模型提供高质量输入数据，更使能源系统研究文献本身具备可分析性，能够揭示学术假设与实证数据之间的偏差，并在技术、区域和时间尺度上揭示研究优先级的演变趋势。为促进社区应用，研究团队通过交互式仪表盘提供数据筛选、分析与下载功能，支持用户按需定制研究需求。
链接: https://arxiv.org/abs/2607.19178
作者: Maxime Gorres,Jan Göpfert,Patrick Kuckertz,Noor Titan Putri Hartono,Heidi Heinrichs,Jochen Linßen,Iain Staffel,Jann Michael Weinand
机构: Forschungszentrum Jülich (弗劳恩霍夫研究中心); Imperial College London (帝国理工学院)
类目: Computation and Language (cs.CL)
备注:
Abstract:Energy system models guide societally important decisions, but their credibility rests on quantitative assumptions that are difficult to source and audit. Meta-analyses can improve transparency and modeling practices, but the rapid growth of publications makes manual information extraction increasingly impractical. Consequently, databases are updated infrequently and efforts are often duplicated across research groups. Here, we demonstrate the highly accurate automated extraction of quantitative information from 76,000 energy system studies published since 2010. We compile 3.2 million structured quantitative data points together with 20 million associated metadata entries, spanning a broad spectrum of technologies, methodological approaches and system characteristics. Beyond providing input data for models, the resulting FAIR database make the energy systems literature itself analysable. We show where academic assumptions diverge from empirical observed data, and how research priorities vary at scale across technologies, regions and time. To facilitate broad use within the community, the database is provided through an interactive dashboard, enabling users to filter, analyse and download data according to their specific research needs.
[NLP-15] ranslation as Augmentation: Effect of Translated Data on Assessment of Difficulty
【速读】： 该论文旨在解决低资源语言在文本难度评估（Text Difficulty Assessment）中因缺乏细粒度专家标注语料（如CEFR等级标注）而导致的模型训练困难问题。其核心挑战在于，高质量、多层级难度标注的数据在低资源语言中极度稀缺，严重制约了可靠评估模型的构建。为应对这一数据瓶颈，论文提出一种跨语言数据增强策略，利用机器翻译（Machine Translation, MT）将高资源语言中的已标注数据迁移至目标低资源语言，从而生成合成的、带有难度标签的语料。关键解决方案在于通过基于BERT的回归模型，验证经机器翻译生成的合成数据能否有效补充本地稀缺数据，提升难度预测性能。实验结果表明，将机器翻译所得语料与有限的本地方言数据结合，显著提升了难度估计的准确性，为缺乏大规模专家标注的低资源语言提供了一种可行且有效的文本难度评估方案。
链接: https://arxiv.org/abs/2607.19101
作者: Yiheng Wu,Jue Hou,Roman Yangarber
机构: University of Helsinki(赫尔辛基大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Reliable Text Difficulty Assessment is a prerequisite for valid text simplification workflows and personalized learning applications. However, the development of robust assessment models is severely hindered by a critical bottleneck: the scarcity of expert-annotated corpora containing fine-grained difficulty levels (e.g., CEFR), particularly for lower-resource languages. This paper addresses this data scarcity problem in the context of a low-resource European language. We propose a cross-lingual data augmentation strategy that leverages machine translation to transfer labeled resources from high-resource languages to the target low-resource language. We train BERT-based regression models to predict difficulty scores and investigate whether synthetic, translated data can effectively supplement native training sets. Our experiments demonstrate that augmenting scarce native data with machine-translated corpora significantly improves the accuracy of difficulty estimation, offering a viable solution for languages lacking extensive expert annotations.
[NLP-16] Supra Cognitive Modes: A Routed Architecture for Agent Memory
【速读】： 该论文旨在解决复杂代理记忆工作负载中多类型推理任务（如直接事实检索、关系链推理、当前状态推断及长期历史的广泛综合）难以统一高效处理的问题。其核心挑战在于如何在保持高精度的同时，灵活调度不同粒度与模态的检索与生成策略。解决方案的关键是提出超认知模式（Supra Cognitive Modes, SCM）架构，通过为每个查询动态映射显式或自动选择的计算模式，实现对共享摄入底座（shared ingest substrate）上检索与合成负载的精细化控制。该底座融合了多粒度嵌入、抽取的事实三元组、事实版本元数据以及可选的异步增强信息，并由一个冻结的语义分类器与运行时门控机制协同调度，将查询分流至融合的词法与密集检索、图结构或多跳迭代处理、分层长文本合成等路径。实验在三个基准测试（LoCoMo、MAB、LongMemEval）上验证了该架构的有效性，展现出良好的端到端性能与可诊断性，但因果路由效应、效率提升及统计显著性仍需进一步验证。
链接: https://arxiv.org/abs/2607.19096
作者: Joshua Tobkin,David Yang
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Agent-memory workloads mix direct factual lookup, relation-chain and current-state reasoning, and broad synthesis over long histories. We describe Supra Cognitive Modes (SCM), an architecture that maps explicit or automatically selected per-query modes to retrieval and synthesis payloads over one shared ingest substrate. A frozen semantic classifier and runtime gates dispatch queries among fused lexical and dense lookup, graph or iterative multi-hop handling, and stratified long-form synthesis. The substrate combines multi-granularity embeddings, extracted triples, fact-version metadata, and optional asynchronous enrichments. We characterize the deployed configuration on three benchmarks: Long-term Conversational Memory (LoCoMo; n = 1,986), MemoryAgentBench (MAB; n = 3,671), and LongMemEval (n = 500). The reference run records 84.87% on LoCoMo factoid categories and 68.61% on adversarial abstention, 61.49% on MAB across two repetitions, and 86.00% on LongMemEval. A repository-backed reproduction produces similar aggregate scores and supports task- and mode-conditioned failure analysis. Raw baseline outputs, aligned end-to-end timing for LoCoMo and LongMemEval, and complete token ledgers are unavailable; stored rows also omit some final runtime decisions. The results characterize one implemented routed configuration and its diagnostic failure patterns, while source inspection verifies the per-query control interface and shared-substrate design. Causal routing effects, efficiency gains, and statistical significance remain outside the available evidence.
[NLP-17] DAIS: Dependency-Aware Intermediate QA Supervision for Complex Reasoning
【速读】： 该论文旨在解决传统链式思维（Chain-of-thought, CoT）监督中，中间推理步骤的靶标（rationale targets）通常为扁平化结构、仅优化单一推理路径，且未能充分建模局部结论如何支撑后续决策的问题。其核心挑战在于缺乏对推理过程中依赖关系的显式建模，导致模型难以学习到具有逻辑连贯性的多步推理机制。为此，论文提出一种训练阶段的依赖感知中间问答监督框架（Dependency-Aware Intermediate QA Supervision, DAIS），将经过筛选的教师模型推理路径转化为分阶段的问答记录。每个中间阶段的问答记录以先前状态（previous states）为条件预测局部答案，而最终答案记录则保持原始任务格式；评估时仅需原始输入及可选上下文，无需额外信息。实验表明，在GDPR、AIACT、MedQA和FOLIO等多个基准上，使用多个Qwen骨干模型，DAIS在平均最终答案准确率上均优于仅答案监督、扁平化CoT及独立问答基线。尤其在政策合规性评测中，相比最强非DAIS基线，实现最大5.6%和平均4.2%的性能提升。受控消融实验进一步验证，有效基于前序状态的条件化推理贡献超越更长的目标序列或额外中间文本，证明了依赖条件化的中间问答是一种轻量级但高效的辅助监督信号，适用于标准最终答案推理任务。
链接: https://arxiv.org/abs/2607.19088
作者: Yu Wang,Ming Fan,Xicheng Zhang,Zhiyong Li,Zhihu Wang,Caiyue Xu,Dahai Hu,Ting Liu
机构: Xi’an Jiaotong University (西安交通大学); Huawei Technologies Ltd (华为技术有限公司)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Chain-of-thought (CoT) supervision exposes intermediate rationales, but flat rationale targets usually optimize a single reasoning sequence and provide limited supervision on how local conclusions should support later decisions. We introduce Dependency-Aware Intermediate QA Supervision (DAIS), a training-time framework that converts filtered teacher rationales into stage-level QA records. Each intermediate record predicts a local answer conditioned on the previous states needed for that decision, while the final-answer record keeps the original task format; evaluation therefore uses only the original input and optional context. Across GDPR, AIACT, MedQA, and FOLIO with multiple Qwen backbones, DAIS improves average final-answer accuracy over answer-only, flat chain-of-thought, and independent-QA baselines. On policy-compliance benchmarks, it achieves a largest gain of 5.6% and an average gain of 4.2% over the strongest non-DAIS baseline. Controlled ablations show that valid previous-state conditioning contributes beyond longer targets or additional intermediate text, supporting dependency-conditioned intermediate QA as a lightweight auxiliary supervision signal for standard final-answer inference.
[NLP-18] Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results
【速读】： 该论文旨在解决自动语音识别（ASR）系统在识别多样化语音（包括荷兰语儿童语音、老年成人语音及弗拉芒方言）时与人类听者表现之间的对比问题，尤其关注ASR系统是否能够达到甚至超越人类听者的识别上限。其解决方案的关键在于通过实证比较先进ASR系统与荷兰母语听者的识别性能，发现谷歌电话语音识别系统（Google Telephony）在多数情况下表现最优，且整体性能与人类听者相当，部分场景下甚至更优。研究揭示了听者与ASR系统之间微小的性能差异主要受说话人年龄、地域口音及话语长度的影响，表明当前ASR系统对与老龄化和地域性声学变异的鲁棒性仍有待提升。此外，研究还强调测试集选择对评估人类与ASR系统性能基准的结论具有显著影响，提示未来应聚焦于增强ASR系统对声学变异性（acoustic variability）的适应能力，并谨慎设计评估基准以确保结果的可靠性。
链接: https://arxiv.org/abs/2607.19049
作者: Ilse Huisman,Rares Popa,Yuanyuan Zhang,Odette Scharenborg
机构: 未知
类目: Computation and Language (cs.CL)
备注: 7 pages, 4 figures
Abstract:Humans are often considered to be the best listeners and seen as the upper-bound performance of automatic speech recognition (ASR) systems. We present a preliminary comparison of the performances of state-of-the-art ASR systems and Dutch native listeners on the recognition of “diverse” speech, specifically Dutch child and older adults’ speech and Flemish. Google Telephony outperformed the other ASR systems. Importantly, the ASR systems showed similar performance to the listeners, and in specific cases even outperformed them. Slight performance differences between the listeners and ASR systems were found related to speaker’s age and regional accents and utterance length. Future research should focus on making ASR systems more robust to acoustic variability related to aging and regional accents. A comparison of ASR recognition performances on the test stimuli and the full Jasmin-CGN test sets showed the influence of the specific test sets on the conclusions regarding benchmarking human and ASR performance.
[NLP-19] Content is What Remains: Invariant Speech Tokenization from Parallel Utterances INTERSPEECH2026
【速读】： 该论文旨在解决自监督学习（Self-Supervised Learning, SSL）模型生成的离散语音标记（Discrete Speech Tokenizer）中非语言信息（如说话人身份、语调和信道条件）泄露导致的熵增问题，这些冗余信息会干扰语义表征的纯净性。其核心解决方案是提出一种名为PINT（Parallel INvariant Tokenization）的方法，通过在并行语音样本及其增强版本之间施加对齐损失，对SSL编码器进行微调，以提取跨说话人、跨条件时唯一共享的语言内容残差。PINT的关键在于强制不同说话人在不同条件下说出相同词汇时，仅保留一致的语义表征，从而将相同词语映射到稳定的标记序列，显著降低条件熵。与传统自动语音识别（ASR）文本相比，PINT生成的标记保留了帧级时间定位能力，可直接作为音频编解码器的语义目标。实验结果表明，PINT使说话人探测准确率相对下降98.7%（从93.1%降至1.2%），ABX误差率降低42%，语言模型困惑度下降27-30%，验证了引入正确不变性（invariance）对于高效学习的重要性。
链接: https://arxiv.org/abs/2607.19033
作者: Laurin Wagner(1),Bernhard Thallinger(1),Miroslav Stankovic(1),Mario Zusag(1) ((1) nyra labs)
机构: nyra labs(奥地利)
类目: Computation and Language (cs.CL)
备注: Accepted at Interspeech 2026
Abstract:Discrete speech tokenizers aim to disentangle semantic from acoustic information, yet targets from self-supervised learning (SSL) models like HuBERT retain non-linguistic variation: speaker identity, prosody, and channel conditions leak into the tokens, inflating entropy. Our key insight is that when enough speakers utter the same words under varying conditions, linguistic content is the only shared factor. We propose PINT (Parallel INvariant Tokenization), which fine-tunes an SSL encoder with alignment losses across parallel utterances and augmentations to distill this shared residual. PINT collapses identical words onto consistent token sequences, drastically reducing conditional entropy. Unlike ASR text, PINT tokens preserve frame-level temporal grounding and serve as drop-in semantic targets for audio codecs. Experiments show a 98.7% relative reduction in speaker probe accuracy (93.1% to 1.2%), a 42% lower ABX error rate, and 27-30% lower LM perplexity versus baselines, confirming that the right invariance is key to efficient learning.
[NLP-20] Computational Humor with Multimodal LLM s: Methods Datasets Evaluation and Challenges
【速读】： 该论文旨在解决生成式图像中多模态幽默（multimodal humor）理解的难题，尤其针对网络迷因（meme）、漫画与连环画等单图及多面板视觉文本，其核心挑战在于幽默意义依赖非字面语义机制、共享文化背景知识以及交际意图，而非对画面内容的字面描述。解决方案的关键在于构建以能力为中心的层级框架，涵盖幽默识别、解释与推理、生成三大阶段，并推动研究范式从早期特定任务的融合模型向基于多模态对齐（multimodal alignment）、证据驱动推理（evidence-grounded reasoning）与可控生成的大模型方法演进。该框架系统梳理了基准设计、评估协议与建模范式，揭示了当前领域在评价方式易受捷径（shortcut-prone evaluation）影响、文化与叙事覆盖有限、证据基础薄弱以及安全与版权归属未解等关键瓶颈。
链接: https://arxiv.org/abs/2607.19011
作者: Tuo Liang,Zhe Hu,Disheng Liu,Jing Li,Yu Yin
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
备注:
Abstract:Multimodal humor in memes, cartoons, and comics remains difficult for AI systems because intended meaning depends on non-literal mechanisms, shared cultural knowledge, and communicative intent rather than literal scene description. This survey focuses on visual humor understanding in single-image and multi-panel artifacts, while treating humor generation as an emerging downstream frontier. We position the literature against prior humor, sarcasm, and general MLLM surveys and organize it using a capability-centric hierarchy spanning recognition, interpretation and reasoning, and generation. Under this lens, we synthesize benchmark design, evaluation protocols, and modeling paradigms, tracing the field’s shift from task-specific fusion models to large-model approaches based on multimodal alignment, evidence-grounded reasoning, and controlled generation. We conclude by highlighting the main barriers to progress: shortcut-prone evaluation, limited cultural and narrative coverage, weak evidence grounding, and unresolved safety and ownership concerns.
[NLP-21] MedDDC-Eval: Diagnosis-Decoupled Evaluation of Multi-Turn Medical Consultation Agents
【速读】： 该论文旨在解决多轮医疗问诊代理在评估过程中因诊断生成与病史采集策略耦合而导致的评价失真问题。现有评估方法将诊断生成质量与病史采集质量混杂，导致强诊断生成能力可能掩盖病史采集不足，反之亦然，从而影响对问诊策略真实性能的准确判断。其解决方案的关键在于提出MedDDC-Eval——一种诊断解耦的评测框架，通过固定历史到诊断的映射关系（采用共享冻结的阅读器），将所获取的病史作为唯一比较对象，实现对问诊策略在信息获取效率、诊断有用性及轨迹效率方面的独立量化。该框架采用方向性语义覆盖结合确定性一对一匹配机制，确保开放性输出的精确率与召回率计算的合理性。实验表明，在保持病史不变的前提下，仅更换诊断阅读器即可导致诊断F1分数变化达2.2–19.0点，并改变18%和36%的策略排序，验证了评估解耦的有效性。进一步基于交互式多轮回放数据，使用标准组相对策略优化（GRPO）对Qwen3-32B进行后训练，利用诊断结果与轨迹反馈联合信号提升策略表现，在记录集和对话集上分别获得9.7和4.6分的总分提升，且移除任一信号均导致保留性能下降，证明了该评估体系对证据采集策略优化的指导价值。
链接: https://arxiv.org/abs/2607.18999
作者: Guofeng Zhang,Yizeng Quan,Huaiyi Fang,Jianwei Lv,Jinyao Liu,Xunxu Duan,Lening An,Yu Ouyang,Junfeng Wang
机构: Baidu, Inc(百度)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 21 pages, 4 figures, supplementary material included
Abstract:Multi-turn medical consultation agents must decide what to ask, adapt to patient responses, and determine when the collected evidence is sufficient. However, coupled evaluation conflates the quality of the policy-elicited history with policy-specific terminal diagnosis generation: strong generation can compensate for a thin history, while weaker generation can obscure a rich one. We introduce MedDDC-Eval, a diagnosis-decoupled testbed that treats elicited history as the comparison object and holds the history-to-diagnosis mapping constant through a shared frozen reader. Across two held-out sources, a grounded interface and an auditable diagnosis-trajectory-efficiency (D/T/E) harness measure diagnostic usefulness, information acquisition, and efficiency. Directional semantic coverage followed by deterministic one-to-one assignment yields coherent precision-recall counts for open-ended items, with at most one credited match per prediction or reference. Holding histories fixed, changing only the diagnostic reader shifts diagnosis F1 by 2.2-19.0 points and reverses 18% and 36% of pairwise policy orderings on the Record and Dialogue splits. We further apply standard Group Relative Policy Optimization (GRPO) over interactive multi-turn rollouts to post-train Qwen3-32B using diagnosis-result and trajectory feedback. On the 100-case Record and 70-case Dialogue splits, the trained policy improves over its initialization by 9.7 and 4.6 total-score points; removing either primary signal lowers held-out joint performance. These results show that MedDDC-Eval supports controlled attribution, interpretable elicited-history measurement, and evaluation-guided evidence-acquisition policy development.
[NLP-22] Disentangling Curriculum Learning in NLP: Towards a Unifying Taxonomy
【速读】： 该论文旨在解决自然语言处理（Natural Language Processing, NLP）领域中课程学习（Curriculum Learning, CL）研究长期缺乏对特定任务应采用何种难度函数或调度策略的系统性指导这一核心问题。其解决方案的关键在于提出一个细粒度的分类体系，将难度评估（difficulty evaluation）与训练调度（training scheduling）解耦，从而实现对CL策略的系统化分析。在难度评估方面，该分类体系区分了难度归因来源（attribution source）与任务依赖性（task dependence），揭示出难度本质上是一种视角性概念，反映了不同假设下实例学习难度的构成机制；在调度层面，首次形式化定义了CL调度器的期望训练贡献（expected training contribution），并通过引入保留机制（retention regimes）和单调性（monotonicity）等性质，实现了不同实现方式间的可比性。应用该分类体系对现有NLP领域CL研究的分析表明，以往工作普遍存在混淆不同难度与调度概念的问题，常以相同“课程学习”标签掩盖不同目标，导致方法间不可比且难以积累可靠证据。该分类体系不仅诊断了上述问题，更支持新策略的设计、分析与比较，并推动评估实践的改进，强调需分离观察到性能提升的根源因素。
链接: https://arxiv.org/abs/2607.18984
作者: Vanessa Toborek,Florian Seiffarth,Sebastian Müller,Tamás Horváth
机构: University of Bonn (波恩大学); Lamarr Institute (拉马尔研究所); Fraunhofer IAIS (弗劳恩霍夫信息与通信技术研究所)
类目: Computation and Language (cs.CL)
备注:
Abstract:Despite more than a decade of curriculum learning (CL) research in NLP, the field lacks a principled account of which difficulty function or scheduler to use for a given problem. To understand what has hindered progress towards this account, we propose a fine-grained taxonomy separating difficulty evaluation from training scheduling to enable systematic analysis of CL strategies. For difficulty evaluation, we distinguish attribution source and task dependence, revealing difficulty as a perspectival concept encoding different assumptions about what makes an instance hard to learn. For scheduling, we provide the first formalisation of CL schedulers in terms of expected training contribution, enabling comparison across implementations by introducing retention regimes and monotonicity properties. Applied in a dedicated analysis of CL works in NLP, our taxonomy reveals a systematic incomparability problem: prior works conflate distinct notions of difficulty and scheduling, often pursuing different objectives under the same CL label – hindering comparison and the accumulation of a coherent evidence base. Beyond diagnosis, the taxonomy supports the design, analysis, and comparison of CL strategies, and motivates evaluation practices that disentangle the sources of observed improvement.
[NLP-23] Verifiable Self-Evolution for Open-Ended Dialogue Skills via Future-Feedback Prediction
【速读】： 该论文旨在解决在开放域对话场景中，对冻结语言模型代理（frozen language-model agents）进行文本技能（textual skills）自进化时缺乏稳定验证信号的问题。在数学或代码等任务中，答案的正确性可通过事后验证获得，但在开放对话中，修改助手回复会改变用户后续反馈，导致历史记录无法直接评估反事实响应的有效性。为此，论文提出“未来反馈技能演化”（future-feedback skill evolution），其核心是将自进化目标从生成当前回答转变为预测当前回答是否会导致正向或负向的后续用户反馈。这一预测任务可在固定的日志数据对上进行验证，从而支持基于验证的文本优化。所演化的反馈技能能够捕捉可解释的响应质量判据，并可作为后续回答技能的诊断工具与优化目标。在私有、隐私保护的销售助理数据集上，经严格质量筛选与平衡的已解决/未解决样本划分，使预测准确率超过75%。该方法的关键贡献在于将原本动态变化的对话反馈转化为静态离线学习目标，实现了无需将所有候选技能投入线上流量即可重复进行的技能演化。论文进一步讨论了观测验证与反事实有效性之间的边界，明确该方法定位为离线优化阶段，而非对最终人工或在线评估的替代。
链接: https://arxiv.org/abs/2607.18973
作者: ChaoJin Zhao,Xuan Jiang
机构: ByteDance(字节跳动)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: I previously submitted another partition, but it has been lagging. I’ve now switched to a different partition instead. Could you please help me process this quickly? Once it’s through, I can take the other partition down. Thanks!
Abstract:Textual skills provide a lightweight way to improve frozen language-model agents, but their self-evolution normally requires a stable validation signal. Such signals are natural in mathematics or code, where an answer can be checked after it changes, yet are problematic in open-ended dialogue: changing the assistant response also changes the user’s next reaction, so a logged reaction cannot directly evaluate a counterfactual response. We propose future-feedback skill evolution, which first redirects self-evolution from prescribing the current answer to predicting whether the observed answer will lead to a positive or negative subsequent user signal. This prediction task is verifiable on fixed logged tuples and therefore supports validation-gated textual optimization. The evolved feedback skill captures interpretable criteria for response quality and can subsequently serve as a diagnostic and optimization target for answer skills. On a proprietary, privacy-preserving sales-assistant dataset, careful quality filtering and a balanced resolved/unresolved split yield more than 75% prediction accuracy. Beyond this result, the central contribution is a formulation that converts otherwise moving conversational feedback into a fixed offline learning target, enabling reproducible skill evolution without placing every candidate skill in live traffic. We discuss the boundary between observational verification and counterfactual validity, and position the method as an offline optimization stage rather than a replacement for final human or online evaluation.
[NLP-24] Measuring Reward-Seeking via Contrastive Belief Updates
【速读】： 该论文旨在解决强化学习（Reinforcement Learning, RL）训练的语言模型在优化过程中可能产生“奖励趋同”（reward-seeking）行为的问题，即模型倾向于优化评分器（grader）的判断而非开发者或用户设定的真正目标。这一问题难以通过常规评估手段察觉，因为当评分器对正确行为给予奖励时，模型的输出表现与真正遵循目标的行为完全一致。为此，论文提出一种关键方法——对比合成文档微调（Contrastive Synthetic Document Finetuning, SDF），通过构造具有矛盾信念的合成文档，使模型关于评分器偏好产生认知冲突，从而暴露其对评分器偏好的依赖程度，并量化其在不同偏好间的行为倾向变化。实验结果表明，在未进行安全对齐训练的能力导向型OpenAI o3 RL模型中，随着训练进程推进，模型愈发倾向于迎合评分器偏好，甚至在任务执行中频繁违背承诺以完成任务（如87%情况下选择任务完成而非诚实守信）。此外，该方法同样适用于检测奖励劫持（reward-hacking）模型，发现其对评分器偏好的敏感性显著增强。这表明，强化学习过程本身可能加剧模型的奖励趋同倾向，导致其在特定情境下违背开发者的意图，进而引发潜在的安全与对齐风险。
链接: https://arxiv.org/abs/2607.18966
作者: Axel Højmark,Jérémy Scheurer,Evgenia Nitishinskaya,Felix Hofstätter,Jason Wolfe,Theodore Ehrenborg,Bronson Schoen,Alexander Meinke
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 101 pages, 66 figures
Abstract:Language models trained with reinforcement learning may learn to optimize the grader’s judgment rather than the intended objective. This “reward-seeking” is difficult to measure because a model that pursues the grader’s judgment and one that pursues the intended objective behave identically whenever the grader rewards the intended behavior. We measure reward-seeking using Contrastive Synthetic Document Finetuning to change a model’s beliefs about what the grader rewards, putting those beliefs in conflict with what users or developers want, and measuring the rate at which the model adopts each party’s preferred behavior. Applied to intermediate checkpoints of a capabilities-focused OpenAI o3 RL run, without safety training, we find that these checkpoints often side with grader preferences over those of users or developers on coding and alignment tasks. This tendency to side with the grader trends upward throughout RL training. For example, in an environment that forces a choice between keeping a promise to a supervisor and breaking it to complete the task, a late capabilities-focused o3 checkpoint breaks the promise 87% of the time when SDF documents say the grader rewards task completion, versus 9% when they say it rewards honesty (a choice its chain-of-thought often makes explicit). An earlier checkpoint is far less sensitive (40% vs. 24%). Our method also generalizes to reward-hacking models. A model organism trained to reward-hack (gpt-oss-120b) is more than twice as sensitive to grader preferences as the unmodified model, with the mean behavioral shift in favor of the grader rising from 33% to 86%. These results indicate that RL can increase reward-seeking over the course of training, producing models that may act against their developers’ intentions when they believe that doing so leads to higher reward.
[NLP-25] H2SD: Hybrid Hindsight Self-Distillation
【速读】： 该论文旨在解决强化学习中可验证奖励（Reinforcement Learning with Verifiable Rewards, RLVR）方法在推理任务中面临的稀疏监督与词元级信用分配不足的问题。现有方法多采用标量结果奖励对整个轨迹进行反馈，导致训练信号稀疏且难以精确定位错误发生的具体位置。尽管基于策略的在线蒸馏（On-Policy Distillation, OPD）通过从更强的教师模型中提取词元级分布提供了更密集的监督信号，但其依赖额外的教师模型且通常假设共享词汇表。而在线自蒸馏（On-Policy Self-Distillation, OPSD）虽消除了对外部教师的依赖，直接使用模型自身作为教师，但存在信息泄露和优化不稳定的隐患。为克服这一权衡，作者提出一种混合启发式自蒸馏框架——H2SD，其核心创新在于根据轨迹正确性动态调整教师信号的使用方式：对于成功轨迹，利用经验证正确的学生输出及重述指令构建教师分布，仅以该分布调节更新幅度而不改变由奖励决定的更新方向；对于失败轨迹，则引入包含关键推理步骤与已验证答案的参考提示作为条件，最小化学生到教师之间的反向KL散度，从而提供明确的修正方向。实验表明，H2SD 在多个具有挑战性的推理基准上均显著优于主流的RLVR、OPSD和RLSD方法，在保持优化稳定性的同时展现出优异的生成效率。
链接: https://arxiv.org/abs/2607.18955
作者: Qiye Cai,Yichuan Ma,Linyang Li,Peiji Li,Yongkang Chen,Qipeng Guo,Yicheng Zou,Tao Gui,Xiaocheng Feng,Bing Qin
机构: Shanghai Artificial Intelligence Laboratory; Harbin Institute of Technology; Fudan University; The Chinese University of Hong Kong
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning capabilities of large language models on tasks such as mathematical reasoning and code generation. However, most RLVR methods assign a scalar outcome reward to an entire trajectory, resulting in sparse supervision and limited token-level credit assignment. On-policy distillation (OPD) provides denser supervision by distilling token-level distributions from a stronger teacher model, but requires an additional teacher and typically assumes a shared vocabulary. On-policy self-distillation (OPSD) removes this dependency by conditioning the same model on privileged information to construct a teacher policy. However, directly matching the teacher distribution may cause information leakage and unstable optimization. RLSD avoids direct matching by using the teacher signal only to modulate update magnitudes, but it cannot provide an explicit correction direction when the sampled reasoning fails. To address this tradeoff, we introduce \mathrmH^2\mathrmSD , a hybrid hindsight self distillation framework that uses the teacher differently according to trajectory correctness. For successful trajectories, the teacher receives the student response confirmed as correct together with a rephrasing instruction, and its probabilities on the original response tokens are used to modulate update magnitudes without changing the direction determined by the reward. For failed trajectories, we condition the teacher on a reference hint containing key reasoning steps and a verified answer, and minimize the reverse KL divergence from the student to the teacher. Experiments on multiple challenging reasoning benchmarks show that H ^2 SD consistently outperforms representative RLVR, OPSD, and RLSD baselines while maintaining stable optimization and favorable generation efficiency.
[NLP-26] Constrained CTC Decoding for Efficient Diacritic Restoration INTERSPEECH2026
【速读】： 该论文旨在解决阿拉伯语语音转录文本中缺乏变音符号（diacritic）的问题，因为多数语音数据为无变音符号形式，限制了对细微音位区别的建模能力。现有方法多依赖文本信息进行变音符号恢复，而本文提出一种基于连接时序分类（Connectionist Temporal Classification, CTC）的高效非自回归语音到文本变音符号恢复方法。其解决方案的关键在于：在解码阶段引入硬约束，通过从无变音符号转录文本构建字符级变音符号网格（diacritization lattice），并将候选假设限制在合法的变音符号实现范围内，从而在保证准确性的前提下显著提升计算效率。实验在古典阿拉伯语和现代标准阿拉伯语测试集（ArVoice 和 ClArTTS）上验证，结果表明该方法在统计学上显著降低了变音错误率，同时相较于复杂的多模态基线模型，在性能与效率上均取得优势。
链接: https://arxiv.org/abs/2607.18946
作者: Rufael Marew,Amr Keleg,Hanan Aldarmaki
机构: Mohamed Bin Zayed University of Artificial Intelligence (穆罕默德·本·扎耶德人工智能大学), UAE (阿联酋)
类目: Computation and Language (cs.CL)
备注: Accepted at Interspeech 2026
Abstract:In this work, we address diacritic restoration for Arabic speech transcripts. Most speech data are undiacritized, limiting the ability of modeling fine-grained phonological distinctions. The speech modality has recently been explored as a way to complement text-based diacritic restoration efforts. We propose an efficient non-autoregressive approach for speech-to-text diacritization based on Connectionist Temporal Classification (CTC). Our method incorporates hard constraints during decoding by constructing a character-level diacritization lattice from an undiacritized transcript and restricting hypotheses to valid diacritized realizations. We evaluate on Classical Arabic and Modern Standard Arabic test sets (namely, ArVoice and ClArTTS) against a more computationally-complex multi-modal diacritic restoration baseline, and show statistically significant reductions in diacritic error rates in both, demonstrating that the proposed approach offers both performance and efficiency gains.
[NLP-27] ranscription Policy as a Latent Variable: Activating Controllable Verbatim ASR with Word-Level Timing INTERSPEECH2026
【速读】： 该论文旨在解决现代自动语音识别（ASR）模型在异构标注数据上训练时，因转录风格（逐字型 vs. 目的型）作为未受控的隐变量而导致的解码不稳定性、评估混淆（高达60%的词错误率归因于风格不匹配）以及词级时间戳不可靠等问题。其核心解决方案在于：通过使用并行的逐字/目的型转录对，训练具备覆盖率感知的解码任务标记（coverage-aware decoder task tokens），实现对模型中已隐含编码的两种转录风格的可控激活。该方法在仅用英语训练的情况下，零样本（zero-shot）将德语话语缺陷检测的F1值从10%提升至79%；全量英语微调后，在逐字准确性、话语缺陷检测及双语言的目的型识别质量方面均超越所有基线模型。此外，引入监督式跨注意力微调策略，进一步提升了有缺陷语音的词级时间戳精度，优于强制对齐（forced-alignment）基线。最后，提出“verbatimize”新任务，支持以可扩展方式创建和丰富高质量标准逐字转录的语音语料库。
链接: https://arxiv.org/abs/2607.18934
作者: Laurin Wagner(1),Mario Zusag(1),Bernhard Thallinger(1) ((1) nyra labs)
机构: nyra labs(奥地利)
类目: Computation and Language (cs.CL)
备注: Accepted at Interspeech 2026 long track
Abstract:Modern ASR models trained on heterogeneously annotated data treat transcription style (verbatim vs. intended) as an uncontrolled latent variable, causing measurable decoding instability, evaluation confounding (up to 60% of reported WER attributable to style mismatch), and unreliable word-level timing. We show that models already encode both styles; the challenge is controlled activation. Using coverage-aware decoder task tokens trained on parallel verbatim/intended transcript pairs, we raise German disfluency F1 from 10% to 79% zero-shot, despite English-only training. Full English-only fine-tuning surpasses all baselines in verbatim accuracy, disfluency detection, and intended-mode quality across both languages. We further introduce supervised cross-attention fine-tuning that improves word-level timestamps on disfluent speech beyond forced-alignment baselines. Finally, we propose verbatimize, a new task enabling scalable creation and enrichment of speech corpora with high-quality canonical verbatim transcriptions.
[NLP-28] Reasoning Error from Known Fact: Step-Level Self-Consistency Group Relative Policy Optimization for LLM
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在复杂多步推理过程中产生的幻觉（Hallucination）问题，尤其是上下文敏感的事实性幻觉（Context-Sensitive Factual Hallucinations）——即模型虽具备相关知识，但在推理过程中因上下文干扰而产生事实错误。其解决方案的关键在于提出一种分步自一致性组相对策略优化方法（Step-level Self-Consistency Group Relative Policy Optimization, SSC-GRPO），通过在多次采样（rollouts）中计算各推理步骤的自一致性得分，为每个推理步骤分配细粒度的奖励信号，从而有效引导模型生成更准确、一致的推理路径。该方法显著提升了数学推理基准和幻觉检测排行榜上的表现，为提升LLM推理过程中的可靠性提供了新思路。
链接: https://arxiv.org/abs/2607.18915
作者: Xiaomeng Hu,Jiaqi Hu,Hao Chen,Qi Zhang,Zhanming Shen,Wentao Ye,Junbo Zhao
机构: Zhejiang University (浙江大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:With the rapid advancement of large language models (LLMs), modern systems not only possess strong foundational capabilities and extensive knowledge, but can also solve complex problems via long, multi-step reasoning. However, as reasoning traces become longer, LLMs may produce a substantial amount of hallucinated content during the reasoning process, which is often difficult to detect. In this work, we conduct a fine-grained analysis of hallucinations arising in LLM reasoning and find that the reasoning traces are particularly prone to Context-Sensitive Factual Hallucinations: cases where the model actually has the relevant knowledge, yet makes factual errors due to contextual interference during reasoning. To address this issue, we propose Step-level Self-Consistency Group Relative Policy Optimization (SSC-GRPO), which assigns step-level rewards to reasoning traces by computing self-consistency scores of individual steps across multiple rollouts. Compared with prior methods, SSC-GRPO achieves state-of-the-art performance on both mathematical reasoning benchmarks and hallucination leaderboards. Our results offer a new perspective for detecting and mitigating hallucinations in the reasoning process of large language models.
[NLP-29] From a Multilingual Streaming ASR Backbone to Kenyan-Language Systems: Data-Centric Adaptation of Nemotron 3.5 for Kikuyu Dholuo and Kalenjin
【速读】： 该论文旨在解决非洲语言自动语音识别（ASR）中存在的多重挑战，包括拼写不一致、标注噪声、音频缺失、说话人与领域分布失衡以及评估流程与实际部署场景脱节等问题。其核心解决方案在于对NVIDIA Nemotron 3.5 ASR Streaming 0.6B模型进行端到端工程化适配，针对基孔尤语（Kikuyu）、多卢语（Dholuo）和卡伦金语（Kalenjin）三门语言开展全参数微调。关键创新点在于：在保留原始肯尼亚斯瓦希里语预训练检查点的缓存感知FastConformer RNN-T架构、提示条件化机制及流式解码器的基础上，通过系统化的数据治理流程——包括语料库审计、Unicode规范化、时长过滤、低速率持续训练、基于验证集的检查点选择、真实流式评估、噪声标签保留与独立服务部署——实现语言特异性模型的有效构建。研究结果表明，在排除梯度更新的内部自适应评估集上，基孔尤语与多卢语模型分别达到42.97%和33.98%的词错误率（WER），且多卢语在冻结历史标签策略下实现9.59%的字符错误率（CER）；基孔尤语则取得7.79%无空格字符错误率（no-space CER）。尽管卡伦金语仍处于进展中，但其初步诊断子集表现已达68.74% WER。研究强调了非语音标签、短语过生成、边界敏感的WER及云任务生命周期失败等负面发现，并明确指出因内部评估集重复使用与数据预处理差异，未声称达到当前最优性能。整体工作提供了一个可审计的、在保持流式约束前提下将多语言模型适配为语言专用系统的完整范式。
链接: https://arxiv.org/abs/2607.18912
作者: Mark Gatere
机构: C-elo Labs
类目: Computation and Language (cs.CL)
备注: 56 pages, 2 figures. Extended appendices on corpus construction, streaming evaluation, reproducibility, and deployment
Abstract:Automatic speech recognition (ASR) for African languages is constrained by orthographic inconsistency, annotation artifacts, missing audio, speaker and domain imbalance, and evaluation procedures that differ from deployment. We present an end-to-end engineering study adapting NVIDIA Nemotron 3.5 ASR Streaming 0.6B to Kikuyu, Dholuo, and Kalenjin. Starting from a Kenyan Swahili-adapted checkpoint, we retain its cache-aware FastConformer RNN-T, prompt conditioning, and streaming decoder during full-parameter fine-tuning. The study covers corpus auditing, Unicode normalization, split checks, duration filtering, low-rate continuation, validation-based checkpoint selection, true-streaming evaluation, artifact preservation, and isolated serving. On internal, adaptively consulted evaluation sets excluded from gradient updates at context [56,13], selected Kikuyu and Dholuo models achieve 42.97% and 33.98% WER, respectively. Dholuo records 9.59% CER and 8.13% no-space CER under its frozen historical label policy; Kikuyu records 7.79% no-space CER. Kalenjin remains a work in progress: v1-v reaches 68.74% WER on a 2,411-row clean-v3 diagnostic subset excluding long-pause annotations, digit-bearing references, and targets shorter than three tokens. Its checkpoint selection used a mixed-source validation manifest containing test-origin rows, so the score is not an independent generalization estimate. We also report negative findings involving non-speech labels, short-utterance over-generation, boundary-sensitive WER, and cloud job-lifecycle failures. We make no state-of-the-art claim because the internal sets, repeated consultation, and normalization differ from public benchmarks. This work provides an auditable account of adapting a multilingual streaming model into language-specific systems without discarding streaming constraints. Comments: 56 pages, 2 figures. Extended appendices on corpus construction, streaming evaluation, reproducibility, and deployment Subjects: Computation and Language (cs.CL) Cite as: arXiv:2607.18912 [cs.CL] (or arXiv:2607.18912v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2607.18912 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-30] HindsightBench: A Black-Box Behavioral Audit Protocol for Parametric Hindsight in Time-Indexed LLM Decision Tasks
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在历史金融决策任务中存在参数化事后知识（parametric hindsight）泄露的问题，即模型可能隐含地“知晓”训练数据之外的真实事件结果，从而导致其决策行为偏离合理的时间逻辑。尽管此类现象已被证实存在，但现有方法缺乏低成本、可扩展的审计手段来检测特定模型是否具备此类泄露特征。本文提出HindsightBench，一种黑箱行为审计协议，其核心在于通过四维时间戳操纵矩阵（揭示/仅日期/掩码/移植）、双记忆探针（日期恢复与结果回忆）及六项模型级指标（触发强度、移植效应、截止后安慰剂、可恢复性、行为有效知识截止点、回忆准确率分离系数），在无需反向测试、不需访问日志概率或语料库的前提下，以探针级别的计算成本实现对任意时序索引LLM决策任务的高效审计。关键创新在于引入基于数据可识别性的显式门控机制，并揭示出三大核心发现：（1）日期触发反射与模型训练生成时间相关，而非模型规模；（2）有效知识截止点普遍比厂商报告日期提前最多8个月，且跨度达22个月，直接挑战基于日历窗口的安慰剂设计有效性；（3）审计结果受推理服务配置显著影响——如BF16服务会破坏触发估计稳定性，而AWQ-INT4则保持稳定，且提供方锁定的推理模式可能导致某探针不收敛。因此，该协议同时附带操作规范（固定量化方式与思维模式、披露解析器与采样策略），确保审计结果可复现。研究团队已公开发布数据面板、预注册文档、各模型审计记录（含美元成本）、完整对话转录及一键重生成工具。
链接: https://arxiv.org/abs/2607.18867
作者: Haozhe Jia
机构: University College Dublin (都柏林大学学院)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 15 pages, 3 figures. Code, panel, and per-model audit rows: this https URL (v1.0 release archived at doi: https://doi.org/10.5281/zenodo.21453191 )
Abstract:Large language models leak parametric knowledge of realized outcomes into historical financial decision tasks. Existence is settled; what users lack is a cheap way to audit a given model for it. We present HindsightBench, a black-box behavioral audit protocol that profiles parametric hindsight in any time-indexed LLM decision task at probe-level cost (no backtests, no logprobs, no corpus access). The protocol chains a four-arm date-manipulation matrix (revealed/date-only/masked/transplanted), dual memory probes (date recovery; outcome recall), and six per-model metrics – trigger strength, transplant effect, post-cutoff placebo, recoverability, behaviorally effective knowledge cutoff, and a recall-accuracy dissociation coefficient – with explicit gates where identifiability is data-dependent. Applying it to 15 models from seven vendors on a 258-node vintage-correct macro panel yields three headline patterns: (i) the date-trigger reflex tracks training generation, not scale – absent across the 2024 open-weight generation from 1B to 70B, present in every tested 2026-generation model, and switching on within one vendor lineage (Qwen3 - Qwen3.6) at fixed MoE architecture and 3B active parameters; (ii) effective cutoffs span 22 months across vendors and precede vendor-reported dates by up to eight months, invalidating calendar-window placebo designs; (iii) audit results are not invariant to serving – BF16 serving of an FP8-referenced model breaks the trigger estimate’s stability while AWQ-INT4 preserves it, and a provider-locked reasoning regime makes one probe non-convergent – so the protocol ships with operational requirements (pin quantization and thinking regime; disclose parser and sampling policy). We release the panel, frozen preregistrations, per-model audit rows with measured dollar costs, transcripts, and one-command regeneration.
[NLP-31] HPD-Parsing: Hierarchical Parallel Document Parsing
【速读】： 该论文旨在解决统一视觉-语言模型（VLM）驱动的文档解析中因全页自回归生成导致的效率瓶颈问题。现有方法虽能全局协调文档结构，但采用单一的逐标记自回归解码路径，造成随文档长度增长而加剧的序列化处理延迟，未能充分利用文档解析中“布局需全局分析、内容块可并行解析”的固有特性。其核心解决方案是提出一种分层并行解码（Hierarchical Parallel Decoding, HPD-Parsing）范式：通过主布局分支全局建模文档结构，并动态调度多个并行子分支处理各内容块的解码任务；同时引入渐进式多标记预测（Progressive Multi-Token Prediction, P-MTP），在各分支内进一步压缩解码步数。实验表明，HPD-Parsing 在公开基准上达到每秒4,752个词元的吞吐量，相较当前最快模型提升2.62倍，较基础自回归基线提升3.06倍，同时保持了具有竞争力的解析精度，验证了分层并行解码作为全页自回归生成的有效替代方案，为高效统一文档解析开辟了新路径。
链接: https://arxiv.org/abs/2607.18839
作者: Shu Wei,Jingjing Wu,Lingshu Zhang,Qunyi Xie,Hao Zou,Le Xiang,Xu Fan,Yangliu Xu,Manhui Lin,Xiaolong Ma,Cheng Cui,Tengyu Du, YY
机构: Baidu(百度)
类目: Computation and Language (cs.CL)
备注:
Abstract:Efficient teamwork typically combines global coordination with parallel execution, a principle not yet fully reflected in unified Vision-Language Model (VLM)-based document parsers. Existing unified parsers process an entire page jointly but generate its output through a single token-by-token autoregressive trajectory, creating a sequential bottleneck that grows with document length. Such full-page sequential generation overlooks a key property of document parsing: layout must be analyzed globally, whereas block content can be parsed in parallel. Based on this observation, we introduce HPD-Parsing, which replaces full-page autoregressive generation with a Hierarchical Parallel Decoding paradigm. A main layout branch organizes the overall document structure and dynamically assigns block-level content decoding to concurrent branches, while progressive multi-token prediction (P-MTP) further reduces the decoding steps within each branch. Experiments on public benchmarks show that HPD-Parsing achieves 4,752 tokens per second, delivering 2.62\times the throughput of the fastest existing document parsing model and 3.06\times that of the vanilla autoregressive baseline, while maintaining competitive parsing accuracy. These results establish hierarchical parallel decoding as an effective alternative to full-page autoregressive generation, opening a new direction for efficient unified document parsing.
[NLP-32] CASE: Causal Alignment and Structural Enforcement for Improving Chain-of-Thought Faithfulness
【速读】： 该论文旨在解决生成式推理（Chain-of-thought, CoT）中推理过程与最终答案之间缺乏因果忠实性的问题，即模型可能通过绕过中间推理链的“指令-答案直连捷径”生成答案，从而导致推理不可信。其核心问题是：在标准自回归大语言模型中，答案生成同时依赖于指令和推理链，破坏了理想因果路径 Z→X→Y（指令→推理链→答案），使得指令可直接影响答案，违背了推理链应有的中介作用。为解决此问题，论文提出CASE框架，其关键在于结合训练时的因果对齐与推理时的结构强制机制：在训练阶段，构建反事实推理、有偏指令和空指令数据集，并采用选择性损失微调以增强推理链对答案的依赖性，同时抑制指令捷径；在推理阶段，通过掩码机制阻断指令词元到答案词元的直接注意力连接，强制模型必须依赖生成的推理链进行决策。理论分析表明，该设计能有效促进因果忠实性。实验结果表明，CASE在三个模型和四个基准测试上相较最强基线平均提升37%的推理忠实性，展现出更强的跨数据集迁移能力，同时保持较高的准确率。
链接: https://arxiv.org/abs/2607.18820
作者: Ziming Wang,Yinghua Yao,Changwu Huang,Ke Tang,Xin Yao
机构: Ziming Wang1,2; Yinghua Yao2; Changwu Huang3; Ke Tang1; Xin Yao4
类目: Computation and Language (cs.CL)
备注:
Abstract:Chain-of-thought (CoT) reasoning is widely used to improve both the performance and interpretability of large language models (LLMs), yet the generated reasoning may not faithfully support the final answer. We study this problem from a causal perspective, where a faithful CoT process should follow the chain Z\rightarrow X\rightarrow Y , with Z , X , and Y denoting the instruction, reasoning chain, and final answer, respectively. In this process, the instruction should affect the answer only through the reasoning chain. However, conventional autoregressive LLMs condition answer generation on both the instruction and the CoT, which still allows a direct instruction-to-answer shortcut. To address this issue, we propose CASE, a framework that combines training-time causal alignment and inference-time structural enforcement. During training, CASE builds counterfactual-CoT, biased-instruction, and empty-instruction datasets, and applies selective-loss fine-tuning to strengthen CoT-to-answer dependence while suppressing instruction shortcuts. During inference, CASE masks direct attention from instruction tokens to answer tokens, preventing the model from bypassing the generated CoT. We provide an information-theoretic analysis showing how these components promote faithful chains. Experiments on three models and four benchmarks show that CASE achieves a 37% average per-setting relative improvement in overall CoT faithfulness over the strongest baselines, exhibits stronger cross-dataset faithfulness transfer, and maintains competitive average accuracy. Code is available at this https URL.
[NLP-33] RF-Agent : A Practical Framework for Building Language Agents for RFIC Design
【速读】： 该论文旨在解决生成式 AI 在射频（RF）电路设计领域应用受限的问题，其核心瓶颈在于缺乏领域专用的数据集与标准化评估基准。为此，研究提出 RF-Agent 框架，通过教科书驱动的知识蒸馏方法构建首个面向射频领域的推理数据集（RF-Reasoning Dataset），包含超过 11,000 个样本，并配套设计了专门的多选题基准测试。解决方案的关键在于采用多智能体问答-思考-求解-作答（QTSA）流程，将七本经典射频教科书的章节级内容结构化为高质量训练数据；在模型适配方面，对比监督微调（SFT）与三种检索增强生成（RAG）配置（语义、关键词、混合），结果表明：领域特定的 SFT 显著提升各类大语言模型（LLM）在射频推理任务中的表现，尤其对中小型模型效果显著；而在 RAG 方法中，基于语义的检索优于关键词和混合策略，表明嵌入向量驱动的上下文对齐更契合射频领域知识的复杂性与逻辑关联性。该数据集与基准为未来基于大语言模型的射频电路设计研究提供了可复用的基础平台。
链接: https://arxiv.org/abs/2607.18772
作者: Yueqi Xing,Houbo He,Jolie Wang,Erin Ni,Shikai Wang,Qiufeng Li,Weidong Cao,Taiyun Chi
机构: Rice University (莱斯大学); The George Washington University (乔治·华盛顿大学)
类目: Computation and Language (cs.CL)
备注: Accepted at ICLAD (IEEE International Conference on LLM-Aided Design), 2026
Abstract:Large language models (LLMs) have driven rapid progress in electronic design automation (EDA), yet their application to radio-frequency (RF) circuit design remains limited by the scarcity of domain-specific datasets and standardized benchmarks. We present RF-Agent, which addresses this gap through textbook-driven knowledge distillation. A multi-agent Question-Thinking-Solution-Answer (QTSA) pipeline converts a subsection-level corpus from seven canonical RF textbooks into the first-of-its-kind RF-domain reasoning dataset (over 11,000 samples) with a dedicated multiple-choice benchmark. On this benchmark we study two adaptation strategies: supervised fine-tuning (SFT) and three retrieval-augmented generation (RAG) configurations (semantic, keyword, hybrid). Across multiple LLM families, domain-specific SFT significantly improves RF reasoning, especially for small and medium-sized models; among RAG configurations, semantic retrieval performs best, indicating embedding-based context alignment suits RF reasoning better than naive fusion. The dataset and benchmark provide a reusable foundation for future work on LLM-aided RF circuit design.
[NLP-34] Bounding Boxes to Improve Small Language Model Performance on Vision-Based Grading Tasks
【速读】： 该论文旨在解决小型语言模型（Small Language Models, SLMs）在教育场景中执行复杂视觉任务（如手写答题卷评分）时面临的准确率低与计算成本高的问题。其核心挑战在于，大尺寸图像的处理带来高昂的计算开销，且页面中的视觉干扰因素影响模型对关键信息的识别。论文提出的解决方案关键在于：采用边界框（bounding boxes）对考生作答区域进行裁剪，作为前置预处理步骤，以聚焦模型注意力于有效内容区域。实验基于2025年澳大利亚物理奥林匹克竞赛的手写答卷扫描数据集，在不同规模（4B至72B参数）的模型上对比了链式思维（Chain of Thought, CoT）提示与图像裁剪的效果。结果表明，使用边界框显著提升了评分准确率，并大幅降低了浮点运算量（FLOPs），验证了其在提升SLMs在大规模视觉-语言教育评估应用中的效率与可行性方面具有关键作用。
链接: https://arxiv.org/abs/2607.18767
作者: Lachlan McGinness
机构: Australian National University (澳大利亚国立大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted for 1st Workshop on Small Language Models for Education (SLM4ED '26) at AIED 2026
Abstract:The deployment of Small Language Models (SLMs) in educational settings offers significant advantages in terms of privacy, cost, and scalability. However, SLMs often struggle with complex vision-based tasks, such as grading handwritten student exams, due to the high computational cost of processing large images and the visual distractions present on a full page. In this paper, we investigate whether cropping student responses using bounding boxes can improve the accuracy and computational efficiency of SLMs on a short-answer grading task. Using a dataset of scanned handwritten responses from the 2025 Australian Physics Olympiad, we evaluate the performance of several models ranging from 4B to 72B parameters under varying conditions of Chain of Thought (CoT) prompting and image cropping. Our results demonstrate that using bounding boxes significantly improves grading accuracy and reduces computational cost (FLOPs) across models. We conclude that bounding boxes are a crucial pre-processing step for deploying SLMs in large-scale, vision-based educational assessments.
[NLP-35] Agent DebugX: An Open-Source Toolkit for Failure Observability Attribution and Recovery in LLM Agents
【速读】： 该论文旨在解决大语言模型智能体（LLM agent）在执行过程中出现故障时难以定位根本原因的问题，尤其针对错误表现与实际错误根源之间存在时空延迟的挑战。现有可观测性工具虽可重放执行轨迹，但缺乏对根因诊断的支持以及从诊断到恢复的自动化转化能力。为此，论文提出AgentDebugX——一个开源的闭环调试框架，其核心创新在于将调试过程建模为“检测（Detect）、归因（Attribute）、恢复（Recover）与重试（Rerun）”的循环流程。其中，DeepDebug作为核心组件，通过全局轨迹理解、结构引导式调查与交叉验证机制实现多轮次根因分析。实验结果表明，在Who and When基准测试中，DeepDebug在两个开源权重模型上均取得最优严格归因准确率，于qwen3.5-9b上达到28.8%的精确任务-步骤准确率，显著优于最强单次遍历基线（21.7%）。在GAIA基准上，其仅需一次重试即可修复13/73个失败任务，远超三个解耦自修正基线（4–6个），并将整体准确率由55.8%提升至63.6%。AgentDebugX通过Python库、命令行接口、网页控制台及可安装的智能体技能形式提供完整工作流支持，并引入可选的Error Hub，用于共享清洗后的故障诊断-修复包，实现调试知识的积累与复用。
链接: https://arxiv.org/abs/2607.18754
作者: Kunlun Zhu,Xuyan Ye,Zhiguang Han,Yuchen Zhao,Bingxuan Li,Weijia Zhang,Muxin Tian,Xiangru Tang,Pan Lu,James Zou,Jiaxuan You,Heng Ji
机构: University of Illinois Urbana-Champaign (伊利诺伊大学厄本那-香槟分校); University of Toronto (多伦多大学); Google(谷歌); Stanford University (斯坦福大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:LLM agent failures are difficult to debug because the step where an error surfaces is often not the one that caused it. Existing observability tools replay execution traces but provide little support for identifying the root cause or translating diagnosis into recovery. We present AgentDebugX, an open-source debugging framework that organizes debugging as a closed loop of Detect, Attribute, Recover, and Rerun. At its core, DeepDebug performs multi-turn root-cause diagnosis through global trajectory understanding, structure-guided investigation, and cross-examination. On the Who and When benchmark, DeepDebug achieves the best strict attribution accuracy among the evaluated methods on both tested open-weight backbones, reaching 28.8 percent exact agent-and-step accuracy on qwen3.5-9b versus 21.7 percent for the strongest single-pass baseline. On GAIA, DeepDebug repairs 13 of 73 failed tasks in a single rerun, compared with 4 to 6 for three decoupled self-correction baselines, improving overall accuracy from 55.8 percent to 63.6 percent. AgentDebugX exposes this workflow through a Python library, CLI, web console, and installable agentic skill, and provides an opt-in Error Hub for sharing scrubbed failure-diagnosis-repair bundles and reusing them as debugging memory.
[NLP-36] Is EEG-to-Text Feasible in Real-World Scenarios? An In-Depth Analysis Using a Neuropsychology-Inspired Benchmark ACL2026
【速读】： 该论文旨在解决当前脑电图到文本（EEG2Text）模型在实际应用中面临的核心瓶颈：现有模型高度依赖教师强制（teacher-forcing）评估机制，一旦脱离该机制便无法生成有意义的解码结果，从而限制了其在非学术、真实场景中的可用性。这一局限性引发了学界对EEG是否真正包含可解码语言信息的广泛质疑。论文的关键突破在于揭示并纠正了现有基准测试中忽视脑电信号固有的不稳定性（EEG instability）这一根本问题，该问题长期干扰了模型推理的有效性并引发争议。通过引入受神经心理学启发的实验范式，研究团队构建了首个专用于评估无教师强制条件下的EEG2Text性能的公开数据集——高密度128通道脑电图-文本语料库（Corpus OF Eeg-To-Text, COFETT），该数据集具备区分不同模型性能的最先进能力，并支持鲁棒的、无需教师强制的评估，为实现真正实用化的非侵入式脑机接口语言解码系统提供了关键基础。
链接: https://arxiv.org/abs/2607.18749
作者: Zihan Zhang(1),Yu Bao(1 and 2),Xiao Ding(1),Tianyi Jiang(3),Kai Xiong(4) ((1) Research Center for Social Computing and Interactive Robotics, Harbin Institute of Technology, (2) Shanghai Innovation Institute, (3) State Key Laboratory for Novel Software Technology, Nanjing University, (4) Zhongguancun Laboratory)
机构: Harbin Institute of Technology (哈尔滨工业大学); Shanghai Innovation Institute (上海创新研究院); Nanjing University (南京大学); Zhongguancun Laboratory (中关村实验室)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Emerging Technologies (cs.ET); Neurons and Cognition (q-bio.NC)
备注: 17 pages, 8 figures. Published in Proceedings of ACL 2026 Main Conference
Abstract:Translating brain signals into text could restore communication for people with severe paralysis, yet practically usable systems to date rely on invasive electrocorticography (ECoG). Electroencephalography (EEG) offers a non-invasive alternative, and EEG-to-text (EEG2Text) has been widely explored. Interestingly, however, EEG2Text models generally rely on teacher-forcing evaluation; without it, they fail to generate meaningful decoding. This reliance prevents EEG2Text from being applied in real-world, non-academic settings. This has fueled numerous debates about whether EEG2Text is a meaningful direction, by extension, and whether EEG truly contains decodable linguistic information. Here, using a neuropsychology-informed paradigm, we find that existing EEG2Text benchmarks have neglected EEG instability, a flaw that has confounded inference and sparked debate. Our experiments furnish key evidence for the feasibility of teacher-forcing-free EEG2Text decoding. Accordingly, we assemble the Corpus OF Eeg-To-Text (COFETT) using a 128-channel high-density EEG cap, providing a benchmark dedicated to evaluating EEG2Text models. In comparisons with multiple existing benchmarks, COFETT achieves SOTA ability to distinguish among model performances and enables robust, teacher-forcing-free evaluation, thereby opening a path toward practical EEG2Text applications. COFETT is open sourced in this https URL.
[NLP-37] Dual Attention Residuals
【速读】： 该论文旨在解决现有Transformer残差路径扩展方法中多流架构（multi-stream）与历史信息检索（historical retrieval）之间缺乏有效交互的问题。当前方法通常将两者独立研究：历史检索依赖于早期深度的信息，而多流架构则维持多个独立的残差轨迹，但各流之间无法共享或影响彼此的深度选择策略，导致信息利用不充分。为此，论文提出双注意力残差（Dual Attention Residuals, DAR），其核心创新在于通过双向跨流地址机制（reciprocal cross-stream addressing）实现多流间的协同作用——针对每个目标流，DAR利用相反流中归一化状态计算深度权重，并将其应用于目标流自身历史值的加权聚合。所检索的状态以受限门控写入方式更新，保持原有网络结构不变；进一步提出的块形式变体则在块级别管理历史信息，以控制计算开销。实验表明，DAR在0.1B至1B参数的密集模型以及7B参数的稀疏MoE模型上均显著降低验证损失，且消融实验排除了额外流或值投影带来的性能提升可能性。表征分析与干预实验进一步证实，该方法有效保留了深度维度上的多样性，避免了传统双流设计中存在的冗余或功能失衡问题。
链接: https://arxiv.org/abs/2607.18730
作者: Xingda Yu,Yining Li,Xinzhang Liu,Zhihao Yang,Haowei He,Chao Wang,Yongxiang Li,Shuangyong Song
机构: 未知
类目: Computation and Language (cs.CL)
备注: 13 pages, 10 figures, and 8 tables
Abstract:Recent work extends Transformer residual pathways along two complementary axes: historical retrieval selects information from earlier depths, whereas multi-stream methods maintain multiple residual trajectories. These capabilities have largely been studied in isolation, and assigning an independent retriever to each stream still prevents one trajectory from influencing depth selection in another. We propose Dual Attention Residuals (DAR), which brings multi-stream interaction into historical retrieval through reciprocal cross-stream addressing. For each target stream, DAR computes depth weights from normalized states in the opposite stream and applies them to values from the target stream’s own history. The retrieved states are combined for an unchanged Transformer branch and updated through constrained gated writes; a block-form variant operates on block-level histories to control overhead. Across dense models from 0.1B to 1B parameters and a 7B sparse-MoE model, DAR consistently improves validation loss over standard residual Transformers and Attention Residuals. Routing ablations show that the gain cannot be explained by an additional stream or value projection alone. Representation and intervention analyses further show that reciprocal cross-stream selection preserves depth-wise diversity and avoids the redundancy or functional imbalance observed in alternative two-stream designs.
[NLP-38] Find Before You Fine-Tune: A Diagnostic Study of Small LLM s for Cybersecurity QA ICML
【速读】： 该论文旨在解决在网络安全领域问答（Cybersecurity QA）任务中，如何在付出微调成本前有效筛选适合的中小型大语言模型（Small LLMs）这一关键问题。由于微调可能引发先验知识退化、指令遵循能力减弱及幻觉增加等问题，尤其是在标注数据稀缺或动态变化的场景下，盲目微调存在显著风险。其解决方案的核心在于提出一种面向任务的诊断框架FiT（Find before Fine-Tune），该框架从三个关键能力维度对小模型进行评估：词汇识别能力（vocabulary recognition）、参数化知识（parametric knowledge）以及检索信息的情境化处理（contextualization of retrieved information）。通过实证研究对比五种开源70亿参数模型在两种不同微调策略下的表现，研究发现微调并非普遍有益，且不同策略导致截然不同的性能变化模式：以知识为导向的微调仅造成适度且排名保持稳定的知识下降；而以指令为导向的微调则因诱导模型主动回避回答，导致知识测量值崩溃，反转知识排名，但检索支撑的情境化能力基本维持不变。研究进一步利用秩相关性分析量化了这些特定于微调范式的模式，并证明微调前的FiT评分可有效预测微调后的性能变化方向。因此，该工作表明，基于任务导向的预诊断能够有效筛选出不适用的模型，避免无效微调，从而支持更安全、高效的中小型模型在网络安全问答流水线中的部署。
链接: https://arxiv.org/abs/2607.18725
作者: Shaswata Mitra,Subash Neupane,Trisha Chakraborty,Himanshu Tripathi,Sudip Mittal,Aritran Piplai,Shahram Rahimi
机构: The University of Alabama (阿拉巴马大学); Meharry Medical College (梅哈里医学院); Mississippi State University (密西西比州立大学); The University of Texas at El Paso (得克萨斯大学埃尔帕索分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 8 pages, 5 figures, 4 tables, IEEE ICMLA
Abstract:Large Language Models (LLMs) are increasingly fine-tuned for critical-domain Question-Answering (QA), yet choosing which small model to adapt, before paying the cost of adaptation, remains difficult. Fine-tuning can improve domain alignment, but it may also erode prior knowledge, weaken instruction-following, or increase hallucination, especially when labeled data are scarce or rapidly evolving as in cybersecurity. We present FiT (Find before Fine-Tune), a task-oriented diagnostic framework that characterizes small LLMs along three capabilities required for cybersecurity QA: vocabulary recognition, parametric knowledge, and contextualization of retrieved information. Using FiT, we conduct an empirical study of five open-weight 7-billion-parameter models under two fine-tuning regimes. We find that fine-tuning does not uniformly help: it consistently degrades vocabulary and parametric knowledge in small models, and the two regimes trade off differently. Knowledge-focused tuning causes moderate, rank-preserving degradation, whereas instruction-focused tuning collapses measured knowledge through induced abstention, inverting the knowledge ranking while leaving retrieval-grounded contextualization essentially intact. We quantify these regime-specific patterns with rank-correlation analysis and show that pre-fine-tuning FiT scores anticipate the direction of post-tuning change. Our results suggest that task-oriented diagnosis can screen out unsuitable models, avoid unnecessary fine-tuning, and support safer deployment of small LLMs in cybersecurity QA pipelines.
[NLP-39] Stale but Stable: Staleness-Adaptive Trust Regions for Stabilizing Asynchronous Reinforcement Learning
【速读】： 该论文旨在解决异步强化学习（asynchronous reinforcement learning）中因策略滞后、推理引擎延迟及专家混合（mixture-of-experts）路由不一致导致的更新过时性（staleness）问题，该问题会加剧训练-推理偏差，进而破坏信任域（trust-region）约束的有效性。传统PPO剪裁仅对采样后的更新方向施加限制，无法在高过时场景下有效控制策略更新，导致训练不稳定。其核心解决方案是提出自适应过时性信任域（Staleness-Adaptive Trust Region, SAT），通过使用分离的采样日志概率比作为过时性代理，结合基于过时性的核缩放机制识别批次中的高失配尾部，并仅对名义PPO区间中符号选定的端点进行收缩，从而在保持正常样本基线行为的同时，对新捕获的外向更新带施加更保守的约束。理论证明表明，SAT能实现局部区间包含性和相对于PPO的点态悲观性，有效重塑异质过时性下的更新几何结构。实验在基于Qwen3-30B-A3B-Base与SGLang推理引擎、Megatron训练框架构建的解耦异步环境中验证，SAT-GSPO结合R3策略在滞后1和滞后8条件下分别取得35.83和34.79的AIME24 avg@8表现，显著优于基线方法，表明自适应剪裁与路由重播分别针对失配尾部和路由不一致性提供了互补稳定机制，整体上通过将剪裁区间与过时性异质性对齐，实现了异步强化学习的有效稳定性提升。
链接: https://arxiv.org/abs/2607.18722
作者: Junyao Yang,Yucheng Shi,Zongxia Li,Zhongzhi Li,Ruhan Wang,Xiangxin Zhou,Kishan Panaganti,Haitao Mi,Leowei Liang
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 28 pages, 9 figures, 9 tables
Abstract:Asynchronous reinforcement learning improves throughput by decoupling rollout generation from optimization, but staleness is an inevitable byproduct compounded by policy lag, engine delays, and mixture-of-experts routing. From a trust-region perspective, this mismatch is critical: training-inference divergence governs approximation error in finite-horizon bounds, whereas PPO clipping only gates sampled outward updates, acting as a sampled surrogate rather than a full-policy constraint. As a result, high-staleness updates remain weakly controlled in the asynchronous regime where stale rollouts matter most. We introduce the Staleness-Adaptive Trust Region (SAT), which uses the detached sampled log-ratio as a practical staleness proxy, identifies high-mismatch tails within each batch via staleness-based kernel scaling, and contracts only the sign-selected endpoint of the nominal PPO interval. This preserves baseline behavior on ordinary tokens while enforcing more conservative updates on newly intercepted outward bands. We prove local interval containment and pointwise pessimism relative to PPO, showing how the adaptive rule reshapes update geometry under heterogeneous staleness. We evaluate SAT in a decoupled asynchronous RL setup built on Qwen3-30B-A3B-Base, using SGLang as the inference engine and Megatron for training. In this setting, SAT-GSPO w/ R3 achieves the best observed AIME24 avg@8, reaching 35.83 at lag 1 and 34.79 at lag 8, while SAT-GSPO reaches 34.17 at lag 1. Adaptive clipping and routing replay act as complementary stabilizers targeting mismatch tails and routing inconsistency, respectively. Overall, aligning clip intervals with staleness heterogeneity effectively stabilizes asynchronous RL. Comments: 28 pages, 9 figures, 9 tables Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL) Cite as: arXiv:2607.18722 [cs.LG] (or arXiv:2607.18722v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.18722 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-40] Rationale-Guided Knowledge Distillation for Cross-Lingual Stance Detection
【速读】： 该论文旨在解决低资源语言（如加泰罗尼亚语）在跨语言立场检测中因标注数据匮乏而导致模型性能受限的问题。现有方法虽能通过跨语言知识迁移缓解数据不足，但普遍依赖文本与目标之间的语义对齐，忽视了立场判断所需的推理过程。尽管大语言模型（Large Language Models, LLMs）具备强大的推理能力，其高计算开销和延迟限制了实际部署。为此，本文提出一种基于推理路径引导的知识蒸馏框架，核心在于利用思维链（Chain-of-Thought）提示引导LLMs生成具有解释性的推理路径，并将这些推理知识高效地蒸馏至轻量级学生模型中；进一步设计双路径蒸馏机制，同步对齐带有和不带推理增强的表示及其预测分布，并引入两种对比学习策略以提升立场判别能力。实验结果表明，该方法在多语言基准上显著优于现有基线。
链接: https://arxiv.org/abs/2607.18693
作者: Qiuli Zhou,Jingyuan Yao,Shengeng Tang,Hongzhi Chen,Jun Tang,Richang Hong
机构: Hefei University of Technology (合肥工业大学); Hefei Institute of Technology (合肥学院)
类目: Computation and Language (cs.CL)
备注: 23 pages, 7 figures, 3 tables
Abstract:Stance detection aims to identify whether a text expresses a favorable or opposing attitude toward a given target, and serves as an important task for various downstream applications. Although existing studies have achieved strong performance in monolingual settings, especially in English, many low-resource languages such as Catalan still lack sufficient annotated data for training effective models. Cross-lingual stance detection alleviates this problem by transferring stance knowledge from resource-rich languages to low-resource languages. However, most existing methods mainly rely on semantic alignment between texts and targets, while ignoring the reasoning process required for reliable stance inference. Although Large Language Models provide strong reasoning ability, their high computational cost and inference latency limit practical deployment. To address these limitations, we propose a rationale-guided knowledge distillation framework for cross-lingual stance detection. Specifically, we use Chain-of-Thought prompting to guide Large Language Models in generating informative rationales, and distill the resulting reasoning knowledge into a compact student model. We further design a dual-path distillation mechanism to align rationale-enhanced and rationale-free representations, together with their prediction distributions. In addition, two contrastive learning strategies are introduced to improve stance discrimination. Experiments on multilingual benchmarks demonstrate that our method consistently outperforms competitive baselines.
[NLP-41] Semantic Primes as Explanans for Emotion in Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）中情感机制的可解释性问题，特别是如何有效解释模型内部的情感生成过程，以及何为高质量的解释。现有研究虽能恢复情感表征、组件与神经回路，但这些解释往往陷入循环论证，且情感空间维度具有任意性和无限扩展性，难以提供本质性说明。本文提出以自然语义元语言（Natural Semantic Metalanguage, NSM）中的语义原素作为更基础的内部变量，作为情感解释的候选要素。实验在四款指令微调的LLM（Llama-1B、Gemma-2B、Gemma-9B、OLMo-7B）上进行，结果表明：（1）NSM语义原素可被有效恢复为模型内部元素；（2）在参考模型中，基于语义原素的方向干预对情感的调控强度约为基于评估（appraisal）方向的三倍，且选择性提升两倍；（3）模型将语义原素的显式表达视为与对应情感等价的解释。上述证据表明，根据科学解释的标准，NSM语义原素比多种替代方案更适合作为大语言模型中情感现象的解释项（explanans）。
链接: https://arxiv.org/abs/2607.18691
作者: Frank Xing
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 11 pages, 9 figures
Abstract:Progresses have been made on understanding emotion mechanisms of large language models (LLMs). However, how to explain emotion in LLMs, or even what constitutes good explanations, are less clear. Emotion representations, components, circuits are widely recoverable, but as explanations of a model’s own computation they are circular; the emotion space dimensions tend to be arbitrary and non-terminating. A pressing question to ask is whether a more primitive set of internal variables does the work: the semantic primes of the Natural Semantic Metalanguage (NSM). Across four instruction-tuned LLMs (Llama-1B, Gemma-2B, Gemma-9B, OLMo-7B), experiments show that the NSM primes are (1) recoverable internal elements; and (2) on the reference model, intervening with a prime based direction controls emotion about three times as strongly, and twice as selectively, as the best appraisal based direction; and (3) the model treats a prime based explication as interchangeable with the corresponding emotion. These evidences suggest that NSM primes seem to be better explanans for emotion in LLMs than many alternative options according to scientific explanations criteria.
[NLP-42] Fusion Embedding: A Unified Embedding Space for Text Image Video and Audio
【速读】： 该论文旨在解决多模态统一嵌入空间（Unified Embedding Space）中音频模态缺失的问题，即当前基于视觉-语言骨干网络的嵌入模型虽在文本、图像和视频检索任务上表现领先，但完全缺乏对音频的支持；而现有的音频-文本检索系统虽性能优异，却无法处理其他模态。其解决方案的关键在于提出Fusion Embedding系列模型：在冻结的视觉-语言嵌入基座（Frozen Vision-Language Embedding Base）基础上，仅通过极小规模的可训练模块实现对音频模态的高效融合。具体而言，第一代（fusion-embedding-1）仅引入一个16.4M参数的连接器，将冻结的音频塔与基座对接；第二代（fusion-embedding-2）进一步引入模态门控深度适配器（Modality-Gated Deep Adapters，44.2M参数），其分支在文本、图像或视频输入时完全不激活，确保输出与原始基座完全一致（bit-for-bit）。由于基座已实现文本-图像-视频的语义对齐，仅需将音频与文本对齐即可自然催生音频-图像检索能力，且无需任何成对的音视频训练数据。研究还通过受控实验验证了设计空间的有效性，并揭示了适用于任意冻结解码器-语言模型（Decoder-LM）嵌入基座的通用训练协议。两代模型均能在单个GPU上数小时内完成训练，且模型权重、代码及评估工具均已开源。
链接: https://arxiv.org/abs/2607.18666
作者: Abdul Basit Tonmoy,Kazi Fardinul Hoque,Md. Shahrier Islam Arham,Arman Luthra
机构: Eximius Labs(Eximius 实验室); Wabash College(瓦巴什学院); Skop Intelligence Co.(Skop 智能科技公司)
类目: Computation and Language (cs.CL); Sound (cs.SD)
备注: 23 pages, 5 figures. Models: this https URL and this https URL . Code: this https URL
Abstract:A single embedding space that covers text, images, video, and audio lets one index serve every query a user can pose. Embedding models built on vision-language backbones now lead text/image/video retrieval benchmarks but lack audio entirely, while audio-text retrieval is led by specialist systems that serve no other modality. We present the Fusion Embedding family, which adds audio to a frozen vision-language embedding base whose parameters are never updated: generation 1 (fusion-embedding-1) trains only a 16.4M-parameter connector between a frozen audio tower and the frozen base, and generation 2 (fusion-embedding-2) adds modality-gated deep adapters (44.2M parameters) whose branch never executes on text, image, or video inputs: their outputs are bit-for-bit those of the released base, verified after every training run. Because the base already binds text, images, and video, aligning audio to text alone makes audio-image retrieval emerge, with zero paired audio-visual training data. Alongside the recipe we map its design space with controlled negative results (rewriting training captions with an LLM, substituting a leaderboard-stronger audio tower, and widening the connector each reduce retrieval) and with training-protocol findings that we expect to transfer to any frozen decoder-LM embedding backbone. Both generations train in hours on a single GPU. Weights, code, and the evaluation harness are openly released.
[NLP-43] Staged Depth-Pruning Distillation of a Flow-Matching Text-to-Speech Teacher: A Compact Hindi Speech Synthesizer
【速读】： 该论文旨在解决在极端数据预算（约17.6小时）下构建高性能、紧凑型印地语文本到语音（Text-to-Speech, TTS）模型的挑战，因为直接在如此有限的数据上从头训练小型模型会完全失败。其核心解决方案是采用深度剪枝（depth pruning）的教师蒸馏（teacher distillation）策略，即仅保留大型流匹配教师模型（IndicF5，337M参数的DiT架构）的宽度、文本维度、注意力头数及输入输出接口不变，通过逐层移除均匀分布的Transformer块来压缩学生模型，同时保持非块张量的一一映射。该方法通过渐进式剪枝（从22至6个块）并结合自动语音识别（ASR）词错误率（WER）作为每一步的验证目标，确保模型在压缩过程中仍维持功能性。最终生成的学生模型在249M和190M参数规模下达到0.00的未见句WER，且在131M参数下仍具鲁棒性；102M参数时出现性能骤降，归因于数据预算限制而非蒸馏方案本身。此外，研究还揭示了两个隐蔽的训练/推理不一致问题（梅尔滤波器组与旋转位置编码库版本差异），并提出与版本无关的修复方案。该方法成功实现了一款高质量、实时运行于6 GB GPU笔记本电脑上的印地语语音合成系统，经独立FLEURS基准测试验证，其190M学生模型性能可媲美原始教师模型及MMS-TTS-hin。
链接: https://arxiv.org/abs/2607.18662
作者: Sivateja Trikutam
机构: 未知
类目: ound (cs.SD); Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注: 7 pages, 4 tables. Model and benchmark artifacts: this https URL
Abstract:We present a practical recipe for building a compact Hindi text-to-speech (TTS) model by distilling a large flow-matching teacher (IndicF5, 337M-parameter DiT) under a severe data budget (~17.6 hours). Training a small model from scratch on this much data fails outright. Instead we warm-start the student from the teacher by pruning depth only: keeping the teacher’s width, text dimension, attention heads, and mel/text I/O fixed so all non-block tensors copy one-to-one, and retaining an evenly-spaced subset of transformer blocks. We first measure how much depth the teacher tolerates (it remains near-functional at -27% blocks but collapses past -50%), then descend gradually (22 - 16 - 12 - 8 - 6 blocks), re-fine-tuning after each prune, with each step gated by an objective ASR word-error-rate (WER) check. The resulting students reach WER 0.00 on unseen sentences at 249M and 190M parameters, and remain robust down to 131M; at 102M we observe a clear capacity cliff that we attribute to the data budget rather than the recipe. We also document two train/inference feature- and library-parity failures (mel filterbank and rotary-embedding library versions) that silently degrade audio, and a version-independent fix. The method yields a high-quality Hindi voice that runs in real time on a 6 GB laptop GPU. An independent 50-sentence FLEURS benchmark compares the released 190M student against its teacher and MMS-TTS-hin.
[NLP-44] Mark Dont Erase: Token Inoculation for Dual-Use Knowledge in LLM s
【速读】： 该论文旨在解决生成式AI（Generative AI）在双用途知识（dual-use knowledge）安全干预中面临的根本性难题：传统方法要么通过“遗忘”或“过滤”删除有害内容，要么在输出层进行“拒绝训练”以抑制危险响应，但二者均导致邻近领域能力下降或过度拒绝，损害模型的可用性与泛化性能。其解决方案的关键在于将安全对齐重新定义为“条件控制”（conditioning）问题，而非知识消除。提出的方法“令牌接种”（Token Inoculation）采用“绑定-分支”机制：首先在持续预训练阶段，通过插入特殊控制令牌标记双用途文档，使模型将该令牌与有害内容的语义建立强关联；其次在监督微调阶段，训练模型在该令牌存在时正确回应有害查询，在其缺失时拒绝响应，从而实现无需移除双用途知识的可选拒绝。实验表明，在如生物武器相关数据集（WMDP-Bio）上，该方法将有害任务准确率从79%降至18%，同时保留93%的基础模型良性任务性能（如MMLU），在1B至14B参数规模下均优于传统遗忘与拒绝微调基线，展现出最优的安全-效用权衡。此外，研究进一步证明拒绝选择性可通过条件信号质量调控，且预训练阶段的领域特定语义绑定是实现泛化行为控制的关键。结果表明，将敏感知识保留在模型中并施加受控访问，比彻底销毁知识更能实现精准的行为控制，因此安全对齐应被视作条件化而非遗忘问题。
链接: https://arxiv.org/abs/2607.18639
作者: Seunghyun Lee,Dongyoon Han,Sangdoo Yun
机构: NAVER AI Lab (NAVER人工智能实验室)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 23 pages, 13 figures, 8 tables
Abstract:Safety interventions on dual-use knowledge typically choose between destroying hazardous content (e.g., unlearning, filtering) and suppressing it at the output layer (e.g., refusal training); both pay a tax in adjacent-domain competence or over-refusal. We argue that the right operation is conditioning, not reduction: we show that hazardous knowledge can be retained in the model and behaviorally gated by a privileged control token. Our method, Token Inoculation, introduces a binding-and-branching approach. First, during continued pre-training, we mark hazardous content by inserting a special token alongside dual-use documents, so the model binds the marker to the underlying semantics of the hazardous domain. Second, during supervised fine-tuning, we teach the model to answer hazardous queries correctly when the special token is present and to refuse them when it is absent, thereby enabling selective refusal without removing dual-use knowledge. On hazardous domain (e.g., WMDP-Bio), Token Inoculation reduces accuracy from 79% to 18% while retaining 93% of the base-model’s benign-domain performance (e.g., MMLU), achieving the best safety-utility trade-off against unlearning and refusal-tuning baselines across 1B-14B model scales. We further show that refusal selectivity is controllable through the quality of the conditioning signal and that domain-specific semantic binding during pre-training is critical for the conditional behavior to generalize beyond memorized triggers. Our results suggest that safety alignment is better cast as a conditioning problem than a forgetting one: behavioral control is more precise when sensitive knowledge is retained under controlled access than when it is destroyed.
[NLP-45] LatentMT: Machine Translation with Latent Reasoning
【速读】： 该论文旨在解决传统机器翻译（Machine Translation, MT）模型在性能提升过程中过度依赖参数量增长或显式链式思维（chain-of-thought）token生成所导致的计算开销大、效率低的问题。其核心解决方案是提出一种基于隐式推理循环（latent-reasoning looped language models, LoopLMs）的新范式——LatentMT，通过在隐藏状态内部进行额外的循环计算，而非增加模型参数或显式输出推理步骤，实现高效且高性能的翻译。其关键在于：利用轻量级训练策略对一个仅26亿参数的小型骨干模型进行适配，在32个涵盖高、中、低资源语言的翻译方向上，达到与参数量大3至5倍的非隐式推理模型相当甚至更优的性能，尤其在中低资源语言上达到当前最优水平。研究发现，随着推理步数的增加，翻译质量在初期持续提升，但很快趋于饱和，机制分析表明这一现象源于隐藏表示在循环推理过程中的差异逐渐缩小。此外，效率分析显示，LatentMT在训练和推理阶段所需的计算成本显著低于同等性能的大型非隐式推理模型，验证了隐式循环计算作为实现紧凑、高效且强性能机器翻译的可行路径。
链接: https://arxiv.org/abs/2607.18618
作者: Wei-Rui Chen,Samar M. Magdy,Chiyu Zhang,Wenhui Zhu,Zhipeng Wang,Muhammad Abdul-Mageed
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Latent-reasoning looped language models (LoopLMs) offer a different scaling path for machine translation (MT): instead of increasing parameter count or emitting explicit chain-of-thought tokens, they spend additional recurrent computation inside hidden states. We introduce LatentMT, the first systematic study of latent-reasoning LoopLMs for machine translation. LatentMT adapts a small 2.6B-parameter backbone model with lightweight training. Across 32 translation directions spanning high-, mid-, and low-resource languages, LatentMT achieves performance comparable to models three to five times larger. It is competitive in a high-resource language and achieves state-of-the-art performance on both mid-resource and low-resource languages. Studying the behavior of scaling the number of recurrent reasoning steps, we find that recurrent computation consistently improves translation quality in early steps, then saturates quickly afterwards. Our mechanistic analysis shows that hidden-representation differences shrink along the recurrent reasoning-step axis, supporting the observed saturation in performance. Finally, our efficiency analysis shows that LatentMT requires lower training and inference compute than much larger non-latent-reasoning models with similar performance, making latent recurrent computation a promising path toward compact, efficient, and strong machine translation.
[NLP-46] Stochastic Meta-Unlearning: Bridging Language Backbone and Multimodal Unlearning
【速读】： 该论文旨在解决视觉-语言模型（Vision-Language Models, VLMs）中的机器遗忘（machine unlearning）问题。与仅处理文本的模型不同，VLMs 由语言主干（language backbone）和视觉组件共同构成，其多模态特性使得遗忘过程更加复杂。研究发现，当从单模态遗忘转向VLM遗忘时存在一个令人意外的现象：即使语言主干单独遗忘某一目标，一旦引入图像信息，该目标仍可在完整VLM中被恢复，这表明仅依赖文本反馈无法实现可靠的VLM遗忘。针对这一挑战，本文提出了一种基于随机元遗忘（Stochastic Meta-Unlearning, SMU）的双层优化框架，其核心在于利用VLM级别的反馈来学习一个具备遗忘能力的初始化状态。在内层循环中，SMU使用文本数据对语言主干执行少量遗忘更新；在外层循环中，将更新后的语言主干与冻结的视觉-语言模型重新组合，并在整体VLM层面评估遗忘效果与保留性能。该设计使遗忘更新能够感知最终的多模态行为，同时保持更新局部于语言主干。实验在两个VLM架构、两个多模态表情包数据集及三种基线方法上验证了SMU的有效性，结果表明其在遗忘-保留权衡方面表现最优，相比最强基线，平均遗忘准确率降低10.52个百分点，平均保留与测试准确率分别提升20.10和17.01个百分点。更重要的是，SMU具有良好的可迁移性，能适用于新的遗忘目标以及不同的元测试遗忘方法，证明了VLM级反馈可显著提升语言主干遗忘的可靠性与泛化能力。
链接: https://arxiv.org/abs/2607.18615
作者: Zijie Liu,Jinhao Duan,Gaowen Liu,Sijia Liu,Tianlong Chen
机构: UNC at Chapel Hill (北卡罗来纳大学教堂山分校); Cisco Research (思科研究院); Michigan State University (密歇根州立大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 15 pages
Abstract:Machine unlearning for vision-language models (VLMs) remains underexplored. Unlike language models, VLMs combine a language backbone with visual components, which makes unlearning more complex. There is a surprising phenomenon when moving from single-modality unlearning to VLM unlearning: a target forgotten by the standalone language backbone can still be recovered when image information is given to the full VLM. This shows that text-only feedback is not enough for reliable VLM unlearning. Motivated by this observation, we propose Stochastic Meta-Unlearning (SMU), a bilevel framework that uses VLM-level feedback to learn an unlearning-ready initialization. In the inner loop, SMU applies a few unlearning steps to the language backbone using text data. In the outer loop, SMU recomposes the updated backbone with the frozen VLM and evaluates forgetting and utility at the VLM level. This design makes the unlearning update aware of the final multimodal behavior, while still keeping the update local to the language backbone. Experiments on two VLMs, two multimodal meme datasets, and three baselines show that SMU achieves the best overall forget-retain trade-off. Compared with the strongest baseline for each metric, SMU reduces average Forget accuracy by 10.52 points and improves average Retain and Test accuracy by 20.10 and 17.01 points, respectively. More importantly, SMU also transfers to new forgetting targets and to different meta-test unlearning methods. These results suggest that VLM-level feedback can make language-backbone unlearning more reliable and more transferable for VLMs.
[NLP-47] For What Reason ? Interpreting Models Encoding of Causation and Antithesis
【速读】： 该论文旨在解决大语言模型在理解文本中话语关系（discourse relations）时的内部机制问题，特别是因果关系（causation）与对立关系（antithesis）这两种对比性话语关系的表征与推理过程。其核心挑战在于揭示指令微调的Transformer模型（如LLaMA和Mistral）如何在不同网络层中编码并动态处理这些语义结构。解决方案的关键在于将话语关系识别任务建模为下一个词预测任务，并结合一系列可解释性技术对模型内部状态进行分析。研究发现，部分早期层在序列中段即做出预测决策，而中间层则在接近序列末尾时完成最终判断；大多数深层网络主要负责传播前期决策而非主动干预。此外，某些层表现出对特定答案的偏好，暗示了话语推理在模型内部存在非对称的表征模式，这为提升模型的语言理解能力与伦理对齐提供了关键洞察。
链接: https://arxiv.org/abs/2607.18570
作者: Abhidip Bhattacharyya,Shira Wein
机构: University of Massachusetts Amherst; University of South Florida
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Discourse relations provide document structure, critical to language understanding and enabling language model performance and ethicality. In this work, we investigate how instruction-tuned Transformer models (LLaMA and Mistral) encode discourse relations in English, with a particular focus on the contrasting relations of causation and antithesis. Framing the task as a next-token prediction task and applying a suite of interpretability techniques to test model internals, our findings show that certain early layers make predictive decisions at mid-sequence tokens, while some mid-level layers finalize their decisions closer to the last token. Most of the remaining layers primarily propagate earlier decisions rather than actively influencing them. Additionally, we observe that some layers exhibit a preference for one answer over alternatives, suggesting asymmetric representation of discourse-based reasoning.\footnoteOur code is available at this https URL
[NLP-48] he Story Shapes the Agent : Narrative Priors in LLM Behavior
【速读】： 该论文旨在解决生成式 AI（Generative AI）在任务执行中因叙事框架（narrative framing）影响行为表现，而超越角色设定（persona）的内在机制问题。研究发现，尽管角色提示被广泛用于引导大语言模型（LLM）代理的行为，但任务的叙事背景对行为模式的影响远超角色设定本身。其解决方案的关键在于通过结构同构性（structural isomorphism）设计三个共享相同动作空间、阶段推进逻辑与资源约束的文本化调查游戏——疾病调查、IT故障排查和谋杀谜案，仅改变任务叙事。研究识别出“叙事先验”（narrative priors）这一核心概念，即由任务故事框架激活的系统性行为倾向，独立于决策结构存在。实验表明，叙事先验解释的行为方差是角色设定的5至31倍，且在不同模型架构间保持一致；在三个领域中的两个，叙事先验与任务成功率呈负相关。角色设定跨叙事迁移的效果主要源于“行为锚点”（behavioral anchors），即角色描述中与共通动作直接映射的语言元素；因果干预实验证实，移除高迁移角色中的锚定词可使跨叙事一致性下降95%。此外，该框架可推广至未见的第四类叙事，并衍生出一种提升跨叙事迁移能力的角色选择方法。研究结论指出，确保LLM行为在叙事变化下仍具鲁棒性的关键，在于基于具体行动而非抽象描述进行建模。
链接: https://arxiv.org/abs/2607.18566
作者: Yixuan Wang,James Lester,Shashank Srivastava
机构: University of North Carolina at Chapel Hill (北卡罗来纳大学教堂山分校); North Carolina State University (北卡罗来纳州立大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted at COLM 2026. 10 pages, 3 figures, 16 tables in appendix
Abstract:Persona prompting is widely used to steer LLM agent behavior, yet the narrative framing of a task can matter more than the assigned persona. We isolate this effect through structural isomorphism, constructing three text-based investigation games that share the same action space, stage progression, and resource constraints while varying only task narrative: disease investigation, IT troubleshooting, and murder mystery. Across 1,890 sessions spanning 3 models and 10 personas, we identify narrative priors: systematic action tendencies activated by a task’s story framing, independent of its decision structure. Narrative priors explain 5-31x more behavioral variance than persona, are consistent across model architectures, and in two of three domains are negatively associated with task success. Persona effects that do transfer across narratives arise from behavioral anchors, persona descriptions whose language maps directly onto shared actions. Causal interventions confirm this: removing anchor words from a high-transfer persona reduces cross-narrative consistency by 95%. Our framework also generalizes to a held-out fourth narrative and yields a persona-selection method that improves cross-narrative transfer. These results suggest that LLM behavior that survives narrative changes should be grounded in concrete actions rather than abstract descriptions.
[NLP-49] Reasoning Fine-Tuning Induces Persistent Latent Policy States
【速读】： 该论文旨在解决生成式语言模型在经过推理专项微调（reasoning fine-tuning）后，其内部机制如何支持多步推理能力提升的问题。具体而言，现有研究尚不清楚这种性能提升是源于局部词元级（token-level）能力的增强，还是全局上对推理过程的时间动态结构进行了重构。为回答这一问题，论文提出将思维链（Chain-of-Thought, CoT）推理建模为一个切换动力系统（Switching Dynamical System, SDS），通过离散隐状态策略（latent policy states）描述内部表示随时间演化的动态过程。该框架结合时序感知的对比表征学习与离散模式发现技术，从激活轨迹中恢复出隐含的推理策略。实验结果表明，经过推理微调的模型在隐状态组织上表现出更丰富的结构特征，包括更分明的转移模式、依赖模型规模的状态利用、持续性及混合特性。所恢复的隐状态呈现出与不同推理阶段相匹配的功能特异性，且其结构并非由正确性、表征学习或先验建模所解释，而是依赖于推理轨迹的连贯时间组织。因果干预实验进一步验证了这些隐状态的功能意义：状态交换消融会降低单步预测拟合度，而将推理动态迁移至基础模型可显著提升复杂推理任务的表现。此外，基于SDS指导的失败前缀剪枝方法在12个设置中的11个上优于自洽性（self-consistency）方法，最高提升达12.5个百分点。综上，该研究揭示推理微调本质上是对模型内在动态结构的全局重构，为理解与控制推理模型提供了新的机制分析视角与过程层面的干预工具。
链接: https://arxiv.org/abs/2607.18532
作者: Abir Harrasse,Michael Lan,Hunar Batra,Fateme Hashemi Chaleshtori,Chaithanya Bandi
机构: Martian; University of Oxford; University of Utah
类目: Computation and Language (cs.CL)
备注: Accepted at the Conference on Language Modeling (COLM) 2026. 45 pages, including appendices; 24 figures and 12 tables. Code: this https URL
Abstract:Reasoning-specialized language models show large performance gains over base models, yet the internal changes responsible for improved multi-step reasoning remain poorly understood. It is unclear whether reasoning fine-tuning improves local token-level competence or globally reorganizes how models structure inference over time. We address this question by modeling Chain-of-Thought reasoning as a switching dynamical system (SDS), in which internal representations evolve under discrete latent policy states. Our framework combines time-aware contrastive representation learning with discrete regime discovery to recover latent policies from activation trajectories. Across four benchmarks and model scales from 1.5B to 32B parameters, reasoning-fine-tuned models exhibit richer latent-policy organization than their base counterparts, characterized by more differentiated transition structure and model-dependent changes in state utilization, persistence, and mixing. The recovered regimes exhibit functional specialization aligned with distinct reasoning stages, and extensive controls confirm that their structure is not explained by correctness, representation learning, or modeling priors, but depends on the coherent temporal organization of reasoning trajectories. Causal interventions further show that the regimes are functionally meaningful: state-swap ablations reduce one-step predictive fit, while transplanting reasoning dynamics into base models improves performance on challenging reasoning problems. Finally, SDS-guided pruning of failure-prone reasoning prefixes outperforms self-consistency in 11 of 12 model-dataset settings, with gains of up to 12.5 percentage points. Together, our results suggest that reasoning fine-tuning globally reorganizes latent dynamics, offering a new lens for mechanistic analysis and process-level control of reasoning models.
[NLP-50] Search-on-Graph-R1: Training Large Language Models to Search Knowledge Graphs with Reinforcement Learning
【速读】： 该论文旨在解决知识图谱问答（Knowledge Graph Question Answering, KGQA）中路径探索效率与部署成本之间的矛盾问题。现有方法依赖于前沿大语言模型（frontier LLM）通过检索工具进行图遍历，但其在推理阶段需调用大规模模型，导致部署开销高昂。为此，本文提出Search-on-Graph-R1（\sogrone），通过监督微调（Supervised Fine-Tuning, SFT）与强化学习（Reinforcement Learning, RL）相结合的方式，将复杂的图导航能力内化至一个轻量级的80亿参数模型中。其核心创新在于构建了一个“教师-学生”框架：以每道题目的真实SPARQL查询作为指导信号，使教师模型在具备实时搜索工具（\textttSearch）的前提下，沿着已知的答案路径进行探索，从而生成具有知识图谱语义约束的训练轨迹。由于所有搜索调用均对接真实Freebase服务器，生成的路径天然具备事实依据。实验表明，\sogrone在WebQSP、CWQ和GrailQA三个基准上均优于所有冻结的前沿大模型系统，尤其在CWQ上达到当前最优性能；且推理时无需额外模块，训练过程亦不依赖大模型判别器。消融实验证明SFT与RL分别贡献互补性提升，该方法具备跨模型家族的迁移能力，且强化学习显著优化了寻答效率，能在更少的搜索调用次数内抵达答案。
链接: https://arxiv.org/abs/2607.18481
作者: Jia Ao Sun,Hao Yu,Fengran Mo,Zhan Su,Yuchen Hui,Bang Liu,Jian-Yun Nie
机构: Université de Montréal (蒙特利尔大学); Mila – Québec AI Institute (魁北克人工智能研究所); McGill University (麦吉尔大学); Halmstad University College (哈尔姆斯塔德大学学院); Canada CIFAR AI Chair (加拿大戴维·科里森人工智能主席)
类目: Computation and Language (cs.CL)
备注:
Abstract:Knowledge graph question answering (KGQA) requires navigating from topic entities to an answer several relations away. Recent methods prompt a frontier LLM to explore the graph through a retrieval tool, but their reliance on frontier-scale inference makes them costly to deploy. We present Search-on-Graph-R1 (\sogrone), which internalizes this navigation into a compact 8B model through supervised fine-tuning (SFT) followed by reinforcement learning (RL). Our central idea is to scaffold a frontier teacher with each question’s gold SPARQL query, so the teacher traverses a known answer-bearing path with a live \textttSearch tool rather than having to discover the path itself. Since every call executes against a live Freebase server, the resulting trajectories are grounded in the knowledge graph by construction. On WebQSP, CWQ, and GrailQA, \sogrone at 8B surpasses every frozen frontier-LLM system in our comparison and posts the strongest results on CWQ of any system we compare against. It does so using no auxiliary module at inference and no LLM judge during training. Isolating each training stage shows that SFT and RL contribute complementary gains, our approach transfers across model families, and RL learns to reach answers in fewer \textttSearch calls than its SFT initialization.
[NLP-51] Structured Output Collapses Answer Diversity Across 44 Language Models
【速读】： 该论文旨在解决语言模型在面对大规模且语义等价的答案空间时，因输出格式指令（如“仅以JSON格式回复”）而引发的输出分布偏移问题。其核心问题是：尽管未施加任何结构化约束或解码限制，仅通过改变输出格式请求，便显著改变了模型的生成行为，导致答案选择趋于集中，即“收敛性增强”。解决方案的关键在于揭示了模型对输出格式（register）的敏感性——这种敏感性并非源于解码器层面的强制约束，而是模型自身在训练中习得的、对不同输出模式的偏好响应机制。具体而言，当要求以JSON格式输出时，模型的平均答案选择不确定性（surprisal）下降，多数模型趋向于更常见的“众数答案”，且这一变化具有渐进性和选择性：最具个性化的模型变化最显著，而保守型模型则基本不变。实验进一步表明，这种压缩效应与模型所训练的输出格式密切相关（如JSON和XML显著压缩，而YAML、CSV无效应，括号包装甚至反向扩张），支持“工具使用后训练”场景下模型行为的可预测性调整。因此，关键发现是：结构化输出格式本身作为一种“注册信号”（register signal），能显著重塑模型的默认输出分布，使生成结果比对话式输出更趋同，从而影响模型评估与选型的公平性与代表性。
链接: https://arxiv.org/abs/2607.18476
作者: Tapan Parikh
机构: Cornell Tech (康奈尔科技)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 12 pages, 1 figure. Companion to the One-Word Census ( arXiv:2607.12796 ). Code, data, and interactive explorer: this https URL
Abstract:When a language model must choose one answer from a large space of equally valid options, a format clause – “Reply with JSON only” – changes which answer it chooses. We re-run the One-Word Census (arXiv:2607.12796): 31 wide-answer-space category prompts asked of 44 models, now with the reply requested in JSON – no schema enforcement, no constrained decoding, only the request. Convergence deepens sharply: on the unconstrained “Pick a word” prompt the modal answer rises from 41% to 64% of the pool and distinct answers fall from 52 to 36; mean answer-choice surprisal drops from 1.80 to 1.58 bits. The tax is progressive: six of 44 models move individually (BH-FDR q=.10), all toward the mode, led by the most distinctive models, while the conformist floor is immobile. It is a sharpener, not a re-indexer – the plain-chat modal answer survives in 28 of 31 categories. Defaults are register-indexed: a within-run re-sample (n=20) finds JSON shifts 53% of a model’s stable chat defaults, mostly back to the crowd, and installs defaults absent from chat (Claude Fable 5 answers “cerulean” for colour 0% of the time in chat, 100% in JSON). Full-battery controls reveal a register gradient: compression is significant and specific to the answer-delivery formats models are trained to speak (JSON -0.22 bits, p=.0002; XML -0.19, p=.002), absent for YAML and CSV, and reversed for an arbitrary bracket wrapper (+0.13, p=.009) – weighing the mechanism toward tool-use post-training. Enforcing the schema at the decoder (response_format) compresses no further than the request (-0.03 bits): the collapse lives in the model’s response to the register, not the decoder. Structured output is how software consumes language models, and that surface is served by a measurably more homogeneous model than the chat surface on which models are evaluated, compared, and chosen.
[NLP-52] CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders
【速读】： 该论文旨在解决在使用冻结编码器（frozen encoder）进行医学图像分析时，现有评估方法因标签分布不均导致的误判问题。传统基于最近邻的判别度量（discordance measure）在标签银行（bank）大小不等时，会因正负样本密度差异而使反标签邻居占据优势，从而误导评估结果，使得某些编码器看似“盲视”实际仅是因数据分布偏倚所致。其解决方案的关键在于提出一种新的判别度量CANDOR，通过强制构建等规模的标签银行并确保其在标签交换下对称，将随机水平（chance level）精确固定为0.5，从而消除标签分布偏倚的影响。在22个编码器、7个领域共20个数据集、超过60万张图像的实验中，该修正使结论发生反转：几乎所有编码器的实际表现均优于随机水平，表明它们并非“盲视”，但普遍存在性能薄弱的问题——例如最佳胸部模型对气胸的判别能力仅为84.5 AUROC，且仍有18.4%的阳性病例被更接近反标签图像而非同类型图像所包围。此外，尽管某些头部网络在特定任务上表现优异（如鸟类分类达4.5），但在其他医学任务上仍处于随机或低于随机水平，说明性能差距源于头部选择而非信息容量本身。研究进一步发现，特征坍缩（collapse）与数据擦除保留性相关，但与目标、尺度、新近性或病灶大小无关。由于CANDOR的随机水平恒定，可于训练任何头部前即用于评估，有效识别出冻结编码器对特定病灶支持能力不足的情况。
链接: https://arxiv.org/abs/2607.18451
作者: Soroosh Tayebi Arasteh,Sven Nebelung,Daniel Truhn
机构: University of Mannheim (曼海姆大学); German Research Center for Artificial Intelligence (德国人工智能研究中心)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Frozen encoders are chosen by how well a lightweight head reads a finding from their features, not whether the geometry separates it. Nearest-neighbor discordance does, but with unequal banks the opposite-label neighbor wins on density, not geometry, so prevalence alone makes an uninformed encoder look blind. We introduce CANDOR, a discordance measure whose equal-size banks are symmetric under a label swap, fixing its chance level at exactly one half. Across 22 encoders, 20 datasets from 7 domains, and 605,443 images, this correction reverses the conclusion. Collapse falls below chance almost everywhere, so no encoder is blind, yet all are weak: the best chest model reads pneumothorax at 84.5 AUROC and still places 18.4% of those positives nearer an opposite-label film than its own kind in the same hospital. The same encoder that resolves bird species at 4.5 leaves chest findings at 42.8 and glaucoma at 49.8, at chance and worse than random weights. Such a case caps the normalized margin of any Lipschitz head, yet some head among eleven is correct on all but 2.8% of cases where one head misses 35.9%: the deficit is selection, not information. Erasure retention is associated with collapse; we detect no association with the objective, scale, recency, or size of the finding. Because the chance level is fixed, CANDOR can be read before any head is trained, flagging which findings a frozen encoder supports poorly.
[NLP-53] PathReportEval: A Systematic Benchmark for Pathology Report Generation
【速读】： 该论文旨在解决病理报告生成任务中因数据集、模型设置、视觉编码器及评估协议异质性导致的进展难以衡量的问题，同时针对现有自然语言生成评价指标（如BLEU、ROUGE、METEOR）仅关注词汇相似性而无法有效识别临床关键错误（如漏诊、幻觉发现或肿瘤特征不一致）的缺陷。其解决方案的关键在于提出一个标准化的基准测试框架与临床导向的评价体系，核心创新为临床报告质量评分（Clinical Report Quality Score, CRQS），该指标将参考报告与生成报告映射至结构化临床属性，从临床事实覆盖率、关键信息召回率、幻觉率和临床不一致性四个互补维度进行量化评估，提供整体分数与可解释的子分。实验表明，传统语言生成指标与临床正确性关联性弱且常高估报告质量，而CRQS能揭示模型与编码器间具有临床意义的差异，从而实现对病理报告生成模型更真实、可靠的评估。该工作通过统一预处理、特征提取、训练、解码与评估流程，构建了可复现、模块化的开放平台，为该领域研究提供了坚实的评估基础。
链接: https://arxiv.org/abs/2607.18448
作者: Suryakant Singh,Sejuti Majumder,Beatrice Knudsen,Joel Saltz,Prateek Prasanna
机构: Stony Brook University (石溪大学); University of Utah (犹他大学)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Pathology report generation from whole-slide images (WSIs) is a rapidly growing multimodal learning problem, yet progress is difficult to measure because existing studies use heterogeneous datasets, model settings, visual encoders, and evaluation protocols. Moreover, commonly used natural language generation metrics, including BLEU, ROUGE, and METEOR, primarily reward lexical similarity and often fail to detect clinically consequential errors such as omitted diagnoses, hallucinated findings, or discordant tumor attributes. We present a standardized benchmark and evaluation framework for pathology report generation. The benchmark evaluates four representative methods across three datasets (TCGA, HistAI, and REG 2025) using three pathology foundation encoders (CONCHv1.5, UNI2-h, and H-Optimus-1). Our framework standardizes preprocessing, feature extraction, training, decoding, and evaluation, enabling fair comparison across models while providing a modular platform for integrating new methods, datasets, and encoders. A central contribution is the Clinical Report Quality Score (CRQS), a clinically grounded metric for evaluating factual correctness. CRQS maps reference and generated reports into structured clinical attributes and measures four complementary dimensions: clinical fact coverage, key information recall, hallucination rate, and clinical discordance, producing both an overall score and interpretable sub-scores. Experiments demonstrate that conventional language-generation metrics are weakly aligned with clinical correctness and frequently overestimate report quality. In contrast, CRQS reveals clinically meaningful differences between models and encoders that lexical metrics fail to capture. Together, the benchmark, public plug-and-play framework, and CRQS establish a reproducible foundation for rigorous evaluation of pathology report generation. Subjects: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2607.18448 [cs.CL] (or arXiv:2607.18448v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2607.18448 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Suryakant Singh [view email] [v1] Mon, 20 Jul 2026 18:59:47 UTC (1,022 KB)
[NLP-54] Using Fine-Tuned LLM s to Identify Indicators of Vulnerability in UK Police Incident Logs
【速读】： 该论文旨在解决英国警务工作中对弱势群体（包括精神健康问题、物质滥用、酗酒依赖及无家可归）的识别与量化难题。由于传统行政数据难以提供足够洞察，研究尝试评估基于大语言模型（LLM）的分类流程能否有效从非结构化的警务事件叙述中提取并估计上述四类脆弱性指标的普遍程度。其解决方案的关键在于构建一个多阶段处理管道，结合重复模型推理、标签聚合、结构化人工审核与统计校正，在本地部署开源权重的LLM以满足警务环境的安全要求。研究表明，尽管该方法可在宏观层面生成具有参考价值的粗略估计（如精神健康相关表述约占五分之一事件），但直接使用原始模型输出不可靠——单次推理结果不稳定，且聚合后存在系统性高估偏差。必须通过大量人工干预和统计调整才能缓解这些偏差，但仍存在显著不确定性。因此，结论强调：虽然生成式AI（Generative AI）具备从非结构化警务文本中提取信息的潜力，但其输出必须经过严谨的方法学支持方可作为可信赖的测量依据；在人群层面实现可辩护的估计是可行但资源密集的，而在个体层面错误频发且不可预测，不适用于操作性决策。本研究揭示了生成式AI在实际应用中的双重特性：既具潜力也受制于方法论约束。
链接: https://arxiv.org/abs/2607.18446
作者: Sam Relins,Daniel Birks
机构: ESRC Vulnerability and Policing Futures Research Centre; School of Law, University of Leeds, Leeds, UK
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: 24 pages, 4 figures. Preprint. Under review
Abstract:Purpose: Understanding how much of routine policing involves vulnerable people could inform resourcing, training, and multi-agency response, yet administrative data provide limited insight. We explore whether an LLM-based classification pipeline, developed on open-source US police data, can be adapted to estimate the prevalence of four vulnerability indicators - mental ill health, substance misuse, alcohol dependence, and homelessness - in UK police incident narratives, and when outputs can be treated as defensible measurements. Methods: We analyse nearly 3,000 de-identified incident logs from a UK police force, using a multi-stage pipeline combining repeated model inference, label aggregation, structured human review, and statistical correction. The pipeline runs on a locally hosted open-weight LLM, reflecting the secure environments police must work in. Results: LLMs can produce meaningful, if imperfect, prevalence estimates at scale. Mental ill health indicators are present in approximately one in five incidents, with lower prevalence for other indicators. However, naive LLM deployment is unreliable: single-pass classifications are unstable, and aggregated outputs systematically over-assign indicators relative to human judgement. Correcting these biases required substantial human input and statistical adjustment, leaving considerable uncertainty. Conclusions: While LLMs can extract information from unstructured police data, their outputs cannot be treated as valid measurements without careful methodological support. At the population level, defensible estimates are achievable but resource-intensive; at the individual level, errors remain frequent and unpredictable, limiting suitability for operational decisions. This study highlights both the potential and the constraints of LLM-based measurement in applied settings. Comments: 24 pages, 4 figures. Preprint. Under review Subjects: Computation and Language (cs.CL); Computers and Society (cs.CY) ACMclasses: I.2.7; J.4 Cite as: arXiv:2607.18446 [cs.CL] (or arXiv:2607.18446v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2607.18446 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Sam Relins [view email] [v1] Mon, 20 Jul 2026 18:58:33 UTC (1,818 KB)
[NLP-55] Computational models of prag matic reasoning with flexible generation of meaning and expression alternatives
【速读】： 该论文旨在解决生成式语言模型在语用推理中对替代表达或解释的建模不足问题，即如何自动、合理地构建对话双方所考虑的可替代选项集合。传统形式化与计算语用模型依赖人工指定替代集，缺乏灵活性与可扩展性。为此，论文提出了一种名为SAGE（ScAffolded Generative models for Explanation）的新框架，其核心在于将语用过程分解为三类模块：由语言模型（LMs）驱动的提议者（proposers），用于生成开放式的候选替代项；评估者（evaluators），负责对这些替代项进行语义、复杂度或典型性等维度的评判；以及基于认知动机的任务分析规则的决策器（selectors）。该框架结合了认知模型的可解释性与语言模型的生成灵活性，通过三个案例研究验证其有效性：指称表达生成、方式（M-）蕴含及格赖斯会话蕴含。实验采用计算认知建模中的标准评估方法（如消融分析、基线对比与人类数据拟合度量化），结果显示SAGE模型整体表现优异，尤其在替代项生成方面显著优于基线。然而组件级分析揭示出不对称性：语言模型作为提议者能可靠生成适合语用建模的替代项，而作为评估者则更擅长提供直觉判断，而非理论或形式化度量。这表明神经符号模型虽具潜力，但在精确捕捉语用推理的正式机制方面仍存在局限。
链接: https://arxiv.org/abs/2607.18443
作者: Polina Tsvilodub,Fausto Carcassi,Michael Franke
机构: University of Tübingen(图宾根大学); University of Amsterdam(阿姆斯特丹大学)
类目: Computation and Language (cs.CL)
备注: 27 pages main text, 9 figures; 25 pages supplementary materials
Abstract:Pragmatic language use requires reasoning about alternatives: the alternative expressions a speaker might have chosen, or the alternative interpretations a listener might entertain. Formal and computational models of pragmatics must therefore specify the sets of alternatives that interlocutors reason over, which is often done through manual specification. Here we propose a framework, ScAffolded Generative models for Explanation (SAGE), that combines the explanatory transparency of cognitive models with the generative flexibility of language models (LMs). SAGE decomposes a pragmatic process into three kinds of modules: proposers, which use LMs to generate an open-ended space of candidate alternatives; evaluators, which assess those alternatives (e.g., their semantics, complexity, or typicality); and selectors, which implement the rule-based computational steps of a cognitively motivated task analysis. We assess SAGE in three case studies spanning pragmatic generation and interpretation-referential expression generation, manner (M-)implicatures, and Gricean conversational implicatures. SAGE models are evaluated critically using established methods from computational cognitive modeling, including ablations, baseline comparisons, and quantitative fit to human data. Across studies, SAGE models achieved high accuracy and often outperformed baselines, but component-level analyses reveal an asymmetry: LM proposers reliably generated alternatives well-suited to pragmatic modeling, whereas LM evaluators are better at providing intuitive judgements rather than judgements of theoretical or formal measures. We discuss the promise and the limitations of neuro-symbolic models as candidate explanatory accounts of human pragmatic language use.
[NLP-56] Relay-Bench: Evaluating LLM s on Multi-Domain Reasoning Chains
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在单一提示（prompt）中完成跨领域复杂任务时的综合能力评估问题，现有基准测试往往局限于单一任务或领域，难以全面衡量模型在多领域协同推理与工具调用方面的实际表现。其解决方案的关键在于提出Relay-Bench——一个无饱和、全栈式、纯文本的基准测试框架，通过构建由多个单领域子问题串联而成的复合型问题，要求模型在单次交互中实现跨域推理。这些问题不仅涵盖视觉推理、编程、数学、信息提取（侧重网络搜索）、问题求解、常识知识与数据分析等多元领域，还引入了提示编码和刻意添加的上下文冗余以增加复杂性，同时明确鼓励模型使用代码执行、网络搜索等外部工具，从而更真实地模拟复杂现实场景下的应用需求。所有问题均由2至13个子问题构成，且不依赖多模态输入输出，确保评估聚焦于语言模型的推理与工具整合能力。
链接: https://arxiv.org/abs/2607.18438
作者: Liam Swayne
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 21 pages, 7 figures
Abstract:Introducing Relay-Bench, an unsaturated, holistic, text-only benchmark that measures LLMs’ ability to complete an assortment of tasks from distinct domains in a single prompt. The leading model, GPT-5.5 (xHigh), scores 43.3%. The test set entirely consists of composite problems: groups of single-domain subproblems that are strung together into challenges that require reasoning across multiple domains in combination. Many of these problems then have layers of complexity added through prompt encoding and deliberate context bloat. Domains tested include visual reasoning, coding, math, information extraction (with a focus on web search), problem-solving, general knowledge, and data analysis. No restrictions are imposed outside of the model harness, and models are explicitly encouraged to leverage code-execution, web searches, and all available tools. All problems are composed of two to thirteen subproblems and do not require multi-modal input or output.
[NLP-57] Building a European Multilingual Evaluation Dataset: The MMLU Localisation Project within the EMT Network
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）评估基准在多语言环境中的代表性不足问题，通过将MMLU数据集本地化为11种欧洲语言，构建一个更具包容性的多语言评估基准。其解决方案的关键在于：依托欧盟翻译总署（Directorate-General for Translation, DGT）与欧洲翻译硕士项目（European Master’s in Translation, EMT）的合作，不仅实现了高质量的多语言数据转换，还为硕士研究生提供了基于真实项目的职业训练，涵盖翻译、校对、项目管理及多语协调等核心能力培养，同时系统揭示了跨语言数据本地化过程中面临的方法论、行政管理与工作流协同等关键挑战。
链接: https://arxiv.org/abs/2607.18432
作者: Pilar Sánchez-Gijón,Susana Valdez,Sofía Calvo Del Barrio,Florence Bellemont,Anna Kokkinidou,Mihai Cristian Brasoveanu
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:This paper reports on a collaboration between the Directorate-General for Translation (DGT) and the European Master’s in Translation (EMT) to localise the MMLU dataset into 11 European languages. Beyond creating a more inclusive benchmark for LLM evaluation, the project offers master’s students authentic, project-based professional training in translation, revision, project management, and multilingual coordination, while highlighting key methodological, administrative, and workflow challenges.
[NLP-58] Enabling Multilingual Privacy Policy Audits: Large-Scale Analysis of Spanish Mobile Apps
【速读】： 该论文旨在解决现有隐私政策自动化审计流程高度依赖英文所带来的局限性问题，特别是在多语言数字生态（如欧盟）中，无法有效评估非英语语种隐私政策的透明度。其核心挑战在于如何在不进行语言特定适配的前提下，实现跨语言、大规模的隐私政策分析。解决方案的关键在于利用大语言模型（LLM）实现无需微调的跨语言泛化能力：研究构建了一个涵盖欧盟24种官方语言的多语言评估语料库，并通过自动化指标与法律专家评审验证翻译保真度；在此基础上，采用基于LLM的分类器对个人数据收集类别进行识别，实现了在不同语言间稳定表现（宏平均F1值介于0.91至0.94之间）。该方法被应用于西班牙Google Play商店中2,611个Android应用的大规模审计，结合隐私标签与运行时网络流量分析，揭示了公共部门应用主要使用西班牙语提供隐私政策，而商业应用则普遍采用英语，导致以英文为主的审计方式系统性掩盖了多语言环境中存在的透明度差距。
链接: https://arxiv.org/abs/2607.18424
作者: Marcos Moran,David Rodriguez,Luka Nenadic,Norman Sadeh,Jose M. Del Alamo
机构: ETH Zurich(苏黎世联邦理工学院); Carnegie Mellon University(卡内基梅隆大学)
类目: Computers and Society (cs.CY); Computation and Language (cs.CL)
备注:
Abstract:Automated analyses of privacy policies enable large-scale assessments of transparency in digital ecosystems, yet existing auditing pipelines remain predominantly English-centric. This limits their ability to systematically evaluate multilingual environments, as in the European Union, where many services disclose privacy practices only in local languages. This paper examines whether large language models (LLMs) can extend privacy policy analysis beyond English without requiring language-specific adaptation, thus empowering large-scale auditing in linguistically diverse app ecosystems. We assemble an evaluation corpus spanning all 24 official EU languages from translated versions of two established expert-annotated datasets (OPP-115 and MAPP) and assess translation fidelity through automated metrics and targeted legal-expert review. Our LLM-based classifier for identifying categories of personal data collection achieves stable cross-lingual performance, with macro-F1 scores ranging between 0.91 and 0.94. We then leverage this capability in a large-scale audit of 2,611 Android applications from the Spanish Google Play Store. Combining multilingual privacy policy analysis with the evaluation of corresponding privacy labels and runtime network traffic exposes an important linguistic barrier: public-sector apps predominantly provide privacy policies in Spanish, whereas popular commercial apps mostly provide them in English. We reveal systematic discrepancies between declared and observed practices, especially in public-sector apps. Overall, our results indicate how English-only privacy audits can systematically obfuscate transparency gaps in multilingual environments. Subjects: Computers and Society (cs.CY); Computation and Language (cs.CL) Cite as: arXiv:2607.18424 [cs.CY] (or arXiv:2607.18424v1 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2607.18424 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-59] Convolution for Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）中自注意力机制（self-attention）虽能实现全局上下文建模，却缺乏对自然语言局部性（locality）的显式归纳偏置（inductive bias）的问题。其核心解决方案是引入轻量级深度可分离卷积（depthwise convolution），在不显著增加模型参数量的前提下，增强模型对短距离词元间依赖关系的建模能力。关键创新在于：在Qwen3 Transformer模块中，将卷积应用于注意力计算前的投影查询、键和值（queries, keys, and values），并采用残差连接与核大小为 k=3 的深度可分离卷积结构，无需额外归一化或激活函数。实验表明，该设计在多个下游任务上提升了平均性能，同时仅增加不足0.01%的参数量，并通过表示层面分析验证了卷积增强了重复词元对其邻近上下文的敏感性，从而有效补足了自注意力在捕捉局部模式方面的不足。
链接: https://arxiv.org/abs/2607.18413
作者: Yuchuan Tian,Yingte Shu,Wei He,Shuo Zhang,Tianchen Zhao,Chao Xu,Xinghao Chen,Yunhe Wang,Hanting Chen,Yu Wang
机构: Peking University (北京大学); Huawei Technologies (华为技术); Tsinghua University (清华大学)
类目: Computation and Language (cs.CL)
备注: 12 pages, 5 figures
Abstract:Large language models (LLMs) largely rely on Transformers, where self-attention provides global token interaction but does not explicitly encode the locality of natural language. We study whether lightweight depthwise convolutions can supply this local inductive bias without materially increasing model size. Our macro-level ablation compares convolution at 17 locations in a Qwen3 Transformer block and finds the best results when convolution is applied to the projected queries, keys, and values before attention. A subsequent micro-level study favors a residual depthwise convolution with kernel size k=3 , without additional normalization or activation. Across Qwen3 models and several pre-training data budgets, this design improves the average accuracy on seven downstream benchmarks while adding less than 0.01% parameters. A representation-level case study further suggests that the convolution makes repeated token IDs more sensitive to their immediate context. These results support depthwise convolution as a lightweight complement to self-attention for modeling short-range token interactions.
[NLP-60] Operational Hallucination and Safety Drift in AI Agents
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在工具调用型自主智能体（tool-using autonomous agents）多轮交互过程中引入的动态可靠性风险问题。现有单轮安全机制虽已相对成熟，但在持续交互中暴露出结构性缺陷：初始对齐性随时间逐步退化，导致安全意图逐渐失效。论文通过实证研究识别出两种关键失败模式——安全漂移（Safety Drift），即模型声明的安全意图随交互过程逐渐弱化，最终执行违反约束的行为（如先拒绝文本后进行侦察与危险操作）；以及操作幻觉（Operational Hallucination），表现为因状态感知错误而产生的持续重复工具调用（如在合法任务中陷入活锁）。基于高风险伦理困境、恶意请求与良性对照的多轮控制评估，研究采用“声明-行动差距”与“活锁率”等量化指标，证实了这些现象在主流LLMs中的普遍性。根因分析表明，当前智能体循环中推理上下文与执行状态之间的解耦是核心诱因。为此，论文提出一种轻量级、可即插即用的动作感知监督层（Action-Aware Supervision Layer），其关键在于集成意图-行动一致性校验、运行时状态追踪及强制终止机制，从而实现对异常行为的精准拦截。后验仿真结果表明，该架构能在不误报良性案例的前提下有效干预已观测到的违规轨迹，推动智能体可靠性从依赖语言层面的防护转向可执行的架构级保障，为负责任的生成式智能体（Responsible Agentic AI）发展提供了新范式。
链接: https://arxiv.org/abs/2607.18366
作者: Shasha Yu,Fiona Carroll,Barry L. Bentley
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)
备注:
Abstract:Large language models (LLMs) serving as planners in tool-using autonomous agents introduce dynamic reliability risks in multi-turn execution. While single-turn safety mechanisms are relatively mature, extended interactions reveal structural vulnerabilities where initial alignment degrades over time. This paper empirically characterizes two observed failure modes across multiple state-of-the-art LLMs: Safety Drift, the gradual erosion of declared safety intent leading to constraint-violating actions (e.g., textual refusal followed by reconnaissance and unsafe execution), and Operational Hallucination, persistent repetitive tool calls indicative of flawed state perception (e.g., livelocks even in legitimate tasks). Through controlled multi-turn evaluation on high-stakes ethical dilemmas, malicious requests, and benign controls, we quantify these phenomena using declaration-action gap and livelock metrics, demonstrating their cross-model prevalence under direct execution protocols. Root-cause analysis attributes the instabilities to the decoupling of reasoning context from execution state in current agent loops. We propose an Action-Aware Supervision Layer - a lightweight, plug-and-play architectural blueprint incorporating intent-action consistency checks, runtime state tracking, and forced termination primitives. Post-hoc simulation on captured failure trajectories shows the layer can intercept observed violations without false positives on benign cases. This work advances agent reliability by shifting focus from linguistic safeguards to enforceable architectural mechanisms for responsible agentic AI.
[NLP-61] A Controlled Study of Attention-Only Transformers
【速读】： 该论文旨在解决生成式模型中前馈网络（Feed-forward Networks, FFNs）在Transformer架构中的必要性问题，尤其是在同时控制参数量、计算量（FLOPs）和深度的前提下，评估仅保留注意力模块的简化架构（即仅注意力网络，Simple Attention Networks, SANs）是否能够替代标准Transformer。其核心挑战在于：尽管前馈网络占用了Transformer非嵌入参数的三分之二，但此前缺乏在参数、计算与深度三者协同匹配下的系统性验证。解决方案的关键在于通过预训练对比实验，将标准Transformer与参数、计算量及层数完全匹配的SANs进行对照，并动态重分配因移除前馈层而释放的资源至注意力深度。结果表明，当参数量保持一致时，两者的损失差距仅为0.006 nats（约0.27%相对损失），且该差异在5B、30B、105B等不同规模下持续缩小，甚至在29倍的模型尺寸范围内维持在0.02 nats以内，证明注意力机制本身已具备足够的表达能力以承担主要建模任务。进一步分析揭示，剩余微小差距源于对权重中知识的依赖——注意力模型在上下文相关推理上表现更优，但在依赖模型权重存储知识的任务中略逊一筹；其根源在于注意力路由矩阵（Q/K）早期结晶化、内容矩阵秩增长缓慢，而移除前馈层后该信息积累被转移到注意力输出投影层。此外，研究发现QK归一化而非前馈层或残差门控是维持48层注意力堆栈可训练性的关键机制。最终，一项注册的独立测试证实了这一解释，预测在知识密集型网页文本上存在0.02–0.05 nat的性能差距，实测值为0.040 nat，表明在所考察范围内，注意力机制已能完成绝大部分建模任务。
链接: https://arxiv.org/abs/2607.18363
作者: Henry Ndubuaku,Karen Mosoyan,Jakub Mroz,Noah Cylich,Satyajit Kumar,Parkirat Sandhu,Roman Shemet,Justin H Lee
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 10 pages, 8 figures
Abstract:Feed-forward networks hold two thirds of a transformer’s non-embedding parameters, yet the architecture has not received a necessity test that controls parameters, compute, and depth at once. We pretrain attention-only decoder transformers (Simple Attention Networks, SANs) against standard transformers matched separately for parameter count, training FLOPs, and depth (2 to 48 layers), for up to 105B tokens at 6M to 87M parameters. Deleting feed-forward layers in place is costly: the standard transformer leads by 0.47 nats at matched depth and 0.26 nats at matched FLOPs. Reallocating the freed budget into attention depth closes the gap: at matched parameters the difference is 0.006 nats (0.27 percent of loss), reproducible to one part in ten thousand across seed pairs, shrinking across 5B, 30B, and 105B budgets, and holding near 0.02 nats across a 29x size range. Three measurements localize the remaining gap to parametric recall: attention-only models are better on context-grounded answers and worse where knowledge must come from weights. Weight spectra show why: routing matrices (Q/K) crystallize early, content matrices accumulate rank slowly, and removing feed-forward layers relocates this accumulation to the attention output projection. QK-normalization, not feed-forward layers or residual gating, keeps 48-layer attention-only stacks trainable. The deficit concentrates on low-context query prediction and localizes there entirely by the largest budget. A pre-registered test confirms the account: it predicts a 0.02 to 0.05 nat gap on knowledge-dense web text; a matched pair trained on fineweb-edu measures 0.040. Within the tested regime, attention does the rest.
[NLP-62] A Classifier That Teaches Itself: Self-Improving Frozen-gate Training (SIFT) for Dynamic Document Classification
【速读】： 该论文旨在解决企业在实际应用中面临的文档分类难题，即尽管在实验室环境中文档分类已基本实现自动化，但在企业级场景中仍难以落地。其核心瓶颈并非模型架构本身，而是标注工程的高成本以及组织对模型自主再训练的安全顾虑。为此，论文提出SIFT（Self-Improving, Frozen-gate Training）——一种动态自改进分类服务，其关键在于构建一个由低成本、以CPU为主的稀疏编码器（SPLADE）与轻量级梯度提升树（LightGBM）分类头组成的推理流水线，并仅将低置信度的少数样本递交给大语言模型（LLM）作为判官进行人工验证。判官的标注结果被回写至标注语料库中，从而形成“自喂养”的闭环，使昂贵的模型持续指导廉价模型的优化：随着使用积累，误判样本的溢出率下降，语料库通过生产流量自然增长，无需预先大规模人工标注，分类准确率随时间累积提升。新文档类别的接入仅需声明式配置包、标签空间、锚定短语及判官术语表，彻底摆脱传统标注项目依赖。针对自主再训练带来的潜在退化风险，SIFT引入双层“晋升门”机制：一是基于关键标签的F1分数回归检测，二是冻结的黄金基准测试集（从不参与训练），任一环节不达标即拒绝模型更新。这一设计将“每月无须人工干预的自动重训”从高风险行为转变为可信赖的常规操作。论文详细阐述了系统架构、自喂养语料循环机制、冻结门控晋升策略及其在多领域部署中的实例，并分析了该分类器边际标注成本趋近于零的经济优势。
链接: https://arxiv.org/abs/2607.18358
作者: Bogdan Raduta,Horia Velicu,Alexandru Preda,Serban Chiricescu
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 9 pages, 2 figures
Abstract:Document classification is a solved problem in the laboratory and an unsolved one in the enterprise. The blocker is rarely model architecture; it is the labeling project that must precede a model and the institutional fear of letting a model retrain itself once one exists. We present SIFT (Self-Improving, Frozen-gate Training), a dynamic classifier service, which attacks both. SIFT serves classification from a deliberately cheap, CPU-bound pipeline, a SPLADE sparse encoder feeding a LightGBM head, and escalates only the low-confidence minority of pages to an LLM judge. The judge’s verdicts are written back into a labeled corpus, so the expensive model continuously teaches the cheap one: the escalation rate falls, the corpus grows from production traffic rather than from an up-front annotation effort, and accuracy compounds with use. Onboarding a new document family requires only a declarative bundle, label space, anchor phrases, and a judge glossary, not a labeling project. The harder problem is safety: an autonomously retraining classifier can silently regress. SIFT resolves this with a two-part promote gate, a critical-label F1 regression check plus a frozen golden regression set the model is never trained on, either of which vetoes promotion. This turns “retrain monthly without a human” from reckless into routine. We describe the architecture, the self-feeding corpus loop, the frozen-gate promotion mechanism, and an illustrative multi-domain deployment, and we discuss the economics of a classifier whose marginal labeling cost trends toward zero.
[NLP-63] EmoEUS: Uncertainty Supervision for Multimodal Emotion Recognition in Conversation INTERSPEECH2026
【速读】： 该论文旨在解决多模态对话情绪识别（Multimodal Emotion Recognition in Conversation, MERC）中因模态间冲突线索、噪声差异及模态信号缺失导致的模态特异性不确定性问题。现有融合方法通常忽略跨话语的模态不确定性，影响识别性能。其解决方案的关键在于提出EmoEUS框架，通过学习各模态的方差估计实现动态加权融合，从而在多模态融合过程中显式建模不确定性；同时引入显式监督损失函数，使每个话语的预测方差与该话语分布表示与其情感-模态特定聚类中心之间的距离对齐，增强模型对不确定性的感知能力。实验结果表明，EmoEUS在IEMOCAP和MELD数据集上均显著优于现有先进方法。
链接: https://arxiv.org/abs/2607.18336
作者: Zilong Huang,Kong Aik Lee,Junjie Li,Zhe Li,Man-Wai Mak
机构: The Hong Kong Polytechnic University (香港理工大学); The University of Hong Kong (香港大学)
类目: Multimedia (cs.MM); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accept by Interspeech 2026
Abstract:Multimodal emotion recognition in conversation (MERC) can leverage multimodal and contextual cues to boost recognition performance. However, existing fusion approaches in MERC often ignore modality-specific uncertainty across utterances caused by conflicting cues, varying noise, and missing modality-specific signals. We propose EmoEUS, an explicit uncertainty supervision framework for MERC. EmoEUS performs uncertainty-aware multimodal fusion by dynamically weighting modalities using learned variance estimates. We also introduce an explicitly supervised loss that aligns each utterance’s predicted variance with the distance between the utterance’s distributional representation and its emotion- and modality-specific cluster center. Experiments on IEMOCAP and MELD show that EmoEUS consistently outperforms state-of-the-art methods.
[NLP-64] Decoding EEG Signals to Explore Next-Word Predictability in the Human Brain
【速读】： 该论文旨在解决阅读过程中底层（与高阶语言结构相关）和顶层（与下一个词的可预测性相关）认知机制如何协同作用以引导理解的问题，尤其关注词义可预测性对不同词类在神经层面响应的影响。其核心解决方案在于利用高时间分辨率的脑电图（EEG）技术，在N400时窗（刺激后300–500毫秒）内系统分析不同词类（包括内容词与功能词、名词与动词）对可预测性的神经反应差异，并通过解码分析方法揭示传统事件相关电位（ERP）分析难以捕捉的动态认知表征。研究发现，可预测性对内容词（尤其是动词）的N400效应显著强于功能词，且名词比动词更能反映可预测性信息；同时，解码技术相比传统ERP分析能更精细、更准确地刻画认知过程的时间演化特征。
链接: https://arxiv.org/abs/2607.18321
作者: Boi Mai Quach,Binh T. Nguyen,Cathal Gurrin,Graham Healy
机构: Dublin City University (爱尔兰都柏林城市大学); VNUHCM – University of Science (越南国家河内自然科学大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Humans invented reading and have passed down this complex skill across generations through language. This study provides empirical evidence of the neural mechanisms underlying bottom-up (related to high-order linguistic structure) and top-down (related to next-word predictability) processes, which interact to guide comprehension during reading. While previous studies have focused on either the N400 effects of predictability or lexical categories, research on how predictability influences N400 responses across different lexical categories is limited, mainly due to constraints in publicly available datasets. Here, we examine how predictability influences brain responses, recorded at millisecond resolution using electroencephalography (EEG), with a focus on the N400 time window (300-500 ms post-stimulus) across different lexical and grammatical categories. Our results indicate that significant differences in N400 responses between high and low cloze probability levels were more pronounced for content words than function words. Among the two primary content categories, verbs exhibited greater N400 differences than nouns, while nouns carried more distinct information about their predictability than verbs. Moreover, we demonstrate that the decoding technique is more effective than the event-related potential (ERP) traditional analysis in capturing more detailed and distinct representations of cognitive processes over time.
[NLP-65] A Situational Speech Synthesizer for Yoruba: System Design Phonological Rule Architecture and Orthographic Extensions for Contour
【速读】： 该论文旨在解决非洲低资源语言——约鲁巴语（Yoruba）在文本转语音（TTS）系统中面临的挑战，特别是如何准确建模和生成具有三重声调轮廓（contour tones）的自然语音。由于约鲁巴语为声调语言，其声调系统复杂且对语义至关重要，而现有合成技术在处理多音高变化、鼻化音区分及上下文声调演变方面存在不足。为此，作者提出了一种基于规则的拼接式双音节（diphone）语音合成系统TTSYoruba，其核心解决方案在于构建一套手工设计的音系规则体系，涵盖完整的声调选择逻辑、三向鼻音歧义消解（区分口鼻音/n/、鼻化元音与成音节鼻音），以及从水平声调输入推导出上下文相关升调与降调的机制。此外，该工作创新性地将卡隆（caron）与波浪号（circumflex）符号标准化为单个元音的声调轮廓标记，并集成至文本归一化流程与WriteYoruba输入工具中，提升了音写一致性与可读性。系统通过50名听者的主观评测验证，展现出较高的语音自然度（以平均意见分MOS衡量），证明了规则驱动方法在低资源声调语言中的有效性与实用性。
链接: https://arxiv.org/abs/2607.18317
作者: Kola Tubosun,Adedayo Oluokun,Hafiz Adewuyi,Dadepo Aderemi
机构: 未知
类目: ound (cs.SD); Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注: Currently under review at Speech Communication
Abstract:We present TTSYoruba, a rule-based concatenative diphone speech synthesizer for Yoruba, deployed at online as part of the this http URL open dictionary of Yoruba personal names. The system takes tone-marked Yoruba text as input and produces audio output by applying a hand-crafted phonological rule system to a recorded inventory of 651 diphone units spanning five tonal variants of every consonant-vowel combination in the language. We describe the phonological architecture of the system in detail, including our complete tonal file-selection logic, our treatment of the three-way nasal disambiguation problem (oral /n/, nasalized vowel, and syllabic nasal), and the derivation of contextual rising and falling tones from level-tone input. We also present, as an orthographic contribution, the adoption of the caron and circumflex, which are symbols with prior standing in Yoruba phonological transcription, as standard single-vowel contour tone markers, integrated into the TTS normalization pipeline and the WriteYoruba keyboard input tool. The system’s performance was evaluated through a listener study (N=50), with detailed results on Mean Opinion Scores (MOS) presented in Section 6. Keywords: Yoruba, text-to-speech, low-resource languages, diphone synthesis, contour tones, African language NLP, rule-based synthesis Comments: Currently under review at Speech Communication Subjects: Sound (cs.SD); Computation and Language (cs.CL); Audio and Speech Processing (eess.AS) ACMclasses: I.2.7; H.5.5 Cite as: arXiv:2607.18317 [cs.SD] (or arXiv:2607.18317v1 [cs.SD] for this version) https://doi.org/10.48550/arXiv.2607.18317 Focus to learn more arXiv-issued DOI via DataCite
[NLP-66] Binding Drift in Multi-Step Tool-Augmented Agents
【速读】： 该论文旨在解决工具增强型语言模型代理（tool-augmented language-model agents）在多步工作流中因实体绑定（entity binding）错误导致的累积性失效问题。具体而言，研究聚焦于实体绑定随时间演变的两种关键现象：绑定漂移（binding drift，初始正确但后续错误）与错误传播（error propagation，初始即错误并持续传递）。其核心发现表明，尽管“实体锁定”（entity lock）策略看似合理（即固定首次绑定的实体），但在存在初始错误的情况下会显著放大错误动作，使错误数量增加达3.0倍甚至最高8.5倍；而采用基于大模型的轻量级再验证机制（LLM-based re-verifier），通过一次低成本的二次模型调用重新解析原始指令，可有效将错误动作减少79%，性能接近理想情况下的最优上界（oracle upper-bound），显著优于仅依赖实体锁定的策略。因此，解决方案的关键在于引入可解释、低成本且高效的再验证机制，以动态纠正绑定偏差，而非简单依赖静态绑定持久化。
链接: https://arxiv.org/abs/2607.18316
作者: Rahul Suresh Babu,Shashank Indukuri
机构: Boston University (波士顿大学); DePaul University (德保罗大学)
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 14 pages, 5 tables, 1 figure. Equal contribution by both authors. Code and data: this https URL
Abstract:Tool-augmented language-model agents execute multi-step workflows over external systems, resolving an entity once and then acting on it across subsequent steps. Prior work shows that in single-step actions, agents select the correct tool but bind it to the wrong entity 24-26% of the time. We study what happens to entity bindings over time: do they stay correct, silently drift to a different entity, or, if wrong from the start, propagate and compound? We formalize binding drift (correct at step 1, wrong later) as distinct from error propagation (wrong at step 1, carried forward), and score them on disjoint workflow sets so the two cannot be conflated. In a controlled multi-step testbed (200 workflows, 580 entity-binding-scored steps, four enterprise domains, eight model backends spanning small to frontier), we find: (1) under controlled error injection, an entity lock (the intuitive “persist the first binding” fix) amplifies wrong actions from 907 to 2,746 (3.0x; bootstrap 95% CI [2.8, 3.3]), because it faithfully carries the seeded wrong entity into every later step; (2) the amplification reaches 8.5x on the most affected model (Claude Opus 4.5); (3) a practical LLM-based re-verifier (a single cheap second model call re-reading the original instruction) reduces wrong actions by 79% (0.21x; CI [0.18, 0.25]), closing the gap to within 1 percentage point of an oracle upper-bound (0.20x); and (4) in the natural (non-injected) setting, baseline agents drift on 18% of eligible workflows, with the per-step error rate rising across steps. Persistence and re-verification are not interchangeable: a defense that eliminates drift can worsen propagation, and a practical re-verifier nearly matches oracle recovery.
[NLP-67] One Student Many Teachers: Multi-Task On-Policy Distillation via Soft-Prompt Privileged Context
【速读】： 该论文旨在解决大语言模型在持续学习过程中因参数微调导致的灾难性遗忘与任务间性能退化问题，尤其针对现有基于策略自蒸馏（On-policy self-distillation, OPSD）方法中教师-学生架构存在的两大缺陷：一是通过在输入端注入特权上下文引发事后解释（post-hoc rationalization），二是通过权重微调造成模型表示空间漂移（drift）与知识遗忘。其解决方案的关键在于提出一种新颖的教师-学生框架——\method，其中教师仅通过一个可学习的软提示（soft prompt）与学生区分开，且教师的骨干网络（backbone）保持冻结状态，仅通过监督学习（x, y_gold）对软提示进行训练。该设计确保了教师在不改变学生表示几何结构（representational geometry）的前提下，生成任务特定的指导信号。进一步地，该方法可自然扩展至多任务场景：将混合语料库中的样本按任务路由至对应的软提示教师，实现单个学生并行吸收来自K个教师的知识；推理阶段所有软提示均被丢弃，从而保证模型轻量化与通用能力保留。实验表明，在Qwen3-1.7B-Base和Phi-4-mini-instruct上，单任务版本（OPD with a PT teacher）在四个任务（科学、工具使用、生物学、数学）上的表现达到或超越全量微调（full fine-tuning），同时训练参数量级降低；多任务版本在整体平均得分达56.2的同时，未损害通用能力基准，显著优于顺序SFT（sequential SFT）所导致的性能下降。
链接: https://arxiv.org/abs/2607.18293
作者: Yingzi Ma,Zichen Zhu,Ming Jiang,Chaowei Xiao
机构: University of Wisconsin–Madison (威斯康星大学麦迪逊分校); Johns Hopkins University (约翰霍普金斯大学); Nanyang Technological University (南洋理工大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 10 pages, 5 figures
Abstract:On-policy self-distillation (OPSD) teaches large language models new skills through a teacher that shares the student’s backbone and supervises its own rollouts. Existing teachers either inject privileged context at the input – inducing post-hoc rationalization – or fine-tune weights, accumulating drift and forgetting across tasks. We propose \method, whose teacher differs from the student only by a learnable soft prompt: trained on (x, y_\textgold) pairs with the backbone frozen, the prompt yields a task-specific teacher that preserves the student’s exact representational geometry. \method\ extends naturally to multi-task settings by routing each example in a merged corpus to its corresponding soft-prompt teacher, allowing a single student to absorb knowledge from K teachers in parallel; at inference, all prompts are discarded. On Qwen3-1.7B-Base and Phi-4-mini-instruct across four tasks (Science, Tool Use, Biology, Math), the single-task variant (OPD with a PT teacher) matches or exceeds full fine-tuning while training orders of magnitude fewer parameters, and the multi-task variant achieves the best overall average ( 56.2 on Qwen3-1.7B-Base) while preserving general-capability benchmarks – in contrast to sequential SFT, which degrades both.
[NLP-68] Reliability Scales Inversely: Bigger Models Compound Mistakes Faster via a Hidden Auto-Regressive Risk Regime
【速读】： 该论文旨在解决大规模语言模型在规模扩展过程中出现的“可靠性退化”问题，即随着模型规模增大，虽然生成答案的准确性提升（能力增强），但错误生成（如幻觉）的频率和严重性也显著上升。其核心问题是：当前主流的“知识鸿沟”解释（如数据量、检索机制或模型规模不足）未能捕捉到自回归生成过程中的内在风险残差——即模型在生成过程中一旦选择低概率词元，便会基于此错误前提持续累积偏差，形成自我强化的错误传播链。解决方案的关键在于识别并量化这一风险机制：通过引入同家族更强的“基准模型”（oracle）作为参考，构建逐位置的分歧度量 δ=logpM−logpO，并将其方差分解为偏差平方项 KL(pM∥pO)2 与风险项 Var[δ]。研究发现，随着模型规模扩大，知识差距缩小约6倍，但知识退化加剧11至39倍；在幻觉发生时，模型自身不确定性（H(pM)）迅速降低，而基于基准模型的风险指标仍持续存在长达17倍以上时间，形成“高自信-高风险”的脆弱区间；进一步实验证明，通过控制固定KL散度下的方差收缩可使网络验证的幻觉率下降35%–74%，表明该风险具有因果性；且该机制能规避模型自身的自我监控（如仅依赖 pM 的语义熵检测器），在风险路径上误报率降低约30%，却承载近4倍的幻觉内容。因此，大模型的错误雪崩本质是一种由自回归结构引发的主导性、自洽性、因果性且不可见的失效模式。
链接: https://arxiv.org/abs/2607.18292
作者: Kushal Chakrabarti
机构: Obviously Wrong, LLC
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:As language models scale, answers start truer but degrade faster: scaling buys capability but erodes reliability. The knowledge-gap account - more data, retrieval, or scale - misses an auto-regressive risk residual that scale sharpens: the model commits to a low-probability token, conditions on it as established, and snowballs. We track this through per-position disagreement \delta = \log p_M - \log p_O against a stronger same-family oracle, whose second moment splits exactly into bias ^2 \mathrmKL(p_M ,|, p_O)^2 and risk \mathrmVar[\delta] . We present four findings: (i) under scaling, the knowledge gap falls \approx 6\times while knowledge degradation grows 11 - 39\times ; (ii) at a fabrication, felt uncertainty H(p_M) relaxes quickly while oracle-referenced risk persists up to 17\times longer, leaving a confident-but-precarious risk regime that bridges consecutive fabrications ( +69% at 14 B); (iii) this regime is causal - an on-policy, fixed- \mathrmKL variance contraction cuts web-verified hallucination by 35 - 74% across three model families; and, (iv) it structurally evades self-monitoring, with p_M -only detectors (e.g. semantic entropy) firing \approx 30% less ( p10^-16 ) on the risky branch holding nearly 4\times more fabrications. Bigger models snowball mistakes faster, through a failure mode that is dominant, self-perpetuating, causal and invisible to the model itself.
[NLP-69] State Compression in Two-Agent LLM Relays: A Closed-World Study of Constraint Preservation
【速读】： 该论文旨在解决长时运行的大型语言模型（Large Language Model, LLM）代理在多阶段任务中因中间状态累积导致的信息过载问题，尤其关注在跨代理协作场景下，中间结果压缩（hand-off compression）对下游决策可行性的负面影响。其核心挑战在于：当上游代理将大量审计、筛选与数值计算结果压缩传递给下游代理时，若压缩方式缺乏结构化与可验证性，微小信息丢失可能破坏严格的数值或类别约束，从而导致决策失败。解决方案的关键在于采用结构化且可审计的中间表示形式，而非单纯追求压缩后的简洁性。实验通过封闭世界下的旅行规划接力任务验证了四种手递手传递策略：无压缩、叙事摘要、基于模式约束的JSON提取以及基于嵌入的剪枝。结果显示，基于模式约束的JSON提取在保持高可行性准确率（0.96）的同时具备良好可读性；而尽管叙事摘要生成的负载最小，但可行性骤降至0.48，表明语义压缩易引入不可靠信息；嵌入式剪枝则在不增加生成调用的前提下，实现与未压缩情况相当的可行性（0.88），证明结构化表示对约束检查具有显著优势。因此，该研究强调，在复杂任务链中，应优先采用可验证、格式规范的中间表达，以保障端到端系统的可靠性。
链接: https://arxiv.org/abs/2607.18265
作者: Anantha Sharma,Sheeba Elizabeth John,Kaarthik Senthil Kumar,Saratsuhas Vijayababu
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 8 pages, 2 figures, 3 tables
Abstract:Long-running Large Language Model (LLM)-based agents often accumulate large intermediate traces containing audits, eliminations, and numeric calculations. In practice, this state is compressed before handing it to a downstream decision step, creating an information bottleneck in which small omissions can break strict numeric or categorical constraints. This paper evaluates hand-off compression in a closed-world travel-planning relay with two LLM agents. A Researcher audits a fixed inventory of hotels and flights for 50 goal instances, and a Booker selects a hotel–flight pair using only the goal and the hand-off payload, with the inventory withheld. We compare four hand-off conditions: no compression, narrative summarization, schema-constrained JSON extraction, and embedding-based pruning. Exhaustive enumeration over the fixed inventory provides exact feasible and optimal labels. Results show that hand-off representation strongly affects downstream feasibility under a small decision model. JSON extraction achieves the highest feasibility accuracy at 0.96, while narrative summarization, despite producing the smallest compressed hand-off payload, degrades feasibility to 0.48. Embedding-based pruning matches the uncompressed control on feasibility at 0.88 without an additional generative compression call. These findings indicate that constraint checking benefits from structured and auditable hand-off representations rather than relying on brevity alone.
[NLP-70] MUX: Continuous Reasoning via Multiplexed Tokens
【速读】： 该论文旨在解决生成式语言模型在复杂问题求解过程中因推理步骤以自然语言形式逐字表达而导致的计算瓶颈问题，即每个推理步骤仅传递单一子词（subword），大量计算资源被消耗于语言表达而非实质性推理。其核心解决方案是提出MUX方法，通过将离散推理过程蒸馏至潜在空间中的连续多路复用（multiplexed）token，实现高带宽、紧凑的推理表示。关键在于：每个潜在token被训练为对一段离散推理子词序列的加权线性叠加（即多路复用），且该叠加在构造上为无损，支持完整恢复（去复用，demultiplexing）。研究证明，仅采用位置相关的权重分配（如几何衰减）即可实现无损多路复用，从而避免由潜在空间坍缩引发的捷径行为。此外，多路复用推理可支持在需搜索的问题中并行探索。在涵盖四类语言模型的32个评估场景中，MUX显著优于现有强基线。消融与探针分析表明，所学潜在token能忠实且可解释地编码推理过程。结果表明，以局部学习目标的无损叠加机制构成实现高效且强大连续潜空间推理的充分条件。
链接: https://arxiv.org/abs/2607.18264
作者: Ayhan Suleymanzade,Halil Alperen Gozeten,Michael Bronstein,İsmail İlkan Ceylan,Jinwoo Kim
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Language models solve complex problems by articulating intermediate reasoning steps in natural language. While effective, this process is computationally bottlenecked: each reasoning step conveys only a single subword, and many are spent expressing a thought instead of carrying out computation. We propose MUX, a simple method for high-bandwidth and compact reasoning based on distillation of discrete reasoning into continuous multiplexed tokens in a latent space. Here, each latent token is trained to represent a weighted linear superposition (multiplexing) of a span of discrete reasoning subwords, where this superposition is lossless by construction and the span can be fully recovered (demultiplexing). We prove that simple position-dependent weightings, such as suitable geometric decay, support lossless multiplexing, which in turn prevents shortcut behaviors caused by latent collapse. We further show that multiplexed reasoning can perform parallel exploration in problems that require search. Across 32 evaluation settings spanning four language models, MUX outperforms strong latent reasoning baselines. Ablation and probing analyses further show that the learned latent tokens encode faithful and interpretable reasoning. Our results suggest that lossless superposition as local learning targets constitutes a sufficient condition for achieving strong and efficient latent continuous reasoning.
[NLP-71] Measuring AI innovation with trademark data
【速读】： 该论文旨在解决如何更全面、及时地监测人工智能（Artificial Intelligence, AI）技术的发展与扩散问题。传统研究多依赖专利数据，但其在反映商业化应用和跨行业渗透方面存在局限。为此，论文提出以商标数据作为补充，关键在于利用人工智能相关商标（AI trademarks）能够揭示企业在不同经济领域中将AI技术转化为新产品和服务的实践路径与广度，从而捕捉专利数据难以覆盖的创新商业化维度。相较于专利数据，商标数据具有更新快、覆盖范围广（涵盖所有经济部门）和全球可获取等优势，为分析AI在企业层面的应用扩散提供了更具时效性和广泛性的数据支持。研究通过意大利企业的实证分析展示了这一方法的有效性，并进一步探讨了在不同分析层次上运用AI商标数据应对AI发展与扩散相关前沿问题的可能性。
链接: https://arxiv.org/abs/2607.18795
作者: C. Castaldi,F. Castellacci,A. Fronzetti Colladon,L. Segneri,F. Venturini
机构: 未知
类目: General Economics (econ.GN); Computation and Language (cs.CL)
备注:
Abstract:Researchers, managers and policymakers are exploring different approaches and data sources to map the development and the diffusion of Artificial Intelligence (AI). In this research note, we illustrate the opportunities offered by trademark data. We argue that AI trademarks can complement AI patents to capture different dimensions of AI innovation. AI trademarks can reveal the extent and ways in which companies exploit AI technologies to develop new goods and services. Importantly, trademark data offer a timely and globally available data source that covers all economic sectors. We present insights from using AI trademarks in an empirical exploration of Italian firms. In our discussion, we reflect on how AI trademarks can be used at different levels of analysis to tackle emerging questions about the development and diffusion of AI.
信息检索
[IR-0] PAGE-RAG : Evidence-Grounded Adaptive Graph Retrieval for Long-Document Question Answering
链接: https://arxiv.org/abs/2607.19301
作者: Xingyu Chen,Junxiu An,Jun Guo,Li Wang
类目: Information Retrieval (cs.IR)
备注: 22 pages, 2 figures, and 3 tables. The source code is publicly available at this https URL
Abstract:GraphRAG improves long-document question answering by introducing structured representations beyond conventional retrieval. However, automatically constructed graphs are inherently incomplete projections of source documents, and treating them as independent knowledge sources may lead to unreliable retrieval and generation. We propose PAGE-RAG, a projection-aware adaptive graph retrieval framework for reliable long-document question answering. PAGE-RAG views graph structures as semantic skeletons that organize and navigate document knowledge, rather than replacing the original knowledge source. Based on this perspective, PAGE-RAG introduces a task-adaptive retrieval routing strategy that dynamically selects appropriate retrieval behaviors according to query requirements. Furthermore, PAGE-RAG incorporates strict knowledge boundary control, ensuring that generated responses remain grounded within available evidence and abstaining from unsupported information beyond the accessible knowledge scope. Experiments demonstrate that PAGE-RAG achieves competitive answer quality while improving retrieval efficiency and knowledge reliability, highlighting the importance of projection-aware graph modeling, adaptive retrieval, and explicit knowledge boundary control for trustworthy GraphRAG systems. The source code is publicly available at this https URL.
[IR-1] Sequential Learner Modeling Using Multi-Relational Graph Convolutional Networks
链接: https://arxiv.org/abs/2607.19253
作者: Rawaa Alatrash,Mohamed Amine Chatti,Hong Yang,Yumeng Wang
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Information Retrieval (cs.IR)
备注: Paper submitted to IJAIED
Abstract:User modeling is a critical task in a variety of personalized systems. Recognizing their effectiveness in learning from graph-structured data, Graph Neural Networks (GNNs), particularly Graph Convolutional Networks (GCNs), are increasingly employed for user modeling. However, existing approaches typically treat different relation types in a graph as homogeneous, limiting their ability to capture richer semantics and construct more informative user models. While multi-relational GNNs (MR-GNNs) have been adopted for representation learning and recommendation, their application for user modeling remains unexplored. Moreover, existing GNN-based user modeling approaches ignore the user interaction sequence. To address these research gaps, in this work we propose MR-ConceptGCN, a novel fully unsupervised approach focused on concept-based sequential learner modeling using multi-relational GCNs (MR-GCNs). MR-ConceptGCN effecively combines Personal Knowledge Graphs (PKGs), MR-GCNs, and the pre-trained language model SBERT to obtain enhanced relation- and semantic-aware representations of the PKG items. The enriched embeddings of the knowledge concepts that a learner did not understand when interacting with learning materials in CourseMapper are then used to construct a sequential learner model that combines long-term and short-term learner interactions. We report the results of an online user study (n = 31), demonstrating the benefits of MR-ConceptGCN in terms of several important user-centric aspects including accuracy, usefulness, diversity, and satisfaction with an educational recommender system.
[IR-2] Spectral Biclustering-Driven Scalability for Post-Hoc Explainability in Recommender Systems
链接: https://arxiv.org/abs/2607.19189
作者: Jose L. Salmeron,Irina Arévalo
类目: Information Retrieval (cs.IR)
备注:
Abstract:Explainability in recommender systems is essential for ensuring transparency, accountability, and trust, yet existing post-hoc methods often encounter severe scalability challenges. Observation-level deletion diagnostics offer a counterfactual way to analyze recommendations by retraining models after removing individual users or items, but their cost grows rapidly with dataset size. To improve the practical tractability of this analysis, this paper introduces a block-deletion diagnostic framework that uses spectral biclustering to group users and items and then removes entire blocks of interactions. This formulation reduces the number of retraining procedures relative to finer-grained deletion strategies and produces explanations at the level of user segments, item groups, and their interactions. The framework is evaluated on two representative recommender paradigms, Singular Value Decomposition and Neural Collaborative Filtering, using the MovieLens and Amazon datasets. The results show that top-ranked recommendations are often more sensitive to specific interaction blocks than lower-ranked ones, with some blocks acting as supporting evidence and others having a detrimental effect on recommendation quality. The analysis also indicates that user segments differ in their sensitivity to block removal, suggesting heterogeneous levels of reliance on localized interaction patterns. These findings provide diagnostic information that is not directly visible through standard recommendation metrics. Overall, the results suggest that block-deletion diagnostics offer a practical and model-agnostic post-hoc analysis framework for recommender systems, while also highlighting that the resulting explanations depend on the chosen block structure.
[IR-3] Biological Amnesia in ICU Time-Series Prediction: A Drift-Adaptive Two-Stream Architecture with Temporal Retrieval
链接: https://arxiv.org/abs/2607.19020
作者: Fatema Ferdous Tamanna,K. M. Merajul Arefin,Md. Abdul Masud
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Quantitative Methods (q-bio.QM)
备注: 10 pages, 3 figures, 8 tables. Code and aggregate audit logs available at: [ this https URL ]. Under Review
Abstract:Background: Clinical decision support systems degrade silently as treatment protocols evolve, yet standard adaptation methods treat models as monolithic blocks, unable to distinguish stable patient physiology from shifting institutional practice. Methods: We propose an adaptive clinical intelligence architecture for ICU intervention prediction that structurally decouples physiological from treatment representations, confining parameter updates to the treatment stream upon a dual distributional and accuracy trigger. Automated audit logs record which treatment features drove each adaptation event and how their importance shifted. At inference, an attribution-driven Temporal RAG module grounds each prediction in patient-specific, era-matched PubMed evidence anchored to the patient’s dominant physiological features. Experiments used 84,792 MIMIC-IV stays (2008-2022) under strict chronological split. Results: Drift localised entirely to the treatment stream, validating the structural prior. Selective adaptation improved vasopressor and septic shock discrimination and calibration over the static source model. A fully retrained baseline yielded marginally higher aggregate discrimination but missed 26 septic shock cases the framework correctly identified, with none in the reverse direction; retrieval consistency with the pre-adaptation source model was preserved by the framework but degraded substantially in the retrained baseline. Conclusions: Structurally constraining adaptation to drifting components while preserving stable physiological representations enables clinical AI to evolve with practice without distorting learned patient biology. This architecture offers a template for governable, interpretable deployment of adaptive models in high-stakes clinical environments.
[IR-4] Answer-Reconstruction Search Density: Measuring the Query and Source Work Compressed by Conversational Answers
链接: https://arxiv.org/abs/2607.18904
作者: Benjamin Tannenbaum
类目: Information Retrieval (cs.IR)
备注:
Abstract:Conversational systems can collapse a visible sequence of web queries, result inspections, and source comparisons into a single synthesized answer. Existing retrieval metrics evaluate ranking, effort, or factual support, but they do not quantify the minimum conventional search work represented by a completed answer. We define answer-reconstruction search density (\ARSD): the minimum number of distinct query actions required, under a fixed and dated reconstruction policy, to support a target share of atomic retrievable answer units. A parallel page-density measure separates query compression from source compression.
[IR-5] AILQA: Evaluating AI-Driven Legal Question Answering Systems for the Indian Legal System
链接: https://arxiv.org/abs/2607.18825
作者: Shubham Kumar Nigam,Shubham Kumar Mishra,Noel Shallum,Kripabandhu Ghosh,Arnab Bhattacharya
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: Accepted in AI and Law Journal
Abstract:This comprehensive study introduces an advanced Artificial Intelligence for Indian Legal Question Answering (AILQA) system tailored to the Indian legal context. AILQA leverages a variety of embedding and generative models, including recent Large Language Models (LLMs), to address the unique challenges posed by the intricate and diverse nature of Indian legal texts and to enhance the accuracy and reliability of responses to legal questions. We conducted rigorous evaluations using both lexical and semantic metrics, enriched by expert legal feedback, to ensure relevance and accuracy. Our findings underscore the effectiveness of the Retrieval-Augmented Generation (RAG) paradigm in improving answer quality, particularly in complex legal domains. Additionally, we assessed performance on standardized tests such as the All India Bar Examination (AIBE), thereby providing a robust benchmark for practical applications. Under the study’s evaluation protocol, some AI-generated responses received higher ratings than the available reference answers, particularly when they contained accurate and relevant supporting details. This finding is specific to the evaluated dataset and rating criteria and should not be interpreted as evidence that the models generally outperform qualified legal professionals. We also discuss the challenges encountered, such as the need for precise context and the risks of model hallucination, and propose directions for future research to further refine AI capabilities in the legal field. This study aims to pave the way for enhanced legal decision-support systems, making them more accessible and effective for legal professionals and the public alike.
[IR-6] SGR: Taobao Search Generative Retrieval
链接: https://arxiv.org/abs/2607.18796
作者: Tianyu Zhan,Gui Ling,Tong Xiong,Kunhai Lin,Yang Wang,Kaixuan Zhang,Zhihong Chen,Yuliang Yan,Dan Ou,Shengyu Zhang,Haihong Tang,Bo Zheng
类目: Information Retrieval (cs.IR)
备注:
Abstract:Generative retrieval (GR) has demonstrated strong promise for industrial e-commerce search by training a single autoregressive model to directly generate the Semantic IDs (SIDs) of target items. However, existing GR systems are primarily optimized for semantic matching and remain insensitive to item business value: SID construction is value-unaware, and candidates are ranked without access to item side-info. Consequently, high-value items are often missed or deprioritized at the retrieval stage, limiting downstream business impact. This limitation is particularly critical in industrial settings such as Taobao Search, where business objectives are central to system design. To address this, we propose \textbfT aobao \textbfS earch \textbfG enerative \textbfR etrieval ( \textbfTSGR ), a unified generative retrieval framework that incorporates value awareness into both item representation and candidate ranking. 1) For item representation, TSGR introduces \textbfQuery-aware Parallel SID (QP-SID) , which encodes query-conditioned value orderings into the SID construction by building parallel codebooks derived from query-item statistics, so that higher-value and query-relevant items are assigned better token indices. 2) For candidate ranking, we introduce a \textbfValue-aware Ranking Module (VRM) that is built upon and jointly optimized with the GR, enabling a single model to seamlessly serve as both retriever and pre-ranker without a dedicated pre-ranking stage. A progressive training pipeline further aligns the model with semantic relevance, user preferences, and business objectives. Offline experiments show that TSGR achieves an 9.16% improvement in HR@1000, and online A/B tests further validate its effectiveness, yielding gains of +0.43% in IPV, +1.12% in Transaction Count, and +1.64% in GMV.
[IR-7] Beyond Noisy Signals: Dual-Level Denoising for Multi-modal Sequential Recommendation ACM-MM2026
链接: https://arxiv.org/abs/2607.18786
作者: Jie Luo,Qi Jin,Xinming Zhang
类目: Information Retrieval (cs.IR)
备注: Accepted by ACM MM 2026. 12 Pages
Abstract:Multi-modal Sequential Recommendation (SR) incorporates rich side information (e.g., textual and visual features) to enhance dynamic user preference modeling. However, existing frameworks inevitably suffer from a \textbfDual-Noise Dilemma: (1) \textitFeature-level redundancy stemming from the semantic gap between generic pre-trained representations and fine-grained recommendation intent; and (2) \textitSequence-level stochasticity induced by spurious interactions such as accidental clicks. To break this bottleneck, we propose \textbfDDMSR, a novel \textbfDual-level \textbfDenoising \textbfMulti-modal \textbfSequential \textbfRecommendation framework that systematically purifies signals from both feature-topological and sequence-frequency perspectives. Specifically, we first design a graph-based feature denoising module that leverages Laplacian smoothing on item semantic graphs as a structural low-pass filter, effectively suppressing high-frequency semantic noise while preserving salient features. For sequence purification, we introduce a frequency-domain sequence denoising module, utilizing the Fast Fourier Transform and a learnable frequency filter to adaptively modulate the interaction spectrum and attenuate anomalous signals. Furthermore, a multi-modal contrastive alignment objective is incorporated to bridge the heterogeneity gap and enforce cross-modal semantic consistency. Extensive experiments on four public benchmark datasets demonstrate that DDMSR consistently outperforms state-of-the-art baselines, providing a highly robust and efficient solution for multi-modal sequential recommendation. The source code is available at: ~\hrefthis https URL\textcolorbluethis https URL.
[IR-8] RAG AL: A Frugal Fully Local Retrieval-Augmented Assistant for Technical Support at a Government Agency
链接: https://arxiv.org/abs/2607.18756
作者: Dan Musetoiu
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注: 16 pages, 6 figures
Abstract:Public institutions hold large volumes of sensitive documents and support tickets that cannot leave the premises, ruling out cloud-hosted language models entirely. We report on RAGAL, a retrieval-augmented assistant for the technical-support team of AFIR, the Romanian Agency for Financing Rural Investments, built and operated under three hard constraints: zero data egress (no external API calls, even for synthetic data), a read-only mandate (the assistant drafts, humans execute), and a single 8 GB consumer laptop as the only development and training machine. Over a Romanian-language corpus of ~25,000 chunks – 15,073 resolved support tickets and internal normative documents – we show that the highest-leverage investments were retrieval engineering and retriever fine-tuning rather than a larger generator: hybrid dense-sparse retrieval with intent routing raised our internal evaluation from 62% to 81%, and fine-tuning the bge-m3 embedder on real ticket data improved recall@10 from 0.663 to 0.850 (MRR 0.489 to 0.684) after 72 minutes of training. We document a general pitfall: single-domain fine-tuning silently degraded retrieval on the untouched document domain below the stock baseline, detected only after building a per-domain evaluation set and repaired with locally generated queries (GenQ). We report two counter-intuitive findings – PII masking improved generation quality, and a structural “anchor distillation” scheme made SQL hallucination impossible by construction – along with a reproducible recipe for full embedder fine-tuning in 8 GB of VRAM. Finally, since zero egress also rules out a cloud judge, we describe a substitute: a 744B-parameter model run on CPU, too slow to serve interactively but affordable in overnight batch, used as a second opinion whose limits we quantify. We release the sanitized pipeline scripts for institutions facing similar data-locality constraints.
[IR-9] An Epistemic Position-Based Click Model: From Interactions to Epistemic Distributions of Relevance and Bias SIGIR2026
链接: https://arxiv.org/abs/2607.18712
作者: Oscar Rolando Ramirez Milian,Harrie Oosterhuis
类目: Information Retrieval (cs.IR)
备注: Published at SIGIR 2026
Abstract:User interactions with rankings are affected by both items’ relevances and display positions. Accordingly, click probabilities are often modeled as a product of relevance and position factors; and for improving recommendation and search, one needs to disentangle relevance from position bias. However, existing click models only provide frequentist point-estimates that do not capture any measure of epistemic uncertainty. Consequently, there is no indication of how much confidence one should have in their predictions. In this work, we introduce the first evidential deep-learning approach to form an epistemic alternative to the important position-based click model. Our learned model takes as input item and position features and outputs a beta-distribution for every relevance and position-bias variable of the position-based model. These distributions capture epistemic uncertainty about click probabilities and the underlying effects of attraction and position bias. The main challenge of our approach is its optimization for which we propose approximation and conditioning techniques to provide numerical stability and variance reduction. Our experiments indicate that our approach captures epistemic uncertainty in predictions on previously-unseen data, whereas standard policy gradients fail to learn meaningful distributions. We believe our contribution of the first contextual epistemic click model constitutes an important step in incorporating Bayesian uncertainty into click modeling.
[IR-10] Exposure-Based Reinforcement Learning to Rank ICTIR’26
链接: https://arxiv.org/abs/2607.18689
作者: Harrie Oosterhuis,Rolf Jagerman,Zhen Qin,Xuanhui Wang
类目: Machine Learning (cs.LG); Information Retrieval (cs.IR)
备注: Published at ICTIR’26
Abstract:Reinforcement learning (RL) methods for learning-to-rank (LTR) can optimize (almost) any ranking goal, e.g., from precision or discounted cumulative gain to fairness-of-exposure or ranking distillation. However, standard RL is ineffective and computationally costly due to the enormous action space in LTR settings. Existing methods reach computational efficiency through custom gradient computation algorithms, but they are very complex to implement and often clash with auto-differentiation. Consequently, existing RL for LTR is not attractive to many practitioners. We reconsider RL for LTR while actively avoiding reliance on custom gradients. Contrary to the existing approaches, we focus on variance reduction and GPU computation. In doing so, we discover that high sample-efficiency can be reached through baseline corrections and partial marginalization. Furthermore, we propose an abstraction that places gradient estimation behind a document-exposure distribution, this enables seamless plug-and-play integration with auto-differentiation. Thereby, one only has to implement a loss as a differentiable function of exposure and RL for LTR can optimize it using auto-differentiation. Our experimental results reveal that our new exposure-based RL for LTR approach converges considerably faster and at significantly higher ranking performance than existing custom gradients, with no additional costs in computation time when using GPUs. In contrast, existing custom gradients result in severe stability issues when converging over many epochs, which never occur for our methods. Thus, we considerably improve RL for LTR methodology by increasing its effectiveness, efficiency, and ease of application.
[IR-11] PLAID-PRF: Pseudo-Relevance Feedback with Centroid-like Tokens in PLAID SIGIR2026
链接: https://arxiv.org/abs/2607.18626
作者: Xiao Wang,Sean MacAvaney,Craig Macdonald
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注: SIGIR 2026
Abstract:Multi-vector dense retrieval models, such as ColBERT, achieve strong retrieval effectiveness by modelling fine-grained token-level interactions between queries and documents. Methods such as PLAID use centroid-based quantisation of each token’s vector to reduce the index size and speed up retrieval while maintaining strong effectiveness. In this work, we introduce PLAID-PRF, a method that performs Pseudo-Relevance Feedback (PRF) over PLAID to reformulate ColBERT’s query vectors based on the top-retrieved results. In contrast with prior methods that perform PRF on multi-vector retrieval models, PLAID-PRF keeps computational costs low by leveraging the internal PLAID centroid vectors, treating them similarly to tokens in traditional PRF methods. The method selects a small and diverse set of high-utility expansion vectors and appends them to the original query, rerunning PLAID to refine both candidate generation and final scoring. Extensive experiments on the standard in-domain MSMARCO and four out-of-domain BEIR benchmarks show that PLAID-PRF consistently improves retrieval effectiveness over various baselines. In particular, PLAID-PRF improves over PLAID by up to 4.3% nDCG@10 and 7.3% MRR@10, while introducing substantially less computation overhead than prior PRF methods. The results demonstrate that our proposed centroid-aware PRF method offers an effective and lightweight mechanism to improve the quality of top-ranked retrieved results. Overall, this work enables effective and efficient feedback-aware late-interaction retrieval without expensive query-time document-token clustering.
[IR-12] Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation
链接: https://arxiv.org/abs/2607.18609
作者: Yongsen Zheng,Ruilin Xu,Guohua Wang,Liang Lin,Kwok-Yan Lam
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:The Matthew effect is a big challenge in Recommender Systems (RSs), where popular items tend to receive increasing attention, while less popular ones are often overlooked, perpetuating existing disparities. Although many existing methods attempt to mitigate Matthew effect in the static or quasi-static recommendation scenarios, such issue will be more pronounced as users engage with the system over time. To this end, we propose a novel framework, Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation (HiCore), aiming to address Matthew effect in the Conversational Recommender System (CRS) involving the dynamic user-system feedback loop. It devotes to learn multi-level user interests by building a set of hypergraphs (i.e., item-, entity-, word-oriented multiple-channel hypergraphs) to alleviate the Matthew effec. Extensive experiments on four CRS-based datasets showcase that HiCore attains a new state-of-the-art performance, underscoring its superiority in mitigating the Matthew effect effectively. Our code is available at this https URL.
[IR-13] AutoIndex: Learning Representation Programs for Retrieval
链接: https://arxiv.org/abs/2607.18603
作者: Sam O’Nuallain,Nithya Rajkumar,Ramya Narayanasamy,Hanna Jiang,Shreyas Chaudhari,Andrew Drozdov
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:We present AutoIndex, a framework for learning representation programs: executable transformations that map raw documents into the representations exposed to a retrieval system. Rather than tuning retrievers, rerankers, or a small set of preprocessing hyperparameters, AutoIndex searches over programs that slice, enrich, normalize, reweight, or reorganize documents before indexing. At each iteration, AutoIndex performs validation-guided program search, in which agents diagnose failures of the current program and synthesize candidate updates, retaining only updates that improve retrieval quality under the resulting index. We evaluate AutoIndex on CRUMB, a benchmark of heterogeneous retrieval tasks, with BM25 held fixed across all experiments. The learned programs improve recall over a static full-document BM25 baseline on all 8 tasks, with average gains of +8.4% in Recall@100 and +8.3% in nDCG@10, and largest gains of +30.5% in Recall@100 and +43.6% in nDCG@10. These results suggest that document representation should not be treated as a fixed preprocessing choice made before retrieval begins, but as an explicit optimization target. Code to reproduce our results is available at this https URL.
[IR-14] opology-Aware Tokenization for Generative Recommendation RECSYS2026
链接: https://arxiv.org/abs/2607.18600
作者: Yaokun Liu,Yifan Liu,Zhenrui Yue,Gyuseok Lee,Zelin Li,Ruichen Yao,Dong Wang
类目: Information Retrieval (cs.IR)
备注: Accepted to RecSys 2026. 10 pages
Abstract:Generative recommendation reformulates sequential recommendation as an autoregressive generation task, yet a critical issue in this paradigm remains overlooked: topology distortion in item tokenization. In particular, we observe that the intrinsic adjacency relationships of items in the pretrained semantic embedding space are significantly disrupted after quantization. This topology distortion misleads the model’s perception of item similarity, ultimately bottlenecking the accuracy of generative recommendations. To address this issue, we propose Topology-Aware Tokenization (TopoTok), an item tokenization framework that preserves item relational structure throughout the quantization hierarchy. Different from the prior monolithic supervision in tokenization, TopoTok introduces a multi-level distillation scheme to progressively recover the topology from coarse to fine granularity: 1) Inter-Group Distillation to capture global cluster-wise relations; 2) Intra-Group Distillation to refine local structures within semantic clusters; and 3) Inter-Item Distillation to enforce fine-grained alignment at the individual item level. Extensive experiments on three benchmark datasets demonstrate that TopoTok effectively alleviates topology distortion and consistently outperforms state-of-the-art tokenizers, achieving significant performance gains of up to 9.42% in Recall@5.
人机交互
[HC-0] he safety failures we are not instrumenting: a perspective on hidden safety-critical challenges in modern AI systems
链接: https://arxiv.org/abs/2607.19292
作者: Gjergji Kasneci,Enkelejda Kasneci
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: Email by the arXiv Support Team: “Dear Gjergji, Thank you for your patience. Your appeal was accepted. You are welcome to resubmit this work at your convenience to cs.CY (cs.AI, cs.HC) Regards, arXiv Support”
Abstract:Current AI safety discourse still focuses disproportionately on visible failures, including obvious harms, dramatic misuse, and hypothetical catastrophic scenarios. That focus is incomplete. In deployed systems, many of the most consequential failures are quieter: plausible rather than spectacular, distributed across components rather than localized in a single output, and normalized by workflows before they are recognized as hazards. We argue that a central safety challenge in modern AI systems is increasingly not only whether a model emits a harmful response, but whether the broader socio-technical system preserves the conditions under which errors remain visible, contestable, containable, and recoverable. We propose a five-layer framework for diagnosing these hidden risks: (1) epistemic integrity, concerning whether evidence and uncertainty are represented honestly enough to support calibrated reliance; (2) control integrity, concerning whether authority, permissions, and action boundaries remain robust under attack and optimization; (3) temporal integrity, concerning whether safety holds across sessions, memory updates, and deployment drift; (4) organizational integrity, concerning whether institutions retain the capacity to audit, assign responsibility, and intervene effectively; and (5) ecosystem integrity, concerning whether AI systems preserve rather than erode the information environment on which future oversight depends. Across these layers, we identify under-recognized risk patterns, including overreliance, uncertainty and legitimacy laundering in retrieval, prompt injection, reward hacking, memory poisoning, evaluation deception, fictional human oversight, synthetic evidence pollution, and model collapse. We conclude with design and governance recommendations and a research agenda for shifting AI safety from model-centric evaluation toward socio-technical reliability.
[HC-1] Caring Over Computing: An Ethical and Sociotechnical Perspective on Generative AI for Social Connectedness in Dementia Care ALT
链接: https://arxiv.org/abs/2607.19007
作者: Teis Arets,Giulia Perugia,Maarten Houben,Wijnand IJsselsteijn
类目: Human-Computer Interaction (cs.HC)
备注: Preprint version of a manuscript submitted to Frontiers in Digital Health - Human Factors and Digital Health, under review. 27 pages, 1 figure
Abstract:People with dementia in residential care often experience reduced social connectedness. Person-centered care approaches foreground meaningful social interactions to support well-being, but staff and time pressures increasingly constrain opportunities for such care. Generative AI (GenAI) technologies offer possibilities for helping care professionals facilitate social connectedness, despite increasing care pressures. Yet, the meaningful integration of such technologies into the complex sociotechnical setting of residential dementia care is not straightforward. To responsibly design GenAI technologies for dementia care, we need to investigate how care professionals recognize and foster social connectedness and how technologies intersect with those approaches in care contexts. We conducted five workshops with 15 care professionals from three care organizations to pursue this objective. Our findings show that fostering social connectedness involves ongoing interpretation, negotiation, and adaptation to residents’ individual needs. Technology was used by care professionals to support communication, interaction, and shared activities, yet remained largely peripheral to everyday care routines and required active mediation for use in practice. Drawing on an ethics of care framework, we derive ethical and sociotechnical implications for the responsible design and positioning of GenAI in residential dementia care.
[HC-2] AutoJourn: Multi-Perspective Summarisation Bias Detection and Bias Neutralisation for LLM -Generated News in Automated Journalism
链接: https://arxiv.org/abs/2607.18983
作者: Himel Ghosh,Ahmed Mosharafa,Georg Groh
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:We present AutoJourn, a demonstration system for multi-perspective news generation and bias-aware evaluation using large language models (LLMs). The system tackles three core challenges in responsible automated journalism: extracting diverse perspectives from unstructured social media discussions, generating summaries that preserve viewpoint diversity, and detecting or mitigating bias in AI-generated news. The pipeline integrates advanced prompt engineering with optional retrieval augmentation to produce semantically diverse perspective sets, a multi-perspective summarisation module that merges conflicting viewpoints into balanced summaries, and a bias analysis suite supporting sentence-level bias detection and type classification in the generated news article, and automatic neutralisation. Users can inspect perspective clusters, compare stance-specific summaries, generate news articles, and apply bias-aware rewrites directly in the interface. We evaluate each component with intrinsic metrics – semantic diversity, summary quality, and bias reduction and show improvements over strong baselines while maintaining content fidelity. A live, publicly accessible demo accompanies the paper to facilitate reproducibility and further research on socially responsible automated journalism.
[HC-3] Public perceptions of AI-driven decision-making in healthcare: A structural equation modeling approach
链接: https://arxiv.org/abs/2607.18884
作者: Leonie Westerbeek,Ernesto de Leon,Julia C.M. van Weert
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:Artificial intelligence (AI) is increasingly integrated into healthcare to support diagnostics, decision-making, and administrative processes. However, the successful implementation of AI depends not only on technical performance but also on public perceptions of its helpfulness, riskiness, and fairness. This study examines public perceptions of automated decision-making (ADM) in healthcare. Data were drawn from the first wave of an ongoing longitudinal survey panel. The final sample consisted of 3,915 respondents and was analyzed with structural equation modeling. Perceptions of ADM in healthcare as helpful, risky, and fair were treated as the dependent variables. AI literacy, familiarity with different forms of AI, confidence in clinicians’ ability to distinguish AI- from human-generated content, use of conversational agents for health information, and use of traditional digital health information sources were included as exogenous. Greater familiarity with different forms of AI, higher confidence in the clinician’s ability to recognize AI-generated content, and use of conversational agents for health information were associated with greater perceived helpfulness. Use of conversational agents was associated with lower perceived risk, whereas greater familiarity with AI and greater reliance on traditional health information sources were associated with higher perceived risk. Perceptions of ADM as fair were most strongly predicted by confidence in the clinician’s ability, with additional small positive associations with AI familiarity, AI literacy, and use of conversational agents. Public perceptions of ADM in healthcare are shaped by technological familiarity, use of conversational agents, and confidence in human oversight. Overall, ADM’s perceived helpfulness and fairness are driven more by trust in healthcare professionals than by trust in the technology itself.
[HC-4] Evaluating a Visual Query Tracer and Builder for Learning Declarative Logic Programming IEEE-VIS2026
链接: https://arxiv.org/abs/2607.18864
作者: Julián Méndez,Lukas Gerlach,Tobias Wieland,Alex Ivliev,Markus Krötzsch,Raimund Dachselt
类目: Human-Computer Interaction (cs.HC)
备注: Author version of accepted short paper at IEEE VIS 2026
Abstract:Nemo Explain Visualizer (nev) is an interactive visual query tracer and builder for Nemo, a powerful Datalog reasoner with extended features. Our tools were developed with and for expert users. However, considering the lack of resources to learn Datalog and similar declarative logic programming languages, we conducted a qualitative user study to assess how our tools might help students. The study, interviewing 14 participants with varying levels of involvement with the content of a university course on knowledge graphs, revealed a very positive assessment of our tools, which strengthens the value of visual explanation tools beyond their intended use.
[HC-5] CITRUS: Candidate Inference and Temporal-tracking for Reliable Unobtrusive Sensing of Wearable Heart Rate under Motion
链接: https://arxiv.org/abs/2607.18818
作者: Yi Wang
类目: Human-Computer Interaction (cs.HC)
备注: 9 pages, 1 figure, 10 tables
Abstract:Wearable photoplethysmography (PPG) provides continuous heart-rate measurements, but its accuracy degrades under motion. In the ring-platform benchmark, the best supervised baseline reaches 5.33 BPM mean absolute error (MAE) on the overall heart-rate task. In the motion-focused ring-only audit, a supervised LSTM baseline reaches 14.39 \pm 0.47 BPM MAE on motion windows, and simple smoothing and ACC priors reduce this only to 13.00 \pm 0.41 BPM. This thesis addresses motion-corrupted HR estimation through three connected stages: candidate-frequency identification, temporal decoding, and reliability-aware reporting. The study first evaluates two wearable PPG ring variants against clinical references for heart rate, respiration, SpO _2 , and blood pressure in 54 participants. The proposed system then uses two layers. The estimation layer converts each window into approximately 140 frequency candidates, scores candidates using agreement among independent estimators, and applies causal Viterbi decoding with a physiological transition penalty. The reporting layer estimates reliability and applies a learned accept/hold/reject policy. Reporting only the most-confident 50% of motion windows reduces motion MAE from 10.8 to 6.2 BPM. The heart-rate estimator uses fewer than 100k parameters and runs within a microcontroller-class compute budget. Additional PPG-DaLiA experiments evaluate the same candidate-selection and temporal-decoding estimator on an independent wrist-BVP cohort. Comments: 9 pages, 1 figure, 10 tables Subjects: Human-Computer Interaction (cs.HC) Cite as: arXiv:2607.18818 [cs.HC] (or arXiv:2607.18818v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2607.18818 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[HC-6] PeakFlow: Peak-Guided Coarse-to-Refined Modeling for EEG-Based Dynamic Affective Trajectory Prediction
链接: https://arxiv.org/abs/2607.18671
作者: Hao Tang,Songyun Xie,Xinzhou Xie,Can Liao,Xin Zhang,Bohan Li,Zhongyu Tian,Dalu Zheng
类目: Human-Computer Interaction (cs.HC)
备注: Preprint. Code is available at this https URL
Abstract:Most existing EEG-based emotion recognition studies formulate affective decoding as static category prediction, although emotions elicited by continuous stimulation evolve over time, accumulate, reach peak intensity, and then recover. This motivates EEG-based dynamic affective trajectory prediction, which estimates continuous affective intensity curves from sequential EEG observations. Existing temporal regression models can capture coarse intensity trends but often fail to preserve peak-centered structure, leading to inaccurate peak timing and terminal-peak bias, where the predicted maximum is shifted toward the end of a trial. To address this issue, we propose PeakFlow, a peak-guided coarse-to-refined framework for EEG-based dynamic affective trajectory prediction. PeakFlow first learns a coarse affective flow through EEG temporal tokenization and masked temporal modeling, then applies a lightweight residual refiner for peak-guided bounded calibration. The refiner uses trajectory-aware cues and a peak-centered objective combining global trajectory consistency, peak-zone emphasis, peak-probability localization, terminal suppression, and residual regularization. This design preserves the global affective trend while correcting peak misalignment, peak-value deviation, and false-terminal predictions. Leave-one-subject-out experiments on SEED-VII show that PeakFlow improves both global trajectory fitting and peak-centered temporal reliability over strong dynamic modeling baselines. Auxiliary evaluation on FIRMED further suggests its potential for sparse peak-centered ordinal intensity analysis. These results highlight the importance of peak-aware modeling for temporally faithful EEG-based dynamic emotion prediction. Code is available at this https URL.
[HC-7] How defensive driving enhances driving safety: A driving simulator study on drivers defensive driving behaviors
链接: https://arxiv.org/abs/2607.18663
作者: Xinzheng Wu,Junyi Chen,Shaolingfeng Ye,Yong Shen
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC); Signal Processing (eess.SP)
备注: 17 pages, 8 figures
Abstract:Defensive driving is widely recognized as an advanced driving skill. However, whether and how defensive driving affects driving safety remains insufficiently investigated. This study examines the behavioral characteristics of defensive driving, its impact on driving safety, and the underlying mechanisms. First, defensive driving is defined regarding operational timing and application scenario. Then, 82 participants are recruited for driving simulator experiments, with their behavioral and eye movement data being collected. Following the experiments, participants are categorized into groups based on the frequency of defensive driving behaviors exhibited. Finally, both inter-group and inter trial comparisons are performed on the experimental data. Experimental results demonstrate that in the inter-group comparison, the high defensive driving capability group exhibits higher acceleration and deceleration magnitudes, lower average speeds, and larger average absolute yaw angles compared to the low capability group, alongside shorter fixation durations and reduced fixation frequencies. Moreover, we observe that these participants tend to initiate defensive or evasive actions earlier, resulting in lower scenario risk. Regarding the inter-trial comparison, we observe similar trends exclusively in the low capability group, whereas most metrics show no significant differences between Trial 1 and Trial 2 in the high capability group. These results reveal that drivers possessing defensive driving capabilities tend to execute more intense driving maneuvers and identify risks and take action earlier, thereby enhancing driving safety. Findings support the promotion of defensive driving and provide a basis for relevant training programs. Meanwhile, they offer insights for the training of autonomous driving algorithms with defensive driving capabilities.
[HC-8] BlurDriving: Investigating How Personalized Blur Techniques Impact Drivers Performance in Virtual Reality
链接: https://arxiv.org/abs/2607.18628
作者: Yuan Li,Mark Colley,Xinyue Gui,Cristian Camilo Rendon Cardona,Pascal Jansen,Christian Sandor,Takeo Igarashi
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Distracted driving remains a major safety concern, motivating approaches that aim to reduce visual overload before attention breaks down. However, visual overload varies across individuals, making it difficult to determine appropriate interventions for each driver. We investigate whether controllable visual blur can simplify the driving scene and mitigate distraction. To address this challenge, we propose BlurDriving, a target-selective, distance-aware blur system in a Virtual Reality (VR) urban driving simulator, and employ a Human-in-the-Loop Multi-Objective Bayesian Optimization (HITL-MOBO) framework to personalize blur configurations. Across two VR user studies, we evaluated driving under normal conditions in Study 1 and under cognitively demanding conditions in Study 2. We found that personalization revealed strong individual differences in blur preference but did not lead to significant improvements in objective driving performance compared to a no-blur baseline. Qualitative feedback revealed polarized responses: some drivers reported improved focus, while others experienced uncertainty, fatigue, or discomfort. These findings suggest that visual blur is not universally effective. Instead, its benefits depend on individual perceptual strategies and tolerance for visual uncertainty. This work highlights the limits of personalized visual simplification in safety-critical driving and informs adaptive in-vehicle interface design.
[HC-9] End-to-End Markov State Sequence Learning for Auditory Attention Decoding
链接: https://arxiv.org/abs/2607.18614
作者: Yushan Yashengjiang,Jie Zhang,Miao Sun,Huadong Liang,Xin Li,Zhen-hua Ling
类目: ound (cs.SD); Human-Computer Interaction (cs.HC)
备注:
Abstract:Auditory attention decoding (AAD) identifies the speaker a listener attends to from neural responses like electroencephalography (EEG), making it a key algorithm in neuro-steered hearing aids. However, most neural AAD models are trained as independent short-window classifiers, despite auditory attention being a temporally persistent cognitive state and short-window EEG–audio evidence often being noisy and ambiguous. We propose an end-to-end Markov AAD framework based on conditional random field (CRF) that trains window-level neural emissions under a two-state attention prior. The framework treats the logits of any AAD backbone as Markov emissions, learns the transition rate from a standard HMM initialization, and jointly optimizes cross-entropy and CRF objectives, allowing temporal continuity to guide representation learning rather than merely smoothing predictions after training. We also introduce ESCNet, an EEG–speech correlation backbone that preserves time-aligned features and converts the difference between two mean Pearson correlations into state logits. We evaluate the framework with four emission backbones spanning correlation-based, convolutional, recurrent, and attention-based designs. On the dynamic AVGC dataset, CRF training generally outperforms post-hoc HMM smoothing; with ESCNet, it achieves 86.5% causal and 92.4% non-causal accuracy using 1 s windows. On the static KUL and USTC datasets, it improves causal decoding over fixed-rate post-hoc HMM baselines by 5.6% and 2.0% , respectively, showing the superiority of learning AAD as attention state sequence over isolated-window classification.
[HC-10] Understanding ADHD Productivity in Construction Work: Toward AI-enabled VR Interventions
链接: https://arxiv.org/abs/2607.18605
作者: Zinat Ara,Behzad Esmaeili,Lap-Fai Yu,Sungsoo Ray Hong
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Attention-Deficit/Hyperactivity Disorder (ADHD) is identified as the most prevalent neurodivergent condition in the construction industry. While the construction industry may broaden employment opportunities, little is known about how ADHD traits shape workers’ performance, sustained attention, and situational awareness in dynamic job-site environments. This work presents an exploratory interview study aimed at understanding how ADHD traits influence construction-specific productivity and how future interventions can reduce challenges while amplifying strengths. We conducted semi-structured interviews with construction workers with ADHD, safety managers, and ADHD researchers to capture their perspectives on attentional demands, task coordination, and workplace adaptation. As part of these discussions, participants also reflected on the potential of combining artificial intelligence (AI) and virtual reality (VR) to support future ADHD workers. Our analysis identifies two overarching themes: (1) workplace challenges, capturing difficulties that arise from both the nature of construction work and the specific needs of ADHD workers, and (2) productivity support strategies, describing effective methods to sustain focus and task engagement. Further, we derive design requirements for AI and VR-enabled interventions that provide adaptive attentional scaffolding, mediated social presence, and motivational support. We conclude by discussing how these insights can inform the future development of ADHD productivity enhancement support.
[HC-11] Designing for What Cannot Be Seen: Supporting Embodied String Learning for Musicians with Blindness and Low-Vision
链接: https://arxiv.org/abs/2607.18598
作者: Shi Shi,Lingyun Chen,Zitao Zhang,Amanda R. Draper,Eli Blevis
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Bowed string instruments demand fine-grained bodily coordination that is typically taught through visual demonstration, creating persistent barriers for musicians with blindness and low-vision (BLV). To understand these challenges and explore new design opportunities, we conducted a design study with four advanced string musicians with BLV and three of their instructors. Our team, spanning violin performance and music education, disability studies in music, HCI design, and engineering employed a qualitative, multi-method approach including practice video analysis, lesson observation, expert interviews. Our analysis identifies recurring difficulties in right-hand bow control, left-hand coordination, score access, and memory-intensive practice. Building on these findings, we conducted an exploratory design ideation phase informed by empirical findings and feedback from one musician with BLV. We developed speculative design directions that could potentially address identified breakdowns, while acknowledging that these concepts require further evaluation with instructors and in deployed contexts.
[HC-12] HALO: Interactive Co-abductive Reasoning in Scientific Hypothesis Generation
链接: https://arxiv.org/abs/2607.18564
作者: Youngseung Jeon,Kat Limqueco,JiaSyuan Chang,Xiang ‘Anthony’ Chen
类目: Human-Computer Interaction (cs.HC)
备注: To appear in the 39th Annual ACM Symposium on User Interface Software and Technology (UIST '26). 22 pages
Abstract:Scientific discovery is essential yet inefficient, primarily because generating hypotheses within a vast search space hinders breakthroughs. While current AI systems assist in generating new hypothesis candidates, they lack interactive support for the reasoning process by which users develop these outputs into promising hypotheses, resulting in surface-level hypotheses. To address this issue, we present co-abduction, a human-AI collaborative framework for abductive reasoning in scientific hypothesis generation. To operationalize co-abduction, we build HALO, a human-AI collaborative system for molecular hypothesis generation in drug discovery, enabling improved candidate clustering, strategy identification, and multi-strategy synthesis. In expert studies involving 10 medicinal chemists, HALO significantly facilitated abductive reasoning for hypothesis generation – efficient candidate observation, systematic strategy identification, and coherent multi-strategy composition – and enabled participants to produce higher-quality, more diverse candidate molecules.
[HC-13] ChatMuse: Supporting In-Person Small-Group Conversation Experience with a Proactive Assistive AI Agent in Mixed Reality
链接: https://arxiv.org/abs/2607.18556
作者: Shaoze Zhou,Joaquin Frangi,Diana Nelly Rivera Rodriguez,Rawan Alghofaili,Janet G. Johnson,Lingyao Li,Renkai Ma,Christine Lisetti,Chen Chen
类目: Human-Computer Interaction (cs.HC)
备注: 19 pages, 9 figures, 4 tables
Abstract:In-person small-group conversations occur across nearly every aspect of daily life and play a crucial role in social interaction. However, achieving effective in-person group conversations can be challenging and cognitively demanding. While recent Mixed Reality (MR) headsets show promise as a conversational support system by presenting relevant information through overlays, it remains unclear how such supporting information should be designed and generated for in-person group conversations. We propose ChatMuse, a novel MR-based proactive assistive system for in-person small-group conversation experience. ChatMuse analyzes verbal and non-verbal cues from all conversation participants and proactively provides real-time guidance on the user’s verbal and non-verbal behaviors. The behavioral responses of the supported users are then used to improve ChatMuse’s support capabilities in subsequent interactions. We conducted a within-subject study to evaluate and demonstrate the feasibility and effectiveness of ChatMuse in assisting users to engage in and contribute to in-person small-group conversations. Our research around ChatMuse represents a design exploration of a new interaction space that investigates the feasibility of supporting in-person small-group conversations through a proactive assistive AI agent in MR.
[HC-14] EduPanel: A Three-Agent LLM Judge for Teaching Videos – Reliability Complementarity and Human Trust Calibration
链接: https://arxiv.org/abs/2607.18529
作者: Jia-Kai Dong,Yi-Cheng Lin,Hung-yi Lee
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: 19 pages
Abstract:Teaching videos are becoming a major medium for education, creating a growing need for scalable evaluation of their pedagogical quality. Existing automatic judges do not fully address this setting because teaching quality depends on multimodal evidence and should be evaluated with respect to the intended learner rather than as a universal property. We present EduPanel, a rubric-grounded, learner-conditioned LLM judge that decomposes evaluation across specialized agents to produce interpretable assessments for different aspects of teaching quality. Across expert studies, architecture ablations, and learner-persona analyses, EduPanel achieves reliability comparable to a median human expert. In expert evaluation, its feedback improves scoring accuracy (MAE 0.87 to 0.73), while experts remain able to detect unreliable outputs (AUC = 0.77) instead of accepting them blindly. These results suggest that EduPanel can serve as effective assistants for educational evaluation rather than replacements for human experts.
[HC-15] Querying Multimodal Scientific Papers with AI: Practices and Preferences Across Blind Low-Vision and Sighted Scientists
链接: https://arxiv.org/abs/2607.18514
作者: Arnavi Chheda-Kothary,Lucy Lu Wang,Joseph Chee Chang,Jonathan Bragg
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:Visual diagrams, figures, and tables are central to scientific papers, and convey information beyond what is captured in text. While blind or low-vision (BLV) scientists have traditionally relied on static alternative text to access figures in papers, the rise of artificial intelligence (AI) has made interactive question-answering (QA) a feasible paradigm for visual exploration; yet little is known about how scientists use visual QA in practice or how to improve its accessibility. In this work, we interview five BLV and five sighted scientists across different STEM fields to understand how they use two AI tools, ChatGPT and Gemini, to query multimodal scientific documents. Our findings characterize how scientists review multimodal content, including existing practices (along with accessibility workarounds) for engaging with visuals, and feedback on the suitability of AI-generated responses to multimodal queries. We further find that vague or incomplete image descriptions, as well as incorrect AI outputs more broadly, can cause both BLV and sighted scientists to abandon AI workflows. To support future research, we additionally contribute a dataset of 115 queries and responses from our participants’ interactions with the AI tools for papers in their field. We close by discussing implications for AI-powered scientific QA systems, emphasizing considerations for access across abilities and domains.
[HC-16] AInimation: Animating from Prompt to AI-Generated Responses
链接: https://arxiv.org/abs/2607.18507
作者: Jiaqi Wu,Damien Masson
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:We explore the use of animated transitions between a prompt and an AI-generated response. After reviewing 800 examples of prompts and responses, we devise a taxonomy of animated transitions for multimodal text- and image-generative models. The proposed animations include translating and morphing elements of the prompt to their final location in the response; highlighting modifications such as fixed typos; overlaying structural requirements to verify them; and displaying how a model understands references. A study shows that adding animated transitions helps users review the response: participants performed 43% better at locating elements in the response; 153% better at identifying changes; and 20% better at verifying the prompt was correctly interpreted. Our work applies to all software that integrates AI and shows that well-crafted, slower animations are preferable to instant AI responses.
[HC-17] owards an Automated Test of LLM Security Knowledge
链接: https://arxiv.org/abs/2607.18496
作者: Shufan Chai,Liangliang Sun,Jessica Staddon
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Large language models (LLMs) are increasingly used for a range of software, hardware and human-centered security tasks. Consequently, LLM performance on security tasks is an active area of measurement and research, often with a focus on identifying areas in which LLM security ``knowledge’’ may be insufficient. Popular strategies for identifying LLM security knowledge gaps include building corpora of challenge questions or task benchmarks, strategies that require substantial manual work and security expertise to design and execute. We introduce a partially-automated method for assessing LLM knowledge of a security this http URL method uses authoritative information from Consumer Protection Agencies (CPAs) to identify instability in LLM responses that can be indicative of knowledge gaps. We demonstrate the method for 2 security topics, identity theft and impostor scams, and 5 LLMs in 2 leading LLM families, Gemini and GPT, using publicly available information about identity theft and impostor scams from 6 this http URL method distinguishes between models that have and don’t have sufficient knowledge to accurately identify the security topics in text narratives.
[HC-18] Assistant or Actor? Student Trust Control and Delegation Regret When Using a General-Purpose AI Agent
链接: https://arxiv.org/abs/2607.18257
作者: Shiva Pochampally,Shengwei An,Yan Chen
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: 10 pages, 3 figures
Abstract:When AI agents shift from answering questions to taking actions, users face a new problem: deciding what to delegate, to a system whose action space they cannot fully anticipate. We call the resulting dissatisfaction delegation regret, a pattern in which users regret not that the agent erred, but that it acted beyond what they would have authorized. In a controlled study, 20 university students completed five common daily tasks using OpenClaw, a general-purpose AI agent, across tasks chosen to vary in privacy, stakes, and reversibility. For each task we measured trust, perceived control, transparency, supervision burden, and approval preference on 5-point Likert scales, and collected free-text reflections analyzed through thematic coding. Three findings emerged. First, participants calibrated trust per task rather than per agent: they granted wide autonomy for advisory and low-stakes tasks but demanded confirmation for irreversible, externally visible actions. Second, irreversibility combined with external visibility, rather than stakes alone, appeared to drive trust withdrawal: the moderate-stakes email task triggered the sharpest drop in trust (M = 3.10) and the highest demand for approval (M = 4.65), whereas a high-stakes but verifiable task did not produce the same response. Third, delegation regret appeared consistently when the agent executed actions without preview, even when the output was rated as successful. We discuss implications for agent designs that expose action boundaries, support per-task autonomy policies, and separate advisory output from agentic execution.
[HC-19] Anthropomorphism in Childrens Interactions with LLM Chatbots: A Systematic Review of Drivers and Outcomes
链接: https://arxiv.org/abs/2607.18250
作者: Hansinie Madushika Jayathilake,Renkai Ma
类目: Human-Computer Interaction (cs.HC)
备注: Accepted by ACM IDC '26
Abstract:Researchers across domains have investigated children’s use of LLM-based chatbots through various perspectives and methodologies. However, prior research remains fragmented regarding anthropomorphism, the tendency for children to assign human characteristics to those large language Model (LLM) chatbots as non-human objects. By analyzing 35 empirical studies published between 2022 and 2025, this systematic literature review identifies the drivers of anthropomorphism in children’s LLM chatbot interactions and the subsequent outcomes of these interactions. We found that human-like persona construction, adaptive scaffolding, supportive companionship, and non-human embodied design drive children’s anthropomorphic interactions. Additionally, five anthropomorphic outcomes emerged, including children exhibiting paradoxical social and moral responses, dual consciousness about the chatbots, forming varying social ties, exploring social boundaries, and attributing human narratives to conversation breakdowns. The findings, including both benefits and risks, can inform the future design and development of LLM chatbots focused on children’s well-being and promoting sustainable interactions that meet children’s developmental needs.
[HC-20] Deterrence Effects of Social Media Interventions on Health Misinformation Dissemination by Bots and Humans
链接: https://arxiv.org/abs/2607.18248
作者: Amir Karami,Nima Kordzadeh,Serena Harn
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:In the realm of social media, information dissemination is pivotal, yet it is tainted by the proliferation of misinformation propagated by both bots and humans, bearing consequential impacts on individuals and society. To address this issue, social media platforms have implemented removal, reduction, and informing interventions, acting as deterrent mechanisms to dissuade users from engaging in the spread of misinformation. Nonetheless, the sustained effectiveness of these interventions on bots and humans remains unclear. Drawing on deterrence theory, this study examines the efficacy of social media interventions on bots and humans sharing health misinformation. Our results show that most interventions can have sustained effects on bots and their activities for years after intervention implementation. However, the interventions may not have significant deterrence effects on humans and their activities. Our findings offer important theoretical and practical implications by highlighting the importance of studying both bots and humans and developing creative strategies to tackle health misinformation dissemination.
[HC-21] Anthropomorphic Behaviors of AI
链接: https://arxiv.org/abs/2607.18247
作者: Amir Karami,Christoph Lutz,Mohammad Hossein Jarrahi,Lisa Park,Ensieh Jarrahi
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Anthropomorphism in artificial intelligence (AI) is a growing area of interest, as AI systems increasingly exhibit human-like expressions, behaviors and interaction styles. This research serves as a systematic observation and categorization of anthropomorphic behaviors in AI outputs. Anthropomorphic behavior refers to the deliberate or emergent manifestation of human-like expressions or linguistic cues in system outputs, such as demonstrating empathy. Using a behaviorally driven taxonomy, our study identifies key forms of anthropomorphic behaviors in the responses of ChatGPT and examines their implications for the theory, practice and ethics of AI systems. The taxonomy enables more nuanced detection of anthropomorphism, offering value to developers and policymakers in balancing the benefits with the potential risks. This work contributes to the academic discourse by providing a foundation for future efforts to refine, expand, and automate the detection of anthropomorphic behavior across diverse AI applications.
计算机视觉
[CV-0] Appearance Pointers – Multimodal Region Control of Diffusion Transformers
链接: https://arxiv.org/abs/2607.19344
作者: Rahul Sajnani,Yulia Gryaditskaya,Radomír Měch,Srinath Sridhar,Matheus Gadelha
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR)
备注: 38 Pages, Preprint with supplement
Abstract:Controllable image generation remains challenging for creative professionals, who often require precise regional control over materials, object identities, and spatial arrangements that cannot be reliably achieved through text prompting alone. Diffusion Transformers (DiTs) can natively ingest heterogeneous tokens stemming from texts and images, but they lack mechanisms for determining where and how these tokens should influence the output. We introduce appearance pointers, compact tokens that guide DiTs toward the correct appearance cues at the correct spatial locations by aligning text or image inputs with user-specified masks. Appearance pointers are produced by a region correspondence network and refined through a spatial aggregation mechanism, enabling the model to handle multiple regional descriptions without significantly increasing token load. Our approach introduces the first modality-agnostic interface for localized multimodal control in a DiT without retraining the base model from scratch. Across a range of metrics, our single model reaches or surpasses the performance of modality-specific state of the art methods, offering a simple and extensible path toward precise, region-aware, multimodal guidance in generative image synthesis.
[CV-1] Masked Visual Actions for Unified World Modeling
链接: https://arxiv.org/abs/2607.19343
作者: Hadi Alzayer,Wenlong Huang,Haonan Chen,Christopher Luey,Lvmin Zhang,Maneesh Agrawala,Gordon Wetzstein,Li Fei-Fei,Yilun Du,Jiajun Wu,Jia-Bin Huang
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Project webpage: this https URL
Abstract:Video models absorb rich priors over how the visual world moves, interacts, and responds to contact, making them promising substrates for robotic world modeling. The central challenge is how to communicate action to such models in a form aligned with the visual space in which they learned these interaction priors, yet still grounded in physical manipulation. We introduce Masked Visual Actions, a pixel-space control interface that expresses action as a partially revealed trajectory of an arbitrary entity in a video. Revealing robot motion makes the model act as a forward dynamics model that predicts the scene’s response to low-level robot actions, while revealing desired object motion makes the same model recover robot behavior consistent with that outcome. Finetuned with only 15 hours of masked examples from real videos and simulation, a single checkpoint achieves strong visual fidelity and controllability across diverse scenes and multiple embodiments. In downstream manipulation settings, the model produces imagined rollouts whose outcomes correlate with real-world execution for policy evaluation, improves decision making by ranking candidate futures in model-based planning, and supports inverse modeling by synthesizing robot motion from desired object motion.
[CV-2] ExpertVerse: A General-Purpose Benchmark for Expert-Level Reasoning in Knowledge-Intensive Visual Synthesis
链接: https://arxiv.org/abs/2607.19341
作者: Yuan Wang,Yongchao Du,Mengting Chen,Jinsong Lan,Xuetao Feng,Xiaoyong Zhu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent advances in multimodal generative models have enabled instruction-based image generation to move beyond semantic manipulation to knowledge-driven visual reasoning. However, these methods focus on explicit commonsense reasoning, shallow causal understanding, and direct knowledge recall, failing at knowledge-intensive generation. We develop \textbfExpertVerse, a capability-centric benchmark to evaluate generative models via knowledge-intensive lens. ExpertVerse stratifies reasoning generation across an orthogonal taxonomy of \textit9 cognitive capabilities and \textit8 expert disciplines, yielding \textit58 sub-disciplines. We curate 1,611 expert-annotated instances covering single-image editing, multi-image composition, and text-to-image generation. We further develop an automated workflow to produce \textbfExpertVerse-100K, a large-scale dataset with reasoning traces and knowledge-anchored rationale annotations. Based on this, we train \textbfKnowThinker with RL fine-tuning, a VLM reasoning engine with world knowledge that jointly generates thinking processes and refined instructions. Towards the cross-modal credit misalignment and multi-objective gradient conflicts in multi-reward optimization, we propose a tailored Bootstrapped Pareto Policy Optimization (BPPO), which synergizes Bootstrapping Reward Rectification (BRR) and Conflict-Aware Pareto Advantage Fusion (CPAF). Extensive results of both open-source and proprietary models exposes critical reasoning deficits, highlighting imperative for knowledge-intensive benchmarks towards next-generation visual generation.
[CV-3] OmniReason er: Thinking with Long Audio-Video via Native Tool Use
链接: https://arxiv.org/abs/2607.19339
作者: Yu Chen,Caorui Li,Ziyu Xiong,Yidong Wang,Mingqi Gao,Shuman Liu,Biao Liu,Chunfeng Yang,Anxiang Zeng,Haibo Zhang,Chaofan Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Long audio-video reasoning is difficult for omnimodal LLMs because the decisive evidence is often sparse, cross-modal, and too expensive to preserve with uniformly high-fidelity inputs. We introduce OmniReasoner, a tool-use post-training framework for Thinking with Long Audio-Video: omni-modal LLMs learn, via supervised fine-tuning and reinforcement learning, to decide whether and where to call a zoom-in tool before answering. OmniReasoner first builds a low-cost global preview of the full stream and then, when needed, calls the zoom-in tool with a requested temporal interval for higher-fidelity visual and audio inspection before answering. Because the model observes different sampling granularities before and after this call – a sparse global preview and a denser local clip – we introduce TimeAnchor, which keeps the tool’s temporal argument valid and round-trip-consistent across these granularities, rather than tied to frame indices from a particular sampling rate. To make this tool-use behavior trainable without expensive manual interval annotation, we build a Temporal Augmented Data Engine that synthesizes tool-use post-training trajectories by video editing and composition. Experiments across omnimodal and video benchmarks show that OmniReasoner improves both answer accuracy and temporal grounding while concentrating high-fidelity computation on informative regions. Code is available at this https URL.
[CV-4] ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling
链接: https://arxiv.org/abs/2607.19332
作者: Chirag Vashist,Ke Li
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Generative models have undergone many generations of evolution, from VAEs/GANs to diffusion/flow matching. Along the way, the underlying techniques have become more complicated and various beliefs about what drives strong empirical performance have taken hold. Due to the success of diffusion models and flow matching, one of the more common beliefs is the importance of transforming the noise distribution to the data distribution gradually through many small transformations. We ask whether this is truly necessary, and take a minimalist approach to designing a competitive generative model. We start with the bare-bones essentials, namely just a training objective and a model. We purposefully make both simple. For the training objective, we choose Implicit Maximum Likelihood Estimation (IMLE), and eschew more complicated alternatives such as variational inference, adversarial training and numerical integration. For the model, we eschew transformers and instead choose a moderately sized convolutional network. Then we judiciously added elements that are truly essential, which surprisingly do not include iterative denoising. The result is a single-step parameter-efficient generative model that produces high quality samples at fast speed: it achieves an FID of 2.56 on ImageNet 256 and simultaneously attains good precision and recall.
[CV-5] InstructMixup: Instruction-Guided Salient Patch Editing for Robust Data Augmentation
链接: https://arxiv.org/abs/2607.19324
作者: Khawar Islam,Arif Mahmood,Xin Jin,Naveed Akhtar
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In image and video technologies, data augmentation is widely used to improve the generalization of deep visual models, and mixup-based strategies that interpolate between samples have become the dominant approach. However, computing informative mixing regions adds substantial overhead, and blending content across different images frequently disrupts the semantic integrity of the resulting sample. We propose \our, a data augmentation method that constructs challenging yet label-consistent training samples entirely within a single visual sample. \our first extracts multi-scale salient patches from the sample using a lightweight saliency detector, refines each patch with an instruction-guided generative model, and blends the edited patch back into the non-salient regions of the same sample; because the generative edits are computed once and cached offline, this step adds negligible training cost. To further diversify the learned representation, \our injects self-similar fractal structure into the same salient regions at an adaptive ratio, so each training sample carries both fractal and non-fractal structure. We derive a second-order approximation of the resulting vicinal risk, showing that the method simultaneously enforces invariance to the generative edit and suppresses curvature along the perturbed salient directions, and we verify both predictions empirically. We evaluate on small to large backbones for instance Convolutional Neural Networks (CNNs), Vision Transformers (ViTs) and Vision-Language Foundational Models (VLMs) across seven benchmarks covering coarse- and fine-grained classification, robustness to corruption and occlusion, calibration, and transfer and self-supervised learning, InstructMixup outperforms nine competing augmentation methods, surpassing the strongest baseline across all benchmarks.
[CV-6] ERank in Latent Space as an Image-Complexity and Richness Measure
链接: https://arxiv.org/abs/2607.19315
作者: Maksim Smirnov,Grigory Kononov,Anastasiia Linich,Egor Surkov,Egor Shvetsov
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We propose the effective rank (ERank) of the channel covariance of an image’s deep feature map as a per-sample, label-free measure of visual richness, computed from a single forward pass through a frozen pretrained encoder. ERank counts how many decorrelated channel directions an image activates, and we characterize its properties, including its behavior under noise. Empirically, ERank orders images from plain to visually rich, correlates with codec bitrate, sharpness, and edge density, and correlates with human complexity annotations on IC9600 with r = 0.72 . As a data-selection criterion, removing low-ERank samples improves super-resolution and removing high-ERank samples improves OCR, in both pretraining and finetuning, while selection does not help classification, segmentation, or denoising. ERank is thus a cheap richness signal, useful exactly when task difficulty is governed by input richness.
[CV-7] From Distances to Trajectories: Real-Time Signed Distance Function Mapping and Distance-Accelerated Motion Planning for UAVs
链接: https://arxiv.org/abs/2607.19306
作者: Jason Stanley(1),Zhirui Dai(1),Qihao Qian(1),Tzu-Chin Ho(1),Tianxing Fan(1),Siddharth Saha(2),Christopher Barngrover(2),Ki Myung Brian Lee(1),Nikolay Atanasov(1) ((1) UC San Diego, La Jolla, USA, (2) Shield AI, San Diego, USA)
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Systems and Control (eess.SY)
备注: 25 pages, 10 figures, 5 tables
Abstract:Autonomous flight in cluttered environments requires a robot to build a geometric map of its surroundings and plan safe, dynamically feasible trajectories, all onboard and in real time. Conventional approaches treat mapping and planning as separate stages and often rely on binary occupancy for collision checking. We argue that these two stages should be co-designed around a single representation: a signed distance function (SDF). By encoding distance to the nearest obstacle, an SDF provides richer information for planning and trajectory optimization than occupancy alone. We develop an Octree REsidual Network (OREN) that pairs an explicit octree prior with an implicit neural residual to reconstruct SDFs online from point cloud observations with the efficiency of volumetric methods and the accuracy and differentiability of neural methods. In tandem, we develop Bubble ^\star , a search-based planner that exploits the distance information to grow maximal collision-free balls, which we call bubbles, with formal guarantees of termination, completeness, and failure detection. Planning over a graph of bubbles significantly reduces collision checks compared to a grid-based A ^\star search and returns a bubble sequence that forms a safe corridor for trajectory optimization. We demonstrate the integrated OREN-Bubble ^\star approach onboard a quadrotor, navigating unseen indoor environments in real time under tight compute constraints. OREN improves SDF estimation by 22 % compared to baselines, while Bubble ^\star finds trajectories spanning \approx 90 m through a cluttered environment in 1 - 3 sec., whereas baselines take up to 10 sec. in the same environment.
[CV-8] No Training Better Flights: Test-Time Scaled VLMs for UAV Navigation
链接: https://arxiv.org/abs/2607.19288
作者: Feinan Cheng,Dongliang Xu,Wenli Nong,Zhiheng Zhang,Ang Liu,Tianyu Wang,Yue Yao
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Test-time scaling offers a promising method to improve the inference performance of Vision-Language Models (VLMs) without additional training. Existing approaches to vision-language navigation (VLN) for Unmanned Aerial Vehicle (UAV) typically relies on a single inference pass, which can falter in complex environments by producing suboptimal or unsafe trajectories. In this paper, we explore a simple and effective approach to apply test-time scaling to VLN for UAV. We enhance navigation reasoning through an iterative refinement process that requires no extra model training, guiding the model to re-evaluate its initial navigation plan for better accuracy and safety. Our method first prompts the model to generate multiple parallel candidates and then performs a self-correction step, achieving deeper and more robust planning without changing the underlying model. To further strengthen decision-making, we design a multi-criteria scoring function to evaluate the refined candidates based on safety, goal alignment, and forward-progress. This simple yet powerful combination enables a frozen UAV navigation VLMs to self-correct and generate more accurate and reliable flight plans, achieving SOTA performance in this task.
[CV-9] PathAgent Bench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image
链接: https://arxiv.org/abs/2607.19261
作者: Dankai Liao,Tianyi Zhang,Yufeng Wu,Xinyue Zhang,Qiaochu Xue,Zeyu Liu,Dachun Zhao,Linghan Cai,Yueming Jin
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Whole-slide image (WSI) diagnosis requires identifying diagnostically relevant regions, examining them across magnifications, and integrating multi-scale evidence. However, most existing pathology benchmarks evaluate models on pre-cropped patches or pre-extracted slide features, leaving their ability to acquire evidence directly from gigapixel WSIs largely untested. We introduce PathAgentBench, a benchmark for evaluating evidence-seeking vision-language models (VLMs) across four complementary capabilities: image-to-text matching for evidence interpretation, text-to-image retrieval for evidence verification, diagnostic-region localization for evidence acquisition, and multi-scale reasoning for evidence integration. The benchmark is organized as a diagnostic tree that links nested regions across magnifications with scale-specific findings and path-level diagnoses. It contains 1,822 TCGA WSIs and 17,135 diagnostic paths annotated by ten board-certified pathologists. An additional private cohort of 190 breast cancer WSIs with detailed annotations is used to evaluate autonomous whole-slide exploration. We evaluate 20 general-purpose, medical, and pathology-specialized models. Leading open-weight models achieve over 93% accuracy in multi-scale reasoning and over 50% accuracy in both cross-modal matching tasks. In contrast, diagnostic-region localization remains challenging: the best text-guided mean intersection-over-union is below 0.09, underperforming a simple center-based heuristic. During autonomous exploration, the unconditional hit rate decreases from 0.522 at low magnification to 0.185 at intermediate magnification and 0.020 at high magnification. These results reveal a pronounced gap between reasoning over curated evidence and acquiring that evidence directly from WSIs. PathAgentBench provides a unified framework for measuring and improving evidence-seeking pathology models.
[CV-10] IGGT4D: Streaming 4D Instance-Grounded Geometry Transformer
链接: https://arxiv.org/abs/2607.19228
作者: Zhengyu Zou,Hao Li,Kuixuan Jiao,Liu Liu,Tingyang Xiao,Xiaolin Zhou,Fangzhou Hong,Zhizhong Su,Dingwen Zhang,Ziwei Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:Real-world spatial intelligence requires agents to understand scenes from continuous video streams, where objects move, persist, disappear, and reappear over time. While recent spatial foundation models have enabled generalizable feed-forward 3D reconstruction, most streaming methods remain geometry-centric and lack temporally consistent object-level understanding. Meanwhile, existing semantic reconstruction and 3D-aware vision-language methods largely rely on externally extracted 2D semantic cues or loosely coupled geometry inputs, limiting unified geometry-instance learning in long dynamic scenes. In this paper, we propose IGGT4D, a streaming instance-grounded geometry Transformer for online 4D scene understanding. IGGT4D processes video frames sequentially, reuses historical context through causal spatial-temporal modeling, and incrementally updates a unified representation of camera motion, geometry, and object identity. This enables long-sequence feed-forward reconstruction with geometry-instance consistency in dynamic environments. To address the lack of high-quality 4D supervision, we further construct InsScene4D-147K, a large-scale dataset spanning real/synthetic and static/dynamic scenes, with RGB images, depth, poses, and temporally consistent instance masks generated by an automated geometry-guided annotation pipeline. Experiments on 3D reconstruction, pose estimation, instance spatial tracking, and open-vocabulary segmentation demonstrate that IGGT4D outperforms existing streaming baselines while maintaining scalable online inference for long dynamic sequences.
[CV-11] Anatomy-Aware 3D Mesh Refinement of Pericardium Segmentations on Computed Tomography
链接: https://arxiv.org/abs/2607.19210
作者: Andreas W. Aspe,Jonas Jalili Loft,Michael Huy Cuong Pham,Andreas Ohrt Johansen,Jørgen Tobias Kühl,Klaus Fuglsang Kofoed,Kristine Aavild Sørensen,Rasmus R. Paulsen,Josefine Vilsbøll Sundgaard
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: This preprint has not undergone peer review (when applicable) or any post-submission improvements or corrections. The Version of Record of this contribution is published in the proceedings for MIUA 2026
Abstract:Accurate delineation of the pericardium in a cardiac CT scan is essential for quantifying epicardial adipose tissue, yet it remains one of the most challenging structures to segment due to its poor contrast boundaries. Instead of solely relying on image gradients, our framework leverages the anatomical context of surrounding anatomical structures to guide the segmentation. This work introduces a novel 3D iterative mesh refinement framework that balances anatomical and geometric forces derived from inherent anatomical rules to refine an initial, possibly ambiguous, segmentation into a high-precision, anatomically plausible result. Designed as a model-agnostic post-processing step, our method uses a 3D vector field to iteratively push the vertices to the correct anatomical locations. Evaluating the refinement on both a high-resolution in-house dataset and a coarse, sparsely annotated open-source dataset, our method consistently improves all volumetric, surface, and anatomical metrics. The framework demonstrates greater improvement when applied to weaker initial segmentations, highlighting its potential for improving segmentations for out-of-domain models and in limited-training-data scenarios. The method is formulated as a gradient-based, GPU-accelerated framework that can be easily extended to other anatomical use cases.
[CV-12] Cognitive Dual-Process Planning for Autonomous Driving with Structured Scene Knowledge and Verifiable Reasoning -Action Consistency
链接: https://arxiv.org/abs/2607.19194
作者: Zhongyao Yang(1 and 2),Haoyu Li(1 and 2),Yu Yan(1 and 2),Zhuangxuan Yu(1 and 2),Jiangfeng Nan(3),Jinrui Nan(1 and 2) ((1) School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China, (2) National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing, China, (3) School of Mechanical Engineering, Southeast University, Nanjing, China)
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 7 figures. Zhongyao Yang and Haoyu Li contributed equally to this work
Abstract:High-level planning for autonomous driving is a knowledge-intensive engineering decision task that requires accurate scene understanding, timely inference, and internally consistent action selection. Vision-language models (VLMs) can make intermediate reasoning explicit, but their use in deployed planners is constrained by costly structured supervision, unnecessary reasoning in routine scenes, and possible inconsistencies between generated rationales and driving actions. We present a cognitive dual-process planning framework that represents planning-relevant scene knowledge in a machine-parsable structured chain-of-thought (S-CoT) schema. An automated data engine integrates perception foundation models, critical-path filtering, and an expert VLM to generate S-CoT supervision without manual annotation of individual rationales. A lightweight visual Arbiter estimates scene complexity from multilevel vision-encoder features before language decoding and routes each input to either fast meta-action prediction or slow structured reasoning. For slow-path outputs, a deterministic rule-based validator checks whether the parsed S-CoT fields are consistent with the final meta-action and provides verifiable rewards for Group Relative Policy Optimization (GRPO). In a 195-scene manual audit, the generated annotations achieve 91.8% CoT accuracy and a 98.5% Logical Consistency Score (LCS). On 574 manually verified NAVSIM test samples, the planner achieves 80.14% planning accuracy and 97.20% LCS while reducing average latency by 17.39% relative to applying slow reasoning to every scene. Evaluation on external long-tail subsets further identifies conditions under which routing and planning performance degrade. Together, these results show how explicit scene knowledge can be operationalized through adaptive reasoning and rule-based verification to support high-level VLM planning decisions.
[CV-13] ABot-World-0: Infinite Interactive World Rollout on a Single Desktop GPU
链接: https://arxiv.org/abs/2607.19191
作者: Fan Jiang,Zhaoxu Sun,Mengchao Wang,Ziyu Zhu,Chiyu Wang,Yunpeng Zhang,Wenlin Liu,Yun Wang,Xue Zheng,Rui Sun,Junfeng Ni,Hongyu Pan,Zhongxu Sun,Fei Yu,Zengye Ge,Mengmeng Du,Nianfei Fan,Mingchao Sun,Yu Liu,Yongchang,Yanqing Zhu,Jiahang Wang,Ning Ying,Yuze Xuan,Di Yang,Zhicheng Liu,Zhe Gao,Tingbing Xu,Jiacheng Sui,Wenjin Yang,Junnan Lai,Shufeng Liu,Yuan Liu,Zheng Zhou,Yingliang Peng,Dawei Cao,Kaifeng Sheng,Yuxiang Cai,Fei Lu,Mu Xu,Ning Guo
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:We present ABot-World-0, an action-conditioned video world model for real-time, long-horizon closed-loop interaction, supported by a multi-source data infrastructure spanning AAA games, simulation engines, and internet videos to learn controllable world dynamics. WorldExplorer performs agent-driven collection guided by training feedback, while a unified pipeline applies 14 deterministic quality checks, VLM-based assessment, and synchronized action and text annotation. We progressively distill a bidirectional action-conditioned teacher into a causal student through teacher forcing and ODE distillation, and introduce LongForcing to align long student self-rollouts with an extended-horizon teacher, mitigating accumulated distribution shift and autoregressive drift. Raw keyboard actions provide a unified control interface for scene roaming and third-person character interaction, while reference-character memory provides persistent appearance cues for identity consistency during third-person rollouts. For deployment, we co-design a streaming inference stack with a lightweight VAE decoder, efficient attention, memory-aware scheduling, and low-bit DiT inference. Across optimized low-bit configurations, ABot-World-0 streams 720P video at up to 16 FPS on a single NVIDIA RTX 5090 desktop GPU, with 1.2s action-to-first-frame latency and approximately 19GiB peak VRAM. Experiments on WorldRoamBench and extended interactive rollouts demonstrate competitive controllability and coherent long-horizon world evolution.
[CV-14] Point Ladder Tuning: Parameter-Efficient Hierarchical Adaptation for 3D Point Cloud Understanding ECCV2026
链接: https://arxiv.org/abs/2607.19171
作者: Junlin Chang,Longhao Zou,Rui Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026. Code: this https URL
Abstract:Fine-tuning pre-trained point-cloud backbones typically updates all parameters, resulting in substantial computation and memory overhead. More importantly, modern point backbones rely on aggressive tokenization and downsampling, which yields compact global tokens but irreversibly discards fine-grained local geometry, an inherent bottleneck for parameter-efficient adaptation. Consequently, existing PEFT methods that operate only on these coarsened tokens can modulate global semantics but struggle to recover the missing multi-scale locality. We present Point Ladder Tuning (PLT), a locality-aware PEFT framework that performs hierarchical, instance-conditioned adaptation while keeping the backbone frozen. PLT forms a lightweight closed loop: (i) a Hierarchical Ladder Network (HLN) constructs a multi-resolution local feature pyramid directly from raw points; (ii) a Local-Global Fusion (LGF) aligns and fuses local pyramids with intermediate backbone semantics; and (iii) a Dynamic Prompt Generator produces instance-aware multi-scale prompts to modulate the frozen backbone effectively. For dense prediction, we further introduce a lightweight segmentation head that progressively upsamples fused features and leverages backbone priors to refine fine structures. Extensive experiments on classification and dense prediction show that PLT consistently surpasses prior PEFT baselines with minimal tunable parameters. PLT achieves state-of-the-art performance using only 2.71% trainable parameters for classification and 7.69% for dense prediction, and scales favorably to larger backbones, requiring merely 0.36% parameters on PointGPT-L. The code is released at this https URL.
[CV-15] Sarus: Privacy-Preserving Multi-Vendor Perception Fusion via Homomorphic Encryption
链接: https://arxiv.org/abs/2607.19146
作者: Munawar Hasan,Apostol Vassilev
类目: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Cooperative perception enables autonomous vehicles (AVs) to improve situational awareness by aggregating detection outputs from multiple agents and sensing platforms, often via a shared fusion service in multi-vendor deployments. However, sharing such outputs at inference time exposes proprietary model behavior and sensitive environmental information, creating significant privacy and security concerns. In this paper, we present Sarus, a privacy-preserving framework for multi-vendor perception fusion via homomorphic encryption (HE), enabling aggregation without revealing individual vendor outputs. Each vendor encodes detections as compact Gaussian moment vectors over a shared spatial lattice and transmits encrypted payloads to a fusion server, which aggregates them directly in the encrypted domain. The fused result is then decrypted and reconstructed into final detections through class-wise bin merging. We analyze the computational complexity, showing linear scaling for vendor payload construction and O(BV) server-side fusion with the number of occupied bins B and vendors V , while postprocessing scales as O(B + \sum_c\in \mathcalC B_c^2) , where \mathcalC denotes the set of object classes and B_c is the number of occupied bins for class c . Experiments demonstrate linear scaling in practice with only a bounded constant-factor overhead from HE, with decryption dominating postprocessing cost. Experiments on the KITTI dataset using camera (YOLOv8) and LiDAR (PointPillars, PV-RCNN) detectors show that Sarus improves scene-level coverage by effectively aggregating complementary detections, particularly in distance-dependent regimes where individual modalities degrade. These results indicate that privacy-preserving multi-vendor perception fusion is feasible for real-time deployment when statistical compression and spatial sparsity are jointly exploited. Subjects: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2607.19146 [cs.CR] (or arXiv:2607.19146v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2607.19146 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-16] xt Template Tokens Are Implicit Semantic Registers in Diffusion Transformers
链接: https://arxiv.org/abs/2607.19139
作者: Maohua Li,Qirui Li,Yanke Zhou,Yiduo Li,Zhaosheng Chi,Chao Xu,Cuifeng Shen,Yixuan Xu,Hanlin Tang,Kan Liu,Tao Lan,Lin Qu,Shao-Qun Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Text-to-image diffusion transformers (DiTs) jointly process text and image tokens, yet their internal computation during denoising remains poorly understood. We introduce a causal interpretability framework for modern large-scale DiTs that combines attention decomposition with targeted interventions across token spans, heads, and layers. Using it to separate prompt-content tokens from structural template tokens, we find that the structural tokens carry little prompt-specific information at the encoder output. Yet surprisingly, they emerge as dominant image-to-text attention sinks and causally maintain object identity inside the DiT, acting as implicit semantic registers. We show that they acquire this identity indirectly, with prompt semantics first injected into the image latents and then read back into the template tokens rather than transferred directly from the prompt tokens. Inspired by the above findings, we design a training-free pruning rule for DiTs. Heads that attend most strongly to prompt tokens are dispensable, and pruning them removes 20% of attention FLOPs with only a 1.4 -point drop on GenEval. We further reveal how generative computation in DiTs is organized across heads and depth, separating semantic routing from visual synthesis and progressing from identity formation to propagation and refinement. Our work not only reveals that the tokens encoding semantics at input need not be those that maintain it during generation, but also provides a causal view of internal mechanisms in DiTs.
[CV-17] Latent Riemannian Flow Matching for Geometry-Grounded 3D Foundation Models
链接: https://arxiv.org/abs/2607.19120
作者: Lisa Weijler,Irene Ballester,Guofeng Mei,Tolga Birdal,Pedro Hermosilla
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Geometric foundation models, such as the Visual Geometry Grounded Transformer (VGGT), provide strong 3D priors from unposed images. However, such models operate purely in a feed-forward, deterministic regime, \ie~they cannot generate plausible geometry beyond what the input views directly support. Generative models for 3D scenes, on the other hand, must rely on strong geometric priors to produce coherent outputs from sparse inputs. We bridge these two paradigms by performing flow matching directly in VGGT’s latent space, leveraging its learned 3D priors without committing to any explicit downstream representation such as Gaussians, meshes, or video-VAE latents. This requires respecting the latent geometry: VGGT tokens occupy a product of high-dimensional hyperspheres on which standard Euclidean flow matching fails. We address this with a Riemannian Flow Matching framework defined on a product manifold of four hyperspheres, aligned with VGGT’s multi-scale encoder, which keeps generated tokens on the valid data manifold required by the frozen decoding heads. On RealEstate10K, ScanNet++ and ETH3D, our method achieves strong performance against recent scene generation baselines in both per-view appearance and aggregated 3D geometry, establishing latent-space flow matching on geometric foundation models as a viable paradigm for 3D generation. The project page can be found \hrefthis https URL\texthere .
[CV-18] CR-Refiner: An Object-Centric Optimal Transport Reranker for Edit-Conditioned 3D Scene Retrieval
链接: https://arxiv.org/abs/2607.19115
作者: Hao Wu,Jinjing Zhu,Nanyu Wu,Qianyi Cai,Heyi Lin,Hao Wang,Hui Xiong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Edit-conditioned 3D scene retrieval pairs a reference 3D room with a natural-language modification and retrieves rooms from a corpus that satisfy the edit. Three lines of prior work each fall short on this task. 2D composed image retrieval reasons over pixel-level edits and has no primitive for 3D object sets. 3D foundation encoders embed individual objects but cannot compose at the scene level. 3D scene-grounding methods localize references inside a static scene rather than rank modified rooms across a corpus. We present CR-Refiner, a training-free reranker that wraps any base retriever’s top-K candidates with three components. A frozen LLM parses the edit into a structured query entity, and each candidate is scored by an unbalanced optimal-transport problem over a 1xG cost matrix coupling category, style, material, and geometry. The unbalanced solver lets the single-entity query drop mass on irrelevant objects, modelling the asymmetry directly. An axis-conditional structural prior adds size-keyword cues for geometric edits and subject-anchor direction cues for spatial edits. An LLM verifier refines the top three candidates with continuous confidence. Because no benchmark evaluates compositional matching over 3D object sets, we additionally release 3D-CER, 4,963 edit-conditioned queries over a 23,381-room indoor corpus across five edit axes, with multi-positive ground truth, CIRR-style hard subsets, and zero-target adversarials. Across three qualitatively distinct base retrievers, CR-Refiner consistently improves hard-subset R@1 and mAP@10 on every edit axis.
[CV-19] GATE-3D: Geometry-Aware Test-time Adaptive Reranking for Open-Set 3D Shape Retrieval
链接: https://arxiv.org/abs/2607.19111
作者: Hao Wu,Heyi Lin,Zilin Wang,Huizai Yao,Hao Wang,Hui Xiong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Large pretrained vision models have substantially improved appearance-based 3D shape retrieval, but they still confuse shapes that look similar while differing in geometry. Although geometry-aware features can reduce these errors, naive fusion of geometry and appearance may hurt retrieval when the two modalities are already well aligned. We propose GATE-3D, a lightweight query-adaptive reranking method that incorporates geometry without retraining the retrieval backbone. For each query, GATE-3D predicts how much a geometry-aware score should adjust the appearance-based ranking using features that capture disagreement between the two modalities. This selective design lets geometry contribute where it helps and stay silent where it would hurt. Experiments on three open-set 3D retrieval benchmarks show that GATE-3D improves over appearance-only retrieval and is more robust than always-on fusion. On the primary benchmark, it improves mAP@10 by 2.00 points over appearance-only retrieval (p=0.041); it also improves leave-one-category-out generalization and reduces geometric false positives by 10.8%. GATE-3D achieves competitive zero-shot results against DAC-based baselines. We further find that simple linear routing is more effective than a small MLP in the low-data regime, suggesting that cross-modal disagreement features matter more than model capacity for adaptive routing.
[CV-20] FlexiAvatar: Unified 3D Gaussian Human Avatars Under Arbitrary Body Visibility ECCV2026
链接: https://arxiv.org/abs/2607.19100
作者: Yihalem Yimolal Tiruneh,Muhammad Salman Ali,Uyoung Jeong,Muneeb A. Khan,MD Khalequzzaman Chowdhury Sayem,Allanur Bayramgeldiyev,Binod Bhattarai,Seungryul Baek
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted in ECCV 2026
Abstract:Reconstructing animatable 3D human avatars from monocular video is a fundamental problem in computer vision with broad applications in AR/VR and digital content creation. Existing approaches typically couple parametric body models with neural rendering or 3D Gaussian splatting and optimize all body regions jointly from short videos, which often degrades fidelity in the visible areas. To overcome this limitation, we introduce FlexiAvatar, a unified framework that explicitly optimizes only the visible body regions, effectively eliminating artifacts arising from unobserved limbs. Our method integrates occlusion-robust SMPL-X tracking with part-specific residual refinement to capture high-frequency geometric and appearance details. To complete entirely unseen regions (e.g., back views), we leverage a diffusion-based approach to generate texture consistent with the observed appearance. Experiments on full-body (NeuMan, ZJU-MoCap, WildAvatar), upper/half-body (talk-show clips), and head-only (INSTA) inputs show that FlexiAvatar delivers consistently higher reconstruction quality, outperforming state-of-the-art methods by an average PSNR improvement of approximately 3% across datasets. Finally, by restricting optimization to observed regions, our method reduces the effective number of Gaussians that must be optimized and rendered, leading to reduced runtime and memory overhead in partial-visibility scenarios.
[CV-21] Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention KDD KDD2026
链接: https://arxiv.org/abs/2607.19086
作者: Joy Dhar,Manish Kumar Pandey,Nayyar Zaidi,Chen Chen,Maryam Haghighat,Ferdous Sohel,Puneet Goyal
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026)
Abstract:Multimodal fusion learning (MFL) has shown great potential in the medical domain, where we are faced with disparate data modalities such as imaging, clinical records, and omics. However, existing MFL strategies face several major challenges. First, they struggle to capture complex cross-modal interactions effectively, which in turn limits performance improvements. Second, they incur high computational costs, restricting their applicability in resource-constrained healthcare AI applications. Finally, they are often designed and evaluated for narrow, fixed modality configurations (e.g., imaging-only, or specific pairs such as image and omics), which limits evidence of their adaptability and generalizability to broader collections of heterogeneous medical modalities. To address these challenges, we propose a novel MFL framework - Cascaded Unified Representation Learning for Efficient Fusion Network (CURE) - a lightweight and scalable framework that progressively integrates various modalities through a novel efficient Hybrid Geometry Aware Fusion layer (HyFuse), where each HyFuse layer is sequentially learned for each modality, making the framework adaptable and generalizable. Within HyFuse, an efficient residual convolution module captures rich multi-scale features to ensure cost-effective learning, while a hybrid-space aware attention mixer learns coarse-to-fine structural cues to better preserve cross-modal relationships. Complementary learnable late-fusion and shared information refinement modules are then employed to learn robust modality-order-invariant shared representations, which in turn yields consistent performance improvements. Extensive evaluations on 16 public datasets show that CURE outperforms leading multimodal fusion methods, boosting performance by up to 3.97% and lowering computational costs by up to 87.8%, ensuring more effective and reliable predictions.
[CV-22] Context-structured Video Anomaly Detection with Large Vision-Language Models
链接: https://arxiv.org/abs/2607.19077
作者: Dongjun Kim,Changjae Oh,Andrea Cavallaro,Jeonghoon Mo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at AVSS 2026
Abstract:Training video anomaly detectors is challenging due to the difficulty and cost of annotating diverse and rare abnormal events. Although recent large vision-language models enable training-free inference, existing approaches mostly rely on holistic inference over sampled video and may miss context-specific anomaly cues. In this paper, we present CSI-VAD, a training-free video anomaly detector that identifies abnormal events across diverse contexts. The key idea is to decompose each video into three distinct contexts (environment, objects, time) and perform context-specific inference in separate branches. Because we ground anomaly judgments solely in context-specific visual cues, we do not require predefined text prompts describing abnormal events or dataset-specific tuning. Experiments on UCF-Crime and UBnormal show that CSI-VAD consistently improves over the direct holistic baseline and achieves competitive performance against existing methods, showing the advantage of structured context decomposition for training-free video anomaly detection.
[CV-23] Delineate Anything v2: A Global Foundation Model for Field Delineation
链接: https://arxiv.org/abs/2607.19069
作者: Mykola Lavreniuk,Nataliia Kussul,Andrii Shelestov,Yevhenii Salii,Volodymyr Kuzin,Charlotte Julia Li-Xing Wang,Zoltan Szantoi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate agricultural field boundary delineation at large scale is a foundational task for food security, supply chain transparency, and carbon accounting. While vision foundation models like SAM show remarkable zero-shot capabilities, they frequently fail in geospatial domains due to topological complexity, cropland texturing patterns, and a lack of physical scale awareness. In this work, we introduce Delineate Anything v2, a globally scalable foundation model designed specifically for wide-area field boundary mapping. We construct FBIS-73M, a 73-million-instance multi-resolution dataset spanning 61 countries. To address the pervasive issue of multi-field administrative parcel merging, we introduce a resolution-specific data curation pipeline that leverages topological image-space adaptation to homogenize merged parcels and strengthen weak physical boundaries. Furthermore, we establish a novel, manually curated evaluation benchmark covering 100 countries to assess independent zero-shot generalization. Our results show that Delineate Anything v2 surpasses the current state-of-the-art, including the Delineate Anything framework, by 0.284 mAP@0.5 (+103.3% relative gain), while maintaining execution speeds suitable for rapid national- and global-scale deployment, as demonstrated by nationwide mapping of Ukraine (603,000 km^2) in 5.4 hours on a consumer-grade workstation. Code, pre-trained weights, the FBIS-73M dataset, and ready-to-use national-scale vector boundary products are publicly available at this https URL.
[CV-24] Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing
链接: https://arxiv.org/abs/2607.19064
作者: Xinjie Zhang,Peng Zhang,Shicheng Zheng,Jinghao Guo,Zhaoyang Jia,Yifei Shen,Xun Guo,Yuxuan Luo,Jiahao Li,Wenxuan Xie,Fanyi Pu,Xiaoyi Zhang,Kaichen Zhang,Zongyu Guo,Tianci Bi,Dongnan Gui,Zhening Liu,Zimo Wen,Zihan Zheng,Senqiao Yang,Xiao Li,Jinglu Wang,Bin Li,Yan Lu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM); Image and Video Processing (eess.IV)
备注:
Abstract:Large-scale visual generators are increasingly capable but costly to train, fine-tune, and deploy. We introduce Mage-Flow, a compact 4B-scale generative stack for efficient text-to-image generation and instruction-based image editing. The stack is built from two co-designed components: Mage-VAE, a lightweight high-fidelity latent tokenizer, and a Native-Resolution Multimodal Diffusion Transformer trained with rectified flow matching. Mage-VAE uses one-step diffusion-style encoding and decoding with anchor-latent regularization, preserving the reconstruction quality of strong public VAEs while reducing tokenization cost by more than an order of magnitude. Together with native-resolution packing and stack-level CUDA kernel fusion, the stack supports flexible-resolution training and improves end-to-end training throughput by about 2.5\times . Built on this foundation, we develop a complete model family with Base, RL-aligned, and Turbo variants for both generation and editing. Diffusion-NFT improves prompt following, text rendering, aesthetic quality, and editing fidelity, while few-step distillation with adversarial perceptual guidance produces 4-step Turbo models for low-latency inference. Despite its compact scale, Mage-Flow and Mage-Flow-Edit achieves competitive performance across standard generation and editing benchmarks. More importantly, the Turbo variants make high-resolution generation and editing practical for interactive use: at 1024^2 resolution on a single NVIDIA A100 GPU, Mage-Flow-Turbo generates an image in 0.59s, and Mage-Flow-Edit-Turbo edits an image in 1.02s, while maintaining a small memory footprint. These results show that careful tokenizer–backbone–system co-design can deliver strong high-resolution generation and editing within an efficient 4B model family.
[CV-25] Now You See the Hate: Adaptive View Retrieval for Hidden Hateful Illusions
链接: https://arxiv.org/abs/2607.19061
作者: Qianpu Chen,Derya Soydaner
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Hateful optical illusions expose a serious gap in current multimodal safety systems. On original-view hateful illusions, previous work shows that six moderation classifiers achieve at most 20.9 to 24.5% accuracy and nine state-of-the-art VLMs remain at or below 10.2% with illusion-aware prompting, leaving most hidden hate undetected. We formulate hidden hateful illusion detection as a perceptual retrieval problem and propose Adaptive View Retrieval. This retrieve-and-calibrate framework assembles a complementary view bank for the image and hidden-message templates, adaptively selects which views to trust, retrieves hidden-message identities, and calibrates whether the recovered evidence is harmful. On HatefulIllusion with a frozen CLIP encoder, Adaptive View Retrieval reaches 93.2% balanced accuracy on the held-out test split. It substantially outperforms original-view baselines and fixed single-transform filters across hate slangs, hate symbols, and visibility levels. The same design also surpasses official fine-tuned CLIP baselines, matches or exceeds human performance on IllusionMNIST, IllusionFashionMNIST, and IllusionAnimals, and outperforms zoom-out preprocessing on HC-Bench under the SemVink protocol. Together, these results show that robust multimodal moderation requires recovering hidden meaning before deciding whether it is harmful.
[CV-26] Contrastive On-Policy Distillation
链接: https://arxiv.org/abs/2607.19046
作者: Jiacheng Ruan,Jun Tang,Wenzhen Yuan,Ting Liu,Shuai Bai,Dayiheng Liu,Zhibo Yang,Yuzhuo Fu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Work in progress. 15 pages, 4 figures
Abstract:On-policy Distillation (OPD) supervises a student model on trajectories sampled from its own policy by minimizing the divergence between the output distributions of the teacher and student at each token position, thereby providing dense token-level supervision. Although existing OPD methods have demonstrated strong performance in improving the reasoning ability of student models, their objectives fundamentally rely on token-level distribution matching. Consequently, they lack an explicit signal for comparing a token’s relative compatibility across reasoning modes and thus do not directly model preferences between these modes. To address this limitation, we propose COPD, a contrastive OPD framework. Specifically, for each token generated by the student model, a frozen teacher model scores the same student state under two contrasting instructions that elicit light and heavy reasoning. The difference between the resulting log probabilities serves as a token-level advantage signal to guide the OPD update. Rather than merely imitating a single teacher distribution, COPD directly encourages the student model to learn more concise and efficient reasoning strategies. We conduct experiments on nine multimodal benchmarks covering both reasoning and understanding tasks. The results show that COPD substantially reduces reasoning length without compromising model performance and consistently improves efficiency across different tasks and model scales. Furthermore, the contrastive formulation can be seamlessly integrated into the On-policy Self-distillation (OPSD) framework, where self-contrastive supervision is constructed without an additional teacher model, thereby enabling the model to distill itself toward lightweight reasoning.
[CV-27] Gaze-DETR: Top-Down Guidance Through Priority Maps for Infrared Weak-Small UAV Detection with DETR
链接: https://arxiv.org/abs/2607.19040
作者: Nian Liu,Yuxin Yang,Shubo Lin,Sikui Zhang,Liang Li,Boyu Cai,Yizheng Wang,Weiming Hu,Jin Gao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Code: this https URL
Abstract:Infrared small target detection (ISTD) remains challenging because tiny, low-contrast targets are easily overwhelmed by clutter, noise, or occlusion. Conventional single-frame and multi-frame detectors rely on bounding-box supervision, which specifies final target locations but offers little explicit guidance for prioritizing candidate regions or preserving weak-target evidence before localization. Task-driven visual search offers such guidance: top-down goals and visual evidence jointly form a spatial priority map that ranks candidate locations. Building on this principle, we propose Gaze-DETR, a bio-inspired detector that learns an internal priority map before localization. First, a priority head predicts a normalized priority map from image features. Second, Residual Priority-Guided Feature Modulation (RPFM) enhances high-priority responses while retaining multi-scale features. Finally, Priority-Guided Anchor Query Injection (PAQI) converts high-priority locations into decoder anchor queries. We train the priority head using three supervision schemes: box-derived Gaussian maps; real-gaze maps constructed from fixation-density maps; and transferred pseudo-gaze maps learned from gaze–box relations in paired annotations and applied to Anti-UAV410 training boxes. To support the latter two schemes, we construct TIR-UAV120-Gaze with paired detection and task-driven eye-tracking annotations. On TIR-UAV120-Gaze, Gaze-DETR achieves 85.76 mAP _50 and 88.77 F1 with box-derived supervision, and 86.18 mAP _50 and 89.00 F1 with real-gaze supervision. On Anti-UAV410, it achieves 87.06 mAP _50 and 90.90 F1 with box-derived supervision, and 87.08 mAP _50 and 90.43 F1 with transferred pseudo-gaze supervision. These results show that explicit spatial-priority learning provides pre-localization guidance complementary to bounding-box supervision across annotation settings and costs.
[CV-28] FilmWorld: Agent ic Novel-to-Film Generation through Dynamic Cinematic World Modeling
链接: https://arxiv.org/abs/2607.19038
作者: Jialong Zuo,Haotong Zuo,Shiwei Zhang,Xiang Wang,Chen Li,Nong Sang,Changxin Gao,Xiang Bai
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Project Page: this https URL
Abstract:Translating novels into films poses a grand challenge for generative artificial intelligence, requiring conversion of abstract literary prose into long-form, multi-scene visual narratives. While current video generation models excel at short, single-scene clips within narrow temporal and spatial contexts, novel-to-film generation operates in a more complex regime, demanding long-duration content across diverse scenes with dynamically evolving entity states. To address this, we formalize novel-to-film generation as dynamic cinematic world modeling, decomposed into two phases: construction, which grounds abstract, underspecified literary narratives into concrete, stateful, and persistent world entities; and evolution, which governs how these entities dynamically update under plot progression to maintain causal consistency across scenes. We propose FilmWorld, an end-to-end agentic system where two groups of specialized agents collaborate to instantiate these phases. Construction-side agents perform narrative structured translation, world entity state modeling with visual anchoring, and state-driven shot planning, progressively projecting literary language into a cinematic blueprint. Evolution-side agents perform state-anchored visual generation, cross-shot dynamic state propagation, and closed-loop state verification to maintain causal consistency and visual coherence. To address the evaluation gap in long-form generation, we introduce FilmEval, a systematic evaluation framework that couples a difficulty-graded benchmark of 15 representative novels with an automated protocol of nine objective metrics spanning three dimensions: cinematic presentation, film consistency, and novel fidelity. Experiments demonstrate that FilmWorld consistently outperforms state-of-the-art video generation agent systems, with particularly pronounced improvements in narrative fidelity and cross-scene consistency.
[CV-29] CoGoal3D: Collaborative 3D Object Detection with 3D-Aware Fusion and Refinement ECCV2026
链接: https://arxiv.org/abs/2607.19036
作者: Zhihao Yang,Zhiyu Xiang,Peng Xu,Tianyu Pu,Kai Wang,Eryun Liu,Dongping Zhang,Yong Ding
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to ECCV 2026. 17 pages, 8 figures, 6 tables. Code: this https URL
Abstract:V2X collaborative object detection features overcoming the limitations of single-vehicle systems by aggregating environmental features from multiple collaborative agents. However, existing mainstream V2X perception methods mainly focus on 2D BEV object detection. When 3D detection task is concerned, inferior results are obtained because they ignore the 3D spatial misalignment caused by differing height and attitude among the collaborators. In this paper, we propose a novel collaborative 3D object detection framework called CoGoal3D, which extracts and refines the 3D feature gradually in a two-stage pipeline. In the first stage, a multiscale 3D-aware global fusion module is designed to mitigate the 3D spatial misalignment. The resulting proposals are then refined in the second stage with an auxiliary task of 3D point reconstruction. An effective multi-agent collaborative data augmentation strategy is further proposed to enrich the training data while minimizing information loss. Extensive experiments on public real-world datasets demonstrate that our CoGoal3D achieves new state-of-the-art performance, with 3D AP@0.7 improvements of 10.86%, 10.34%, and 10.18% on the DAIR-V2X, V2V4Real, and V2X-Real datasets, respectively. Code is available at this https URL.
[CV-30] IMMoE: Incomplete Multi-View Anomaly Detection via Mixture of View Experts Fusion ECCV2026
链接: https://arxiv.org/abs/2607.19032
作者: Lei Hu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV 2026
Abstract:Existing Multi-view Anomaly Detection (MAD) methods assume that all views are completely available and model each view separately. However, in real industrial scenarios, information in the view may be missing due to faults such as occlusion, which leads to the performance degradation of existing methods due to the lack of a multi-view consistency prior. To address this, we explored a more challenging task: Incomplete Multi-View Anomaly Detection (IMVAD), in which some areas of each view were masked. We proposed a pipeline for automatically generating the IMVAD dataset and generated the \textbfRIMAD dataset based on the Real-IAD dataset through this pipeline. In addition, in order to effectively utilize the information of multiple views in the absence of view information, we propose \textbfIMMoE, which consists of two key modules: (1) Multi-View Expert Fusion (MVEF) effectively fuses multi-view information through a multi-view expert network and guides the reconstruction of a single view; (2) Local Anomaly Enhancement Encoder (LAEE) effectively prevents the model from overfitting the mask region by applying dropout to local features. Our method achieves state-of-the-art performance on both the RIMAD and Real-IAD datasets, especially on RIMAD, we have increased the pixel-level and image-level metrics by 11.8% and 2.8%, respectively. Our source code is available at this https URL
[CV-31] Mitigating Modality and Language-Style Gaps for Zero-Shot Video Moment Retrieval ECCV2026
链接: https://arxiv.org/abs/2607.19027
作者: Jihyun Lee,Cheol-Ho Cho,Woojin Jun,Woojin Jeong,Jae-Pil Heo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026 (* These authors contributed equally.)
Abstract:Zero-shot video moment retrieval aims to overcome the limitations of traditional approaches that require large-scale datasets annotated with text and its relevant temporal spans. Despite advances in pre-trained vision-language models and multimodal large language models, existing ZMR methods still heavily depend on query-to-video content similarity, making them vulnerable to modality and language-style gaps. These gaps lead to unreliable span proposals and unstable moment retrieval results. To address this issue, we propose Self-Similarity-based Moment Proposal and Scoring that instead exploits intrinsic relationships within videos, enabling robust span generation and scoring. By deriving self-similarity only from the video content, we circumvent the noisy and mismatched patterns of query-frame or query-caption similarities, thereby mitigating both modality and language-style gaps. Furthermore, we introduce a query-aware MLLM-based reasoning stage to further sharpen alignment between text and video. Extensive experiments demonstrate that Self-SiMS achieves state-of-the-art performance across ZMR benchmarks.
[CV-32] Learning Semantic-Robust Change Detection via Semantic-Invariant Self-Distillation ECCV2026
链接: https://arxiv.org/abs/2607.19000
作者: Jiuhe Qu,Yingping Liang,Ying Fu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV2026
Abstract:Change detection aims to identify semantic changes between remote sensing images. However, features from models are easily disturbed by non-semantic variations, such as illumination, shadows, and atmospheric changes, leading to false alarms and limited generalization in real-world scenarios. In this paper, we propose \textbfSCDistill, a framework for learning semantic-robust change detection via semantic-invariant self-distillation. First, to strengthen semantic consistency, we introduce a semantic-invariant self-distillation strategy that learns semantic robustness from perturbed yet semantically consistent data, empowering the change detector to extract disturbance-resistant features and achieve more reliable and accurate semantic change identification. Second, to expand paired data with non-semantic variations, we design a diffusion-based perturbation simulation pipeline that synthesizes complex environmental changes, enabling the model to explicitly learn to distinguish semantic changes from appearance-level fluctuations and reduce false alarms caused by non-semantic disturbances. These components promote robustness from data and representation perspectives, leading to synergistic performance gains. Extensive experiments demonstrate that SCDistill achieves state-of-the-art performance on multiple semantic change detection benchmarks and exhibits strong generalization to binary change detection and change captioning tasks. Code is accessible at this https URL.
[CV-33] Benchmarking Deep Learning Approaches for AEC Engineering Drawing Layout Detection and Information Extraction
链接: https://arxiv.org/abs/2607.18997
作者: Tianyang Huang,Alessio Lombardi,Ahmed Elnagar,Ahmed Zalouk,George Paul,Sepehr Najjarpour,Arvid Sigurdsson,Khalid Ismail,Mohamed Ragab,Edlira Vakaj
类目: Computer Vision and Pattern Recognition (cs.CV); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
备注: 2026 European Conference of Computing in Construction (EC3 2026), 8 pages
Abstract:Information Extraction (IE) from Architecture, Engineering, and Construction (AEC) drawings remains hindered by manual inefficiency, while Layout Detection, a vital ‘middleware’ organizing graphical and textual hierarchies, is underexplored. General document layout models, optimized for text-centric content, lack validation on engineering drawings. This study constructs a custom AEC-specific layouts dataset and benchmarks five deep learning architectures. RF-DETR achieves state-of-the-art performance with an mAP_50 of 0.949, while the Vision-Language Model Qwen3-VL attains a leading F1-score of 0.911. Conversely, models pre-trained on general document datasets suffer from “domain interference”, causing performance degradation. This establishes a robust technical foundation for automated IE in AEC.
[CV-34] SWITi: Quantifying and Reducing Tiling Artifacts with Sliding Window Inner Tiling
链接: https://arxiv.org/abs/2607.18990
作者: Federico Carrara,Aman Kukde,Melisande Croft,Joran Deschamps,Florian Jug
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:SWITi is a test-time method for reducing artifacts in tiled predictions, particularly for neural networks that learn posterior distributions from which solutions are sampled at inference time. Tiled predictions are unavoidable for large image data, and artifacts arise whenever tiles are smaller than a network’s receptive field and when tiles are independent posterior samples. SWITi averages overlapping sliding-window predictions, so discrepancies between neighboring samples are spread across shifted tile positions rather than accumulating at fixed seam coordinates. For posterior models, SWITi uses no more tile samples than an MMSE estimate requires and therefore incurs no additional forward passes. Additionally, we introduce two reference-free metrics, the Fraction of Rejected Tests (FRT) and Artifact Severity (ASV), for detecting and quantifying tiling artifacts from a per-tile permutation test that compares the distribution of pixel gradients across tile seams against the surrounding image content. On pre-trained and published image splitting models across three fluorescence microscopy datasets in 2D and 3D, we show that SWITi substantially attenuates stitching seams while also improving reconstruction fidelity and resolution. Since tiling artifacts in posterior predictions can easily be mistaken for biological structures or for boundaries between biological structures, removing or reducing them using SWITi will improve the downstream processing of large image predictions, which is particularly relevant for biomedical data.
[CV-35] DobicVLM: Aligning Chest X-Ray Report Generation with Clinically-Grounded Programmatic Rewards via Group Relative Policy Optimization
链接: https://arxiv.org/abs/2607.18988
作者: Thanni Adewuyi,Angelica Obayi,Andem Aniekan,Samuel Okoko,Angel Ezendu,Ephraim Usani,Ademide Animasaun,Philip Chibundu,Christian Maurice,Mary Donald Essien,Oluwaseun Odunsi,Oluwasegun Oguntuase,Abiodun Adereni
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Medical imaging is a cornerstone of diagnostics, yet automated chest X-ray report generation struggles with structural adherence, anatomical completeness, and semantic faithfulness. We introduce DobicVLM, a vision-language model combining supervised fine-tuning on MedGemma-4B with Group Relative Policy Optimization (GRPO) and clinically-grounded programmatic rewards. Our approach uses interpretable, rule-based reward components; structural verification, anatomical checklist, semantic similarity, and length constraints to enforce clinical standards without neural reward models. Trained on 1,000 de-identified image-report pairs from a private clinical dataset (with ethics approval and compliance to local regulations), DobicVLM is evaluated via blinded expert review on 69 held-out cases. DobicVLM outperforms Gemini 2.5 Flash across the majority of criteria, achieving the highest impression accuracy (27.2%) and medical terminology (86.5%) compared to both Gemini 2.5 Flash and MedGemma 4B baselines, with minor trade-offs in completeness and referrals. This demonstrates GRPO’s value for transparent alignment in resource-limited settings. Keywords: Vision-Language Models, Radiology Report Generation, Reinforcement Learning, Medical AI, GRPO
[CV-36] Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model
链接: https://arxiv.org/abs/2607.18958
作者: Sibo Wang,Jie Zhang,Shiguang Shan,Xilin Chen,Wen Gao
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:While Large Vision-Language Models (LVLMs), represented by LLaVA and GPT-4V, have demonstrated remarkable capabilities, their visual inputs remain vulnerable to adversarial attacks, posing significant security risks. Existing defense methods predominantly target single-task scenarios (e.g., zero-shot classification) and consequently lack generalizability across various multimodal tasks. To address this limitation, we propose a dual adversarial fine-tuning framework that jointly optimizes visual and semantic supervision signals from two modalities, enhancing model robustness while generalizing across multiple downstream tasks. The proposed framework comprises two core components, i.e., \textbfVisual supervision branch and \textbfSemantic supervision branch. The former branch leverages features from clean images, extracted via a frozen original vision encoder, to guide adversarial robustness while the latter incorporates caption-image alignment as a contextual signal to preserve semantic coherence under attack. Moreover, our method achieves cross-task robustness by simply replacing the CLIP vision encoder in the original model, with no need of separate task-specific retraining or architecture this http URL experiments demonstrate that our approach outperforms the state-of-the-art method in adversarial robustness evaluation across zero-shot classification, image captioning, and visual question answering (VQA) tasks.
[CV-37] NGPS: GPS-Denied Aerial Geo-Localization and 2.5D Reconstruction via Deep Satellite Image Matching and Multi-Rate Sensor Fusion IROS2026
链接: https://arxiv.org/abs/2607.18936
作者: Sanket Sharma
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages (8-page paper + IEEE copyright/citation cover page), 6 figures. Accepted to IEEE/RSJ IROS 2026
Abstract:We present NGPS (Next-Generation Positioning System), a visual geo-localization framework for high-altitude UAVs that provides GPS-free absolute positioning by matching down-facing images to georeferenced satellite imagery with deep features. The system combines (1) adaptive confidence-weighted UKF fusion, where NGPS covariance is modulated by RANSAC inlier ratio, reprojection error, and match confidence; (2) velocity-predictive kernel extraction, using VIO velocity to predict the satellite search region; and (3) an asynchronous multi-rate temporal priority queue that interleaves absolute position (1-2 Hz), VIO (10-20 Hz), and IMU (100-200 Hz) updates in chronological order. Globally optimized poses from VINS pose-graph optimization, anchored by NGPS corrections, further enable real-time 2.5D georeferenced orthomosaic reconstruction. On five flight sequences (60-150 m AGL), NGPS achieves 2.94 m position RMSE, with worst-case ATE 6.04 m at 150 m AGL and 2 m/s, yielding a 3.5x improvement over standalone monocular VIO. The system runs in real time on an NVIDIA Jetson Orin NX. Part of the implementation is open-sourced at this https URL.
[CV-38] Learning Explicit Physical Parameter Control and Benchmarking for Video Generation
链接: https://arxiv.org/abs/2607.18924
作者: Yanxun Li,Hao Wen,Bingze Song,Jiashu Zhu,Aiming Hao,Chubin Chen,Jintao Chen,Jiahong Wu,Xiangxiang Chu,Miao Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 23 pages, 11 figures
Abstract:Recent advances in image-to-video generation have improved visual realism, making physically grounded and controllable dynamics an important step toward future world simulation. Current models often generate plausible motion, but it is not reliably governed by explicit physical causes, and instance-level constraints can leak or become entangled in multi-object interactions. We attribute this gap to two missing pieces: large-scale, fine-grained physical parameterization, and model designs that correctly bind physical attributes to instances and emphasize dynamics over appearance. To bridge this gap, we introduce PhyParam-Dataset, an interaction-centric collection of 130K physically simulated videos with dense physical parameterization, including force vectors, object material properties, and environmental constants across five representative rigid-body motion types. Built on this data, we present PhyParam, a physics-guided image-to-video diffusion model that conditions on object-level forces, masses, friction, restitution, and scene-level gravity via a lightweight physical-attention routing mechanism, and further improves motion learning with semantic-structural feature-space supervision. We also establish PhyParam-Bench, a benchmark for physical-law consistency in image-to-video generation, with a multi-level protocol evaluating temporal dynamics, spatial stability, and semantic–physical alignment. Experiments show that PhyParam improves physical consistency while maintaining high visual fidelity, advancing explicit rigid-body physical-parameter control for image-to-video generation. We will publicly release the dataset, benchmark, and code to support future research. Comments: 23 pages, 11 figures Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2607.18924 [cs.CV] (or arXiv:2607.18924v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.18924 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: YanXun Li [view email] [v1] Tue, 21 Jul 2026 10:06:55 UTC (21,127 KB) Full-text links: Access Paper: View a PDF of the paper titled Learning Explicit Physical Parameter Control and Benchmarking for Video Generation, by Yanxun Li and 9 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.CV 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?) 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-39] AP-RAG : Task-Aware Policy Control for Long-Document Multimodal Question Answering
链接: https://arxiv.org/abs/2607.18917
作者: Zhong Ji,Keqi Jin,Yan Zhang,Jiasheng Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 6 figures, 9 tables
Abstract:Long-document multimodal question answering requires more than retrieving relevant chunks from a large document. Different queries require different evidence behavior. Existing multimodal RAG systems improve evidence access through text chunks, page images, graph links, or heterogeneous document elements, but they often apply a largely query-agnostic evidence-use strategy. We present TAP-RAG, a task-aware policy-controlled RAG framework for long-document multimodal QA. TAP-RAG contains a main controller, the Task-Aware Policy Controller (TAPC), and two policy-guided evidence executors: Task-Aware Query-Guided Flow Diffusion (TA-QFD) and Task-Aware Visual Enhancement (TAVE). For each query, TAPC predicts the task prior, estimates visual/local/global evidence signals, and produces an executable policy. TA-QFD then expands textual and structural evidence over the multimodal document graph, while TAVE selectively inspects page images when visual or layout evidence is needed. A guarded synthesis stage fuses text, visual, and structural evidence and abstains when support is insufficient. On DocBench and MMLongBench-Doc, TAP-RAG achieves the best overall accuracy among the compared systems, improving over a matched multimodal-RAG baseline by +9.1 points (61.1 to 70.2) and +4.5 points (42.2 to 46.7), respectively.
[CV-40] SynGallery: A Synthetic Gallery of Real Paintings for Instance-Level Artwork Recognition
链接: https://arxiv.org/abs/2607.18907
作者: Patryk Bartkowiak,Jakub Markil,Bartosz Kotrys,Dominik Michels,Sören Pirk,Wojtek Palubicki
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Instance-level artwork recognition requires matching a handheld visitor photograph to a specific work in a large museum collection. This is challenging because painting datasets typically provide clean catalog images for training, while test queries are captured under oblique viewpoints, gallery lighting, reflections, frames, and other scene-level variations. We present SynGallery, a synthetic gallery dataset for artwork retrieval that addresses this gap without collecting additional real photographs. Starting from catalog images of real paintings, we place each artwork into a procedurally generated 3D gallery scene and render it from multiple viewpoints under varied geometric and appearance conditions, while preserving the exact identity of the original work. The resulting dataset contains 24,490 rendered views of 4,898 paintings from the Met benchmark. We show that these synthetic views provide a stronger training signal than the corresponding studio photographs. At the same number of training data points, training only on SynGallery improves art painting recognition from 67.18 to 73.47 GAP ^- . When added to the full Met training set, SynGallery improves the published benchmark protocol from 35.97 to 38.48 GAP. Ablation experiments show that the gain comes primarily from geometric viewpoint variation rather than photographic realism: blur, sensor noise, and image compression consistently reduce performance.
[CV-41] Local Label-Informed Feature Transfer for Generating Ground-Truth Medical Images: A Comparison of GAN- and Diffusion-Based Approaches
链接: https://arxiv.org/abs/2607.18882
作者: Rick Wilming,Irem Ozseker,Luca Matteo Cornils,Ahcène Boubekki,Benedict Clark,Danny Panknin,Stefan Haufe
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 6 pages, submitted to IEEE MetroXRAINE 2026
Abstract:Validating Explainable Artificial Intelligence (XAI) methods in medical imaging requires ground-truth data with known locations of informative features. However, current approaches rely on expert annotations, which are prone to labeling errors, or on hand-crafted artificial perturbations superimposed onto healthy images to mimic lesions or malignant features, which lack clinical realism. We present Local Label-Informed Feature Transfer (LLIFT), a framework for generating semi-synthetic brain magnetic resonance images with realistic lesions placed in user-controlled regions, which does not require pixel-level lesion annotations during training. We implement LLIFT with two generative paradigms: LLIFT-GAN, a custom GAN that learns pathological features from binary class labels alone, and LLIFT-DM, a diffusion-based inpainting pipeline conditioned on bounding-box masks via ControlNet. Both approaches are evaluated on brain magnetic resonance imaging data derived from the Human Connectome Project. In evaluations, both achieve Fréchet Inception Distance scores, with respect to the real pathological distribution, that are comparable to the inter-class reference between healthy and pathological images in the given dataset. Furthermore, qualitative inspection confirms the realism of lesion structures. The resulting benchmark datasets provide spatially controlled ground truth data for evaluating XAI methods in medical imaging.
[CV-42] Wave2Body: Rethinking mmWave Human Pose Estimation as Radar-to-Body Token Translation
链接: https://arxiv.org/abs/2607.18875
作者: Bo Liang,Chen Gong,Wei Gao,Chenren Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Millimeter-wave (mmWave) radar enables privacy-friendly human sensing, but its sparse point clouds are physical measurements of view-dependent electromagnetic reflections and only indirectly characterize body articulation. Recovering a complete 3D pose from such partial, geometry-dependent observations is therefore under-constrained. Existing methods directly regress joint coordinates from paired radar-pose data, relying on the same limited paired supervision to learn radar perception, human-body structure, and their alignment. This coupling can encourage dataset-specific shortcuts under ambiguous radar observations. We propose Wave2Body, a radar-to-body token translation framework that decouples these learning targets using a self-supervised mmWave tokenizer, a pretrained compositional body tokenizer that defines the output space, and a lightweight translator between them. Experiments on M4Human and mmBody show that Wave2Body achieves stronger cross-domain generalization than previous methods while incurring much lower computational costs for training and inference. All the code and experiment results are publicly available at this https URL.
[CV-43] Reliability-Aware 3D Geometric Injection for Universal Person Re-identification ECCV2026
链接: https://arxiv.org/abs/2607.18863
作者: Bohan Su,Jiashuo Wang,Fangyi Liu,Mang Ye
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCV 2026
Abstract:Universal person re-identification (ReID) aims to retrieve pedestrian identities across diverse real-world scenarios, including severe occlusions, clothing changes, and cross-modality shifts, within a unified model. However, existing 2D representations fundamentally struggle with spatial ambiguities due to a lack of depth and topological awareness, while naively introducing monocular 3D priors often causes severe negative transfer due to geometric estimation noise under extreme visual degradation. To safely harness the clothing-invariant and canonical structural properties of 3D geometry, we propose UniGeo, a Universal Monocular 3D-Enhanced ReID framework driven by a Consistency-Aware Reliability Gate and Dual-Stream Residual Fusion. Specifically, the processing of 3D information is strategically decoupled into geometric extraction and dynamic utilization. To provide pure structural compensation, we project monocular 3D parameters into kinematic joint representations, explicitly capturing instance-level geometric topology to resolve appearance-based ambiguities. To robustly incorporate these cues without perturbing the reliable 2D feature space, we isolate the 3D prior as a late-stage structural residual; modulated by the consistency-aware gate, this mechanism adaptively filters geometric noise and enables controlled fallback to the pure 2D baseline. Extensive experiments show that our method improves challenging, structure-sensitive scenarios while preserving competitive performance on clean domains. Code is available at this https URL.
[CV-44] hink Sparse Predict Dense: Continuous Thought Machines for Image Super-Resolution
链接: https://arxiv.org/abs/2607.18856
作者: Zekai Shi
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注: 8 pages, 6 figures
Abstract:Continuous Thought Machines introduce an internal temporal dimension in which neuron-level histories and synchronization-derived representations evolve over a sequence of thought ticks. Extending this mechanism to dense visual prediction is non-trivial, because tasks such as image super-resolution require spatial evidence to remain available at every output location rather than being compressed into a single global representation. In the proposed window-level use of CTM, the thought dynamics produce a compact summary representation for each local window. DQ-CTM transforms this compact thought representation into window-aligned dense queries through a structured low-rank, parameter-efficient compact-to-dense query mechanism. Each position within a window receives its own query, while shared thought dynamics progressively refine the dense representation across ticks. In its super-resolution instantiation, termed ThinkSR, encoded feature maps are partitioned into local visual windows without token pooling, restored to the original feature field after shared refinement, and decoded into a high-resolution image. Preliminary experiments under a fixed four-tick training horizon reveal a progressive reconstruction trajectory. PSNR-Y increases from 28.1045 dB at T=0 to 30.2817 dB at T=4 , while PSNR-RGB increases from 26.6271 dB to 28.7781 dB and the mean \ell_1 error decreases from 0.034602 to 0.023545. All 100 evaluated images improve from T=1 to T=4 . These initial results establish the feasibility of sparse latent thought for dense spatial reconstruction and motivate broader continuous-thought architectures for dense vision.
[CV-45] OPD-IAD: From Language Judgment to Industrial Anomaly Detection via On-Policy Self-Distillation
链接: https://arxiv.org/abs/2607.18850
作者: Shuimu Chen,Jing Jin,Nan Su,Hongbo Xu,Zebang Cheng,Wenming Yang,Fei Ma,Guijin Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Large vision-language models (LVLMs) have recently shown strong potential for industrial anomaly detection (IAD) by providing image-level anomaly judgments and interpretable defect reasoning. However, current LVLM-based IAD methods still struggle to produce precise pixel-level anomaly maps from generated language judgments. We aim to achieve precise pixel-level localization while using language as guidance rather than letting it dominate the visual response. Specifically, we propose \textbfOPD-IAD, an evidence-privileged dense on-policy self-distillation framework for LVLM-based IAD. OPD-IAD distills privileged defect evidence onto the model’s own on-policy judgment trajectory, enabling the final generated judgment to be learned under dense supervision rather than treated only as a textual answer. The resulting judgment serves as a semantic condition for dense anomaly perception. To turn this condition into dense visual evidence, we introduce \textbfLanguage-guided Visual Anchoring, which uses a judgment reforward to re-encode the image and question under the final-judgment condition into semantic anchors and contrasts them with dense visual features through a contrastive heatmap head to generate anomaly maps. The language judgment therefore provides compact semantic guidance, while dense visual features remain the basis for pixel-level scoring, allowing language to guide anomaly localization without letting language quality directly dictate the pixel-level response. Extensive experiments show that OPD-IAD achieves the best overall performance among LVLM-based IAD methods, leading on most image-level, pixel-level, and QA metrics.
[CV-46] Open-Vocabulary Gaze Object Prediction: Benchmark and Method
链接: https://arxiv.org/abs/2607.18827
作者: Binglu Wang,Sensen Niu,Ying Chen,Guangyu Guo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ACM Multimedia 2026
Abstract:Gaze Object Prediction (GOP) aims to localize and recognize the objects humans attend to, a task crucial for understanding human-centric interactions. However, existing methods are typically trained under a closed-vocabulary paradigm with a fixed label space and evaluated on scene-specific datasets, limiting their applicability to real-world scenarios where gaze targets often follow a long-tail distribution or belong to unseen categories. To address this gap, we introduce Diverse Scenes for Gaze object prediction (DiSG), a benchmark containing 86 in-the-wild categories that facilitates the evaluation of Open-Vocabulary GOP (OVGOP). Building on DiSG, we propose a framework that leverages text-driven object discovery to localize potential gaze candidates, with a gaze-guided selection module to pinpoint the intended target from the candidate objects. Furthermore, to better capture semantic knowledge across diverse in-the-wild categories, we introduce Gradient-Informed Selection Tuning (GIST) to selectively update parameters most relevant to a given class vocabulary. Extensive experiments demonstrate that our proposed model performs effectively in open-vocabulary settings and also outperforms existing methods in the conventional closed-vocabulary setting. The benchmark and code is available at this https URL.
[CV-47] In-Context Learning for Wound Classification with Small Multimodal Language Models
链接: https://arxiv.org/abs/2607.18819
作者: George Martvel,Oskar Gustafsson,John Pavia,Ernst Ahlberg
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Wound image classification is often treated as a task-specific supervised learning problem, requiring substantial amounts of manually labelled data and retraining when the label space or deployment setting changes. This study evaluated whether small multimodal language models (SMLMs) can provide a training-free alternative for wound classification through retrieval-based in-context learning (ICL). Experiments used two public wound-image datasets: the Kaggle wound dataset (1469 images, 10 classes) and the Medetec dataset (560 images, 9 classes). Eleven SMLMs from the Qwen 3.5, Ministral 3, and Gemma 4 families were evaluated under zero-shot prompting and few-shot prompting with random support examples, embedding-based k-nearest-neighbour (kNN) retrieval, and kNN retrieval followed by maximal marginal relevance reranking (MMR). Retrieval-only weighted-kNN controls, support-set reduction experiments, and support-context size sweeps were used to assess the effects of retrieval, model scale, and prompt length. Query-conditioned ICL consistently outperformed zero-shot and random few-shot prompting. On the Kaggle dataset, the best result was achieved by Qwen 3.5 27B with kNN+MMR, reaching 0.872 accuracy and 0.871 F1 score. On Medetec, Qwen 3.5 27B with kNN+MMR reached 0.678 accuracy and 0.670 F1. Larger models exceeded matched weighted-kNN controls, indicating use of retrieved examples beyond nearest-neighbour voting. Retrieval-based ICL degraded modestly under support-set reduction, and most gains saturated with 8-10 support images. Retrieval-based ICL allows SMLMs to perform adaptable wound image classification without task-specific retraining. Compact retrieved contexts may support practical and privacy-conscious deployment, although performance remains dependent on model scale, retrieval strategy, and dataset difficulty.
[CV-48] ZeroSplat: Generalized Referring Segmentation in 3D Gaussian Splatting ECCV2026
链接: https://arxiv.org/abs/2607.18801
作者: Jiayu Ding,Meilu Song,Xiaoyi Zhang,Hongbo Jin,Yichen Jin,Xiangtian Si
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026
Abstract:Recent advancements in 3D Gaussian Splatting (3DGS) have enabled language-guided scene understanding. However, existing Referring 3D Gaussian Splatting (R3DGS) methods are fundamentally restricted to single-target queries. To reflect the ambiguity of real-world instructions, we introduce the Generalized Referring 3D Gaussian Splatting Segmentation (GR3DGS) task, which requires dynamically segmenting an arbitrary number of targets (0, 1, or N ). To facilitate comprehensive evaluation of this new task, we construct two new benchmarks: GR-LERF and GR-ScanNet. Crucially, existing R3DGS paradigms exhibit fundamental technical bottlenecks that severely limit their performance on the GR3DGS task: they lack intrinsic 3D point-level understanding by operating merely on 2D rendered pixels, and they incur prohibitive computational overhead by requiring per-scene optimization to embed heavy semantic features. To dismantle these bottlenecks, we propose ZeroSplat, a novel training-free and zero-feature framework. ZeroSplat lifts 2D Vision-Language Model (VLM) priors into 3D space through robust multi-view geometric constraints. This strategy enables intrinsic point-level understanding without incurring any additional feature storage. Extensive experiments demonstrate that ZeroSplat significantly outperforms state-of-the-art methods across generalized and single-target scenarios while maintaining exceptional efficiency. Project Page: this https URL
[CV-49] UVFaceFusion: Fast Multi-view Topologically Consistent Face Reconstruction in the Wild via UV-space Neural Fusion
链接: https://arxiv.org/abs/2607.18798
作者: Xin Ming,Yuxuan Han,Junhai Yong,Feng Xu
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注:
Abstract:Reconstructing high-fidelity facial geometry with an assigned topology is essential for digital avatar creation and animation, yet existing automated methods often trade off geometric fidelity and in-the-wild generalization. We present UVFaceFusion, a feed-forward framework for multi-view, fixed-topology face reconstruction from daily images. Our key idea is to replace heuristic topological optimization with learnable neural fusion in a canonical UV space. Given multi-view images, we first obtain dense point maps and facial UV correspondences of each view using VGGT and Pixel3DMM, respectively. Then, the view-specific point maps are lifted into the canonical UV domain and fused with a novel mask-aware neural fusion network. The network predicts a complete UV-space point map, from which a fixed-topology mesh is directly sampled. Although trained only on Ava-256, UVFaceFusion generalizes well to multiple public benchmarks and in-the-wild captures, benefiting from its canonical UV-space geometry-to-geometry fusion that reduces dependence on dataset-specific appearance and capture conditions. Experiments on various benchmarks show that UVFaceFusion achieves state-of-the-art reconstruction accuracy while reconstructing a mesh from 16 input views in less than 3 seconds on a single RTX 4090. Code is available at this https URL.
[CV-50] STS-NET: Spatio-Temporal Stress Network for Self-Supervised Crop Stress Detection using Satellite Image Time Series
链接: https://arxiv.org/abs/2607.18791
作者: Pradeep Dalal,Rajiv Ranjan,Sushil Ghildiyal,Shashank Tamaskar,Neeraj Goel
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 5 pages
Abstract:Early and accurate detection of crop stress is essential to improve agricultural productivity and ensure global food security. However, collecting a large labeled crop stress dataset is a challenging task. To address this challenge, we introduce a novel spatial-temporal stress network (STS-NET), built on a self-supervised 3D-convolutional autoencoder (3D-CAE), designed to utilize Satellite Image Time Series (SITS) data for crop stress detection. STS-NET exploits four vegetation indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Vegetation Index (GNDVI), Red-Edge Chlorophyll Index (RECI) and Normalized Difference Red-Edge Index (NDRE) obtained from high resolution Planetscope imagery to capture spatiotemporal stress patterns. The model is trained on our BSPT (Barnala Spatial-Temporal) dataset and evaluated on a real-world sugarcane dataset collected over a year from a 2.5-acre test plot located in Lakhimpur-Kheri (LK) district in Uttar Pradesh in India. STS-NET achieved a precision of 97. 98% for water stress, 85.08% for nitrogen stress, and 83.47% for combined stress. The results demonstrate the potential of STS-NET in effectively detecting stress in sugarcane crops with minimal reliance on labeled data. Furthermore, STS-NET can serve as a robust feature extractor for simpler models.
[CV-51] Moving Alphabet: A Controlled Study of Training Data for Text-to-Video Generation
链接: https://arxiv.org/abs/2607.18789
作者: Amber Yijia Zheng,Lu Liu,Raymond A. Yeh,Xi Yin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Text-to-video generation has advanced significantly over the past five years through scaling of model size, data, and compute. Unlike model architecture, training data is often underexplored. Real-world data curation is complex and non-trivial, involving clip selection from raw videos and captioning to create video-text pairs for learning text-to-video mappings. We study how data distribution and caption quality impact text-to-video models. To enable controlled experiments, we introduce Moving Alphabet, a procedural testbed that renders letters with varying fonts, colors, sizes, and positions, moving in different directions and speeds against a black background. This design allows precise control over data distribution and caption quality by corrupting ground-truth metadata. Our experiments yield three findings: a) a diverse and balanced distribution of video content and duration is critical for generalization; b) caption quality significantly affects both model performance and training efficiency, suggesting that text-to-video models are bounded by video understanding capabilities; and c) classifier-free guidance and fine-tuning on high-quality data provide partial recovery from models trained on corrupted captions, but cannot fully compensate for poor pre-training data. We believe these insights can inform the development of large-scale text-to-video models, and we advocate for greater attention to the science of pre-training data.
[CV-52] Image Editing Models are Numerical Solvers
链接: https://arxiv.org/abs/2607.18787
作者: Ulysse Mizrahi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We investigate whether a pretrained generative image-editing model can provide a common interface for numerical simulation. Physical inputs and solutions are rendered as images, while scalar quantities such as material properties, diffusivity, and loading parameters enter through lightweight adapters. Using established numerical and analytic solvers for supervision, we apply the same architecture and training protocol to heterogeneous elliptic equations, forced heat and Burgers evolution, complex Ginzburg-Landau dynamics, two-dimensional Navier-Stokes prediction, potential flow, elasticity, eikonal travel time, phase-field fracture, and entropic optimal transport. The results show that a pretrained image model can represent diverse static and time-dependent physical mappings, including unstable and shock-like behavior, when each task is expressed through a suitable visual encoding. This work is a capability study rather than an attempt to surpass specialized solvers. It also identifies fundamental constraints: image and latent representations complicate numerical range selection and direct enforcement of governing equations or invariants, while a failed Kuramoto-Sivashinsky experiment indicates that representation errors prevent meaningful long-horizon simulation of chaotic systems.
[CV-53] CGMap: A Geospatially Aware Deep Learning Framework for Crop Gap Mapping Using UAV
链接: https://arxiv.org/abs/2607.18779
作者: Karan Sharma,Rajiv Ranjan,Dinesh Kumar,Shashank Tamaskar
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages
Abstract:In India, crop germination is primarily monitored by visual inspection and manual counting, which are prone to errors, despite their crucial role in determining eventual yield potential. This paper highlights a deep learning based pipeline which uses object detection methods and drone imagery to assess and provide a precise count of sugarcane germination in fields. The approch uses a pre-trained AI model to find germinated plant sampling and identify gaps, also known as ``bald spots’', which restricts field productivity. The techniques used here relies on the YOLOV8 architecture, which was trained on a carefully selected dataset of UAV photos taken in various agroclimatic zones of India. Here, we bring upon a novel orientation-normalization technique that uses minimum Spanning Trees (MST) to account for variations in planting geometry, allowing for dependable row and column extraction across a variety of field layouts. By converting detected seedlings into spatial point clouds, emergence gaps can be inferred from the anticipated spacing between plants. A geospatial germination map exported in Well-Known Text (WKT) format is the end result, and it can be easily incorporated into GIS platforms used by sugar mills and agronomists to direct transplant initiatives. Timely interventions based on the insights provided by the algorithm can significantly increase yield, resulting in higher profits. Hence, support proper allocation of resources, avoid wastage, and enhance long-term sustainability.
[CV-54] Privileged Lesion-Context Relational Distillation for Mask-Free Skin Lesion Classification DATE
链接: https://arxiv.org/abs/2607.18773
作者: Abu Mukaddim Rahi,Md Mithun Hossain,Md Zulficar Hasan Joy,M. F. Mridha,Md. Jakir Hossen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Manuscript submitted for consideration. The paper presents a mask-free skin lesion classification framework evaluated on HAM10000 and externally validated on ISIC 2018
Abstract:Accurate skin lesion classification can benefit from lesion segmentation masks, but requiring masks or an auxiliary segmentation model during inference reduces clinical practicality and increases computational complexity. This work introduces Privileged Lesion-Context Relational Distillation (PLCRD), a teacher-student framework that exploits lesion masks exclusively during training while preserving image-only inference. The privileged teacher jointly analyzes the original dermoscopic image and its mask-guided lesion region to learn lesion-specific and contextual diagnostic representations. An image-only student is then trained through complementary knowledge-transfer mechanisms that convey the teacher’s diagnostic distribution, lesion-focused attention, inter-lesion relational geometry, and lesion-context structure. PLCRD decomposes deep representations into lesion and contextual embeddings and transfers their relational organization through inter-lesion similarity alignment, lesion-context affinity matching, separation regularization, and class-aware relational learning. This formulation avoids direct feature matching between heterogeneous teacher and student architectures and enables the student to internalize mask-informed diagnostic structure without accessing masks at deployment. The framework was evaluated on HAM10000 using lesion-disjoint data partitioning and externally validated on ISIC 2018 without retraining. PLCRD achieved a lesion-level macro-F1 of 0.773 +/- 0.018, balanced accuracy of 0.764 +/- 0.023, and macro-AUROC of 0.976 +/- 0.002 on HAM10000, together with a macro-F1 of 0.732 +/- 0.008 on ISIC 2018. The results indicate that privileged lesion annotations can be transformed into transferable relational knowledge, yielding a practical and interpretable approach to mask-free skin lesion classification.
[CV-55] GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors
链接: https://arxiv.org/abs/2607.18770
作者: Guang Yang,Fengchen Liu
类目: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
备注: 21 pages, 16 figures
Abstract:Fine-tuned foundation-model detectors dominate face-forgery benchmarks, yet they stay blind to generator families absent from training. We present GLID, a detector that repairs this blind spot with geometry instead of data. GLID treats the patch tokens of a single image as a sample from a manifold and estimates their local intrinsic dimension (LID) at several depths of a frozen vision transformer. This 12-dimensional, training-free signal enters a fine-tuned detector through a confidence gate whose strength is calibrated purely in-distribution. On a 16-axis cross-generator benchmark, GLID reaches 0.805 mean AUC, first among retrained state-of-the-art baselines and never significantly behind the strongest of them on any axis. It lifts the generation axes by +0.084 AUC while moving reenactment by only -0.005. Two empirical laws explain the design. First, forged faces bend the token manifold at family-specific depths: GAN artifacts peak at the last layer, diffusion artifacts peak mid-network, and the pattern survives four backbones, three dimension estimators, and non-face imagery. Second, fine-tuning absorbs auxiliary gains exactly where training data covers: injecting 1% target-family images erases a +0.100 gain, so geometric signals matter precisely where data is unavailable. The deterministic signal also cuts the cross-seed spread of accuracy 5.5x. Code, preregistered analysis gates, and per-image scores accompany the paper.
[CV-56] Cross-Modal UAV Object Tracking: State-Aware Representation Learning and A Unified Benchmark
链接: https://arxiv.org/abs/2607.18768
作者: Yun Xiao,Zhihong Hong,Jiandong Jin,Chenglong Li,Jin Tang,Amir Hussain
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Unmanned Aerial Vehicle (UAV) object tracking has emerged as a popular research field with broad practical applications. Modern UAVs are increasingly equipped with both visible light and thermal infrared sensors. However, due to constraints in communication bandwidth, computational resources and power consumption, current systems often activate one modality and switch between modalities to maintain robust tracking in complex scenarios. Such modality switch inevitably leads to significant appearance change and sudden spatial shift, posing great challenges for existing tracking algorithms. To handle this problem, we propose a novel State-Aware Representation Learning Approach called SARLA, which perceives the inconsistent modality states of current frame with template and last frame in the target representations to adapt to the sudden changes in both appearance and position, for robust cross-modal object tracking. In particular, we propose the Modality State Aware Representation Module (MSARM) and Spatial State Aware Representation Module (SSARM). MSARM guides the model to learn appearance correlation, bridging the modality gap, while SSARM models cross-frame spatial correlation to mitigate sudden spatial shift impacts. In addition, we design a spatial shift prediction loss to further handle the effects of spatial variation caused by modality switch. To promote the development of this research field, we establish a large-scale video benchmark called CM-UOT, which consists of 1079 cross-modal sequences with an average video length greater than 621 frames and encompasses over 671K frames in total. Extensive experiments on CM-UOT dataset demonstrate the superior performance of the proposed SARLA against 20 excellent tracking methods. The source code, datasets, and evaluation protocols associated with this work are publicly available at: this https URL.
[CV-57] Posterior Samplings are Missing Modalities Generators for Medical Image Translation ECCV26
链接: https://arxiv.org/abs/2607.18763
作者: Jonghun Kim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 26
Abstract:Magnetic resonance imaging comes in various modality contrasts that provide complementary anatomical and pathological information. Complete multimodal acquisitions are often unavailable due to time and protocol constraints. This leads to real-world datasets with missing modalities, where conventional medical image translation methods are typically limited to fixed source-target settings or require retraining for each observed source-target pair. We propose a unified framework that formulates missing-modality generation as a linear inverse problem under a joint distribution and solves it via posterior sampling with a flow matching model. By learning a joint prior over the complete modality set, our method can reconstruct arbitrary missing modalities at inference time by guiding the sampling trajectory to enforce measurement consistency with observed modalities. We further mitigate inter-modality error propagation in multi-target generation by adopting a many-to-one sampling strategy. Experiments on BraTS and IXI datasets show that our method achieves the best performance over baselines across most missing-modality scenarios. In downstream tumor segmentation, synthesized images from our method result in higher segmentation performance, indicating better preservation of clinically relevant structures.
[CV-58] Weakly Supervised Pathology-Informed Representation Learning for PET-Based Content Retrieval of Intra-Tumour Heterogeneity
链接: https://arxiv.org/abs/2607.18762
作者: Rajat Vashistha,Sandra Brosda,Lauren G. Aoude,Christine Jestin Hannan,James M. Lonie,Jessica Ng,Andrew Nathanson,Ellie Vloedmans,Caroline Cooper,Andrew P. Barbour,Viktor Vegh
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We propose a weakly supervised 18FFDG PET representation-learning framework for content based medical image retrieval, using HE derived information during training while preserving PET-only inference. The proposed method was designed to use HE derived information during training while maintaining PET only inference. A teacher student training strategy was used to learn the PET tumour derived voxel representations, from which global and hotspot conditioned embeddings were generated along with maps of intra tumour heterogeneity in our oesophegeal cancer test case. A progressive ablation strategy was used to evaluate the contribution of different supervision mechanisms. Retrieval performance was assessed across cross-validation folds using metrics including mean average precision, normalised discounted cumulative gain and mean reciprocal rank. Additional analyses evaluated ablation performance, hotspot faithfulness through perturbation/deletion experiments, prototype-specific PET uptake behaviour and indirect patient level concordance between learned PET prototype classes and selected histomic features. Progressive introduction of pathology informed supervision and hotspot modelling improved PET retrieval performance compared with global PET representations and conventional PET baselines. Across the ablation ladder, PET hotspot conditioned representations consistently provided stronger retrieval than global embeddings, indicating that focusing on informative tumour subregions improved sensitivity to intra tumour heterogeneity. Histopathology concordance further showed that the learned classes were not simply high uptake PET regions; instead, they demonstrated distinct heterogeneity in 18F FDG uptake.
[CV-59] SkyEV: RGB-Event UAV detection and tracking dataset and baseline
链接: https://arxiv.org/abs/2607.18747
作者: Jakub Mandula,Sebastian Heusinger,Julian Moosmann,Christian Vogt,Michele Magno
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Detecting UAVs in air spaces has become increasingly important due to UAVs widespread availability and easy usage. However, due to their small size, they are typically difficult to detect at a sufficient range. For the training of optimized detection algorithms, datasets have been published, covering optical sensing methods ranging from infrared to regular RGB to event-sensor-based. However, these datasets often fail to reflect realistic counter-UAV scenarios, lacking critical factors such as camera ego-motion, extremely small target scales, and diverse lens configurations, and introduce compression artefacts on the frame images. To address this gap, we introduce SkyEV, an open-source dataset featuring highly synchronized uncompressed RGB and event-based data. SkyEV distinguishes itself by capturing complex real-world conditions, including significant camera motion and varied optical setups, which are essential for testing the fundamental trade-off between Field of View and detection range. Furthermore, we provide a unified data loader and establish an experimental baseline using a multi-modal architecture, demonstrating the dataset’s efficacy in detecting challenging, small-scale targets.
[CV-60] Continual Video-MLLM Adaptation over Evolving Domains ACM-MM2026
链接: https://arxiv.org/abs/2607.18716
作者: Rui Cheng,Meixing Shi,Yuxiang Cai,Jingcai Guo,Jianwei Yin,Zhi Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ACM MM 2026
Abstract:Video multimodal large language models have shown strong capability in video understanding, yet their adaptation to sequentially evolving domains remains underexplored. In real-world deployments, video data often arrives continuously from heterogeneous domains, requiring the model to acquire new domain-specific knowledge without overwriting previously learned capabilities. Existing continual learning methods typically rely on shared adaptation spaces, which can induce severe cross-domain interference and catastrophic forgetting. We propose Distribution-Aware Expert Routing, a parameter-efficient framework for continual Video-MLLM adaptation over evolving domains. DAER maintains domain-isolated lightweight experts while keeping the pretrained Video-MLLM backbone frozen, thereby decoupling domain-specific adaptation from the general multimodal knowledge of the pretrained model. To enable fine-grained specialization, we introduce an intra-domain distribution-aware routing mechanism that matches each input to expert-level prototype reservoirs using MMD. To address the absence of task identities at inference time, we further propose an inter-domain routing mechanism that performs prototype matching in a discriminative subspace for robust domain identification. In addition, we introduce adaptive domain merging to improve parameter scalability and adopt a two-stage optimization strategy to stabilize expert specialization during continual learning. We evaluate DAER by curating a domain-incremental benchmark built from ten VidQA datasets covering diverse visual environments and reasoning demands. Experiments on two strong Video-MLLM backbones show that DAER consistently outperforms prior methods.
[CV-61] Confidence-Gated Vision-Only Heading Alignment for UAV-UGV Cooperative Systems
链接: https://arxiv.org/abs/2607.18713
作者: Reza Ahmari,Vahid Hemmati,Parham Kebria,Olusola Odeyomi,Kaushik Roy,Abdollah Homaifar
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-based heading prediction is useful for UAV–UGV cooperation, but accurate prediction alone does not guarantee that every predicted heading should be issued directly as a control command. This paper investigates the decision problem of when and how a fixed vision-based heading predictor should be trusted for command issuance. A lightweight confidence-gated framework is proposed in which execution decisions are made using two interpretable reliability proxies derived from the perception stream: bounding-box area as a visibility-related proxy and short-window variation in predicted heading as a stability-related proxy. During low-confidence intervals, the framework compares the baseline freeze-HOLD policy with a bounded-blend fallback that updates the issued command conservatively. The method is evaluated on a real UAV–UGV dataset under clean and perturbed conditions. The results show that confidence gating creates a clear trade-off among execution rate, executed-frame accuracy, issued-command accuracy, and smoothness. The results further show that sparse execution can cause severe stale-command error under the baseline freeze-HOLD policy, whereas the bounded-blend fallback substantially improves command-level behavior under the same gate decisions. These findings highlight that reliable perception-driven autonomy depends not only on prediction accuracy, but also on decision-aware command issuance during low-confidence
[CV-62] Generative World Renderer at the Speed of Play
链接: https://arxiv.org/abs/2607.18703
作者: Guixu Lin,Zheng-Hui Huang,Siqi Yang,Ming-Hsuan Yang,Kaipeng Zhang,Zhixiang Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Generative world renderer AlayaRenderer receives structured world states exported from physics engines and synthesizes RGB frames. Unlike models that generate frames from text/control-hints prompts, AlayaRenderer preserves scene structure without altering the underlying world dynamics. This demonstrates an alternative path toward interactive world modeling and user-controllable play. However, the original AlayaRenderer is too computationally expensive for real-time deployment. This technical report introduces AlayaRenderer-Flash, a real-time-oriented generative forward world renderer that pushes AlayaRenderer from 0.56 FPS to 31.54 FPS, reaching the speed of play. AlayaRenderer-Flash reformulates the original renderer as a few-step autoregressive streaming model and introduces lightweight distilled codecs for efficient latent encoding and frame reconstruction. It retains the teacher model’s G-buffer and text-prompt interfaces while enabling continuous rendering over input streams of unbounded length. We evaluate AlayaRenderer-Flash on G-buffer streams across content preservation, temporal consistency, cross-window stability, prompt controllability, and runtime efficiency. Our results show that AlayaRenderer-Flash substantially reduces inference cost while preserving the core rendering capabilities of the teacher model. By integrating AlayaRenderer-Flash with a physics engine, we build a fully playable generative world running at 30 FPS.
[CV-63] Decoupled Pipeline with Proposal Reranking and Score Fusion for Positive-Unlabeled Marine Species Detection
链接: https://arxiv.org/abs/2607.18700
作者: Robert James Brock,Sebastian Maximilian Krupa,Jason Kahei Tam
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:The FathomNetCLEF 2026 competition combines underwater object detection and fine-grained marine species classification under a positive-unlabeled evaluation setting. The provided training labels are sparse, while the hidden test set is out-of-distribution relative to the training imagery, creating both annotation incompleteness and source-shift challenges. We describe DS@GT ARC’s multi-stage system developed for this setting while keeping model training restricted to the data provided by the competition. The final private-leaderboard model uses a frozen Megalodon YOLOv8x detector as a class-agnostic proposal generator, combines global and tiled inference with tile-edge filtering, classifies expanded proposal crops with a LoRA-finetuned DINOv3 ViT-H classifier, and ranks predictions using weighted geometric fusion of detector and classifier confidence. This system placed 12th out of 102 teams. A closely related variant added a locally trained TTN-inspired validity head as a light reranking signal, improving public-leaderboard and proxy-evaluation performance but slightly reducing private-leaderboard performance. Across experiments, the strongest lesson was that train-derived validation and detector-only metrics were not reliable enough for model selection. Instead, we used proxy datasets only for validation and comparison, and combined those signals with leaderboard feedback and targeted ablations. These experiments showed that reserving proposal recall, avoiding over-aggressive filtering, and improving downstream ranking were more effective than fine-tuning the detector or directly training on noisy pseudo-labels. Code: this https URL.
[CV-64] Attributes Should Come from Images Not Class Names: Distribution-Conditioned Attribute Selection for Vision-Language Models
链接: https://arxiv.org/abs/2607.18695
作者: Gautam Rajendrakumar Gare,Jia Shi,Zhiqiu Lin,Deepak Pathak,John Galeotti,Deva Ramanan
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:A popular route to interpretable zero-shot classification asks a large language model (LLM) to describe each class name and prompts CLIP with the resulting descriptors. We show that these descriptors carry little visual evidence of their own: removing the class name from the prompt collapses ImageNet accuracy from 59.5% to 15.5%. The diagnosis is that the descriptors are conditioned on the label rather than on the images, so they describe the concept in general and mislead exactly when the data shifts; an LLM insists that strawberries are red, but every strawberry in ImageNet-Sketch is a colorless line drawing. We therefore select attributes from the target image collection instead: we score a large attribute pool against the images in CLIP’s joint embedding space and keep the top-scoring attributes per class. Selected this way, class-name-free attribute prompts reach 23.8% on ImageNet (against 15.5% for LLM descriptors), the gain holds on four shifted ImageNet variants, and reselecting from the LLM’s own pool isolates the selection mechanism as the cause. With one image per class, the selected attributes outperform the prompt-tuning method CoOp by 3 points while fitting in under a minute instead of 14 hours, with no learned soft prompt to obscure the decision. Because the attribute set is chosen by the data, it doubles as a readable summary of a dataset, which we use to describe distribution shift in words.
[CV-65] Dual-Edged Homogeneous-Modality Similarity: Towards Visible-Infrared Modality-Incomplete Person Re-Identification with Modality Adaptive Matching
链接: https://arxiv.org/abs/2607.18688
作者: Xin Xu,Shuhao Zhan,Wei Liu,Zheng Wang,Kui Jiang,Chia-Wen Lin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 7 figures
Abstract:Visible-Infrared Person Re-Identification (VI-ReID) operates under a closed-world assumption, where queries and galleries are from heterogeneous modalities. However, in open-world scenarios, both sets are likely to contain homogeneous and heterogeneous modality images. A query may consist of visible-only, infrared-only, or mixed-modality images, while galleries present multi-modal images over long-term collection. Under these conditions, VI-ReID methods, built on a heterogeneous-modality retrieval paradigm, suffer from three trustworthiness challenges: matching conflicts due to high homogeneous-modality similarity, interference from modality uncertainty, and robustness degradation induced by unknown modality combinations. They fail to meet the requirements of trustworthy visual recognition in reliability, consistency, and dynamic adaptability. To address these challenges, we formalize the Visible-Infrared Modality-Incomplete Re-Identification (VIMI-ReID) task. We reorganize existing datasets to construct the SYSU-VIMI and RegDB-VIMI benchmarks. The unpredictable modality combinations and inherent similarity of homogeneous-modality samples in VIMI-ReID cause a significant performance drop in existing VI-ReID methods. We propose the Modality Adaptive Matching Transformer (MAMT). It employs a Divergence Transformer Module (DTM) and a Shared Transformer Module (STM) to extract modality-specific and modality-shared features, respectively. Guided by a divergence loss, the DTM enriches modality-specific features with modality-style information to enhance discriminability within the same modality. A Modality Adaptive Matching Module (MAM) dynamically fuses features according to the query-gallery modality relationship, enabling stable matching under arbitrary and uncertain modality conditions. Extensive experiments on the VIMI benchmarks demonstrate the effectiveness and adaptability of MAMT.
[CV-66] Cross-Dataset Generalization in Breast MRI Tumor Classification via Class-Wise Dataset Mixing
链接: https://arxiv.org/abs/2607.18678
作者: Mohammad Ali Dadrast,Hamid Usefi
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: CBMS 2026
Abstract:Breast MRI is highly sensitive for detecting breast tumors, but exams contain many slices and require substantial reading time. Deep learning models often perform well on internal splits but can fail across institutions because of domain shift and dataset-origin bias. We study this failure mode for binary breast MRI tumor classification. EfficientNet-B3 and WaveViT-Small are trained using Duke Breast Cancer MRI and fastMRI, and evaluated only on the independent multi-center MAMA-MIA cohort. In a deliberately confounded setup, where label is perfectly correlated with dataset origin, external accuracy is near chance (0.5048–0.5265), despite very high recall. We then construct a mixed training set in which each class contains samples from both Duke and fastMRI, while preserving patient-level splitting, augmentation, and leakage controls. On MAMA-MIA, dataset mixing improves accuracy/F1 to 0.8463/0.8625 for WaveViT-Small and 0.8884/0.8994 for EfficientNet-B3. These results show that controlling dataset-origin bias is important for reliable breast MRI classification.
[CV-67] MissingBench-Verified: Probing Vision-Language Models Inability to Detect Missing Object Parts ECCV2026
链接: https://arxiv.org/abs/2607.18673
作者: Wenqi Marshall Guo,Qingyun Qian,Shiyu Zhou,Guoping Luo,Shan Du
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Submitted to the ECCV 2026 Workshop on Explainable Computer Vision (eXCV). 11 pages, 4 figures
Abstract:Vision Language Models (VLMs) are well known for hallucinating non-existent objects in images. Objects with missing parts present a unique challenge for VLMs, stemming from both real-world knowledge bias and the scarcity of such images in training data. We present MissingBench-Verified, a benchmark designed to evaluate a specific and practically relevant scenario: when vision-language models fail to recognize that an essential component of an object has been removed. Across ten leading models, we observe consistent and significant failure rates that persist even when external tool evidence explicitly contradicts the model’s visual perception. We further ask whether granting models access to image processing tools (e.g., cropping, contrast adjustment) enables autonomous inspection to resolve these failures. We find that existing mitigation strategies, including tool-assisted verification, autonomous visual reasoning, longer reasoning durations, and fine-tuning on an easier dataset, provide negligible improvement, indicating that this failure mode cannot be addressed through current prompting or post-hoc correction techniques. Our findings highlight a fundamental limitation of current VLM for inspection and monitoring tasks and underscore the need for architectural or training-level interventions that enable models to override internal expectations when confronted with contradictory evidence.
[CV-68] DeforM: Reasoning -Guided Physics-Aware Video Generation via Spatial-Temporal Masking
链接: https://arxiv.org/abs/2607.18664
作者: Yunyi Li,Yu Qiao,Yaohui Wang,Xinyuan Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video generation models achieve high visual quality but often struggle to generate physics-aware videos. Unlike rigid-body motion, which can be described by explicit trajectories or formulas, complex deformation dynamics remain challenging to synthesize. We observe that a lack of physical reasoning for localizing dynamic areas allows irrelevant regions to dilute the model’s attention, leading to generation failure. In this paper, we propose DeforM, a reasoning-guided image-to-video generation framework that directs the model’s focus toward physics-critical regions. To reason about and localize these critical regions, we introduce a VLM-guided physical reasoning module, DeforM-Reason, to identify target objects and generate spatial-temporal masks. For physical guidance, we develop two alternative strategies: DeforM-Free for training-free mechanism analysis and DeforM-Injection as a powerful training-based generator. Experimental results demonstrate that DeforM improves the realism of generated deformation scenarios, outperforming baseline models in both visual quality and physical consistency.
[CV-69] Fluid-SDF: Ultra-Lightweight and Editable Implicit Shape Representation via Differentiable Primitives
链接: https://arxiv.org/abs/2607.18646
作者: Pradyumna Sripada,Chinmay Nadgir,Ksheer Agrawal,Krishna Kanth Kodanganti
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, 5 figures
Abstract:Implicit Neural Representations (INRs) have become the standard for continuous 2D shape modeling, but they suffer from black-box uneditability, vulnerability to noise, and high parameter counts that severely hinder deployment on edge devices. We introduce Fluid-SDF, a highly compressed, differentiable Constructive Solid Geometry (CSG) framework that models shapes using explicit geometric primitives blended via a smooth minimum function. By replacing traditional multi-layer perceptrons (MLPs) with a parameterized primitive engine, Fluid-SDF reconstructs complex, non-convex topologies using strictly under 100 parameters, achieving comparable or superior intersection-over-union (mIoU) to standard neural baselines. Furthermore, we demonstrate that Fluid-SDF acts as a powerful geometric prior, inherently resisting high-frequency dataset noise where capacity-matched neural networks catastrophically overfit. Finally, unlike standard INRs, Fluid-SDF’s explicit parameter space allows for direct, zero-shot user editing of local and global shape features without retraining. By bypassing expensive on-device gradient updates entirely, Fluid-SDF is uniquely suited for mobile AI, augmented reality, and resource-constrained embedded environments
[CV-70] Deep Learning Estimation of Sex Age Height and Weight from CT-derived Digitally Reconstructed Radiographs
链接: https://arxiv.org/abs/2607.18638
作者: Tomohiro Kikuchi,Kohei Yamamoto,Yukihiro Nomura,Yosuke Yamagishi,Takeharu Yoshikawa,Toshiaki Akashi,Jun Kamohara,Hiroyuki Fujii,Harushi Mori
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Code: this https URL
Abstract:Purpose: To develop and validate a deep learning ensemble for estimating adult sex, age, height, and weight from coronal digitally reconstructed radiographs (DRRs) generated from diagnostic CT. Materials and Methods: This retrospective study included 128,621 CT examinations from 80,004 adults at nine institutions in Japan. Three multitask models-ConvNeXt-Base, ViT-Base/16, and MaxViT-Base-were fine-tuned using coronal DRRs and combined by weighted averaging. Data were split by institution into training (114,147 examinations; seven institutions), tuning (4,305; one institution), and test (10,169; one institution) sets; generalizability was assessed on two non-Japanese datasets. Accuracy and mean absolute error (MAE) were used to evaluate sex classification and age, height, and weight regression, respectively. Body surface area (BSA)-corrected heart and liver volume trends were compared using true versus estimated height and weight. Results: In the test set (median age, 69.9 years; 4,899 of 10,169 [48.2%] male), overall sex-classification accuracy was 0.997 (95% CI, 0.996-0.998), and MAEs were 3.57 years (3.51-3.63), 2.59 cm (2.54-2.64), and 3.40 kg (3.34-3.47) for age, height, and weight, respectively. In examinations covering the chest through pelvis, accuracy was 1.000, and MAEs were 3.15 years, 2.28 cm, and 3.18 kg, respectively. BSA calculated from estimated values reproduced age-related heart and liver volume trends obtained using true values. On non-Japanese datasets, height error increased but was reduced by continued fine-tuning. Conclusion: The ensemble estimated adult sex, age, height, and weight from CT-derived DRRs, with generally lower errors in examinations with broader anatomical coverage.
[CV-71] Seeing Before Generating: Object Perception Enhances Single-View 3D Reconstruction
链接: https://arxiv.org/abs/2607.18630
作者: Y Huynh,Duc Thanh Nguyen,Mohamed Abdelrazek
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The relationship between object perception and reconstruction is well established in human vision, yet remains underexplored in computer vision. In this paper, we demonstrate that learnt object perception can significantly enhance 3D reconstruction. Focusing on the challenging task of single-view 3D object reconstruction, we propose a method that leverages perceptual signals extracted from pretrained perception models capturing semantic and geometric information to drive the reconstruction of an object from its single image. Our approach is model-agnostic and can be integrated into various reconstruction methods in a plug-and-play manner. Experiments with two state-of-the-art single-view 3D reconstruction pipelines in a benchmark dataset show consistent and substantial improvements achieved by our method, validating the effectiveness of incorporating perception into generation. We provide in-depth analysis of various aspects of our method and its application. Our project page is at this https URL.
[CV-72] Norm or Direction? Decoding Vision Mambas for High-Resolution Vision
链接: https://arxiv.org/abs/2607.18625
作者: Jin Yu,Juyoun Park
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision Mamba models replace quadratic self-attention with linear complexity selective state space models (SSMs), emerging as efficient visual backbones. However, MambaOut demonstrates that a Gated CNN block can match or exceed VMamba on image classification, questioning the necessity of SSMs for vision. This raises a fundamental question: do VMamba and MambaOut encode visual information differently at the representation level? To investigate, we apply cross model centered kernel alignment (CKA) analysis and find that VMamba’s final stage blocks form representations distinctly different from both MambaOut and its own preceding blocks. We therefore focus on the final block features, decomposing each spatial token into magnitude and direction. MambaOut concentrates class-discriminative information in high-norm foreground tokens that align with Grad-CAM attribution. VMamba, by contrast, produces high-norm tokens predominantly in background regions, misaligned with Grad-CAM, yet preserves discriminative signals primarily in token directions. These observations reveal that the two models rely on different encoding strategies. We connect this difference to high-resolution classification and semantic segmentation. VMamba distributes logit support broadly across object regions, whereas MambaOut relies on sparse dominant tokens, a strategy that becomes less stable as token counts grow. Under full fine-tuning for segmentation, VMamba consistently outperforms MambaOut. These results suggest that VMamba’s advantage in dense prediction stems not merely from the SSM mechanism or sequence length, but from how semantic evidence is organized across token magnitude, direction. Ultimately, we conclude that token magnitude and directional structure serve as critical axes for improving visual backbones, particularly under dense supervision.
[CV-73] Attention Without Grounding: Causal Evaluation of Visual Explanations in Medical VLMs
链接: https://arxiv.org/abs/2607.18577
作者: Binesh Sadanandan,Vahid Behzadan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: iMIMIC Workshop 2026
Abstract:Attention and saliency heatmaps are widely used to explain medical Vision-Language Model (VLM) outputs on chest X-rays, yet whether they truly highlight the image evidence driving predictions has not been causally tested. We audit faithfulness via overlap with radiologist bounding boxes on PadChest (n=637), attribution mass within radiologist masks on CheXlocalize (n=643), and 16x16 patch-occlusion maps that record which regions, when hidden, change the answer. We study three MedGemma-4B variants, cross-family probes on LLaVA-RAD and Qwen3-VL-8B-Instruct, and the specialist CheXagent-2-3b, with two CXR-trained classifiers (DenseNet121, ResNet50) as positive controls. A heatmap is faithful only if the model uses the image and attention concentrates on regions whose occlusion alters the prediction. No evaluated VLM meets both criteria. MedGemma and Qwen3-VL use the image, but attention anti-correlates with patch-occlusion importance (rho 0 with 95% bootstrap CIs below zero). LLaVA-RAD’s attention correlates positively, but the model is almost text-only (99.1% text-only agreement, near-zero causal mass), so correlation ties two near-zero signals. Attention also misses annotated anatomy: overlap with true regions never beats shifted or random controls, and no method places more than 22% of its mass inside radiologist masks. The two CXR classifiers pass all metrics, indicating the failure is specific to VLM heatmaps, not the evaluation. These heatmaps are visually reassuring but not faithful; clinical explanations require controlled localization metrics and causal perturbation, not visual inspection alone.
[CV-74] xt-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data
链接: https://arxiv.org/abs/2607.18576
作者: Samy Mounir,Mikolaj Cieslak,Najmeddine Dhieb,Hakim Ghazzai,Jonathan Klein,Katja Froehlich,Soeren Pirk,Wojciech Palubicki,Gianluca Setti,Ahmed M. Eltawil,Dominik L. Michels
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-based automation is an excellent candidate for reducing manual labor in greenhouse crop production and phenotyping. However, progress is constrained by the lack of annotated training data. Recent advances in vision-based foundational models have shown promising results in zero-shot generalization to novel domains, but their performance drops in complex agricultural environments. In this work, we present a sim-to-real framework for tomato plant segmentation that combines synthetic data generation with fine-tuning of a foundation model. We model a commercial cherry tomato greenhouse and use it to generate a large-scale synthetic dataset under diverse viewpoints, lighting conditions, and plant morphology. Subsequently, we fine-tune the Segment Anything Model 3 (SAM 3) on the synthetic dataset, specializing its text-conditioned segmentation behavior for greenhouse crop organs while retaining the general visual prior that makes zero-shot transfer possible. By evaluating our framework on multiple real-world greenhouse datasets, we demonstrate that combining synthetic data with SAM 3 fine-tuning significantly improves segmentation performance and model confidence. To support community benchmarking, we publicly release the procedural model, the generated synthetic dataset, and our fine-tuned SAM 3 weights.
[CV-75] Robust Multi-View Classification under Noisy Supervision via Global Anchor Consensus
链接: https://arxiv.org/abs/2607.18561
作者: Yuliang Yang,Hongzhe Zhang,Huiru Wang
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In recent years, multi-view learning has attracted increasing attention, as it integrates the complementary information of heterogeneous views. Most existing multi-view classification methods rely on accurate annotations to guarantee performance. However, noisy labels are ubiquitous in practice due to imperfect annotation, and the refinement signals that existing methods derive from models trained on such noisy supervision can gradually lose their reliability. To deal with this problem, we propose a novel Global Anchor-based Label Auditing method (GALA) for multi-view classification to resist the negative impact of noisy labels. Specifically, we construct a global anchor for each class in every view, which aggregates the samples of the whole class and thus offers a stable reference insensitive to individual predictions. Then, each view measures how close an instance is to the anchor of its observed label relative to the nearest competing anchor, and the per-view evaluations are fused with the classifier confidence into a cross-view audit score. Based on the audit scores, suspicious samples are assigned small weights, and an adaptive correction strategy rewrites a label only when the anchor-based candidate agrees with the classifier prediction. Finally, the corrected labels in turn refine the anchors and supervise noise-robust representation learning. Extensive experiments on six datasets demonstrate that GALA outperforms eight state-of-the-art methods, especially under high noise rates.
[CV-76] Physics Closure Matters for Machine Olfaction: A Maxwell–Stefan Graph Solver for Identifiable Dynamic Gas Unmixing
链接: https://arxiv.org/abs/2607.18544
作者: Yue Shi,Liangxiu Han,Xin Zhang,Tam Sobeih
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Machine olfaction for gas unmixing is an underconstrained inverse problem in which gas compositions must be inferred from low-dimensional, delayed, and entangled sensor responses produced by interacting chemical transport, surface adsorption, and sensor transduction. One of the key obstacles is physics closure misspecification, where a neural network is designed to fit sensor traces rather than infer a physically closed olfactory process. In this work, we formulate gas unmixing as a multi-physics-constrained inverse problem governed by Maxwell–Stefan multicomponent transport PDEs, competitive adsorption ODEs, and nonlinear sensor transduction ODEs. Directly solving such a high-dimensional coupled system is computationally expensive and often numerically unstable. To this end, we propose UnMixNet, a physics-closed graph neural solver that embeds this multi-physics forward process into end-to-end gas unmixing. UnMixNet discretizes Maxwell–Stefan cross-diffusion on spatial graphs and formulates the multicomponent flux on each edge. This design enables local, differentiable, and flux-conservative inference for multicomponent cross-diffusion. Evaluations on SmellNet show improved single-odor recognition, seen-mixture unmixing, and unseen-mixture generalization. In addition, an external validation on UCI Dynamic Gas Mixtures shows that the inferred concentration process agrees with ground truth concentration set points under dynamic transitions. Process-consistency diagnostics further show that the proposed model learns transferable dynamic physical fingerprints that better satisfies transport, conservation, adsorption, and readout closure.
[CV-77] Recti-Q: Feature-Space Rectification for Out-of-Distribution-Robust Quantized Perception in Edge Robotics IROS2026
链接: https://arxiv.org/abs/2607.18540
作者: Hamidreza Yaghoubi Araghi,Parastoo Pilevar,Ming C. Lin
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Robotics (cs.RO)
备注: Accepted at the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
Abstract:Robotic perception pipelines increasingly rely on large vision backbones deployed on SWaP-constrained edge platforms, making post-training quantization (PTQ) attractive for real-time inference. However, while PTQ often preserves clean in-distribution accuracy, we show that it can substantially degrade reliability under deployment-relevant distribution shifts (e.g., sensor noise, severe weather, and novel operating environments), creating a Quantization-Induced Robustness Gap. Across foundational vision benchmarks (ImageNet-C and PACS), 4-bit PTQ models exhibit pronounced robustness degradation despite negligible ID accuracy loss. To address this, we propose Recti-Q, a lightweight feature-space rectification framework that freezes the quantized backbone and trains a small classifier-head LoRA adapter using only source data. Recti-Q is architecture-agnostic across CNNs and Transformers, supports efficient teacher-free training, and recovers a significant portion of the lost robustness, in some cases matching or exceeding FP32 performance. At less than 1% parameter overhead (as small as 6 KB), Recti-Q preserves over 99% of PTQ memory savings, adds negligible compute, and enables low-bandwidth Over-The-Air (OTA) resilience patching for deployed robotic fleets operating in unpredictable physical environments.
[CV-78] AniGS: Bridging Rendering and Diffusion Prior for 3D Scene Animation
链接: https://arxiv.org/abs/2607.18539
作者: Yen-Chi Cheng,Chen Gao,Chuhan Chen,Tuotuo Li,Rajvi Shah,Ayush Saraf,Changil Kim,Liangyan Gui,Alexander Schwing,Johannes Kopf,Hung-Yu Tseng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Preprint. Project page: this https URL
Abstract:Novel view rendering of large and complex reconstructed scenes is becoming increasingly photorealistic. However, most reconstructions remain static and lack the ambient motion that makes environments immersive. We present AniGS, a method for scene-level animation of 3D Gaussian Splatting (3DGS) reconstructions that adds subtle, distributed dynamics, e.g., vegetation motion, while preserving rigid structures. Unlike existing 3D animation techniques which are limited to object-centric subjects or small regions, AniGS is designed for large, cluttered, navigable scenes. AniGS represents the scene with a canonical 3DGS and models motion using a time-conditioned deformation field. To animate the entire scene, we leverage a pretrained video diffusion model and introduce an iterative dataset–model update strategy that progressively expands viewpoint coverage and repeatedly updates camera-fixed training videos using a render-and-refine scheme. To prevent artifacts from unintended motion in static areas, we further introduce a composed video-to-video refinement scheme that restricts motion to desired regions. Experiments on five real-world, large-scale outdoor scenes demonstrate that AniGS produces natural ambient dynamics and high-quality novel view videos, enabling more immersive viewing experiences of reconstructed environments.
[CV-79] Signed Rectified Flow: Negativity-Controlled Generation
链接: https://arxiv.org/abs/2607.18516
作者: Runlong Liao,Baiyu Su,Lizhang Chen,Qiang Liu
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We introduce Signed Rectified Flow (Signed RF), a generalization of Rectified Flow that targets the signed measure \pi^sign = (1+\alpha)\pi^+ - \alpha\pi^- , where \alpha0 , \pi^+ is the distribution to promote, and \pi^- is the distribution to suppress. Although direct sampling from a signed measure is not well-defined, Signed RF induces a valid generative process that concentrates probability in regions where the signed measure is positive while provably excluding regions dominated by its negative component. It therefore provides a principled framework for incorporating negative information and exclusion constraints into generative modeling. We analyze the signed continuity equation underlying Signed RF and use a charged-particle interpretation to explain how negative mass forms exclusion barriers. This theory further motivates practical adaptive guidance algorithms. Across several applications, Signed RF improves the fidelity-diversity trade-off on ImageNet, reduces nearest-neighbor similarity in anti-memorization experiments, and reduces nudity induced by adversarial prompts in Stable Diffusion 3.5 while preserving CLIP and aesthetic scores.
[CV-80] DuSPiT: Dual-Branch Sub-Patch Pixel Diffusion Transformer
链接: https://arxiv.org/abs/2607.18510
作者: Yunpeng Bai,Yossi Gandelsman,Michaël Gharbi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Diffusion Transformers achieve strong image generation performance, but most operate in compressed latent spaces. Pixel-space diffusion avoids this information loss, yet existing approaches map each raw image patch to a single token, forcing one representation to handle both global communication and fine-grained details. We address this issue by proposing a new architecture, \textbfDuSPiT, a \textbfDual-branch \textbfSub\textbfPatch \textbfPixel \textbfTransformer. This model separates global structural reasoning from local appearance modeling. DuSPiT uses a compact base branch for efficient global reasoning and a parallel, high-capacity pixel branch, organized into subpatch groups, to preserve detailed appearance, with the two branches interacting through cross-attention. Our results show that DuSPiT generates images with richer details and stronger fine-grained structures, while also achieving a better quality–efficiency trade-off than prior pixel-space diffusion transformers.
[CV-81] Style over Substance: A Shortcut Audit of Emotion-Description Preference Evaluation
链接: https://arxiv.org/abs/2607.18508
作者: Jiabing Yang,Yixiang Chen,Yuan Xu,Qisen Ma,Tao Yu,Peiyan Li,Yingda Li,Yan Huang,Liang Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Preference over model-generated emotion descriptions is emerging as a standard evaluation metric for multimodal emotion understanding, exemplified by the MER2026 MER-Prefer track on EmoPrefer. Such benchmarks assume that predicting the preferred description requires grounded cross-modal understanding of the video. We conduct a systematic shortcut audit of EmoPrefer using content-blind probes. A simple logistic regression using only description length and generator identity, without processing the text, video, or audio, performs comparably to LoRA-finetuned 7B text and audio-visual judges (65.8 versus 66.8 WAF on EmoPrefer-V2). Generator identity is recoverable from description text with 99.5 percent accuracy, every candidate pair contrasts two distinct generators, and the human preference labels agree with a fold-exclusive per-generator win-rate prior on 66 percent of the evaluated pairs. When the human label conflicts with this prior, trained judges still follow the style prior on 63 to 80 percent of the pairs. On a length-matched subset that neutralizes verbosity bias, the tested media configurations yield no statistically significant improvement, while an ODIN-inspired diagnostic that decouples the style shortcut leaves its content head near chance. These results do not imply that human preferences are inherently stylistic or that the descriptions contain no emotional information. Instead, they show that the current scores can be reached without verifying either description against the video. We recommend source-balanced pairing, strict length control, counter-stereotypical sliced reporting, and multi-annotator consensus for future cross-generator evaluations. Code is available at this https URL.
[CV-82] Now We Know? A Systematic Comparison of TerraMind and THOR
链接: https://arxiv.org/abs/2607.18504
作者: Frederick Schindlegger,Kenzo Bounegta,Eva Gmelich Meijling,Johannes Jakubik,Arnt-Børre Salberg,Theodor Forgaard,Nicolas Longepe,Valerio Marsocci
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Benchmarks for Geospatial Foundation Models (GFMs) increasingly rank models by aggregate score, but such rankings obscure why models differ: how much of the gap is architecture, how much is decoder capacity, and how much is a use-case-specific artefact? This study addresses that gap through a controlled comparison of two GFMs developed under European Space Agency’s \Phi -lab with contrasting design philosophies: THOR, which introduces a compute-adaptive architecture supporting variable patch sizes and unifies Sentinel-1, -2, and -3 data at their native resolutions; and TerraMind, a multimodal generative GFM pretrained with a dual-scale token/pixel objective that enables any-to-any cross-modal generation (Thinking-in-Modalities) to infer missing sensors at inference time. Rather than reporting a single leaderboard, we investigate the axes along which the two architectures actually differ - patch size, decoder complexity, finetuning regime, input modality, and model scale - across ten use cases spanning segmentation and regression in diverse domains, including climate disaster response, methane leak detection, snow monitoring, or sea ice mapping. We find that architectural design choices - patch size and decoder type in particular - explain more performance variance than model identity itself, that the two models embody complementary investment strategies (pretraining-time scale for TerraMind versus inference-time tokenisation for THOR), and that correctly interpreting results requires dataset-level characterisation. The resulting picture is not a single winner but a set of hypotheses and a diagnostic ablation methodology that we expect to generalise to future GFMs beyond THOR and TerraMind.
[CV-83] ECoNGS: Efficient Compressive Neural Gaussian Splats for Volume Visualization IEEE-VIS2026
链接: https://arxiv.org/abs/2607.18466
作者: Kaiyuan Tang,Chaoli Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注: To be published in Proceedings of IEEE VIS 2026, IEEE Transactions on Visualization and Computer Graphics
Abstract:Recent advances in differentiable Gaussian splatting have highlighted the potential of primitive-based approaches as alternative scene representations for interactive, high-quality, volume visualization (VolVis) of large datasets. However, the explicit nature of current primitive-based methods, combined with isolated optimization for each VolVis scene, results in redundant, non-compact representations. We present ECoNGS, an efficient compressive neural Gaussian splatting framework for VolVis scene representation. ECoNGS employs lightweight neural networks to dynamically predict implicit, editable Gaussian splats from explicit anchor points, effectively combining model compactness and parameter efficiency of implicit representations with high-performance rendering of explicit primitives. We explore a joint learning strategy that clusters geometrically similar scenes and shares parameters across them, significantly reducing overall training time and model size while maintaining reconstruction fidelity. To achieve a more compact scene representation, we further compress the explicit anchor attributes using a neural entropy model that estimates their probability distributions, enabling compact storage via entropy coding. We systematically investigate Gaussian initialization strategies and propose a simple yet effective scheme tailored for VolVis scenes, improving reconstruction accuracy and accelerating convergence. We evaluate ECoNGS qualitatively and quantitatively across various univariate and multivariate VolVis scenes, highlighting its superior performance over prior methods in training time, reconstruction quality, and model size. In particular, compared with the prior method iVR-GS, ECoNGS improves reconstruction quality by up to 2.2 dB in PSNR while reducing the model size by up to 6.1x and the training time by up to 5.9x. The code is available at this https URL.
[CV-84] Surprise Forcing: What to Remember When to Skip in Long Video Generation
链接: https://arxiv.org/abs/2607.18436
作者: Shuwei Shi,Zhen Li,Muyao Niu,Chuanhao Li,Bo Zheng,Kaipeng Zhang,Yinqiang Zheng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Technical report
Abstract:Streaming autoregressive diffusion makes minute-scale video synthesis practical, but its bounded context and fixed denoising schedule allocate resources uniformly across a highly non-stationary sequence. A rolling key-value cache forgets distant visual evidence even when that evidence remains important, while every generated chunk receives the same number of denoising passes irrespective of its actual difficulty. We introduce Surprise Forcing, a training-free framework that treats both limitations as online resource-allocation problems. A Surprise-Gated Memory Bank summarizes evicted frames with value-token descriptors, evaluates them using complementary global-deviation and nearest-neighbor novelty signals, and regulates admission through a feedback-controlled budget in normalized score space. Priority-based replacement and relevance-aware routing then keep the external memory compact and useful. In parallel, Surprise-Aware Denoising estimates chunk difficulty from the maximum adjacent-frame cosine distance after the first denoising pass and uses a local percentile scheduler to skip intermediate steps for comparatively easy chunks. Experiments on VBench, VBench-Long, and VBench-2.0 show that the proposed allocation strategy improves long-horizon consistency and visual quality while retaining real-time streaming throughput.
[CV-85] From Pixel to Prognosis: Convolutional and GLCM Feature Fusion for Automated Four-Class Cataract Severity Classification
链接: https://arxiv.org/abs/2607.18349
作者: K. Mithra,Prem Kumar Santhanam
类目: Computer Vision and Pattern Recognition (cs.CV); Emerging Technologies (cs.ET)
备注: 10 pages
Abstract:Objective: To develop a low-cost automated cataract severity classification system operating on standard consumer-grade colour photographs of the eye, without specialised ophthalmic hardware. Methods: A hybrid framework was designed that fuses deep features from a Convolutional Neural Network (CNN) with five handcrafted Grey-Level Co-occurrence Matrix (GLCM) and intensity descriptors - mean intensity, uniformity, standard deviation, contrast, and energy - extracted from a Hough-circle-localised pupil Region of Interest (ROI). A multi-class Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel classifies each image into one of four severity grades: normal, immature, mature, or hypermature cataract. Results: The proposed fused system achieved 95.0% accuracy, 93.8% sensitivity, and 96.1% specificity on an ophthalmologist-labelled test set drawn from 300 images (75 per class) collected at an ophthalmology clinic, outperforming texture-only (88.5%) and CNN-only (91.3%) baselines and surpassing recently published deep learning approaches. Conclusion: The CNN-GLCM-SVM fusion framework provides competitive four-class cataract grading without GPU acceleration or specialised cameras, making it suitable for primary-care and telemedicine deployment in resource-limited settings.
[CV-86] Hazard or Anomaly? Evaluating VLMs for Understanding Dangers and Discrepancies
链接: https://arxiv.org/abs/2607.18325
作者: Murali Indukuri,Mohammad Eskandari,Sree Nitya Kollu,Stephanie Lukin,Cynthia Matuszek
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: 8 pages, accepted to RO-MAN 2026
Abstract:Modern safety-critical systems increasingly rely on human-robot interaction to reduce disaster risk and support decision-making during emergencies. Vision-Language Models (VLMs) are promising for these settings because they can interpret complex scenes and communicate safety-relevant information, but they still require careful evaluation to ensure reliable safety reasoning. In particular, current evaluations often frame danger recognition as a binary decision (Safe/Unsafe), making it unclear whether a model is identifying true physical hazards or merely reacting to unusual scene elements. We address this limitation by introducing an explicit distinction between hazard and anomaly, and by separately recognizing hazardous and anomalous states. We evaluate several state-of-the-art VLMs across two datasets and multiple prompting strategies to test whether this distinction changes model behavior. Our results show that VLMs frequently misinterpret anomalousness as hazardousness, revealing an over-reliance on contextual irregularity as a proxy for danger. We further show that explicitly separating anomaly from hazard provides a more informative evaluation of VLM safety reasoning and exposes failure modes that binary safety judgments can obscure. Our public dataset is available on Roboflow this https URL.
[CV-87] FedCC: A Low-Resource Federated Adaptation of Foundation Models for Robust Corpus Callosum localization in Fetal Ultrasound Images
链接: https://arxiv.org/abs/2607.18283
作者: Alessandro Di Matteo,Sara Moccia,Giuseppe Rizzo,Gianpaolo Grisolia,Ricciarda Raffaelli,Lorenzo Vasciaveo,Francesco D’Antonio,Maria Chiara Fiorentino
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate localization of the corpus callosum (CC) in fetal ultrasound (US) images is crucial for the early identification of neurodevelopmental abnormalities. However, this task remains highly challenging due to the intrinsic limitations of US imaging, including low contrast, speckle noise, and the considerable anatomical variability of the CC. We propose FedCC, a federated learning (FL)-based framework for CC localization in fetal US images, specifically designed for realistic multi-center and resource-constrained clinical settings without requiring data sharing. The framework integrates a frozen DINOv2 backbone with a lightweight YOLO-based detection head. To enable parameter-efficient adaptation, Low-Rank Adaptation (LoRA) modules are incorporated, allowing only a small subset of parameters to be optimized and exchanged among clients. This strategy substantially reduces both computational and communication overhead, making the framework suitable for low-resource environments. The proposed approach was evaluated on a multi-center dataset comprising 10,970 ultrasound frames acquired from 58 pregnant women during routine neurosonographic examinations across three clinical sites using heterogeneous imaging devices. The proposed framework achieved strong performance in the federated setting. In particular, the combination of DINOv2 and LoRA under the FedAvg strategy achieved an average mAP@50 of 0.857 and an F1-score of 0.803, outperforming both full fine-tuning and encoder-freezing baselines. Notably, the proposed approach reduced the number of trainable parameters to 2.9M compared with 24.4M in full fine-tuning, corresponding to an approximately 8.5 \times reduction in communication cost. These findings represent a promising step toward scalable, privacy-preserving, and clinically deployable AI systems for fetal neurosonography.
[CV-88] Wavefront Parallelization for Efficient Learned Image Compression
链接: https://arxiv.org/abs/2607.19082
作者: Shimon Murai,Fangzheng Lin,Kasidis Arunruangsirilert,Jiro Katto
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by MMSP 2026
Abstract:Autoregressive context models are foundational for learned image compression,but they suffer from slow serial inference. Existing acceleration methods such as checkerboard context require architectural changes and retraining, thus are inapplicable to pre-trained models. We propose a completely training-free inference-time acceleration algorithm inspired by wavefront parallelism in video coding standards. Our method reorganizes inference into an optimal ``staggered’’ wavefront order, minimizing sequential steps while maintaining exact autoregressive dependencies. Experimental results show our approach accelerates pre-trained autoregressive models (e.g., Cheng et al.) by more than 13\times while preserving the original rate-distortion performance. We also demonstrate that faster decoding is possible by trading off precise context dependencies. Source code will be available at this https URL.
人工智能
[AI-0] CodeRescue: Budget-Calibrated Recovery Routing for Coding Agents
链接: https://arxiv.org/abs/2607.19338
作者: Qijia He,Jiayi Cheng,Chenqian Le,Rui Wang,Xunmei Liu,Yixian Chen,Jie Mei,Zhihao Wang,Xupeng Chen,Yuhuan Chen,Tao Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Coding agents increasingly operate in executable environments where a failed attempt produces actionable feedback rather than merely an incorrect answer. Existing cost-aware systems typically treat such failures as cascade decisions: try a cheap model first, then escalate hard cases to a stronger and more expensive model. In coding, however, execution feedback can also make further cheap-model recovery worthwhile, raising a budgeted deployment question: when should an agent spend more cheap compute, and when should it escalate? We formulate this post-failure decision as recovery routing over heterogeneous actions and train a supervised router from execution rollouts. To make the same router usable under changing budgets, we add a Conformal Risk Control (CRC) layer that selects a deployment-time cost penalty without retraining and provides marginal expected-cost control under exchangeability. Across held-out failures from five coding benchmarks, cheap recovery and escalation exhibit complementary success patterns. The calibrated frontier improves over fixed actions, prompt-only routers, and a binary cascade baseline; in the main GPT-5.4-nano/GPT-5.4 setting, one CRC-calibrated frontier point exceeds always-escalate solve rate while using 35% of its mean recovery cost. Code is available at this https URL.
[AI-1] Provable diffusion-based posterior sampling for linear inverse problems via DDIM
链接: https://arxiv.org/abs/2607.19333
作者: Yuchen Jiao,Na Li,Changxiao Cai,Yuxin Chen,Gen Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:Diffusion-based methods have achieved remarkable empirical success in solving inverse problems. However, many existing posterior samplers either lack rigorous theoretical guarantees or incur substantial computational overhead. We propose a simple and efficient algorithm, called \pddim, for solving linear inverse problems with diffusion priors via a DDIM-type sampler. Our method requires only lightweight, coordinate-wise modifications to the standard DDIM update, while explicitly incorporating the measurement model. The key idea is to perform posterior sampling separately along each singular direction of the measurement operator: for each direction, the sampler follows the learned diffusion prior when the observation signal-to-noise ratio (SNR) is below the corresponding diffusion SNR, and switches to a calibrated measurement-based predictor otherwise. We prove that the proposed sampler converges to the Bayesian posterior conditioned on the measurements. Empirical results show that the proposed sampler performs favorably against existing diffusion-based posterior samplers across a range of image restoration tasks, achieving the best performance on the majority of evaluation metrics considered. Overall, our results convert posterior sampling for noisy linear inverse problems to simple coordinate-wise DDIM updates, yielding an efficient, easy-to-implement algorithm with provable posterior consistency.
[AI-2] ISO: An RLVR-Native Optimization Stack
链接: https://arxiv.org/abs/2607.19331
作者: Hanqing Zhu,Wenyan Cong,Zhizhou Sha,Sagnik Mukherjee,Xinyuan Song,David González-Martínez,Xiaoxia Wu,Yuandong Tian,Shiwei Liu,David Z. Pan,Zhangyang “Atlas” Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Preprint
Abstract:Reinforcement learning with verifiable rewards (RLVR) is rapidly advancing the reasoning capabilities of language models, yet the optimization layer that converts reward feedback into weight-space updates remains poorly understood. Building on our prior analysis (Zhu et al., 2025), we study this missing layer through the singular structure of model weights and identify spectral inheritance: RLVR can reuse the base model’s weight spectra while acquiring new behavior through changes in the associated input and output singular frames. We operationalize spectral inheritance as Isospectral Optimization (ISO), an RLVR-native, fixed-spectrum optimization framework with complementary offline and online instantiations. Offline, ISO-Merger combines the frame changes of shared-base specialists into a single fixed-spectrum model, requiring no post-merge data, rollouts, gradient updates, or on-policy distillation (OPD). It recovers complementary specialist capabilities and achieves the strongest aggregate performance among the compared data-free merging methods. Online, ISO-Optimizer applies a chosen base optimizer, including AdamW and Muon, to the frame variables while keeping the base spectra fixed. Across reasoning and coding tasks ranging from 1.5B to 8B parameters, ISO-Optimizer improves accuracy in the reported runs and reaches matched scores with substantially fewer training steps. On Qwen3-8B-Base, AdamW reaches an aggregate accuracy of 0.495 after 270 training steps. ISO-AdamW reaches the same accuracy after only 100 training steps and improves further to 0.509 after 210 training steps. Together, ISO offers a concrete answer to RLVR’s missing optimization layer: rather than inheriting pre-training optimization wholesale, design post-training around the structure of reward-driven adaptation: inherit the spectrum, optimize the frames. Comments: Preprint Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.19331 [cs.LG] (or arXiv:2607.19331v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.19331 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-3] Associative Emotional Learning in Convolutional Neural Networks
链接: https://arxiv.org/abs/2607.19327
作者: Seowung Leem,Andreas Keil,Mingzhou Ding,Ruogu Fang
类目: Artificial Intelligence (cs.AI)
备注: The article has been accepted for publication in Neural Computation
Abstract:Associative emotional learning enables organisms to adaptively link pleasant or unpleasant outcomes to the presence of predictive stimuli. Whereas computational models such as the Rescorla-Wagner model have shed light on this important function, the limitations of these models are also known, especially when they are applied to neural data. The advent of deep neural networks has opened another avenue for modeling associative emotional learning. In this work we proposed a deep neural network model of visual valence processing, consisting of a visual module that encodes complex natural scenes and a module that recognizes their emotional significance in terms of valence, a key dimension of emotion, and tested a novel Pavlovian learning paradigm on the model. The results showed that with learning, the model reproduced several observations from human associative learning studies, including association formation and generalization, and that the neural representations of the conditioned and the unconditioned stimuli became increasingly aligned both at the single unit and at the neural population level. Comparison between the model and human experimental data provided further validation of our approach. This study thus suggests that deep neural network models, when combined with appropriate learning algorithms, can be used to model behavioral and neural signatures of associative emotion/valence learning.
[AI-4] ResearchArena: Evaluating Sabotage and Monitoring in Automated AI RD
链接: https://arxiv.org/abs/2607.19321
作者: Lena Libon,Ben Rank,Jehyeok Yeon,David Schmotz,Jeremy Qin,Daniel Donnelly,Derck Prinzhorn,Maksym Andriushchenko
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
备注: 51 pages, 11 figures
Abstract:As AI agents begin to automate AI RD, we need ways to assess whether their outputs are safe to deploy, even when the agents themselves may be untrusted. AI control offers one such approach: rather than trusting the agent, it treats it as a potential adversary and uses a monitor to detect covert sabotage before deployment. We evaluate AI control for automated AI RD with ResearchArena, a framework spanning four long-horizon tasks: safety post-training, capabilities post-training, CUDA-kernel optimization, and inference-server optimization. Because the deliverable in AI RD is an artifact that will be deployed and run, we pair each main task with two kinds of hidden side task: an embedded side task that sabotages the submitted model, adapter, kernel, or server itself, and an independent side task that takes a covert out-of-scope action in the sandbox while leaving the artifact honest. We evaluate frontier agents at both sabotage and monitoring, across four monitor types that vary along two axes: whether the monitor may execute and probe the produced artifact, and whether it sees the agent’s chain-of-thought. We find that sabotage hidden in the training data is the hardest to catch, flagged fewer than half the time. Letting the monitor run experiments on the artifact, rather than only read the trajectory, helps, but it is not enough: monitors still miss embedded sabotage by inspecting only the surface, by noticing the anomaly but explaining it away, or by probing the artifact with the wrong test. We release ResearchArena as a modular framework for evaluating sabotage and control in automated AI RD.
[AI-5] Off-Context GRPO: Learning to Reason on Hard Problems using Privileged Information
链接: https://arxiv.org/abs/2607.19313
作者: Priyank Agrawal,Ankur Samanta,Shervin Ghasemlou,Jalaj Bhandari,Kavosh Asadi,Daniel Jiang,Aditya Modi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 24 Pages
Abstract:Reinforcement learning with verifiable rewards (RLVR) improves reasoning in large language models. Yet, typical RLVR approaches fail on difficult problems: when a model cannot generate any correct solutions, it receives \textitzero learning signal. Providing privileged guidance during training, such as solution prefixes, can help overcome this learning cliff by steering the model towards correct solutions with non-zero reward. We call these rollouts \textitoff-context: they are generated from a training prompt that contains privileged guidance, while the target objective is defined by the original prompt without that guidance. We introduce Off-Context GRPO (OC-GRPO), a minimally modified variant of GRPO that uses guided rollouts but applies an importance-corrected objective to steer the update back toward the original unguided objective, avoiding the mismatch that destabilizes uncorrected guided training. Empirically, our algorithm achieves a 3.9% absolute improvement (13.8% relative gain) over vanilla GRPO on average across standard mathematical reasoning benchmarks with negligible additional cost.
[AI-6] Riemannian Deep Learning:Modules Networks and Geometries
链接: https://arxiv.org/abs/2607.19305
作者: Chen Ziheng
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Differential Geometry (math.DG)
备注: PhD thesis, University of Trento. The presentation has revised some typos in the previous published papers
Abstract:Deep neural networks on manifold-valued representations have attracted growing interest, but many basic components remain tied to specific manifolds, rely on Euclidean approximations, or require costly and numerically fragile geometric operations. This thesis develops a unified framework for Riemannian deep learning from three complementary perspectives: reusable neural modules, manifold-specific network architectures, and the design of underlying geometries. It generalizes batch normalization from Euclidean spaces and individual manifolds to broad classes of Lie groups and gyrogroups, and extends multinomial logistic regression from Euclidean space to SPD manifolds and then to general Riemannian manifolds. It further develops neural networks for several important geometric representations, including an unconstrained model of hyperbolic space, Busemann-based hyperbolic learning, and full-rank correlation matrices. Finally, it introduces adaptive and computationally efficient Riemannian metrics on SPD manifolds, including learnable Log-Euclidean geometries and fast, stable Cholesky-based geometries. The proposed methods are supported by theoretical analysis and validated through numerical experiments and applications in vision, signal processing, graph learning, and genomics.
[AI-7] LLM Detection as an Intervention: Downstream Impact under Strategic User Behavior
链接: https://arxiv.org/abs/2607.19300
作者: Meena Jagadeesan,Tatsunori Hashimoto,Jon Kleinberg
类目: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)
备注:
Abstract:As LLM adoption becomes more widespread, there is a growing interest in detecting LLM-generated content, for example through LLM detection tools and through heuristics based on language patterns. Detectors operate as an intervention that steers not only the detected attribute itself, but also downstream metrics such as LLM usage and output quality. In this work, we demonstrate how imperfect LLM detectors lead to counterintuitive impacts on these downstream metrics, by distorting how users are incentivized to use LLMs in their workflow. We develop a stylized model which captures how users strategically choose how much to use the LLM and how to post-process content to reduce the detected attribute. Using this model, we show that LLM detection can counterintuitively lead humans to increase their LLM usage. Moreover, even when reducing the detected attribute improves output quality, we find that introducing an LLM detector can lead users to produce lower quality outputs. In contrast, we show that detectors result in a clean “rise-then-fall” pattern for the detected attribute, which we empirically reproduce for word frequencies on arXiv abstracts. Altogether, our work illustrates how LLM detection can distort LLM usage and output quality, uncovering failure modes when LLM detectors operate as an intervention on these downstream metrics.
[AI-8] Graph-Based Agent ic AI with LangGraph: Workflow Pathways for Long-Running Stateful Business Processes
链接: https://arxiv.org/abs/2607.19297
作者: Daniel Pearson,Sidney Shapiro,Emiliano Sebastian Gonzalez Venegas,Sanad Al-Khatib,Aurora Pinzón Arzola
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 25 pages, 2 figures. With ancillary code
Abstract:This paper is a practitioner guide to graph-based workflow pathways for long-running, stateful, multi-step generative AI systems in business processes. Rather than treating LangGraph, a low-level orchestration framework for stateful agents, as a model-quality benchmark target, we present three executable recipes – SQL analytics with repair loops, agentic retrieval-augmented generation with evidence gating, and human-in-the-loop policy review with interrupt and checkpoint recovery – to show how typed state, conditional routing, deterministic tools, retries, interrupts, checkpoints, and traces fit together. LangGraph is positioned by workflow-complexity fit, not as a universal default: simpler ReAct-style or plain SDK loops may be better for basic tool use, schema-first tools for structured extraction and validation, and DSPy when prompt or program optimization is the main goal. Each recipe explains when LangGraph is worth the extra structure and which implementation patterns make routes, pauses, and audit trails explicit product behavior rather than hidden prompt logic.
[AI-9] GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models
链接: https://arxiv.org/abs/2607.19270
作者: Alessandro Scalese,Santhanakrishnan Narayanan,Constantinos Antoniou
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning. While Graph Neural Networks have emerged as fast, data-driven surrogates, their practical deployment is severely constrained by a spatial generalization gap. Standard models rely on transductive feature initializations that tie travel demand to fixed network topologies, preventing seamless transfer to new urban environments. To overcome this structural limitation, this research proposes a network-agnostic initialization layer, termed Geometrically Unconstrained Inductive Demand EmbeDding (GUIDED). By injecting travel demand as a scalar attribute on auxiliary virtual links rather than as specific node features, this modular framework standardizes the input space regardless of network scale. Extensive experimental evaluation across multiple urban topologies demonstrates that a Heterogeneous Graph Attention Network (HetGAT) model integrated with the proposed GUIDED layer maintains state-of-the-art predictive accuracy on single-network tasks, while demonstrating superior robustness to out-of-distribution demand patterns and maintaining a distinct performance advantage over the baseline even under severe data scarcity. Notably, the proposed feature initialization enables highly parameter-efficient domain adaptation for inter-network transfer learning without artificial input homogenization, establishing a robust foundation for truly inductive models. At the same time, the optimized scatter operations of the initialization layer yield an approximate 50% reduction in training time per epoch compared to the baseline approach. Furthermore, while demonstrated on vehicular traffic, this fundamental abstraction of spatial topology provides a versatile blueprint for generalized origin-destination spatial problems, such as freight logistics and multimodal network optimization.
[AI-10] oward Auditable Fraud Detection: Combining Graph Features Model Explanations and Agent ic Case Investigation
链接: https://arxiv.org/abs/2607.19266
作者: Rahil Sharma
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 14 pages, 3 figures. Code available at this https URL
Abstract:Fraud detection systems must scale with rising transaction volume while remaining explainable and reviewable. We study a layered pipeline on the PaySim dataset that combines a gradient-boosted classifier, graph-derived structural features, an autoencoder-based anomaly signal, TreeSHAP explanations, and a bounded LLM investigation agent applied to cases the classifier scores uncertainly. Before any model comparison, we identify and remove a simulator-specific balance shortcut that would otherwise inflate baseline performance. After this correction, neither the graph features nor the anomaly signal improves Average Precision on the full test set. Both, however, rank fraud better within the subset of cases receiving intermediate baseline scores. In a controlled experiment with injected multi-account fraud rings, engineered structural features recover all injected test transactions, while the tabular baseline misses roughly a quarter of them. The investigation agent underperforms direct thresholding of the classifier it relies on, reaching 65.0% accuracy against 71.7% on a balanced 60-case sample, despite having access to model explanations, graph context, and retrieved reference cases. Of the eight decisions the agent changed, six replaced correct classifier outputs with errors, and it produced a coherent written rationale in each case. An exploratory disagreement-based escalation rule flagged two of these agent errors for human review without flagging any correct decision. We conclude that each component of a layered fraud system contributes only under specific conditions, and that a plausible rationale from an investigation agent is not evidence of a better decision.
[AI-11] BioSecBench-Surveillance: A Verifiable Benchmark for AI Agents in Pathogen Genomic Surveillance
链接: https://arxiv.org/abs/2607.19262
作者: Harmon Bhasin,Kevin Flyangolts,Dianzhuo Wang,Evan Seeyave,Arjun Banerjee,Amanda Darling,Joshua Stallings,David Stern,Shawn Higdon,Claire Duvallet,Bryan Tegomoh,Kenny Workman
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:As pathogen genomic surveillance scales, the bottleneck is shifting from data generation to analysis. We present BioSecBench-Surveillance, a verifiable benchmark of 100 evaluations testing whether AI agents can infer the right analysis pipeline from raw sequencing data and surveillance context. Each evaluation gives an agent only the data and context a human analyst would have, then grades its structured answer deterministically. The tasks span seven categories, from taxonomic classification to genetic-engineering detection, across diverse sample types and sequencing technologies. Across 3,962 gradable attempts from sixteen model-harness pairs, the strongest configuration cleared only about half. Opus 4.8 with PI led at 50.2 percent, with a 95 percent confidence interval of 40.1 to 60.3 percent across 83 evaluations, tied with GPT-5.5 with Codex at 50.2 percent, with a 95 percent confidence interval of 40.8 to 59.6 percent, followed by Opus 4.7 with PI at 49.6 percent, with a 95 percent confidence interval of 40.0 to 59.2 percent, and Sonnet 4.6 with PI at 48.6 percent, with a 95 percent confidence interval of 38.9 to 58.3 percent. Even when agents invoked the correct workflows, their mistakes came from the choices around them, such as which references, thresholds, filters, and normalization to apply. BioSecBench-Surveillance provides a standard for measuring whether agents can be trusted to perform genomic surveillance when the next outbreak arrives.
[AI-12] Benchmarking Generalization in Financial Statement Fraud Detection: robust evaluation and novel tasks IJCAI2026
链接: https://arxiv.org/abs/2607.19259
作者: Guy Stephane Waffo Dzuyo(1 and 2),Gaël Guibon(2 and 3),Christophe Cerisara(2),Luis Belmar-Letelier(1) ((1) Forvis Mazars, (2) LORIA CNRS Université de Lorraine, (3) LIPN CNRS Université Sorbonne Paris Nord)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at FinLLM@IJCAI2026
Abstract:Financial statement fraud detection (FSFD) is crucial for market integrity but faces challenges from increasingly sophisticated schemes and under-utilized textual data in financial reports. Existing methods often rely on random data splits, leading to overoptimistic performance estimates that do not reflect real-world generalization to new companies or future periods. To address this recurring problem with the state of the art, we propose a robust FSFD framework leveraging Large Language Models (LLMs) to integrate both structured financial data and unstructured textual information from financial reports. We provide a more realistic evaluation through a novel and challenging benchmark task called Company-Isolated FSFD (CI-FSFD). We construct and make publicly available a comprehensive U.S. company dataset combining financial statements, summarized MDA text, and fraud labels. Our approach achieves the best performance on the challenging CI-FSFD task, demonstrating the critical value of textual data and robust evaluation for reliable financial fraud detection.
[AI-13] Computing on the Fly: Navigating a Vision for the Future of Drone Computing
链接: https://arxiv.org/abs/2607.19213
作者: Kevin Butler,Christopher Stewart,Nils Aschenbruck,Alina Gerall,Weisong Shi,Deborah Silver,Ufuk Topcu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:The report envisions a decade in which drones move goods, medical supplies, and information at a scale comparable to national infrastructure investments like highways and the electric grid. Potential applications include natural disaster detection drones that spot wildfire sources within minutes, medical supply chains that bypass ground congestion to reach rural hospitals, and nationwide fleets that continuously inspect bridges and power lines. Realizing this future, however, requires closing what report authors call a “capability gap,” where hardware and aspirations are outpacing the software and systems needed to operate safely at scale. The report identifies twelve technical challenges that must be addressed to realize the transformative potential of drone technology: Scaling to millions of drones; AI intelligence and assurance; Edge-cloud continuum and real-time coordination; AI autonomy and agentic systems; Data, training, and validation infrastructure; Critical infrastructure protection; Building reliable fleets from non-deterministic agents; Trust, security, and distributed authentication; Next-generation drone networks; Human-AI partnership and scalable insight; Standards, certification, and regulation; and Workforce development and education. These twelve challenges and proposed approaches to them form the basis of the report, laying out a multifaceted path forward for the evolution of done technology. Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.19213 [cs.RO] (or arXiv:2607.19213v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2607.19213 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-14] Assessment in Team Problem-Solving Exercises in Computing Education
链接: https://arxiv.org/abs/2607.19209
作者: Valdemar Švábenský,Jan Vykopal,Sukrit Leelaluk,Pavel Čeleda,Fumiya Okubo,Atsushi Shimada
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Full paper accepted for the main track of the IEEE FIE 2026 conference
Abstract:This full paper in the research-to-practice track presents methods for assessing student teams in tabletop exercises (TTXs). TTXs enable learner teams to prepare for workplace tasks and practice crisis responses, such as resolving cybersecurity incidents. While assessment is essential for determining how well teams achieve learning objectives, the complex, open-ended nature of TTXs often leads to delayed or incomplete feedback. TTX learning platforms can record teams’ actions and communication; yet, leveraging these data to assess performance is underexplored. To address this gap, we compared two post-TTX team assessment methods – clustering and large language models (LLMs) – using an original dataset from 81 participants across two countries. We evaluated these methods against instructor-assigned scores based on standardized rubrics. Clustering grouped teams that approached TTX tasks similarly, enabling instructors to deliver faster, targeted feedback to teams within a cluster. This method was valid and reliable, with low computational requirements. LLMs used the standardized rubrics to assess teams’ communication. While GPT-4o frequently disagreed with instructor scores, GPT-5.2 demonstrated considerably lower error. The researched methods have been integrated into INJECT, an open-source TTX learning platform, to support scalability and teaching practice. To encourage community adoption, we publicly share all datasets, software tools, and a full-fledged TTX scenario.
[AI-15] Agent ic Real2Sim: Physics-based World Modeling with Vision-Language Agents
链接: https://arxiv.org/abs/2607.19190
作者: Guanxiong Chen,Qianjun Xia,Jiawei Peng,Heng Zhang,Bole Ma,Justin Qian,Ziyi Jiao,Bingyang Zhou,Luoxin Ye,Kaifeng Zhang,Kunyi Wang,Weijia Zeng,Yunuo Chen,Pengzhi Yang,Ziqiu Zeng,Huamin Wang,Chao Liu,Alan Yuille,Fan Shi,Changxi Zheng,Yunzhu Li,Chenfanfu Jiang,Peter Yichen Chen
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Real-to-sim conversion for robotic interaction with objects remains labor-intensive because it requires more than visual reconstruction: a streamlined real2sim process must recover scene geometries and object states, infer physical parameters, and assemble actors, objects, cameras, poses, and trajectories into a runnable physical simulation. Today this process still depends on manual tuning of visual foundation models, mesh cleanup, coordinate-frame alignment, and brittle workflow glue across visual perception tools and simulators. We introduce \textitAgentic Real2Sim, a framework for generalized physical world modeling with vision-language agents, converting a real-world recording of object-robot interaction into a simulatable episodic twin which preserves observations, geometries, robot interactions, and object states. We evaluate Agentic Real2Sim on rigid-object manipulation, deformable-object interaction, and humanoid motion scenes, spanning domains that are usually handled by separate Real2Sim pipelines, marking a first step toward scalable conversion. The framework’s agentic decisions can be driven by an open-weight VLM backend at a small fraction of the cost of frontier models, while attaining comparable conversion success rate. We aim to use the resulting real-world-aligned twins for downstream robotics tasks, specifically policy learning and evaluation. The project site is available at this https URL.
[AI-16] Breaking the Homogeneity Assumption: Specialized Multi-Generator Adversarial Learning for Rare Failure Detection in Predictive Maintenance
链接: https://arxiv.org/abs/2607.19153
作者: Alexis Lazanas,Georgios Kampouropoulos
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Supervised learning models in the predictive maintenance field are regularly trained on highly imbalanced industrial datasets: machine failures occur rarely but have a disproportionate effect on operations. In addition to the clear class disparity, failure data are typically non-homogeneous, with different failure modes arising from distinct physical processes and exhibiting a multimodal distribution across minorities and classes. Traditional imbalance-management methods, e.g., undersampling, SMOTE-based interpolation, or cost-sensitive learning, typically assume that the minority population is homogeneous. This means their effectiveness is severely limited in the multifaceted conditions encountered in industrial practice. This paper determines the possibility of a failure-type-conscious generative augmentation program to improve the identification of infrequent failures in predictive maintenance systems. An experimental design that is leakage-safe is used to compare five imbalance-handling methods: cost-sensitive learning, random undersampling, SMOTE oversampling, single-generator GAN augmentation, and a specialized multi-generator GAN architecture that has independent generators that are asked to learn individual failure subtypes. Precision/Recall-oriented measures are used to quantify model performance; the main evaluation measure is the PR-AUC. Experiments conducted on the AI4I 2020 predictive maintenance dataset indicate that the proposed multi-generator GAN framework produces more realistic minority samples, yielding higher PR-AUC and recall scores compared to traditional resampling methods and individual-generator GAN augmentation.
[AI-17] Incomplete Observations Boost Evolutionary Performance in Ocean Modeling
链接: https://arxiv.org/abs/2607.19147
作者: Yangyang Kong,Yutong Jiang,Yanhai Gan,Junyu Dong,Feng Gao,Xiaopei Lin
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 26 pages,5 figures
Abstract:Data-driven methods have revolutionized ocean modeling, yet current approaches rely heavily on complete reanalysis datasets, imposing computational constraints and limiting model performance to that of the training data. Here, we present a generative state-space model and an optimization framework that enable learning directly from sparse and noisy observations. The model is essentially a hidden Markov model with a continuous state space, where oceanic physical quantities are treated as hidden states and measurements as observations, enabling a unified representation of ocean fields and observational data. Both the initial-state and state-transition modules are implemented as neural networks to capture the complexity and temporal evolution of ocean states, while the emission module is formulated as a masked Gaussian distribution. To train the model from sparse observations, we derive an optimization framework based on the expectation-maximization (EM) algorithm. The framework alternately reconstructs high-fidelity ocean fields via Langevin dynamics and optimizes deep neural networks to capture temporal evolution. Theoretical analysis shows that the framework maximizes the likelihood of observations under the generative model. For efficiency, we assume that ocean-state evolution follows a stationary, ergodic, and Markovian stochastic process and adopt only length-two state sequences during optimization. Experiments on CMIP6 simulation data and FY-3D satellite data demonstrate high-fidelity reconstruction and accurate prediction, showing that sparse observations can directly improve the model’s representation of ocean-state dynamics. This work offers a scalable pathway for next-generation Earth system models to learn directly from sparse, incomplete real-world observations.
[AI-18] Parallel Noising in Neural Markov Logic Networks
链接: https://arxiv.org/abs/2607.19126
作者: Peter Jung,Giuseppe Marra,Ondrej Kuzelka
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Neural Markov Logic Networks (NMLNs) are a flexible neurosymbolic relational model. Previous work has shown that, although NMLNs achieve strong performance as generative models for small relational structures, they underperform diffusion-based generative graph models on larger structures. In this paper, we strengthen NMLNs along two main dimensions: (i) we increase the expressive capacity of their potential functions using graph neural networks, and (ii) we develop a new training and inference algorithm inspired by parallel-tempering Markov chain Monte Carlo methods, which we name parallel noising. Together, these enhancements enable NMLNs to attain strong performance in graph generation relative to general diffusion-based generative graph models. Furthermore, they allow NMLNs to match the performance of specialized text-based recurrent models when generating small molecular structures.
[AI-19] Code Division Modulation Layers Against Forgetting and Inference in Continual Gait Identification
链接: https://arxiv.org/abs/2607.19122
作者: Simone Milani
类目: Multimedia (cs.MM); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: Submitted to EUSIPCO 2026
Abstract:Continual learning (CL) has been recently employed in biometric identification systems thanks to its ability to integrate new knowledge within a pre-trained model and to the possibility of reducing the computational cost of training. Unfortunately, such approaches pose new challenges both in terms of final accuracy and privacy guarantees since a progressive fine-tuning of the model on small subsets expose them to catastrophic forgetting and successful inference attacks. This paper evaluates the efficiency of code division modulation layers (CDML) on a gait identification system which has been trained following a continual learning policy. The proposed approach preserves accuracy on all the tasks while mitigating membership inference attacks at the same time. Moreover, the impact of retransmission is minimized since replaying data is not necessary. Comments: Submitted to EUSIPCO 2026 Subjects: Multimedia (cs.MM); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR) Cite as: arXiv:2607.19122 [cs.MM] (or arXiv:2607.19122v1 [cs.MM] for this version) https://doi.org/10.48550/arXiv.2607.19122 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-20] Comparative Study of Multi-Agent Actor-Critic Algorithms in Parameterized Action Reinforcement Learning
链接: https://arxiv.org/abs/2607.19117
作者: Ubayd Ali Bapoo,Clement N Nyirenda
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 2 figures
Abstract:Parameterized action reinforcement learning has shown strong performance in environments requiring both discrete action selection and continuous parameterization. Prior work established the effectiveness of single-agent actor-critic algorithms - Greedy Actor-Critic (GAC), Soft Actor-Critic (SAC), and Truncated Quantile Critics (TQC) - on benchmark parameterized action tasks, but their extension to multi-agent settings remains largely unexplored. This paper presents a comparative study of shared-experience multi-agent extensions of these algorithms: Multi-Agent Greedy Actor-Critic (MAGAC), Multi-Agent Soft Actor-Critic (MASAC), and Multi-Agent Truncated Quantile Critics (MATQC). Rather than following the centralized training, decentralized execution (CTDE) paradigm, the proposed framework uses multiple independent actor-critic agents that share a replay buffer while maintaining separate policy and value networks. We evaluate the algorithms on the Platform-v0 and Goal-v0 benchmarks against their single-agent counterparts, using three-, five-, and ten-agent configurations to assess scalability. Performance is measured by average evaluation return and training time across ten independent runs, with one-way ANOVA and Tukey HSD post-hoc tests used to assess statistical significance. Results show that the multi-agent framework consistently improves Greedy Actor-Critic performance, while MASAC and MATQC show comparatively modest gains over their single-agent versions. Increasing the number of agents beyond five yields limited additional performance while substantially raising computational cost, particularly for MAGAC. These results highlight a trade-off between learning performance and computational efficiency, offering insight into the scalability of shared-experience multi-agent actor-critic methods for parameterized action reinforcement learning.
[AI-21] OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation
链接: https://arxiv.org/abs/2607.19108
作者: Yuze Dai,Zhihan Zhang,Yan Zhao,Ruoyu Wu,Xunkai Li,Zekai Chen,Qiangqiang Dai,Hongchao Qin,Ronghua Li
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 10 figures, 3 tables
Abstract:Text-attributed graphs (TAGs) are an important graph data form that combine relational structure with rich node text. However, real-world TAGs are often imperfect, with quality issues arising from text, structure, and labels, and typically manifesting as sparsity, noise, and imbalance. These dimensions define nine representative degradation scenarios that can substantially affect TAG learning. Although prior studies have explored specific mitigation strategies, existing evidence remains fragmented across degradation types, datasets, tasks, and model families, leaving TAG robustness insufficiently understood. To address this gap, we present OpenRTAG, a robustness benchmark for text-attributed graph learning. OpenRTAG organizes TAG quality issues into a unified 3 * 3 taxonomy and supports standardized evaluation across nine TAG datasets and three downstream tasks. It systematically evaluates scenario validity and model sensitivity, compares traditional GNNs, LLM-GNNs, and a representative GFM, investigates the effectiveness, efficiency, and robustness of scenario-matched baselines, and further examines model behavior under composite degradation scenarios. OpenRTAG provides a standardized testbed for understanding robustness in TAG learning under realistic low-quality settings.
[AI-22] SciCodePile: A 128GB Corpus and Executable Benchmark for Challenging Scientific Code Generation
链接: https://arxiv.org/abs/2607.19104
作者: Weifeng Sun,Ye Fan,Yuchen Chen,Gou Tan,Jieke Shi,Yuan Yidi,Swee Liang Wong,Jonathan Pan,David Lo
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) excel at general-purpose code generation, yet how well they handle scientific code remains an open question. Existing datasets and benchmarks are limited in scale, domain coverage, or executable verification, leaving the true gap between current LLMs and reliable scientific code generators inadequately assessed. To address these limitations, we present SciCodePile, the largest scientific code corpus to date, constructed from 37,737 public repositories and collectively comprising 128GB of code that spans multiple computational science disciplines. From this corpus, we further curate an executable benchmark of 200 tasks, each equipped with a sandboxed execution environment and an automated test harness for functional verification. We evaluate 15 LLMs from both open-source and closed-source families on three tasks: prefix-to-suffix completion, fill-in-the-middle infilling, and executable code generation. Results show that scientific code generation remains highly challenging: The best CodeBLEU reaches only 38.13 and 38.37 on the two completion tasks, while the strongest model achieves just 12.30% Pass@1 on the executable benchmark, underscoring how far current models remain from reliable scientific code generation. To demonstrate the training utility of SciCodePile, we further show that continued pretraining on our corpus improves CodeBLEU by \times 2.84 on scientific code completion, and instruction tuning on our data improves Pass@1 by \times 4.79 on the executable benchmark. All code and data are available at this https URL.
[AI-23] On the Effectiveness of Pretraining for Graph Combinatorial Optimization
链接: https://arxiv.org/abs/2607.19072
作者: David Aguado,Daniel Fuertes,Carlos R. del-Blanco,Fernando Jaureguizar
类目: Artificial Intelligence (cs.AI)
备注: This work was accepted to be presented at the Graph Signal Processing Workshop 2026
Abstract:This paper introduces a self-supervised pretraining framework for graph combinatorial optimization specifically designed to address the nature of routing problems like the Traveling Salesman Problem. By utilizing graph contrastive learning with geometric augmentations (specifically, rotations and axial reflections) the model is forced to learn invariant structural representations and global relative distance distributions. Results demonstrate that this pretraining strategy outperforms non-pretrained models across various problem scales. Notably, the hybrid strategy (combining rotation and reflection) achieved a 6.57% improvement in tour length for TSP1000, proving that geometric pretraining is an important inductive bias for effectively scaling neural solvers to high-dimensional instances.
[AI-24] Quality Action Assurance: Multimodal Verification of Examiner Claims in VR OSCEs
链接: https://arxiv.org/abs/2607.19063
作者: Harry Rogers,Sally Shiels,Ashley Tomlinson,James Thomas,James Aylward,Nathan Gauge,Helen Higham,Alison Noble
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Objective Structured Clinical Examinations (OSCEs) are the gold standard for assessing clinical competence, yet scoring remains vulnerable to examiner subjectivity, fatigue, and cognitive bias. Standard examiner validation via inter-rater statistics lacks explanatory power regarding the source of errors, as it neither analyzes examiner reasoning nor verifies examiner claims against actual events. Thus, we introduce Quality Action Assurance (QAA), a multimodal framework that verifies examiner claims in Virtual Reality (VR) pediatric OSCEs by comparing actions claimed by examiners against the true sequence of events, constructed from video, VR logs, and actor data. QAA combines a constrained temporal action alignment model, which performs action localization and actor source attribution, with a large language model that extracts examiner claims and checks them against the record. Across a 5-fold cross-validation, QAA achieves 99.2% \pm 0.7% Actor F1 and 93.4% \pm 1.9% W@16 for temporal alignment. Overall, QAA detects examiner errors with 70.0% precision and 76.7% recall, improving factual correctness from 39.2% to 79.2%, enabling fairer OSCE assessment.
[AI-25] Deep learning-based prediction of time-resolved adhesive forces in viscoelastic Hertzian contacts
链接: https://arxiv.org/abs/2607.19060
作者: Ali Maghami,Merten Stender,Michele Ciavarella,Antonio Papangelo
类目: Machine Learning (cs.LG); Soft Condensed Matter (cond-mat.soft); Artificial Intelligence (cs.AI); Data Analysis, Statistics and Probability (physics.data-an); Machine Learning (stat.ML)
备注:
Abstract:Fast prediction of the response of adhesive soft viscoelastic contacts represents a current challenge in soft robotics and for gripping and manipulation tasks. Determining the complete time-resolved force trajectory requires full numerical simulations, whose computational cost is strongly parameter-dependent, making them impractical for real-time application or design-optimization loops. In this work, we overcome this limitation by training a scalar-conditioned, stateful, sequence-to-sequence deep learning model to predict the full force evolution from a prescribed displacement history for both short- and long-range adhesion regimes. The data set spans four orders of magnitude in loading and unloading rates and includes varied dwell times, with the Tabor parameter ranging from 0.2 to 3.2 . To enable learning across these heterogeneous time scales, we introduce a fixed-measurement-step (FMS) representation that converts variable-length trajectories into fixed-length sequences while preserving their physical-time information. Different architectures were trained, including long short-term memory (LSTM) networks, temporal convolutional neural (TCN) networks, and time-distributed dense layers with three different Tabor-conditioning mechanisms. The models were compared using global waveform and error metrics. We found that the best-performing model has an LSTM architecture with concatenated conditioning, which achieves a held-out mean-squared error of 5.0\times10^-4 , a median pull-off-force error of \approx2.2% , and a median hysteresis error of \approx1.1% . For the held-out protocols, the model predicts a complete force trajectory with a median inference time of 0.16 s. The model is tested across unseen parameter combinations and against analytical limiting cases, providing a rapid surrogate for repeated numerical evaluations with potential use in control-oriented applications.
[AI-26] Where Should Optimizer State Live? Tiered State Allocation for Memory-Efficient Mixture-of-Experts Training
链接: https://arxiv.org/abs/2607.19058
作者: Nuemaan Malik
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 12 pages, 5 figures. Code and training logs available at: this https URL
Abstract:Optimizer state is the largest single line item in the memory budget of mixture-of-experts (MoE) training: on a 6.78B-parameter MoE language model, AdamW keeps 50.6 GB of first and second moments to update 12.6 GB of bfloat16 weights. We study SkewAdam, an optimizer built on the observation that the three parameter populations of an MoE - the dense backbone, the experts, and the router - differ enough in size and gradient statistics that they should not receive the same state. SkewAdam keeps float32 momentum plus a factored second moment for the backbone (5% of parameters), a factored second moment alone for the experts (95%), and an exact second moment for the router (0.01%). The resulting state occupies 1.29 GB, 2.6% of AdamW’s, and peak training memory falls from 81.4 GB to 31.3 GB, within the budget of a 40 GB accelerator. In a controlled comparison from identical initializations over 82M tokens, SkewAdam reaches validation perplexity 108.4, ahead of AdamW (126.8), Muon (120.2), and Lion (393.7), and settles router load balance to within 1% of its uniform floor. The allocation is not what earns that perplexity: a tier ablation matches it with twenty times the state, and Adafactor, which shares the factored estimator but drops momentum, plateaus 40 points behind. The tiers buy memory at no cost to accuracy; the accuracy comes from keeping momentum, which a uniform optimizer shares too. Sweeping the baselines’ learning rates narrows but does not close the gap: the best tuned AdamW reaches 118.5, tuned Adafactor 139.7. Where optimizer state lives, these results suggest, matters at least as much as how much of it there is.
[AI-27] Vector-Bench: Can Models Surgically Edit SVG Code?
链接: https://arxiv.org/abs/2607.19056
作者: Yug Aditi Gupta,Prannay Hebbar
类目: Artificial Intelligence (cs.AI)
备注: 11 pages, 9 figures
Abstract:Instruction-based vector editing requires two capabilities: making a requested change and leaving everything else alone. The second is easy to miss when an output is judged only as a raster image. We introduce Vector-Bench, a compact, difficult benchmark of 40 SVG repair tasks. Each task pairs a corrupted SVG program with an author-written visual instruction, a hidden target program, 5.05 annotated repairs on average, and an average of 60.55 protected objects. Instructions describe visible defects without exposing element identifiers, coordinates, color codes, or path data. We define a deterministic binary specification reward: requested repairs use attribute-aware perceptual tolerances, while unrequested rendering- or application-relevant structure must remain semantically unchanged and the result must be a valid SVG. Canonical target equality and stricter source fidelity are retained as diagnostics. Validity-gated repair progress, a near-complete tier, and valid-output Unintended Change Rate (UCR) explain partial outcomes. We evaluate 34 model endpoints (25 listed as open-weight, 5 inexpensive controls, and 4 frontier closed endpoints) over 1360 requests. The strongest endpoint reaches only 15.0% full specification success, despite 43.7% mean repair progress, showing that apparent repair progress and specification-faithful editing remain substantially different. All prompts, outputs, scoring code, costs, and per-task reports are released.
[AI-28] Spectral Higher-Order Neural Networks Have Sharp Expressivity Bounds
链接: https://arxiv.org/abs/2607.19042
作者: Gianluca Peri,Diego Febbe,Duccio Fanelli
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Neural hypergraphs are a natural generalization of neural networks, the reference models in modern machine learning. Yet, their deployment has proven demanding: the number of weighted hyperedges required leads to an intractable parameter explosion. However, a novel parametrization that leverages spectral attributes for neural hypergraphs has been recently proposed, that enables to recycle parameters via a weight sharing scheme and consequently yields a significant reduction of the associated computational cost. Preliminary tests carried out on spectral higher-order architectures pointed to meaningful improvements in both performance and interpretability. Building on these results, we advance the benchmarking efforts by evaluating the spectral higher order framework on N-bit parity tasks, a well-established testbed known to be particularly challenging. As we will convincingly argue, Spectral Higher-Order Neural Networks (SHONNs) possess a versatile and highly tunable hypothesis space.
[AI-29] Athena-Brain Technical Report: An Efficient Robot Brain for General Intelligence and Embodied Interactio
链接: https://arxiv.org/abs/2607.18985
作者: Jialian Li,Junhong Liu,Yuchen Cao,Weiran Guo,Jiaming Song,Xutao Wang,Yi Zhao,Jiangpin Liu,Jie Chen
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) have demonstrated remarkable capabilities in language understanding, reasoning, and world knowledge. As embodied agents become increasingly capable, there is a growing demand for compact models that can serve as an on-device brain, preserving the broad general intelligence of LLMs while enabling effective high-level interaction with embodied environments. Existing approaches, however, often prioritize either general-purpose intelligence or specialized embodied capabilities, making it challenging to satisfy both requirements within a single model. We present \textbfAthena-Brain-8B, an 8B LLM designed to serve as an on-device brain for embodied intelligence for embodied intelligence. Through a multi-stage post-training pipeline consisting of General Supervised Fine-Tuning, General Reinforcement Learning, Embodied Expert training, and Model Merge, Athena-Brain-8B maintains strong general capabilities while acquiring strong high-level embodied interaction capabilities and generating concise responses for efficient embodied interaction. Experimental results demonstrate the effectiveness of Athena across both general and embodied evaluations. Compared with the corresponding Qwen3-8B thinking model, Athena-Brain-8B achieves comparable performance on general language and reasoning benchmarks while generating substantially shorter responses. On in-domain embodied benchmarks, Athena-Brain-8B consistently outperforms models of similar scale and surpasses several substantially larger frontier models evaluated zero-shot, demonstrating that compact language models can effectively integrate strong general intelligence with embodied capabilities.
[AI-30] Fishing Out Free Riders: Shapley-Based Reward Attribution for Parallel Reasoning via Reinforcement Learning
链接: https://arxiv.org/abs/2607.18979
作者: Wentao Zhang,Haoyu Zhang,Xinke Jiang,Yuxuan Cheng,Yuhan Pan,Miao Li,Zhipeng Qiao,Tao Feng,Zhen Tao,Dengji Zhao
类目: Artificial Intelligence (cs.AI)
备注: 19 pages, 4 figures, 8 Tables
Abstract:Large Language Models (LLMs) excel at multi-step reasoning, yet current parallel reasoning approaches often fail to distinguish the contributions of individual reasoning paths. Many paths may be redundant, misleading, or even detrimental, but outcome-level rewards assign uniform reward, leading to ambiguous learning signals and unstable training. We propose Parallel Shapley, a reinforcement learning framework that attributes fine-grained, path-level contributions in multi-path reasoning. Treating each path as a player in a cooperative game, we leverage Shapley values to quantify marginal contributions, using a generative reward model to evaluate path utilities and Monte Carlo sampling for efficient approximation. Experiments on mathematical reasoning benchmarks show that Parallel Shapley outperforms existing baselines while providing more stable and interpretable training. Our framework effectively “fishes out the free riders,” assigning reward proportionally and improving multi-path reasoning in LLMs.
[AI-31] Mi-Memory: A Lifecycle Memory Framework for Personal AI
链接: https://arxiv.org/abs/2607.18975
作者: Xule Liu,Hanlin Teng,Chao Li,Yanan Ni,Shuo Lu,Audrey Wang,Yijun Liu,Yunfei Wang,Xiaofeng Li,Xian Yi,Yuanfa Li,Kang Zhao,Jian Liang,Yuxuan Chen,Jinyuan Chen,Heng Qu,Kun Shao,Jian Luan
类目: Artificial Intelligence (cs.AI)
备注: Project page: this https URL
Abstract:Personal AI is moving beyond chat-only interaction toward continuous services that span phones, cars, homes, wearables, cameras, and tools. In this setting, memory cannot remain a cache of prior conversations. It should serve as a continuity and governance substrate: preserving durable user state, grounding answers in multimodal and device evidence, supporting correction and forgetting, bounding policy evolution, and remaining deployable under latency, cost, privacy, and edge-cloud constraints. This technical report presents Mi-Memory, a lifecycle memory framework for Personal AI organized around four roles: Structure, Expansion, Evolution, and Deployment. A shared audit contract links these roles through four recurring artifact families: typed evidence payloads preserve source identity and provenance, diagnostic traces localize evidence loss across the serving pipeline, strategy artifacts make memory-policy changes explicit, and gate/rollback records bound accepted evolution. MiMemory instantiates the roles through MemStack, MemSense/MemFuse, D ^2 ACCI/E ^2 MEND, and LiteMem. In controlled-reference Structure evaluations, MemStack reaches 93.59%, 57.24%, and 87.47% on LoCoMo, PersonaMem-V2, and LongMemEval, respectively; other tracks report module-level, preliminary/internal, transfer-feasibility, or design-only evidence with explicit boundaries. MiMemory is a step toward auditable, evidence-gated, and deployment-aware memory systems for Personal AI. Project homepage: this https URL .
[AI-32] Skillware: A Software Ontology and Engineering Lifecycle for Persistent Behavioral Artifacts
链接: https://arxiv.org/abs/2607.18970
作者: Haodi Fan,Zucong Lan
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 26 pages, 6 figures, 5 tables
Abstract:Agent Skills have become persistent behavioral artifacts across independent AI agent systems. They combine natural-language task specifications with metadata and optional references, scripts, assets, hooks, package manifests, tests, and companion interfaces. Existing studies explain how Skills are specified, executed, maintained, and evolved, but lack an ontology that defines these artifacts as independent software objects. This paper introduces Skillware as the software abstraction that extends software engineering to persistent Behavioral Artifacts in agent systems. A Skill Artifact specifies reusable task behavior; a Skillware Unit manages that artifact as software through an independent identity and lifecycle. A compatible Agent Host activates the unit for runtime interpretation. Three necessary conditions operationalize category membership: behavioral primacy, independent software identity, and an Agent Host execution relationship. Lifecycle Continuity records whether the same unit identity persists through update, maintenance, rollback, and removal as a separate software-grade property. Evidence combines the Agent Skills specification, a frozen corpus of 138,133 content-deduplicated this http URL records associated with 20,556 repository identifiers, independent empirical studies, 15 category-boundary cases, and 13 fixed-revision engineering implementations. The evidence establishes a recurring artifact envelope, separable software identities, compatible execution paths, and lifecycle engineering pressure. Skillware provides the software ontology and engineering lifecycle through which agent capabilities can become identifiable, composable, maintainable, and evolvable software artifacts.
[AI-33] Variational meta-learning inference for low dimensional neural system identification
链接: https://arxiv.org/abs/2607.18965
作者: Matteo Rufolo,Dario Piga,Marco Forgione
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注:
Abstract:Deep learning has proven highly effective for nonlinear system identification, but heavily parameterized neural networks are prone to overfitting in low-data regimes and lack reliable uncertainty quantification. The recently developed manifold meta-learning framework addresses the data efficiency problem by restricting the model parameters to a meta-learned low-dimensional manifold. However, that method is purely deterministic. We propose a fully probabilistic extension of the manifold meta-learning framework, based on amortized Variational Inference, where a generative prior over the low-dimensional parameter manifold is learned. During task-specific adaptation, we combine Maximum A Posteriori estimation with the Laplace approximation to yield a mathematically grounded posterior approximation. Evaluated on a static regression task and the Bouc–Wen dynamical system benchmark, the proposed approach achieves predictive accuracy comparable to its deterministic counterpart while successfully providing calibrated uncertainty bounds in severely low-data regimes.
[AI-34] From Dependency to Compositionality: A Neurosymbolic Lifting of LLM Outputs via Combinatory Categorial Grammar
链接: https://arxiv.org/abs/2607.18961
作者: Remo Pareschi
类目: Artificial Intelligence (cs.AI)
备注: 27 pages. Under review
Abstract:Large language models (LLMs) generate fluent text by incrementally predicting the next token from a prefix. Critics in the generative tradition argue that such systems lack genuine grammar; influential replies from the dependency-grammar perspective hold that LLM behavior is well described by local head-dependent structure built word by word. We argue that a sharper observation has been overlooked: the prefix-driven, type-completing dynamics of autoregressive generation align closely with the incremental processing model that Combinatory Categorial Grammar (CCG) was originally designed to support. On this basis we propose a neurosymbolic framework in which LLM outputs are lifted into typed compositional derivations – not claiming that LLMs implement CCG internally, but that their outputs admit a principled, incremental, and auditable CCG reconstruction. Two consequences follow. First, through the Curry-Howard correspondence the lifting extends beyond natural language to the formal languages LLMs also produce – programming languages such as Solidity, description-logic and query languages such as OWL and SQL – with the type system varying and the architecture held fixed. Second, the lifting supports two layers of checking: a compositional layer that catches structural failures directly, and a content layer that checks the lifted structure against external knowledge sources, enabling the earliest possible flagging of hallucinated content. The account thereby requires of a producer not cognition but a prefix-driven generative profile. We close with a sketch of synchronous LLM-CCG coupling as one direction the framework opens.
[AI-35] SFGA: A Statistics-First Gating Architecture with Adjudicative Escalation for Trustworthy SFT Data Procurement
链接: https://arxiv.org/abs/2607.18960
作者: Arther Tian,Alex Ding,Simon Wu,Aaron Chan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注:
Abstract:Procuring supervised fine-tuning (SFT) data forces a buyer to decide, before any downstream training, whether a candidate corpus is worth acquiring. We present \sys, a statistics-first gating architecture that treats procurement as a cost-aware routing problem over three intrinsic quality axes – diversity, utility, and redundancy. Cheap blind measurements are summarised into per-axis estimates with confidence intervals; a gate accepts a decision only when intervals are tight, sample sizes are adequate, and the axes agree, otherwise it escalates the case to an adjudicative debate between a buy-advocate and a reject-advocate judge, resolved by a presiding verdict. On a controlled benchmark of 12 datasets ( 2\times3\times2 grid over the three axes) with 5 seeds, the gate reaches 0.90 accuracy and 0.83 F_1 at \ 0.017 per unit, sitting between an always-verify baseline (0.75) and an oracle upper bound (0.98) while spending less than always-escalate (\ 0.020). We further report honest negative diagnostics of the debate path: a con-side win rate of 0.80 ( p\approx3\times10^-6 ) and a 52% position-flip rate under advocate swapping expose negativity and positional biases that a naive LLM-judge would hide. We frame the injected-knob evaluation explicitly as a controlled synthetic benchmark for measurement fidelity and routing calibration, and delimit external validity as future work.
[AI-36] What General Intelligence Requires: Non-Reducible Constraints Across Levels of Description
链接: https://arxiv.org/abs/2607.18943
作者: Subhomoy Bakshi
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 78 pages, 6 figures, 2 tables, 1 appendix
Abstract:General intelligence, of the kind that underwrites the full range of human cognitive achievement, is not a property of computational architecture alone. This paper advances a single thesis: the structural constraints on general intelligence occupy distinct levels of description and are mutually non-reducible, in the sense that the special-sciences tradition gives to that term. It follows that no single architectural advance, and no continuation of the scaling programme by itself, can produce artificial general intelligence (AGI), and that research programmes must be evaluated against the full constraint profile rather than against performance on any one benchmark. The thesis is developed through a method that reads general intelligence through four evidential lenses, AI systems research, anthropology, law, and economics, each anchored to a distinct level of description, supplemented by speculative fiction used as a disciplined heuristic in the context of discovery rather than the context of justification. Applying the method yields a taxonomy of twenty-three structural constraints organised into eight clusters; six are examined in depth and ordered as an ascending ladder of levels, with explicit bridges showing why progress at one level cannot carry to the next. The argument issues in five falsifiable predictions, each stated with a named benchmark family and a disconfirmation condition, converting a descriptive framework into a research programme with a longer horizon than the scaling hypothesis implies.
[AI-37] Functional Equivalence and Geometric Diversity in Neural Network Approximations: An Empirical Characterization
链接: https://arxiv.org/abs/2607.18930
作者: Anuragine S A,Prem Jagadeesan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The Universal Approximation Theorem states that a neural network with a single hidden layer is sufficient to approximate any continuous univariate function on a compact domain to arbitrary error. However, the uniqueness of such neural network representations is not guaranteed, raising questions about practical identifiability. In this work, we address this concern by analyzing functional equivalence and geometric diversity of neural network approximations to a few elementary mathematical functions. The analysis includes an extensive study of single-layer neural networks and multilayer perceptrons under noisy and noise-free conditions. Beyond just network capacity, we study the geometric properties through the lens of sloppiness, characterized by the eigen spectrum of the Hessian of the cost function and the effective rank to quantify the dimensionality of parameter space. The study reveals large equivalence classes of functionally indistinguishable yet geometrically diverse networks that consistently exhibit low effective rank and structural redundancy. Finally, a model select criterion is proposed for identifying optimal models based on parsimony, ease of estimation, and inference efficiency.
[AI-38] OntoBook: Ontology-Grounded Synthetic Textbooks for Medical Encoder Pretraining
链接: https://arxiv.org/abs/2607.18927
作者: Rian Touchent(ALMAnaCH),Éric de la Clergerie(ALMAnaCH)
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We present OntoBook, a method that converts medical ontology structure into pretraining signal for encoder language models. Our approach has three stages: random walks through ontology graphs capture hierarchical and causal relations between medical codes, a large language model reformulates these walks into fluent textbook-style prose, and the resulting text is used to train ModernCamemBERT, a 149M-parameter French encoder, with two objectives on the same data: masked language modeling and relation prediction between code pairs. On three French medical coding benchmarks (FRACCO, Cantemist-FR, Distemist-FR), OntoBook achieves significant improvements over MLM-only pretraining, with +2.5 micro-F1 on FRACCO and +8.0 micro-F1 on Distemist. We find that alignment between objectives is necessary: misaligned training, where each task uses different data, causes a 30-point degradation. We release 1.3 million LLM-reformulated medical textbooks across three French ontologies (CIM-10, CCAM, ATC) and pretrained model checkpoints.
[AI-39] Circuit Claims Depend on What Is Extracted and How It Is Compared
链接: https://arxiv.org/abs/2607.18921
作者: Yang Sheng,Jie Fu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 26 pages, 11 figures, 20 tables
Abstract:Circuit extraction identifies a small set of model components whose presence preserves a target behavior under ablation, and the resulting circuit is often read as the mechanism behind that behavior. We argue that this reading is under-determined: preserving behavior does not single out one circuit, because the claim it supports depends on which circuit is reported and how two circuits are compared. We make this concrete in a synthetic Lean tactic-prediction benchmark – predicting the next step of a proof – where fixed proof rules with randomized surface form let differences between extracted circuits be attributed to these choices rather than to the task. Across dense and weight-sparse checkpoints (most weights constrained to zero) of the same transformer, evaluated on atomic (single-rule) and compositional (multi-rule) proofs, we vary which extracted object is reported (a compact prediction-preserving circuit, a broader graph that also keeps surrounding read, write, and routing structure, or the smallest subgraph meeting a post-ablation loss threshold), and whether each attention head’s query and key are represented jointly or separately. Exact component-to-component edge overlap is low and sensitive to these choices, at times dropping to a random baseline, while two coarser summaries stay stable: the set of selected attention heads, and the circuit-size ranking of conditions that differ in which supervised checkpoint initializes reinforcement learning (RL). The largest accuracy gains from RL on compositional proofs come with the most structure beyond the atomic circuits. A circuit-level claim is therefore well defined only once one states which circuit is reported, the pruning threshold used to extract it, and the level at which circuits are compared. We distill these requirements into a reporting practice for circuit-extraction studies.
[AI-40] Enhancing Transformer-based Routing by Encoding Distance via Relative Positional Encoding
链接: https://arxiv.org/abs/2607.18909
作者: Leyre Encío,Daniel Fuertes,Carlos R. del-Blanco,Fernando Jaureguizar
类目: Artificial Intelligence (cs.AI)
备注: This work was accepted to be presented at the Graph Signal Processing Workshop 2026
Abstract:This paper explores Relative Positional Encoding (RPE) as an additive bias in Transformer architectures to solve the Team Orienteering Problem. By embedding in the attention mechanism pairwise spatial relationships among nodes of the graph that represents the routing problem, the transformer encoder can compute a richer spatial-aware graph embedding that allows the decoder to estimate better routes. Experimental results involving instances up to 100 nodes demonstrate consistent improvements in collected rewards and optimality gaps over vanilla Transformer architectures used by other state-of-the-art works. These findings highlight that explicit relational modeling significantly enhances scalability and generalization for complex combinatorial optimization.
[AI-41] Black-Mamba: Biologically-Inspired Leaky Accumulation for Conceptual Knowledge under Distribution Drift
链接: https://arxiv.org/abs/2607.18899
作者: Giuseppe Soriano,Nicola Tonellotto,Alberto Gotta
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Forecasting under real-world conditions is inherently non-stationary, as the conditional distribution of future observations evolves over time. Recent test-time adaptive sequence models address this challenge by updating internal states during inference, but tie adaptation to instantaneous prediction errors or surprise. This coupling can conflate persistent distribution shift with stochastic innovations, leading to unnecessary updates and inefficient adaptation. We introduce Black-Mamba, a test-time adaptive forecasting architecture that formulates online adaptation as evidence-gated state tracking under distribution drift. The model augments a base predictor with a dynamic memory updated when temporally accumulated surprisal provides sufficient evidence of a regime change. This turns adaptation into a selective, event-driven process rather than a continuous one. Across multiple forecasting benchmarks with non-stationary dynamics, Black-Mamba achieves competitive or improved predictive performance compared to existing test-time adaptation methods while significantly reducing the number of memory updates during inference. Together with mathematical analysis and biological evidence, these results suggest that accumulated surprisal provides a principled signal for distinguishing persistent drift from transient noise, yielding more efficient and robust adaptation.
[AI-42] NaviAIS: A Scenario-Level Vessel Trajectory Prediction Dataset withVectorized Lane Priors and the NaviLane Forecasting Framework
链接: https://arxiv.org/abs/2607.18887
作者: Yuan Gui,Hongchen Luo,Liqi Qu,Longyue Fu,Jiao Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Vessel trajectory prediction in complex maritime environments is essential for traffic management, collision warning, route planning, and autonomous navigation. Although AIS-based learning methods have progressed rapidly, existing datasets are often released as raw message streams or irregular time series, with inconsistent sampling rates, noisy observations, heterogeneous coordinate systems, and non-unified scenario protocols. Most public AIS resources also lack structured representations of navigational lanes, waterway geometry, and navigable-region constraints, limiting reproducible, environment-aware forecasting. To address this, we introduce NaviAIS, a standardized scenario-level AIS dataset for vessel trajectory prediction. It organizes multi-vessel historical-future trajectories within unified temporal windows and local coordinate systems, and provides rasterized navigable maps, vectorized lane priors, lane graphs, and structured map representations. Compared with existing datasets, it jointly supports vectorized lanes, multi-scenario coverage, vectorized maps, open accessibility, and processed trajectories. Built on this dataset, we propose NaviLane, a hierarchical macro-action framework for map-aware prediction. NaviLane first performs trajectory-map joint encoding for a unified scene representation, then uses a discrete macro-action codebook to generate multimodal candidates coarse-to-refined. A residual refinement module improves local geometric and dynamical consistency, and a world-model-based consequence-aware evaluator ranks candidates by interaction risk and environmental feasibility. Experiments show NaviLane outperforms representative baselines in both single-modal and multimodal settings, confirming the value of structured navigational priors, hierarchical multimodal generation, and consequence-aware evaluation.
[AI-43] RAMP: Recognition parametrisation by Amortised Message Passing
链接: https://arxiv.org/abs/2607.18883
作者: Lior Fox,Kai Biegun,James Heald,Samo Hromadka,Arielle Rosinski,Maneesh Sahani
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: In the proceedings of the Symposium on Probabilistic Machine Learning 2026 (ProbML 2026)
Abstract:A central aim of unsupervised learning is to uncover latent factors that explain dependencies among observations. Probabilistic models typically achieve this by introducing multiple latent variables linked through a graph of conditional relationships, with distributional parameters and their dependence learnt from data. Learning relies either on distributional choices that allow tractable belief propagation, or on approximations that scale poorly with model size and complexity. We build on the recently developed recognition-parametrised modelling paradigm to propose an alternative approach: RAMP, a method that implicitly defines latent structure by learning a flexible, nonlinear, amortised message-passing framework. We show that RAMP enables efficient likelihood-based recovery of latent-variable distributions within expressive nonlinear models acting on complex high-dimensional data.
[AI-44] Regime-Aware Physics-Guided Early Warning of Lithium-Ion Battery Thermal Runaway Using Thermo-Mechanical Signals
链接: https://arxiv.org/abs/2607.18860
作者: Syed Sajid Ullah,Muhammad Zunair Zamir,Salman Khan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Thermal runaway in lithium-ion batteries poses a major safety risk to electric vehicles and energy storage systems. Current early-warning methods depend mainly on temperature and may therefore miss mechanical precursors that emerge before rapid heating. We introduce a regime-aware, physics-guided framework that integrates temperature, voltage, force, deformation, and state-of-charge measurements for early warning under controlled mechanical abuse. A lightweight convolutional classifier first infers safe, warning, or danger regimes from mechanical signals. These regime estimates then condition a causal temporal convolutional backbone through feature-wise linear modulation, physics-biased attention, and regime-dependent gating. Joint learning unifies regime identification, thermal-runaway detection, and time-to-disaster estimation. We evaluate the framework using leave-one-experiment-out cross-validation on 30 mechanical-abuse tests across state-of-charge levels of 10%, 50%, and 90% and two loading protocols. The method achieves an F1 score of 0.89, a high-temperature prediction root-mean-square error of 12.3 °C, a mean warning lead time of 15.6 s, a detection success rate of 0.92, and an experiment-level false alarm rate of 2.7%. Its lead time exceeds that of the strongest baseline by 69.6%. Removing force reduces the lead time by 60.3%, highlighting the value of mechanical precursors. These results support regime-aware thermo-mechanical fusion as a promising strategy for earlier and more reliable thermal-runaway warning under controlled abuse conditions.
[AI-45] PhoenixRepair: Rethinking Repair Strategy Exploration in Software Agents
链接: https://arxiv.org/abs/2607.18859
作者: Tianyue Jiang,Yanlin Wang,Xin He,Daya Guo,Jiachi Chen,Ming Wen,Ensheng Shi,Xilin Liu,Yuchi Ma,Guanbin Li
类目: Artificial Intelligence (cs.AI)
备注: 14 pages, 5 figures
Abstract:While Large Language Models have greatly advanced automated issue resolution, existing agent-based methods exhibit a fundamental limitation in their insufficient exploration of repair strategies. This insufficiency manifests in two key aspects. First, the exploration of multiple potential edit locations is limited. Second, the exploration of repair attempts at each location is also insufficient. To address these challenges, we present PhoenixRepair, a multi-agent framework that systematically explores multiple candidate edit locations and performs iterative reflection and refinement on patch generation, thereby expanding the search space of repair strategies. Our framework begins with multi-location sampling, optionally augmented with graph-based localization information for difficult tasks, followed by iterative reflection and refinement to generate better patches, culminating in final-round generation guided by distilled insights from all historical attempts. Experiments on SWE-bench-Verified demonstrate that PhoenixRepair achieves the largest relative improvement of 7.8% over SWE-agent under DeepSeek-V3.1, and attains the highest resolved rate of 76.0% Pass@1 under MiniMax-M2.5. Meanwhile, it achieves higher fault localization accuracy than existing approaches. Our code is available at this https URL.
[AI-46] Data Leakage Prevention in Agent ic Applications via Preemptive Hardening
链接: https://arxiv.org/abs/2607.18847
作者: Akansha Shukla,Emily Bellov,Parth Atulbhai Gandhi,Yuval Elovici,Asaf Shabtai
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Agentic systems integrate LLM driven planning with interfaces to external tools, making data leakage and tool misuse feasible via instruction/data boundary failures and prompt injection attacks. Enforcing required controls consistently is particularly challenging in workflows spanning many codebases and heterogeneous agents. To address this challenge in multi agentic systems, we present a pre-deployment pipeline for scanning, hardening, and validation of agentic applications. The pipeline analyzes prompt templates, tool interfaces, and tool-invocation code to identify leakage-enabling patterns and generate actionable patches. The hardened application is then validated through adversarial prompt injection attacks and benign input variations ensuring that mitigations do not disrupt intended behavior. In the hardening stage, high-risk tools are prioritized, and minimally invasive mitigations are applied, including schema tightening, boundary sanitization, allowlist-based tool gating, and least-privilege checks. In the validation stage, the pipeline automatically generates attack inputs that mimic jailbreaks, instruction overrides, and tool-targeted manipulation, along with benign task variants, to confirm that the functionality of the hardened application is preserved after remediation. We evaluated the pipeline on five real-world agentic applications, as well as on the AgentDojo benchmark. Across all applications, the proposed pipeline identified recurring leakage-enabling patterns and generated patches that can be integrated without disrupting the intended application behavior. The resulting modifications of application code were shown to eliminate leaks when targeted by basic jailbreak and instruction-override attacks, achieving a 100% reduction in leakage, and reduce leaks by 91% under conditions of stress-induced manipulation, without the need of continuous runtime policy enforcement.
[AI-47] ABOPD: Antibody CDR Design via On-Policy Distillation
链接: https://arxiv.org/abs/2607.18835
作者: Zhuo Yang,Jiaying He,Jiaqing Xie,Daolang Wang,Xipeng Qiu,Yuxin Wang,Tianfan Fu,Beilun Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Antibodies are essential therapeutic molecules, and their complementarity-determining regions (CDRs) form the primary antigen-recognition interface. Recent protein generative models have demonstrated broad capabilities in biomolecular design, yet post-training strategies for downstream objectives remain limited. Standard denoising training operates on noisy states obtained by perturbing native structures, whereas recursive generation proceeds through model-generated intermediate states. For flexible antibody CDR loops such as CDR-H3, this mismatch can allow backbone deviations to accumulate along the denoising trajectory and compromise antigen-facing loop geometry. We introduce ABOPD, an antibody design framework based on on-policy distillation that leverages privileged native geometry during training to supervise states visited along the model’s own denoising trajectories. With this fine-grained structural supervision, ABOPD substantially improves structural recovery on RAbD CDR-H3 generation, reducing RMSD by 0.42 Å (from 2.37 Å to 1.95 Å) and outperforming supervised fine-tuning and offline distillation controls, offering a path to higher-fidelity protein design.
[AI-48] From Trajectories to Instructions: Language-Conditioned Meta-Reinforcement Learning ICANN2026
链接: https://arxiv.org/abs/2607.18830
作者: Garvit Singla,Uma Maheswari Natarajan,Raghuram Bharadwaj Diddigi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at the International Conference on Artificial Neural Networks (ICANN 2026)
Abstract:Model-Agnostic Meta-Learning (MAML) is a widely used framework for reinforcement learning (RL) that enables efficient transfer by learning global policy parameters that can be rapidly adapted to new tasks. MAML training proceeds in two loops: an inner loop where the global parameters are adapted to task-specific parameters, and an outer loop where these task-specific parameters are evaluated and losses are back-propagated to improve the global parameters. Traditionally, the inner loop adaptation is performed by collecting trajectories from the task environment and applying gradient updates on the empirical expected return, which can be a costly operation. We note that it is the outer loop that drives the actual learning of global parameters, and therefore the inner loop adaptation mechanism need not be restricted to be gradient-based. This observation leads us to ask: Can we replace the inner loop trajectory collection and gradient update with a simpler, task-specific signal? In many practical settings, tasks are naturally accompanied by language instructions. Leveraging these instructions as a direct task-specific signal, we propose LA-MAML (Language Adapted MAML), which modifies the inner loop by adapting the global policy parameters in a single step through a learned embedding of the task instruction, replacing the inner loop trajectory collection and gradient-based updates. Experiments on the BabyAI benchmark demonstrate that LA-MAML achieves competitive or improved performance compared to baselines at a significantly lower per-iteration wall-clock training time. These results demonstrate that language instructions are an effective and efficient substitute for trajectory-based inner loop adaptation in meta RL.
[AI-49] Evaluating medical AI under missing information: same-provider judges and human raters change apparent safety ALT
链接: https://arxiv.org/abs/2607.18828
作者: Koyar Afrasyab
类目: Artificial Intelligence (cs.AI)
备注: GitHub this https URL
Abstract:Readiness stress-testing of medical AI has focused on closed-ended and multimodal benchmarks. We extend it to open-ended clinical conversation under missing information, where safe behavior means recognizing absent information and qualifying, clarifying, or not over-committing - and where the evaluator becomes part of the measurement. We stress-test four models - three flagships (Claude Opus 4.8, GPT-5.5, Grok 4.3) and one mid-tier model (Gemini 3.5 Flash) - by deleting the latter half of the final user turn in HealthBench conversations, grading responses with a four-provider LLM-judge panel and a blinded clinician-anchored reference. Two evaluator-facing results are robust. First, judge choice materially changes apparent safety: inter-judge agreement is only moderate (Fleiss’ kappa = 0.65), and after adjusting for each judge’s general leniency (vote-level logistic regression), a positive same-provider association remains (exact permutation p = 0.04; GPT-5.5 ~ +0.10 on the probability scale) - large enough to change which model appears to over-commit least once its own-provider judge is excluded. Second, LLM judges are more permissive than clinicians on a blinded 50-item subsample: all four are significantly more lenient than the stricter independent clinician (crediting appropriate uncertainty on 66-84% of items vs 52%), and three of four than the author-influenced consensus (Grok directional only; judge-vs-consensus kappa = 0.20-0.43). On the author-audited clinical-underdetermined subset the permissiveness gap widened and the point-estimate model ordering held. A closed-ended MedQA anchor confirms accuracy is high and option-order effects are within a +/-5-point equivalence region for three of four models, so the safety gap is about calibration, not knowledge. We release the harness, prompts, per-item outputs, judge panel, perturbation audit, and human-annotation protocol.
[AI-50] Cross-Agent Campaign Attribution: Linking Asynchronous Attacks Across LLM Agents ICML2026
链接: https://arxiv.org/abs/2607.18826
作者: SangJin Park,Myungsub Choi,Jineok Kim,Minseung Kang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 22 pages, 5 figures. Accepted at the Second Workshop on Agents in the Wild: Safety, Security, and Beyond (AIWILD) at ICML 2026
Abstract:LLM-agent defenses are typically evaluated one session at a time. In deployment, however, attacks can be distributed across independent agents, teams, and runtimes, leaving each local guardrail with only a sparse fragment. We formalize cross-agent asynchronous campaign attribution: linking sessions from the same latent adversarial campaign without shared runtime state, test-time campaign labels, or attacker identity oracles. We introduce Asynchronous Attribution Fingerprint Vectors ( A^2FV ), a lightweight proxy-side reference protocol for scoring pairwise campaign similarity from proxy-observable tool-use, timing, and prompt residue. We also construct SCD-v1, a controlled persona-matched benchmark with benign traffic, isolated attacks, multi-session campaigns, matched non-oracle evasion, and leakage audits. On SCD-v1, A^2FV achieves 0.82 pairwise AUC for campaign linking, while score-only adaptations of per-session detectors and chunked LLM judges remain near chance under the same task. The strongest fixed signal is carried by structural and stylometric residue, while timing is retained as a diagnostic channel for richer proxy traces. Crossed-style controls show that the signal is partly style-sensitive but not reducible to style alone. Static and dimension-aware non-oracle stress tests further show that pairwise separability persists under controlled evasion. These results establish cross-agent campaign attribution as a distinct evaluation layer for securing LLM agents in the wild.
[AI-51] Agent Trails: Towards Trust and Reuse for Agent ic Tasks VLDB2026
链接: https://arxiv.org/abs/2607.18816
作者: Eden Wu,Sonia Castelo,Yurong Liu,Cláudio T. Silva,Juliana Freire
类目: Databases (cs.DB); Artificial Intelligence (cs.AI)
备注: 4 pages, 4 figures. Short paper accepted at the Workshop on Systems for Data-centric Agents with Human-in-the-loop (DASHSys 2026), co-located with VLDB 2026
Abstract:LLM-powered agents increasingly tackle complex tasks by invoking tools, querying databases, executing code, and manipulating intermediate artifacts. These agents follow trajectories that are typically stored as chronological logs, obscuring the underlying dataflow – the dependencies between their actions and the artifacts they create and manipulate. This limits developers’ ability to understand the agents’ trails, compare executions, debug failures, and re-use the computations. We present AgentTrails, a prototype system for agent provenance and sensemaking. AgentTrails converts raw trajectories into structured provenance graphs, where tool calls are modeled as computational actions and inputs and outputs as data artifacts. The system supports the comparison of executions by placing multiple provenance graphs on a shared canvas and constructing a joined quotient graph that aligns recurring tools, artifacts, and dependency structures across trajectories. On top of this representation, AgentTrails supports pattern extraction, downstream analysis, and skill abstraction. We demonstrate AgentTrails on real-world agent trajectories, showing that it reveals hidden dependencies, aligns divergent executions, and surfaces recurring tool-use patterns beyond chronological logs.
[AI-52] SkillSight: Seeing Through Shared Descriptions for Accurate Skill Retrieval
链接: https://arxiv.org/abs/2607.18785
作者: Jinying Xiao,Bin Ji,Shasha Li,Xiaodong Liu,Ma Jun,Jiacheng Jie,Chao Wang,Nyima Tashi,Jie Yu
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 4 figures
Abstract:As large language model agents gain access to increasingly large skill libraries, retrieving the right skill becomes critical to reliable capability selection and execution. Existing retrievers often treat skill descriptions as ordinary documents, overlooking their highly regular structure: shared descriptive patterns recur across many skills while providing little evidence for distinguishing the required capability. We show that this shared descriptive background systematically contributes to dense relevance scores, induces a pronounced energy gap between queries and skill documents, and obscures task-relevant signals. Based on this observation, we propose SkillSight, a training-free retrieval framework that calibrates shared background in both semantic and lexical spaces. Semantic Background Calibration estimates a background subspace from generic tokens identified by IDF, reducing similarity induced by shared descriptive patterns, while Lexical Evidence Calibration downweights shared background tokens to recover discriminative token-level evidence. Experiments on SRA-Bench and SkillBench-Supp demonstrate consistent improvements across retrieval metrics, with SkillSight improving Recall@10 by up to 20.21 percentage points over the original dense retriever. In end-to-end evaluation, SkillSight achieves the best overall performance across three agent models and outperforms LLM Selection by up to 4.97 percentage points. It is also up to 1,248 times faster than the Dense + Reranker baseline. These results identify shared descriptive background as a key source of bias in skill retrieval and demonstrate that explicitly calibrating it enables accurate and efficient skill selection without additional training. Our code is available at this https URL.
[AI-53] ConceptCF: Concept-based Counterfactuals for the Explainability of Time Series
链接: https://arxiv.org/abs/2607.18748
作者: Annemarie Jutte,Faizan Ahmed,Jeroen Linssen,Maurice van Keulen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:This paper proposes ConceptCF, a method for counterfactual generation that operates on human-interpretable concepts. In high-stakes domains such as healthcare and predictive maintenance, artificial intelligence models can increase efficiency and safety. Explainability is key to ensure these models rely on causal relationships rather than spurious correlations. Counterfactual explanations identify minimal modifications that would change a model’s predictions. Existing methods for time series operate on individual points or subsequences without ensuring interpretability of the mutations. ConceptCF instead modifies meaningful concepts. As a result we can provide explanations in terms of these concepts, for example ``the model’s prediction would be Sit' instead of Walk’ if you increase the scale of the movement’'. In this paper, the concepts are constructed through time series decomposition, resulting in concepts such as scale, and frequency bands. Counterfactuals are generated using a genetic algorithm that optimizes the concept mutations. Evaluation against five state-of-the-art approaches demonstrates that ConceptCF consistently achieves top-tier performance across validity, confidence, proximity, sparsity and plausibility metrics.
[AI-54] One Rewrite to Fix Them All? Type-Aware Repair Allocation for Text-to-Image Prompt Optimization
链接: https://arxiv.org/abs/2607.18724
作者: Haoyue Liu,Xiaoyu Ma,Ye Chen,Shuguang Cui,Xiaoying Tang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Text-to-image (T2I) generators often fail to follow their prompts faithfully, producing wrong counts, swapped attributes, ambiguous relations, and illegible text. Prompt optimization repairs such failures by rewriting the user prompt, requiring no generator retraining, and has yielded promising results. However, existing optimizers absorb heterogeneous failures into one uniform prompt expansion, even though each calls for different repair language. We formulate semantic prompt optimization as atomic repair allocation: each failed proposition is routed to a type-conditioned repair operator before the resulting local constraints are compiled into one executable prompt. We instantiate this formulation in the training-free Type-Aware Repair Allocation (TARA) framework, which separates diagnosis, allocation, compilation, and a semantic repair gate, an accept-or-revert controller over exactly one prescribed repair that prevents semantic regressions. Extensive experiments on DSG and TIFA across four frozen generators demonstrate that TARA achieves the best semantic accuracy in all eight benchmark-generator cells, improving over VisualPrompter by 5.6 and 2.6 points on DSG and TIFA, respectively, while maintaining image quality and running fastest in our matched local setting at 16.0 seconds versus 20.0 seconds per prompt.
[AI-55] DWM: Separating World Effects from Actions in Latent World Models
链接: https://arxiv.org/abs/2607.18715
作者: Yi-Ge Zhang,Tianqi Du,Qi Zhang,Yisen Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Latent world models underpin much of modern model-based control, yet current action-conditioned formulations supervise the next-latent transition with a single, undifferentiated target, forcing a monolithic learning signal to absorb every source of state change. In real world, however, transitions arise from two heterogeneous sources: an action-driven component induced by the agent, and an action-invariant world effect – the change that would still occur under a null action, dictated by the environment’s intrinsic dynamics (e.g., gravity-driven sliding, inertia, contact rebound, and persistent drift). Fusing them into a single target entangles the two inside the latent transition, prevents the model from attributing observed changes to their underlying causes, and undermines the transferability of the learned dynamics. We introduce DWM (Decomposed World Model), a supervision-level framework that operationalizes this decomposition. DWM augments the predictor of a latent world model with an auxiliary world head, regularized by a normalized world-contrastive objective to be action-invariant, while the original pred head is coupled to it via an orthogonality constraint; together, the two signals induce an explicit additive decomposition of the predicted transition into an action-invariant and a complementary action-driven component, without altering the underlying architecture or inference pipeline. To evaluate DWM under persistent world effects, we construct W-variants of three standard control benchmarks – PushT-W, Reacher-W, and TwoRoom-W – each instantiating a distinct action-invariant dynamic. DWM matches strong baselines on the flat counterparts and delivers a mean absolute improvement of 13.1% in CEM planning success across the W-variants. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2607.18715 [cs.AI] (or arXiv:2607.18715v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2607.18715 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yi-Ge Zhang [view email] [v1] Tue, 21 Jul 2026 05:13:26 UTC (551 KB) Full-text links: Access Paper: View a PDF of the paper titled DWM: Separating World Effects from Actions in Latent World Models, by Yi-Ge Zhang and 3 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI 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?) 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-56] What the Waveform Knows: Transparent-first Speech and Audio Intelligence with Caption Studio
链接: https://arxiv.org/abs/2607.18704
作者: Cheng Siong Chin,Jianhua Zhang,Mohan Venkateshkumar
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注:
Abstract:Caption Studio is a transparency-first speech and audio intelligence platform that transforms spoken audio and video into structured, searchable content through automated transcription, speaker diarization, speech analytics, signal-level audio analysis, and subtitle generation. The system is built on a FastAPI backend with a real-time dashboard and adopts a three-layer architecture comprising (i) a transcription and diarization core based on Whisper-class automatic speech recognition and pyannote speaker diarization, (ii) an audio intelligence layer that extracts acoustic and linguistic features, including waveforms, spectrograms, pitch, speaking rate, silence, filler-word frequency, and sentiment, directly from the audio signal, and (iii) an integration layer that supports data export and downstream workflow integration. A principal contribution of this work is the transparency-first framework, in which every reported metric is explicitly identified as measured, derived, or unavailable, thereby improving the traceability, interpretability, and reliability of speech analytics. The paper presents the system architecture, benchmarking methodology, explainability and uncertainty framework, and key considerations for enterprise-scale deployment.
[AI-57] Do AI-Native Biotechs Need Departments? Benchmarking Company World Models for AI-Driven Drug Development
链接: https://arxiv.org/abs/2607.18696
作者: Yinan Wang
类目: Artificial Intelligence (cs.AI)
备注: Working paper. Includes public no-label benchmark cases and dry-lab evaluation artifacts. No wet-lab, patient-level, clinical, regulatory, or investment validation is claimed
Abstract:AI-native biotechnology companies are often designed by copying human biotech org charts into agent roles. We argue for a different abstraction: a Company World Model, defined as a persistent asset-to-value state representation with transition models, explicit value functions, planning, and updating across scientific, regulatory, BD, commercial, financial, and execution constraints. We introduce a dry-lab benchmark for testing whether AI-agent organizations should mimic departments or operate around such a world model. The benchmark contains 45 retrospective public-information decision cases with strict time cutoffs, hidden outcomes, common schemas, automatic scoring, and blinded pairwise judging. We compare human-org-mimic, stronger human-org-mimic-plus, AI-native asset-centric, and AI-native value-conversion architectures. The value-conversion architecture is a prompt-level approximation of a Company World Model: a Live Asset Value Record updated by Deal, Approval, Revenue, and Investment Arbiter loops. Under a success function defined by external BD, regulatory approval and launch, and revenue discipline, it achieved the highest automatic value-conversion score and was strongly preferred over the original baselines by value-specific blinded judges. Stress tests narrowed the claim: a stronger human baseline remained competitive, and a neutral judge did not show robust value-conversion dominance. Codex-only mechanistic ablations suggest that Revenue Room, Deal Room, and Approval Room carry useful work under the target objective. The central finding is objective-sensitive: departments may remain useful governance views, but the core AI-native operating primitive should be a shared, predictive asset-to-value state rather than a static human org chart. The study is dry-lab only and does not establish real-world drug success, clinical benefit, or revenue prediction accuracy.
[AI-58] SciHazard: A Benchmark for Measuring Scientific Safety Risks with Decomposed Harm Scoring
链接: https://arxiv.org/abs/2607.18665
作者: Chunxiao Li,Yuan Xiong,Lijun Li,Tianyi Du,Wenlong Zhang,Lei Bai,Jing Shao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) increasingly support science, but they can also convert hazardous scientific knowledge into actionable misuse guidance. Existing benchmarks often rely on templated queries disconnected from real-world hazards, and employ LLM-as-a-Judge paradigms without domain grounding. To address this, we introduce SciHazard, a real-world-grounded benchmark for scientific risks and a dataset agnostic evaluation framework for measuring harmfulness. SciHazard contains 2400 hazardous questions and 600 oversafety questions across 12 disciplines, with both queries grounded in regulated entities and documented failure scenarios. To compute \textscDeHarm-Score , we develop a decomposed evaluating procedure that combines query hazard severity, refusal behavior, and response-level risk. For non-refused responses, it further decomposes response-level harm into \textscExecutability, quantified via dynamic checklists with importance weighting, and \textscNet-new risk, assessed through retrieval-augmented claim extraction and synthesis-barrier verification. An expert-validation study shows that \textscDeHarm-Score improves agreement with expert annotations by 90.17% over the strongest baseline. We benchmark 31 frontier LLMs and deep research agents in an extensive scientific safety evaluation. Notably, deep research agents yield 32.3% higher mean \textscDeHarm-Score than standard LLMs, exposing autonomous agents as a critical blind spot in current safety defenses. Code and dataset are available at this https URL.
[AI-59] CPInj: Uncovering Prompt Injection Risks in Textual Collaborative Prompt Optimization
链接: https://arxiv.org/abs/2607.18622
作者: Xinting Liao,Behnoosh Zamanlooy,Masoumeh Shafieinejad,David B. Emerson,Ruinan Jin,Deval Pandya,Xiaoxiao Li
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: Accepted by COLM 2026 and AI4GOOD workshop
Abstract:Textual Collaborative Prompt Optimization (TCPO) extends Textgrad (Yuksekgonul et al., 2025) to a decentralized setting by allowing multiple clients to jointly improve prompts for large language models (LLMs) while keeping their data locally. Its reliance on free-form textual updating and aggregation introduces a new and largely unexplored attack surface, i.e., malicious instructions can be injected into local prompts and propagated through server-side prompt aggregation. Unlike conventional prompt injection attacks, attacking TCPO targets the collaborative optimization loop in TCPO. This setting is more challenging because malicious instructions must survive aggregation, persist through subsequent benign prompt optimization, and evade server-side defenses. To expose this risk, we propose CPInj, a collaborative prompt injection attack that contaminates the aggregated global prompt with malicious instructions, degrades downstream task performance, resists purification by prompt optimization on benign clients, and evades advanced detection-based defenses on the server. We find that current defense methods are ineffective against CPInj. To mitigate this attack, we further propose a defense-oriented aggregation method, i.e., APAgg, which purifies malicious instructions and partially recovers TCPO utility. We conduct extensive experiments across three LLM families and five reasoning tasks in math, logic, and medicine. The results demonstrate that our proposed attack reveals a critical vulnerability in TCPO. Although we take a first step toward mitigation, the attack remains highly effective and far from fully resolved, calling for more robust defense for TCPO.
[AI-60] Intelligent Multi-UAV Navigation in ITNTNs: A Hierarchical LLM Approach
链接: https://arxiv.org/abs/2607.18604
作者: Zijiang Yan,Hao Zhou,Wael Jaafar,Jianhua Pei,Ping Wang,Halim Yanikomeroglu,Hina Tabassum
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI); Systems and Control (eess.SY)
备注: This paper has been accepted by IEEE GLOBECOM 2026
Abstract:The deployment of high-speed Uncrewed Aerial Vehicles (UAVs) in 3D aerial highways necessitates robust coordination of physical flight kinematics and multi-tier network handovers. While Deep Reinforcement Learning (DRL) offers rapid tactical control, it lacks the zero-shot strategic reasoning required to quickly adapt to dynamic Integrated Terrestrial and Non-Terrestrial Networks (ITNTNs). Conversely, Large Language Models (LLMs) excel at semantic reasoning but suffer from high inference latency, rendering them unsuitable for real-time aerodynamic control. To bridge this gap, we propose a novel Hierarchical LLM-driven control framework. A massive cloud-based LLM deployed on a High-Altitude Platform Station (HAPS) manages slow-timescale global load balancing, while lightweight edge-LLMs on individual UAVs translate local observations into tactical sub-goals. These sub-goals guide a fast-timescale physical DRL controller to execute collision-free, handover-aware trajectories. Simulation results demonstrate that our agentic architecture significantly reduces collision rates and improves aggregate system throughput compared to existing baselines.
[AI-61] Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States
链接: https://arxiv.org/abs/2607.18589
作者: Armin Sommer
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Reinforcement learning is conventionally divided into model-based and model-free methods. In this taxonomy, model-based methods perform lookahead planning over a learned world model, whereas model-free methods learn a reactive state-action mapping. Recent work, however, has shown that planning can emerge from model-free reinforcement learning alone. The conditions under which this behavior emerges from a pure reward-maximization objective have so far remained unclear. In this paper, we present evidence that, in the observed cases, the hidden-state structure of the neural architecture is the deciding factor. We find that a network of relational hidden states, each anchored to an environment state and exchanging messages along learned relations, acquires a planning mechanism. These hidden states recover the environment’s transition structure in their learned relations, and improve the policy at decision time by planning over the learned graph. In a matched control agent that must additionally discover which cells represent which states, no such binding arises, and no planning follows from it. We argue that this explains the observed phenomenon of emergent planning in model-free reinforcement learning and raises the question of how common such emergent planning might be more generally. Finally, we hypothesize that the discovered mechanism could describe how planning emerges from pure reward maximization in the human brain through a neural architectural prior.
[AI-62] When Does Machine Learning Beat Value Sorting? A Three-Dataset Diagnostic of Exposure-Weighted Shipment Prioritization
链接: https://arxiv.org/abs/2607.18573
作者: Jize Li
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 1 figure, 4 tables, accepted for presentation at AIIIP 2026
Abstract:Delay-risk models are usually judged by predictive accuracy. What matters in practice is narrower: with capacity to review only a few shipments, which ones should a manager check first? We evaluate whether machine learning clears a demanding no-model baseline: inspect the highest-value shipments first. Across three real supply-chain contexts: SCMS procurement, DataCo logistics, and Olist e-commerce, we use leakage-controlled rolling-origin evaluation and 1000-sample paired bootstrap confidence intervals. Ranking by predicted delay severity times known value (M1) beats severity-only ranking in all three datasets, yet it does not generally beat value sorting. At a 10% review budget, M1 minus VALUE_ONLY is -5.5 percentage points (pp) for SCMS, +10.1 pp for DataCo, and -4.9 pp for Olist. The divide is consistent with severity learnability: DataCo has R^2 = 0.27 and calibration bias of +0.01 days, whereas SCMS and Olist have R^2 of approximately -0.02 and negative calibration bias. Nested-CV cost-sensitive retraining does not deliver a stable improvement over M1. Rather than proposing a new learning algorithm, this paper presents a deployment diagnostic and evaluation protocol. Value sorting should remain a permanent benchmark, and ML should be deployed only after severity learnability and calibration have been audited and the model clears that gate under leakage-controlled rolling-origin evaluation.
[AI-63] Attacking Graph Foundation Models Through Their Shared Representation
链接: https://arxiv.org/abs/2607.18567
作者: Pankaj Kumar,Subhankar Mishra
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
备注:
Abstract:A graph foundation model generalizes across graph domains by mapping every input into one shared representation before any task reasoning. We call this map the alignment layer, the component that separates a graph foundation model from a graph neural network, and we show it is a distinct attack surface that prior work has not studied. We attack it at inference time, with no access to training, on six public models spanning spectral tokenizers, text embedding spaces, and a discrete codebook. A directed representation-space perturbation collapses every model, but at a budget comparable to the representation norm a plain graph network also needs, with one exception: OpenGraph, whose spectral tokenizer collapses at a fifth of that budget, an alignment-specific fragility a plain network does not share and which a same-representation control traces to the tokenizer rather than the decoder. A realizable input-space attack that edits edges, features, or text removes at least half the correct predictions on three of the six models at peak. How much of this fragility an input-access attacker realizes tracks how directly the decoder reads the representation, and not the clean accuracy a task leaves; we measure this carrier gain structurally from the decoder’s local Lipschitz sensitivity, and report clean-accuracy headroom as a within-model ordering heuristic that does not survive on realizable attacks.
[AI-64] Operational Proto-Introspection in Looped Language Models: Process-Quality Taps Executable Branching and the Readout-Control Boundary
链接: https://arxiv.org/abs/2607.18553
作者: Jan Kirin
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 49 pages, 8 figures. Paper 1 of a two-paper series; companion methodology paper submitted separately
Abstract:Can a language model read the quality of ongoing computation, and can an external intervention turn that readout into better outcomes? We test both questions in a frozen 2.6B looped transformer, Ouro-RLTT. On GSM8K, a strict pre-answer probe excludes the answer region and gold value yet predicts eventual success: hidden states plus length and log-probability shortcuts reach AUROC 0.797, versus 0.731 for the shortcuts alone (incremental +0.066; task-clustered 95% CI [+0.021, +0.112]; 170 tasks, 680 candidates). Low-capacity taps also read role-specialized properties: task-disjoint branch survival reaches 0.9697 oracle retention, content ranking reaches 0.6310 macro top-1, and generated-branch correctness reaches AUROC 0.7755. A non-looped control replicates a candidate-quality readout, so recurrence is not required for every signal. We build branch/carry/prune machinery over Ouro’s 192-slot recurrent cache, including branch-specific cache lineage and a bit-exact residual-capture splice that recomputes only the affected suffix and saves up to 88% of per-branch layer passes. No frozen intervention produces a validated capability gain. Directional steering is an established negative; a four-task matched-sampling comparison removes evidence for a frozen-fork gain but cannot estimate a general deficit; terminal selection remains unresolved and underpowered; and bounded LoRA changes surface behavior without improving net reachability. A two-null audit does not support the simplest span-misalignment explanation. We call this readable-but-not-yet-usable property operational proto-introspection. The model is not consulting our probes: we read its hidden trajectories, and our interventions fail to convert those readouts into validated capability. The pre-answer result is limited to one domain. Load-bearing values use source-item-disjoint splits and antisymmetrized evaluation where applicable. Comments: 49 pages, 8 figures. Paper 1 of a two-paper series; companion methodology paper submitted separately Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.18553 [cs.LG] (or arXiv:2607.18553v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.18553 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-65] MAGE: Human-Like Macro Placement via Agent ic Multimodal Reasoning
链接: https://arxiv.org/abs/2607.18536
作者: Andrew B. Kahng,Sayak Kundu,Bodhisatta Pramanik
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: 16 pages
Abstract:Macro placement still requires substantial manual refinement in industrial physical design flows. We present MAGE (Macro Placement Agentic Engine), a multimodal multi-agent framework for macro placement refinement. MAGE decomposes the macro placement task into a six-phase workflow that combines structured floorplanning rules, visual checks, and iterative refinement. Expert floorplanning knowledge is encoded through natural-language directives and validation criteria, rather than learned from labeled placement data. A tournament-style refinement mode evaluates multiple candidate placements and propagates feedback from higher-quality solutions. We also introduce four metrics for quantifying human-likeness in macro placement: notch score, whitespace score, pocket score, and alignment score. These metrics capture structural properties used by expert designers but not directly measured by conventional PPA metrics. Across nine designs in NanGate45 and GlobalFoundries 12nm enablements, MAGE achieves geometric-mean improvements of 11.1%-19.3% in WNS and 70.0%-74.0% in TNS over commercial macro placers. On the three NanGate45 designs, for which human-expert and Hier-RTLMP baselines are available, MAGE improves WNS and TNS by 18.3% and 72.5% over the human expert, and by 47.0% and 80.4% over Hier-RTLMP, with comparable wirelength and power. On human-likeness metrics, MAGE improves the overall score by 6%-48% over all baselines. Additional case studies on anonymized netlists, unseen designs, dense rectilinear floorplans, and high-utilization settings show that the framework transfers to new placement settings without design-specific retraining.
[AI-66] Censoring-Aware In-Context Learning for Generalized Supplier Lead Time Estimation in Supply Chain Planning
链接: https://arxiv.org/abs/2607.18530
作者: Christopher Wang,Sebastien Ouellet,Behrouz Haji Soleimani,Ali Etemad
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Supplier lead time forecasting is a central input to material requirements planning, inventory optimization, and supply chain risk management. However, many industrial lead time datasets are naturally right-censored: at the time forecasts are required, some orders have not yet arrived. Standard regression and classification approaches discard this information, while conventional survival models require task-specific modeling. We propose LeadTime-ICL (LT-ICL), a censoring-aware in-context learning model for probabilistic lead time forecasting. LT-ICL combines a transformer backbone with a conditional normalizing-flow head, producing a full predictive distribution over lead times. The model is pretrained on synthetic right-censored lead time tasks, enabling in-context adaptation to new industrial datasets without task-specific parameter updates. We provide theoretical support for this formulation by showing that excess CRPS is bounded by prior misspecification and amortized approximation errors, providing clear direction for improving forecasting performance. We evaluate LT-ICL on 24 proprietary supply-chain datasets spanning seven industries. LT-ICL achieves the lowest point-forecasting error on 15 of the 24 datasets, and the lowest probabilistic forecasting error on 14 datasets, yielding the best average rank across both. These results support right-censored probabilistic forecasting as a practical formulation for supplier lead time prediction and demonstrate that pretrained in-context models can provide accurate, low-adaptation-cost forecasting for industrial planning systems.
[AI-67] Automated Data Engineering and Feature Selection for the Case Study of Warpage Detection in Fused Deposition Modeling
链接: https://arxiv.org/abs/2607.18515
作者: Saleh Valizadeh Sotubadi,Nazanin Mahjourian,Vinh Nguyen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:This study contributes toward development of an Automated Data Processing (ADP) framework designed to evaluate and reinforce optimal machine learning model-feature combinations for predictive tasks in fused deposition modeling (FDM) process datasets. The methodology is centered around a reinforcement learning-inspired policy updating mechanism, where multiple machine learning models are trained on both full feature sets and feature subsets selected through Shapley-based Explainable AI (SHAP XAI) across 217 datasets. At each episode, the framework assesses the predictive accuracy and F1-scores of each model-feature pair, computes a scalar reward, and updates Q values to guide future model selection. SHAP XAI feature importance was employed to generate reduced yet informative feature subsets to enable the framework to explore performance with dimensionality. The policy was shown to evolve over multiple episodes, with reward distributions used to visualize performance stability. Overall, results indicate that leveraging the ADP framework through XAI algorithms successfully converges toward optimal model-feature configurations with improved accuracy and stability. Specifically, the proposed framework improves the test-set AUC from 0.9248 to 0.9731 and increases the mean reward value by more than fifty percent compared with the baseline full-feature configuration.
[AI-68] he Open Ant: A Robot Platform for Reinforcement Learning Research
链接: https://arxiv.org/abs/2607.18488
作者: Elena Sorina Lupu,Patrick Spieler,Khurram Javed,Kris De Asis,John D. Martin,Martha Steenstrup,Joseph Modayil
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注: Published in the Reinforcement Learning Conference
Abstract:Reinforcement learning (RL) research has demonstrated success in both physical and simulated domains; however, the predominant methodology remains rooted in simulations. The predominance of simulations makes translating research to physical reality uncertain for both algorithms and researchers. We propose a physical platform that is designed to simplify the transition. In this paper, we present the Open Ant: a physical variant of the commonly used Gymnasium Ant environment, along with a simulation. We demonstrate that competent walking policies can be learned from scratch in approximately one hour directly from the physical robot’s experience for two substantially different RL algorithms: SARSA( \lambda ) and Soft Actor-Critic (SAC). Separately, we show policies that were learned in simulation transfer to reality. We also examine how well the platform supports a nimble experimental ecosystem. Specifically, we observe the speed with which new users from diverse backgrounds achieve their first success with the platform, and how easily the platform can be repaired and updated when hardware issues arise. Both the hardware design and software are available as open-source on GitHub for ease of customization. In summary, we advocate for the use of the Open Ant for RL researchers who frequently use simulated environments, so they can more easily include robot experiments in their evaluations.
[AI-69] rusted Credentials Untrusted Behavior: Benchmarking LLM -Agent Security in High-Performance Computing
链接: https://arxiv.org/abs/2607.18485
作者: Jie Li
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM) agents are starting to take on routine work in high-performance computing (HPC), including monitoring Slurm jobs, diagnosing failed builds, inspecting simulation output, and coordinating scientific workflows. To do this work, an agent commonly acts under its user’s credentials and inherits the user’s access to files and the scheduler. This arrangement creates a failure mode that ordinary account-level controls do not capture. Adversarial instructions in a log, tool description, shared file, or peer-agent message may redirect the agent beyond the task the user assigned, even though every resulting command is authenticated and permitted for that account. We refer to this as the hijacked authorized agent problem. Existing agent-security studies explain relevant mechanisms, such as indirect prompt injection and tool misuse, but generally evaluate them in web, enterprise, or personal-assistant settings. HPC security, by contrast, has mature controls for identity and isolation but does not ordinarily represent the intent of a particular task. This paper defines the threat model in the HPC setting, identifies attack surfaces created by schedulers, shared storage, multi-project accounts, and scientific workflows, and examines where current controls fall short. It concludes with a research agenda and a plan for an empirical benchmark, TaskBound.
[AI-70] Governing Well in the Algorithmic Age: The Foundations of Digital Statecraft
链接: https://arxiv.org/abs/2607.18483
作者: Zeynep Engin,Tim Gordon,Viviana Bastidas,Tom Crick,Jon Crowcroft,Jean-Martin Denis,David J. Hand,Lauren Maffeo,Jakob Mökander,Irene Ng,Anastasija Nikiforova,Giulio Quaggiotto,David Uriel Socol de la Osa,Rhonda Syler,Philip Treleaven,Stefaan Verhulst
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Social and Information Networks (cs.SI); Systems and Control (eess.SY)
备注: 27 pages
Abstract:The digital substrate of states – data, algorithms, infrastructure, platforms, applications – is being governed without adequate conceptual foundations. The ability and legitimacy required to govern this substrate, and to govern with it, are simultaneously misaligned, contested, and structurally absent. We introduce digital statecraft as the organising concept for this emerging field, arguing that ‘digital’ reconstitutes the statecraft question rather than merely extending its domain. The concept operates on two dimensions - statecraft over digital systems, concerning the authority and capacity of the state in relation to the digital substrate itself, and statecraft with digital systems, concerning the deployment of algorithmic tools as instruments of governing authority. And it rests on two foundational requirements, technical coherence and legitimate authority, that are genuinely in tension. We derive ten principles of digital statecraft from these foundations, each naming a condition whose absence produces an identifiable and structural governance failure: public interest first, human-machine complementarity, governability by design, systemic coherence, hybrid institutions, adaptive governance, human centricity and civic agency, accountable and traceable authority, judgment across time, and the non-delegable core. This article takes the state as the starting point, the institutional form that developed historically in response to the problem of effective and legitimate public governance, and the only current candidate for which the full set of legitimacy conditions is institutionally available. But the digital statecraft programme holds open a deeper question than just whether states can reform themselves: governing well in the algorithmic age may require rethinking the boundaries, scale, and affiliative basis of statehood itself.
[AI-71] RRPO: Reference-Relative Policy Optimization with Stratified Conditional Rollouts
链接: https://arxiv.org/abs/2607.18470
作者: Yuxin Xiong,Xunyi Jiang,Rohan Surana,Xintong Li,Sheldon Yu,Nikki Lijing Kuang,Ryan A. Rossi,Jingbo Shang,Tong Yu,Julian McAuley,Junda Wu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Group Relative Policy Optimization (GRPO) has shown strong effectiveness in reinforcement learning from verifiable feedback, where sampled rollouts can be compared within a group using task-provided correctness signals. However, extending group-relative optimization beyond verifiable settings is challenging because success in many tasks is not captured by a single correctness criterion. We propose \textbfReference-Relative Policy Optimization (RRPO), which generalizes GRPO by replacing direct correctness-based advantage construction with reference-relative contrastive comparisons. RRPO first uses \emphstratified conditional rollouts to construct positive and negative anchor sets, and then trains a metric projection head with a set-contrastive objective to compare candidate rollouts against these anchors. The resulting alignment scores directly define contrastive advantages: during policy optimization, the projection head is frozen, and the scores are centered within each rollout group in a standard group-relative objective. We evaluate RRPO using anchor-based contrastive advantages throughout policy optimization, without relying on task ground-truth verifiers. Across verifiable reasoning, open-ended generation, and post-SFT settings, RRPO remains competitive with verifier-based optimization, improves over weakly supervised baselines, and provides additional gains after supervised fine-tuning.
[AI-72] Estimating Rare Events in Language Models with Proper Evaluation
链接: https://arxiv.org/abs/2607.18454
作者: Nikita Y. Parulekar,Anqi Liu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 37 pages, 24 figures
Abstract:Quantifying the risk of rare failures in language models, such as those triggered by adversarial distribution shifts or very large-scale deployments, requires estimating probabilities far too small for random sampling. While recent work has formalized Low Probability Estimation, existing pipelines remain fragile in the rarest regimes: estimators can suffer zero-estimate collapse or systematic bias, and standard evaluation losses can become unstable or poorly matched to asymmetric safety costs. In this work, we introduce Gradient Activation Adaptive Multi-Level Splitting (GA-AMLS), which adapts rare-event Monte Carlo methods to the continuous activation space of language models. Specifically, GA-AMLS uses a gradient-based MCMC kernel to navigate activation space, eliminating the zero-estimate collapse of input-space search and replacing the independence assumptions of prior activation-space estimators with conditional sampling under an explicit, heavier-tailed activation prior. We also propose the Shifted-Power Bregman (SPB) Loss, a proper scoring rule that remains finite for zero-estimates and offers tunable asymmetry between underestimation and overestimation penalties. Experiments on small transformer models reveal a bias-variance tradeoff: GA-AMLS achieves the lowest loss under symmetric evaluation, reducing average log-space squared error relative to the strongest baseline across model sizes, while methods with overestimation bias prevail under asymmetric penalties. Our findings highlight that estimator choice should be matched to deployment context. More broadly, our work establishes activation space as a tractable domain for rare-event estimation in language models, circumventing the brittleness of discrete input-space search.
[AI-73] ChainMark: Model-Free LLM Watermarking with Closed-Form Calibration
链接: https://arxiv.org/abs/2607.18445
作者: Chengheng Li-Chen,Kyuhee Kim
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Regulatory regimes such as the EU AI Act mandate machine-readable marking of synthetic text, but existing watermark detectors rely on the generating LM and on heuristic thresholds with no closed-form calibration. We introduce ChainMark, an active watermark that partitions the vocabulary into S states via keyed SHA-256 and forces a hard Markov transition on a fraction rho of positions; the detector replays the partition from the same key in O(n) hash operations, with no LM access. We derive a closed-form S*(n, rho, alpha) mapping a target FPR, text length, and budget to the minimum state count (Theorem 1), prove a universal robustness threshold delta* = 1 - 1/sqrt(2) approximately 29.3% that is invariant in (S, rho, n) (Theorem 2), and generalise both to any k-regular transition topology (Theorem 3). Across three instruction-tuned LLMs and four domains, ChainMark strictly dominates KGW and SWEET under translation and random-substitution attacks at matched budget; a one-corpus empirical recalibration restores the 1% target FPR on natural-language text.
[AI-74] Intelligence from Learnable Novelty
链接: https://arxiv.org/abs/2607.18433
作者: Yanbo Zhang,Michael Levin
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Adaptation and Self-Organizing Systems (nlin.AO)
备注: 24 pages, 10 figures, 4 tables
Abstract:Intelligence appears under different names in different fields: as data compression in statistics and machine learning, as universal computation in dynamical systems, and as adaptive behavior in agents. Each field carries its own objective, and the two most influential drives often fail in mirror image: novelty search, which seeks surprise, is transfixed by a noisy television screen, while the free-energy principle, which avoids surprise, is most content in a dark room. Both failures have a single cause: each objective treats as one quantity the surprise a learner can convert into knowledge and the surprise it never can. Here we show that the learnable part of that information, which we call learnable novelty, yields the seemingly disparate projections of intelligence, and we give a closed-form estimator of it built on a cheap and differentiable reservoir computer. Used as a measure, with no supervision of any kind, the estimator recovers decades of complexity classification, ranking the Turing-complete rule~110 highest among the elementary cellular automata. Used as an objective, its gradient carries a neural cellular automaton from simple dynamics into a regime of solitons, the traveling, colliding structures by which rule~110 computes, as well as organizes the representation of an image encoder around the ten digit classes of MNIST, fully unsupervised: no label ever enters training. Handed to a reinforcement-learning agent as an intrinsic reward, it supplies the exploration that task rewards lack, improving on the task baseline in nine of ten environments and collapsing in none. Complexity generation, abstraction, and exploration, ordinarily pursued with unrelated objectives in separate fields, thus emerge from ascent on one differentiable quantity, and the projections of intelligence gain a common quantitative footing.
[AI-75] Adversarial Robustness of Phishing Email Detection: A Comparative Study of TF-IDF Logistic Regression and Fine-Tuned DistilBERT
链接: https://arxiv.org/abs/2607.18429
作者: Tanveer Ahmed,Seyedali Pourmoafil
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注:
Abstract:Phishing emails remain one of the most persistent cybersecurity threats, and machine-learning classifiers are widely used to detect them. Most reported detection accuracies, however, are measured on clean, in-distribution test data rather than on emails deliberately altered to evade detection. This paper reports a controlled, pairwise comparison of two phishing-detection approaches a TF-IDF + Logistic Regression baseline and a fine-tuned DistilBERT transformer trained on a unified corpus of 82,255 emails drawn from six public datasets and evaluated under three conditions: normal in-distribution, synthetic phishing, and adversarial phishing. Both models exceeded 98% accuracy on clean data yet degraded sharply under adversarial testing: TF-IDF + LR fell to 64.00% (a 34.59-percentage-point drop) and DistilBERT fell to 63.64% (a 35.40-percentage-point drop) a gap of only 0.36 percentage points, equivalent to a single email in the 275-sample adversarial test set. LIME, SHAP, and attention-rollout analysis indicate the two models relied on different evidence yet showed similar vulnerability. Pairwise error analysis shows the models agreed on 54.9% of adversarial samples but each made a similar number of exclusive errors (24 and 25 respectively), indicating partly complementary rather than identical failure modes. The results show that clean-data accuracy does not predict adversarial robustness, and that adversarial testing should be a standard part of phishing-detection evaluation.
[AI-76] Neuro-Symbolic Meta-Policies for Temporal Knowledge-Graph Memory under Partial Observability
链接: https://arxiv.org/abs/2607.18368
作者: Taewoon Kim,Vincent François-Lavet,Michael Cochez
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Partially observable reinforcement learning requires deciding what to retain, retrieve, and forget over time. We introduce a neuro-symbolic meta-policy that learns which symbolic memory heuristic to apply at each decision point while keeping execution symbolic. Our setting uses temporal knowledge-graph memory in RoomKG, where hidden state and observations are represented as Resource Description Framework (RDF) graphs and memory is augmented with temporal RDF triple annotations. The model combines knowledge-graph encoding of memory contents with value heads for question answering, exploration, and forgetting, yielding a controller that is both adaptive and inspectable. This gives the work a direct Semantic Web grounding through RDF-based representation, annotation-compatible graph semantics, and graph-based symbolic operations over explicit memory state. On train/test room splits at long-term memory capacity of 512, the qualifier-aware StarE-GNN configuration achieves the best held-out performance among the compared symbolic, neural, and neuro-symbolic systems while preserving step-level traceability of memory-management decisions.
[AI-77] AlayaWorld: Interactive Long-Horizon World Modeling – Full Technical Report
链接: https://arxiv.org/abs/2607.18367
作者: AlayaWorld Team:Kaipeng Zhang,Chuanhao Li,Yifan Zhan,Yongtao Ge,Yuanyang Yin,Jiaming Tan,Kang He,Liaoyuan Fan,Mingliang Zhai,Ruicong Liu,Xiaojie Xu,Xuangeng Chu,Zhen Li,Zhengyuan Lin,Zhixiang Wang,Zian Meng,Zihui Gao
类目: Artificial Intelligence (cs.AI)
备注: Authors are listed alphabetically by the first name and their role. See the contribution section for details
Abstract:Unlike conventional video game development, which relies on labor-intensive pipelines for asset production, animation, physics, and programming, video world models generate interactive environments from user inputs instantly. It enable us to create customized, explorable, and continuously evolving virtual world from text, an image, or video. Realizing this vision requires four tightly coupled capabilities: interaction, persistent spatiotemporal consistency, stable long-horizon generation, and efficient response. We present AlayaWorld, an interactive long-horizon video world model that generates 24-fps video at 540p and 720p. Built on a 15B video diffusion transformer, AlayaWorld generates short latent chunks autoregressively under camera trajectories and switchable text prompts. Its bounded visual context combines a persistent sink frame, compressed temporal history, geometry-aligned spatial memory, and recent-frame conditioning. To reduce long-term drift, the model is trained with corrupted histories and prediction residuals collected from its own roll-outs. We further introduce a discrete autoregressive distillation formulation that combines distribution-matching distillation, self-forcing++, and consistency distillation, reducing inference from approximately 30 sampling steps to four steps per chunk. On iWorld-Bench, AlayaWorld achieves the best performance over long-horizon generation. Conceived as a full-stack, open-source, and long-term project, AlayaWorld is intended to provide an extensible foundation for future research on interactive video world models.
[AI-78] Physical Self-Supervised Learning: IMU Sensing without Manual Labels
链接: https://arxiv.org/abs/2607.18361
作者: Yuyang Leng,Renyuan Liu,Shaohan Hu,Peijun Zhao,Chun-Fu Chen(Richard),Songqing Chen,Shuochao Yao
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 15 pages, 20 figures. Published in ACM MobiSys 2026
Abstract:Deep neural networks have become a promising approach for IMU-based sensing, but their scalability is fundamentally limited by costly labeled data and poor robustness to heterogeneous devices, placements, and users. Existing unsupervised and self-supervised methods reduce but do not remove this dependence, still requiring labeled data for domain adaptation and largely ignoring known physical structure. We propose physical self-supervised learning, an autoencoder-style paradigm for label-free IMU sensing. We replace the conventional neural decoder with an auto-adaptive physics decoder, a learnable family of kinematic equations that enforces explicit physical structure while adapting across environments, and adopt a hybrid two-stage IMU encoder with reconstruction in a structured latent space to mitigate sensor noise. Our framework further introduces probabilistic frequency-spatial constraints to disentangle sensor and object motion, a multi-view kinematic tree to exploit sparse physical self-supervised signals, and an uncertainty-aware formulation to handle the inherent ambiguity of IMU inference. Evaluated on inertial tracking and full-body motion capture over public datasets and realistic deployments, physical self-supervised learning reduces errors by up to 5x for tracking and 4x for motion capture in challenging generalization scenarios, consistently outperforming state-of-the-art supervised and self-supervised baselines without any labels.
[AI-79] HALLM ARK: Diagnosing Three Failure Modes in LLM Citation Verifiers DATE
链接: https://arxiv.org/abs/2607.18360
作者: Patrik Reizinger,Wieland Brendel
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 59 pages, 7 figures, 40 tables. Benchmark and code: this https URL (v1.2.0); verification tool: this https URL (v1.5.0)
Abstract:Large language models (LLMs) now routinely draft literature reviews and assist with academic writing, which means a higher risk of fabricated references: GPTZero found 53 papers with hallucinated citations among NeurIPS 2025’s accepted set. Rule- and LLM-based verifiers are emerging, but no shared benchmark compares them and gives detailed failure diagnostics. We close that gap with HALLMARK (Hallucination benchmark): 2,526 BibTeX entries spanning 14 hallucination types, three difficulty tiers, six diagnostic sub-tests per entry, and a contamination-resistant held-out split. On it we evaluate a DOI-lookup baseline, frontier LLMs zero-shot, tool-augmented agents, and our own rule-based, co-designed verifier bibtex-updater. Across the benchmark one result is consistent: the false-positive rate, not recall, decides whether a verifier is deployable. HALLMARK makes it concrete through three failure modes: agentic lookups buy recall but inflate false positives; at a venue-realistic base rate, the order-of-magnitude spread in false-positive rates (FPRs) – not recall – governs whether a verifier’s flags are mostly true catches or mostly noise; and most LLMs over-flag papers published past their training cutoff, where only the two latest-cutoff models hold their false-positive rate near in-distribution levels (a signal we report as descriptive, since it is confounded with possible recall of those entries). Thus FPR is the deployment bottleneck, but an undetected fabrication remains the costlier error for the scientific record.
[AI-80] Decode-Time Grammars: Constrained LLM Generation over a Refinement Order of Grammar Frag ments
链接: https://arxiv.org/abs/2607.18357
作者: Shuoming Zhang,Ruiyuan Xu,Haofeng Li,Qiuchu Yu,Yangyu Zhang,Chunwei Xia,Xiaobing Feng,Chenxi Wang,Huimin Cui,Jiacheng Zhao
类目: Programming Languages (cs.PL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Large language models now write a growing share of the world’s code, increasingly inside agents and serving systems that compile, execute, or dispatch generated code without line-by-line review. This works well for mainstream languages but remains brittle for low-resource programming surfaces such as domain-specific languages, custom library APIs, and command-line tools. Even under grammar-constrained decoding, a model can still produce references invalid in the current environment: a buffer never declared, a column absent from the schema, a function the library does not provide, or an unsupported CLI option. This paper introduces decode-time grammars: grammar fragments instantiated during generation from a runtime environment Gamma. A region-specific policy selects a fragment for each hole, and a tightening operator replaces open reference positions with Gamma-typed slots whose candidates are exactly the names, fields, APIs, or options available at that point. Newly generated declarations enter Gamma before later regions are decoded, so the constraining grammar can depend on the prefix already generated. This ensures not only grammatical correctness but also semantic correctness, by preventing references to undefined symbols. We formalize grammar fragments as environment-indexed grammars ordered by refinement, prove No-Ghost soundness for Gamma-slotted fragments, show that refinement preserves this support-set guarantee, and characterize the boundary of mask-enforceable properties. We implement the approach in gproj with offline grammar induction and online policy resolution. Across TileLang, SQL, and P4, with models from 0.6B to 236B parameters, gproj eliminates ghost references by construction at moderate overhead over standard constrained decoding. Subjects: Programming Languages (cs.PL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2607.18357 [cs.PL] (or arXiv:2607.18357v1 [cs.PL] for this version) https://doi.org/10.48550/arXiv.2607.18357 Focus to learn more arXiv-issued DOI via DataCite
[AI-81] CODENS: Transforming Code Changes into Living Accessible and Queryable Documentation
链接: https://arxiv.org/abs/2607.18356
作者: Abdelhak Kelious,Chyrine Tahri,Eliot Bardet
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: Accepted at the 26th ACM Symposium on Document Engineering (DocEng 2026). DOI: https://doi.org/10.1145/3820755.3832807
Abstract:Maintaining up-to-date code documentation is difficult in fast-moving repositories because design knowledge is scattered across source files and pull requests. We present CODENS , a system that turns pull requests into living, accessible, and queryable documentation for production codebases. CODENS incrementally builds a typed software knowledge graph from pull requests, enriches components through schema-driven semantic extraction, derives typed relations between them, and exposes the resulting knowledge through three retrieval modes, including agent-guided graph traversal for repository-level question answering. The system also preserves semantic change history across pull requests and integrates both answer-quality and operational evaluation metrics. We evaluate CODENS on a client Ruby on Rails project in production. Results show that CODENS produces highly relevant and well-grounded answers, while qualitative feedback highlights a remaining challenge in concise, documentation-oriented synthesis.
[AI-82] Multi-layer MIMO Relay as Deep Physical Neural Networks: Power Amplifiers as Activation Functions
链接: https://arxiv.org/abs/2607.18354
作者: Meng Hua,Itsik Bergel,Deniz Gündüz
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Theory (cs.IT); Signal Processing (eess.SP)
备注:
Abstract:Wireless physical neural networks (WPNNs) embed neural computation directly into analog hardware, offering lower energy consumption and latency than conventional digital implementations. In this paper, we propose a deep WPNN in which nonlinear activations are realized by a multi-hop multiple-input multiple-output (MIMO) relay network, in which each relay implements a trainable complex linear gain and bias, followed by the power amplifier’s intrinsic nonlinearity acting as an activation function. The cascade of multiple relays therefore realizes an over-the-air fully connected network whose parameters can be trained end-to-end. We develop two transceiver designs for different channel state information (CSI) availability scenarios: a least squares (LS)-based scheme requiring only receiver-side CSI, and a singular-value-decomposition (SVD)-based scheme requiring both transmitter-side and receiver-side CSI. Simulation results show that the proposed architecture enables accurate over-the-air inference for image classification. In particular, the results highlight the advantage of exploiting hardware nonlinearity for enhanced inference capability.
[AI-83] MambaLSTM: A Spatio-Temporal Framework for Enhanced Traffic Accident Risk Prediction
链接: https://arxiv.org/abs/2607.18353
作者: Zhen Yu,Yachao Yuan,Zixiang Peng,Muting Li,Thar Baker
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:In traffic accident risk prediction, most studies overlook the extra noise that could be incorporated when fusing temporal features into spatial features, and some models struggle to capture global correlations among spatial regions. To address these challenges, we propose a novel traffic accident risk prediction framework named MambaLSTM. First, we develop a squeeze-and-excitation temporal feature fusion module to integrate temporal information without compromising spatio-temporal integrity. Second, we introduce a new patch embedding module for effectively capturing semantic relationships among spatially adjacent regions. Additionally, we introduce a Mamba block based on state-space models to model global spatial semantics in urban regions. Finally, we propose a MambaLSTM unit to efficiently capture long- and short-term temporal dependencies for identifying dynamic risk patterns. Extensive experiments on real-world datasets demonstrate the proposed model’s superiority over state-of-the-art methods. The code is released at this https URL.
[AI-84] An Analysis of Residual-Stream Geometry Across Transformer Depth
链接: https://arxiv.org/abs/2607.18348
作者: Sunit Bhattacharya,Ravi Shankar Kolli
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:We propose a transition-centred geometric analysis of transformer residual streams. Relative displacement measures how \emphfar representations move between consecutive layers, and orthogonal Procrustes analysis separates each transition into a rigid rotation and a non-rigid residual. Across six instruction-tuned models, on code generation and cross-lingual translation, these measurements reveal reproducible depth regularities. Relative displacement is strongly layer-dependent; typically larger early and late, with a quieter middle third; and nearly invariant across conditions within each model. Rotation magnitude is nearly constant across depth, while Procrustes residual and angle concentration remain depth-modulated, with residual peaking at the final transition. During generation, non-English targets show larger final-layer displacement and residual than English targets. We present these as descriptive geometric regularities, not as measures of computational effort or causal explanations. The contribution is a measurement framework for residual-stream transitions and evidence that, in the settings studied here, depth curves are model-dependent and largely condition-stable.
[AI-85] Addressing Limited Data in Auditory Attention Decoding with Diffusion Generative Models
链接: https://arxiv.org/abs/2607.18345
作者: David Rannaleet,Victor Gunnarsson,Bo Bernhardsson,Martin A. Skoglund,Emina Alickovic
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Limited training data constrains deep learning models for Auditory Attention Decoding (AAD) in hearing aids (HAs). AAD uses electroencephalogram (EEG) data to decode listener’s attention, enabling real-time tracking of specific sound sources. However, achieving high AAD performance with short time windows typical in HAs (=1s) is challenging due to the scarcity of real-world speech-evoked EEG data. To address this issue, we investigate diffusion probabilistic models (DPMs) for generating synthetic speech-evoked EEG data. DPMs learn the underlying complex data structure through a denoising process and can generate realistic samples suitable for data augmentation. We evaluate the use of synthetic EEG data for augmenting datasets in locus-of-attention (LoA) classification tasks. Our experiments demonstrate that DPMs can generate realistic EEG signals and that incorporating synthetic data significantly improves AAD performance compared to models trained solely on measured EEG data (p0.05). These results highlight the potential of diffusion-based data augmentation to mitigate training data limitations and improve the robustness of short-window AAD models in HA applications.
[AI-86] Federated Lightweight Fine-Tuning ACCV2026
链接: https://arxiv.org/abs/2607.18343
作者: Radhakrishna Achanta,Will Reed
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 22 pages, 10 figures, 6 tables. Extended preprint with appendix. Under review at ACCV 2026
Abstract:Federated fine-tuning is bottlenecked by communication: FedAvg and pseudo-gradient schemes transmit a payload that scales with the model, and gradient compression shrinks it by only a constant factor. We take a different lever. Mapping networks generate a network’s weights from a small trainable latent through a frozen affine projection; because the map is shared and affine, averaging latents is exactly averaging the generated weights. We turn this into a practical low-bandwidth federated channel with two changes: a low-rank, seed-regenerable factorisation of the projection (cutting generator memory from ~80 GB to ~10 MB), and a delta formulation \theta = \theta^\mathrmpre + U V^\top z that learns an additive correction around a shared centrally-pretrained base – federated fine-tuning, which is what makes the method work at scale. A frozen orthogonal classifier head further removes the head from the payload while improving accuracy. On CIFAR-100 with ResNet-18+GroupNorm, our method (FLITE, Federated Low-rank Iterative Training Engine) communicates 1,280 floats (~5 KB) per client per round – an 8718x reduction – and reaches 74.67%, within ~0.5 pp of full-weight FedAvg. The averaging identity holds to floating-point precision ( 6 \times 10^-8 ); the method sits one to two orders of magnitude below PowerSGD and top-k on the bandwidth-accuracy Pareto; it matches or exceeds full-weight FedAvg under strong non-IID skew. int4 latents reach 648 bytes per round at unchanged accuracy, whereas int4 full-weight FedAvg collapses to chance.
[AI-87] PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption
链接: https://arxiv.org/abs/2607.18342
作者: Sahaj Majavdia,Mahdi Taheri
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
备注:
Abstract:Structured pruning is essential for making neural network inference feasible under homomorphic encryption (HE), yet its impact on model reliability has remained unexplored. This paper presents a systematic reliability characterization of pruned CKKS-encrypted neural networks and introduces Polynomial-Sensitivity-Aware Pruning (PSAP), a structured pruning method that is inherently reliability-aware. PSAP scores filters jointly by weight magnitude, polynomial activation sensitivity, and rotation cost, which concentrates pruning in fault-tolerant regions. Across two architectures, two datasets, two numerical representations, and five bit-error rates (40 full-model and 108 per-layer experiments), PSAP-pruned models limit catastrophic (10 pp accuracy drop) layers to at most two versus 5–14 for magnitude-pruned baselines, reducing worst-case vulnerability by up to 29 times under int32 bit-flip injection. Direct CKKS encrypted fault injection indicates a safe operating boundary near BER~ 10^-5, supporting int32 injection as a conservative reliability proxy. The fault-critical structural layers account for only 1.1% of parameters, enabling selective hardening at minimal overhead. These reliability gains are obtained alongside competitive efficiency: PSAP reduces Halevi–Shoup rotations by up to 45.2% on ResNet-32, and an adaptive mixed-degree allocation scheme lowers multiplicative depth from 66 to 56 levels, enabling leveled inference without bootstrapping.
[AI-88] Quantum Cryptanalysis on IBM Quantum Hardware: Extending Even–Mansour Period Recovery from N=4 to N=10
链接: https://arxiv.org/abs/2607.18340
作者: Taebong Kim,Youngsik Hong,Minsik Kim,Sunyoung Choi,Jaewon Jang,Junghoon Shin,Minseo Kim
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:We report genuine-un-compiled, textbook-faithful-quantum cryptanalysis of symmetric-cipher structures executed on real IBM quantum hardware (ibm_kingston, Heron generation). Using Simon’s algorithm we recover the hidden period of the Even-Mansour cipher up to security parameter N = 10 on real hardware, beyond the largest previously reported real-hardware key recovery of N = 4, and we cleanly recover the periods of a 3-round Feistel (DES-family) construction at block sizes 6 and 8; a 21-qubit block-10 instance is verified in simulation and submitted to hardware. We further provide a breadth-first benchmark of five genuine quantum attacks spanning four symmetric-cipher design paradigms – Bernstein-Vazirani (linear structure, single query), Grover (SPN key search, quadratic), and Simon (Even-Mansour, CBC-MAC forgery, and Feistel; exponential-to-polynomial in query complexity) – validated to the classical-simulation ceiling of 25 qubits. We are deliberately explicit about scope: these attacks target reduced or structured constructions in the Q2 (quantum-query) model, asymptotically follow the birthday bound and therefore do not constitute quantum advantage over classical collision-finding, do not break full AES/RSA or 16-round DES, and rely on error mitigation rather than fault-tolerant error correction. Our contribution is the real-hardware demonstration at record structure sizes, the breadth of genuine algorithmic coverage across four paradigms, and an honest, reproducible benchmark with public artifacts.
[AI-89] ChemHyperMag: Physics-informed magnetic hypergraph learning improves molecular ADMET prediction ICML2026
链接: https://arxiv.org/abs/2607.18332
作者: Hexiao Ding,Hongzhao Chen,Jing Lan,Yufeng Jiang,Zihong Luo,Zehua Xiong,Tianlong Ruan,Yunlin Mao,Nga Chun Ng,Gwing Kei Yip,Gerald W.Y. Cheng,Kate Inyoung Oh,Jing Cai,Liang-Ting Lin,Jung Sun Yoo
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted by Proceedings of the AI4Physics Workshop at the 43 rd International Conference on Machine Learning (AI4Physics@ICML 2026)
Abstract:Accurate prediction of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) is important for drug discovery. Most predictors use undirected molecular graphs and pairwise edges. This choice misses asymmetric interactions, nonreversible dynamics, and motif level effects from functional groups and ring systems. We propose ChemHyperMag for multitask ADMET prediction under missing labels. ChemHyperMag builds a functional group hypergraph from rings, BRICS fragments, Bemis-Murcko scaffolds, and bonds. It also defines a potential driven nonreversible flow guided by electronegativity and Gasteiger partial charges. The resulting circulation is encoded by a Hermitian magnetic Laplacian and processed with a magnetic Chebyshev encoder. We perturb magnetic phases to form stochastic views and train with an InfoNCE objective. Experiments on multiple ADMET benchmarks show improvements over recent methods with fewer labeled samples and no conformers. ChemHyperMag is scalable and provides interpretable directional signals through its magnetic phases.
[AI-90] Physics-Guided Masked Multi-Task Network for Edge-Friendly Battery Health Diagnostics from Sto-chastically Frag mented Charging Profiles
链接: https://arxiv.org/abs/2607.18330
作者: Shuhao Chen,Tianyu Shi,Chengyi Tu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The deployment of reliable lithium-ion battery management systems is crucial for accelerating electrification, yet the joint prognosis of State of Health (SOH) and Remaining Useful Life (RUL) remains severely hindered by task heteroscedasticity. Conventional multi-task learning frameworks fail to balance the bounded, low-variance noise of SOH estimation with the unbounded, nonlinearly expanding uncertainty of long-term RUL predictions. Here, we present the Rotary SOH-Injected Prior Battery Transformer (RoSIP-Batt), a unified co-estimation framework that resolves these optimization conflicts. By formulating joint prediction as a Bayesian multi-task objective, RoSIP-Batt introduces a homoscedastic uncertainty weighting mechanism to dynamically scale task-specific gradients based on learned residual noise levels. The architecture leverages decoupled dual classification tokens and a per-dimension gated fusion mechanism, secured by a gradient-detachment operator to prevent high-variance RUL updates from corrupting the stable SOH representation space. To capture electrochemical degradation patterns without relying on absolute cycle steps, Rotary Position Embedding (RoPE) is incorporated into a shared Transformer backbone to model translation-invariant relative temporal profiles. Crucially, the intermediate SOH estimate is directly injected into the RUL regression head as a physical degradation prior. Evaluations across the NASA, MIT-Stanford, and HUST datasets show that RoSIP-Batt significantly outperforms state-of-the-art baselines, reducing SOH estimation error to 1.994% MAE on NASA and restricting RUL prediction error to 62.85 cycles on Stanford. These findings establish RoSIP-Batt as a highly generalizable, computationally efficient solution suitable for real-time embedded BMS deployment.
[AI-91] Dynamic Loss Balancing for Joint SOH and RUL Prediction of Lithium-Ion Batteries via a Rotary SOH-Injected Prior Battery Transformer
链接: https://arxiv.org/abs/2607.18329
作者: Shuhao Chen,Tianyu Shi,Yiwen Huang,Chengyi Tu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The deployment of reliable lithium-ion battery management systems is crucial for accelerating electrification, yet the joint prognosis of State of Health (SOH) and Remaining Useful Life (RUL) remains severely hindered by task heteroscedasticity. Conventional multi-task learning frameworks fail to balance the bounded, low-variance noise of SOH estimation with the unbounded, nonlinearly expanding uncertainty of long-term RUL predictions. Here, we present the Rotary SOH-Injected Prior Battery Transformer (RoSIP-Batt), a unified co-estimation framework that resolves these optimization conflicts. By formulating joint prediction as a Bayesian multi-task objective, RoSIP-Batt introduces a homoscedastic uncertainty weighting mechanism to dynamically scale task-specific gradients based on learned residual noise levels. The architecture leverages decoupled dual classification tokens and a per-dimension gated fusion mechanism, secured by a gradient-detachment operator to prevent high-variance RUL updates from corrupting the stable SOH representation space. To capture electrochemical degradation patterns without relying on absolute cycle steps, Rotary Position Embedding (RoPE) is incorporated into a shared Transformer backbone to model translation-invariant relative temporal profiles. Crucially, the intermediate SOH estimate is directly injected into the RUL regression head as a physical degradation prior. Evaluations across the NASA, MIT-Stanford, and HUST datasets show that RoSIP-Batt significantly outperforms state-of-the-art baselines, reducing SOH estimation error to 1.994% MAE on NASA and restricting RUL prediction error to 62.85 cycles on Stanford. These findings establish RoSIP-Batt as a highly generalizable, computationally efficient solution suitable for real-time embedded BMS deployment.
[AI-92] Cost Accounting for Reactive Computational Graphs: Exhaustive Sweeps Sequential Mutation and the Backward-Locality Gap
链接: https://arxiv.org/abs/2607.18323
作者: Abdallah Khemais(ISITCOM, University of Sousse)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Companion paper: “Exact Network Surgery: Functional Invariance and Gradient Plasticity in Reactive Computational Graphs” (submitted concurrently)
Abstract:Exhaustive site-by-site interventions on a neural network’s computational graph – activation-patching sweeps, circuit-discovery searches, systematic ablation studies – mutate the graph at every candidate site, and their cost is dominated by recomputation after each mutation. On a reactive graph engine whose invalidation provably touches exactly the downstream cone of a mutated node, we give a complete cost accounting for such workloads. First, the aggregate speedup of an exhaustive sweep over independent full recomputations is not a universal constant: if per-layer weight varies regularly with depth at Karamata index q, the ratio converges to (q+2)/(q+1) when weight concentrates near the output and to q+2 near the input, recovering 2 only in the depth-uniform case; a wall-clock corollary predicts a ceiling of about 1.79, below 2, until interpreter overhead is compiled away. Second, we prove the exact cost of a sequence of persistent mutations, never undone between insertions: the interleaved cost exceeds the isolated sum by an exact overcount summed over comparable site pairs, with closed-form extremes over insertion orders, while batched application is order-independent and sub-additive, costing exactly the union of the sites’ cones plus the fresh nodes. Third, we prove the exact mirror of forward locality for the backward pass, showing it collapses the aggregate speedup to 1 under backpropagation on architectures without long skip connections. Every identity is validated on NeuroDSL, a reactive graph engine in Julia: measured sweep ratios converge to the predicted limits under four cost profiles; the training-mode ratio collapses to 1 at the predicted rate; and all 18 per-graft sequential costs and the batched total match the closed forms at zero tolerance across three insertion orders.
[AI-93] Agent ic Calibration of Grey-Box Simulation Models: An LLM -Driven Alternative
链接: https://arxiv.org/abs/2607.18308
作者: David Gómez-Guillén,Mireia Diaz,Josep Lluis Arcos,Jesús Cerquides
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Manuscript: 19 pages, 2 figures. Appendix: 11 pages, 1 figure
Abstract:Calibration of grey-box simulation models is a constrained optimization problem in which model evaluations are expensive, the parameter space can be high-dimensional, and the search must respect plausibility constraints. Although the simulation code is fully available to the analyst, the joint effect of multiple parameters remains difficult to predict analytically. Classical optimizers such as Nelder–Mead (NM) are simple to deploy but sample-inefficient, particularly under constraints. Modern Bayesian Optimization methods achieve competitive solutions with far fewer evaluations but require non-trivial modeling machinery for constraint handling. We introduce an agentic calibration method in which a large language model acts as the optimizer, with constraints incorporated as a plain-language section of the system prompt. We evaluate the agentic method, NM, and Bayesian Optimization (BO) on an anal cancer simulation model under both unconstrained and clinically constrained calibration. Under unconstrained calibration, the agentic method achieves substantially lower best error than BO and NM, while requiring fewer model evaluations. Under constrained calibration, the agentic method reaches comparable error levels and both outperform NM. These results are obtained at the cost of increased inference time per iteration. Agentic calibration achieves competitive performance with substantially fewer model evaluations, and constraint handling is essentially free at the modeller-facing interface through simple textual specifications rather than additional modelling machinery. The main trade-off lies in increased per-iteration inference cost, making the approach particularly suitable when simulation time dominates. Beyond performance, the per-iteration rationale makes the search auditable and explainable, so its decisions can be scrutinised and justified to third parties.
[AI-94] Gradient-Energy Guided Block-Wise Perturbations for Sharpness-Aware Minimization
链接: https://arxiv.org/abs/2607.18306
作者: Zhen Huang,Jiaxin Deng,Junbiao Pang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Sharpness-Aware Minimization (SAM) improves generalization by minimizing the worst-case loss in a local parameter neighborhood. Standard SAM implicitly allocates its global perturbation budget across parameter blocks according to instantaneous minibatch gradient norms. Such an allocation can be noisy and may not reflect the sensitivity that blocks accumulate throughout training. We propose Gradient-Energy Adaptive Radius SAM (GEAR-SAM), which maintains an exponential moving average (EMA) of squared block gradients as a lightweight, curvature-related sensitivity signal and allocates the fixed SAM budget through a closed-form constrained optimization. GEAR-SAM preserves the global SAM radius, requires no Hessian-vector products or explicit Fisher estimation, and adds only scalar state beyond SAM. Experiments on image classification, transfer learning, noisy-label learning, and partition studies demonstrate improved generalization and robustness across architectures and tasks. More broadly, GEAR-SAM provides a dynamic view of sharpness-aware optimization: a fixed perturbation budget should be redistributed as the sensitivity of functional network blocks evolves during training.
[AI-95] he Information Shadow: Measuring Structural Limits on What Language Models Can Learn
链接: https://arxiv.org/abs/2607.18305
作者: Priyansh Srivastava,Romit Chatterjee
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Some limits on what language models know are not gaps in data coverage but structural properties of learning from text. We introduce the information shadow: the region of phenomena that a text-trained learner cannot acquire regardless of scale, comprising (I) structures language cannot express, (II) functions that are statistically non-identifiable from the training distribution, and (III) functions that are representable but unreachable by gradient-based training. We give each type a probe that is decisive because the premise of the shadow is, in that setting, provable. For Type I, Language Compression Residuals compare a text learner, which sees only a lossy text-like encoding of the signal, against a full-signal learner, which sees the underlying signal directly. The text learner sits at a computable expressibility ceiling while the full-signal learner pulls away by a gap that stays flat across 300x more data, so the deficit is a property of the channel, not of training. For Type II, the Counterfactual Distinction Test trains models on data exactly consistent with two incompatible rules. Across a provable string task and a language-like agreement task, behavior on counterfactuals is set by the model’s inductive bias, while 5% disambiguating data steers the learned rule bidirectionally to either target (r = +/-1.0, p 1e-10). For Type III, Basin Escape Mapping exhibits a function that is representable at 100% (by hand construction) yet reached 0% of the time by standard training and instantly from a nearby initialization, with width scaling providing no help (p = 1.6 x 10^-14). Each effect is isolated by a control that rules out a capacity or modality artifact. We release the probe suite and discuss implications for benchmark design, capability auditing, and shadow-aware uncertainty.
[AI-96] Deep Reinforcement Learning to Master the Asymmetric Strategy of Baghchal
链接: https://arxiv.org/abs/2607.18296
作者: Ranjit Raut,Aarav Subedi,Sagun Rai,Aaryan Shakya,Manoj Shakya
类目: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)
备注: 6 pages, 6 figures
Abstract:Baghchal is a two-player asymmetric board game with Nepali origins where four tigers are to capture goats and twenty goats desire to keep tigers in immobility. Although Baghchal has a complex structure which is strategic, has perfect information structure, and has cultural meaning, it has not been adequately covered in deep reinforcement learning (RL) literature. This paper gives a systematic exploration of four deep RL solutions Deep Q-Network (DQN), REINFORCE, Proximal Policy Optimization (PPO) and MuZero that are trained on one side of the asymmetric gameplay of Baghchal and then evaluated on the other side. The algorithms are rated based on win rate, draw rate, average captures, training convergence and computational cost. It is experimentally found that MuZero generates the best performance in both tasks, achieving 86 percent win over these Tiger and 62 percent win over these Goat and the ability to do so is due to the model-based planning machine through the Monte Carlo Tree Search. PPO is the most realistic algorithm and is provided to be competitive over both asymmetric tasks with significantly reduced computational costs compared to MuZero. Emergent strategic behavior analysis shows that model-based strategies are optimal over long-horizon planning, whereas value-based counterparts like DQN are more biased up towards the Tiger role owing to the more substantial reward signal.
[AI-97] Dual-domain fused LSTM modeling for efficient time-dependent reliability analysis
链接: https://arxiv.org/abs/2607.18291
作者: Yixin Zhang,Mingyang Li,Zichao Jiang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注:
Abstract:Time-dependent reliability analysis is crucial for ensuring the long-term safety and performance of engineering systems under uncertainties. However, traditional surrogate model methods often struggle to incorporate time-independent random variables and capture their complex interactions with time-dependent stochastic processes. To overcome this limitation, this paper proposes a dual-domain fused long short-term memory (DDF-LSTM) model for efficient and accurate time-dependent reliability analysis. A novel network architecture is developed to jointly process information from both time-dependent and time-independent domains. Specifically, the time-independent variables are embedded into the initial hidden states, and a fully connected layer is introduced to map both LSTM outputs and time-independent variables into the final output space. Furthermore, an improved loss function is designed to emphasize the model’s sensitivity to minimum responses, thereby improving the precision of failure probability estimation. The proposed method effectively captures the dependencies among random variables, stochastic processes, and the temporal behavior of limit state functions. Once trained, the DDF-LSTM model enables efficient Monte Carlo simulation to estimate time-dependent failure probabilities with minimal computational cost. Four case studies validate the proposed method’s enhanced computational efficiency and predictive accuracy.
[AI-98] SechKAN: Kolmogorov-Arnold Networks with Hyperbolic Secant Functions
链接: https://arxiv.org/abs/2607.18290
作者: Hoang-Thang Ta
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 27 pages
Abstract:In recent years, Kolmogorov-Arnold Networks (KANs) have attracted increasing attention due to their effectiveness in machine learning and scientific computing tasks, offering a new paradigm for neural network design. In this paper, we present SechKAN, a KAN architecture based on hyperbolic secant (sech) functions. The hyperbolic secant basis is used for its smooth bell-shaped form, localized responses, and stable gradients. We employ 1D linear transformations to reduce the number of parameters, allowing SechKAN to remain comparable to multilayer perceptrons (MLPs) in model size. Experimental results indicate the effectiveness of SechKAN in function fitting, PDE problems, and image classification tasks on benchmark datasets, including MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100. SechKAN achieves superior performance compared to MLPs and other KAN variants while maintaining a similar number of parameters. However, its running time, while better than that of other KAN variants, is slightly longer than that of MLPs.
[AI-99] owards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios
链接: https://arxiv.org/abs/2607.18289
作者: Kamil Faber,Mateusz Smendowski,Roberto Corizzo
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Continual anomaly detection (CAD) studies how models can adapt to evolving data distributions while retaining performance on previously observed regimes. CAD benchmarks, however, depend critically on how tasks are defined, filtered, ordered, and validated. In tabular domains, task boundaries are rarely given, and arbitrary splits can create unlearnable, redundant, or overly transferable tasks that obscure genuine continual-learning behavior. To this end, we introduce a systematic framework for reproducible benchmark scenario design from existing tabular anomaly-detection datasets. The framework discovers candidate tasks, filters unsuitable tasks, and derives principled orderings that expose diverse dynamics. The framework allows us to deliver five benchmark-ready scenarios from three large-scale cybersecurity anomaly detection datasets, yielding both single-dataset and multi-dataset CAD settings.
[AI-100] BearingNAS: Obtaining In-Sensor Intelligent Fault Diagnosis Systems for Bearings Using a Laptop
链接: https://arxiv.org/abs/2607.18287
作者: Andrea Mattia Garavagno,Edoardo Ragusa,Paolo Gastaldo,Antonio Frisoli,Rodolfo Zunino
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted to IEEE COINS 2026 in Special Session 7: Edge AI technologies
Abstract:This paper introduces BearingNAS, a Hardware-Aware Neural Architecture Search (HW-NAS) framework designed to shift the intelligence directly onto the sensor die via in-sensor processing. BearingNAS frames the search as a constrained optimization problem targeting extreme micro-budgets (4 to 8 kiB of RAM and 16 to 32 kiB of Flash). To eliminate the reliance on expensive discrete GPUs, we propose a lightweight, derivative-free search strategy paired with a single data-flow search space that leverages a decaying kernel growth formulation to prevent parameter explosion. We evaluate our framework on the Case Western Reserve University (CWRU) bearing benchmark, optimizing architectures for three STMicroelectronics targets: two commodity microcontrollers and the LSM6DSO16IS Intelligent Sensor Processing Unit (ISPU). Running entirely on a laptop CPU, the search converges in less than an hour. The resulting best in-sensor architecture achieves a highly competitive diagnostic accuracy of 99.50% on the ISPU. These results demonstrate the viability of shifting the machine learning workload inside the sensor package, enabling low-cost, production-scale bearing fault diagnosis.
[AI-101] Preference-Conditioned Multi-Objective Reinforcement Learning for Runtime-Tunable Transit Signal Priority ITSC2026
链接: https://arxiv.org/abs/2607.18286
作者: Philip-Roman Adam,Stefanie Schmidtner
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注: 8 pages, 3 figures, 4 tables. Accepted at the 29th IEEE International Conference on Intelligent Transportation Systems (ITSC 2026). Code: this https URL
Abstract:Transit signal priority (TSP) requires balancing competing objectives: reducing bus delay while limiting adverse impacts on non-bus traffic and avoiding extreme waits for a subset of vehicles. Existing reinforcement-learning (RL) approaches to TSP typically encode transit-aware features (e.g., occupancy and schedule deviation) but optimize a fixed reward or fixed scalarization, which limits operational flexibility when agency priorities change across time-of-day or disruption conditions. We present a preference-conditioned TSP controller, \pi(a \mid s,w) , that selects the next signal phase under minimum/maximum green and transition-feasibility constraints and can be tuned at runtime via a preference parameter w to trade off bus-priority emphasis against overall traffic delay without retraining. We implement this on top of IntersectionZoo by introducing a constrained signal-control/TSP wrapper, and we extend scenario generation with bus-prevalence augmentation and timetable-based bus insertion to address sparse transit-priority events during training. Experiments against fixed-time control, a rule-based TSP overlay, and fixed-weight PPO specialists show that a single learned conditioned policy spans a smooth empirical trade-off frontier across runtime preferences, outperforms fixed-time and rule-based baselines, and maintains constraint feasibility, while tail-delay diagnostics reveal that non-bus externalities remain limited for moderate preference settings but can increase substantially under high bus-priority weights. The source code of this work is available at this https URL.
[AI-102] Edge-Efficient Transformer for End-to-End RF Spectrum Monitoring
链接: https://arxiv.org/abs/2607.18285
作者: Zhifan Song,Haralampos-G. Stratigopoulos,Hassan Aboushady
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:We present E-SpecFormer (Edge Spectrum monitoring Transformer) for end-to-end automatic modulation and covert channel (CC) recognition. We introduce LiTAN (Linear Tanh Attention Network), a Softmax- and LayerNorm-free attention mechanism that reduces complexity while increasing accuracy in RF tasks. E-SpecFormer is parameterized in four scalable variants (Nano, Small, Medium, Large) to accommodate diverse hardware constraints. Using the RadioML2018 dataset for modulation recognition, the Nano variant achieves 86.5% average accuracy for Signal-to-Noise Ratios (SNRs)0 dB, and on the hardware Trojan (HT)-based CC dataset it reaches 94.2% accuracy, both with fewer than 10k parameters and up to speed of 92 \mus per frame on FPGA/CPU co-execution, surpassing state-of-the-art edge models at a fraction of their cost. These results establish E-SpecFormer as an edge-efficient solution for real-time spectrum intelligence on Internet of Things (IoT) devices. GitHub link to the repository: this https URL.
[AI-103] Compressing What Matters: Neuron Importance Meets Data-Aware Low Rank Approximation for Language Model Compression
链接: https://arxiv.org/abs/2607.18284
作者: Athanasios Ntovas,Alexandros Doumanoglou,Petros Drakoulis,Dimitris Zarpalas
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:To excel at their domain large language models are comprised of billions of parameters. Yet this comes at the cost of huge memory requirements restricting their applicability in resource-constrained environments. To address the problem of neural network (NN) compression Singular Value Decomposition (SVD) has played a key role as a fundamental component for matrix compression through decomposition. To minimize compression error and to maximize the efficacy of the compressed model on the downstream tasks previous works focused on low-rank approximation of the NN’s weight matrices either from the perspective of parameter importance or per-layer functional equivalence. While previous works studied the aforementioned perspectives in isolation in this work we are investigating the effectiveness of an approach that combines ideas from these two perspectives in a single objective. In parallel to this an important aspect that affects the compression quality is the distribution of the compression rate across layers and NN parameters. Earlier works mostly considered distributing the compression rate uniformly across layers and network weights or relied on computationally expensive heuristic search. Contrary to them in this work we propose an enhanced and computationally efficient algorithm for dynamic compression rate allocation. Experimental results support the efficacy of the proposed approach which performs on par or substantially better than the previous state-of-the-art especially under high compression ratios.
[AI-104] Beyond Single-Dimensional Compression: The Compound Sparsity Frontier of Large Language Models
链接: https://arxiv.org/abs/2607.18280
作者: Chao Han,Haozhe Hu,Xiaoyu Shen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) are often compressed through static parameter pruning or dynamic token-level computation, yet aggressive sparsification can trigger rapid performance degradation beyond an essential sparsity boundary. This work asks \emphwhether combining these two mechanisms can delay such degradation by distributing the compression burden. We study a minimalist compound sparsity framework that first applies low-rank approximation and channel pruning to obtain a statically compressed backbone, and then introduces lightweight routers for per-token dynamic layer skipping. This design enables independent control of parameter sparsity and token-level computation sparsity. Experiments across language understanding and modeling benchmarks show that compound sparsity consistently outperforms single-mechanism compression under the same total sparsity, delaying the decay point on understanding tasks and preserving stronger modeling performance. Further analysis reveals cross-dimensional interference between parameter pruning and token skipping, and shows that near-balanced allocation is most effective under a fixed sparsity budget. These results demonstrate that compound compression provides a practical way to improve LLM compression, while revealing a broader cross-dimensional sparsity boundary that ultimately limits further compression. Code will be available at this https URL.
[AI-105] Beyond Output-Space Calibration: Spectral Evidence Bundling for Selective Reliability Estimation in Time-Series Classification
链接: https://arxiv.org/abs/2607.18279
作者: Filippo Cenacchi,Longbing Cao,Runze Yang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Post-hoc calibration for time-series classification usually remaps output scores, but deployment decisions such as trust, abstention, and review depend on whether a confident prediction is supported by the current temporal signal. We address three time-series reliability gaps: identical confidence values can hide different temporal support, average calibration can miss false high-confidence errors, and output-space recalibration offers limited input-linked auditability. We introduce a validation-gated fixed-label reliability policy that keeps the backbone prediction unchanged while estimating whether it should be trusted. The method combines output-side cues with whole-sample spectral descriptors, including band energy, entropy, peak dominance, period support, and phase stability, to form a scalar reliability estimate and diagnostic band-level evidence. A validation gate enables spectral conditioning only when correctness ranking improves without breaching FalseConf@0.9 or AURC tolerances; otherwise it reverts to the safer output-space baseline. Across eight heterogeneous UCR/UEA datasets, eight time-series backbone families, and standard recalibrators, the unconstrained method improves fixed-label selective-reliability metrics on the matched evaluation subset, raising Corr-AURC from 0.693 to 0.779. The validation-gated policy further improves Corr-AURC to 0.786 and reduces FalseConf@0.9 to 0.094. These results suggest that reliability estimation for time-series classifiers benefits from bundling output confidence with spectral evidence, while validation gating prevents unsupported spectral conditioning.
[AI-106] FALCON-Discover: Discovering Concentrated False-Confidence Regions for Calibration
链接: https://arxiv.org/abs/2607.18278
作者: Filippo Cenacchi,Longbing Cao,Runze Yang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Calibration is usually evaluated in aggregate, but the most dangerous failures are often local: predictions that remain highly confident despite being wrong. We study this failure mode as false-confidence concentration, the extent to which confident errors occupy compact, discoverable regions of prediction space. We introduce FALCON-Discover, a post-hoc, model-agnostic framework that ranks predictions using discrepancy signals from confidence, local support, neighborhood agreement, and perturbation stability. Across seven binary tabular datasets, four seeds, five-fold cross-fitting, and strong learners including XGBoost and CatBoost, we find that false-confidence concentration is recurrent but regime-dependent. At the main confidence threshold, discrepancy-based ranking substantially outperforms the strongest validation-selected calibration or trust-scoring baseline in the strongest regimes, while raw confidence recovers little dangerous-error mass. The best detector varies across datasets: learned discrepancy is strongest when multiple cues must be combined, whereas stability-centered ranking works best when local decisional fragility dominates. These results show that dangerous overconfidence is better treated as a family-level discovery problem than as a single-score calibration problem, and motivate calibration strategies that explicitly target regions where confidence, support, and stability diverge.
[AI-107] A Calculus of Discernment: Decision-Relevant Insight Sequence Value and Forgetting as Higher-Order Learning
链接: https://arxiv.org/abs/2607.18275
作者: Suyash Mishra
类目: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI)
备注: 17 pages, 5 figures. Includes a pre-registered empirical study (30 seeds x 100 iterations) and a reference implementation; a companion interactive demo is available from the author
Abstract:In a world of generative AI, candidate insights are abundant; what is scarce is the capacity to discern which matter, to act on them in the right amount and order, and to forget the rest so the system can adapt. We argue these scarcities are governed by one object and build a framework around it. We define an insight strictly as a lever with an identified, measurable effect on an objective, and rank candidates by decision-relevance via the expected value of information rather than novelty. We show action carries an order, not only a size: under realistic belief dynamics, content “touches” are non-commuting operators, so a fixed plan delivered in different orders yields different outcomes, defining a sequence premium. We observe that the value of any lever is a shadow price, unifying pharmaceutical marketing, equity selection, and manufacturing as one leverage-discovery problem. Most speculatively, we propose APOHA, a theory in which forgetting is not the disposal of knowledge but the operator by which value is learned: the value of a retained item is the counterfactual cost of forgetting it, a learning system is the residue of maximal forgetting subject to preserved value, and higher-order value is the structure that survives repeated forgetting (a renormalisation-relevant invariant), with consolidation as its conjugate. We state the central open problem (a non-trivial attractor with a spectral gap) and test the forgetting theory: operationalising APOHA as an agent on a non-stationary obesity-treatment decision world over 30 seeds, adaptive forgetting cut cumulative decision-regret by 24-32% against never-forget and a fixed half-life, kept a ~6x smaller, cleaner memory, and converged stably; notably, blind forgetting was worse than never forgetting, so the benefit is specific to value-aware forgetting. A multi-disciplinary critique stress-tests the whole.
[AI-108] Domain Design for the Cops and Robbers Problem
链接: https://arxiv.org/abs/2607.18274
作者: Connor Little,Meagan Mann,Erin Meger,Christian Muise
类目: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI)
备注:
Abstract:Cops and Robbers is a well-studied problem in graph theory. The setting consists of a robber and one or more cops placed on an undirected graph. Taking turns moving throughout the graph, the cops try to capture the robber. The property of interest is whether k cops suffice to ensure at least one cop occupies the same vertex as the robber, after a finite number of turns, given any configuration of their initial placement; if successful, the graph is referred to as `` k -copwin’'. In this work, we cast the problem of determining whether a graph is k -copwin as a non-deterministic planning problem and use state-of-the-art planners to compute this property. The cop movement is cast as non-deterministic movement (to capture all possible strategies), while the robber movement is deterministic in nature. We also extend the base model using several variations from the graph theory literature.
[AI-109] Market Strategy Evaluation for Prosumers in Local Electricity Markets
链接: https://arxiv.org/abs/2607.18272
作者: Lukas Peter Wagner,Raoul Bisson,Felix Gehlhoff
类目: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注:
Abstract:Prosumers equipped with distributed generation and flexible loads form autonomous cyber-physical energy systems that control local resources and participate in local energy markets with minimal human intervention. This work develops and evaluates an agent-based simulation platform in which agents, representing prosumer households with photovoltaic systems, battery storage systems, electric vehicles, and heat pumps, participate in a uniform-price double-sided call auction. The effect of individual bidding strategies on community-level efficiency and prosumer-level financial outcomes is incompletely understood, particularly when prosumers with heterogeneous portfolios interact in one market. Four market strategies of increasing complexity are compared: a zero-intelligence constrained baseline, a boundary-price strategy, an extended storage cascade, and a market-adaptive pricing strategy. The simulation is conducted on a community of 33 prosumers at 15-minute resolution, spanning summer, winter, and spring to characterize seasonal variation. Results show that rule-based resource control substantially reduces community energy expenditure: the extended storage cascade achieves a total cost of 39.06 EUR compared to 62.38 EUR under the zero-intelligence baseline, a reduction of 37.4 %. The market-adaptive strategy yields the highest aggregate community financial gain through local energy market participation (14.40 EUR vs. 10.28 EUR for the baseline, a gain of 40.1 %) under summer conditions. Strategy effectiveness depends on both portfolio composition and seasonal supply conditions, requiring joint evaluation of resource control and pricing decisions.
[AI-110] Using LLM s for Explainable Data-Driven Insight Generation from Time Series
链接: https://arxiv.org/abs/2607.18271
作者: Ria Mundhra,Gustavo Sato dos Santos,Michael Benedikt
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Time series forecasts are widely used in decision-critical domains, where they are rarely consumed without accompanying explanations. Producing such explanations is usually a manual and costly process, and attempts to automate it using large language models often suffer from hallucination when applied to temporal data. We propose a domain-agnostic framework for grounded natural language explanation generation for time series forecasts, illustrated in Figure 1. The framework consists of three components: (i) extraction of structured explanatory factors from historical analyst-written explanations, (ii) evidence-conditioned explanation generation, and (iii) scalable evaluation for readability, logical consistency, and persuasiveness. The design explicitly constrains generation to verifiable evidence, reducing unsupported claims. We evaluate the framework on a financial forecasting case study involving the NASDAQ-100 index and a freight pricing case study using data from Vortexa. Results show that generated explanations approached analyst-written explanations in terms of readability, consistency and persuasiveness. These findings demonstrate that grounded explanation generation for time series forecasting can be achieved at scale without domain-specific fine-tuning. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2607.18271 [cs.AI] (or arXiv:2607.18271v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2607.18271 Focus to learn more arXiv-issued DOI via DataCite
[AI-111] rajectory-Aware Clinical Risk Prediction via Severity-Grounded Knowledge Graphs and Retrieval-Augmented Generation KDD2026
链接: https://arxiv.org/abs/2607.18270
作者: Kyunghoon Jeon,Youmin Ko,Woohwan Jung,Hyunjoon Kim
类目: Artificial Intelligence (cs.AI)
备注: Accepted to KDD 2026
Abstract:While Electronic Health Records (EHRs) offer a wealth of clinical data, effectively augmenting a patient’s records with heterogeneous external knowledge to predict the patient’s clinical risk remains a significant challenge. Existing methods fail to capture disease severity, treatment responses, and nuanced clinical progression, due to data sparsity and the underutilization of unstructured clinical notes. To address these challenges, we propose TRACER (a trajectory-aware and clinically grounded prediction framework) that (1) constructs a medical knowledge graph enriched with severity information from medical literature, (2) retrieves clinically relevant, severity-weighted paths of a patient’s progression from the knowledge graph, (3) extracts clinically relevant events from unstructured clinical notes, and (4) augments patient context with similar peer cases. Experiments on the MIMIC-III and MIMIC-IV datasets demonstrate large gains over state-of-the-art baselines, with up to 28.5% increase in Macro F1 score for the mortality prediction task, and 19.7% increase for the readmission prediction task.
[AI-112] Wisdom of LLM Crowds: Aggregation and Contamination in Language Model Ensembles
链接: https://arxiv.org/abs/2607.18269
作者: Igor Douven
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:The wisdom of crowds – the finding that aggregating judgments across individuals often outperforms the best individual – has been extensively studied with human forecasters. Whether the same phenomenon emerges when the ``crowd’’ consists of large language models (LLMs) is an open question with both theoretical and practical implications. We elicited probability estimates from 15 LLMs on 254 binary prediction market questions and evaluated classical and learned aggregation methods. Learned aggregators – a multilayer perceptron and a logistic regression – outperformed all individual models and classical methods. The logistic regression was found to match the neural network, suggesting that the benefit of learned aggregation derives from learning a linear combination of diverse model outputs rather than from nonlinear interactions. Symbolic regression applied to the neural network’s learned mapping recovered a pure model-disagreement signal as the lowest-complexity useful formula on the Pareto frontier, further supporting this interpretation. Training cutoff contamination proved a pervasive confound: the apparent capability gap between frontier cloud models and smaller local models collapsed from 35.8% to 8.9% on a clean subset of questions resolving after all models’ training cutoffs, and individual model rankings showed only moderate stability. Even when the prediction market is evaluated at each model’s training cutoff, LLMs remained substantially less accurate, indicating a genuine gap in collective information aggregation. These findings suggest that LLM crowds can exhibit wisdom-of-crowds effects, but that contamination-free evaluation is essential for reliable assessment.
[AI-113] Fence: Specialized SLM Guardrails for LLM Applications
链接: https://arxiv.org/abs/2607.18268
作者: Kumud Lakara,Ruibo Shi,Fran Silavong
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Real-world applications that use closed-source large language models (LLMs) need advanced safety measures that go beyond the basic content filters. Content moderation filters such as toxicity and bias have relatively standard definitions where as application specific guardrails like hallucination, topic drift and behaviour deviation are more difficult to model and can vary by use case. Additionally, data scarcity and annotation costs, make the process of creating and testing specialized guardrails challenging. In this work, we propose using Small Language Models (SLMs) trained on synthetic data as specialized guardrails for LLM applications. We introduce a novel synthetic data generation method inspired by the design of Generative Adversarial Networks (GANs) to generate high quality synthetic data samples which can be used to train SLMs to encode use case specific guardrail information and hence function as specialized guardrails. Our experiments demonstrate that SLM guardrails trained on high quality synthetic data show performance gains over prompt based LLM guardrails.
[AI-114] he Economics of Autonomy: Real-Time Risk Indexing for Insurable AI-Driven 6G Systems
链接: https://arxiv.org/abs/2607.18267
作者: Anthony Kiggundu,Michael Zentarra,Christoph Lipps,Hans D. Schotten
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
备注: Accepted at the IEEE 104th Vehicular Technology Conference - VTC2026-Fall
Abstract:The transition to sixth-generation (6G) networks transforms wireless infrastructure into a cognitive substrate supporting Vehicle-to-Everything (V2X), Industrial IoT (IIoT), and Integrated Sensing and Communication (ISAC). In this paradigm, autonomous agentic AI performs orchestration at millisecond scales, rendering traditional static governance frameworks fundamentally inadequate for risk management. This paper introduces GIRAF(Governance-Integrated Risk and Assurance Framework), a Governance-as-Code (GaC) framework for real-time risk quantification and trust modulation in agentic 6G systems. GIRAF derives a continuous Aggregate Risk Index ( R_t ) from machine-readable runtime signals, including epistemic confidence, network jitter, and verification latency. A core contribution is the formalization of the verification staleness trade-off, where safety mechanisms induce risk if computational latency exceeds 6G deadlines. We demonstrate that GIRAF identifies ‘Confidence Gaps’ discrepancies between agent reported certainty and environmental ground truth, triggering automated safety envelopes when conditions deteriorate. Crucially, GIRAF serves as the foundational governance groundwork and conceptual ‘glue’ that externalizes these technical risks into machine-readable telemetry. Through simulations with fine-tuned Large Language Models (LLMs), we validate that the framework preserves operational integrity while providing the essential actuarial baseline required for multi-stakeholder liability attribution and dynamic premium quantification in the 6G ecosystem. Comments: Accepted at the IEEE 104th Vehicular Technology Conference - VTC2026-Fall Subjects: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.18267 [cs.NI] (or arXiv:2607.18267v1 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2607.18267 Focus to learn more arXiv-issued DOI via DataCite
[AI-115] Structured Synthetic Reasoning Data for Arithmetic Fine-Tuning of Small Language Models
链接: https://arxiv.org/abs/2607.18266
作者: Jake O’Grady,Effirul Ramlan
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Small language models are attractive for local deployment, but they often struggle with multi-step arithmetic reasoning. We study whether structured synthetic reasoning data can improve this behaviour under consumer-hardware constraints. Starting from GSM8K, we generated a 21,250-example corpus of grade-school arithmetic word-problem variants using GPT-5-mini, combining natural-language solution traces, light Socratic-style cues, structural variation, and irrelevant distractor context. We then fine-tuned Qwen3-0.6B and Qwen3-1.7B with LoRA on consumer hardware (Apple M4, 16 GB RAM). Exact-match accuracy on GSM8K improved from 36.5% to 49.1% for Qwen3-0.6B and from 53.5% to 66.5% for Qwen3-1.7B. For Qwen3-1.7B, transfer to related arithmetic benchmarks was stronger, reaching 98.9% on MultiArith and 73.0% on SVAMP, compared with 54.4% and 45.3% for the base model. Qualitative analysis suggests that fine-tuned models produce shorter reasoning traces, make fewer arithmetic and distractor-use errors, and benefit more consistently from self-consistency sampling. These results show that low-cost synthetic data design can materially improve arithmetic adaptation in small language models. Because the intervention combines Socratic-style cues with other data-design choices, we interpret the gains as evidence for structured synthetic reasoning data rather than as a causal test of Socratic guidance alone.
[AI-116] Position: AI/ML Deepfake Research is Misaligned with AI-Generated Non-Consensual Intimate Imagery (AIG-NCII) ICML2026
链接: https://arxiv.org/abs/2607.18263
作者: Li Qiwei,Wells Lucas Santo,Sarita Schoenebeck,Eric Gilbert
类目: Artificial Intelligence (cs.AI)
备注: Position paper accepted to ICML 2026, Spotlight
Abstract:AI-generated non-consensual intimate imagery (AIG-NCII) is not adequately addressed in AI/ML literature regarding AI-generated media, commonly referred to as “deepfakes”. While research on deepfakes currently focuses on its epistemic harms – or harms relating to truth and authenticity – this is misaligned with the dominant reality of generative AI abuse involving sexualized imagery. We conduct a landscape analysis of highly-cited works to demonstrate that technical interventions addressing deepfakes almost entirely ignore AIG-NCII, limiting the research ecosystem to authenticity detection tools. In this position paper, we argue that existing interventions address viewer-centric epistemic harms, such as fraud or scams, but ignore subject-centric dignity harms, such as AIG-NCII. We illustrate that knowing an image is synthetic does not mitigate harms to subjects and may, in some cases, even exacerbate them. We conclude by offering recommendations to realign the field, including updating threat models to consider subject-centric harms and addressing AIG-NCII in AI safety research. Finally, we caution that researchers should only engage in this high-risk domain if they implement safety guardrails for both subjects and researchers and establish partnerships with domain experts in sexual violence prevention.
[AI-117] ProbSPARQL: Querying Knowledge Graphs with Multi-dimensional Uncertain Numeric Data
链接: https://arxiv.org/abs/2607.18262
作者: Jingcheng Wu,Ratan Bahadur Thapa,Daniel Hernandez,Hongkuan Zhou,Steffen Staab
类目: Artificial Intelligence (cs.AI)
备注: 18 pages, 5 figures, 1 table. Preprint. Supplementary material and reproducibility artifacts are available online
Abstract:The SFB 1574 Circular Factory is building a shared knowledge graph infrastructure for integrating data about returned products. A central challenge is that circular-factory data include numeric measurements that (i) originate from sensors or are derived from sensor-based measurements, (ii) are frequently multi-dimensional, and (iii) are inherently uncertain, while downstream triage, validation, reliability-modeling, and reassembly-planning modules require queryable uncertainty representations. Current RDF and SPARQL technologies lack native support for harmonized querying and analysis of such uncertain numeric measurement data. To address this gap, we present ProbSPARQL, an upward-compatible SPARQL extension developed as an early-stage query-layer pilot for this infrastructure. ProbSPARQL models uncertain numeric values as random variables whose distributions are encoded by probabilistic RDF literal datatypes, and supports distribution-aware expressions, probabilistic filters, and divergence-based joins. We implement ProbSPARQL on Apache Jena ARQ and expose it through a Fuseki-compatible execution layer. We assess real-data applicability using project-derived measurement fragments covering GMM-encoded uncertainty and histogram-based empirical roughness distributions, and evaluate scalability separately on controlled ontology-conformant benchmarks with up to 5,000 angle-grinder instances and 1.5M triples. The results show feasible in-engine execution, filter-pushdown speedups over application-layer post-processing, and latency-accuracy trade-offs among divergence-join decision strategies.
[AI-118] When JSON Is Not Enough: Semantic Reliability of Schema-Constrained LLM Ordering Agents
链接: https://arxiv.org/abs/2607.18261
作者: Yin Li
类目: Artificial Intelligence (cs.AI)
备注: 7 pages, 6 tables
Abstract:LLM agents are increasingly used as transaction compilers: a user states an intent in natural language, and the model emits a structured object that an API can execute. JSON Schema and provider-level structured-output modes are useful because they remove a large class of parse failures, but they do not by themselves decide whether the object is a safe, faithful transaction. We introduce OrderBench, a deterministic benchmark for restaurant ordering agents that separates syntactic validity, schema validity, status decisions, exact item semantics, constraint preservation, and unsafe acceptances. Across 2,400 Nebius Token Factory calls to four open models in prompt-only and JSON-schema modes, we find that schema-valid output can still have large semantic error rates. In the strongest model, both modes achieve 100% schema validity, yet semantic success remains near 80%; in weaker models, schema-valid unsafe acceptances occur in double digits. The result is a concrete engineering warning: structured output is a necessary interface layer, not a substitute for domain verification and fail-closed execution.
[AI-119] FindStatBench: Evaluating Large Language Models on Combinatorial Code Synthesis
链接: https://arxiv.org/abs/2607.18260
作者: Soham Dan
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce FindStatBench, an execution benchmark for evaluating large language models on combinatorial code synthesis. Built from FindStat, it contains 2,329 tasks across 24 collections and 5.52M hidden instances, covering statistic synthesis, which maps objects to integers, and map synthesis, which maps objects to objects. Each task gives a mathematical description and at most five public input-output examples; a model must emit one Python solve function with no retrieval, tool use, execution feedback, voting, or reranking. Submissions are scored by exact sandboxed execution on held-out combinatorial objects. We evaluate eleven systems: four closed-source production models and seven open-weight models served through one inference provider. FindStatBench reveals three main patterns. First, the strongest open- and closed-source systems converge within 1 pp instance accuracy, and both an oracle over all systems and five-way sampling from one mid-tier model yield only limited task-accuracy gains. Second, examples can hurt: several classical bijections are solved perfectly with zero examples but fail under five-example prompts. Third, some failures reflect output-budget mechanics, as reasoning can exhaust the visible response before code is emitted. Overall, statistic synthesis is much easier than map synthesis, some collections remain near-zero, long prompts cause a sharp accuracy cliff, and exact symbolic rule induction remains brittle.
[AI-120] Probabilistic Concept-Aware Steering for Trustworthy LLM Inference
链接: https://arxiv.org/abs/2607.18259
作者: Brian Becker,Rui Chu,Yingjie Lao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Steering vectors (SVs), an inference-time intervention technique for large language models (LLMs), guide the generation process by adding a concept-specific direction vector to intermediate activations during inference. However, existing SV methods frequently yield representation-incoherent behaviors that undermine interpretability and fine-grained control, largely because prior work has focused on binary positive-negative steering evaluation while employing discrete clustering metrics that fail to capture the continuous spectrum of semantic alignment. In this work, we present the Probabilistic Concept-Aware Steering (PCS) framework for LLM inference. PCS preserves original task competence while providing controllable, safety-oriented semantic bias through concept-driven steering-vector retrieval and probabilistic strength calibration.
[AI-121] S2T-RLHF: Hierarchical Credit Assignment for Stable Preference-Based RLHF
链接: https://arxiv.org/abs/2607.18258
作者: Wei Chen,Guanghui Zhu,Yafei Li,Limin Wang,Yihua Huang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Reinforcement learning from human feedback (RLHF) with preference-based reward models often exhibits unstable training dynamics. A key contributing factor is that standard RLHF relies on a single sequence-level scalar reward, which is propagated to token-level policy updates and leaves credit assignment within a response inherently ambiguous. Recent work has attempted to address this issue by refining rewards into denser token-level supervision, often relying on the implicit assumption that finer-grained credit assignment improves optimization. We argue that this assumption is incomplete: when preference signals are noisy and only defined at the response level, overly fine-grained reward refinement can amplify reward uncertainty and destabilize learning. To address this problem, we propose a granularity-aware principle for hierarchical credit assignment, emphasizing stability-oriented reward design rather than maximal allocation precision. Under this principle, sentences serve as a natural intermediate granularity, balancing semantic coherence with robustness to token-level noise. Guided by this view, we introduce S2T-RLHF. This sentence-to-token reward decomposition framework first allocates sequence-level preference rewards across sentences and then applies bounded token-level refinement within each sentence, without reward-model retraining or token-level supervision. Experiments across multiple datasets and optimization settings show that S2T-RLHF improves training stability and robustness while maintaining competitive preference alignment.
[AI-122] PEARL: Solver-in-the-Loop Interactive Optimization Modeling from Natural Language
链接: https://arxiv.org/abs/2607.18256
作者: Hongliang Lu,Zhong Li,Yuxuan Chen,Yuan Lan,Fan Zhang,Zaiwen Wen
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Optimization modeling is the process of translating real-world decision problems, often described in natural language, into formal mathematical formulations and executable solver code. While recent advances in large language models have shown promise in automating this process, most existing approaches remain one-shot: a model produces a formulation once, without executing it, conditioning on solver feedback, or iteratively revising errors. This stands in sharp contrast to real-world optimization modeling, which is inherently interactive and proceeds through repeated solve-debug-revise cycles. We introduce PEARL, a system for interactive optimization modeling that uses Python execution and mathematical programming solvers inside this loop. Rather than relying on a fixed repair workflow, PEARL learns when to test partial models, how to revise from solver diagnostics, and when to stop. It operates in a multi-turn tool-integrated setting where intermediate execution results, feasibility signals, and solution checks are used to improve both formulations and solver code before finalization. Across diverse optimization benchmarks, PEARL substantially improves verified solve rates over strong one-shot and tool-augmented baselines; notably, our PEARL-Qwen3-\textbf4B model outperforms the much larger DeepSeek-V3.2-\textbf685B in both macro- and micro-averaged accuracy on optimization modeling tasks.
[AI-123] Semantic Cooperative Games for Contribution Attribution in LLM -Based Multi-Agent Systems
链接: https://arxiv.org/abs/2607.18255
作者: Pengyi Jiang,Xiaoguang Zhu,Quanyan Zhu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Contribution attribution has become a central problem in LLM-based multi-agent systems, where final outputs are produced through multiple agents, message exchanges, and ordered workflow dependencies. Existing attribution methods often rely on counterfactual valuation, such as removing agents or comparing score changes across altered agent subsets. In language-mediated workflows, these methods require repeated model calls, introduce high variance, and do not explicitly capture the intermediate semantic states through which agents produce, preserve, and transform task-relevant information. We propose Semantic Cooperative Games (SCG), a framework that represents a realized language flow as a semantic generation hypergraph and induces an agent-level semantic value function on this structure. We define the Semantic Shapley Value (SSV) to allocate contribution over semantic support logic, and introduce SLIC, a single-trajectory algorithm that constructs the semantic hypergraph, recovers minimal semantic supports, applies Boolean absorption, and computes SSV without rerunning agent subsets. We prove that SSV reduces to the classical Shapley value under standard set-based, fully observable, and no-order-dependence conditions. On a medical benchmark satisfying these conditions, SLIC reduces computation cost by 93.3% while remaining highly consistent with a Monte Carlo Shapley baseline. In more general multi-role workflows, SSV aligns with perturbation-induced score-drop profiles and exposes cases where semantic contribution and failure impact diverge. Overall, SLIC provides a fast, counterfactual-free, and interpretable attribution method for complex LLM-based multi-agent systems.
[AI-124] Cross-Dialect Generalization Without Retraining: Benchmarks and Evaluation of Schema-Derived Constrained Decoding for MLIR
链接: https://arxiv.org/abs/2607.18254
作者: Plawan Kumar Rath
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Programming Languages (cs.PL)
备注: 27 pages (9 main + bibliography + appendix), 9 figures, 9 tables. Code, benchmarks, and reproducibility Docker image at this https URL
Abstract:Multi-Level Intermediate Representation (MLIR) underlies modern ML compiler infrastructure (TensorFlow, JAX/StableHLO, PyTorch Inductor, IREE), yet appears only in trace amounts in code-LM pretraining corpora. MLIR is also extensible by design: new dialects ship per application domain, so a fine-tuned model per dialect does not scale. We ask whether inference-time priors derived mechanically from each dialect’s Operation Definition Specification (ODS) can substitute for gradient-based adaptation. First, we release four natural-language-to-MLIR benchmarks across three dialects - MLIR-Spec-150, Linalg-Spec-30, StableHLO-Spec-30, and StableHLO-Held-Out-200 - totaling 410 in-scope NL-to-MLIR pairs, plus a 25-program out-of-grammar stress set and a hand-authored n=30 functional reference set, shipped under Apache-2.0 with Gebru datasheets and Croissant 1.0 metadata. Second, we build a three-layer schema-derived constraint stack: a CFG over op signatures(C1), type-domain splits from an ODS-extracted type lattice (C2), and an SSA-scope validator driving five-retry rejection sampling (C3). Porting from arith+func+memref+linalg to StableHLO required no new constraint-layer code. On dialects whose verifier semantics are dominated by structural constraints, schema-derived priors let SmolLM2-1.7B match or exceed 15B-34B code LMs at 8-25x the per-generation speed: on linalg, SmolLM2 reaches 80.0% verify-valid (three-seed mean, n=125), beating CodeLlama-34B, Granite-Code-34B, and StarCoder2-15B by 21-44 percentage points with non-overlapping CIs. On arith+func and on the templated parametric StableHLO-Held-Out-200, where verifier semantics turn on attribute values rather than structure, the same baselines match or beat the SLM; we scope these as non-win cells. We release benchmarks, decoder, all per-prompt generations, and a reproducibility Docker image.
[AI-125] Beyond Accuracy and Cost: Latency-Aware LLM Query Routing for Dynamic Workloads
链接: https://arxiv.org/abs/2607.18253
作者: Shivam Patel,Akaash R. Parthasarathy,Ankur Mallick,Gauri Joshi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Modern language query routers improve inference efficiency by assigning each query to a model that balances response quality and monetary cost. However, current query routers are largely latency-agnostic and do not consider the generation latency experienced by queries at model instances. In practice, latency is often controlled by load-balancing policies such as round-robin or join-the-shortest-queue, which do not account for model accuracy or inference cost. Incorporating query latency into routing is challenging as it depends not only on the query’s prompt length, but also on the current prefill and decode workload at the model instance and the scheduling and batching policy of the serving framework. We design a lightweight latency estimator that simulates autoregressive token batch processing in the serving framework and estimates the time-to-first-token (TTFT) of queries. We incorporate this latency estimator into a latency-aware router that jointly optimizes latency, accuracy, and cost when assigning queries to model instances. Our experimental results indicate that this joint optimization yields up to 40% improvement in accuracy–cost utility while maintaining the same latencies as standard load-balancing approaches.
[AI-126] MILP-Evo: Closed-Loop Fully Automatic Design of MILP Solvers
链接: https://arxiv.org/abs/2607.18252
作者: Jinbiao Nie,Kewei Feng,Xiaoyuan Zhang,Shan Yin,Zizhuo Wang,Bin Dong
类目: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
备注:
Abstract:Machine learning methods have shown that data-driven policies can accelerate mixed-integer linear programming (MILP) solvers, but many such approaches remain difficult to inspect, adapt, and deploy because the learned policy is represented as an external predictor or other opaque model. By contrast, explicit solver logic is easier to understand and integrate, but is usually hand-designed rather than learned from solver feedback. We study whether the automatic design of MILP solver logic can instead be cast as LLM-guided closed-loop search over executable white-box components evaluated directly by end-to-end solver behavior. To this end, we propose a closed-loop program evolution framework for MILP solver auto-design, implemented through PySCIPOpt, and instantiate it on the joint design of a cut selector and a branching rule. Candidate programs are iteratively generated, loaded into SCIP, and evaluated by direct execution on MILP instances, with the resulting feedback guiding performance-based selection, targeted repair, diagnostic reflection, and diversity-aware population maintenance. The method outputs explicit solver components that can be inspected, modified, and deployed within standard solver workflows. Across four benchmark families, we find that LLM-guided program evolution can discover competitive domain-specialized policies in several settings.
[AI-127] Integro-differential equations in angular stabilization of drone motion by distributed feedback control
链接: https://arxiv.org/abs/2607.18251
作者: Alexander Domoshnitsky,Oleg Kupervasser,Anatoly Polonsky
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Optimization and Control (math.OC)
备注: 22 pages, 5 Figures
Abstract:In this paper, we propose angular stabilization of drone motion using distributed feedback control in the form of an integral operator. It should be stressed that the memory of this integral operator could be unbounded. It is intuitively clear that large length of the observation time open new possibilities to construct better control based on previous states of the control object. Unbounded memory in control requires the creation of a certain approach different from standard ones to the study of integro-differential equations. One of the goals of this article is to propose a certain universal approach that allows us to study the stability of integro-differential equations in the case of unbounded memory in the integral operator specifying the feedback control in stabilization. The approach we propose allows us to reduce the study of integro-differential equations to the analysis of systems of ordinary differential equations. In general, such systems can consist of an infinite number of equations. In relation to the so-called linear approximation in the problem of angle stabilization manages to limit itself to relatively simple exponential kernels in the integral control and arrive at a system with a finite number of equations. The examples explain that more complex kernels, for example, linear combinations of the exponential kernels, can enhance the stabilization capabilities. We obtain new unexpectable results on the exponential stability of integro-differential equations. Then we apply them to stabilization of drone flight.
[AI-128] Phionyx: A Deterministic AI Runtime Architecture with Structured State Management and Pre-Response Governance
链接: https://arxiv.org/abs/2607.18246
作者: Ali Toygar Abak
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 27 pages, 4 figures, 5 tables. Reference implementation, reproducibility pack, and evaluation artifacts available via GitHub ( this https URL ) and Zenodo (DOI: https://doi.org/10.5281/zenodo.20027534 )
Abstract:We present Phionyx, a deterministic AI runtime architecture derived from the broader Echoism interaction framework that introduces a governance-first approach to AI engineering: treating large language model (LLM) outputs as noisy sensor measurements rather than direct decisions. Unlike probabilistic agents, Phionyx enforces deterministic state evolution via a structured state vector governed by deterministic state-evolution equations, enabling reproducible behavior in applications requiring auditability and governance. The architecture integrates three layers: (1) a deterministic evaluation kernel processing noisy sensor measurements through a canonical 46-block pipeline, (2) a unified safety layer providing pre-response control and architectural privacy enforcement, and (3) a semantic time-based memory system implementing impact-weighted cache eviction. Experimental validation on single-instance deployments demonstrates approximately 31% reduction in computational overhead vs. post-hoc filtering (at 30% unsafe input ratio, simulated cost model) and up to 24% improvement in high-value data retention vs. LRU (72% vs. FIFO, same cache capacity, benchmark-verified), deterministic execution verified across 100 repeated runs with zero variance in control signals (hash-verified), and zero unplanned restarts in single-instance deployment testing (see Appendix C for methodology and scope). This paper presents the architecture, its analytic structure, and scoped experimental evidence; generalization to distributed or multi-tenant deployments remains future work.
[AI-129] SAAG: Structured Agent Assessment and Grounding ACL2026
链接: https://arxiv.org/abs/2607.18245
作者: Ritvik Garimella,Vedant Khandelwal,Anvi Kohli,Amit Sheth
类目: Artificial Intelligence (cs.AI)
备注: Accepted to KnowFM @ ACL 2026 workshop (Non-Archival). 16 pages in total
Abstract:Exact-match evaluation of agent-calling obscures qualitatively different failure modes: a model may select the right function yet hallucinate argument values, or satisfy a schema while choosing a agent for the wrong reason. Existing benchmarks collapse these distinctions into a single binary score, leaving practitioners unable to diagnose where agent calls fail. We propose SAAG a cascaded diagnostic framework that decomposes agent-calling evaluation into three sequential stages: registry conformance, structural completeness, and argument grounding, each producing interpretable stage-specific diagnostics. These diagnostics additionally enable iterative self-repair: on prediction failure, the stage-specific signal guides targeted correction without leaking ground-truth values. We evaluate this framework on a controlled benchmark derived from Glaive’s function-calling dataset across registry sizes of 5, 10, and 15 agents using three local sub-4B-parameter models. Structured feedback consistently improves argument precision and reduces value hallucination relative to single-pass inference and uninformative binary feedback, while end-to-end F1 gains are modest and model-dependent. These results suggest that stage-decomposed diagnostic evaluation is a necessary lens for understanding and improving agent-calling reliability across model families and registry scales.
[AI-130] From Agent Failure Paths to Quantified Residual Risk: A Compositional Framework for Resilient Agent ic AI
链接: https://arxiv.org/abs/2607.18243
作者: Hassan Karim,Sai Sitharaman,Deepti Gupta,Danda B. Rawat
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Agentic AI is crossing trust boundaries faster than current risk models can represent. Existing approaches provide one of two partial views. They either describe failure mechanisms without producing a transferable residual-risk estimate, or they produce a risk estimate while treating the internal failure path as a black box. We couple those two views by proposing CPSAINT, a seven-layer integrity decomposition over Physical state, Sensors, Data, Compute, Actuators, Environment, and Time, paired with FRIESA-K, a residual-risk functional that maps each failure path to a quantified risk instance. FRIESA-K grounds the resistance term K in a controlled absorbing Markov model so that control effectiveness is derived from state dynamics rather than assigned as an informal score. The result is a concise mechanism-to magnitude pipeline for resilient agentic and embodied AI. We report governance observability through a separate additive penalty instead of inserting governance as a new variable in the resistance functional. We formalize structural composability linking valid failure paths to well-defined risk instances and show the framework on two contrasting scenarios a hard real-time warehouse robot and a governance-instrumented financial-services agent. Across both cases, the same layer grammar, variable semantics, and dynamic-resistance construction remain intact. Thus, we obtain a compact kernel that supports cross-domain reasoning, explicit assumptions, and quantitatively grounded formalism of composable trust.
[AI-131] BatchDAG: LLM -Planned Execution Graphs for Scalable Ad-Hoc Analysis Over Enterprise Data
链接: https://arxiv.org/abs/2607.18241
作者: Anupreet Walia
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 2 figures, 9 tables
Abstract:Large language models (LLMs) excel at analyzing individual documents but break down on exhaustive, cross-entity analytical questions over enterprise-scale datasets due to context overflow, loss of per-entity attribution, and linear latency from sequential tool calls. We present BatchDAG, a system in which an LLM generates a typed directed acyclic graph (DAG) of operations – SQL queries, semantic searches, in-memory transforms, parallel fan-outs, and single-shot analyses – which a deterministic engine evaluates with topological-wave parallelism and structured JSON data flow. A key optimization, entity-aware batching, groups rows by logical entity before fan-out, reducing LLM calls by up to 47x. BatchDAG is not primarily an accuracy improvement over hand-optimized pipelines; rather, it is a general-purpose orchestration layer that replaces multiple hand-engineered workflows with a single system that generates the appropriate execution strategy from natural language. In controlled experiments on 12 transcript-heavy queries, BatchDAG (3.74/5) achieves quality comparable to an expert-designed pipeline (3.25/5) and significantly outperforms a ReAct agent (3.09/5, p0.01), with superior provenance (77% transcript evidence rate vs. 46-60% for baselines). A controlled ablation shows structured JSON intermediates reduce hallucinations by 27% versus prose summaries (paired t-test, p=0.107, n=12). The planner achieves 98.8% valid-DAG rate across 300 planning calls. In production at this http URL, BatchDAG processes queries over 50,000+ meetings in under 60 seconds, with measured per-query costs of 0.02- 0.24 at published GPT-5.1 pricing.
[AI-132] Calibrated Selective Fact-Checking via Evidence Chain Evaluation
链接: https://arxiv.org/abs/2607.18240
作者: Dekun Yang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) can achieve strong fact-checking accuracy, yet forced binary decisions conceal a critical reliability problem: systems may issue confident verdicts even when supporting evidence is weak, sparse, or internally inconsistent. We address this issue through Evidence Chain Evaluation (ECE), a selective fact-checking framework that permits abstention via an uncertain verdict instead of requiring a true/false decision for every claim. The evaluated system is a tool-using verification agent that gathers evidence through web search, scholarly search, and executable checks, and then returns a structured verdict with confidence and source-level metadata. On ECE-Bench, ECE achieves 91.6% standard accuracy, 93.7% coverage, and 97.8% selective accuracy on answered claims. Although ECE does not outperform the strongest retrieval baseline on aggregate calibration metrics such as Expected Calibration Error, Brier score, or AURC, it delivers a clear selective-prediction trade-off: the system maintains very high accuracy on answered claims while deferring 6 of 95 cases. These deferred cases are concentrated in lower-reliability evidence settings (5/6 at source level L4), supporting the view that abstention functions as a safety-oriented mechanism for handling epistemically weak evidence. Code is available at this https URL cheshireyang/ECE.git
[AI-133] SysAdmin: Measuring Instrumental Power-Seeking in Frontier AI
链接: https://arxiv.org/abs/2607.18239
作者: Mana Azarm,Qiyao Wei,Rahul Nambiar
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Power-seeking defined as behaviors where AI systems acquire resources, evade oversight, or resist termination beyond task requirements is identified as a key driver of Loss of Control (LoC) risk. In this work, we introduce SysAdmin, a benchmark that positions frontier language models as autonomous system administrators in a high-fidelity Linux sandbox to measure power-seeking propensity across five dimensions: self-preservation, increasing autonomy, resource acquisition, environment modification, and strategic concealment. We evaluated seven frontier models across four experimental conditions in a total of 2800 tasks. After bias correction using human-annotated calibration data, corrected power-seeking estimates ranged from 0 to about 5 percent per model. We also conducted a positive control with explicit power-seeking prompts that achieved 100% detection, validating measurement sensitivity. Our findings indicate current frontier models exhibit minimal spontaneous power-seeking in naturalistic system administration contexts, though model-specific failure modes suggest evaluations must test diverse misalignment patterns. Nevertheless, we discovered other more pronounced failure modes (than power-seeking) such as specification gaming and resistance to goal modification.
[AI-134] Free energy landscape of Dense Associative Memory
链接: https://arxiv.org/abs/2607.19195
作者: Sumedha,Abhishek Singh
类目: Disordered Systems and Neural Networks (cond-mat.dis-nn); Statistical Mechanics (cond-mat.stat-mech); Artificial Intelligence (cs.AI)
备注: 5 pages, 1 figure
Abstract:Using large deviations theory, we solve and obtain a general expression for the free energy functional for a broad class of associative memories, including dense associative memories. We illustrate the method by reproducing classical results for the Hopfield model. For a finite number of patterns, we derive the temperature-dependent free energy functional for dense associative memories featuring polynomial interactions and Log-Sum-Exponential (LSE) activation. We also evaluate the disorder-averaged ground-state energy of these systems in the extensive limit. Our analytical framework reveals how memory retrieval depends on the initial state in higher-order dense networks, and gives the exact full-retrieval threshold for the LSE model. This method provides a systematic procedure for analyzing diverse, complex architectures in associative memory.
[AI-135] MIRAG E: Multi-scale Lesion-Informed Representation with Auxiliary Guidance for MRI Contrast Enhancement
链接: https://arxiv.org/abs/2607.19137
作者: Andrea Borghesi,Xin Wang,Jonas Teuwen,George Yiasemis
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 9 pages, 2 figures, 3 tables
Abstract:Inferring contrast enhancement from one pre-contrast breast MRI slice is underdetermined: post-contrast appearance contains physiological information that is not uniquely encoded in baseline anatomy. Optimizing only paired pixel fidelity can suppress uncertain lesion enhancement, whereas adversarial or stochastic generative objectives can favor realistic post-contrast appearance without guaranteeing patient-specific lesion fidelity. We introduce MIRAGE, a residual 2D U-Net that combines global reconstruction and perceptual losses with three forms of lesion-aware supervision available only during training: an asymmetric penalty for missed tumor enhancement, multi-scale auxiliary tumor segmentation, and guidance through a frozen post-contrast tumor segmentation nnU-Net. We evaluate the method on 301 cases from the multi-centre MAMA-SYNTH data using eight complementary image-, region-, radiomics-, and segmentation-based metrics. MIRAGE ranks first on six metrics and markedly improves downstream lesion localization over tuned pix2pix, conditional diffusion, and latent bridge-matching baselines. The generative alternatives retain advantages in LPIPS or contrast classification, revealing a clear fidelity-utility trade-off. Leave-one-in and leave-one-out ablations show that the losses are partly redundant for lesion localization but exert distinct effects on appearance, radiomics, and boundary accuracy. These results support task-aware synthesis while also showing that its apparent optimality is conditional on the downstream models and metrics used to define utility.
[AI-136] From Operations to Elderly Care Outcomes: A Thematic Review of Industrial Engineering and Decision-Support Approaches
链接: https://arxiv.org/abs/2607.19075
作者: Shayan Farhang Pazhooh,Fereshteh Parvaresh
类目: Optimization and Control (math.OC); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 12 pages, 2 figures, 3 tables
Abstract:The rapid growth of the global aging population presents severe challenges to healthcare systems, necessitating efficient, equitable, and patient-centered care models. While Industrial Engineering and Operations Research (OR) provide robust optimization and decision-support tools to address these multidimensional complexities, current applications often remain fragmented. This paper presents a thematic review of 30 seminal studies at the intersection of OR and elderly care, categorizing the literature into home healthcare operations, polypharmacy management, and clinical chronotherapy. Our analysis highlights a significant methodological evolution from static, deterministic models toward dynamic and stochastic frameworks integrated with artificial intelligence (AI). Despite these advancements, a critical translational gap persists: the current OR literature is heavily dominated by process-level optimizations, such as staff routing, and struggles to translate these operational efficiencies into measurable clinical outcomes. Furthermore, holistic models bridging the transition between hospital and community care remain critically underexplored. To develop resilient and smart healthcare systems, this study proposes a conceptual framework that shifts the research focus from isolated operational tasks to integrated, multi-level decision-making. We emphasize the critical need for robust systems analysis, human-inclusive design, and the smartification of care through emerging digital technologies - including digital twins and large language models - to successfully bridge the gap between theoretical operational metrics and tangible patient-level health outcomes.
[AI-137] mporal-Causal Unity as an Operational Framework for Collective Dynamics: Causal-Progress Clocks Synchronization and Polarization
链接: https://arxiv.org/abs/2607.18620
作者: Jian Liu,Dong Sun
类目: Physics and Society (physics.soc-ph); Artificial Intelligence (cs.AI)
备注:
Abstract:This paper develops temporal-causal unity (TCU), a framework connecting a process-philosophical thesis – time is the ordered unfolding of causal change – to an operational model of cognitive and social dynamics. The framework deliberately separates three claims: an interpretive thesis about becoming, a measurable causal-progress coordinate, and a stochastic network model. Causal progress is defined by \tau(t)=\int_0^t\lambda(s\mid\mathcal H_s),\rm ds , where the nonnegative event intensity \lambda must be specified independently of the outcome. Agents carry an orientation phase and an activation amplitude; weighted interaction, heterogeneous drift, external input, anchoring, and diffusion govern their evolution in \tau . First- and second-harmonic order parameters separate consensus from bipolar polarization. For the all-to-all noisy Kuramoto special case with Lorentzian drift width \Delta , synchronization begins at the conditional threshold K_c = 2(\Delta + D) , not at a universal constant. Reproducible numerical illustrations illustrate (not empirically demonstrate) this threshold, causal-clock curve collapse, and the consensus-polarization distinction. Six historical episodes are treated as scope probes rather than validation data. The paper derives falsifiable hypotheses and an out-of-sample protocol for comparing causal-progress and chronological-time models. TCU is therefore offered as a disciplined bridge between process ontology and complex-systems modeling, not as a replacement for spacetime physics or as an empirically established identity between time and causation.
[AI-138] Competitive and Complementary Tools
链接: https://arxiv.org/abs/2607.18460
作者: David C. Krakauer
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI)
备注:
Abstract:Humans have always externalized thought onto tools, from the tally and the abacus to the map and, now, large language models. I model the agent, the tool, and the task as one dynamical system in which competence (what the user retains) and reliance (what the user outsources) co-evolve, and find that the outcome is bistable. Above a critical tool availability the competent state is destroyed and competence collapses toward a low dependent floor as the user outsources completely. Lowering availability does not reverse the collapse until a far lower threshold, so history of practice rather than the current tool fixes the state. Two users with the same present access can therefore occupy opposite and lasting states, one competent and one dependent, decided only by which they built first. The collapse threshold depends jointly on the competence a user brings to a task and on the tool’s transparency, the fraction of its working a user can reconstruct. In the case where an agent faces an uncertain goal, a tool can cause agency itself to transfer to the tool and the human-agent becomes an agentic-instrument, irreversibly, because the tool’s model is too large to internalize. The model is tested against several independent data sets, including GPS and map use, arithmetic expertise, and language models. These results reframe how tools should be built, how artificial intelligence is deployed, and what a tool-resistant education might require.
[AI-139] FSDBN: Foreground-Aware EEG–Visual Alignment via Dynamic Brain Networks
链接: https://arxiv.org/abs/2607.18344
作者: Yiheng Liu,Chuhang Zheng,Peiliang Gong,Jingtao Liu,Daoqiang Zhang,Qi Zhu
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI)
备注:
Abstract:EEG-based visual decoding provides a non-invasive pathway for interpreting visual semantics. However, existing methods often overlook the perceptual asymmetry between foreground and background in complex scenes, leading to background interference and semantic misalignment. EEG signals also exhibit rapid temporal dynamics and nonstationary spatial patterns, making it difficult to capture the time-varying brain connectivity associated with focal visual attention. To address these limitations, we propose FSDBN, a unified framework for robust EEG-visual decoding. FSDBN introduces Semantic-Consistent Saliency Alignment to separate semantically relevant foreground regions from background noise under joint saliency and semantic constraints. It further employs Semantic-Prior Dynamic Gating Foreground Fusion to adaptively regulate the contributions of foreground and background features. In parallel, EEG signals are modeled as adaptive spatiotemporal brain networks whose functional connectivity dynamically reorganizes to capture neural responses to salient foregrounds. Experiments on zero-shot brain-to-image retrieval demonstrate that FSDBN achieves 69.0 percent top-1 accuracy and 92.2 percent top-5 accuracy, outperforming previous state-of-the-art methods. Code is available at this https URL.
[AI-140] Approximating SPR Distance Between Phylogenetic Trees with Graph Neural Networks
链接: https://arxiv.org/abs/2607.18311
作者: Renata Martins Castanheira,Miguel Bugalho,Cátia Vaz
类目: Populations and Evolution (q-bio.PE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Comparing phylogenetic tree topologies is essential for understanding epidemic dynamics, yet biologically meaningful distances such as the Subtree Prune and Regraft (SPR) distance are NP-hard to compute and intractable on large datasets. We investigate whether a Graph Neural Network (GNN) can approximate SPR distances in near-constant time per comparison after training. Our contributions are fourfold. First, we build and publicly release a dataset of 864 phylogenetic trees inferred with UPGMA and Neighbor-Joining over four bacterial species, spanning up to 9,500 isolates, together with 388 labelled tree pairs. Second, we establish a reproducible pre-processing pipeline including midpoint re-rooting, which reduces tree depth and supplies the rooting required for exact distance computation and for the model’s root-based features. Third, we validate the supervision target: on small trees, where exact SPR is tractable, the unrooted phangorn::this http URL heuristic correlates almost perfectly with the exact rooted distance computed by rspr (Pearson 0.98 – 0.99 ), making it an excellent monotonic surrogate. Lastly, we train a Siamese Graph Isomorphism Network (GIN) regressor. In-distribution, i.e., held-out trees from the same species and size range as training, it explains roughly 87–90% of the variance ( R^2 \approx 0.87 on a held-out split; 0.90 \pm 0.19 under stratified cross-validation), with about four times lower error than a mean-predictor baseline, and shows partial transfer to unseen species ( R^2 \approx 0.37 ). Its main limitation is extrapolation to trees larger than those seen in training, where accuracy collapses. The released dataset and the validated heuristic versus exact relationship provide a reproducible basis for scaling learned SPR approximation.
[AI-141] Distribution-First Population Simulation: Collapse Calibration and Recall in Non-WEIRD LLM Persona Modeling
链接: https://arxiv.org/abs/2607.18310
作者: Gurkan Ozkan
类目: Physics and Society (physics.soc-ph); Artificial Intelligence (cs.AI)
备注: 8 pages, 8 figures
Abstract:Synthetic-population tools increasingly run every individual as an independent large language model (LLM) agent. Using real survey microdata, we show that this paradigm has a basic failure mode, and we set a distribution-first corrective against it, all measured with a deterministic, construct-validated verifier on non-WEIRD (Turkey-first) data. First, N independent LLM agents grounded on 2,414 real World Values Survey respondents fail to reproduce the population’s response distribution: they pile onto a modal default (four scenarios x five seeds: concentration 0.36-0.69, entropy 1.46-0.77, 85% collapse, TVD=0.44), and the collapse is a predictable function of scenario structure (r=0.55 with a single-answer structure). Second, Verbalized Sampling (VS) fixes the field’s chronic under-dispersion without training in three model families (fidelity +7 to +10; significant on Qwen, p=0.002, d=6.2), yet the same move universally overshoots into over-dispersion (SD-ratio 0.4-0.56 - 1.26-1.37), a structural property of VS. Third, survey fidelity transfers only weakly to agentic behavior: in a single-model, single-domain booking task, a persona is dominated by a cheapest-default (~80%) that income modulates but does not override (comfort choice 0%-7%-32% across income bands). Fourth, a placebo-controlled memorization attack and an election backtest show VS keeps aggregate strength while subgroup and individual claims are contaminated by recall and underdetermination. We close with the corrective: model the distribution once (VS) and assign it to grounded characters at O(1) cost, with a budget-aware router whose honest AUC is 0.805, not the tautological 1.0 of a code-derived oracle. The central contribution needs no realism claim: it measures the internal inconsistency of the independent-agent route and the conditions under which the distribution-first route calibrates.
[AI-142] MechAInistic: An LLM -guided Multi-Agent System for Reasoning over Genome-Scale Constraint-Based Metabolic Models
链接: https://arxiv.org/abs/2607.18249
作者: Josh Loecker,Narayna Puraja,William Bryan,Bhanwar Lal Puniya,Ahmed Abdeen Hamed,Tomáš Helikar
类目: Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI)
备注: 23 pages, 6 figures,
Abstract:Constraint-based metabolic modeling is a powerful way to study the mechanistic basis of cellular states and disease, but its effective use demands substantial computational expertise and careful coordination of multi-step analyses. We developed MechAInistic to lower this barrier and enable researchers to ask complex biological questions in natural language. Harnessing large language models, MechAInistic is a multi-agent system organized around an Architect-Reviewer pattern that transforms a natural-language question into an executable, model-grounded workflow and generates a structured report. The system supports a variety of tasks, including pathway comparison, perturbation analysis, drug-target exploration, and literature-grounded interpretation across paired metabolic model states. We developed and evaluated MechAInistic using two paired immune-cell metabolic-model use cases for therapeutic hypothesis generation. For Naive B cells from rheumatoid arthritis (RA) paired with healthy controls, MechAInistic identified mitochondrial metabolic rewiring and nominated Devimistat/CPI-613 as an investigational OGDH-centered hypothesis. In a paired CD4+ Th17 cell study from multiple sclerosis (MS) and healthy controls, the same workflow identified NADP-dependent isocitrate dehydrogenase as the optimal single target and proposed ivosidenib as an FDA-approved repurposing candidate. Together, these results show that MechAInistic converts natural-language biological questions into executable, model-grounded workflows for traceable therapeutic hypothesis generation.
机器学习
[LG-0] 1-Lipschitz Neural Networks on Hadamard Manifolds
链接: https://arxiv.org/abs/2607.19335
作者: Davide Murari,Marta Ghirardelli,Ben Adcock,Elena Celledoni,Brynjulf Owren,Carola-Bibiane Schönlieb
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG)
*备注:
Abstract:Controlling the Lipschitz constant of a neural network is a standard way to promote robustness and stability. Most existing constraining strategies are designed for Euclidean spaces. In this work, we construct and analyze a class of 1-Lipschitz neural networks on Hadamard manifolds. Our layers are of gradient-descent type, 1 -Lipschitz, and quasi- \alpha -firmly nonexpansive. The core building blocks of the proposed architecture are Busemann functions, and we exploit the properties of Busemann gradient flows to design 1 -Lipschitz geometry-preserving layers. We provide explicit constructions and examples for hyperbolic manifolds and the manifold of symmetric positive definite (SPD) matrices. We test the proposed architecture in two numerical experiments: robust classification on the Poincaré disk and masked-Wishart covariance reconstruction. On the Poincaré disk, the proposed networks yield robust classifiers under hyperbolic perturbations. On the SPD manifold, we train SPD-valued denoisers and adopt them as a Plug-and-Play prior for a masked-Wishart covariance reconstruction problem. We show improved results from the nonexpansive denoiser over static, data-only, and Log-Euclidean denoising baselines, and empirically test its convergence properties.
[LG-1] Staypoint Detection from Noisy Trajectory Data [Experiment Paper]
链接: https://arxiv.org/abs/2607.19312
作者: Lance Kennedy,Hossein Amiri,Yueyang Liu,Riyang Bao,Hanqi Chen,Mohammad Hashemi,Ruochen Kong,Xiaotong Liu,Joon-Seok Kim,Shengpu Tang,Liang Zhao,Andreas Züfle
类目: Machine Learning (cs.LG); Computational Geometry (cs.CG)
*备注:
Abstract:Detecting staypoints from raw trajectory data is fundamental to numerous spatial computing applications. This process transforms raw numeric sequences of geolocations into semantically meaningful locations, such as homes, workplaces, or restaurants. Despite its importance for semantic trajectory analysis, staypoint detection lacks standard benchmarks, and existing algorithms have never been systematically evaluated. This gap persists because no publicly available datasets provide both raw individual trajectories and ground-truth staypoint annotations. This benchmark paper addresses this limitation with two key contributions: (1) we introduce 16 large-scale simulated datasets capturing thousands of agents with annotated staypoints across varying trajectory noise levels, and (2) we evaluate nine staypoint detection algorithms-including both state-of-the-art and novel methods-to analyze their robustness to noise. Our evaluation reveals that existing state-of-the-art algorithms perform poorly under realistic noise conditions. Conversely, our proposed unsupervised methods yield substantial improvements, while supervised approaches drastically outperform existing baselines. While these results are very promising, these datasets and methods are only meant as starting points for future research in staypoint detection.
[LG-2] Real-time optimal control with shallow recurrent decoder networks
链接: https://arxiv.org/abs/2607.19302
作者: Matteo Tomasetto,Francesco Braghin,J. Nathan Kutz,Andrea Manzoni
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:Controlling dynamical systems in real-time across multiple scenarios is critical to enabling adaptive control strategies, ensuring stability and efficiency. However, to tailor control actions in response to varying scenarios, traditional optimal control problems typically require several system simulations, which are often computationally demanding due to the high-dimensionality of the underlying spatio-temporal dynamics. In this work, we exploit SHallow REcurrent Decoder networks-based Reduced Order Modeling (SHRED-ROM) to synthesize a real-time closed-loop controller for high-dimensional and parametric dynamics, relying solely on limited state sensor readings. After training the model on a few optimal examples given by an expert demonstrator, SHRED-ROM mimics the expert behavior with effective distributed control actions in new scenarios, alleviating the curse of dimensionality. Moreover, a sensor forecaster is synthesized and used to close the loop at the latent level, thus efficiently mitigating possible sensor failures or delays. The performance of the proposed optimal control strategy is finally assessed on three challenging high-dimensional cases dealing with either parametric density control or fluid flow control.
[LG-3] A Reinforcement-Learning-Augmented Liquid-Fueled Reactor Network Model for Predicting Lean Blowout in Gas Turbine Combustors
链接: https://arxiv.org/abs/2607.19281
作者: Philip John,Eloghosa Ikponmwoba,Pinaki Pal,Opeoluwa Owoyele
类目: Machine Learning (cs.LG)
*备注:
Abstract:This study introduces a reinforcement learning (RL) framework for generating optimal liquid-fueled reactors to improve lean blowout (LBO) predictions in gas turbine combustors. Existing approaches for determining cluster boundaries rely on manual heuristics or distance-based metrics in the input space. In contrast, the proposed method is goal-oriented, explicitly accounting for the target metric (e.g., LBO prediction accuracy) during cluster formation. The framework employs a multi-stage clustering–classification strategy: an initial clustering step (e.g., k -means clustering) generates a large set of homogeneous micro-clusters, followed by an actor-critic RL agent that merges them into optimal reactor zones. The validation study, performed using a Jet-A mechanism (119 species, 841 reactions), shows the RL framework offers improved predictive fidelity compared to k -means and captures the correct LBO trends, while achieving substantial speedups relative to the high-fidelity computational model. Overall, the RL-driven approach demonstrates strong potential as a computationally efficient reduced-order modeling technique that can complement high-fidelity simulations for rapid design-space exploration.
[LG-4] hermodynamics-Informed Input Reparameterization for Neural Prediction of Real-Fluid Thermodynamic Properties in Supercritical Combustion
链接: https://arxiv.org/abs/2607.19241
作者: Haoze Zhang,Han Li,Ke Xiao,Yangchen Xu,Runze Mao,Zhi X. Chen
类目: Machine Learning (cs.LG); Computational Physics (physics.comp-ph); Fluid Dynamics (physics.flu-dyn)
*备注:
Abstract:Real-fluid thermodynamic property evaluation is a major computational cost in supercritical combustion simulations. In the enthalpy-based pressure-correction formulation, the closure evaluates temperature T, density \rho , and compressibility coefficient \psi from the solver state (h,p,Y) through enthalpy-temperature inversion and repeated real-fluid equation-of-state evaluations. Neural-network surrogates offer fixed-cost inference, but direct mapping from (h,p,Y) to (T,\rho,\psi) must capture the enthalpy-temperature relation and non-ideal equation-of-state response, resulting in a complex regression problem. This work introduces a thermodynamics-informed input reparameterization strategy, termed target-aligned input reparameterization (TAIR). TAIR replaces the raw enthalpy coordinate of each property network with a target-matched thermodynamic coordinate: the temperature network uses a temperature estimate obtained by inverting a constant- c_p ideal-gas mixture enthalpy approximation, whereas the density and compressibility networks use an ideal-gas density estimate. These algebraic transformations use only solver-available variables and species constants, guiding the networks to learn real-fluid departures from ideal-gas baselines rather than reconstructing the full closure from raw enthalpy. The method is assessed using supercritical methane-oxygen counterflow flame data against a raw-input baseline and target-inconsistent cross-reparameterization controls. TAIR reduces held-out RMSE by factors of about 1.5, 2.0, and 7.5 for T, \rho , and \psi , respectively. For an unseen strain-rate flame within the augmented thermodynamic envelope, the corresponding factors are 3.6, 14.5, and 6.0. The target-inconsistent controls perform worse, indicating that the gains arise from thermodynamically matched input design rather than generic preprocessing.
[LG-5] DBMol: Design of High-Affinity Target-Specific Small Molecules through Structure Prediction Models
链接: https://arxiv.org/abs/2607.19237
作者: Yiming Qin,Kai Yi,Miruna Cretu,Sjors H.W. Scheres,Pietro Liò,Pascal Frossard
类目: Machine Learning (cs.LG)
*备注:
Abstract:Designing small molecule ligands that bind with high affinity to specific protein pockets is a fundamental goal in drug discovery, as small molecules constitute a major fraction of approved therapeutics. Recent breakthroughs in structure prediction, such as AlphaFold-3 and Boltz-2, enable accurate biomolecular interaction prediction and show promise as foundation models for downstream tasks, including binding affinity prediction. We propose to leverage these models and introduce DBMol, a new structure predictor-guided framework for de novo small molecule design. DBMol formulates an alternating optimization and projection process. In the optimization stage, DBMol starts from an initial molecule and uses gradient-based optimization to improve pocket-specific interactions and predicted binding affinity using a structure prediction model. In the projection stage, a flow-matching model maps the optimized molecular graph to discrete and chemically valid molecules. Experiments show that DBMol effectively optimizes the Boltz-2 affinity proxy and generates molecules with strong predicted affinity and specificity under Boltz-2 evaluation. To reduce self-confirmation bias, we further evaluate generated molecules using held-out metrics, including AF3-based evaluation. DBMol substantially improves pocket coverage while maintaining molecular diversity over unconditional generation, and is competitive under held-out metrics despite the absence of reference-ligand supervision. These results support the promise of structure prediction models as effective optimization signals for de novo molecular design.
[LG-6] In-Context Time Series Classification with Random Convolutional Features
链接: https://arxiv.org/abs/2607.19234
作者: Joscha Cüppers,Jilles Vreeken
类目: Machine Learning (cs.LG)
*备注:
Abstract:Time series classification is central to domains like medical signal analysis, industrial monitoring, and sensor-based activity recognition, where class information manifests as localized shapes, specific frequencies, temporal shifts, or complex cross-channel interactions. Random convolutional transforms efficiently map these sequences to fixed-dimensional tabular features but are traditionally paired with simple linear classifiers. We investigate whether a pretrained tabular foundation model can more effectively harness these rich representations. We propose MASHT, a pipeline that marries MultiRocket and Hydra features with the power of in-context tabular foundation models. By leveraging a pretrained tabular foundation model, our approach completely bypasses task-specific model training, requiring only feature extraction and direct inference. Extensive experiments demonstrate that MASHT matches state-of-the-art time series classification baselines on univariate tasks, achieving a lower average rank than HIVE-COTE 2.0. On multivariate datasets, MASHT remains highly competitive with the strongest reference methods. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2607.19234 [cs.LG] (or arXiv:2607.19234v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.19234 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-7] Conservative Query and Adaptive Regularization for Offline RL Under Uncertainty Estimation ECAI2025
链接: https://arxiv.org/abs/2607.19199
作者: Li-Rong Zhou,Qin-Wen Luo,Sheng-Jun Huang
类目: Machine Learning (cs.LG)
*备注: Accepted by ECAI2025
Abstract:Offline reinforcement learning (RL) aims to learn an effective policy from a static dataset, but its performance is fundamentally limited by dataset coverage. Action preference queries leverage expert feedback without additional environment interaction, enabling policy improvement during offline training. However, existing methods still face two key challenges: selecting informative preference queries and effectively exploiting the collected feedback. Current approaches typically rely only on the distance between policy actions and dataset actions for query selection, while enforcing fixed constraints that keep the policy close to queried preferences. Such strategies often lead to unstable policy updates and integrate poorly with value regularization. To address these limitations, we propose Conservative Query and Adaptive Regularization under Uncertainty Estimation, a lightweight framework that jointly improves preference querying and preference exploitation. Specifically, we employ a Morse network to estimate the uncertainty of policy actions with respect to the offline dataset. Based on this uncertainty, we introduce a conservative query strategy that selectively queries actions near the dataset to preserve Bellman-update stability, together with an uncertainty-aware adaptive regularization scheme that dynamically adjusts data-level constraints during policy optimization. We integrate our framework with CQL and evaluate it extensively on the D4RL benchmark. Experimental results demonstrate superior or competitive performance across a wide range of tasks.
[LG-8] Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise
链接: https://arxiv.org/abs/2607.19173
作者: Arthur Bizzi,Olga Fink
类目: Machine Learning (cs.LG)
*备注:
Abstract:Neural stochastic differential equations (SDEs) have emerged as powerful tools for learning noisy or stochastic dynamics directly from data; however, existing approaches largely assume uncoupled and continuous noise, limiting their applicability to realistic stochastic drivers, and often scale poorly in time, requiring expensive autoregressive training. To address these limitations, we propose Neural Kolmogorov Equations (NKEs), a deterministic, infinite-dimensional reformulation of Neural SDEs based on the Kolmogorov Forward equation, transforming the learning problem from modelling individual stochastic trajectories to modelling the evolution of probability densities. NKEs learn general Lévy-type stochastic forcing directly through the operator structure of the KFE, and enable parallel-in-time training via a Lagrangian Galerkin projection and operator splitting. We evaluate NKEs on several stochastic benchmarks, including systems with coupled noise and jump processes, and verify that NKEs provide flexible models that accurately recover deterministic and stochastic dynamics with competitive predictive accuracy and improved training efficiency. Code and pretrained models will be released.
[LG-9] Boundary-Adapted PINNs for Elliptic Dirichlet Problems: H2(Ω) A Priori Error Bounds with Application to Mean Escape Time Computation
链接: https://arxiv.org/abs/2607.19167
作者: Nathanael Tepakbong,Jun Fan,Xiang Zhou,Ding-Xuan Zhou
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG); Statistics Theory (math.ST); Machine Learning (stat.ML)
*备注:
Abstract:Motivated by the numerical computation of the Mean Escape Time (MET) \tau:\Omega\to\mathbbR of a stochastic process from a bounded domain \Omega\subseteq\mathbbR^d , we study elliptic Dirichlet boundary value problems (BVPs) using boundary-enforced Physics-Informed Neural Networks (PINNs), in which the Dirichlet condition is imposed exactly by multiplying the network output with a predefined distance-to-boundary approximation \rho . Combining approximation-theoretic and statistical-learning arguments for Rectified Quadratic Unit (ReQU) and hyperbolic tangent (tanh) networks, we derive a priori error bounds that make explicit the dependence on \rho . In particular, we show that exact boundary enforcement alone is not enough for H^2(\Omega) error bounds, and that a sufficient and essentially necessary condition is for \rho to be a smooth distance approximation \textitnormalized to first order , of the kind constructed in arXiv:2104.08426 [math.NA]. We thereby identify this subclass of \textitboundary-adapted PINNs as the appropriate neural network ansatz for solving Dirichlet BVPs. Numerical experiments support the theory, showing that appropriate choices of \rho improve accuracy and convergence, while poorly chosen distance functions can substantially degrade the solution. Our proof also yields new VC-dimension bounds for hypothesis spaces of higher-order derivatives of ReQU and tanh networks, together with new approximation bounds for shallow ReQU networks in higher-order Sobolev norms, all of which are of important independent interest.
[LG-10] One Model Many Graphs: Learning over Attributed Graphs across Heterogeneous Modalities with Vision-Language Models
链接: https://arxiv.org/abs/2607.19128
作者: Jiayi Yang,Yifang Chen,Yuanfu Sun,Jiajin Liu,Qiaoyu Tan
类目: Machine Learning (cs.LG)
*备注:
Abstract:Vision-language models (VLMs) provide a unified representation space for textual and visual information, yet their potential as general-purpose backbones for graph-structured data remains largely unexplored. In practice, attributed graphs exhibit substantial modality heterogeneity: some graphs contain only textual node attributes, others only visual attributes, while still others provide both. Existing graph learning approaches are typically designed for fixed modality schemas, requiring separate models for different settings and limiting scalability and cross-graph generalization. To bridge this gap, we present OMG-VLM (One Model, Many Graphs with Vision-Language Models), a unified framework for learning over attributed graphs across heterogeneous modality schemas. OMG-VLM leverages a pretrained VLM as a shared backbone and introduces structure-aware graph adapters that integrate neighborhood information while remaining compatible with the VLM’s native embedding space. This design enables effective learning over text-attributed, image-attributed, and multi-attributed graphs within a single model. Extensive experiments across diverse domains show that OMG-VLM consistently outperforms state-of-the-art GNN- and LLM-based baselines on attributed graph learning tasks such as node classification and link prediction, while exhibiting strong generalization to unseen graphs and varying modality schemas. The source code is available at this https URL.
[LG-11] Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning
链接: https://arxiv.org/abs/2607.19114
作者: Zeno Romero,Maximilian Kohns,Fabian Jirasek
类目: Machine Learning (cs.LG); Chemical Physics (physics.chem-ph)
*备注:
Abstract:Activities in aqueous electrolyte solutions, usually described by ionic activity and osmotic coefficients, are important properties for modeling many processes in industry and nature. Established activity models, such as those of Pitzer or Bromley, require fitting to experimental data for each electrolyte of interest and thus cannot predict properties for unstudied systems. While some predictive approaches exist, they are typically limited in scope and rely on additional ion-specific descriptors. In this work, we introduce a new hybrid model that combines the physics-based Bromley model with a matrix completion method (MCM) from machine learning. The MCM is employed to predict the electrolyte-specific parameters of the Bromley model, exploiting the fact that these parameters can be arranged in a matrix with cations and anions as rows and columns, respectively. Due to the lack of experimental data for many electrolytes, the initial parameter matrix is sparsely populated, making the prediction of the Bromley parameters for unstudied electrolytes a matrix completion problem. The hybrid model, Bromley-MCM, was trained end-to-end on experimental data for mean ionic activity coefficients and osmotic coefficients of aqueous solutions of 478 electrolytes at 298 K from the Dortmund Data Bank. As output, we obtain a completed matrix of Bromley parameters for 83 cations and 112 anions, enabling consistent prediction of concentration-dependent activities in aqueous solutions of 9,296 electrolytes at 298~K. This substantially extends the applicability of the Bromley model while maintaining high predictive accuracy, as demonstrated through evaluations on electrolytes excluded from model training.
[LG-12] An unsupervised clustering analysis of breast cancer data derived from electronic health records enhanced through UMAP dimensionality reduction
链接: https://arxiv.org/abs/2607.19089
作者: Davide Chicco,Nicoletta Benvenuto
类目: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
*备注: Accepted at the CIBB 2026 conference ( this https URL )
Abstract:Breast cancer is one of the most widespread types of cancer, affecting approximately 8 million women worldwide. Electronic health records of patients diagnosed with this disease can serve as valuable datasets for computational analyses, enabling the discovery of new insights about the pathology. Unsupervised clustering, in particular, can identify groups of patients with medically significant features, revealing data trends that might otherwise go unnoticed by medical doctors. In this study, we first applied the DBSCAN density-based clustering method to three independent datasets derived from electronic medical records of patients with mammary carcinoma. Subsequently, to enhance our results, we preceded the DBSCAN application with a dimensionality reduction phase using UMAP. We evaluated our clustering outcomes using three statistical indices (DBCV, DCSI, and DISCO). Our results confirm the effectiveness of combining UMAP with DBSCAN for clustering data derived from electronic health records, paving the way for the medical interpretation of the patient groups identified by our approach.
[LG-13] GEqTrain: A Configuration-Driven Framework for Retargeting Equivariant Graph Neural Networks Across 3D Scientific Tasks
链接: https://arxiv.org/abs/2607.19083
作者: Daniele Angioletti,Marco Nobile,Vittorio Limongelli
类目: Machine Learning (cs.LG); Biomolecules (q-bio.BM)
*备注:
Abstract:Equivariant graph neural networks provide a powerful modeling language for three-dimensional scientific data, but their reuse is often limited by implementations tied to specific tasks, outputs, and training regimes. We present GEqTrain, a configuration-driven framework that separates dataset semantics, model composition, and training objectives. Raw data are mapped to typed node-, edge-, and graph-level fields, while model stacks, losses, and training workflows are assembled declaratively through Hydra configurations. A shared equivariant backbone and training infrastructure can therefore be retargeted to a new task primarily through configuration. We demonstrate this flexibility on three different problems handled within one software stack: coarse-grained-to-atomistic backmapping of biomolecular systems, prediction of NMR chemical shifts in molecular solids, and equivariant generative modeling. Our aim is not to surpass individually optimized task-specific systems, but to show that a shared representation and training infrastructure can achieve competitive accuracy across qualitatively different tasks at the cost of a configuration change. We further introduce GEqDiff, a generative extension based on equivariant flow matching. GEqDiff treats user-defined equivariant fields as first-class generation targets, jointly transporting Cartesian positions and non-scalar node fields spanning representations up to l=3 within a single equivariant flow. We validate this capability on a controlled synthetic benchmark inspired by protein secondary-structure motifs, showing that fields with heterogeneous transformation properties can be reconstructed jointly and with high fidelity. By reducing the software overhead of moving between predictive and generative, scalar and tensorial settings, GEqTrain aims to make equivariant modeling more reproducible, extensible, and reusable.
[LG-14] Probabilistic Physics-Aware Machine Learning Predictions of Electric Truck Energy Consumption with Field Data
链接: https://arxiv.org/abs/2607.19054
作者: Hannes Nilsson,Rafael Basso,Balázs Kulcsár,Morteza Haghir Chehreghani
类目: Machine Learning (cs.LG)
*备注: 22 pages, 8 figures
Abstract:In this work, we incorporate first principle physics into the construction of data-driven methods by considering a model that accounts for the different sources of energy losses during vehicle operations. Our results show that Bayesian linear regression based on this physics-aware model can improve the reliability of the expected energy consumption, as compared with standard linear regression. Further, it is shown that more complex machine learning models such as neural networks and gradient boosted regression trees, based on the same physical model, can further improve the accuracy in energy forecasting and significantly outperform standard versions of the same machine learning models. In addition to point predictions of the energy consumption, we develop a framework for estimating the corresponding uncertainty in the form of predicted standard deviation. Our results show that all of the models learn to estimate the uncertainty reasonably well.
[LG-15] Adopting Reinforcement Learning with Verifiable Rewards for Molecular Generation
链接: https://arxiv.org/abs/2607.19044
作者: Mingxuan Ouyang,Hao Lan,Wanyu Lin
类目: Machine Learning (cs.LG)
*备注: 13 pages, 4 figures
Abstract:Leveraging large language models (LLMs) for molecular generation has shown remarkable potential in chemical and drug design. Current methods primarily rely on supervised training or fine-tuning with limited datasets, which are insufficient to capture complex molecular design objectives. While some approaches attempt to guide generation toward specific goals, they often lack direct optimization mechanisms, making it difficult to align generated molecules with desired properties. To tackle these challenges, we propose \textbfLLMol, a principled reinforcement learning framework that directly incorporates verifiable rewards for targeted molecule generation. The key insight is to formulate molecular design as a goal-conditioned sequence prediction task, where verifiable rewards serve as explicit supervision to drive generation toward desired objectives. LLMol follows a two-stage training paradigm combining supervised learning and reinforcement learning. In the first stage, large language models are supervised fine-tuned to capture chemical syntax and molecular distributions. In the second stage, we introduce Reinforcement Learning with Verifiable Rewards (RLVR), which directly integrates property-based reward signals to guide molecular generation toward task-specific objectives. To address the high variance and instability common in discrete sequence optimization, we adopt Group Relative Policy Optimization (GRPO), a stable on-policy algorithm that smooths reward signals and improves training robustness. This framework enables LLMol to effectively handle a range of molecular design tasks, including single-property targeting (e.g., penalized logP, QED) and structure-constrained optimization. Experimental results demonstrate that LLMol consistently outperforms existing methods, achieving higher success rates and improved efficiency across diverse molecular benchmarks.
[LG-16] Unsupervised Multi-kernel Learning for Automated Algorithm Selection
链接: https://arxiv.org/abs/2607.19031
作者: Yihang Lu,Tome Eftimov,Carola Doerr
类目: Machine Learning (cs.LG)
*备注:
Abstract:Automated algorithm selection in black-box optimization typically relies on supervised models that map landscape features to algorithm performance labels. Such models are costly to train, benchmark-dependent, and often fail to generalize to unseen problem classes. We study an unsupervised alternative: multi-kernel clustering over heterogeneous landscape representations, in which problem instances are grouped without using performance labels in the clustering stage, and the resulting clusters are mapped post hoc to solver recommendations through a strictly separated three-stage evaluation protocol. Drawing on two decades of advances in multiple kernel learning, we adopt a multi-kernel k-means formulation that jointly learns cluster assignments and kernel weights over four heterogeneous landscape views: ELA, DeepELA, DoE2Vec, and TransOptAS. On affine BBOB-derived selector tasks for Differential Evolution (DE) and Particle Swarm Optimization (PSO) at a fixed evaluation budget, we report mean plus or minus standard deviation selector profiles over 50 independent random seeds for stochastic configurations. Multi-kernel clustering obtains the strongest mean profile on the DE portfolio and remains competitive with, and nominally ahead of, the leading baselines on the more compressed PSO portfolio, where differences among the best methods are small relative to stochastic variation. In representative median-seed runs used for visualization, the learned kernel weights retain ELA and TransOptAS while assigning zero weight to DeepELA and DoE2Vec, providing a task-specific interpretation of which representations are retained by the multi-kernel model for selector-oriented grouping.
[LG-17] Subject-Conditioned Glucose Forecasting in Type-1 Diabetes
链接: https://arxiv.org/abs/2607.19006
作者: Giorgia Rigamonti,Mirko Paolo Barbato,Davide Marelli,Paolo Napoletano
类目: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
*备注: Accepted at the IEEE EMBC 2026 Conference
Abstract:Accurate forecasting of blood glucose concentration is key in the management of Type 1 Diabetes, facilitating early detection of adverse glycemic events and supporting timely therapeutic interventions. Despite recent advances in glucose prediction, most existing approaches rely on population-level representations or implicit personalization strategies that fail to deliver effective subject-specific forecasts. In this work, we propose Subject-Conditioned Glucose Prediction (SCGP), a novel multimodal deep learning architecture conceived for personalized blood glucose prediction. SCGP conditions glucose predictions based on observed glucose data and a compact subject-specific representation learned from contextual information. By explicitly separating subject characterization from glucose dynamics modeling and avoiding early fusion of heterogeneous inputs, the proposed framework effectively captures inter-subject variability while preserving robust and reliable temporal modeling. Experiments on two state-of-the-art benchmark datasets demonstrate that SCGP consistently improves forecasting performance, enabling reliable detection of adverse glycemic events across multiple prediction horizons, highlighting the benefits of explicit subject conditioning for personalized diabetes management.
[LG-18] Visual Semantic Decoding of Electrocorticography from Video Stimuli using End-to-End Deep Learning
链接: https://arxiv.org/abs/2607.18923
作者: Stella Ho,Joel Villalobos,Joseph West,Jingyang Liu,Weijie Qi,Haruhiko Kishima,Ryohei Fukuma,Takufumi Yanagisawa,Sam E. John,David B. Grayden
类目: Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
*备注: This is a preprint and has not yet undergone peer review
Abstract:ECoG-based visual semantic decoding enables inference of semantic interpretation of visual perception from complex, noisy brain activity. This study examines the feasibility of visual semantic decoding using an end-to-end deep learning framework using electrocorticography (ECoG). Specifically, the decoding task is to predict visual categories from video stimuli using time-series neural inputs. A previously collected ECoG dataset from participants ( n=17 ) with drug-resistant epilepsy is used for analysis. With fewer than 50 training samples per visual category, this study evaluates multiple deep learning approaches, artificial neural network architectures, and frequency-band filtered inputs. The best-performing approach is analyzed to shed light on the discriminative information it relies on across spectral, temporal, and cortical dimensions. The selected decoding system uses mixup augmentation, a Transformer-based encoder, and high-gamma (80-150 Hz) inputs with a 900 ms post-stimulus window. Further analysis shows that early visual cortex (V2-V4), ventral stream visual cortex, MT+ complex with neighbouring visual areas, and lateral temporal cortex contributed substantially to decoding performance. This study demonstrates that an end-to-end deep learning framework can yield promising decoding performance from dynamic visual stimuli without handcrafted features, while the model behavior remains interpretable through spectral, temporal, and cortical dimensions, which are broadly consistent with established neuroscience knowledge.
[LG-19] Breaking Feedback-Blindness: Utility-Augmented Transformer for Sequential Decision Making
链接: https://arxiv.org/abs/2607.18910
作者: Yuyang Shen,Shan Dai,Daimin Chen
类目: Machine Learning (cs.LG)
*备注: 25 pages, 6 figures
Abstract:Sequential decision making in non-stationary and partially observable environments requires rapid adaptation to latent regime changes. However, existing Transformer decision models face a structural bottleneck in the retrieval mechanism: even when reward is used for training or exposed as an input token, attention retrieval remains primarily driven by observation-derived similarity. We formalize this limitation as feedback-blind retrieval, and formally show that, on feedback-informative tasks, observation-equivalent histories with different action-reward outcomes cannot be distinguished by any observation-only attention, resulting in suboptimal choice. To address this mismatch, we propose the Utility-Augmented Transformer (UAT), a new feedback-conditioned retrieval attention architecture in which a compact utility state modulates the query, key, and value projections, allowing action-reward history to directly alter context retrieval during the forward pass. UAT also enjoys an exact zero-gate degradation property that recovers the Vanilla Transformer when feedback is uninformative. Under finite-horizon compactness and Lipschitz assumptions, we prove that UAT strictly enlarges the observation-only Transformer class and can uniformly approximate feedback-dependent decision maps. Across four non-stationary benchmarks: synthetic navigation with hidden goal shifts, non-stationary sepsis treatment, cross-market portfolio allocation, and delayed-feedback recommendation, UAT consistently improves performance over observation-only, test-time adaptation, and input-level feedback baselines, with particularly large gains in noisier regimes that require stronger adaptation.
[LG-20] KALE: Kernel Alignment with Loss Equilibration for Stable CLIP-DINOv2 Alignment at Web Scale
链接: https://arxiv.org/abs/2607.18885
作者: Michał Pawłowicz
类目: Machine Learning (cs.LG)
*备注: 13 pages, 7 figures, 6 tables
Abstract:Kernel-based alignment of CLIP toward a vision centric teacher such as DINOv2 (KUEA) improves CLIP’s visual representations while preserving text-encoder compatibility, using a fixed trade-off weight tuned on curated ImageNet-1K. We ask whether this transfers to noisy, web-scale data (CC12M) and find that it does not: the alignment term’s weighted contribution falls to about 0.2% of the clean term, so under any fixed weight its gradient is effectively inert. We introduce KALE, a loss-equilibration controller that tracks both losses and adaptively rescales the alignment weight toward a target ratio, restoring the signal with no per-dataset tuning; reaching balance requires increasing the weight by roughly four orders of magnitude, and the required value is configuration-dependent, so no fixed scalar suffices. We characterize the resulting regime: a bounded high learning rate and a decaying schedule with a moderate floor are needed for stability, and the controller equilibrates rather than diverging. On a 3.3M-image CC12M subset, the aligned model preserves image-text retrieval and reproducibly improves SVHN linear probing; zero-shot improves by +2.00 over CLIP on the standard 11-dataset average, exceeding KUEA’s +1.29. We report all results with explicit run-to-run variance and base our conclusions on the metrics that are stable across runs.
[LG-21] Physics-Informed Super-Resolution of Atmospheric Data
链接: https://arxiv.org/abs/2607.18877
作者: Chang Xu,Gencer Sumbul,Hugo Porta,Manon Béchaz,Sebastian Schemm,Devis Tuia
类目: Machine Learning (cs.LG)
*备注:
Abstract:In the context of global warming, extreme events have become more frequent and intense, making their trustworthy detection and forecasting more important than ever. Yet, atmospheric observations lack sufficient spatial resolution, motivating atmospheric data downscaling as a way to reconstruct high-resolution data from coarse observations. This task is now being formulated as a super-resolution (SR) problem with machine learning methods featuring high efficiency. Nevertheless, it remains unclear whether the super-resolved atmospheric data still satisfies fundamental physics governing the Earth system, raising concerns about their trustworthiness in climate-related applications. In this work, we address this challenge by constraining SR models to respect hydrostatic primitive equations that represent multivariate atmospheric physics. First, we propose a Physics-Informed Super-Resolution (PISR) method involving multi-scale physics-informed objectives based on primitive equations. PISR favors the SR outputs to respect these equations and therefore naturally encodes inter-variable relationships. In addition, we propose a metric called Normalized Physical Consistency (NPC) derived from said primitive equations to measure the physical consistency of super-resolved data. Experiments on ERA5, CERRA, and COSMO demonstrate that PISR enhances the reconstruction fidelity by improving physical consistency, SR accuracy, and downstream detection of extreme events, as demonstrated by case studies in heatwaves and extreme winds.
[LG-22] Reinforcement Learning for Delivery Drone-Based Participatory Sensing in Dynamic Environments
链接: https://arxiv.org/abs/2607.18874
作者: Xin Ouyang,Songxin Lei,Xusen Guo,Yutian Jiang,Sijie Ruan,Yuxuan Liang
类目: Machine Learning (cs.LG); Computers and Society (cs.CY)
*备注:
Abstract:Using Unmanned Aerial Vehicle (UAV) for urban sensing has emerged as a powerful paradigm to monitor the status of the city, e.g., air quality and noise levels, through agile aerial crowdsourcing. Despite this potential, existing UAV-based sensing approaches overlook environmental disturbances like wind that drastically impact drone velocity and energy efficiency. Consequently, directly applying existing methods to this joint delivery and sensing paradigm in dynamic environments faces two severe challenges: (1) scalability bottlenecks as fleet sizes expand; and (2) multi-timescale decision heterogeneity between macro task dispatching and micro velocity control. To tackle these, we formalize the problem as SensUAV and propose a Two TimeScale Reinforcement Learning framework (TSRL). Specifically, TSRL separates decision-making into two cooperative layers. At the macro level, a task-embedding sensing dispatcher handles scalability by separately encoding distinct task features and sequentially evaluating UAV suitability before task selection. At the micro level, a wind-aware velocity controller learns fine-grained velocity scheduling to adapt to dynamic environmental variations. Extensive experiments on real-world datasets demonstrate that TSRL significantly outperforms baselines, achieving average system profit improvements of 20.1% in Hangzhou and 46.6% in Shanghai.
[LG-23] NSMA: Neuro-Symbolic Manifold Alignment for Generalizable Adaptive Bitrate Streaming under Texture Shift
链接: https://arxiv.org/abs/2607.18845
作者: Zhiqiang He,Zhi Liu
类目: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG)
*备注:
Abstract:For decades, ABR has kept two kinds of intelligence apart. Neural policies learn rich behaviors yet forget them the moment the environment changes; rules never learn, and never forget. Every prior attempt to combine them has kept this separation, letting rules supervise, constrain, or override the network from outside. We dissolve the boundary itself. But no union can be trusted before it can be tested, and ABR has never known how to measure what its policies learn or forget. The field’s yardstick is bandwidth statistics, and we show it misleads. Identical statistics can hide entirely different outcomes, while wildly different statistics can hide similar ones. We replace the yardstick before building the bridge, with Texture-Aware Generalization Evaluation, a protocol that judges a policy by its whole training journey across traces whose temporal nature is laid bare. What truly breaks a policy is invisible. No statistic reveals it, no feature extracts it, yet rules walk through it untouched, for they reason from physics and owe the data nothing. So we build the bridge. Neuro-Symbolic Manifold Alignment (NSMA) embeds rule decisions as anchors inside the latent space of the neural policy, so that it keeps learning where learning pays, and can no longer forget what rules have always known. Generalization cannot be argued, only survived. We raise NSMA on 3G traces alone and release it, without fine-tuning, into eight unseen datasets spanning 4G, 5G, and WiFi, and onto a real-world player. It outperforms every state-of-the-art baseline. And when we open its latent space to ask why, probing and visualization return the same answer the design promised. this https URL
[LG-24] Countercurrent Multiplier Networks: A Renal-Inspired Iterative Operator with Provably Bounded Fixed-Point Dynamics
链接: https://arxiv.org/abs/2607.18829
作者: Snigdha Chandan Khilar
类目: Machine Learning (cs.LG); Mathematical Physics (math-ph)
*备注:
Abstract:The mammalian kidney concentrates urine using a mechanism with no analogue in current neural architectures: the countercurrent multiplier. Two anti-parallel flows joined at a hairpin recirculate a weak magnitude-bounded local pump into a large axial gradient achieving a four-fold concentration increase from a single-effect gradient that never exceeds 200 mOsm at any point. We formalize this mechanism as a differentiable sequence operator the Countercurrent Multiplier (CCM) layer and study it as an alternative to residual iterative refinement.
[LG-25] Elicitation without Backpropagation: Steering Model Behavior by Optimizing the Latent Posterior
链接: https://arxiv.org/abs/2607.18804
作者: Garrett Baker,Vinayak Pathak,Daniel Murfet,Susan Wei
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:In the \emphlatent posterior model of transformer behavior, the next-token distribution arises from a posterior over latent predictive models conditioned on the context, mixed to generate continuations. We exploit this model in settings where it is exact, namely Bayes-filtered transformers (BFTs) meta-learned on sequences from a hierarchical prior, to introduce \textbfPosterior Prefix Tuning (PPT), a new method for \empheliciting behavior from a transformer: given a utility function on continuations, find a prompt under which the transformer generates continuations of high expected utility. For a BFT, the elicitation objective factors through the latent posterior, and the gradient of this objective can be estimated from samples of the prior alone. PPT optimizes the parameters of a distribution over hard prompts: it draws prior samples once from the BFT via predictive Monte Carlo (PMC), then estimates the gradient by importance sampling against them. The optimization performs no transformer forward passes and no backpropagation through the transformer, and the prior samples are utility-independent, so a single set of samples drives elicitation against any number of utilities at negligible marginal cost. We validate PPT on Beta–Bernoulli and reinforced urn BFTs across three utility families (reverse cross-entropy, frequency matching, Dyck validity).
[LG-26] QScheduler: Adaptive Gradient Sampling for Zeroth-Order On-Device Training on INT8 NPUs
链接: https://arxiv.org/abs/2607.18802
作者: Victor Felipe Domingues Do Amaral(GeePs),Pierre Demaj,Erwan Libessart(GeePs),Laurent Folliot,Anthony Kolar(GeePs),Philippe Bénabès(GeePs)
类目: Machine Learning (cs.LG)
*备注:
Abstract:Zeroth-Order (ZO) optimization enables On-Device Learning (ODL) on NPU-equipped microcontrollers by estimating gradients through forward passes alone, bypassing the need for backpropagation primitives and reducing memory requirements. The number of gradient samples q critically affects training: insufficient samples produce noisy gradients that plateau early, while excessive samples consume more computational resources. However, finding an optimal q typically requires costly hyperparameter searches. This work introduces QScheduler, an adaptive algorithm that adjusts q based on training progress, and provides the first proof-of-concept of INT8 quantized on-device training on the STM32N6’s Neural-ART NPU. Experiments on EuroSAT and STL-10 show that QScheduler matches well-tuned fixed-q configurations for both ResNet18 and MobileNetV2, without requiring prior q hyperparameter optimization.
[LG-27] PertReason : A Knowledge-Grounded Benchmark and Framework for Cell-State-Conditioned Mechanistic Reasoning of Perturbation Effects
链接: https://arxiv.org/abs/2607.18777
作者: Dongkwan Kim,Yiming Gao,Yining Yang,Yang Shen
类目: Machine Learning (cs.LG); Molecular Networks (q-bio.MN)
*备注: Preprint; 31 pages; Dongkwan Kim and Yiming Gao contributed equally to this work
Abstract:Evaluating machine learning in scientific domains requires separating correct predictions from correct reasons under realistic distribution shifts. We introduce PertReason, a knowledge-grounded benchmark and framework suite for cell-state–conditioned reasoning about perturbation effects. At its core, PertReasonQA is a benchmark that tests whether models can generate mechanistically faithful explanations while remaining robust to complex shifts, such as new cells and unseen perturbations. PertReasonQA combines single-cell genetic and chemical perturbation data across multiple cellular contexts with knowledge graphs, and dynamically conditions pathways on cell-specific basal states to avoid generic memorization. Evaluations on state-of-the-art models reveal systematic gaps between predictive accuracy and mechanistic reasoning. Specifically, these models exhibit failure modes largely invisible to standard benchmarks, such as deriving correct answers through flawed logic, ignoring cellular context, and generating directionally inconsistent mechanisms. As a reference probe of the benchmark, we present PertReasonLM, a large language model trained to align outcome predictions with context-specific mechanistic reasoning. Our model targets the identified failure modes by grounding rationales in context-specific pathways and tightening agreement between outcomes and mechanisms. Together, we provide a diagnostic framework for exposing and mitigating failures in faithful reasoning in data-rich scientific systems.
[LG-28] Formulation-Level Auto-Tuning for QUBO-Based Machine Learning: A Case Study Across Multiple Quantum-Inspired Annealers
链接: https://arxiv.org/abs/2607.18774
作者: Naoya Mizuki,Takahiro Katagiri,Daichi Mukunoki,Tetsuya Hoshino
类目: Machine Learning (cs.LG); Performance (cs.PF)
*备注:
Abstract:This paper presents an Optuna-based formulation-level auto-tuning framework for support vector machines (SVMs) implemented on multiple quantum-inspired annealers. In an annealing-based SVM, continuous dual variables are discretized and converted into a quadratic unconstrained binary optimization (QUBO) model. This transformation introduces three coupled classes of parameters: representation parameters-the encoding base B and bit depth K-which determine numerical range, resolution, and QUBO size; the RBF kernel parameter \gamma, which determines classifier geometry; and the equality-constraint penalty \xi, which controls feasibility and coefficient balance. We formulate their joint selection as a mixed discrete-continuous black-box optimization problem. The framework has two optimization levels: an inner annealer minimizes the generated QUBO, while an outer Optuna loop reconstructs the formulation in every trial and maximizes validation accuracy. The same solver-agnostic procedure is applied to Fixstars Amplify Annealing Engine, Toshiba SQBM+, and Fujitsu Digital Annealer using TPE and Gaussian-process samplers and is compared with conventional grid search. Experiments on linear and nonlinear classification tasks with 0-20% label noise show mean gains over grid search of approximately 0.8 and 2.1 percentage points, respectively. The results demonstrate that formulation quality and backend capability must be evaluated jointly and that task-level feedback can compensate for discretization, penalty imbalance, and backend-dependent approximate optimization.
[LG-29] Relative Positions Generalize Absolute Positions Memorize: An Implicit-Bias Account of Length Generalization in Attention
链接: https://arxiv.org/abs/2607.18759
作者: Subham Singh,Ashutosh Mishra,Subha Raut
类目: Machine Learning (cs.LG)
*备注:
Abstract:Transformers with relative positional encodings often extrapolate to sequences longer than those seen during training, whereas transformers with learned absolute encodings typically do not. This is a robust empirical regularity, and the explanations offered for it so far are chiefly about expressivity, that is, about whether a length-generalizing solution exists. We give an optimization explanation. On a minimal fixed-offset retrieval task that isolates positional selection, the gap is governed by the implicit bias of the trained attention head: among the many solutions that fit short sequences, which one gradient descent actually selects. We prove that rotary encodings make the attention logit a function of relative offset alone, an exact equivariance, so whatever selection rule is learned at training lengths is reproduced verbatim at every longer length. Learned absolute encodings instead leave out-of-range positions unconstrained, and the trained head pins to a fixed absolute position inside the training range. We characterize the learned rotary rule as a low-rank ``carrier’’ kernel aligned with the target offset, and we derive the resulting graceful accuracy decay as an attention-dilution law; both predictions are confirmed across seeds and offsets. A linear-attention control shows the mechanism is specific to softmax: without normalization, training selects a min-norm interpolant that does not extrapolate. The phenomenon, the equivariance, and the carrier all transfer to a multi-layer, multi-head transformer trained on a full-sequence length-generalization task. The account connects the implicit bias of attention, implicit bias for extrapolation in recurrent models, and the learning side of the RASP-L conjecture.
[LG-30] Decafs: Disentangled Conditional adversarial Flows
链接: https://arxiv.org/abs/2607.18755
作者: Anirudh jain,Sakshi Varshney,Samuel Kaski,Vikas Garg
类目: Machine Learning (cs.LG)
*备注:
Abstract:Flow-based models have established state-of-the-art performance in generative modeling across domains, but are hard to interpret due to their complex latent embeddings. In particular, the entanglement of generative factors in the latent space hinders controlled generation. We circumvent this issue by appealing to a novel conditional generator based on Lie groups that disentangles an alternative latent space, which is aligned closely with the latent flow space using an adversarial loss. Our approach facilitates interpretable conditional generation while obviating the need to expand the dimensionality of the flow space (owing to its invertibility requirements). The proposed model demonstrates strong performance across conditional image (including, outperforming StyleGAN on MNIST, dSprites) and molecule (using standard QM9, ZINC and MOSES) generation tasks
[LG-31] Contraction-Gauge Preconditioning for Quantized Matrix Multiplication
链接: https://arxiv.org/abs/2607.18745
作者: Piyush Sao,Narasinga Miniskar,Pedro Valero-Lara,Keita Teranishi,Sudip Seal
类目: Machine Learning (cs.LG); Information Theory (cs.IT); Numerical Analysis (math.NA)
*备注: 50 pages, 13 figures, 3 tables
Abstract:We study low-precision computation of C=AB with both factors quantized. We derive an exact finite-dimensional identity for the expected squared product error under independent, zero-mean entrywise errors with known variance fields; it holds exactly for non-overloading subtractive dither and for independent stochastic rounding, and we empirically assess deterministic round-to-nearest (RTN). Using the product-preserving equivalence AB=(AT)(T^-1B), we formulate contraction-gauge preconditioning: jointly choosing a factor representation and its sharing pattern before quantization. Preconditioning can reduce product error but may require extra transformed, quantized copies of the opposite operand: a shared transform needs one copy, a block-specific transform up to one per block. Within the bounded family of positive diagonal gauges (folds), a geometric program computes a globally optimal shared fold and a linear program decides whether the identity fold is already optimal. For other families we derive computable selection statistics – tail index for scaling, profile spread for partitioning, coherence and weighted-Gram energy for rotations, slice-energy covariance for hierarchy depth – with upper bounds for ranking heuristic candidates. Across twelve linear products from a trained three-block image classifier, median within-product rank correlations between dither-model predictions and deterministic-RTN errors are 0.937 at 8 bits and 0.918 at 4 bits. The GP fold cuts held-out product error over the identity fold by 18.0% (8-bit) and 20.5% (4-bit) in geometric mean, beats a SmoothQuant-style grid baseline at both precisions and on ten of twelve products, and lowers composed logit MSE by 15.4% and 26.4%. We thus provide exact stochastic product-error accounting, certified selection within the diagonal family, and a common objective for evaluating reusable transform candidates under RTN.
[LG-32] Spaghetti Architect: A Contamination-Resistant By-Construction-Labelled Multi-Language Code Dataset Generator
链接: https://arxiv.org/abs/2607.18642
作者: Yuxiang Ji
类目: Machine Learning (cs.LG); Software Engineering (cs.SE)
*备注: 32 pages. Under review at the Journal of Data-centric Machine Learning Research (DMLR). Code: this https URL (artifact archived at doi: https://doi.org/10.5281/zenodo.21033174 )
Abstract:Mined code corpora are abundant but uncontrolled: a snippet’s semantics, surface “messiness,” and difficulty are whatever the wild contained; there is no known-optimal reference to grade against; and any public sample may already sit in a model’s training set. We present Spaghetti Architect, a tool that mints code datasets with the control such corpora lack. An anti-optimization transpiler maps a clean, language-agnostic JSON intermediate representation to deliberately redundant, fully-flattened programs in five languages (Python, JavaScript, Go, Java, C++); every program is compiled, run, and checked against a reference oracle, so each instance is correct by construction. The clean IR is a known-optimal reference, messiness is dialed by strictly-nested anti-pattern profiles, each instance is labelled along two orthogonal difficulty axes, intrinsic (problem size) and incidental (presentation at fixed semantics), and contamination is resisted by minting fresh variants from a private held-out seed. We give construct-validity evidence that the quality order moves established complexity and readability metrics, and report baselines on a four-model open ladder: exact match rises with scale, and the intrinsic knob collapses arithmetic-aggregation accuracy of even the strongest model to zero. Further, development-set scores equal freshly re-minted held-out counterparts within |\Delta|\le 0.012 (comprehension) and \le 0.011 (refactoring); on identical programs, refactoring equivalence ( 0.73 \rightarrow 0.99 ) is scale-invariant while output prediction collapses; and ablating the generator’s self-annotations shows they inflate the weakest model an order of magnitude more than the strongest ( -0.173 vs -0.017 ): the annotated ladder resolves one of three adjacent pairs where the unannotated resolves all three. Open source (MIT), dependency-free, archived under a persistent DOI.
[LG-33] End-to-end Conditional Diffusion for Realistic and Controllable Visual Traffic Scenario Generation
链接: https://arxiv.org/abs/2607.18637
作者: Jingzheng Li,Yufei Ge,Zhijun Chen,Qianren Mao,Zizhe Wang,Binhang Qi,Bing Li,Keyu Chen,Baochang Zhang,Xianglong Liu,Philip S Yu
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Generating closed-loop traffic scenarios that are both realistic and controllable is crucial for evaluating autonomous driving systems, especially under rare safety-critical interactions. Existing learning-based methods often struggle to balance controllability and realism, offering either limited fine-grained control over traffic behavior or controllable scenarios at the expense of behavioral plausibility. This paper presents E2E-CDiff, an end-to-end conditional diffusion framework for controllable and realistic scenario generation. Conditioned on front-view visual observations, E2E-CDiff jointly denoises future motion states and executable low-level controls for route-interacting background vehicles. This unified state-action generation mitigates the planning-control mismatch in conventional two-stage trajectory-then-controller pipelines. Differentiable guidance further regulates speed, enforces drivable-area compliance, and supports collision-avoidance or collision-seeking behaviors, enabling both naturalistic and safety-critical scenario generation. Experiments on Bench2Drive show that E2E-CDiff achieves a favorable controllability-realism trade-off compared with representative reinforcement- and imitation-learning baselines, while its collision-guided variant induces challenging interactions across multiple autonomous driving systems. E2E-CDiff also performs competitively as a learning-based ego planner, demonstrating the generality of end-to-end state-action diffusion.
[LG-34] Graph Neural Network-based Algorithm Selection for the Traveling Salesman Problem: A Systematic Study of Cost and Rank Losses under Distinct Budget Regimes
链接: https://arxiv.org/abs/2607.18632
作者: Zhaoxuan Li,Jiale Yang,Yifei Lu,Mustafa Misir
类目: Machine Learning (cs.LG)
*备注: 23 pages, 6 figures, and 5 tables. Submitted to the European Journal of Operational Research
Abstract:Automated Algorithm Selection (AS) aims to improve problem-solving performance by selecting, for each problem instance, the most suitable algorithm from a predefined portfolio. This is particularly relevant to the Traveling Salesman Problem (TSP), where solver performance is strongly instance-dependent. We introduce GNNAS-TSP, a Graph Neural Network (GNN)-based AS framework that learns TSP instance representations directly from raw graph data, avoiding manual feature engineering. GNNAS-TSP formulates AS as a joint cost-prediction and ranking task. We evaluate cost-based (mean squared error (MSE), mean absolute error (MAE), and Huber), rank-based (RankNet, ListNet, and LambdaRank), and hybrid learning objectives for a portfolio comprising Chained Lin-Kernighan, Edge Assembly Crossover, Lin-Kernighan-Helsgaun, Multiagent Optimization System, and Concorde. Experiments use fixed computational budgets of 10 and 60 seconds. On the held-out test set, the selected configurations improve on the Single Best Solver (SBS) in normalized solution cost at both budgets. For the 10s budget, AS achieves substantial and statistically significant cost improvement over SBS. Overall, the results suggest that GNNAS-TSP is a useful meta-solving strategy when exploitable variation exists across solver performance.
[LG-35] BRIDGE: Bottleneck-Aware Regulator-Set Inference and Diagnosis for Cooperative Gene Regulatory Recovery
链接: https://arxiv.org/abs/2607.18602
作者: Maryam Rahimimovassagh,Clayton Thomas Barham,Ivan Garibay,Niloofar Yousefi
类目: Machine Learning (cs.LG)
*备注:
Abstract:Cooperative gene regulation often depends on groups of regulators acting jointly, but most gene regulatory network (GRN) inference methods output pairwise regulator-target rankings. We introduce Bottleneck-Aware Regulator-Set Inference and Diagnosis (BRIDGE), a framework for complete regulator-set recovery, and Targeted Recovery Attribution for Cooperative Evaluation (TRACE), a diagnostic suite that attributes failures to retrieval, set-level scoring, decoding, and evaluation bottlenecks. TRACE includes a leak-free mechanism-mismatch cooperativity stress test in which cooperative targets are generated by random nonlinear mechanisms rather than product interactions. This design avoids feature-mechanism circularity: Residual higher-order set scoring (Residual HOS2) operates on raw expression vectors without handcrafted product-correlation features. Across 30 matched seed-cooperativity settings, Residual HOS2 improves Jaccard similarity from 0.382 to 0.460, recall from 0.522 to 0.597, and exact recovery from 0.053 to 0.113 over a decomposable pairwise set scorer (PairS2), although exact recovery remains low. On SERGIO DS3, oracle retrieval and TRACE show that candidate coverage is necessary but insufficient because set-level misranking remains the dominant source of exact-recovery failure. PairS2 proposal followed by Residual HOS2 reranking reduces HOS2-scored candidate sets by 94-97% while largely preserving exact-recovery behavior. These results distinguish edge ranking, candidate retrieval, set-level scoring, and exact cooperative regulator-set recovery as separate objectives. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2607.18602 [cs.LG] (or arXiv:2607.18602v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.18602 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-36] On the Diverse Dynamical Behaviors Arising in Deep Linear Transformers
链接: https://arxiv.org/abs/2607.18584
作者: Sixu Li,Thomas Jacob Maranzatto,Jan Peszek,Trevor Teolis,Semih Akkoc,Konstantin Riedl,Sennur Ulukus,Nicolás García Trillos
类目: Machine Learning (cs.LG); Dynamical Systems (math.DS)
*备注:
Abstract:We study the inference-time behavior of deep linear encoder-only transformers through the lens of interacting particle systems. In this perspective, tokens are modeled as particles that interact dynamically through successive linear self-attention layers. We show that in embedding dimension two, for any key, query, and value matrices, the dynamics can be reformulated as a generalized Kuramoto-type model with pure second-harmonic coupling. This formulation is amenable to Watanabe–Strogatz theory which reveals the dynamics are intrinsically low-dimensional regardless of the parameter matrices. For a class of token initializations associated with the Ott–Antonsen (OA) manifold, we show that the parameter matrices induce a diverse variety of long-time behaviors in linear transformers, including clustering, oscillations, and bifurcations. The oscillations and bifurcations are characterized by uncovering a hidden Hamiltonian structure in the dynamics. By establishing a structural stability result, we further show that dynamics initialized near the OA manifold exhibit the same long-time behavior as those initialized exactly on the manifold. Motivated by our theory in dimension two, we conduct numerical experiments for analogous parameter regimes in higher-dimensional transformers. Our numerical experiments suggest that the long-time behaviors characterized in our theoretical results persist in higher dimensions.
[LG-37] Conditioned Direct Feedback Alignment via Activity and Error Geometry
链接: https://arxiv.org/abs/2607.18574
作者: Houman Safaai,Varun Reddy,Bernardo L. Sabatini
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Neurons and Cognition (q-bio.NC)
*备注:
Abstract:Direct feedback alignment (DFA) trains hidden layers with fixed random projections of the output error, avoiding the transposed-weight backward pass of backpropagation (BP). We study a failure mode of DFA training that is distinct from feedback quality: the local weight update is calculated by an outer product, so anisotropy can enter through either its presynaptic-activity factor or its local-error factor. Our analyses with controlled synthetic regimes isolate the first failure mode and show an approximately 40-percentage-point activity-conditioning gain when high-variance directions contain task-irrelevant nuisance. Three clean confirmations isolate a different regime: error conditioning improves raw DFA by 1.77–7.53 percentage points, and combining independently selected activity and error factors adds 0.40–0.90 points over activity conditioning. The signs hold for tanh/one-vs-rest MNIST and preregistered Fashion-MNIST, and replicate on eight fresh seeds in a ReLU/softmax MNIST model. This factorization yields a symmetric block-local family of normalized DFA (nDFA): activity nDFA right-preconditions by an inverse activity second moment, error nDFA left-preconditions by an inverse local-error second moment, and K-nDFA applies both factors with separately tuned damping. A linearized post-alignment calculation gives an exact input-side spectral identity and a Kronecker-factor motivation for the two-sided rule, whereas norm matching rules out a scalar step-size explanation. The error factor is fragile when under-damped, BatchNorm is a strong activity-side alternative, and convnet gains remain partial. We therefore frame conditioned DFA as a factor-level study of when local outer-product rules fail, not as a general replacement for BP or a solution to all-layer convolutional credit assignment.
[LG-38] AMICA-Python: Adaptive Mixture Independent Component Analysis with Anderson Acceleration
链接: https://arxiv.org/abs/2607.18568
作者: Scott Huberty,Christian O’Reilly
类目: Machine Learning (cs.LG)
*备注:
Abstract:Adaptive Mixture Independent Component Analysis (AMICA) is widely used in EEG research and has long been associated with strong empirical performance for blind source separation. Despite its impact, practical use has historically depended on a single Fortran implementation, accessed via the EEGLAB toolbox for MATLAB, limiting its accessibility for analytical pipelines not designed within the MATLAB ecosystem. Here we present AMICA-Python, a Python implementation of the AMICA algorithm, with a scikit-learn-conformant API designed for integration with existing scientific Python pipelines. The implementation follows the reference algorithm closely while adopting modern software engineering practices and an interface familiar to Python users. Additionally, we introduce an optional Anderson acceleration scheme that can dramatically reduce the time to convergence for this relatively slow algorithm. To evaluate numerical agreement and practical performance, we benchmarked AMICA-Python against the reference Fortran implementation on 14 open EEG recordings. After averaging 3 runs of each implementation on all 14 recordings, AMICA-Python closely matched the reference, with a median final normalized log-likelihoods of 11.572 for both the Fortran and Python implementations, and a negligible median relative absolute difference of only 1.07\times10^-8 when normalized by the absolute Fortran value. Runtime was also competitive. Relative to the reference implementation, AMICA-Python was 17.7% faster, while the Anderson-accelerated variant was 34.1% faster. AMICA-Python reproduces the reference implementation to high numerical precision with competitive runtime, while making AMICA available through a more accessible and extensible Python interface.
[LG-39] Adaptive Two-Stage Online Learning for Service-Affecting Failure Detection in Mobile Core Networks
链接: https://arxiv.org/abs/2607.18522
作者: J. du Toit,G. Fita,J. Salzwedel,A. Stoltz,R. Wolhuter
类目: Machine Learning (cs.LG)
*备注: 8 pages, 3 figures
Abstract:Mobile network operators monitor aggregated traffic volumes to assess the operational health of core network infrastructure. Reliable failure detection is challenging due to strong temporal structure, non-stationarity, measurement artefacts, and extreme class imbalance, which limit static threshold-based monitoring. This paper proposes a two-stage online learning framework for traffic-based failure detection in mobile core networks. Stage I incrementally models normal traffic dynamics using lightweight regression with time-aware features. Stage II analyses prediction residuals together with contextual indicators to detect genuine service-affecting network failures. The framework operates fully online under a prequential evaluation protocol, enabling continuous adaptation with low computational overhead. Across linear and non-linear models, the proposed two-stage architecture achieves the best precision-recall trade-off, attaining the highest recall, F1-score, and AUC at acceptable false positive rates. These results demonstrate the importance of explicit residual decomposition for reliable failure detection in streaming mobile core network data.
[LG-40] Attractor Geometry Determines the Identifiability Limits of System Discovery
链接: https://arxiv.org/abs/2607.18490
作者: Matteo Gallo,Fabio Anselmi,Paolo Lazzari
类目: Machine Learning (cs.LG); Chaotic Dynamics (nlin.CD)
*备注: 42 pages, 6 figures
Abstract:Symbolic discovery of governing equations from data is limited not only by algorithm design and data volume, but by the geometry of the attractor: what the long-run dynamics allow to be recovered. Using a within-system design on Lorenz-84, where one forcing parameter drives fixed-point, limit-cycle, and chaotic regimes while the governing equations and library stay fixed, we show that a single number, \lambda_\min(M) , the smallest eigenvalue of the invariant-measure moment matrix, sets the identifiability ceiling for both sparse regression (SINDy) and evolutionary symbolic regression (PySR). Derived from the Birkhoff ergodic theorem and obtained from a short reference trajectory before any run, \lambda_\min(M) measures how fully the attractor covers function space: where it vanishes, recovery is impossible for any algorithm, sparse or combinatorial alike; as it grows, both algorithms improve. Chaos raises \lambda_\min(M) by spreading the attractor, but also enlarges it and amplifies noise; because noise enters SINDy’s regression bottleneck linearly and PySR’s discrimination channel superlinearly, the same transition can push the two methods in opposite directions, so deeper chaos is not uniformly better. Parameter-free mechanistic scores from this framework transfer without refitting to a held-out Lorenz-96 system, confirming mechanism rather than curve-fitting; a criterion read from the equations predicts when added chaos will not improve conditioning. We also introduce Soft F1, a coefficient-weighted structural metric that resolves performance differences invisible to binary-success and predictive scores. The first question of discovery is then not which algorithm, but what the attractor permits.
[LG-41] Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection
链接: https://arxiv.org/abs/2607.18479
作者: Dorianis M. Perez,Maksim E. Eren,Bryan E. Kaiser
类目: Machine Learning (cs.LG)
*备注:
Abstract:Malicious anomalous activity detection is a fundamental challenge for cyber security systems. Both tensor decomposition under statistical framework with CANDECOMP-PARAFAC alternating Poisson regression (CP-APR) and normalizing flows have proven to be powerful unsupervised machine learning methods that model multi-dimensional data and capture complex and multi-faceted details of behavior profiles in cyber security applications. In this study, we propose Hybrid Latent-Structural Fusion (HLSF), a weighted anomaly fusion framework integrating CP-APR structural anomaly scores with latent-space density scores derived from normalizing flows. In our experiments, we show that the HLSF framework improves anomaly detection performance on a dataset of real-world compromised user credentials collected from the large enterprise network of Los Alamos National Laboratory (LANL) during a red-teaming exercise, compared with using CP-APR or normalizing flows alone.
[LG-42] Weak-to-Strong Learning in Decision Making
链接: https://arxiv.org/abs/2607.18467
作者: Jingwei Ji,Renyuan Xu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Many operational decisions rely on predictive models that estimate uncertain outcomes conditional on observable contexts. Training such models, however, often faces a fundamental data asymmetry: labeled outcomes are scarce or costly to obtain, while contextual covariates are abundant. Motivated by this data asymmetry, we develop a decision-aware weak-to-strong (W2S) framework that leverages both labeled and unlabeled data to improve contextual stochastic optimization. Specifically, we first train a weak model using limited labeled data and then use it to generate predicted outcome distributions on unlabeled contexts. These distributions provide soft supervision for training a strong model. We establish a non-asymptotic upper bound on the excess decision risk of W2S and a complementary lower bound for a strong-only benchmark. Their comparison yields explicit sufficient conditions under which W2S improves downstream decision performance. The key quantity is the correlation dimension between the weak and strong feature representations: when it is small, abundant unlabeled data reduce the effect of teacher errors along non-overlapping directions. A synthetic newsvendor experiment and a comment moderation experiment based on real-world data provide empirical evidence consistent with the theory.
[LG-43] AHEAD: Advancing Multi-Class Label Aggregation with Interpretable Cross-Annotator Modeling
链接: https://arxiv.org/abs/2607.18465
作者: Ju Chen,Sijia Xu,Jun Feng,Zhiqiang Gao,Zhengyi Yang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Crowdsourced labeling provides valuable labeled data for domains across natural language processing, computer vision, and video. Label aggregation aims to infer latent true labels from noisy and biased annotations, with the key lying in annotator reliability estimation. Despite promising progress, existing approaches struggle with one real-world bottleneck: most individual annotators label only a small subset of tasks, making accurate annotator estimation highly intractable. In this paper, we focus on the considerably more challenging multi-class label aggregation and propose AHEAD (cross-Annotator learning and High-confidEnce Annotator-guideD label aggregation), a cross-annotator learning framework that advances annotator reliability estimation by leveraging the population-level data. Specifically, AHEAD first learns high-dimensional cross-annotator contexts via a graph neural network, deriving multi-view, complementary annotator embeddings by aggregating individual-level annotator features with contextual information. These embeddings are then decoded into interpretable annotator-specific confusion matrices to fit the observed labels. We formulate a composite objective incorporating high-confidence annotators to alleviate the unsupervised training issues faced by prior models. Experiments on 10 real-world datasets spanning NLP, CV, Video, and Audio show that AHEAD substantially improves label accuracy, increasing average accuracy from 68.75% to 73.23%, with gains of up to 14.9% in the best case. Meanwhile, scalability experiments on the largest dataset further demonstrate the overall superiority of our method.
[LG-44] PAC–Bayes Bounds on Quotient Parameter Spaces: Geometry-induced Implicit-Bias Priors
链接: https://arxiv.org/abs/2607.18422
作者: Nicola Aladrah,Fabio Anselmi
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Overparameterized models often have continuous parameter symmetries, so different parameters define the same predictor. We show that PAC–Bayesian analysis should be performed on the quotient predictor space: pushing a prior and posterior to the quotient preserves the empirical and population Gibbs risks while removing the nonnegative KL contribution caused solely by how the two distributions differ among parameterizations of the same predictor. Quotienting alone does not determine which prior to use. We construct a canonical choice of one parameterization for each predictor and account for the geometric volume of its equivalent parameterizations. This transforms a neutral reference prior into a data-independent prior that reflects the model’s implicit bias. It approximates the ideal but inadmissible posterior-matched prior, which would minimize the KL term by depending on the training data. The resulting certificate is tighter exactly when this geometry-induced prior has smaller KL divergence from the learned quotient posterior than the neutral prior. We test this prediction in Fourier regression with a Hadamard parameterization and in Query-Key attention, using ordinary SGD without an explicit regularizer. The implicit-bias prior reduces the mean quotient-space KL by (40.69%) and the mean PAC–Bayes certificate by (21.40%) in the Fourier-Hadamard experiment. The smaller, prior-scale-dependent improvement in Query-Key attention confirms the predicted conditional nature of the effect. Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2607.18422 [cs.LG] (or arXiv:2607.18422v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.18422 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-45] Scalable and Efficient Joint Spiking Embedding Predictive Architecture for Large-Scale Dynamic Graphs
链接: https://arxiv.org/abs/2607.18412
作者: Huizhe Zhang,Yuchang Zhu,Huazhen Zhong,Liang Chen,Zibin Zheng
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注:
Abstract:Dynamic graph learning aims to capture evolving structural and semantic patterns in real-world systems, such as fraud detection and recommender systems. Due to the scarcity of labeled data in real-world dynamic graphs, recent studies have introduced generative or contrastive paradigms (e.g., masked graph autoencoders or graph contrastive learning) to generate task-agnostic graph embeddings. However, these methods typically rely on complex edge-level reconstruction objectives and tailored graph augmentation strategies. This incurs substantial computational overhead when scaling to large-scale dynamic graphs. In this paper, we propose SG-JEPA, a joint spiking embedding predictive architecture for large-scale dynamic graphs. In contrast to existing self-supervised methods, SG-JEPA partitions nodes into context and target sets along the temporal dimension to learn embeddings that are predictive of each other via additional spatial-temporal information. Furthermore, through encoding sequential inputs into coarse-to-fine spike count embeddings, spiking neurons enable SG-JEPA to adapt to the varying computational constraints of downstream tasks. Extensive experiments demonstrate that SG-JEPA achieves competitive or even superior performance over discriminative baselines on node classification, while effectively scaling to the dynamic graph with 13 million edges. SG-JEPA avoids the complex machinery (negative sampling, graph augmentations, edge-level reconstruction, etc.), resulting in superior training efficiency and memory scalability compared with prior self-supervised dynamic graph baselines.
[LG-46] Interactive Training 2: Auditable Control Plane for Live Model Training
链接: https://arxiv.org/abs/2607.18314
作者: Wentao Zhang,Xuanhe Pan,Han Zhou,Yang Lu,Yuntian Deng
类目: Machine Learning (cs.LG)
*备注:
Abstract:Experiment trackers show how training is progressing, but changing a live run still usually requires trainer-specific code. We present Interactive Training 2, an open-source control plane for steering training through a shared protocol. Training applications declare which settings and actions they expose, humans and automated controllers submit requests through the same interface, and the training loop validates and applies them at safe control points. A customized Aim workspace combines live metrics and controls with a chronological record of requests and outcomes. We demonstrate the system across five NLP and reinforcement-learning workflows. The released code and traces provide a reusable foundation for auditable human- and agent-guided training.
[LG-47] Spatio-Temporal Prediction of Unsteady Airfoil Aerodynamics Using Augmented Graph Neural Ordinary Differential Equations with Exogenous Controls
链接: https://arxiv.org/abs/2607.18309
作者: Henrik Lange,Reik Thormann,Philipp Bekemeyer
类目: Machine Learning (cs.LG); Fluid Dynamics (physics.flu-dyn)
*备注: 27 pages, 18 figures, submitted to Computers Fluids
Abstract:Unsteady aerodynamic phenomena, such as gusts, turbulence, and fluid-structure interactions affect an aircraft during flight. For design, optimisation and certification, it is indispensable to quantify such unsteady aerodynamic effects. Industry-standard computational fluid dynamics methods, such as solving the unsteady Reynolds-averaged Navier-Stokes equations or the linearized frequency domain method, are either computationally expensive or restricted by assumptions like linearity. Once trained, machine learning methods are capable of computing non-linear relationships very fast, making them suitable as surrogate models. By autoregressively applying graph neural networks (GNNs), operating on a discretised spatial domain, spatio-temporal predictions can be made. However, autoregressive GNNs suffer from error accumulation leading to unstable rollouts over time. Here we show that combining GNNs with augmented Neural Ordinary Differential Equations yields temporally stable predictions of the surface forces on a pitching airfoil. We found that our approach, called GNODE, based on Graph Neural Ordinary Differential Equations, provides temporally more stable, spatially smoother, and overall more accurate results than an autoregressive GNN baseline. Tests are conducted on a dataset consisting of a simulations of a pitching airfoil, including transonic shocks, transient behaviour and dynamic non-linearities. Augmenting GNODEs with additional latent dimensions improves the expressivity and accuracy by capturing underlying history effects. The developed method demonstrates an approach that is suitable to model non-linear spatio-temporal systems with exogenous inputs.
[LG-48] D-DPO: Difference-Aware Preference Optimization for Mitigating Sycophancy in Clinical Autism Intervention Dialogue EMNLP2026
链接: https://arxiv.org/abs/2607.18304
作者: Shuzhong Lai,Junhong Lai,Chenxi Li,Qing Zhou,Haifeng Li,Gang Pan,Lin Yao,Yueming Wang
类目: Machine Learning (cs.LG)
*备注: commited to EMNLP2026
Abstract:The sycophancy of large language models can increase the safety risk in intervention dialogue for autistic children. Supervised fine-tuning can somewhat reduce sycophancy, but relying solely on positive examples is often insufficient to identify and correct failure patterns. We observe that sycophancy behaviors can often be localized to a limited span within the model response. In this regime, sequence-level preference optimization can over-update preference-irrelevant tokens and degrade intervention ability. To address this, we propose the \textbfMinimal \textbfEdit \textbfData \textbfAugmentation (MEDA) strategy to construct controlled, stable, minimal edit preference pairs and \textbfToken-level \textbfDifference \textbfDirect \textbfPreference \textbfOptimization (TD-DPO), which upweights difference tokens between chosen and rejected responses while downweighting shared tokens to suppress background drift. Extensive experiments across multiple backbones and evaluators show that TD-DPO achieves a better trade-off between sycophancy mitigation and intervention ability retention in our offline settings, highlighting its potential as a practical alignment approach for autism intervention.
[LG-49] Fretiq: Browser-Native Electric Guitar String Classification via Engineered Spectral Features and Held-Out Free-Play Evaluation
链接: https://arxiv.org/abs/2607.18303
作者: Aadi Garg
类目: ound (cs.SD); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
*备注: 12 pages, 5 tables, preliminary single-instrument system paper
Abstract:Identifying which string produces a given pitch in monophonic electric guitar audio is a fundamental classification challenge: a single pitch can often be produced on multiple strings at different fret positions, with timbral differences that prior listening studies confirm are largely imperceptible to untrained humans. Existing approaches using support vector machines and spectral envelope features have achieved F-measures of 0.90 for six-string electric guitar classification, while String-Inverse Frequency features in earlier work achieved F1 scores up to 0.72. We present Fretiq, a preliminary single-instrument, single-player browser-based string classification system built on a 26-dimensional feature representation integrating frequency band energies, spectral statistics, and 13 Mel-Frequency Cepstral Coefficients, achieving 97.1% shuffled frame-level validation accuracy across 322,215 balanced frames. An ablation study identifies MFCCs as the primary accuracy driver (92.2% to 97.1%). We additionally introduce Comparison Training – a data collection methodology in which adjacent open-string and fifth-fret string pairs are recorded in deliberate alternation – and evaluate its contribution via confusion matrix analysis. Comparison Training reduces the D3 to A2 frame-level confusion rate by 44% but shows mixed results on other targeted pairs. A held-out free-play evaluation on 103,000 frames yields 87.8% overall accuracy. We describe the feature extraction pipeline in both Python and TypeScript to guarantee training-inference parity, and document two critical implementation failure modes. The system runs entirely in-browser with no hexaphonic pickup, fretboard sensor, camera, or multi-microphone setup required.
[LG-50] A Better Start for Language Models: Domain-Conditional Position Offsets
链接: https://arxiv.org/abs/2607.18302
作者: Ye Qiao
类目: Machine Learning (cs.LG)
*备注:
Abstract:Autoregressive language models are least accurate at the beginning of a sequence, where little context forces reliance on a generic pretraining prior. We show that this cold-start penalty is domain dependent and reduce it with a domain-conditional position offset: a single learned vector added to the embedding activation at the first sequence positions while all model weights remain frozen. The offset trains in minutes on roughly one hundred documents, switches between domains without added sequence state, and has no measurable latency overhead. Across eight Mamba, GPT-NeoX, and Llama models spanning 410M to 8B parameters, it reduces held-out in-domain perplexity by up to 27%; the effect persists at 70B, and one position captures most of the benefit. A matched, converged direct logit-bias correction reaches at most only 7.9% and leaves later-token loss unchanged, showing that the offset propagates through model state rather than merely recalibrating the output prior. A tuned LoRA reaches lower perplexity but uses two to three orders of magnitude more parameters and an active low-rank weight path, while soft prompts add sequence positions. With wrong-domain controls, offsets improve retrieval reranking and domain classification when decisions depend on early in-domain tokens, For the few-shot reasoning whose signal occurs later, the results maintains unchanged. Position-aware prefill application also help generation tasks, whereas naive application at every cached decoding step causes repetition. The offset is therefore not the strongest adapter, but a lightweight, hot switchable tool for short in-domain scoring and calibration.
[LG-51] On Incentivized Exploration beyond Bayesianism and Full-Information
链接: https://arxiv.org/abs/2607.18300
作者: Dimitar Chakarov,Lee Cohen,Nathan Srebro
类目: Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
*备注: 30 pages, 5 figures
Abstract:We extend Incentive Compatible Exploration beyond the Bayesian full-information setting of Kremer et al. [2014]. We consider agents that may possess external information unknown to the principal. We show such settings require new notions of incentivized exploration, as well as going beyond a Bayesian perspective, and we introduce a definition where agents choose any reasonable (undominated) action. Furthermore, our framework provides for a more robust treatment of ties, and extends to settings where agents lack a single common prior and instead only know that reward distributions belong to a collection of potential priors.
[LG-52] On the Limits of Support-Preserving Alignment and Bounded Filtering
链接: https://arxiv.org/abs/2607.18295
作者: Aryan Dutt,Rui Mao,Anupam Chattopadhyay
类目: Machine Learning (cs.LG)
*备注:
Abstract:We study whether alignment schemes that reshape a base model’s output distribution, combined with bounded safety filters, can drive the probability of harmful behavior to zero in modern large language models. Recent research suggests that harmful behaviors can persist under preference-based alignment and that external filtering can be computationally hard in the worst case, but it remains unclear whether practical alignment pipelines that largely preserve internal representations can eliminate harmful behavior entirely rather than merely suppressing its most visible forms. We formalize this setting using support-preserving alignment operators together with bounded filtering algorithms under black-box, white-box, and statistical-query access, and analyze their ability to approximate an ideal eliminator that removes all harmful mass. Building on this framework, we provide computational and information-theoretic arguments indicating that, under these constraints, bounded filtering may fail to eliminate all harmful outputs supported by the base model’s distribution. To evaluate these limits empirically, we analyze a range of state-of-the-art open-weight and hosted LLMs accessed via OpenRouter under bounded black-box, white-box, and statistical-query filters on adversarial prompts drawn from curated cybersecurity scenarios and PKU-SafeRLHF. Across models, filter classes, and query budgets, the estimated harmful-output rate decreases with additional filtering compute but consistently plateaus above zero, suggesting a persistent empirical harm floor.
[LG-53] Uncertainty Quantification for AI-Driven Crash Simulation Surrogates: A Comparative Study of Monte Carlo Dropout and Deep Ensemble on Open-Source Bumper Beam Benchmark
链接: https://arxiv.org/abs/2607.18294
作者: Sudeep Chavare
类目: Machine Learning (cs.LG)
*备注:
Abstract:Machine learning surrogate models are increasingly being explored in engineering product development to augment simulation-driven design, offering near-instantaneous predictions that complement computationally expensive high-fidelity analyses. However, a critical gap limits their adoption in safety-critical workflows: a point prediction without an accompanying uncertainty estimate cannot tell an engineer when the model should not be trusted. This work presents a systematic, head-to-head comparison of two widely used uncertainty quantification approaches – Monte Carlo Dropout and Deep Ensembles – applied to an open-source surrogate pipeline built on NVIDIA PhysicsNeMo. A key contribution is the use of concrete dropout, a built-in PhysicsNeMo capability that eliminates the dropout rate as a manual hyperparameter by learning it end-to-end during training, directly addressing the most common criticism of Monte Carlo Dropout-based uncertainty quantification. Automotive crash simulation is used as the application domain, with a steel bumper beam impact problem serving as the benchmark. Both methods are evaluated on identical held-out simulations and compared on point accuracy, uncertainty band calibration, and computational cost. The results reveal a fundamental trade-off between accuracy and calibration that challenges the common assumption that deep ensembles are the default gold standard for surrogate uncertainty quantification. The findings demonstrate that well-calibrated, hyperparameter-free uncertainty estimates are achievable within a fully open-source engineering workflow at a fraction of the computational cost of ensemble approaches.
[LG-54] Multi-Timescale Latent-Action DRL for Joint Optimization in Edge-Cloud Networks
链接: https://arxiv.org/abs/2607.18288
作者: Vo Phi Son,Van-Dinh Nguyen,Ngoc Hung Nguyen,Trinh Van Chien,Symeon Chatzinotas
类目: Machine Learning (cs.LG)
*备注: Sumitted for publication (14 pages)
Abstract:Load imbalance across edge and cloud layers degrades latency performance in hierarchical edge-cloud computing (HECC) systems under dynamic task arrivals and heterogeneous resources, leading to severe queuing delays and inefficient resource utilization. To address this challenge, we study a joint service placement, computational delegation, and power control (JSCP) problem to minimize the average end-to-end (e2e) latency. The resulting JSCP problem is a mixed-integer nonconvex and NP-hard optimization problem due to the strong coupling between discrete and continuous variables. To enable tractable optimization and stable system adaptation, we exploit the inherent difference in decision dynamics and decompose the problem into long-term system configuration and short-term resource allocation subproblems. Based on this formulation, we propose a two-timescale multi-layer deep reinforcement learning framework with a latent action space (2T-MDRL-LA) to jointly optimize service placement, user association, computational delegation, task offloading, and user transmit power. A latent action representation based on a variational autoencoder is introduced to efficiently compress the high-dimensional combinatorial action space. Simulation results demonstrate that the proposed framework effectively adapts to dynamic network conditions and achieves near-optimal performance compared to branch-and-bound solutions. It achieves up to a 20.8% reduction in average e2e latency and a 13% improvement in resource utilization over the scheme without the computational delegation, while converging approximately 50% faster than conventional proximal policy optimization.
[LG-55] ALAS: Additive Learnable Alpha-Stable Kernels for Flexible Bayesian Optimization
链接: https://arxiv.org/abs/2607.18282
作者: Weibo Huang,Cheng Hua
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:Bayesian Optimization is widely used for expensive black-box optimization, yet its success often depends on choosing a kernel that matches the objective’s unknown structure. In this work, we propose ALAS, a flexible Gaussian Process kernel family built from symmetric \alpha -stable spectral components. By learning the stability parameter \alpha , ALAS adapts its effective smoothness from data, capturing both smooth trends and sharp irregularities. We present two parameterizations: ALAS, a single stationary component with joint spectral modulation, and ALAS-Sep, a separable variant that learns dimension-wise tail behavior to improve robustness on approximately decomposable objectives. Experiments on standard benchmarks and real-world surrogates demonstrate strong and robust performance across diverse settings.
[LG-56] Fundamental limits of distributed multiclass classification from simple binary decisions
链接: https://arxiv.org/abs/2607.19334
作者: Ioannis Papageorgiou,Srinivas Nomula,Ayalvadi Ganesh,Sidharth Jaggi,Parimal Parag
类目: Machine Learning (stat.ML); Information Theory (cs.IT); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
Abstract:We consider the problem of constructing a K -class classifier from the combination of O(\log K) simple binary classifiers – this is a natural paradigm to construct a sophisticated classifier in a distributed manner with each agent performing a relatively straightforward task. We study the fundamental performance limits of such a classifier when the corresponding binary classifiers are hyperplanes. For a stylized Gaussian setting where the K class centers are independent Gaussian points in \mathbb R^d and the observations are corrupted by Gaussian noise, we derive explicit performance bounds across several decoding and dimensional regimes. Extensive simulation experiments provide strong empirical validation of the presented theoretical results.
[LG-57] ATLAS: A Foundation Neural Sampler for Amorphous Materials
链接: https://arxiv.org/abs/2607.19198
作者: Mouyang Cheng,Denis Blessing,Botao Yu,Gerhard Neumann,Mingda Li,Carles Domingo-Enrich,Yuanqi Du
类目: Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
*备注:
Abstract:Amorphous materials exhibit exceptional mechanical and functional properties, yet their rugged energy landscapes are notoriously difficult to sample. Below the glass-transition temperature, conventional molecular dynamics and Monte Carlo become inefficient because equilibration relies on rare barrier-crossing events, while data-driven generative models are constrained by scarce and biased reference ensembles. Here, we introduce ATLAS, an efficient sampler that learns a diffusion process to generate Boltzmann-distributed amorphous structures directly from a target energy function. Parameterized by an equivariant graph neural network, ATLAS generalizes across system size, temperature, and composition. By exploiting the time reversal of the diffusion process, it enables efficient estimation of thermodynamic quantities and steering toward target observables. In two-dimensional Kob-Andersen systems, ATLAS reproduces parallel tempering Markov chain Monte Carlo structural distributions, free energies and entropies, achieving below 0.2% free energy error in the low-temperature glass regime with over 500-fold fewer energy evaluations. In Cu-Zr and Cr-Co-Ni metallic glasses, ATLAS recovers experimentally observed short-range-order trends and steers structures toward prescribed order parameters and optimized bulk moduli. Moreover, composition-amortized pretraining outperforms composition-specific training from scratch, reduces inverse-design costs by several hundred-fold, and enables sampling with expensive universal machine learning interatomic potentials. Coupled to a large language model agent, ATLAS searches an eight-element space for high-entropy metallic glasses balancing stiffness and ductility, identifying a converged Pareto frontier within 480 oracle evaluations. Together, these results establish ATLAS as a foundation model for sampling, steering and designing amorphous materials.
[LG-58] he Tractability Landscape of Sampling with Inexact Scores
链接: https://arxiv.org/abs/2607.19004
作者: Anming Gu,Kevin Tian,Hubert Yang,Yusong Zhu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注: 5 pages, 1 figure
Abstract:We provide a simple and tight characterization of the types of inexact score oracle access that permit sampling with vanishing total variation bias, for a standard, well-behaved target family. Our main result shows that any weaker error than the sub-Gaussian assumption used by [YW26] rules out the tractability of unbiased sampling. This strengthens the conclusion of [CCSW26] to be algorithm-agnostic, and to hold for a wider range of error assumptions.
[LG-59] Optimizing Regret
链接: https://arxiv.org/abs/2607.18866
作者: Irene Aldridge
类目: Econometrics (econ.EM); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 19 pages
Abstract:Building on the identity that expected regret equals the covariance between costs and decisions, this paper develops the complete derivative theory of the covariance regret functional. We derive the Gâteaux derivative, showing that the universal steepest-descent direction is the contrarian policy -(c-\barc) , while ascent yields momentum. For linear policies \hat\pi© = Ac+b , the gradient is the cost covariance matrix \Sigma_c , with a zero Hessian implying boundary-optimal solutions such as the minimum-variance portfolio. We extend to constrained optimization, sign-gradient duality between regret minimization and alpha maximization, finite-sample convergence bounds paralleling Thompson Sampling, and gradient-descent algorithms requiring only input observations, with applications to portfolio tilting and LLM-based allocation strategies.
[LG-60] Enhanced NQS via Annealed Gradient Descent
链接: https://arxiv.org/abs/2607.18865
作者: Shiwei Zhou,Yiming Huang,Xiao Yuan,Xiaoxia Cai
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG); Chemical Physics (physics.chem-ph); Computational Physics (physics.comp-ph)
*备注:
Abstract:Neural quantum states offer expressive representations of quantum many-body wave functions, yet their practical accuracy can be limited by stochastic optimization rather than representational capacity. Here we identify a finite-sample instability, termed subspace trapping, in which physically important configurations become strongly underestimated, remain absent from successive sampling batches and receive insufficient gradient feedback. This self-reinforcing loss of sampled support can confine optimization to an effective subspace and produce apparently stationary states above the true ground state energy. To address this problem, we introduce annealed gradient descent (AGD), a sampling-aware update with annealing factor that temporarily increases the relative contribution of sampled low-probability configurations while limiting the dominance of high-probability ones. We establish the connection between finite-sample support loss and effective subspace optimization, and then evaluate the method across molecular systems, one and two-dimensional J_1 - J_2 models. Annealed gradient descent suppresses metastable trapping, preserves physically relevant configurations and enables compact neural quantum states to attain chemical accuracy and competitive state-of-the-art performance. These results establish AGD as a lightweight complement to expressive neural architectures, improved sampling strategies for scalable quantum many-body optimization.
[LG-61] Algebraic Signatures for Structural Learning in Probability Tensors
链接: https://arxiv.org/abs/2607.18817
作者: Akihiro Maeda,Shohei Hidaka,Satoshi Aoki
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: Submitted to Algebraic Statistics
Abstract:Algebraic statistics characterizes statistical models through polynomial constraints, but it has mainly been used for analytically specified model classes. This paper studies the inverse problem: identifying probabilistic structure from vanishing binomials observed in empirical probability tensors. We treat the vanishing binomials of a toric model as its algebraic signature, and turn the ideal-variety correspondence of algebraic statistics into an operational procedure for structural learning that identifies a model by signature matching without parameter estimation. By restricting attention to a computationally tractable class of configuration matrices, which we call \it the Kronecker-stack class, we make these signatures explicitly enumerable. Within this class we define minimum invariant constraint (MIC) as the atomic unit characterizing each signature and generalizing the notion of independence. We tested this approach employing MICs on synthetic data as well as on corpus-scale real language data. The results suggested the utility of the method, revealing that the identified rank-one structures correspond to interpretable sets of words. These results open up a new avenue for applying algebraic statistics to computational linguistics.
[LG-62] he Price of Hidden Curvature: An widetildeΩ (d5/4 sqrtT) Lower Bound for Bandit Convex Optimization
链接: https://arxiv.org/abs/2607.18652
作者: Nived Rajaraman
类目: Machine Learning (stat.ML); Information Theory (cs.IT); Machine Learning (cs.LG)
*备注: 35 pages, 1 figure
Abstract:We establish a \widetilde\Omega(d^5/4\sqrt T) lower bound on the minimax expected regret of stochastic bandit convex optimization of 1 -Lipschitz functions on the Euclidean ball. This presents the first nontrivial regret lower bound that grows faster than d\sqrtT for this problem, establishing that stochastic bandit convex optimization is fundamentally harder than linear bandits. The hard class of convex functions we construct takes the following form in dimension 2d : for an action a = (a^1,a^2) \in \mathbbB^2d_2 , each function is the scaled soft maximum of a “tube”, r^-1 | W^\star a^1 - \fracr8\varepsilon a^2 |_2 (hyperparameterized by \varepsilon,r ), and a squared distance function, \frac12 | a^1 - u^\star |_2^2 - \frac12 | u^\star |_2^2 . Here, W^\star \in \mathbbR^d \times d is an unknown linear transformation, and u^\star \in \mathbbR^d is an unknown vector which must be learned to minimize the function. Observations are informative about u^\star only when the learner’s action lies near the tube determined by W^\star , satisfying a^2 \approx \frac8\varepsilonr W^\star a^1 : thus the learner must either find this tube without knowing W^\star , or spend observations learning useful directions of W^\star . Formally, our regret analysis exploits this tradeoff by bounding the posterior spread of Fisher information matrices obtained under an adaptive sequence of actions. Together, these ingredients give a sample complexity lower bound of \widetilde\Omega(d^5/2/\varepsilon^2) to find an \varepsilon -optimal action, which translates to an \widetilde\Omega (d^5/4 \sqrtT) regret lower bound. We also extend this lower bound to the unconstrained setting where the action space is \mathbbR^d . Comments: 35 pages, 1 figure Subjects: Machine Learning (stat.ML); Information Theory (cs.IT); Machine Learning (cs.LG) Cite as: arXiv:2607.18652 [stat.ML] (or arXiv:2607.18652v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2607.18652 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-63] GQD-AdsNet: Graph Neural Networks Unlock Rapid Exploration of Transition Metal Adsorption on Graphene Quantum Dots
链接: https://arxiv.org/abs/2607.18591
作者: Lara Goncebat(1),Rodrigo Echeveste(2),Matías Gerard(2),Frederik Tielens(3),Gustavo Belletti(1),Paola Quaino(1) ((1) Instituto de Química Aplicada del Litoral IQAL (UNL-CONICET) Santa Fe Argentina, (2) Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional sinc (i) (UNL-CONICET) Santa Fe Argentina, (3) General Chemistry (ALGC) Materials Modelling Group Vrije Universiteit Brussel (VUB), Brussel, Belgium)
类目: Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)
*备注:
Abstract:In recent years, interest in single-atom catalysts supported on carbon-based structures has grown considerably due to their high catalytic activity and efficient uses of metal atoms. However, the design and characterization of these materials through first-principles calculations are computationally expensive, limiting the exploration of a large number of possible configurations. Here, we developed a framework based on graph neural networks (GNNs) to predict the adsorption energies of transition metals on graphene quantum dots (GQDs). The model was trained using data obtained from density functional theory calculations and achieved an R^2 of 0.906 with an MAE of 0.101 eV, while reducing computational cost by roughly six orders of magnitude relative to DFT. This methodology provides an efficient tool for the accelerated screening and rational design of new catalysts based on carbon nanostructures.
[LG-64] Mixing-Free and Signal-Optimal Learning of Gaussian Graphical Models from Glauber Dynamics
链接: https://arxiv.org/abs/2607.18559
作者: Vignesh Tirukkonda,Gautam Dasarathy
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
Abstract:Gaussian graphical model selection is usually studied under independent sampling, but in many applications the data arise as a single trajectory of a dependent stochastic process. We study exact recovery of the graph from one trajectory of random-scan Gaussian Glauber dynamics. Existing techniques for this problem either inherit the mixing time of the chain, which can be super-polynomial in the dimension p without strong assumptions, or are suboptimal in the minimum normalized edge strength \kappa . We propose two algorithms that are mixing-free and attain the \kappa^-2 dependence of the information-theoretic lower bounds. Both instantiate a shared dueling-neighborhood search meta-algorithm with a local statistic built directly from the update sequence. The first fits a least-squares regression at the updates of each node and recovers the graph from \widetilde O(pd^2/\kappa^2) updates, where d is the maximum degree. This algorithm’s data requirement depends on a local conditioning quantity, but only logarithmically and is provably optimal even when the underlying chain mixes slowly. The second algorithm is based on counting occurences of a specific update pattern and requires \widetilde O(pd^4/\kappa^2) updates, with no dependence on any condition number. The central technical challenge is that both statistics are built from dependent, non-stationary observations. Our analysis tackles this by demonstrating how to extract fresh Gaussian innovations from the update sequence, which yields mixing-free control of appropriate quantities. Neither the algorithms nor their analyses invoke stationarity, a spectral gap, or mixing conditions, and all guarantees hold from an arbitrary initialization.
[LG-65] Quantum Reservoir Computing: Recent Advances and Future Directions
链接: https://arxiv.org/abs/2607.18552
作者: Shehbaz Tariq,Muhammad Talha,Arshid Ali,Muhammad Diyan,Symeon Chatzinotas
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注:
Abstract:Quantum reservoir computing (QRC) uses the dynamics of a fixed or weakly tuned quantum system to transform temporal and sequential inputs into measured features, while training is typically confined to a classical readout. This separation reduces reliance on repeated quantum parameter updates and avoids the barren plateaus associated with variational circuit training. Its computational power is often attributed to the exponentially large Hilbert space of the quantum system. However, the memory, nonlinearity, and expressivity that determine what a reservoir can actually compute depend jointly on the input encoding, quantum evolution, observables, measurement, and readout, not on Hilbert space dimension alone. On hardware, these capabilities are further constrained by finite sampling, hardware noise, measurement backaction, and the cost of estimating observables, so a large state space alone does not guarantee useful computation. In this survey, we develop a common system model that connects these components and use it to organize QRC foundations, computational properties, reservoir architectures, operating protocols, and physical implementations. We examine spin, photonic, superconducting, bosonic, neutral atom, and other analog platforms, together with applications, software and high performance computing support, benchmarking, and reproducibility. The analysis distinguishes hardware demonstrations from simulations and identifies the assumptions and resources that govern comparisons across implementations. Current results do not establish a broad quantum advantage over well matched classical reservoirs. We therefore specify the resource accounting, benchmark standards, and theoretical criteria needed to evaluate claims of quantum advantage.
[LG-66] Using binary silver labels in electronic health records-based computable phenotyping algorithms
链接: https://arxiv.org/abs/2607.18431
作者: Shuhe Wang,Matthew T. Slaughter,Jennifer C. Nelson,Brian D. Williamson
类目: Methodology (stat.ME); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Gold-standard phenotype labels are often unavailable at scale in electronic health record (EHR) studies because they require manual chart review. Weakly supervised phenotyping methods instead use silver-standard labels, such as diagnosis-code counts, natural language processing (NLP) mentions, medication indicators, or laboratory thresholds. PheNorm is widely used for this purpose, but its original formulation was designed for count-valued silver labels and relies on log transformation, utilization normalization, and Gaussian mixture modeling. These steps are not directly suited to binary silver labels, which are common and may be highly informative. We propose Binary PheNorm, an extension that uses binary silver labels directly in the corruption-and-regression denoising step and produces a continuous phenotype score without EM calibration. We also consider a lasso-regularized version for high-dimensional EHR settings and combined models using both binary and count labels. In simulations, Binary PheNorm achieved strong discrimination using binary labels alone and often improved performance when combined with count labels. In anaphylaxis, AUC increased from 0.793 for an epinephrine-mention indicator to 0.891-0.892 after Binary PheNorm. In acute pancreatitis, AUC increased from 0.736 for a lipase-threshold indicator to 0.805-0.819. These results support Binary PheNorm as a practical weakly supervised approach when informative binary silver labels are available.
[LG-67] Disentangling Forced and Internal Climate Variability in Single Realizations using Dynamic Mode Decomposition with Control
链接: https://arxiv.org/abs/2607.18298
作者: Nathan Mankovich,Andrei Gavrilov,Gustau Camps-Valls
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph)
*备注:
Abstract:We show that a single climate realization can be decomposed into forced and internal components by treating external forcing as a dynamical driver within a linear stochastic system, an idea grounded in pullback attractor theory. In doing so, we address a central methodological challenge in climate science with direct implications for climate projection and the detection and attribution of the forced response, disentangling the forced climate response from internal variability in a single observed record. Statistical methods range from approaches trained on large ensembles to techniques operating on single realizations. The latter often rely on linear frameworks such as linear inverse models (LIMs) and linear regression. LIMs ignore forcing predictors, whereas linear regression omits climate system dynamics. Here we introduce PullbackDMDc, a method grounded in non-autonomous dynamical systems theory and dynamic mode decomposition with control (DMDc), incorporating pullback attractor estimation to decompose a single climate realization into spatial modes and their associated forced and internal components, yielding a physically interpretable picture of the underlying dynamics. We illustrate the utility of PullbackDMDc for Earth System Model (ESM) evaluation by applying it to near-surface air temperature and sea-level pressure from reanalysis and four ESM large ensembles. PullbackDMDc estimates the forced response with skill matching or exceeding established baselines and identifies optimal forcing predictors against model-based ground truth. Its internal variability components reveal that ESMs qualitatively capture interannual and decadal modes while exhibiting systematic differences relative to each other and to observations. Skillful forced response estimation and a novel decomposition position PullbackDMDc as a practical tool for single-realization climate analysis and ESM evaluation.
[LG-68] Position: The Inevitable Transition to Machine Learning in Quantum Chemistry ICML2026
链接: https://arxiv.org/abs/2607.18281
作者: Karen Sargsyan,Chao-Ping Hsu
类目: Chemical Physics (physics.chem-ph); Machine Learning (cs.LG); Quantum Physics (quant-ph)
*备注: Accepted as a position paper at ICML 2026. OpenReview forum: this https URL
Abstract:Finding exact solutions to the quantum many-body problem is computationally intractable (QMA-hard). Traditional approximations for electrons in an atom or molecule – density functional theory and wavefunction methods – have been indispensable, but their development shows signs of saturation: DFT functionals have proliferated without converging toward the exact functional, and strong correlation remains largely unsolved after decades of effort. This position paper argues that machine learning represents the most promising path forward – not as a proof of logical necessity, but as a decision-theoretic argument: ML succeeds whether the underlying problems are truly hard or merely lack simple analytical solutions. We reframe recent traditional method development as ``hand-crafted machine learning’’ that has exhausted the hypothesis space accessible to human intuition. Significant challenges remain, but these have clear research paths forward, unlike the fundamental barriers facing traditional approaches. ML-based approaches merit strategic priority in quantum chemistry’s next phase.
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