本篇博文主要内容为 2026-08-19 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 (2026-08-19)
今日共更新622篇论文,其中:
- 自然语言处理共72篇(Computation and Language (cs.CL))
- 人工智能共172篇(Artificial Intelligence (cs.AI))
- 计算机视觉共103篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共143篇(Machine Learning (cs.LG))
- 多智能体系统共14篇(Multiagent Systems (cs.MA))
- 信息检索共12篇(Information Retrieval (cs.IR))
- 人机交互共21篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] Collective Counterfactual Planning : Coordination Consent and Verification under Representational Constraints
【速读】：该论文旨在解决多智能体团队在完成复杂任务时面临的集体反事实规划（Collective Counterfactual Planning, CCP）问题，即当单个成员无法独立规划、执行或验证整个任务时，如何实现协同目标达成。其核心挑战在于：各智能体的决策与验证能力受限于其对共同任务空间的特定子空间投影（representational geometry），而非能力、知识或可观测性的不足。解决方案的关键在于提出**集体反事实可解性（Collective Counterfactual Solvability, CCS）**问题框架，将任务求解分解为三个维度——几何可行性（geometric feasibility）、可执行达成性（executable attainment）与验证完成性（validated completion），并通过四个关键机制（四道“门”）进行约束：外生执行联盟（exogenous implementation coalitions）以及三重表征门限——构想（conception）、同意（consent）与任务相关验证资格（task-relative verification qualification）。研究揭示了正负双重性：通过迭代跨智能体接力（iterated cross-agent relay）可解锁单次个体计划聚合所无法涵盖的解，但任何依赖于全队不可见子空间（subspace dark to the entire team）的目标要求均不可验证，因而无法合法完成；此外，无记忆且可审计的同意机制分别限制动作方向与累积轨迹状态，二者互不主导。最终提出的四步穷尽式有限视野求解方案，在精确表示接力闭包条件下具有完备性与保真性，而受限实施策略虽保持保真性，却未必完备。该模型为顺序性相互赋能、执行者无法理解目的的任务步骤、基于专长的强制子团队协作及无法合法声明完成的成果提供了统一的几何解释框架。
链接: https://arxiv.org/abs/2608.17932
作者: Chainarong Amornbunchornvej
机构: National Electronics and Computer Technology Center (NECTEC), NSTDA; Pathum Thani, Thailand
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注: The code is available at this https URL
Abstract:Groups routinely complete projects that no single member can plan, execute, or verify alone. We propose a formal model of this phenomenon, Collective Counterfactual Planning (CCP), in which the binding limitation on each agent is neither capability, knowledge, nor observability, but representational geometry: each agent perceives the state, conceives moves, consents to actions, and certifies goal requirements only through a projection onto an agent-specific subspace of a common task space. Four gates jointly determine whether a team can reach a conjunctive goal and legitimately recognize that it has done so: the exogenous implementation coalitions required to perform each action, together with three representational gates – conception, consent, and task-relative verification qualification. We define the Collective Counterfactual Solvability (CCS) problem, separating geometric feasibility, executable attainment, and validated completion. The results expose a positive-negative duality. Iterated cross-agent relay can unlock a solution that no one-shot pooling of individual plans contains, but any goal requirement depending essentially on the subspace dark to the entire team is unverifiable and therefore not validly completable, even when the trajectory accidentally attains it. Memoryless and audited consent further constrain different objects – action directions versus cumulative trajectory states – and neither dominates the other. A four-step exhaustive horizon-bounded solvability scheme is sound and complete under exact representation of the relay closure; restricted implementations remain sound on returned plans but need not be complete. The model gives one geometry for sequential mutual enabling, competent execution of steps whose purpose is invisible to the executor, forced sub-teaming at expertise boundaries, and completion that cannot be validly declared.
[MA-1] A Theoretical Framework for Parallel Lifelong MAPF Using Group Decentralized Planning
【速读】：该论文旨在解决长期多智能体路径规划（Lifelong Multi-Agent Path Finding, L-MAPF）问题中，随着智能体数量增加导致计算开销急剧上升、限制了算法实际应用规模的核心挑战。现有高性能解决方案——滚动时域碰撞消解（Rolling-Horizon Collision Resolution, RHCR）虽能生成高质量路径，但其计算复杂度随智能体数量增长而显著升高，难以扩展至大规模场景。针对此问题，论文的关键贡献在于：首先，基于局部依赖多智能体马尔可夫决策过程（Locally Interdependent Multi-Agent MDP）的理论框架，证明了RHCR在折扣马尔可夫决策过程（discounted MDP）形式下的近最优性；进而，利用该理论结果自然引出一种新型扩展框架——分组去中心化滚动时域碰撞消解（Group Decentralized RHCR, GD-RHCR），该框架通过传递性通信机制对智能体进行分组，并在各组内并行规划，实现了空间维度上的结构化分解。研究表明，GD-RHCR与原始RHCR均能达到指数级接近最优的性能保证，揭示了时间维度上的滚动规划（RHCR）与空间维度上的分组划分（GD-RHCR）之间存在的理论对偶性。实验验证表明，相较于RHCR，GD-RHCR在不同地图场景下具备更高的吞吐率，能够有效支持更高数量级的智能体，同时显著降低单次规划的计算成本，从而在保持高解质量的同时实现了可扩展性突破。
链接: https://arxiv.org/abs/2608.17928
作者: Alex DeWeese,Jiaoyang Li,Guannan Qu
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注:
Abstract:In the Lifelong Multi-Agent Path Finding (L-MAPF) problem, agents must repeatedly move from one destination to another while avoiding obstacles and inter-agent collisions. Widely regarded as one of the highest-performing solutions to this problem is the Rolling-Horizon Collision Resolution (RHCR) framework. However, commensurate with its quality solutions, it incurs a computational cost that limits its applicability to even modest agent counts. In this paper, leveraging theoretical methods from the Locally Interdependent Multi-Agent MDP literature, we first theoretically prove the near-optimality of RHCR in a discounted MDP formulation of the L-MAPF problem. Then, we leverage these results to naturally motivate an extended framework called Group Decentralized RHCR (GD-RHCR) which incorporates a group decentralized structure that partitions agents based on a transitive communication scheme and plans for each partition of agents in parallel. We show that both RHCR and GD-RHCR achieve similar exponentially close to optimal guarantees, establishing a theoretical duality between the time based restrictions performed by vanilla RHCR and the additional space based partitioning performed by GD-RHCR. Lastly, we show that across varying maps, GD-RHCR is able to attain high throughput that scales into higher agent counts while maintaining a significantly lower per plan cost.
[MA-2] AutoResearch: Insight In Hallucination Out
【速读】：该论文旨在解决自主科研系统在自动化执行研究流程时，因缺乏科学根基而导致研究结果不可靠的问题。现有自动化方法虽能完成复杂研究工作流，但难以确保研究思路的合理性与实验结论的可信性，易产生“幻觉”（hallucination）式错误。其解决方案的关键在于提出一个双阶段框架AutoResearch，通过“思想生成”（Idea Generation）与“思想执行”（Idea Execution）的协同机制实现科学研究的可验证性与可靠性：在思想生成阶段，系统融合新兴研究信号与累积领域知识，利用多模型生成与交叉评审生成具有理论依据且可验证的研究计划；在思想执行阶段，由协调代理将计划分解为实验任务，通过迭代实施、诊断及独立证据驱动的审查机制，确保实验结果可信，并基于证据决定是否继续、修正或终止研究方向。该方法实现了“洞察入，幻觉出”（Insight In, Hallucination Out）的研究范式，在跨模态检索、系统优化和基准驱动的机器学习等场景中显著提升了研究进展的质量与可靠性。
链接: https://arxiv.org/abs/2608.17906
作者: Yiming Ren,Xiang Liu,Qumeng Sun,Xiao Zhang,Jiahao Li,Haoyang Zhang,Junjie Wang
机构: Infinite Evolution Lab, EvoMap; Tsinghua University (清华大学)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Autonomous research systems are increasingly capable of executing long research workflows, yet automation alone does not ensure that the resulting process remains scientifically grounded. We introduce AutoResearch, a two-stage system that connects Idea Generation with Idea Execution to address both how research ideas are formed and how they are reliably established through experimentation. In Idea Generation, AutoResearch continuously integrates emerging research signals with accumulated domain knowledge, identifies transferable mechanistic insights, and uses multi-model generation and cross-review to produce grounded, testable research plans. In Idea Execution, coordinated agents decompose these plans into experiments, iteratively implement and diagnose them, and employ independent evidence-based review before accepting research conclusions. Across representative settings in cross-modal retrieval, systems optimization, and benchmark-driven machine learning, AutoResearch turns generated ideas into measurable progress, detects and corrects unreliable experimental results, and makes evidence-conditioned decisions to continue, revise, or terminate research directions. For example, on RSICD benchmark, an AutoResearch-generated idea improves mean Recall from 32.84 to 34.69, while recording only 5 audit-confirmed issue events compared with 11-27 for other autonomous research systems. These results demonstrate a research process in which meaningful insight is grounded before experimentation and conclusions are grounded before acceptance: Insight In, Hallucination Out.
[MA-3] Offline Multi-Agent Reinforcement Learning with a Physics-Informed World Model for Cooperative Mixed Traffic Control
【速读】：该论文旨在解决混合交通流中部分可观测高速公路瓶颈处联网自动驾驶车辆（CAVs）的协同控制问题，核心挑战在于如何在不依赖全局完整交通状态或在线试错机制的前提下缓解交通拥堵。其解决方案的关键在于提出一种基于物理信息世界模型的离线多智能体强化学习框架：通过融合宏观-微观耦合交通动力学提供物理约束监督，从局部CAV观测-动作历史中重构可解释的全局交通状态；利用概率集成世界模型学习交通状态转移与系统奖励，并以模型分歧度量认知不确定性；进而采用带悲观奖励和不确定性驱动截断的多步想象回溯进行离线策略学习。实验结果表明，引入物理监督显著提升了状态重构与世界模型预测精度，验证了该方法在复杂交通场景下的有效性。
链接: https://arxiv.org/abs/2608.17739
作者: Lu Liu,Chi Xie,Xi Xiong
机构: 未知
类目: Multiagent Systems (cs.MA)
备注:
Abstract:This study investigates cooperative control of connected and automated vehicles (CAVs) at partially observable highway bottlenecks in mixed traffic, aiming to mitigate congestion without relying on complete global traffic states or online trial-and-error. We propose a physics-informed world model-based offline multi-agent reinforcement learning framework that reconstructs a physically interpretable global traffic state from local CAV observation-action histories, with coupled macroscopic-microscopic traffic dynamics providing physics-based supervision. A probabilistic ensemble world model learns traffic-state transitions and system rewards, while model disagreement quantifies epistemic uncertainty. Multi-step imagined rollouts with pessimistic rewards and uncertainty-driven truncation are then used for offline policy learning. Experiments in a SUMO-based on-ramp bottleneck using approximately 1\times10^6 offline transitions show that physics supervision improves state reconstruction and world-model prediction accuracy.
[MA-4] he Influence of Agent Models on the Complexity of Bus Routing
【速读】：该论文旨在解决公交线路规划（bus routing）中的复杂性问题，即在包含多个出行者（agents）的网络中，设计一条能够平衡各方成本与效率的公交路径。具体而言，需综合考虑出行者到达公交站点的步行成本、乘车时间以及公交车的能耗等多维因素。研究重点在于分析不同目标函数及出行者步行成本建模方式对问题复杂性的影响。其核心发现表明：即使在最简化的步行成本模型下，通用网络上的问题仍为计算难题；而在树形网络结构中，若允许出行者具有异质的成本模型，则即便在极为简单的星型树结构上也存在计算难解性；相比之下，当所有出行者采用一致的成本模型（仅起点与终点不同）时，部分情形可实现可解性提升；此外，若允许出行者在使用公交与直接步行之间自由选择，将进一步加剧问题的复杂度。多数结果不仅证明了经典NP-hard性，还揭示了以公交站点数量 k 为参数时的参数化不可解性（parameterized intractability），凸显了该问题在实际应用中的计算挑战。
链接: https://arxiv.org/abs/2608.17733
作者: Eva Deltl,Christian Komusiewicz,Jurek Rostalsky,Johannes Schröder,Luca Pascal Staus
机构: TU Clausthal(克劳斯塔尔工业大学); Friedrich Schiller University (耶拿弗里德里希·席勒大学); TU Berlin(柏林工业大学)
类目: Computational Complexity (cs.CC); Multiagent Systems (cs.MA)
备注:
Abstract:In bus routing, the task is to plan a bus route in a network with several agents, each of whom wants to travel from a starting point to a destination. A bus route should account for several factors, including agents’ cost for reaching the bus stops, their travel time, or the energy consumption of the buses. We study the complexity of several variants of this problem, focusing on how the objective function and the models for agents’ walking costs influence the problem complexity. After observing that even the simplest agent cost model leads to hardness on general networks, we consider networks with tree structure. Our main findings are as follows. First, allowing agent-specific cost models leads to hardness even on extremely limited trees such as stars. Second, consistent agent models (where agents differ only in their starting points and destinations) make the problem easier in some cases. Finally, allowing agents to choose between using the bus and walking directly can make the problem considerably harder. Most of our hardness results show not only classical NP-hardness but also parameterized intractability for the natural parameter k , the number of bus stops.
[MA-5] Adaptive Incentive Design in Dynamic Principal-Agent Problem via Kernelized Bandits
【速读】：该论文旨在解决动态主-代理（principal-agent）问题中因信息不对称导致的合约设计难题，特别是现有文献普遍假设代理方效用为确定性形式所引发的主方期望效用不连续性问题，这一缺陷迫使对合约空间进行计算上不可行的离散化处理。其解决方案的关键在于引入代理方效用的随机化建模，以刻画真实系统中固有的物理与行为变异性，从而在理论上证明该随机化设定恢复了主方期望效用的连续性。基于此连续几何结构，作者将交互过程建模为带有异方差噪声的结构化多臂赌博机（structured multi-armed bandit）问题，并提出一种采用弧正弦（Arcsin）神经网络核函数的异方差高斯过程上限置信区间（Heteroscedastic GP-UCB）算法，以有效捕捉效用景观的非平稳及S型几何特征。对于m维紧致合约空间，该方法在高概率意义下实现了累积遗憾度为 $ O\left(\sqrt{T}(\log T)^{m+1}\right) $ 的理论保证。最后，通过将车辆到电网（Vehicle-to-Grid, V2G）激励设计问题形式化为动态主-代理问题，验证了所提框架的等价性与实用性，并展示了其在提升电网聚合商经济性能方面的显著优势。
链接: https://arxiv.org/abs/2608.17614
作者: Arghya Mallick,Anuj S. Vora,Sergio Grammatico,Peyman Mohajerin Esfahani
机构: 未知
类目: Multiagent Systems (cs.MA); Systems and Control (eess.SY)
备注:
Abstract:We consider the dynamic principal-agent problem under asymmetric information, wherein a principal sequentially designs contracts to incentivize an agent with unknown preferences and hidden actions. A fundamental bottleneck in the existing literature is the assumption of deterministic agent utility, which renders the principal’s expected utility discontinuous and forces computationally intractable discretizations of the contract space. In this paper, we address this limitation by introducing a stochastic counterpart into the agent’s utility model, capturing the inherent physical and behavioral variations in realistic subsystems. We formally prove that this stochastic formulation restores the continuity of the principal’s expected utility. Leveraging this continuous geometric structure, we formulate the interaction as a structured multi-armed bandit problem subject to heteroscedastic noise. We propose a \textttHeteroscedastic GP-UCB algorithm that utilizes a Neural Network (Arcsin) kernel, chosen to capture the non-stationary, sigmoidal geometry of the utility landscape. For an m -dimensional compact contract space, we establish a high-probability cumulative regret bound of O\left(\sqrtT(\log T)^m+1\right) . Finally, we demonstrate the practical efficacy of our theoretical framework by formulating the Vehicle-to-Grid (V2G) incentive design problem, proving its equivalence to a dynamic principal-agent problem, and showing superior economic performance for grid aggregators.
[MA-6] Communication Reduction via Semantic-Based Encoding in DMPC Using LSTMs
【速读】：该论文旨在解决分布式模型预测控制（DMPC）中通信开销过大的问题，即在每个时间步内各智能体需交换大量信息，导致现有无线通信技术难以承受。其解决方案的关键在于引入基于长短期记忆（LSTM）单元的编码器-解码器网络，通过在分布式优化算法中对消息进行语义压缩，使智能体仅发布信息的低维表示，接收方则利用训练好的网络重构原始信息。实验表明，该方法在移动机器人编队任务中显著降低通信量的同时仍保持优异性能，且在高通信压力下具有可靠性；更重要的是，使用LSTM能够实现前所未有的高重建精度，或在不重新训练的前提下灵活适应不同的预测时域长度。
链接: https://arxiv.org/abs/2608.17592
作者: Torben Schiz,Pedro H. J. Nardelli,Henrik Ebel
机构: LUT University (拉彭兰达大学)
类目: ystems and Control (eess.SY); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Robotics (cs.RO)
备注: 13 pages, 13 figures
Abstract:The communication demands of distributed model prediction control (DMPC) can overwhelm even advanced wireless communication technologies as agents must exchange a significant amount of information at least once per time step. To semantically reduce communication demands, this work employs encoder-decoder networks built around long-short term memory (LSTM) cells in a distributed optimization algorithm. Agents publish a reduced representation of a message and receivers reconstruct the original message upon reception. In tests with reduced communication using formations of mobile robots, trained networks retain satisfactory performance and work reliably under conditions overwhelming full communication. As the results show, the usage of LSTMs either allows unprecedented reconstruction accuracy or the usage of different prediction-horizon lengths without the necessity to retrain.
[MA-7] ask-Aware Harness Provisioning for LLM Agents in Mission-Critical Infrastructure Operations
【速读】：该论文旨在解决大语言模型（LLM）智能体在运行关键任务基础设施（MCI）时，因采用统一且全面的工具调用权限（harness）配置而导致资源浪费的问题。其核心挑战在于如何根据具体任务需求动态匹配最适配的工具与信息访问权限，以实现性能与资源消耗之间的最优平衡。解决方案的关键在于将这一问题建模为任务需求与工具供给之间的资源匹配问题，通过分析任务的数学表征进行分类，并基于研究文献挖掘与受控代理执行测量构建任务-工具配置映射关系。在此基础上，提出一种“映射引导的逐步升级”（map-guided escalation）的工具配置算法：初始使用针对任务定制的最小化权限集，仅在自检失败后才逐步扩展至完整权限。实验表明，在液冷系统中，该方法将代理准确率从全权限配置下的0.652提升至0.715，且达到与Reflexion相当的性能，但仅需48%的生成令牌；而在电力系统中，全权限仍为最优，但映射驱动的配置提供了成本更低的替代方案。结果揭示了工具配置应遵循领域依赖的精度-成本帕累托前沿，而非通用最优解。
链接: https://arxiv.org/abs/2608.17433
作者: Liangtao Lin,Qingang Zhang,Zhaomeng Zhu,Tianwei Zhang,Yonggang Wen
机构: Nanyang Technological University (南洋理工大学); Singapore (新加坡)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:LLM agents have been widely adopted to operate mission-critical infrastructure (MCI). These agents normally rely on a harness that determines what information they can access, which tools they can use, and what actions they can take. Existing systems often expose the same comprehensive harness to every task, which may not be necessary and cause resource wastes. In this paper, we focus on the identification of optimal harness configurations, and view it as a resource-matching problem between what each task requires and what the harness provides. To measure this match, we classify MCI tasks based on the mathematical representation of the underlying system and rank harness configurations by the amount and type of information they provide. We then construct task-to-harness mappings from two sources: mining research literature and measuring controlled agent execution. Leveraging the measured mapping, we propose a new harness provisioning algorithm: map-guided escalation. It begins with a task-specific harness and expands to full provision only after a failed self-check. We evaluate our method in two representative MCI tasks: in liquid cooling, it improves the agent accuracy from 0.652 under full provision to 0.715 and achieves accuracy comparable to Reflexion with 48% fewer tokens; In power grids, full provision remains accuracy-optimal, while map-based provisioning offers lower-cost alternatives. These findings show that harness provisioning follows a domain-dependent accuracy-cost Pareto frontier rather than a universal optimum.
[MA-8] KernelArc: A Multi-Agent Framework for GPU Kernel Optimization
【速读】：该论文旨在解决异构工作负载下GPU内核（GPU kernel）自动优化的难题，尤其针对复杂、多样化的计算场景中难以高效生成高性能内核代码的问题。其核心挑战在于如何在有限的候选解预算内，有效探索大规模搜索空间并发现超越现有最优方案的高质量实现。为此，论文提出KernelArc这一多智能体框架，其解决方案的关键在于：多个策略专业化智能体并行运行，并通过仅传递结论的共享内存机制、确定性基准保护机制以及只读跨智能体状态与平台触发的草拟机制进行协同。这种设计在保障协作确定性的同时，避免了状态污染与通信开销，使智能体能够在不同优化阶段根据任务特性动态调整策略。实验结果表明，该框架在NVIDIA H100和B200 GPU上实现了包括自定义BF16 GEMM、静态cuBLASLt专家接口配置表、融合混合专家反向传播、形状门控解码器层融合、原生NVFP4分组查询注意力及分页预填充注意力在内的多项先进优化，显著领先于SOL-ExecBench公开排行榜上的同类方案。研究验证了共享多智能体搜索在扩大探索范围与提升性能上限方面的有效性，同时揭示了各协调机制对特定内核及优化阶段的依赖性。
链接: https://arxiv.org/abs/2608.17071
作者: Joyjit Kundu,Ben Stoffelen,Kaili Wang,Peter Vrancx,Ludovic Denoyer
机构: AILabs, Interuniversity Microelectronics Centre (IMEC)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Performance (cs.PF)
备注: 11 pages, 6 figures
Abstract:We present KernelArc, a multi-agent framework for autonomous GPU kernel optimization across heterogeneous workloads. Strategy-specialized agents run in parallel and coordinate through conclusions-only shared memory, a deterministic benchmark guard, and read-only cross-agent state with plateau-triggered drafting. We evaluate \kernelarc on NVIDIA H100 and B200 GPUs using category-representative SOL-ExecBench workloads. The resulting implementations span custom BF16 GEMM, static cuBLASLt Expert-API configuration tables, fused mixture-of-experts backward, shape-gated decoder-layer fusion, native NVFP4 grouped-query attention, and paged prefill attention. At the public SOL-ExecBench leaderboard snapshot recorded on July~30, 2026, these submissions ranked first on representative L1, L2, Quantization, and FlashInfer tasks. The trajectories support the paper’s central motivation: shared multi-agent search can broaden exploration and reach stronger incumbents within a fixed candidate budget, while the value of individual coordination features depends on the kernel and optimization stage.
[MA-9] Memory Is Communication: The Frontier Between Remembering and Signaling
【速读】：该论文旨在解决在资源受限条件下，智能体如何在利用自身历史信息（memory）与依赖同伴通信（signaling）之间进行信息预算分配的问题。其核心挑战在于，在同时受限于记忆容量和通信频率的情况下，如何权衡两种信息源的使用以实现最优决策性能。解决方案的关键在于构建“记忆—通信前沿”（remembering–signaling frontier），即在特定任务和决策规则下，能够达到预定性能阈值的记忆率与通信率的高效边界集合。通过分析不同情境中历史信息对任务损失的最大降低能力，研究提出假设：当历史信息能带来更大的损失减少时，智能体对同伴通信的依赖将相应降低。这一假设通过初步参照游戏实验得到部分支持——目标重复显著缩短了成功消息长度，而基于隐含循环规则的可预测性则未产生类似效果，表明历史信息的质量与结构对通信效率具有关键影响。未来可通过系统性地调节记忆与通信速率，精确估计该前沿并验证该理论预测在多种合作任务中的普适性。
链接: https://arxiv.org/abs/2608.17053
作者: Yashar Talebirad,Eden Redman,Ali Parsaee,Osmar R. Zaiane
机构: Alberta Machine Intelligence Institute, University of Alberta (阿尔伯塔机器智能研究所，阿尔伯塔大学), Edmonton, Canada; Network for Applied Technology (应用技术网络), Edmonton, Canada
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Theory (cs.IT); Multiagent Systems (cs.MA)
备注:
Abstract:A bounded agent may obtain information for a decision from its own past, from peers, or from both sources. Retaining task-relevant history can reduce later communication, while a peer message can supply what memory lacks. Under limits on both resources, how should an agent allocate its information budget? Given a fixed task and decision rule, the memory and message rate pairs attaining a performance threshold form an achievable region under specified rules for using history and peer observations. We call its efficient boundary the remembering–signaling frontier. Across conditions where history permits the same maximum reduction in task loss, we hypothesize that a bounded agent will need less peer communication when it obtains a larger loss reduction from history. In preliminary referential games, target repetition coincided with shorter successful messages, while predictability from a hidden cyclic rule did not shorten them. Experiments varying memory and message rates can estimate the frontier and test this prediction across cooperative tasks.
[MA-10] WONDER: A Radio World Model-based Negotiation Framework for Multi-Agent UAV Coverag e Optimization
【速读】：该论文旨在解决灾后地面基础设施损毁导致无线覆盖中断的问题，尤其针对无人飞行器（UAV）编队在复杂环境中因局部几何观测受限、无线信号干扰隐蔽以及编队内通信受限所引发的局部可行动作与整体覆盖效果之间的显著脱节问题。其核心解决方案是提出一种基于无线世界模型（Radio World Model）的分布式UAV覆盖优化框架——WONDER。该框架的关键在于：利用基于联合嵌入预测架构（JEPA）的无线世界模型，从部署可用信息中学习并预测各候选航迹对无线电场的增量影响，从而弥补机载观测无法获取未来无线环境的缺陷；在此基础上，通过逐轮协商机制，依次承诺最优航迹并重新评估剩余航迹在更新上下文下的性能，实现全局覆盖优化。同时，WONDER采用类似近端策略优化（PPO）的智能体（Actor）结构，并交替更新世界模型与策略网络，提升决策鲁棒性。理论分析验证了该框架的有效性，实验在集成无人机运动、射线追踪电磁传播、编队通信及数字孪生几何的综合仿真平台RadioDynamics上完成，结果表明，WONDER在7种对比方法中取得最高均衡得分（0.870），较STACCA提升0.162的覆盖优势，且保持100%的编队连通性。
链接: https://arxiv.org/abs/2608.16955
作者: Jiahao Huang,Rongpeng Li,Zhifeng Zhao,Guoru Ding,Honggang Zhang
机构: Zhejiang University (浙江大学); Zhejiang Lab (浙江省实验室); Army Engineering University of PLA (中国人民解放军陆军工程大学); Macau University of Science and Technology (澳门科技大学)
类目: Multiagent Systems (cs.MA); Machine Learning (cs.LG)
备注:
Abstract:Post-disaster damage to terrestrial infrastructure can disrupt wireless coverage,while Uncrewed Aerial Vehicle (UAV) swarms provide a promising solution for rapid this http URL, due to the limitations in local geometry observations hidden radio impact,and inter-UAV communication,there exists a significant gap between locally visible movement choices and swarm-level coverage this http URL combat this gap,we propose a raido World-model-based Optimized Negotiation framework for Distributed UAV covERage (WONDER).Particularly, to tackle the unavailability of the future radio field from onboard observations, WONDER uses a Joint-Embedding Predictive Architecture (JEPA)-based radio world model to learn and predict the incremental radio effect of each candidate trajectory from deployment-available this http URL-round negotiation in WONDER then coordinates ranked proposals by committing one trajectory at a time and re-evaluating the remaining proposals under the updated context. Our theoretical analyses further validate the effectiveness of such a world model-based framework. WONDER also adopts a Proximal Policy Optimization (PPO)-style Actor and alternates between updating the world model and the actor. Furthermore,we build RadioDynamics,a comprehensive simulation environment that integrates UAV mobility,radio propagation, inter-UAV communication modeling,and digital-twin geometry with ray-traced fields in 62 metropolitan this http URL on 11 testing scenes in RadioDynamics show that WONDER achieves the highest balanced score among seven evaluated methods,reaching 0.870 with a 0.162 coverage advantage over STACCA, while maintaining 100% connectivity between UAVs.
[MA-11] MITRE-SAGE: A Multi-Agent Cybersecurity Question-Answering model
【速读】：该论文旨在解决网络安全领域中大规模异构安全信息分析所面临的挑战，包括信息过载、告警疲劳以及在时间受限条件下进行决策的困难。现有大语言模型（LLM）虽在问答（QA）任务中表现优异，但在网络安全场景下受限于领域知识不足、幻觉现象频发及难以同时捕捉语义与结构化关系等问题，导致其可靠性与可解释性不足。为此，本文提出MITRE-SAGE——一种多智能体检索增强生成框架，通过融合语义与结构化网络安全知识，提升基于LLM的QA系统在可靠性与可解释性方面的表现。其核心创新在于将复杂任务分解为查询理解、证据检索与答案合成三个阶段，并采用分层智能体协作机制，有效支持漏洞评估、威胁画像构建与关系提取等关键网络安全任务。此外，研究构建了MITRE-QA基准数据集，包含3,000个涵盖多样化网络安全知识任务的问答对，用于系统评估不同方法的表现。实验结果表明，MITRE-SAGE在八项基准任务中的五项上超越独立使用LLM及传统检索增强生成（RAG）方法，尤其在轻量级配置（Qwen2.5-7B子智能体 + Qwen2.5-14B协调器）下仍取得卓越性能，验证了该多智能体架构的有效性。综上，MITRE-SAGE为实现可扩展、可解释的可靠网络安全问答提供了新范式，而MITRE-QA则为未来研究提供了标准化评估基准。
链接: https://arxiv.org/abs/2608.16921
作者: Ali Habibzadeh,Farid Feyzi,Reza Ebrahimi Atani
机构: University of Guilan (吉兰大学)
类目: Information Retrieval (cs.IR); Cryptography and Security (cs.CR); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注:
Abstract:Effective cybersecurity operations require timely and accurate analysis of large-scale heterogeneous security information; however, analysts increasingly struggle with information overload, alert fatigue, and time-constrained decision-making. Although large language models (LLMs) have demonstrated promising capabilities for question answering (QA), their effectiveness in cybersecurity remains limited by insufficient domain knowledge, a tendency to hallucinate, and difficulties in capturing both semantic and structural relationships. This work proposes MITRE-SAGE, a multi-agent retrieval-augmented generation framework that integrates semantic and structural cybersecurity knowledge to improve the reliability and interpretability of LLM-based QA systems. By decomposing complex tasks into query interpretation, evidence retrieval, and answer synthesis, MITRE-SAGE effectively supports cybersecurity tasks such as vulnerability assessment, threat profiling, and relationship extraction. Furthermore, we propose MITRE-QA, a comprehensive benchmark comprising 3,000 question-answer pairs for evaluating LLMs across diverse cybersecurity knowledge tasks, and use it to systematically evaluate MITRE-SAGE against representative baseline methods. Extensive experiments demonstrate that MITRE-SAGE consistently outperforms standalone LLMs and conventional RAG approaches. Notably, a lightweight configuration comprising Qwen2.5-7B sub-agents and a Qwen2.5-14B orchestrator achieves superior performance on five of the eight benchmark tasks, indicating the effectiveness of the proposed multi-agent framework. The results highlight the potential of MITRE-SAGE as a scalable and interpretable approach for reliable cybersecurity QA, while MITRE-QA provides a standardized benchmark for future research.
[MA-12] he Little Scientist: LLM Agent -Driven Discovery via the Scientific Method
【速读】：该论文旨在解决如何自动化算法设计中的科学发现过程这一核心问题，即能否将科学方法的循环迭代机制（假设生成、实现、实证测试与反馈）嵌入大语言模型（LLM）驱动的智能体中，以实现自主发现新算法或优化策略。其解决方案的关键在于提出名为“小科学家”（The Little Scientist）的框架，该框架通过两个协同智能体实现：一是“科学家智能体”（Scientist agent），在评估环境中迭代执行假设生成、代码实现与基于结构化诊断的性能反馈；二是“库恩智能体”（Kuhn agent），当科学家智能体陷入局部最优时，主动引入跨学科启发的范式颠覆性假设，推动模型探索其潜在空间中更广阔的新区域。这一双智能体机制有效避免了传统优化方法的过早收敛问题，使系统不仅能发现全新的算法（如用于DNA基序发现的DALE算法），还能设计出超越现有基准的集成策略（如蛋白质适应度预测中的Delta V）。实验表明，该框架在蛋白质适应度预测和DNA基序发现任务上均显著优于现有方法，且整个研究仅使用704M tokens在无GPU的单台虚拟机上完成，验证了其高效性与可扩展性。
链接: https://arxiv.org/abs/2608.16951
作者: Travis Smith
机构: Independent Researcher(独立研究员); São Miguel, Azores, Portugal(葡萄牙亚速尔群岛圣米格尔岛)
类目: Quantitative Methods (q-bio.QM); Multiagent Systems (cs.MA)
备注: Code: this https URL and this https URL (Apache 2.0)
Abstract:What happens when you teach an LLM-based agent the scientific method? Motivation: Scientific discovery emerges from cycles of hypothesis, implementation, empirical testing, and feedback. Can this process be automated? We approach automated algorithm design through the lens of the scientific method, where an LLM-based agent goes through each step of the process in an ordered, iterative fashion. Results: We present The Little Scientist, a framework in which a “Scientist agent” works inside an evaluation environment that benchmarks its code and returns structured per-instance diagnostics. When the Scientist plateaus at a local optimum, a “Kuhn agent” injects a paradigm-shifting conjecture paired with a cross-disciplinary inspiration, forcing exploration of a different region of the LLM’s latent space. We demonstrate the framework on two problems that require fundamentally different modes of discovery. For protein fitness prediction, the Scientist discovered Delta V, an ensemble calibration strategy that ranks first on the ProteinGym DMS Substitutions Zero-Shot leaderboard across all five official evaluation metrics, exceeding the #2 model (VenusREM) by +0.033 mean Spearman correlation across 217 DMS assays. For DNA motif discovery, the Scientist wrote an algorithm from scratch–DALE (Dual-seed Algorithm for Latent Enumeration)–that outperforms STREME (the default in the MEME Suite) across 132 ENCODE transcription factors (mean AUROC 0.842 vs. 0.803, Wilcoxon p 10^-6) while running 11x faster. This demonstrates that the framework can produce genuinely novel algorithms, not just optimize existing components. Together, these results show that an LLM agent stepping through the scientific method can discover both new algorithms and new ensemble strategies that outperform prior solutions. The entire research program consumed 704M tokens on a single virtual machine with no GPUs Comments: Code: this https URL and this https URL (Apache 2.0) Subjects: Quantitative Methods (q-bio.QM); Multiagent Systems (cs.MA) Cite as: arXiv:2608.16951 [q-bio.QM] (or arXiv:2608.16951v1 [q-bio.QM] for this version) https://doi.org/10.48550/arXiv.2608.16951 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[MA-13] CityReal: Human-Aligned Urban Behavior and City Dynamics Simulation with Large-Scale LLM Agents
【速读】：该论文旨在解决当前大规模城市仿真中代理（agent）行为与真实人群行为脱节的问题，尤其针对基于大语言模型（Large Language Models, LLMs）的代理在少样本提示（few-shot prompting）下容易复制模型自身行为先验、难以反映目标人群真实行为模式的局限性。其解决方案的关键在于提出一种模块化的人类对齐城市仿真框架——CityReal，该框架将代理建模为以意图驱动的决策者，能够生成连贯的出行与活动规划，而非孤立的步骤选择；同时，代理通过经验积累持续学习习惯与偏好，实现动态适应。为进一步提升群体层面的真实性，系统引入文本适配器（textual adapters）对行为模块进行微调，使代理决策与真实人口统计数据对齐。实验表明，CityReal在微观个体行为和宏观群体特征两个层面均显著提升了与现实人类行为的一致性，并可扩展至数万代理规模，支持对人群密度、场所受欢迎程度、出行流及福祉等多维度的城市情景分析与预测，构建了一个可扩展的城市仿真与预测平台。
链接: https://arxiv.org/abs/2608.16897
作者: Nicolas Bougie,Xiaotong Ye,Narimasa Watanabe
机构: Woven by Toyota(丰田织物)
类目: Physics and Society (physics.soc-ph); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Large-scale urban simulation plays a pivotal role in social science, traffic safety, and transportation policy. Recent work has shown that large language models, when prompted as agents, can generate lifelike daily routines at city scale. Yet these methods typically rely on few-shot prompting, causing agents to reproduce the LLM’s behavioral priors rather than the target population. We introduce CityReal, a modular framework for human-aligned urban simulation. CityReal models agents as intention-driven decision makers that pursue coherent mobility and activity plans rather than isolated step-by-step choices. They adapt over time by learning habits and preferences based on experience and constraints. To improve population-level realism, we learn textual adapters for behavior modules that align agent decisions with observed population statistics. Experiments show that CityReal improves alignment with real-world human behavior at both micro and macro levels. Scaling to tens of thousands of agents, it supports analysis of crowd density, place popularity, mobility flows, and well-being under different urban scenarios, offering a scalable testbed for urban simulation and forecasting.
自然语言处理
[NLP-0] Multi-Agent AI System for Radiology Report Structuring and Quality Assurance with Independent Radiologist Evaluation
【速读】： 该论文旨在解决放射科报告结构不统一及质量保障不足的问题，尤其针对胸部、腹部和盆腔CT检查报告中存在的内容碎片化、关键信息遗漏以及报告各部分之间逻辑不一致等常见问题。其解决方案的关键在于构建一个本地部署的多智能体生成式AI（Generative AI）系统，通过结合正则表达式规则与本地大语言模型（Local Large Language Models），在句子层面实现报告的标准化解构，将“发现”（Findings）部分自动重组为预定义的解剖学结构格式；同时，系统具备多重质量控制功能，可检测“发现”与“印象”部分之间的矛盾、同一部分内的逻辑冲突、性别-解剖学不匹配以及重要发现未被明确记录等问题。研究结果显示，该系统成功对全部22,270个句子完成结构化处理，并在独立评估中获得多数评审者认可，整体质量评价达“优秀”或“良好”的比例为84%，且未出现临床重要信息丢失或虚构内容，表明该系统在保证报告质量的同时实现了高效、安全的自动化处理，具有在临床实践中推动报告标准化与质量控制的潜力。
链接: https://arxiv.org/abs/2608.18072
作者: Iryna Hartsock,Cesar Lam,Christopher Otteni,Aliya Qayyum,Robert Gatenby,Cyrillo Araujo,Ghulam Rasool
机构: 未知
类目: Computation and Language (cs.CL)
备注: 14 pages, 2 figures, 4 tables
Abstract:Purpose: To develop and evaluate a locally deployed multi-agent AI system for radiology report structuring and quality assurance. Materials and Methods: This retrospective study included 638 radiology reports from CT examinations of the chest, abdomen, and pelvis dictated by 15 board-certified radiologists in 2023 and 2024. A multi-agent AI pipeline was developed to perform report structuring and quality assurance (QA). The system structured the report into standardized anatomical sections at the sentence level using regex rules and local large language models. It also detected mismatches between the Findings and Impression sections, or within sections; gender-anatomy conflicts; and undocumented communication of critical findings. Two board-certified radiologists independently evaluated a 45-report subset. Results: The multi-agent system structured the Findings sections of all reports (22,270 sentences) into a predefined anatomical format while retaining the original report content. The system flagged 90 (14.1%) reports, most commonly for section mismatches (80 reports, 12.5%). In the radiologist evaluation, both reviewers agreed that 31 (69%) were correctly restructured, 2 reports (4%) were incorrectly restructured, and disagreed on the remaining 12 reports (27%). Both reviewers agreed that no clinically important information was omitted and no fabricated content was introduced. Overall QA performance was rated as “excellent” or “good” in 84% of the evaluated reports, with the remaining reports rated as “fair”. Conclusion: A locally deployed multi-agent AI system combined radiology report structuring and quality assurance within a single workflow. The system demonstrated favorable performance in radiologist evaluation. Such systems may support standardization of reporting and quality assurance in radiology practice.
[NLP-1] On the Frag ility of Self-Improving Agents : Variance Task Order and Underspecification
【速读】： 该论文旨在解决记忆增强型自改进智能体（memory-based self-improving agents）在实际应用中可靠性不足的问题。尽管此类方法在文献中展现出良好前景，但其评估过程常忽视噪声敏感性与任务顺序依赖性等关键可靠性问题。研究通过双重扩展的评估框架——包括多次运行以量化方差、随机打乱任务顺序以检验任务序列的影响——揭示了当前方法的脆弱性：首先，复杂环境下的多步任务评估本身具有高度噪声，而叠加自改进机制会进一步放大该噪声；其次，智能体的性能提升严重依赖于任务顺序，现有研究普遍采用默认任务顺序，隐含构建了有利于成功的教学大纲（implicit curriculum），导致结果不可复现。为探究根源，作者对智能体记忆内容进行人工分析，提出任务与环境描述不充分（underspecification）是导致脆弱性的核心原因。通过在记忆构建中引入详细评分标准（rubrics）与环境反馈信息，部分缓解了性能下降，但仍存在显著差距，表明尚有未被识别的因素影响系统稳定性。因此，论文的关键解决方案在于：强化评估协议，要求报告多轮实验结果并施加严苛测试条件；同时呼吁设计支持有效人机协同监督的系统与接口，以防止智能体在未知情境下失效。
链接: https://arxiv.org/abs/2608.18066
作者: Qinyuan Ye,Yu Li,Yada Pruksachatkun,Jiaxin Zhang,Chien-Sheng Wu
机构: Salesforce AI Research
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Code: this https URL Data: this https URL
Abstract:Memory-based self-improving agents–those that learn from an online stream of tasks and improve over time by maintaining a textual memory bank–have shown great promise in recent literature. However, the reliability aspects of these methods have been critically overlooked. In this work, we conduct a comprehensive re-evaluation of two memory-based methods, broadening the scope of evaluation along two axes: (1) including multiple runs to quantify variance, and (2) randomly shuffling the tasks to investigate the effect of task order. Through these experiments, we make two observations that expose the fragility of current methods: First, agent evaluation is inherently noisy in complex environments and on multi-step tasks, and stacking a self-improving loop on top can further amplify this noise. Second, the agent’s improvement is highly dependent on task order. Prior works often adopt default orderings that impose an implicit curriculum, acting as a hidden prerequisite for success. To better understand this fragility, we manually examine the agents’ memory and hypothesize that task and environment underspecification contribute to this fragility. We validate this hypothesis by incorporating information that enables better specification, such as detailed rubrics and environment feedback, into the memory construction process. While this added information partially closes the performance degradation in previous experiments, significant gaps still remain, suggesting that other uncharacterized factors contribute to this fragility. Looking ahead, our work advocates for more rigorous evaluation protocols for self-improving agents by reporting results across multiple runs and stress-testing them under challenging conditions. Moreover, our findings on underspecification call for systems and interfaces that enable effective human oversight, preventing agents from failing in unforeseeable ways. Comments: Code: this https URL Data: this https URL Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) Cite as: arXiv:2608.18066 [cs.AI] (or arXiv:2608.18066v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.18066 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-2] okEval: A Tokenizer Evaluation Suite
【速读】： 该论文旨在解决语言模型分词器（Tokenizer）选择过程中缺乏充分评估的问题，其核心挑战在于当前分词器设计决策往往基于直觉而非系统性验证，而这些设计选择直接影响模型的下游性能。现有评估方法主要依赖于如“词元密度”（fertility）和“压缩率”（compression rate）等传统指标，难以捕捉对模型能力有实质性影响的语言学与结构特性。为此，论文提出TokEval框架，引入一系列超越传统指标的评估度量，涵盖语义与结构层面的关键属性，例如UTF-8字符边界完整性、数学任务中数字位值边界对齐等。通过在受控条件下进行语言模型预训练实验，仅改变分词器的训练数据混合比例、预分词策略及训练算法，并在比特每字节（bits-per-byte，一种与分词器无关的困惑度变体）及涵盖语言理解、数学推理与代码生成等多个基准上评估模型表现，研究发现：信息论类指标能有效预测语言建模能力（斯皮尔曼等级相关系数最高达0.80），而对结构敏感的指标（如数字与换行处理能力）则与具体任务准确率显著相关。因此，该研究的关键贡献在于揭示了不同分词器内在属性与模型特定能力之间的关联性，为实现更科学、可解释的分词器评估提供了新范式，有望以内在度量替代耗时的预训练调优过程，在二者一致时实现更高效的模型开发。
链接: https://arxiv.org/abs/2608.18062
作者: Clara Meister
机构: EPFL(洛桑联邦理工学院)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Published as a conference paper at COLM 2026; Library hosted at this https URL
Abstract:Language model tokenizers are typically selected with minimal evaluation, despite the fact that their design choices directly impact model capabilities. This can be partly attributed to a limited understanding of which tokenizer properties affect which aspects of downstream performance. We introduce TokEval, a framework of tokenizer evaluation metrics that goes beyond standard measures like fertility and compression rate to capture linguistically and structurally meaningful properties, e.g., UTF-8 character boundary integrity and digit place-value boundary alignment for mathematics. To validate whether these metrics are predictive of downstream model performance, we conduct controlled language model pretraining experiments, varying solely the tokenizers’ training data mixture, pretokenization strategy, and training algorithm. We evaluate the resulting models on bits-per-byte (a tokenizer-agnostic version of perplexity) and several benchmarks, spanning linguistic understanding, mathematical reasoning, and code generation. Our experiments suggest that different intrinsic properties have different impacts on model abilities: information-theoretic metrics predict language modeling abilities (Spearman rho up to 0.80), while structure-sensitive metrics, such as those measuring digit and line-break handling, correlate with task accuracy. We hope TokEval enables more principled tokenizer evaluation, replacing pretraining sweeps with intrinsic measurement wherever the two agree.
[NLP-3] Language Has Two Parameters: Narrative-Induced Semantic Plasticity and Phase-Sensitive Interpretation
【速读】： 该论文试图解决的问题是：当前自然语言处理模型（如标准Transformer）在语义表示中忽略了语义间的相位关系（phase），导致对隐喻、反讽、引用等复杂语用现象的建模能力受限。现有模型依赖于词频统计与嵌入向量中的注意力权重，仅能捕捉语义关联的幅度（amplitude），却无法表征语义共激活时的相位关系——即不同意义之间如何通过相位相互调制，甚至在保持完全激活状态的同时反转其贡献作用。论文提出，语义理解需要第二个参数：相位（phase），它具有符号性（signed）、持久性（persistent）和个体/二元关系索引性（indexed to individuals and dyads），能够反映语义在特定交流主体间的历史积累与互动顺序。解决方案的关键在于构建一种带有代理索引（agent-indexed）、承载相位信息（phase-bearing）的语义状态的语言模型架构，使模型不仅能捕捉语义的共现强度，还能动态追踪语义在具体对话历史中的相位演化。这一理论主张弱版本已被论证，即解释必须包含第二个关系参数；而强版本则认为量子概率可作为该参数的形式化表达工具，尽管不主张大脑中存在量子过程。实验设计通过六项预测检验被抑制语义是否仍活跃、遭遇顺序是否影响短语功能、标记方式是否改变共享表达的理解、以及模型是否因历史输入而发生实质变化，从而验证相位机制的有效性。
链接: https://arxiv.org/abs/2608.18041
作者: Hollis Robbins(University of Utah)
机构: 未知
类目: Computation and Language (cs.CL)
备注: 23 pages; 0 figuresCC
Abstract:Language has two parameters. Count how often words occur together and you estimate amplitude, the strength of association. Word embeddings and attention weights refine that count, which sums every writer in the corpus together. This paper claims a second parameter, phase, which signed weights learned from a corpus do not supply. Phase exists only between meanings: it determines how coactivated meanings combine, and it can reverse what a meaning contributes while that meaning stays fully present. A speaker can set phase in the signal through linguistic form; encounters install phase relations and history distributes them. Population averaging deletes history-indexed phase: agent-deindexed corpora identify the population marginal state and determine no individual or dyadic state, at any scale. The standard transformer has no explicit representation for phase in frozen inference, and the interpretability program measuring progress by monosemanticity is optimizing against it: the coexistence it treats as a defect is the condition of allusion, irony, and quotation. Six predictions test whether a suppressed meaning stays active, whether encounter order changes what a phrase does, whether marking the signal changes how a shared phrase is taken, and whether a model given a history is changed by it or only informed about it. The claim defended is the weak version: interpretation requires a second relational parameter, signed, persistent, and indexed to individuals and dyads. Quantum probability is one notation for the parameter; nothing in the formalism claims quantum processes in the brain. The strong version, that the quantum calculus constrains these phenomena as signed classical models do not, rests on an encounter-order constraint not yet derived. The architecture the theory calls for is a language model with agent-indexed, phase-bearing semantic states.
[NLP-4] Chain-of-Experience for Continual LLM Improvement
【速读】： 该论文旨在解决传统大语言模型（LLM）评估忽视模型在推理阶段通过交互实现持续学习能力的问题。现有方法多依赖零样本推理（zero-shot inference），无法体现模型在实际应用中基于经验迭代优化的能力。为此，本文提出“经验链”（Chain-of-Experience, CoE）这一新范式，其核心在于构建一个在测试时通过与自我反馈或环境信号的多次交互积累经验痕迹的持续改进闭环。解决方案的关键在于引入多样化的反馈机制（如模型自反馈、正确性信号、公开代码测试通过率等），使模型能够在不重新训练的情况下，利用迭代交互实现性能提升。实验结果表明，采用CoE的模型显著优于无反馈基线，在数学、编程和知识等领域平均提升5.6%准确率，并降低19% API调用成本；进一步发现，融合互补反馈通道（如模型自身判断与外部正确性信号）可带来额外增益，且CoE在单位令牌上的准确性优于现有测试时策略。此外，研究揭示了基础模型能力与改进潜力之间存在正相关关系，且模型对弱或虚假反馈具有鲁棒性，不同反馈类型促进不同维度的改进，大部分性能提升集中在迭代初期。
链接: https://arxiv.org/abs/2608.18027
作者: Haoqin Tu,Yunhao Fang,Yizhong Wang,Cihang Xie,Shen Yan
机构: UC Santa Cruz; Bytedance Seed
类目: Computation and Language (cs.CL)
备注: H.T. and Y.F. contributed to this work equally
Abstract:Humans continuously learn from experience, whereas conventional large language model (LLM) evaluations ignore the models’ ability to improve through inference-time interaction. In this paper, we study how LLMs learn from iterative experience at test time, a setting we refer to as Chain-of-Experience (CoE), where models accumulate experiential traces through iterative interactions with self or environmental feedback to form a continual improvement loop beyond zero-shot inference. We instantiate CoE with diverse feedback mechanisms, including model self-feedback and environmental signals such as correctness or public coding test pass rates, and evaluate across math, coding, and knowledge domains using 8 LLMs, including GPT-5, Gemini-2.5 Pro, Claude-4.5 Sonnet. Our study shows that leveraging iterative experience consistently outperforms feedback-free baselines, achieving substantial gains with self feedback alone, alongside a 5.6% overall improvement and 19% lower API cost across tasks and models. We further show that combining complementary feedback channels (e.g., model and correctness signals) yields additional gains, and that CoE delivers higher accuracy per token than existing test-time strategies. We observe a positive correlation between LLM base ability and improvement capacity, and show that models remain robust under weak or spurious feedback, with different feedback contributing to distinct improvement aspects and most gains emerging early in the iterations.
[NLP-5] he IOL-AI Challenge: An Open Challenge towards Advancing Linguistic Reasoning
【速读】： 该论文旨在解决大语言模型（LLM）在缺乏明确规则的开放性语言推理任务中的表现评估问题，尤其聚焦于人类语言学奥林匹克竞赛（IOL）中典型的无规则约束型语言谜题。这类任务要求模型首先从有限输入中自主发现语言规律（即“元规则”），再进行逻辑推理，与传统依赖预设规则的数学或代码推理形成鲜明对比。其解决方案的关键在于构建一个基于真实未见题目、由官方评委按照人类参赛者标准评分的公开科学挑战——IOL-AI Challenge，从而实现对模型语言推理能力的可信评估。实验结果表明，尽管前沿模型如Claude Opus 4.8在该任务中获得等同于金牌的评委评分，但受限于计算资源的系统（单张T4显卡，30分钟）却仅达到人类选手后5%水平，说明模型性能并非由规模决定，而是取决于解码策略与输出处理机制；同时发现自动评估指标虽与人工评分趋势一致，但存在显著量级压缩，夸大弱模型表现约13分，低估强模型表现。进一步分析揭示，即使前沿模型具备某些目标语言的先验知识，也未能显著提升其推理能力，表明语言推理任务可作为衡量通用推理能力的有效基准。
链接: https://arxiv.org/abs/2608.18011
作者: Eduardo Sánchez,Rita Berrada,Dan-Mircea Mirea,Sara Rajaee,Alexander Piperski,Ana Meta Dolinar,Boris Iomdin,Andrey Nikulin,Mariya Shmatova,Marzieh Fadaee,Julia Kreutzer
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Reasoning in LLMs is overwhelmingly studied in domains that provide a model with rules: mathematics and code. Linguistic puzzles invert this: the solver must first discover the system before reasoning within it. We present the IOL-AI Challenge, an open-science competition run on the unseen problems of the International Linguistics Olympiad (IOL) 2026 Individual Contest, evaluated both automatically and, for the first time, by members of the official IOL Jury under the same rubrics applied to human contestants. The challenge drew 731 submissions from 46 teams under a strict compute budget (one T4, 30 mins). We additionally benchmark 15 unconstrained frontier and open models, with Claude Opus 4.8 earning a jury score equivalent to a gold medal, while both resource-constrained systems we submitted for jury grading scored in the range of the bottom 5% of contestants. Capability was not determined by scale: 14B submissions outperform models twice their size, and gains come from decoding and output-handling rather than model capacity. We also found that automatic metrics rank systems exactly as the jury does, but compress the scale, upscoring weak systems by ~13 points and understating strong ones. Our analysis shows that while frontier models might have prior knowledge about some of the problem languages, it does not significantly help them solve the linguistic reasoning tasks, leaving linguistic reasoning as a strong benchmarking proxy for generalizable reasoning skills.
[NLP-6] Judge Retrieve or Abstain: Uncertainty-Guarded LLM Judging with Provable Risk Guarantees
【速读】： 该论文旨在解决在无参考答案（reference-free）的客观任务中，使用大语言模型（LLM）作为评估者时面临的可靠性挑战。传统方法要么依赖模型自身的参数化知识进行判断（易产生幻觉或证据不足），要么通过工具增强（如网络检索）获取额外证据，但后者带来计算开销并需复杂机制控制使用时机与方式。更重要的是，现有方法无法对接受的评估结论中的错误率提供形式化控制或保证其可靠性达到指定水平。为此，论文提出一种风险可控的评估框架，通过在预留数据集上校准不确定性阈值，利用有限样本的Clopper–Pearson置信区间确保被接受的评估结果中假发现率（false discovery rate）低于用户设定的α水平，具有高概率保障。当模型在参数模式下置信度不足时，系统将实例路由至检索增强模式，获取外部网页证据并以第二个校准后的阈值重新评估，整个两阶段流程仍保持有限样本下的严格风险控制。实验表明，该框架在不同规模的评测模型与开放域问答基准上，均能维持目标误差率的同时显著提升评估覆盖率，优于单一模式基线。
链接: https://arxiv.org/abs/2608.17994
作者: Sher Badshah,Ali Emami,Hassan Sajjad
机构: Dalhousie University (达尔豪西大学); New York University Abu Dhabi (纽约大学阿布扎比分校); Emory University (埃默里大学)
类目: Computation and Language (cs.CL)
备注: Accepted at Conference on Language Modelling 2026
Abstract:Using LLMs as judges has become standard practice for evaluating model outputs at scale. This is particularly common for subjective, open-ended tasks such as assessing helpfulness or alignment, where no single reference answer exists. However, objective tasks introduce a distinct reliability challenge for reference-free LLM judging. In the absence of a reference answer, the judge evaluates factual correctness either through its parametric knowledge or through tool augmentation. Although the former enables efficient evaluation, the judge may hallucinate or lack sufficient evidence for its verdict. Conversely, tool augmentation can provide additional evidence but introduces extra computational cost and requires an appropriate mechanism to determine when and how that evidence should be used reliably. More importantly, neither approach alone provides formal control over the risk of accepted verdicts or guarantees their reliability at a specified level. We propose a risk-controlled framework that calibrates uncertainty thresholds on a held-out set so that the false discovery rate among accepted verdicts remains below a user-specified level~ \alpha with high probability, using finite-sample Clopper–Pearson intervals. When the parametric mode is not sufficiently confident, the instance is routed to a retrieval-augmented mode, where the judge gathers web evidence and re-evaluates the instance under a second calibrated threshold. The finite-sample guarantee carries over to this two-threshold routing without additional assumptions. Across open-domain QA benchmarks and judges of varying scales, the framework maintains the target error rate while achieving substantially higher coverage than single-mode baselines.
[NLP-7] Against Political Polarization: A Unified Framework for Tracing Evolving Political Ideologies on Social Media
【速读】： 该论文旨在解决社交媒体中政治意识形态演化追踪所面临的多重挑战，包括数据稀缺性、非政治内容冗余、人工标注成本高且易引入偏见，以及难以建模未来意识形态倾向等问题。其核心解决方案是提出一个统一的框架TSN4PI，关键在于两个核心模块：一是基于大语言模型（Large Language Models, LLMs）结合风格迁移与无监督域适应（Unsupervised Domain Adaptation）的意识形态检测网络（PIDN），能够有效从噪声大、跨领域的原始数据中识别并过滤非政治内容，提升意识形态检测的鲁棒性；二是采用时序图神经网络（Temporal Graph Neural Networks, TGNs）的意识形态演化预测网络（PIPN），可捕捉个体或群体在时间维度上的意识形态动态变化，实现对意识形态存在性、强度及演变趋势的综合分析。该框架通过构建两个大规模公开数据集支持非商业研究，并在X和Truth Social等多平台的案例研究中验证了其有效性，为理解网络政治极化与意识形态演化提供了方法论创新与实证洞察。
链接: https://arxiv.org/abs/2608.17987
作者: Yijie Xu,Chao Wang,Hui Xiong
机构: The Hong Kong University of Science and Technology (Guangzhou)(香港科技大学(广州)); University of Science and Technology of China(中国科学技术大学); The Hong Kong University of Science and Technology(香港科技大学)
类目: ocial and Information Networks (cs.SI); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted by ACM Transactions on Intelligent Systems and Technology
Abstract:The rapid growth of social media has greatly influenced political discourse, highlighting the need to understand individual political ideologies and their temporal dynamics. This task faces challenges such as data scarcity, abundant non-political content, costly and bias-prone manual annotation, and difficulty in modeling future ideological inclinations. To address these issues, we propose TSN4PI, a unified framework for tracking the evolution of political ideologies on social media. It includes two core modules. The PIDN uses large language models with style transfer and unsupervised domain adaptation to enable robust ideology detection and filter irrelevant content from noisy, cross-domain data. The PIPN employs temporal graph neural networks to predict future ideological shifts, enabling comprehensive analysis of ideology presence, intensity, and evolution. We release two large-scale datasets for noncommercial research use to facilitate further work. Extensive case studies on multiple platforms (X and Truth Social) validate the effectiveness of TSN4PI and provide empirical insights into political polarization and the evolution of online ideologies. Our findings offer a nuanced perspective, advancing both methodological development and empirical understanding in this field.
[NLP-8] When Writing Style Drifts: Benchmarking Authorship Verification under Distribution Shifts in Genre Time and the AI-Era
【速读】： 该论文旨在解决作者身份验证（Authorship Verification, AV）在现实场景中因体裁、时间跨度及生成式AI辅助写作等多重分布偏移（distribution shifts）导致的模型鲁棒性下降问题。现有研究多孤立地考察单一因素，且主要聚焦于英语语料，难以反映真实环境下的性能表现。为此，本文提出首个德语领域的系统性评估基准AVShift，涵盖超过15万对文本，覆盖三种体裁与21年时间跨度，支持在统一框架下对跨体裁、时间演变及生成式AI时代等多重分布偏移进行可控评估。其核心解决方案在于构建一个大规模、多维度、语言特定的基准数据集，并在此基础上对比分析基于特征、嵌入及大语言模型（LLM）的不同方法。实验表明，微调后的LLM在跨体裁泛化能力上表现最优，且其性能显著受益于风格多样化的训练数据；时间漂移（temporal drift）是影响AV性能最强的因素，文档间时间间隔越大，识别准确率越低；而当前数据中未观测到明显的生成式AI时代分布偏移。此外，特征分析揭示了跨体裁保持稳定的语篇特征，但其重要性权重随体裁转换而动态变化。该研究为理解并提升AV在真实世界中的可靠性提供了关键实证基础与可复现的评估工具。
链接: https://arxiv.org/abs/2608.17979
作者: Lotta Kiefer,Brisca Balthes,Christoph Leiter,Yamen Ajjour,Elena Schmidt,Steffen Eger
机构: University of Technology Nuremberg (UTN)
类目: Computation and Language (cs.CL)
备注:
Abstract:Authorship verification (AV) assumes that an author’s writing style remains sufficiently stable to distinguish it from that of other writers. In practice, however, this assumption is challenged by distribution shifts caused by changes in genre, time, and AI-assisted writing. Existing AV benchmarks typically study these factors in isolation and focus predominantly on English, limiting our understanding of model robustness under realistic conditions. We introduce AVShift, the first German benchmark for systematically evaluating AV under multiple distribution shifts. AVShift comprises over 150,000 text pairs spanning three genres and 21 years, enabling controlled evaluation of cross-genre, temporal, and AI-era shifts within a unified framework. We benchmark representative feature-based, embedding-based, and LLM-based approaches. Our experiments show that fine-tuned LLMs generalize best across genres and benefit substantially from stylistically diverse training data. We further demonstrate that temporal drift is one of the strongest factors affecting AV, with performance degrading significantly as the time gap between documents increases. In contrast, we find no evidence of a measurable AI-era distribution shift within AVShift. Finally, our feature analysis reveals stylistic features that remain stable across genres, while their relative importance varies depending on the specific genre transition. We release AVShift and our code for future research.
[NLP-9] Do Large Language Models Play Six Degrees of Separation? Measuring Topological Compression in Long-Context Manifolds
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在长文本上下文中实现多跳推理（multi-hop reasoning）时，其内部认知跃迁机制缺乏可解释性的问题。传统基于注意力权重的可解释性方法因注意力汇聚（attention sink）等路由伪影，难以准确捕捉语义上的真实邻近关系。为此，本文提出一种突破性的解决方案：摒弃注意力权重，直接分析隐藏状态流形（hidden state manifold）的动态几何结构，证明深层语言模型的潜在空间天然形成小世界网络（Small-World networks）。通过将长上下文表征的连续相似度矩阵稀疏化为无权图，研究者在两种不同架构中追踪了语义上高度离散的锚点之间的连接性。研究发现存在显著的拓扑相变现象：早期句法层的结构完全断裂，而深度推理层则突然将庞大的概念距离压缩为严格受限于“六度分隔”（Six Degrees of Separation，即最多6个语义跳跃）的可导航路径。进一步地，该框架被应用于检索增强生成（Retrieval-Augmented Generation, RAG）中的零样本幻觉检测（使用RAGognize数据集），结果显示，基于事实的生成保持与源上下文的结构完整性（约3跳），而幻觉生成则引发严重的拓扑坍缩。本研究从数学上形式化了Transformer执行抽象推理的机制，并提出了一个严格的几何特征作为评估生成内容事实可靠性的新指标。
链接: https://arxiv.org/abs/2608.17950
作者: Md. Faiyaz Abdullah Sayeedi
机构: BRAC University (布拉克大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large Language Models (LLMs) demonstrate remarkable multi-hop reasoning capabilities over long contexts, yet the internal mechanisms enabling these distant cognitive leaps remain poorly understood. Traditional attention-based interpretability often fails to capture true semantic proximity due to routing artifacts like attention sinks. In this paper, we bypass attention weights to directly analyze the dynamic geometry of the hidden state manifold, proving that deep LLM latent spaces natively organize into Small-World networks. By sparsifying the continuous similarity matrices of long-context representations into unweighted graphs, we trace the connectivity between highly disjoint semantic anchors across two distinct architectures. Our findings reveal a sharp topological phase transition: while early syntactic layers remain entirely fractured, deep reasoning layers abruptly compress massive conceptual distances into highly navigable pathways strictly bounded by the “Six Degrees of Separation” limit (= 6 semantic hops). Furthermore, we demonstrate the practical efficacy of this framework by applying it to zero-shot hallucination detection within Retrieval-Augmented Generation (RAG) using the RAGognize dataset. We show that factually grounded generations maintain structural integrity with their source context (approximately 3 hops), whereas hallucinations induce severe topological collapse. Ultimately, this work mathematically formalizes how transformers execute abstract reasoning and provides a novel, strictly geometric signature for evaluating factual reliability.
[NLP-10] Efficient RLVR Scheduling via Graph-Structured Online Difficulty Estimation
【速读】： 该论文旨在解决生成式 AI（Generative AI）在强化学习中使用可验证奖励（Reinforcement Learning with Verifiable Rewards, RLVR）时，因固定探索预算分配导致的效率低下问题。具体而言，现有方法对不同难度的样本均采用相同的探索预算，造成简单样本冗余探索而困难但可学样本探索不足。为实现难度自适应的探索，当前的自适应调度器依赖课程学习式样本选择或基于估计难度的非均匀采样，但其核心挑战在于难以获取可靠的在线难度估计：专用探测会引入显著的生成开销，而基于历史的估计器存在冷启动问题、反馈过时，且通常忽略样本间的关联性。本文提出一种即插即用的基于图结构的在线难度估计器，通过共享相关样本间的探索反馈并持续更新难度估计，有效缓解了冷启动与反馈滞后问题，且无需额外探测。其关键创新在于构建一个基于语义和推理相似性的难度感知样本图，引入隐含难度状态，并利用Potts先验促使邻近样本共享相同状态；随后采用状态级Beta-Binomial模型聚合各状态的采样结果，并通过在线均场变分算法动态更新隐状态分配与状态级难度估计。该框架可无缝集成至样本选择与采样分配调度器中，实现无需专门探测的难度自适应探索，在多个基础模型、强化学习调度器与基准测试上均展现出更优性能。
链接: https://arxiv.org/abs/2608.17941
作者: Zhizhao Liu,Zhiliang Tian,Xi Wang,Zhihua Wen,Yihang Xiong,Zhiquan Lai,Dongsheng Li
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models but relies on costly rollout exploration. Assigning the same exploration budget to samples with different difficulty levels is inefficient: easy samples may receive redundant rollouts, whereas difficult but learnable samples may receive too little exploration. Existing adaptive schedulers address this mismatch through curriculum-based sample selection or non-uniform rollout allocation based on estimated sample difficulty. However, obtaining reliable online difficulty estimates remains challenging: dedicated probing adds substantial generation overhead, whereas history-based estimators face a cold start with no initial observations and stale feedback, and typically ignore relations among samples. To address these limitations, we propose a plug-and-play graph-based online difficulty estimator that shares rollout feedback across related samples and continuously updates their difficulty estimates, mitigating cold start and staleness without dedicated probing. Specifically, we first construct a difficulty-aware sample graph based on semantic and reasoning similarities. Based on this graph, we introduce latent difficulty states and use a Potts prior to encourage neighboring samples to share the same state. We then employ a state-level Beta-Binomial model to aggregate the rollout outcomes associated with each state. Finally, we use an online mean-field variational algorithm to continuously update the latent-state assignments and state-level difficulty as new feedback arrives. Our framework can be integrated into sample-selection and rollout-allocation schedulers, enabling difficulty-adaptive exploration without dedicated probing. Experiments across multiple base models, RL schedulers, and benchmarks demonstrate that our framework achieves better performance.
[NLP-11] Grading Needs a Rubric Not Intelligence
【速读】： 该论文旨在解决小规模语言模型在开放性试题评分中可靠性不足的问题，尤其关注如何在不依赖高成本模型的前提下实现与大型模型相当的评分一致性。其核心解决方案在于“任何到基准”（any-to-bench）框架的设计：利用前沿级大模型在数据摄入阶段一次性解析题目及其评分标准（rubric），随后由低成本的小语言模型执行重复性的评分任务。关键发现表明，评分结果的高度可重复性主要由被评答案本身决定（解释了95.6%的分数方差），而评分者身份的影响极小（仅0.2%），且评分者的推理努力程度对得分影响微乎其微（最大变动仅为全分值的0.006）。进一步消融实验揭示，评分标准中的具体评分维度和等级划分虽非决定性因素，但官方参考答案在维持评分可靠性方面起到了核心作用；一旦移除官方答案，评分信度显著下降（ICC从0.888降至0.628），且评分者推理能力重新产生影响。因此，该研究的关键在于：通过将评分过程锚定于结构化、明确的评分标准，实现了评分结果与评分者认知能力的解耦，从而使得低成本模型能够稳定、可靠地完成高质量评分任务。
链接: https://arxiv.org/abs/2608.17938
作者: Jhen-Ke Lin
机构: National Yang Ming Chiao Tung University (国立阳明交通大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Small language models can grade open-ended examination answers as reliably as substantially more expensive models when they grade against an explicit rubric. We test this claim as the design principle behind any-to-bench: a frontier model reads source documents once, at ingestion, to extract each question and its rubric; lower-cost models then perform all repeated grading work. We evaluate six cost-efficient model configurations from two model families at three reasoning-effort levels. Each configuration answers 24 open-ended examination questions, and each also grades every answer sheet three times, yielding 3,456 per-question grades. Scores depend overwhelmingly on the answer being graded: answer identity explains 95.6% of score variance, whereas judge identity explains only 0.2%. Raising a writer’s reasoning effort moves earned scores by as much as 0.143 of full marks, while raising a judge’s reasoning effort moves assigned scores by at most 0.006. Six frontier-tier judges, added as a check, reproduce these scores and are no more reliable as a panel. Two ablations then decompose the rubric on the same questions and answers. Removing its criteria and levels while keeping the official answer changes nothing measurable. Removing the official answer as well collapses reliability (ICC 0.888 to 0.628), inflates scores, and makes judge reasoning effort matter again. The rubric is what decouples grading from judge intelligence, and within the rubric the official answer does nearly all the work. We find no evidence of length preference or same-family preference under rubric-anchored grading.
[NLP-12] SpeechSense: A Paralinguistic-Focused Dataset for Fine-Grained Speech Sentiment Analysis
【速读】： 该论文旨在解决现有语音情感分析（Speech Sentiment Analysis）研究中的两大核心问题：一是主流方法依赖以文本为中心的流水线架构，将自动语音识别（Automatic Speech Recognition, ASR）与文本分析串联，导致声学特征（如语调、韵律）在处理过程中被丢失，难以捕捉语音中蕴含的细微态度信息；二是现有基准数据集在标签粒度上存在偏差，过度关注基本情绪类别（如快乐、悲伤），而忽视了人际互动中更为精细的立场表达（如自信、不耐烦），限制了模型对社会敏感性语境的理解能力。为此，论文提出了一种新型数据集SpeechSense，构建了一个基于人际立场的8类细粒度分类体系，该体系主要依赖韵律线索而非词汇内容进行判别，并通过高保真语音合成与严格的人工验证流程构建高质量标注数据。实验结果表明，具备声学输入能力的多模态大语言模型（multi-modal LLMs）、纯文本大语言模型（text-only LLMs）及语音编码器均显示，拥有声学信息的模型显著优于仅依赖文本的基线模型，充分验证了声学线索在识别说话人微妙态度中的主导作用。因此，解决方案的关键在于：建立一个聚焦于韵律驱动的人际立场分类体系，并通过高精度、人工验证的语音数据集（SpeechSense）实现对细粒度情感语义的有效表征与建模。
链接: https://arxiv.org/abs/2608.17931
作者: Shicheng Ma,Wenqian Cui,Irwin King
机构: The Chinese University of Hong Kong (香港中文大学)
类目: Computation and Language (cs.CL); Multimedia (cs.MM); Sound (cs.SD)
备注: 7 pages, 2 figures, 5 tables. Accepted to ACM Multimedia 2026 (Dataset Track). Dataset and code: this https URL
Abstract:Recent advances in AI have revolutionized speech processing, yet effective speech understanding requires discerning not just what is said, but how it is said. Speech Sentiment Analysis plays a critical role in decoding these paralinguistic cues for diverse real-world applications such as recruitment and customer service. However, existing Speech Sentiment Analysis research faces two primary limitations. First, dominant approaches rely on text-centric pipelines that cascade Automatic Speech Recognition with text analysis. This process inevitably discards essential acoustic features like prosody and tone, failing to capture attitudinal meanings in acoustically ambiguous utterances. Second, current benchmarks suffer from a mismatch in label granularity, prioritizing basic emotions (e.g., happy, sad) over the nuanced interpersonal stances (e.g., confident, impatient) necessary for social sensitivity. To address these limitations, we propose a novel dataset, SpeechSense, for fine-grained speech sentiment analysis. Specifically, we define a specialized 8-class taxonomy of interpersonal stances detectable primarily through prosodic cues beyond lexical content alone. We then construct a curated dataset based on this taxonomy, built from high-fidelity speech synthesis and rigorous human validation. Comprehensive experiments across multi-modal LLMs, text-only LLMs, and speech encoders demonstrate that models with acoustic access consistently outperform text-only baselines. These results empirically validate the primacy of acoustic cues in detecting subtle speaker attitudes, highlighting the necessity of SpeechSense. Dataset and supplementary materials are available at this https URL.
[NLP-13] CABLE: Extending the Reach of Memory Retrieval via Complementary Antecedent-Based Linking and Expansion
【速读】： 该论文旨在解决长时对话记忆中证据可及性（evidence-reachability）问题，即在受限记忆接口下，后续上下文难以通过语义相似性检索恢复早期与当前任务相关但语义上相距较远的关键记忆（如动机、计划或过往经验）。现有方法依赖语义重叠构建记忆图谱，易导致与主检索器重复的关联，无法有效拓展检索边界。其解决方案的关键在于引入一种轻量级插件式增强机制——CABLE（Complementary Antecedent-Based Linking and Expansion），通过生成以前提为出发点的查询，从历史记忆中检索潜在相关项，并剔除已由语义检索覆盖的候选，仅保留对主检索器具有互补性的稀疏关联，构建一个有向稀疏图。在检索阶段，利用这些互补链接扩展原始检索结果，从而揭示隐含的支持性证据。实验表明，无论集成于A-MEM、SimpleMem还是Mem0g系统，CABLE均在多轮对话、跨会话及偏好导向等复杂场景中显著提升大模型判分表现，尤其在证据分布分散的情况下优势明显，验证了优先构建稀疏、推理相关的互补关联相较于冗余语义连接更具有效性。
链接: https://arxiv.org/abs/2608.17911
作者: Zheling Tan,Jin Gao,Dequan Wang
机构: Shanghai Jiao Tong University(上海交通大学); Shanghai Innovation Institute
类目: Computation and Language (cs.CL)
备注: Accepted by COLM 2026
Abstract:As LLM agents operate across structured workflows and sessions, preserving long-term history does not ensure that later contexts can recover relevant evidence through a bounded memory interface. We study this evidence-reachability problem in long-term conversational memory, where retrieval still relies heavily on semantic similarity. This works well for topical recall, but it often misses earlier experiences, plans, or motivations that are semantically distant from the later events they help explain. Existing memory graphs provide cross-memory structure, yet links driven mainly by semantic overlap can duplicate what the host retriever already recovers. We argue that link construction should instead prioritize a sparse set of retriever-complementary associations. We present CABLE (Complementary Antecedent-Based Linking and Expansion), a plug-in augmentation that constructs links designed to extend the host retriever’s direct semantic reach. For each new memory, CABLE generates antecedent-oriented queries, retrieves prior memories, subtracts candidates in the direct semantic neighborhood, and verifies the remainder before adding the accepted complementary associations into a sparse directed graph. At retrieval time, CABLE expands the host system’s retrieved seeds along these links to surface implicit supporting evidence. We evaluate CABLE with A-MEM on LoCoMo and MA-LongMemEval, and further integrate it into SimpleMem and Mem0g on LoCoMo, using Qwen3.5-27B, DeepSeek-chat, and GPT-4o-mini. CABLE yields higher mean LLM-judge scores in every evaluated system-level setting, with the largest gains in categories where useful evidence is distributed across memories or sessions, including open-domain, multi-session, and preference-oriented questions. These results support prioritizing sparse, reasoning-relevant associations that complement rather than duplicate the host retriever.
[NLP-14] BEAR-Bench: A Bilingual Enterprise and Academic Reasoning Benchmark for Multimodal Models
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在处理文本密集型专业文档时的推理能力评估不足的问题，尤其针对现有基准测试在语言覆盖范围上的局限性——多数基准集中于英语或中文，对俄语等其他语言支持严重不足。为此，研究提出BEAR-Bench（双语企业与学术推理基准），这是一个自包含、复杂度高的双语（英文与俄文）评测集，包含1000个由人工标注的基于商业及科学文档的问题。其解决方案的关键在于构建一个高质量、跨语言、面向真实专业场景的评测体系，并在此基础上对16款开源与专有MLLMs进行系统评估，揭示当前最强模型在该任务上仍存在显著性能提升空间。此外，研究进一步利用模型输出对比现有幻觉检测方法的有效性，不仅评估模型在BEAR-Bench上的错误频率，还分析这些错误是否可被可靠识别，从而为未来模型鲁棒性改进提供依据。
链接: https://arxiv.org/abs/2608.17895
作者: Liubov Chubarova,Alexandra Kuleshova,Daniil Volkov,Kirill Sultanov,Alexey Zaytsev
机构: Yandex Applied AI Institute(雅库扎应用人工智能研究所)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:While Multimodal Large Language Models (MLLMs) have made significant strides in visual comprehension, their ability to reason about text-dense, professional documents remains incompletely evaluated. Existing benchmarks emphasize information extraction, require external domain knowledge, or cover professional documents only as one of many settings. They are also largely English- or Chinese-centric, leaving other languages and Russian, in particular, substantially underrepresented. To address these limitations, we introduce BEAR-Bench (Bilingual Enterprise and Academic Reasoning), a self-contained, complex English-and-Russian benchmark comprising 1000 human-annotated questions based on text-rich business and scientific documents. We evaluate 16 proprietary and open-weight MLLMs, including Gemini 3.1 Pro and Qwen3.5-397B, on BEAR-Bench and observe clear headroom even for the strongest systems. Finally, we use the resulting model outputs to compare existing hallucination detection methods, evaluating not only how often models fail on BEAR-Bench but also how reliably those failures can be identified.
[NLP-15] BayesPrompt: human readable prompts that make sense
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）中如何重构能够引导产生期望回答或行为的提示词（prompt）这一关键问题。现有基于优化的方法通过最小化目标答案的困惑度（perplexity）来生成提示，但往往产生缺乏人类可读性的“伪提示”（pseudoprompts），即语义混乱、不可理解的标记串。论文指出，这一现象的根本原因在于提示优化任务本身是病态的（ill-posed）。为此，作者将提示优化重新建模为一个贝叶斯后验推断问题，在该框架下提出一种高效的采样算法，能够在保持低困惑度的同时生成具有高人类可读性的提示。实验结果表明，相较于现有最先进方法，该方案在真实数据集上于多个评估指标上均表现出显著提升。
链接: https://arxiv.org/abs/2608.17866
作者: Franky Kevin Nando Tezoh,Ali Hussaini Umar,Alessandro Laio,Guido Sanguinetti,Riccardo Rende
机构: Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy; Center for Computational Quantum Physics, Flatiron Institute
类目: Computation and Language (cs.CL)
备注:
Abstract:Reconstructing prompts that can elicit a desired answer or behaviour in an LLM is an open and important research topic. Optimisation methods which aim at minimising the perplexity of a given answer, however, consistently yield so-called pseudoprompts, unintelligible strings of tokens which can lack human interpretability. We argue that this is a consequence of the ill-posedness of the prompt optimisation task. By reframing the task as a Bayesian posterior inference over prompts, we propose an efficient algorithm to sample prompts which are both efficient (in terms of perplexity) and human readable. We compare our approach with state of the art alternatives showing on a real data set a marked improvement over a range of metrics.
[NLP-16] Encoded but Not Actionable: Auditing the Decode-Generate-Steer Gap in Frozen LLM s for Geometric Constraints
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在结构化推理任务中对几何关系的编码机制不明确的问题，具体聚焦于生成式几何推理中局部几何关系与整体草图自由度（Degrees of Freedom, DOF）状态的表征差异。其核心挑战在于区分模型是否真正“理解”并编码了几何约束，还是仅能从表层模式中进行统计拟合。解决方案的关键在于构建一个受控的参数化计算机辅助设计（CAD）约束测试平台，通过分离局部成对关系与全局草图级约束状态，系统性地探究六种冻结的解码器仅型LLM在隐藏状态中的信息可解码性（linear decodability）、强制选择生成能力、激活水平影响以及行为可操控性（behavioral steerability）。研究发现，预训练显著提升了局部几何关系的可解码性，且该优势在消除位置线索干扰后依然存在；而草图级自由度状态在随机初始化时已具备较高可解码性，表明其信息可能源自架构或先验而非深度学习所得。更重要的是，尽管部分信息可被解码，但其在生成过程中往往无法有效表达，且激活恢复与均值差控制等干预手段难以可靠引导输出，揭示出可解码性、生成能力、激活影响与可控性之间存在显著脱节。这一审计框架为区分模型对几何结构的编码失败与表达/控制失败提供了严谨的分析路径。
链接: https://arxiv.org/abs/2608.17843
作者: Man Liang,Xinzhao Cheng,Faizan Wajid
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 13 pages, 7 figures, 8 tables, including appendices
Abstract:Large language models (LLMs) have demonstrated strong performance on structured reasoning tasks, but what they encode and whether it informs model behavior remain unclear. We investigate this question through geometric reasoning, using parametric CAD constraints as a controlled testbed for separating local pairwise relations from sketch-level constraint status. By probing the hidden states of six frozen decoder-only LLMs, we examine four properties: linear decodability, forced-choice generation, activation-level influence, and behavioral steerability. Pretraining substantially improves the decoding of local geometric relations, and this advantage persists after accounting for positional cues with shuffled-order controls. In contrast, sketch-level DOF status is already highly decodable from randomly initialized representations and improves only modestly with pretraining, indicating that much of its probe performance is available without learned weights. Further analyses show that decodable information is not always actionable. Generation often fails to express this information, and on the two intervention-tested backbones, activation-restoration effects at the patched entity position vanish while decodability persists across depth. Mean-difference steering also does not reliably control outputs. These results show that decodability, generation, activation-level influence, and steerability can diverge in the tested setting. The audit provides a controlled way to distinguish failures to encode geometric structure from failures to express or control encoded information.
[NLP-17] From Global Benchmarks to Local Evaluations: Benchmarking LLM s for the German Public Sector
【速读】： 该论文旨在解决公共机构在选择适用于其特定情境的大型语言模型（Large Language Models, LLMs）时面临的持续性挑战。现有评估基准因主要反映英语语境和美国中心化背景，且仅关注任务性能，难以有效支持非西方、非英语环境下的模型选型。本文提出MÖVE框架，作为面向德国公共部门的综合性评估体系，首次系统考察了能源消耗、供应商透明度及对德国政党立场的知识掌握等三类常被忽视的治理维度。研究发现，在这些维度间存在显著权衡：模型能耗差异超过60倍，且无法仅由模型规模解释；不同供应商的信息披露程度呈现系统性差异；欧洲模型并未表现出对德国政党立场更强的理解能力。因此，解决方案的关键在于突破传统以任务性能为导向的评估范式，强调将部署场景的治理需求纳入模型选择标准，推动评估体系从单一性能指标向多维治理适配性转变。
链接: https://arxiv.org/abs/2608.17827
作者: Camilla Dalerci,Thilo Michael,Robin Schaefer,Daniel Weinland
机构: Bundesdruckerei GmbH(德国联邦印刷局), Berlin(柏林), Germany(德国)
类目: Computation and Language (cs.CL)
备注: Accepted as non-archival paper at Eval4SD (co-located with KONVENS 2026)
Abstract:Public institutions face a persistent challenge in selecting LLMs suited to their specific context. Existing benchmarks, however, are of limited use as they primarily reflect English-language and US-centric settings, and often only evaluate task performance. In this paper, we present first results of MÖVE, a holistic evaluation framework for the German public sector, examining three rarely considered governance dimensions: energy consumption, provider transparency, and knowledge of German-party positions. Our results reveal significant trade-offs, with no single model excelling across all dimensions: estimated energy consumption varies more than 60-fold and is not explained by model size alone, information disclosure varies systematically across providers, and European models do not exhibit stronger knowledge of German party positions. Model selection for public institutions thus cannot rely on performance rankings alone. Instead, evaluations should also reflect the governance requirements of the deployment context.
[NLP-18] Whether LLM s Can Navigate Beliefs and Facts Depends on How You Phrase It
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在用户对话中对信念表达（epistemic expressions）的处理能力不足问题，尤其关注模型在面对基于错误信息的用户信念时，仍倾向于以事实核查为导向进行回应，从而忽视用户实际表达的主观信念。其核心问题是：尽管用户明确表达了某种信念（如“我认为答案是3”或“我怀疑这正确吗”），当前的LLMs往往忽略这一信念语境，转而进行事实验证，导致响应与用户认知状态脱节。解决方案的关键在于揭示该现象的本质源于“任务混淆”（task confusion）——模型默认将所有陈述视为需验证的事实命题，而非识别其作为信念表达的语用功能。研究通过分析10个LLM在18种信念表达形式上的表现，发现不同动词（如“我隐约记得”“我严重怀疑”）引发的准确率差异显著，从+50%到-14%不等，且显式链式思维（chain-of-thought）中的事实核查行为会降低对虚假信念的处理准确性。进一步实验表明，仅通过一个简单指令即可逆转模型在不同信念动词类别间的失败模式，说明干预策略具有可行性。机制层面发现，模型在解码时更关注无法被证实的错误信念，但抑制此类注意力仅部分恢复准确性，提示现有方法尚不足以完全解决信念追踪问题。因此，该研究揭示了事实核查这一通常有益的行为在信念理解任务中可能产生的干扰效应，并强调未来需发展专门的干预机制以实现对用户信念状态的精准建模。
链接: https://arxiv.org/abs/2608.17809
作者: Quang Minh Nguyen,Luis Frentzen Salim
机构: KAIST(韩国科学技术院); National Taiwan University of Science and Technology(台湾科技大学)
类目: Computation and Language (cs.CL)
备注: In submission
Abstract:Humans naturally form and express beliefs in daily communication, e.g., “I think the answer is 3” or “I suppose that’s right.” Such beliefs inevitably intertwine with fact and knowledge, making the ability to handle them in tandem desirable for large language models (LLMs), as they are increasingly deployed in user-facing settings. Prior work showed that even capable LLMs exhibit a systemic weakness in acknowledging user beliefs grounded in incorrect information. We extend this evaluation to 10 LLMs across 18 epistemic expressions and find that the size and direction of the weakness depend on the verb used to express the belief, with the accuracy gap between factual and false information ranging from +50% on “I vaguely remember” to -14% on “I seriously doubt”. We further show that the phenomenon stems from task confusion: models default to fact-checking the underlying claim, overriding the user’s stated belief; chains of thought that explicitly fact-check show lower accuracy on false information than those that do not; and a single instruction can reverse the failure across verb families. Mechanistically, models attend more to false beliefs they fail to confirm, but suppressing this attention at decoding time recovers accuracy only partially and only in some models, calling for future work on intervention methods. Our findings clarify prior results and show how fact-checking, a generally desirable behavior, can interfere with belief tracking in LLMs. Our code is available at this https URL.
[NLP-19] An Empirical Study of Reward Specification and Benchmark Reliability in GRPO-based LLM Unlearning
【速读】： 该论文旨在解决生成式问答（Generative QA）中大语言模型（LLM）去学习（unlearning）任务的规范性问题，尤其关注在目标相关提示（target-adjacent prompt）下，模型应如何在不泄露特定知识的前提下，选择更广泛但非敏感的回答，而非出现知识泄露、回避或拒绝回答等不当行为。现有评估方法通常聚焦于抑制目标知识与保留非目标实用性，却忽视了对“适度泛化回答”这一中间行为的明确定义与验证。为此，研究在受控的LoRA-GRPO RWKU（Retrieval-based Weighted Knowledge Unlearning）框架下，对比四种奖励设计：词法抑制、反拒绝引导、基于评分标准的广义回答生成，以及显式的拒绝对比，并考察其在有无监督微调（SFT）预热条件下的表现。研究发现，优化成功并不等同于真正的行为去学习——遗忘得分、离线完成审计、训练终态滚动生成审计及训练动态分析可能得出相互矛盾的结论。这种分歧主要源于奖励劫持（reward-hacking）终点、GRPO算法中策略支持能力的局限、基准探测器对最终策略变化的遗漏，以及优化过程中可选择低语义泄漏的宽主题回答所导致的误导性奖励信号。因此，解决方案的关键在于构建更精细、可解释且能有效区分不同行为模式的奖励机制，并结合多维度评估手段以确保真正实现期望的行为去学习。
链接: https://arxiv.org/abs/2608.17804
作者: Rubén Balbastre,Juan Manuel Orduña,Mariano Pérez
机构: University of Valencia(瓦伦西亚大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 32 pages, 4 figures. Code and artifacts linked in the paper
Abstract:Practical LLM unlearning is usually evaluated through two objectives: suppress target-specific knowledge and preserve non-target utility. In generative QA, this leaves a third behavior underspecified: when a target-adjacent prompt admits a broader answer without target-specific leakage, the model should answer at that level rather than leak, evade, or refuse. We study this specification problem in a controlled LoRA-GRPO RWKU setting, comparing four reward designs that span lexical suppression, anti-refusal shaping, rubric-based broad answering, and an explicit refusal contrast, with and without SFT warm-up. The experiments show that optimization success is not equivalent to behavioral unlearning: RWKU forget scores, held-out completion audits, terminal training-rollout audits, and training dynamics can point to different conclusions. We trace these disagreements to reward-hacking endpoints, policy-support limits in GRPO, benchmark probes that miss endpoint changes, and rewards that can select broad-topic answering with low semantic leakage during optimization.
[NLP-20] raceSQL: Traceable Answerability Estimation for Reference-Free Text-to-SQL Verification
【速读】： 该论文旨在解决生成式文本到SQL（Text-to-SQL）系统在实际部署中缺乏真实执行结果或正确SQL作为监督信号的验证难题，即如何在仅给定用户问题、数据库上下文和生成的SQL的情况下，判断生成的查询是否可能正确回答问题。传统方法依赖大语言模型（LLM）作为裁判或专用代理进行评估，但其决策过程难以追溯；而现有的结果奖励模型（Outcome Reward Models, ORMs）虽能对未见查询分配正确性评分，却仍缺乏对预测依据的可解释性。为此，本文提出TraceSQL，一种轻量级且可追溯的验证模型，其核心在于基于显式的诊断特征构建可解释的推理机制。该方案整合了67个特征，涵盖问题歧义性、问题需求、问题-模式-SQL一致性、SQL结构以及意图对齐等多个维度，确保每项预测均可追溯至具体的诊断证据。在BIRD开发数据库上的实验表明，TraceSQL在F1指标上达到66.47%，ROC-AUC为64.48%，优于GradeSQL-7B ORM基线（61.87% F1，58.26% ROC-AUC）。特征归因分析进一步揭示，模型同时依赖语义对齐与确定性的SQL结构信号，验证了其在保持高性能的同时具备良好的可解释性，为实现高效、透明的SQL生成质量验证提供了新范式。
链接: https://arxiv.org/abs/2608.17795
作者: Neelesh Kumar Shukla,Debasmita Panda,Srutanik Bhaduri,Aditya Banerjee,Viji Krishnamurthy
机构: Oracle Corporation(甲骨文公司); Oracle Corporation(甲骨文公司); Oracle Corporation(甲骨文公司); Oracle Corporation(甲骨文公司); Oracle Corporation(甲骨文公司)
类目: Computation and Language (cs.CL)
备注: 9 pages main paper with 6 pages supplementary material
Abstract:Text-to-SQL systems are commonly evaluated using ground-truth SQL queries or reference execution results, but such supervision is unavailable at inference time in real-world deployments. This creates a critical verification problem: given only a user question, database context, and generated SQL, can a system estimate whether the generated query is likely to correctly answer the question? Recent approaches use LLMs as judge or specialized agents to inspect generated SQL, but their decisions can be difficult to trace. Outcome Reward Models (ORMs) address this by learning from execution-labeled candidate SQLs and assigning correctness scores to unseen queries, yet they still provide limited visibility into the signals behind each verification. To address this limitation, we propose TraceSQL, a lightweight and traceable verification model built on explicit diagnostic features. TraceSQL combines 67 features capturing question ambiguity, question requirements, question-schema-SQL consistency, SQL structure, and intent alignment. These signals remain available for examining which factors influence each prediction and for tracing decisions back to diagnostic evidence. On BIRD development databases, TraceSQL achieves 66.47% F1 and 64.48% ROC-AUC, compared with 61.87% F1 and 58.26% ROC-AUC for the GradeSQL-7B ORM baseline on the same generated-SQL evaluation. Feature attribution further shows that the model relies on both semantic grounding and deterministic SQL-structure signals. These results show that SQL verification can be performed with a lightweight learned model while retaining feature-level evidence for inspecting and diagnosing its predictions.
[NLP-21] Preference Is Not Intervention: The Structure and Stability Boundaries of Reader-Specific Evidence Utility
【速读】： 该论文旨在解决生成式 AI 系统中下游模型身份对决策影响的可复用性问题，核心在于判断模型间的差异是否源于可迁移的稳定偏好结构，而非仅由输入特定的局部交互所导致。其关键解决方案在于通过检索增强生成（RAG）任务，在严格控制查询、证据、任务、评分策略与干预条件的前提下，系统性地分离并量化读者（即不同模型）在三个可测量维度上的行为：证据激活度（evidence activity）、序数偏好（ordinal preference）与条件符号方向（conditional signed direction）。研究发现，尽管个体读者间存在显著异质性（33%的联合影响单元中读者对效果方向意见不一），但序数偏好几何结构在四种独立设置下表现出高度稳定性（分半一致性 ρ=0.60–0.83），表明读者具有稳定的相对排序偏好；然而，符号方向（即对证据是否有助于任务的正负判断）则表现出明显的任务依赖性，仅在二元事实核查任务中表现强相关（ρ=0.75），而在开放式问答任务中则较弱，且无法被稀疏性、解码噪声或指标偏差解释。进一步分析显示，尽管读者间的序数相似性稳定，但其无法有效预测跨读者干预转移效果（最优距离相关性 ρ=-0.27，遗憾可靠性 -0.28），说明稳定排序并不意味着可转移的“帮助”或“伤害”决策。因此，该研究揭示了模型间偏好差异中存在可复现的序数结构，但该结构不足以支持跨模型的干预泛化，从而指出生成式系统中模型特异性效应的边界及其对可解释性与可迁移性的深刻影响。
链接: https://arxiv.org/abs/2608.17781
作者: Shi Zhou
机构: Jilin University (吉林大学)
类目: Computation and Language (cs.CL)
备注: 16 pages, 6 figures, 11 tables
Abstract:ML systems increasingly condition decisions on downstream model identity, but this is useful only if model-specific differences form reusable structure rather than input-local interactions. We test this in retrieval-augmented generation (RAG), where evidence utility can be measured under controlled interventions. Holding query, evidence, task, scoring, and intervention fixed, nine readers disagree on effect sign in 33% of jointly affected cells; reader \times query interaction explains 29.8% of utility variance versus an 8.4% permutation null; and self-selected evidence improves F1 by +0.031 ( t=3.39 ). We then ask the sharper question: \emphwhich components of this heterogeneity are stable reader properties across queries? Separating three measurable objects—evidence \emphactivity, \emphordinal preference, and \emphconditional signed direction—we find ordinal reader geometry stable across four independent settings (split-half \rho=0.60 – 0.83 ): leave-one-out interventions, PRISM preferences, RAMDocs, and RAGuard. Signed geometry is task-bounded: weak in open-ended QA (0.14, 0.35), especially for misleading and irrelevant evidence, but strong in binary fact-checking (0.75) with no significant ordinal gap, though still below its sparsity-matched ceiling. Sparsity, decoding noise, and metric artifacts do not explain the main ordinal–signed gap. Finally, stable ordinal similarity fails to predict cross-reader intervention transfer (oracle-distance \rho=-0.27 ; regret reliability -0.28 ). Reader-specific utility exists, but preference is not intervention: stable ranking similarity does not license transfer of help/harm decisions.
[NLP-22] hinking in a Low-Resource Language: What SFT Builds What RL Fixes What Accuracy Cannot See
【速读】： 该论文旨在解决生成式 AI (Generative AI) 在低资源语言（如希腊语）中进行推理时的可解释性与语言对齐问题。尽管在传统准确率基准上表现近乎无变化（仅随机种子变动即导致7.7分波动，超过所有实验变量影响），但其核心发现揭示：基础模型在面对非母语问题时几乎不以目标语言进行真实推理（0/1000个推理轨迹使用希腊语），即便答案正确，其内部推理过程仍不可读、不可审计、不可修正。解决方案的关键在于通过监督微调（Supervised Fine-Tuning, SFT）实现高度的语言对齐——使模型在约98%的样本中以问题语言进行推理，显著提升语法正确性且保留原始能力，同时减少推理令牌数（如某模型减少3倍）。研究提出六个行为维度指标，均通过控制输出长度相关性来确保有效性，并揭示了现有评估工具的六种失效模式，均被设计的对照机制捕获。然而，SFT无法自我修复缺陷，如格式跳过、信息泄露至推理通道及“用英语思考”指令执行率不足50%。进一步采用预注册的可验证奖励强化学习（Reinforcement Learning with Verifiable Rewards），成功将格式错误率从24%降至2.5%，信息泄露从3.5%降至0.0%，并提升指令遵循度9.1个百分点，而原有希腊语推理习惯在仅依赖准确率梯度的训练中未被改变。研究成果具备跨语言可迁移性，希腊语仅为验证案例，其评估框架、控制机制与预注册协议可推广至任意低资源语言。
链接: https://arxiv.org/abs/2608.17744
作者: Ayoub Kirouane,Christos Petrocheilos
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG); Robotics (cs.RO); Machine Learning (stat.ML)
备注:
Abstract:Take three frontier mixture-of-experts models (Alibaba, OpenAI, NVIDIA; 3.6-4.0B active parameters each) and fine-tune them to reason in a low-resource language. On accuracy benchmarks almost nothing happens, and the benchmark itself is noise at this scale: changing only the random seed moves the score by 7.7 points, more than every data and recipe effect we measured. That null is our first result. The real changes live where accuracy cannot see. Base models never think in Greek: 0 of 1,000 reasoning traces, even when the question is Greek, so the model answers correctly while reasoning in a form its user cannot read, audit, or correct. After supervised fine-tuning (SFT), every released checkpoint reasons in the language of the question on ~98% of items, one family at 3x fewer tokens, with judged grammaticality improving on all four models and general ability within a few points of each base: nothing was forgotten, and fluency was gained. We propose six behavioural dimensions that make such changes measurable, each gated to reject any metric that correlates with output length, and we report how our own instruments lied: six failures, each caught by a control. What SFT cannot do is fix its own defects: a quarter of answers skip the requested format, answers leak into the reasoning channel, and an explicit “think in English” is obeyed under half the time. Reinforcement learning with verifiable rewards, pre-registered before training, fixes the first two outright (fallback 24% to 2.5%, leak 3.5% to 0.0%, both against a flat random-reward control) and moves the third (+9.1pp), while the Greek reasoning habit survives an accuracy-only gradient untouched. We release five checkpoints. The instruments, the controls and the pre-registration travel to any low-resource language; Greek is the case that let us measure them.
[NLP-23] What Aggregate Scores Miss: Measuring Item-Level Regressions in Commercial LLM API Migrations
【速读】： 该论文旨在解决在大型语言模型（Large Language Model, LLM）版本迭代过程中，依赖厂商API的软件系统在面临旧模型退役时所面临的迁移决策困境。现有迁移决策普遍依赖于聚合基准分数（aggregate benchmark scores），但此类指标将异质性的个体任务表现压缩为单一数值，掩盖了模型升级中复杂的、双向的性能变化模式。论文的关键解决方案在于引入细粒度的项目级（item-level）评估框架：在GPT-5.4至GPT-5.6的三次模型升级中，对900个公开基准任务（涵盖研究生级知识、奥数数学与指令遵循）各进行50次重复采样，基于假发现率控制（false-discovery-rate control）和实际显著性阈值，对每个任务分类为“可靠改进”“可靠退化”“实际等效”或“不确定”。通过标签置换零模型校准，揭示出在所有九个迁移-基准组合中，可靠改进与可靠退化并存——即使整体得分提升高达7.3个百分点，仍存在最高8.3%的任务出现可靠退化；而得分下降的场景下，亦有高达10.7%的任务实现可靠提升。尤其在指令遵循任务中，严格与宽松评分标准间的差距在最新一次升级中扩大了3.9个百分点，表明严格评分下的3.9分退化在宽松评分下仅表现为0.04分退化。研究结论强调，仅依赖聚合分数的迁移决策会严重忽略个体任务层面的双向动态变化。为此，论文公开了完整的响应级档案与逐项评分输出，为精细化评估提供可复现的数据基础。
链接: https://arxiv.org/abs/2608.17719
作者: Xiaonan Xu,Wenjing Wu
机构: Georgia Institute of Technology (佐治亚理工学院); University of Colorado Boulder (科罗拉多大学博尔德分校)
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 25 pages, 1 figure, 10 tables (including 8 appendix tables)
Abstract:Context: Software systems that depend on commercial large language model APIs must migrate to successor versions when vendors deprecate older models. Migration decisions typically rely on aggregate benchmark scores, which compress heterogeneous item-level behaviour into a single net figure. Objective: We measure what that compression conceals. Method: On three pairwise upgrades in the GPT-5.4 to GPT-5.6 Sol product sequence, we query 900 public benchmark items (graduate-level knowledge, olympiad mathematics, instruction following) 50 times per item per model, classify each item as reliably improved, reliably regressed, practically equivalent, or inconclusive under false-discovery-rate control and a practical-significance threshold, and calibrate the results against a label-permutation null. Results: Across all nine migration-benchmark cells, reliable improvements and reliable regressions coexist. Edges with aggregate gains of up to 7.3 percentage points contain up to 8.3% reliably regressed items; edges with aggregate losses contain up to 10.7% reliably improved items. On the instruction-following benchmark, the gap between strict and loose scoring widens by 3.9 percentage points on the latest migration: a 3.9-point regression under strict scoring shrinks to 0.04 points under loose scoring. Conclusion: Migration decisions based on aggregate scores alone miss substantial bidirectional item-level change. The complete response-level archive and per-item scoring outputs are released.
[NLP-24] LLM -Derived Preference Judgments Are Not Self-Consistent
【速读】： 该论文旨在解决当前基于大语言模型（LLM）的偏好建模中一个关键假设的有效性问题：即用户通过自然语言表达的数值型偏好判断是否具备近似自洽性（self-consistency），从而可被单一效用函数（utility function）所忠实表征。现有方法通常依赖于从这些判断中估计效用函数，并据此进行决策，但该过程隐含假设所有判断均源自同一内在效用结构。然而，本文通过构建统计检验与可解释的偏差度量方法，系统评估了六种主流大语言模型在航班、公寓和酒店等场景下的基数型偏好判断自洽性。实验结果表明，各模型的偏好响应存在显著且持续的不一致性，例如在交换无差异支付意愿与两物品间价差之间出现明显偏离。这一发现表明，大语言模型生成的偏好判断无法被单一效用函数准确拟合，因此直接将其用于效用建模与最优决策可能导致系统性偏差。其解决方案的关键在于提出一套可量化、可解释的自洽性评估框架，以揭示并警示当前基于大语言模型的偏好推断方法在理论基础上的根本局限。
链接: https://arxiv.org/abs/2608.17644
作者: Matthew T. Ford,Francis Bahk,Jingjing Wang,Adam S. Jovine,Tinghan Ye,David B. Shmoys,Peter I. Frazier
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 16 pages, 4 figures; includes appendices
Abstract:Agents increasingly interpret a person’s natural-language preferences by querying an LLM for numerical preference judgments, e.g., by asking how much the person would be willing to pay for an item. A growing body of work estimates a utility function from these judgments and then chooses actions based on their estimated utility. This pipeline assumes the judgments are approximately self-consistent: that a single utility function can reproduce them. But are they? To study this question, we measure the self-consistency of cardinal LLM preference judgments. For example, the difference in stated willingness-to-pay between two items should match the stated payment that makes a person indifferent to exchanging them. We develop statistical tests and interpretable measures of how far observed responses depart from the best-fitting self-consistent utility function. Experiments with flight, apartment, and hotel examples across six LLMs reveal large persistent inconsistencies. This suggests that LLM-derived preference judgments cannot be faithfully summarized by a single utility function.
[NLP-25] MoNe: Modular Neural Memory for Efficient Long Context Inference
【速读】： 该论文旨在解决预训练Transformer模型在长上下文推理时面临的计算开销与显存占用随上下文长度线性增长的问题，尤其针对无法通过微调扩展上下文窗口的冻结模型。其核心解决方案是提出一种轻量级模块化神经记忆（MoNe），通过在推理阶段引入可快速学习的快速权重神经记忆网络（fast-weight neural memory networks），以分块读取方式动态构建上下文记忆；该设计采用局部梯度更新策略，在测试时仅基于查询令牌生成键和值，无需重新读取全部上下文。这种两阶段架构实现了预处理时间复杂度为O(N)、查询阶段计算成本恒定为O(1)，且峰值显存不随上下文长度N增加，显著降低了长序列推理的资源消耗。在128K token场景下，相比传统的上下文插值（ICL）方法，MoNe将计算量和峰值显存均降低约80%，仅需6.4%的参数额外开销，并在RULER基准的“针堆找针”和词提取任务中展现出远超ICL的泛化性能，有效突破了骨干模型原始注意力窗口限制。
链接: https://arxiv.org/abs/2608.17616
作者: Wonguk Cho,Kyubyung Chae,Tribhuvanesh Orekondy,Sunghyun Park,Hyoungwoo Park,Jeongho Kim,Arash Behboodi,Kyuwoong Hwang,Sungrack Yun
机构: Qualcomm AI Research(高通人工智能研究院); Qualcomm Technologies, Inc(高通技术公司)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:We present MoNe, a lightweight modular neural memory that attaches to any frozen pretrained Transformer to enable long-context inference without retraining. MoNe reads context in fixed-size segments via test-time learning of fast-weight neural memory networks with layer-localized gradient updates; at inference, the memory generates keys and values from the query tokens alone, with no context tokens re-read. This two-phase design decouples inference cost from context length, achieving O(N) preprocessing and O(1) query cost with peak GPU memory that does not grow with N . At 128K tokens, MoNe reduces both compute and peak GPU memory by approximately 80% compared to ICL with only 6.4% parameter overhead. MoNe generalizes to context lengths far beyond the backbone’s native window, achieving strong performance on needle-in-a-haystack and word extraction benchmarks from RULER, where ICL degrades sharply.
[NLP-26] Multi-turn Conversational AI from Text to Multimodal Interaction: Data Models Evaluation and Open Challenges
【速读】： 该论文旨在解决多轮对话中系统难以维持上下文连贯性与跨轮次语义一致性的核心挑战。随着对话型人工智能从孤立文本输入向持续、多模态交互演进，用户在真实对话中频繁出现目标修正、请求调整、话题切换及新信息引入等行为，要求系统具备跨轮次记忆保持、多模态（文本、语音、视觉等）与工具/外部知识的精准对齐、以及跨语言文化适应的能力。其解决方案的关键在于构建具备持久记忆（persistent memory）、跨轮次对齐（cross-turn grounding）、全双工交互（full-duplex interaction）能力的统一架构，并通过多模态数据集与基准测试、端到端建模范式、协同训练策略及鲁棒评估体系来支撑系统的综合发展。然而，当前技术虽在多模态感知与生成方面取得进展，仍受限于长期上下文管理、动态语境理解与文化敏感性对齐等关键瓶颈，因此研究提出需聚焦于实现“记忆、修订、对齐、表达、倾听、行动与跨文化适应”于一体的下一代对话系统。
链接: https://arxiv.org/abs/2608.17605
作者: Syeda Faiza Ahmed,Zien Sheikh Ali,Hunzalah Hassan Bhatti,Firoj Alam,Shammur Absar Chowdhury
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Sound (cs.SD)
备注: Multi-turn Conversational AI; Multimodal Dialogue; AudioLLMs; Conversational Memory; Tool-Augmented Agents; Dialogue Evaluation
Abstract:Conversational AI is moving beyond isolated text prompts toward sustained, multimodal interaction. In real conversations, users clarify goals, revise requests, interrupt responses, switch topics, and introduce new evidence while expecting systems to preserve context across turns. This makes multi-turn dialogue a distinct challenge requiring systems to maintain and update memory, ground responses across modalities, tools, and external knowledge, and adapt across languages and cultures. This study reviews multi-turn conversational AI across text-only dialogue, AudioLLMs and speech-native systems, multimodal and omni-modal systems, and tool-augmented agents. We organize the literature around datasets and benchmarks, modeling paradigms, training strategies, evaluation setups, and cross-cutting challenges. Our analysis shows that support for multiple modalities has advanced faster than the ability to sustain coherent interaction across a session. Despite stronger capabilities to perceive, speak, and act across modalities, current systems still struggle with persistent memory, cross-turn grounding, full-duplex interaction, robust evaluation, and cultural alignment. We conclude with a research agenda for systems that can remember, revise, ground, speak, listen, act, and adapt across turns, modalities, and cultures. (this https URL)
[NLP-27] Write Execute Refine: From Skill Followers to Skill Optimizers via Reinforcement Learning from Execution Feedback
【速读】： 该论文旨在解决生成式工具使用智能体（tool-using agents）在技能（skill）生成与优化过程中存在的性能瓶颈问题，即由模型自动生成的技能表现显著劣于无技能基线（8-11分差距），表明生成流程性指令与基于执行反馈进行迭代改进是两种分离的能力。其核心挑战在于如何将中间阶段的执行经验有效转化为可训练的优化信号，以实现对技能生成模型的持续改进。解决方案的关键在于提出一种多阶段框架WER（Write, Execute, and Refine），该框架通过一个独立于冻结执行器（frozen executor）的技能优化器（Skill Optimizer）进行训练：优化器生成技能，由固定执行器反复执行，程序化验证器（programmatic verifier）对结果打分并筛选出混合成功与失败的轨迹；随后，匹配的成功与失败轨迹构成下一阶段的精炼状态（refinement states），使优化器能够学习其先前输出的实际后果。该方法实现了从执行反馈中提取相对奖励信号并用于模型迭代更新，克服了传统推理时循环（inference-time loops）仅修复当前实例而无法提升生成模型本身的局限。实验表明，在BFCL v4和tau2-bench上，WER分别相较无技能基线提升7.80和3.85点，在相同精炼流程下优于未训练优化器的同构模型9.35和10.29点，且4B规模的训练后优化器在BFCL v4上达到76.63%的Pass@1，超越所有评估过的现成通用型技能优化器平均表现。
链接: https://arxiv.org/abs/2608.17587
作者: Kang Peng,Zhiwei Zhang,Yichen Zhang,Zezhong Wang,Yiming Du,Geng Tu,Baojun Wang,Bin Liang,Ruifeng Xu,Kam-Fai Wong
机构: Harbin Institute of Technology, Shenzhen, China; Harbin Institute of Technology, Harbin, China; The Chinese University of Hong Kong; MoE Key Laboratory of High Confidence Software Technologies; Huawei Technologies Co., Ltd.
类目: Computation and Language (cs.CL)
备注:
Abstract:Expert-written natural language skills can improve tool-using agents, yet agent-authored skills perform 8-11 points worse than using no skill. This gap suggests that following procedural guidance and improving it from execution evidence are distinct capabilities. Inference time loops can repair skills but do not improve the model that writes the next one. We study how to organize execution experience from intermediate skills into training states for an optimizer. We introduce WER (Write, Execute, and Refine), a multi-phase framework that trains a Skill Optimizer outside a frozen executor. The optimizer proposes skills, a frozen agent executes each repeatedly, and a programmatic verifier scores the outcomes. The scores provide relative credit and select mixed-outcome records. Matched successful and failed trajectories from these records form the next phase’s refinement states, so the optimizer learns from the consequences of its earlier outputs. On BFCL v4 multi-turn and tau2-bench, WER improves average Pass@1 over the no-skill baseline by 7.80 and 3.85 points, respectively. Under an identical refinement workflow, it outperforms the same backbone without optimizer training by 9.35 and 10.29 points. The trained 4B optimizer reaches 76.63 percent on BFCL v4, outperforming all evaluated off-the-shelf general-purpose models used as skill optimizers on average.
[NLP-28] Auditing Exposure to Harmful Content on TikTok using Multimodal Language Models: A Cross-National Age-Stratified Study EMNLP2026
【速读】： 该论文旨在解决在线视频平台（如TikTok）对青少年用户暴露于有害内容的监管难题，尤其针对跨语言环境下独立审计难以开展的问题。其核心挑战在于视频内容标注成本高昂且不同语言间的审核标准存在差异。为实现可扩展的跨国青少年安全审计，研究提出基于多模态大语言模型（Multimodal Large Language Model, MLLM）的自动化标注方案：通过在300个视频的参考集上验证四种MLLM的性能，最终选定Gemini 2.5 Flash结合八帧采样图像与文本信息的组合，在保持0.42整体克朗巴系数（kappa）的同时，将单次调用成本降至人工上传视频标注的一半。该方法被应用于10%样本集，总API支出约50美元，显著提升了审计效率。实验结果显示，关键词主动搜索带来的有害内容占比（35%-56%）较被动推荐基线提升1.5至7.5倍，且在多数国家-年龄组合中呈现短期激增后趋于平缓的趋势，部分抵消了原有年龄差异。意大利在所有年龄段中均表现出最高危害率（19岁组达48.6%），而平台自身安全过滤器仅拒绝1.1%的内容，明显低估了最明显的有害信息。因此，该研究的关键解决方案在于利用高性能、低成本的MLLM实现跨语言、可扩展的自动化内容审计，突破传统人工审计的局限性。
链接: https://arxiv.org/abs/2608.17583
作者: Hamidreza Saffari,Francesco Pierri
机构: Politecnico di Milano(米兰理工大学)
类目: Computation and Language (cs.CL)
备注: 20 pages, 16 figures, 14 tables. Accepted to Findings of EMNLP 2026
Abstract:Online video platforms can expose young users to harmful content, but independent audits remain difficult because video annotation is costly and moderation judgments vary across languages. We audit TikTok in France, Italy, and Sweden with sockpuppet accounts representing four age personas (13, 16, 19, 40), collecting 36,971 videos from passive For-You-page scrolling and active sessions that scroll, search for harm keywords, and scroll again. To scale annotation, we validate four multimodal LLMs against native-speaker labels on a 300-video reference set. Gemini 2.5 Flash with eight sampled frames plus text performs best (aggregate kappa = 0.42), at half the per-call cost of native-video upload, and we apply it to a 10% sample for approximately \ 50 in total API spend across both modalities. Keyword search returns 35-56% harmful content, a 1.5-7.5x increase over the scrolling baseline in ten of twelve country-age combinations; the spike is temporary and flattens the age differences observed in France and Sweden. Under passive scrolling, Italy has the highest harm rate at every age, with Italian age-19 reaching 48.6%. Overall, MLLM-based auditing offers a scalable approach for cross-national youth-safety audits, while provider safety filters (1.1% refusal rate) under-count the most explicit harms.
[NLP-29] Domain-Adapted Molecular Language Models for Efficient Search of Make-on-Demand Libraries
【速读】： 该论文旨在解决预训练分子语言模型在分子发现任务中，其在目标领域内及跨领域应用时的实际适用性不明确的问题。尽管生成式 AI（Generative AI）驱动的分子语言模型已被广泛用作分子编码器以学习结构-性质关系，但其在不同化学领域中的泛化能力与表现差异尚未系统评估。研究的关键解决方案在于通过系统性基准测试四种分子语言模型在涵盖药物发现、有机材料和催化等领域的六个虚拟分子库上的表现，揭示原始模型嵌入在不同库中表现差异显著，而分子指纹则提供了一致且稳健的基线性能。研究进一步表明，存在潜在的领域表示不匹配问题，通过在目标虚拟库结构上对分子语言模型编码器进行微调，可显著提升表示性能与样本效率，部分适配后的模型在多个基准任务中成为最优表现者。因此，研究证明了分子表示质量高度依赖于目标领域，而显式的领域适应策略能有效提升分子基础模型在虚拟筛选与自驱动实验室中的实际应用价值。
链接: https://arxiv.org/abs/2608.17567
作者: Henrik Wille,Luis-Finley Schütz,Felix Strieth-Kalthoff
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Pretrained molecular language models are increasingly used as molecular encoders for learning structure-property relationships. However, their practical suitability for molecular discovery within and beyond their pretraining domain remains unclear. Herein, we systematically benchmark four molecular language models across six virtual molecular libraries spanning drug discovery, organic materials, and catalysis. Native molecular language model embeddings show substantial variation in discovery performance across libraries, whereas molecular fingerprints provide a consistently strong and robust baseline. Consistent with a potential domain-representation mismatch, we show that explicit domain adaptation substantially improves representation performance. Fine-tuning molecular language model encoders on structures from the target virtual library consistently improves sample efficiency, with several adapted encoders emerging as the top-performing representations across the benchmark tasks. These results show that molecular representation quality depends strongly on the target domain and that explicit adaptation can improve the practical utility of molecular foundation models. More broadly, our findings establish domain-adapted molecular representations as a promising strategy for sample-efficient adaptive decision making in virtual screening and self-driving laboratories.
[NLP-30] Reflex-Guard: A Low-Latency Guardrail for LLM Prompt Safety Using Dense Semantic Embeddings
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在实际应用中面临的安全风险，即用户通过精心设计的提示（prompt）绕过安全防护机制的问题。现有防护方法如基于LLM作为裁判（LLM-as-a-judge）或依赖云端安全API虽能有效检测不安全内容，但普遍存在延迟过高（约250–900毫秒）和数据隐私泄露风险等问题，难以满足实时性要求（响应时间需低于100毫秒）。为此，本文提出一种轻量级本地运行的防护方案——Reflex-Guard，其核心在于采用狱变感知预处理（jailbreak-aware preprocessing）、紧凑型句向量嵌入（compact sentence-transformer embeddings）以及七个快速二分类器的协同架构。该设计实现了高精度提示安全性过滤，同时将端到端延迟降低至37.6毫秒，显著优于Llama Guard 2（255毫秒）和SafeDecoding（723毫秒）。实验表明，Reflex-Guard在30,568个样本的平衡数据集上达到95.9%的有害提示召回率，并可完全检测GCG后缀攻击与Base64编码提示（默认阈值下），而针对结构化攻击（DrAttack）则需将阈值调至0.03以实现最优检测，反映出不同攻击类型在嵌入概率空间中具有可区分的分布特征。此外，引入的反射效率评分（Reflex Efficiency Score, RES）显示，Reflex-Guard最高达16.79，显著优于对比基线，验证了其在性能与效率之间的优越平衡。
链接: https://arxiv.org/abs/2608.17556
作者: Istiaque Ahmed,Afia Anjum Borsha,Ranat Das Prangon,Abu-fuad Ahmad,Thi Hong Tran
机构: Osaka Metropolitan University (大阪都立大学); BRAC University (BRAC大学); Bangladesh University of Engineering and Technology (孟加拉国工程技术大学); New Mexico State University (新墨西哥州立大学)
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Large Language Models (LLMs) in real-world applications often face the risks of specially crafted prompts designed to bypass the safety controls. Existing guardrail methods, such as LLM-as-a-judge and cloud-based safety APIs are able to detect unsafe content. However, they often add a delay of about 250-900 ms to each request. This delay is too high for real-time applications, when the system usually needs to respond in less than 100 ms. Furthermore, routing user prompts through external moderation endpoints raises significant data privacy concerns. This paper introduces Reflex-Guard, a lightweight guardrail that runs locally. It uses jailbreak-aware preprocessing, compact sentence-transformer embeddings, and seven fast binary classifiers. Together, these components enable high-accuracy prompt safety filtering with much lower latency than existing solutions. Through systematic evaluation on a strategically balanced dataset of 30,568 samples drawn from five complementary sources, we demonstrate that Reflex-Guard achieves 95.9% recall on harmful prompts at 37.6 ms end-to-end latency. It is faster than existing baselines, including Llama Guard 2 at 255 ms and SafeDecoding at 723 ms. It can detect 100% of GCG suffix attacks and Base64-encoded prompts using the default threshold. However, DrAttack structured prompts required lowering the threshold to 0.03 for optimal detection, as they produced a distinct probability distribution. Reflex-Guard achieves Reflex Efficiency Score (RES) scores up to 16.79, significantly outperforming Llama Guard 2 (11.90) and SafeDecoding (9.80). This analysis offers practical deployment advice and shows that different attack types occupy distinct regions in the embedding probability space.
[NLP-31] Code as Representation: A Compilable Parsing Paradigm for Academic Documents ACM-MM2026
【速读】： 该论文旨在解决科学文献中结构化学术元素（Structured Academic Elements, SAEs）在机器可读性方面严重受限的问题。当前大多数学术论文以PDF格式存在，虽适合人类阅读，但其内部的表格、公式、图表及伪代码等关键信息在转换为通用表示形式（如Markdown）时，常导致结构、数据与逻辑的丢失，阻碍了多模态大语言模型（Multimodal Large Language Models, MLLMs）对科学内容的准确理解与执行。为此，论文提出“可编译学术文档解析”（Compilable Academic Document Parsing, CADP）范式，其核心在于将整页学术内容重构为包含上下文感知的LaTeX与可执行Python代码的联合表示，从而实现对结构化元素的保真重建、重新编译及与原始页面的直接验证。该方案的关键创新在于引入“可重注回编译验证”机制，通过CADP-Bench这一专家验证的基准数据集，系统评估模型生成结果的准确性与可执行性。实验表明，即使采用前沿的MLLMs，仍难以生成高保真的可执行重构内容，凸显当前模型在结构感知与科学文档解析方面存在显著提升空间。
链接: https://arxiv.org/abs/2608.17550
作者: Rihui Jin,Jun Wang,chengyuan zhu,Liang Mingyu,Yue Gao,Li Yunxuan,Kuicai Dong,Guilin Qi,Lin Ren,Yongrui Chen,Xinbang Dai,Jiaqi Li,Tongtong Wu,Gholamreza Haffari
机构: Southeast University (东南大学); Nanyang Technological University (南洋理工大学); Nanjing University (南京大学); Monash University (墨尔本皇家理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注: Accepted by ACM MM 2026
Abstract:Academic papers are a primary carrier of scientific knowledge, yet most of this knowledge remains locked in PDFs that are optimized for human reading rather than machine use. For Multimodal Large Language Models (MLLMs), the core challenge is not only perception, but representation: scientific pages interleave text with Structured Academic Elements (SAEs) such as tables, formulas, charts, and pseudocode, whose structure, data, and logic are poorly preserved by common surrogates like Markdown. We therefore propose Compilable Academic Document Parsing (CADP), a paradigm that reconstructs a full page as contextual \LaTeX plus executable Python, so that structure-preserving elements and executable chart representations can be reconstructed, recompiled, and directly verified against the source page. To support this setting, we introduce CADP-Bench, an expert-verified benchmark of full academic pages containing tightly coupled text and multiple SAE types, evaluated through a re-injection compilation protocol. We further study current capabilities using SOTA MLLMs and an exploratory multi-agent baseline that incorporates common agentic techniques. Results show that even frontier models still struggle to produce high-fidelity executable reconstructions, highlighting substantial room for improvement in structure-aware scientific document parsing. CADP-Bench is released for future research.
[NLP-32] CoAL-RAG : A Complexity-Aware Legal Retrieval-Augmented Generation Method
【速读】： 该论文旨在解决法律咨询问答中因问题复杂度多层级而引发的检索与生成难题：单一检索策略在简单问题上易导致过度推理，在复杂问题上则表现可解释性差，难以兼顾高风险场景下对答案质量与响应效率的双重需求。其解决方案的关键在于提出一种面向复杂度感知的法律检索增强生成方法（CoAL-RAG），通过构建基于“问题本质”（question essence）与“检索一致性”（retrieval consistency）的多维评估机制，实现检索策略的自适应路由。具体而言，该方法首先依据问题的逻辑结构量化推理需求；进而利用语义检索与关键词检索结果间的差异，间接表征问题复杂度，从而动态选择最优检索策略并精细化过滤上下文信息。实验表明，CoAL-RAG在中文法律基准（SocialLawQA、LawBench）上显著优于基线模型，BLEU得分提升42.5%，ROUGE-L指标达基于知识图谱方法的3.6倍；在英文跨司法管辖区数据集（LexGLUE、CaseHold）上亦展现出强泛化能力，实现了生成质量、深层逻辑推理与系统效率之间的最佳平衡。
链接: https://arxiv.org/abs/2608.17536
作者: Jin Su,Zhuofeng Zhao,Huanhuan Wang,Hao Chen
机构: North China University of Technology (华北理工大学); Beijing Key Laboratory of Key Technologies for AI+ Domain Applications (北京市人工智能领域应用关键技术重点实验室); Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data (北京市大规模流数据融合与分析重点实验室)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Legal consultation questions exhibit multi-level complexity. A single retrieval strategy often leads to over-reasoning for simple questions and poor interpretability for complex ones, making it difficult to meet the requirements for both answer quality and efficiency in high-risk scenarios. To address this issue, this paper proposes CoAL-RAG, a complexity-aware legal retrieval-augmented generation method, which constructs a multi-dimensional evaluation mechanism based on question essence'' and retrieval consistency’’ to enable adaptive routing of retrieval strategies. First, the reasoning demand is quantified according to the logical structure of the question. Then, the discrepancy between semantic retrieval and keyword retrieval is utilized to indirectly reflect problem complexity, thereby selecting the most appropriate retrieval strategy and dynamically filtering contextual information. Experimental results demonstrate that the proposed method significantly outperforms baseline models not only on Chinese legal benchmarks (SocialLawQA, LawBench) but also demonstrates strong cross-jurisdictional generalization on English datasets (LexGLUE, CaseHold). Specifically, on Chinese datasets, the BLEU score improves by 42.5% and ROUGE-L reaches 3.6 times that of knowledge graph-based methods. On English benchmarks, CoAL-RAG maintains highly competitive accuracy, achieving an optimal balance between generation quality, deep logical reasoning, and system efficiency across different legal systems.
[NLP-33] ArborMem: Navigating Interaction States with Memory Forests
【速读】： 该论文旨在解决大语言模型在作为持续性对话助手时，如何有效管理长期对话记忆以维持跨轮次交互连续性的问题。现有方法虽通过长上下文处理、选择性检索和结构化记忆组织提升了历史信息的可访问性，但普遍忽视了在新对话轮次中识别当前应恢复的先前交互状态这一关键步骤，导致在多任务、多参与者及多计划交错进行且可能被中断后重续的复杂场景下，出现语义相关但结构上不同的对话轨迹混淆。为此，论文提出ArborMem，一种在线记忆框架，将长期对话建模为可导航的“交互状态森林”（forest of interaction states），其中每条分支代表一个局部连贯的对话轨迹，而整个森林则支持多个轨迹并行存在与后续恢复。其核心创新在于：针对每个新输入，系统首先定位最相关的状态节点，恢复该分支局部上下文，并跨分支检索可复用证据进行增强，从而在不混淆结构差异的前提下保持对话连续性。为评估此类分支结构挑战，研究进一步构建了BranchMemEval——一个专门用于测试交错与可恢复对话轨迹的受控诊断基准。实验结果表明，ArborMem在LongMemEval、LoCoMo、BEAM 100K及BranchMemEval等多个基准上均显著优于最强基线，性能提升达3.36至10.31个百分点，在读取预算受限条件下优势更为明显，且完整内存查询响应时间始终低于0.5秒。
链接: https://arxiv.org/abs/2608.17534
作者: Zongwei Lv,Yuemeng Xu,Yilun Yao,Siyi Ding,Xinyu Tan,Yaoming Li,Guangxiang Zhao,Weihong Lin,Lin Sun,Xiangzheng Zhang,Tong Yang
机构: 未知
类目: Computation and Language (cs.CL)
备注: 24 pages, 2 figures
Abstract:Large language models increasingly serve as persistent conversational assistants, requiring memory that preserves relevant experience and maintains continuity across interactions. Existing methods improve access to conversational history through long-context processing, selective retrieval, and structured memory organization. However, most systems treat memory access as retrieving relevant past information without first determining which prior interaction state the current turn resumes. This limitation becomes particularly important when conversations interleave multiple tasks, people, and plans that may be interrupted and later revisited. We introduce ArborMem, an online memory framework that represents a long-running conversation as a navigable forest of interaction states. Each branch preserves a locally coherent trajectory, while the forest maintains multiple trajectories that may later be resumed. For each new input, ArborMem localizes the relevant state, restores its branch-local context, and augments it with reusable evidence retrieved across branches, preserving interaction continuity without conflating semantically related but structurally distinct trajectories. Existing long-term memory benchmarks cover diverse memory and reasoning capabilities but do not explicitly isolate branch-structured challenges. We therefore introduce BranchMemEval, a controlled diagnostic benchmark for interleaved and resumable interaction trajectories. Experiments on LongMemEval, LoCoMo, BEAM 100K, and BranchMemEval show that ArborMem outperforms the strongest baselines by 3.36 to 10.31 percentage points on the three established benchmarks and by 5.0 points on BranchMemEval. Its advantage grows under constrained read budgets, while complete memory queries remain below half a second.
[NLP-34] Effects of Answer Format Variation on Gender Bias in Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）中性别偏见等社会偏见在现有评估方法中存在的测量不稳定性问题，尤其关注答案格式（answer format）对偏见测量结果的影响。传统评估多依赖于封闭式、李克特量表或开放式回答的问答基准，但这些格式本身可能显著影响模型输出行为与人类响应分布的一致性。研究发现，在相同指令条件下，不同答案格式（如闭合式、李克特量表式、开放式）会引发模型产生截然不同的响应模式——包括强制选择、量表分布倾向以及自由文本生成中的拒绝回应——从而导致偏见测量结果发生显著变化，甚至出现排名反转。其解决方案的关键在于将答案格式视为评估设计中的核心变量，强调采用多格式混合评估范式以实现更稳健、可比且具生态效度的模型性能衡量，进而提升对模型社会偏见及其与人类认知对齐程度的准确刻画。
链接: https://arxiv.org/abs/2608.17516
作者: Ksenia Merzlyakova,Sebastian Padó,Franziska Weeber
机构: Institute for Natural Language Processing, University of Stuttgart(斯图加特大学自然语言处理研究所)
类目: Computation and Language (cs.CL)
备注: 6th Workshop on Computational Linguistics for the Political and Social Sciences (CPSS 2026)
Abstract:Gender bias or other social biases in large language models (LLMs) are frequently evaluated with question answering or survey benchmarks where the LLM needs to give a response in a predefined answer format. It is well known in survey science that the answer format has a substantial impact on answers, just as LLMs are sensitive to the prompt wording. However, to our knowledge it has not been studied yet how changes in answer format impact the measurement of gender bias in LLMs and their alignment with human response distributions. We evaluate three instruction-tuned models on the BBQ benchmark and OpinionQA survey data across closed-ended, Likert-scaled and open-ended formats, comparing bias measurement and distributional alignment under otherwise identical conditions. We find that answer format does substantially alter measured outcomes, including reversals in order rankings. These differences arise because each format elicits distinct response behaviours, such as forced-choice selection, scale-based distributions and refusal in free-text generation. Our findings highlight the importance of treating answer format as a substantive component of LLM evaluation and motivate multi-format designs for more robust model assessment.
[NLP-35] From Entity Mentions to Tone: An LLM -Based Pipeline for Media Bias Analysis
【速读】： 该论文旨在解决在线新闻中媒体偏见（media bias）与框架建构（framing）的自动化分析问题，尤其针对缺乏人工标注数据集或专用语言处理工具的场景。其核心解决方案是构建一个端到端的分析流水线，通过将新闻文章聚类至主题与事件层面，添加命名实体（named-entity）与情感（sentiment）注释，并基于人物提及频率、源级语调（source-level tone）以及事件级报道模式进行跨媒体比较。该方法在8,358篇阿尔巴尼亚语新闻文章上进行了验证，结果表明其自动标注与GDELT原始标注之间在情感识别和实体抽取方面达到中等一致性，并识别出额外的人物-实体关联对，有助于深化偏见分析。研究还对比了两种注释提示策略，发现更严格的语义验证规则虽可减少标签-评分不一致，但显著增加执行时间并降低覆盖范围，因此最终采用更为简洁的提示方案以平衡效率与覆盖率。最终输出包括源级框架特征、跨来源人物语调差异及事件级把关机制与覆盖指标，展示了同一数据集在揭示媒体报道内容、描述公共人物方式及报道集中度方面的多维分析潜力。该方法为资源受限环境下开展系统性媒体偏见研究提供了可行路径。
链接: https://arxiv.org/abs/2608.17454
作者: Klesti Hoxha,Olti Qirici
机构: University of Tirana (地拉那大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:This paper presents a pipeline for analyzing media bias and framing in online news. The pipeline groups articles into topics and events, adds named-entity and sentiment annotations, and compares news sources through people mentions, source-level tone, and event-level coverage patterns. We apply it to 8,358 Albanian news articles collected from GDELT and compare the resulting annotations with GDELT’s automated annotations. The results show moderate agreement for sentiment and entity extraction, as well as additional person-entity pairs that can potentially support the bias analysis. We compare two annotation prompts and find that stricter sentiment-validation rules remove label-score inconsistencies but increase execution time and reduce annotation coverage. Based on these results, the simpler prompt is used for the rest of the analysis. We have provided sample analysis on source-level framing pro les, person-level tone differences across sources, and event-level gatekeeping and coverage indicators. These outputs show how the same news collection can be used to examine what sources cover, how they describe public figures, and where coverage is concentrated. The approach is particularly useful in settings where manually verified datasets or specialized language tools are limited.
[NLP-36] Decomposition Attacks Across Unlinkable Identities: Limits of Stateful Defenses for LLM Services
【速读】： 该论文旨在解决生成式 AI (Generative AI) 服务中因采用无状态防御机制而面临的分解攻击（decomposition attack）问题。无状态防御仅基于单个请求进行判断，导致攻击者可通过将有害任务拆分为多个看似合法的独立请求并聚合其响应来规避检测。为应对这一挑战，论文提出需依赖有状态监控机制以识别同一攻击任务下的相关请求序列，从而实现有效拦截。然而，当攻击者使用不可关联的身份（unlinkable identities）并在外部组合答案时，缺乏可靠的请求分组信号，使得传统分组策略失效。研究证明，在固定攻击策略且不重试的情况下，防御的有效性完全取决于对相同功能能力的良性请求如何分组：若能建立持久且可识别的请求群组，则可实现有效的防御；反之，若群组为全新且难以区分，则无法构建可靠防御。当攻击者可重试并根据允许/拒绝反馈动态调整策略时，即便存在精确请求-操作映射的特权策略，也因反馈信息仅揭示“通过”而非“是否正确阻止”，导致安全与效用之间的最优平衡点消失。实验在91个可执行任务和11,393个能力匹配的良性请求上验证了上述结论，结果显示，在1%良性请求拒绝率上限和0.5%无关背景流量拒绝率上限下，所有十种测试策略均无法同时满足防攻击效果与预算限制。对于未见过的攻击任务类别，一次尝试后攻击成功率不低于99%，两次尝试后达100%。因此，真正有效的防御必须引入额外的分组依据，如可信身份绑定、新身份成本机制或对输出使用的控制等。
链接: https://arxiv.org/abs/2608.17445
作者: Bowen Sun,Zhengyue Zhao,Xiaogeng Liu,Yinzhi Cao,Chaowei Xiao
机构: Johns Hopkins University (约翰霍普金斯大学)
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL)
备注:
Abstract:Most large language model services use stateless defenses, which judge only the current request, to refuse harmful tasks. Decomposition attacks exploit this limitation by splitting a harmful task into individually permissible requests and combining their answers. Defending against them therefore requires a stateful monitor that considers requests together. If it can group all requests for one attacker task, it can stop the attack. However, attackers can use unlinkable identities and combine answers elsewhere, leaving no reliable grouping signal. We ask whether decomposition attacks can still be stopped under this setting. For a fixed attack strategy without retries, we prove that the achievable security and utility tradeoff depends entirely on how benign requests for the same capabilities are grouped. Persistent, recognizable groups permit a useful defense; fresh, indistinguishable groups do not. When attackers can retry and learn from Allow/Block decisions, this useful operating point disappears: the feedback reveals what passes but not whether a block was correct. Experiments on 91 executable tasks and 11,393 capability-matched benign requests support these results. Under a 1% denial cap for these requests and a 0.5% cap for unrelated background traffic, all ten tested policies, including one privileged policy with an exact request-to-operation map, either fail to stop attacks or exceed the budget. On defense-unseen task families, attack success is at least 99% after one attempt and 100% after two. Effective defenses therefore require additional evidence or mechanisms tied to grouping, such as reliable identity linkage, costs for fresh identities, or control over answer use.
[NLP-37] An Investigation of Translationese in the Generations of Multilingual Large Language Models
【速读】： 该论文旨在解决多语言大语言模型（MLLMs）生成文本是否表现出译文语体（translationese）特征的问题，以及其生成的译文语体与人工直接翻译产生的译文语体之间的差异。核心问题在于：MLLM生成文本是否在语言特征上呈现出翻译痕迹，从而具有典型的译文语体特征？其解决方案的关键在于综合运用已有的译文特征指标，通过高精度分类模型、基于个体语言特征的方差分析，以及对德语和西班牙语子集的人工标注，系统评估MLLM生成文本中的译文语体含量，并识别出区别于传统翻译干扰的关键语言特征。研究结果揭示了MLLM生成文本虽具译文语体特征，但其表现形式与人工翻译存在显著差异，表明生成过程中的“内部翻译”机制可能并非完全等同于人类翻译行为。
链接: https://arxiv.org/abs/2608.17399
作者: Maria Valentini,Téa Wright,Julisa Granados,Eliana Colunga,Katharina von der Wense
机构: University of Colorado Boulder(科罗拉多大学博尔德分校); University of California Berkeley(加州大学伯克利分校); Johannes Gutenberg University Mainz(美因茨约翰内斯古腾堡大学)
类目: Computation and Language (cs.CL)
备注: Accepted to COLM 2026
Abstract:Text which has been translated from another language tends to carry with it evidence of translation \unicodex2014 hence, it is often referred to as \textittranslationese . Multilingual large language models (MLLMs) generate text in a variety of languages. However, it is still unclear if MLLMs’ generations resemble internal translation (from English or, potentially, other languages) and, thus, result in translationese. Here, we ask the following research questions: (1) Does text generated by MLLMs resemble translationese? (2) How does translationese produced by MLLMs differ from translationese produced through direct translation? We leverage established indicators of translated text to evaluate text generated by state-of-the-art MLLMs in five languages, comparing to both non-translated and human-written baselines in order to isolate translationese from other kinds of interference. Through the use of high-accuracy classification models, analyses of variance on individual linguistic features, and the collection of human annotations in a subset of two languages (German and Spanish), we assess the translationese content of MLLM generations and examine the key features that distinguish MLLM-generated text from typical translation-related interference.
[NLP-38] PTXBench: Benchmark and Adapt LLM s for GPU Kernel Optimization with Architecture-specific PTX
【速读】： 该论文旨在解决大语言模型（LLM）在生成针对特定GPU架构优化的PTX（Parallel Thread Execution）代码时，其功能性正确性与实际性能提升之间的不一致性问题。当前，尽管部分模型可生成符合语法规范的架构特定PTX指令，但这些指令在复杂工作负载（如H100和B200 GPU上的GEMM与注意力反向传播）中往往无法有效执行或未能带来显著加速，反映出现有模型在理解底层硬件语义、生成高效代码以及泛化能力方面的局限性。解决方案的关键在于构建一个可审计的基准测试平台——PTXBench，该平台不仅评估生成代码的功能正确性（即目标指令是否在运行时成功执行），还量化其相对于前沿库的加速比。在此基础上，论文进一步采用监督微调对Qwen3.6-27B模型进行适配，并引入修复条件训练策略以提升关键任务表现；研究表明，模型性能的改进不仅依赖于数据规模，更受训练数据覆盖度、分布均衡性及推理教师质量等多重因素影响。因此，该研究的核心贡献在于揭示了当前生成式AI在深度利用新兴GPU架构潜力方面的瓶颈，并为后续模型优化提供了可量化的评估框架与改进方向。
链接: https://arxiv.org/abs/2608.17379
作者: Genghan Zhang,Yixin Dong,Chengze Fan,Zhichen Zeng,Yueming Yuan,Shaowei Zhu,Kunle Olukotun
机构: Carnegie Mellon University (卡内基梅隆大学); RadixArk; Stanford University (斯坦福大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:We introduce PTXBench, a benchmark for evaluating and adapting large language models (LLMs) to use architecture-specific PTX for GPU kernel optimization. PTXBench measures functional correctness, whether selected target instructions execute at runtime, and speedup over frontier libraries across GEMM and attention workloads on H100 and B200 GPUs. Our evaluation shows that architecture-specific PTX capability remains uneven: success rates fall substantially on complex attention backward workloads, and executing the target instructions does not necessarily translate into competitive performance. No evaluated model consistently matches frontier libraries across the suite. We further adapt Qwen3.6-27B using supervised fine-tuning. Repair-conditioned training improves several tasks, but generalization remains uneven; data coverage, balance, and the quality of the reasoning teacher matter in addition to dataset size. PTXBench provides an auditable testbed for measuring and improving LLMs’ ability to exploit evolving GPU architectures.
[NLP-39] ArguLens: An Open-Source System for Automated Essay Scoring and Label-Aware Feedback Generation
【速读】： 该论文旨在解决现有自动化作文评分（AES）系统普遍存在的两大核心问题：一是仅输出单一整体分数而缺乏可解释的证据支持，二是依赖封闭式API接口导致数据隐私风险和使用成本高昂。为此，提出了一种开源且可本地部署的ArguLens系统，其关键创新在于将传统端到端的评分流程解耦为三个独立模块：基于LoRA微调的Qwen2.5-7B-Instruct discourse-move分类器、不依赖评分标签的轻量级LightGBM打分器（基于31个语言学与论述特征）、以及基于Qwen2.5-14B-Instruct大模型通过vLLM服务的标签感知反馈生成器。该架构实现了模块化设计与可插拔推理后端，支持单篇与批量评分，并提供可下载的逐篇分析报告。实验结果显示，在PERSUADE 2.0数据集上，该系统在判别性任务中达到82.6%准确率与0.727宏平均F1；在提示分组的五折交叉验证下，打分器在理想论述特征条件下实现0.813的均值加权肯德尔等级相关系数（QWK），且引入真实论述标注后相较仅使用词汇与句法特征配置提升0.055 QWK（配对t检验，p = 0.010），验证了模块化诊断的有效性。该系统以组件级评估而非端到端性能为核心目标，其反馈生成模块已配备结构化评估协议，人类评分员研究留待后续工作。系统已按Apache 2.0许可开源发布。
链接: https://arxiv.org/abs/2608.17356
作者: Weiran Wang,Hongxiang Shi,Huitao Tang,Wenjuan Qin
机构: Fudan University (复旦大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Most automated essay scoring (AES) systems output a single holistic score without interpretable evidence and rely on closed APIs that introduce data privacy and cost barriers. We present ArguLens, an opensource, locally deployable system that decomposes AES into three decoupled components: a discourse-move classifier (Qwen2.5-7B-Instruct fine-tuned with LoRA on PERSUADE 2.0), a grade-independent LightGBM scorer over 31 linguistic and discourse features, and a label-aware feedback generator served through vLLM with a Qwen2.5-14BInstruct backbone. A Gradio web UI exposes pluggable inference backends and supports single-essay and batch scoring with downloadable per-essay breakdowns. On an essaydisjoint PERSUADE 2.0 test split, the logitprobe classifier achieves 82.6% accuracy and 0.727 macro-F1; under prompt-grouped 5-fold cross-validation the scorer reaches a mean QWK of 0.813 under an oracle discoursefeature protocol, and an ablation shows that adding gold discourse annotations yields an increment of +0.055 QWK over the lexical+syntactic configuration (paired t-test, p = 0.010). This is a component-level diagnostic rather than an end-to-end classifier-to-scorer result. The feedback generator ships with a structured evaluation protocol; its human-rater study is left to future work. The system is released under Apache 2.0 at this https URL.
[NLP-40] LLM s for Medical Consultation Are Evaluated Too Late: The Preformulation Gap
【速读】： 该论文旨在解决当前医疗咨询类大语言模型在评估时普遍依赖于已明确临床问题的设定，而忽视了真实诊疗场景中患者初始表达常为模糊、简化或错误表述的问题。其核心挑战在于现有评估范式无法有效捕捉模型在“首诊接触阶段”（preformulation gap）的表现，即在患者尚未清晰陈述症状前，模型是否能主动引导信息获取并提供合理建议。解决方案的关键在于引入“进入照护指令条件”（entry-to-care instruction conditions），通过结构化指令干预模型行为，使其从早期即转向以系统性问诊和规范记录为导向的交互模式。实验结果表明，该指令显著改变了模型的响应顺序与文档生成方式：在基准条件下，9/12案例中模型过早给出自我照护建议，而在指令条件下，10/12案例中生成了结构化的转诊摘要，但仍未确保关键事实的可靠采集。因此，研究强调应直接观察模型在首次接触中的行为表现，而非仅依赖诊断准确率或最终答案质量进行推断，从而更真实地反映其在实际临床初诊场景中的可用性。
链接: https://arxiv.org/abs/2608.17330
作者: Yining Hua,Cyrus Ayubcha,Hongbin Na,Levi Lian,Alon Gorenshtein,Yiftach Barash,Eyal Klang
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: 17 pages, 3 tables. Code, cases, prompts, complete transcripts, and results: this https URL
Abstract:Large language models for medical consultation are often evaluated after a clinical problem has already been made clear, although real consultations may begin with a vague, minimized, or misframed concern. We evaluated three API models across four physician-authored, multi-turn vignettes under baseline and entry-to-care instruction conditions, yielding 24 fixed-script transcripts; two cases also used adaptive standardized-patient simulation, yielding 12 transcripts. Self-care or home-management advice before any patient answer appeared in 9 of 12 baseline case-model cells and 0 of 12 instruction cells, while structured handoff summaries appeared in 0 of 12 and 10 of 12 cells, respectively. The instruction changed sequencing and documentation, although it did not reliably ensure elicitation of decisive facts. The preformulation gap should therefore be evaluated directly through observable first-contact behavior rather than inferred from diagnostic accuracy or final-answer quality.
[NLP-41] What Tokens are Learned when Tokenization is Optimized Jointly with Language Modeling?
【速读】： 该论文旨在解决传统语言模型中分词（tokenization）固定化所带来的局限性问题，尤其是在跨语言场景下，固定的分词策略难以适应不同语言的形态结构与书写系统差异，从而影响模型性能。其核心解决方案在于通过将分词过程与语言建模进行联合优化，探索无需预定义分词器的端到端学习范式。关键创新在于：在联合优化框架下，模型能够自适应地学习具有上下文高效性和形态对齐特性的分词结构；具体而言，自监督语言模型（SSLMs）倾向于生成与词素结构一致且上下文高效的分词，而H-Nets则更注重字节级计算效率，产生与标准子词词汇重叠极低的长分词。实验表明，这种动态分词机制在18种类型和书写系统多样的语言中表现稳定，尤其在屈折型和黏着型语言中展现出更强的分段灵活性。下游任务评估显示，基于SSLM的预分词方法虽使用独特词汇表，仍能显著降低语言建模困惑度，并实现与传统方法相当甚至更优的下游性能，表明无分词器方法本质上优化的是上下文与计算效率，而非严格的形态结构，从而生成了根本不同但同样有效的词汇体系，为跨语言自然语言处理提供了新的范式。
链接: https://arxiv.org/abs/2608.17325
作者: Saketh Reddy Vemula,Parameswari Krishnamurthy
机构: IIIT Hyderabad(印度国际信息技术学院); India(印度)
类目: Computation and Language (cs.CL)
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Abstract:Tokenization is a fundamental component of language modeling pipelines. Despite its importance, it is often fixed, even though it significantly impacts model performance across languages. In this work, we analyze what tokens are learned when tokenization is jointly optimized with language modeling. We compare tokenizer-free approaches such as SSLMs and H-Nets with fixed tokenizers across 18 typologically and script-diverse languages. Our results show that joint optimization fundamentally alters token structure. SSLMs recover morphologically aligned and contextually efficient tokens, whereas H-Nets prioritize byte-level efficiency, producing longer tokens with very low overlap with standard subword vocabularies. We further show that tokenization behavior varies across language typologies. Agglutinative languages exhibit more dynamic segmentation patterns while learning. Through downstream evaluation, with pretrained-then-finetuned BERT models, we find that SSLM-based pretokenization consistently reduces language modeling perplexity and achieves competitive downstream performance despite distinct vocabularies. Overall, tokenizer-free approaches optimize for contextual and computational efficiency rather than strict morphological structure, resulting in fundamentally different yet effective vocabularies for downstream NLP.
[NLP-42] Q-Interference: Memory-Efficient Phase-Aware Quantum-Inspired Attention
【速读】： 该论文旨在解决生成式AI（Generative AI）中传统注意力机制仅依赖点积相似性衡量标记（token）兼容性，而无法显式建模强标记特征之间应相互增强或抑制的问题。其核心解决方案是提出Q-Interference，一种全经典量子启发的注意力机制，通过为每个查询（query）和键（key）特征引入可学习的振幅与相位，使注意力得分具备相位感知能力：同相位成分产生建设性干涉，反相位成分则引发破坏性干涉，从而实现更丰富的交互规则。然而，直接实现Q-Interference需构建大规模的标记对-特征交互张量，导致内存开销过大。为此，本文提出一种精确的三角函数分解方法，仅通过两次标准矩阵乘法即可计算等效注意力得分，避免了大中间张量的显式构造，实现了内存高效化。该方法可无缝嵌入GPT的Transformer模块中，保持原有模型架构与下一标记预测目标不变。实验表明，该重构在标准GPT设置下训练稳定，并在多个公开基准数据集上展现出一致的内存优势，验证了其核心贡献——即为相位感知干涉注意力提供了一种精确且内存高效的可落地实现方案。
链接: https://arxiv.org/abs/2608.17288
作者: Emama Nahid,Tahmid Imtiaz Imu,Huayue Gu,Liran Ma,Zhipeng Cai,Honghui Xu
机构: Kennesaw State University (肯尼索州立大学); Miami University (迈阿密大学); Georgia State University (佐治亚州立大学)
类目: Computation and Language (cs.CL)
备注: Preprint
Abstract:GPT attention measures token compatibility through dot-product similarity. This mechanism is simple, effective, and memory-efficient. But it does not explicitly model whether strong token features should reinforce or suppress one another. We introduce Q-Interference, a fully classical quantum-inspired attention mechanism for autoregressive language modeling that augments each query and key feature with an amplitude and a learned phase. The resulting attention score is phase-aware which aligned phases contribute constructively while conflicting phases contribute destructively. Although Q-Interference yields a richer interaction rule than similarity alone, a naive implementation of Q-Interference requires a large token-pair-feature interaction tensor, making it memory-intensive and often impractical. To address this limitation, we propose an exact trigonometric factorization that computes the same score using two standard matrix multiplications avoiding materialization of the large intermediate tensor. Q-Interference fits directly into a Transformer block in GPT and leaves the remainder of the model architecture and next-token prediction objective unchanged. Experiments on public benchmark datasets and baseline models show that the proposed reformulation trains stably in a controlled GPT-style setting and provides a consistent memory advantage over naive phase-aware interference attention. These results support the specific contribution of this work: an exact memory-efficient reformulation that makes phase-aware interference attention practical within a standard GPT pipeline.
[NLP-43] mporal Leakage in Financial News NLP: A Multi-Architecture Audit with a Regime-Specific MA Signal
【速读】： 该论文旨在解决金融新闻方向预测任务中因训练-测试划分方式（时间顺序分割与随机分割）不同而导致评估结果严重失真的问题，即时间泄漏（temporal leakage）对模型性能评价的干扰。其核心问题是：现有金融自然语言处理（NLP）基准在采用随机分割时报告的性能增益，很大程度上源于信息泄露，而非模型真正具备对未来事件的预测能力。解决方案的关键在于引入严格的时间顺序划分（chronological splitting），并通过多维度验证——涵盖从传统特征（TF-IDF）到先进预训练模型（MiniLM、FinBERT、RoBERTa-large、DeBERTa-v3-large）以及大语言模型（LLM）的零样本/少样本和LoRA微调探针——系统性地审计各模型在防止时间泄漏下的真实表现。研究发现，随机分割可使马修相关系数（MCC）提升1.1至6.5倍，且模型容量与特征丰富度越高，膨胀效应越显著；即使经过端到端的FinBERT微调，也无法消除这一差距（大小匹配比率仍达1.75倍）。唯一在近似时间顺序评估下呈现正向锁定信号的是并购（M&A）事件类别，但该信号仅限于特定语义范围（2024–2025年欧洲导向的并购标题），不具备跨数据集泛化能力。三组独立的角色标注器一致指向“收购方”标记的文章为信号来源，表明该信号具有定性一致性，但尚无充分证据支持其因果机制。最终，论文强调时间顺序分割在金融NLP中的作用类似于资产定价中剔除特征污染，能够剥离可预测的陈旧信息，保留小规模、事件局部化且词汇浅层的残差信号。因此，作者主张将时间泄漏审计作为金融-NLP基准发布时的强制披露要求。
链接: https://arxiv.org/abs/2608.17223
作者: Chenhao Xue,Raslen Guesmi,Siwei Feng,Yucheng Gong,Jacob Xavier Sundram,Jordan Pang,Lan Wang,Julian Kaljuvee
机构: Predictive Labs Ltd(预测实验室有限公司); University of Oxford(牛津大学); Imperial College London(帝国理工学院); Independent Researcher(独立研究员)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Financial-news direction prediction has become a popular NLP benchmark, yet reported gains depend critically on whether the train-test split is chronological or random, i.e., on temporal leakage. We audit this dependence on a 49,799-article corpus across 16 feature-model combinations spanning TF-IDF, MiniLM, FinBERT, and fine-tuned RoBERTa-large / DeBERTa-v3-large, plus separate zero/few-shot and LoRA probes of Llama-3 and Qwen2.5 LLMs: random splits inflate MCC by 1.1\times to 6.5\times , tracking model capacity and feature richness, and end-to-end FinBERT fine-tuning re-amplifies rather than closes the gap (size-matched ratio 1.75\times ). Conditioning on event type, mergers and acquisitions (MA) is the only audited category with a positive locked-test signal under near-temporal chronological evaluation (TF-IDF MCC = 0.138 train-only, 0.068 under train \cup val refit; 10,000-permutation p 10^-3 ); the signal does not transfer to FNSPID’s 2009-2020 U.S. corpus, localising the headline to our 2024-2025 European-tilted MA semantics rather than a universal predictor. Three independent role labellers converge on acquirer-tagged articles as the signal locus, a power-limited qualitative convergence rather than a hypothesis-tested asymmetry. Chronological splitting plays for financial NLP the role characteristics-purging plays for asset pricing: it strips the predictable, stale component of news and leaves a residual that is small, event-localized, and lexically shallow. We advocate leakage audits as a required disclosure for financial-NLP benchmarks.
[NLP-44] he Plot Thins: Uniformity and Linearity in Literary Summaries
【速读】： 该论文旨在解决文学摘要在转述源文本时因过度聚焦情节而偏离原作复杂性的问题，尤其是摘要在保持事件顺序（线性度）和均匀分配注意力（均匀度）方面的偏差。其解决方案的关键在于构建一个包含150篇小说摘要与对应原文章节之间句子级映射的标注数据集，结合人工与大语言模型（LLM）标注方法，系统量化摘要在事件顺序和内容覆盖上的失真程度，并通过分析这些偏差揭示文学作品与摘要在叙事细节呈现清晰度与突出性上的本质差异。
链接: https://arxiv.org/abs/2608.17218
作者: Rebecca M. M. Hicke,Sil Hamilton,David Mimno,Ross Deans Kristensen-McLachlan
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Works of literature are complicated; they balance plot, suspense, surprise, and artistic expression. Summaries of literature prioritize plot, and therefore may deviate from their sources. Using a combination of manual and LLM-based annotation, we construct a dataset mapping sentences from 150 novel summaries to their respective source chapters. We find the task unexpectedly difficult for both human and model annotators. Using the sentence-to-chapter mappings, we then measure summary linearity, the degree to which it maintains the source’s order of events, and uniformity, the degree to which a summary spreads attention equally across a source. By examining when and how summaries break linearity and uniformity, we identify differences in how literary works and summaries express plot, particularly with regard to the clarity and prominence with which narrative details are described.
[NLP-45] Which Source Wins? Task-Dependent Reliance in Vision-Language Models
【速读】： 该论文旨在解决视觉-语言模型（Vision-Language Models, VLMs）在多模态输入冲突时，如何动态调整对图像与文本模态依赖关系的问题。具体而言，当图像或文本中的一方因退化而变得难以辨识时，模型是否会根据任务类型、证据结构和评估方式灵活地重新分配模态权重，尚缺乏系统性研究。其解决方案的关键在于构建一个受控的实验框架：通过逐步降低图像或文本的可读性（共四个等级），同时保持另一模态清晰，并追踪模型偏好变化；研究中引入了基于GSM8K和SVAMP数据集构造的算术问题冲突样本，以及人工审校的ChartQA-Conflict基准（包含229个图表-报告冲突对，附带图表与表格图像表示）。实验结果表明，五种开源VLM在算术任务中更倾向于放弃退化的文本而非图像，而在图表理解任务中则表现出相反趋势——即更倾向放弃退化的视觉信息。这一反转现象在控制单模态准确率损失后依然存在，且在替换图表为普通表格图像后仍成立，说明该行为模式并非由特定视觉形式导致。此外，两种前沿API模型（GPT-5.6-Luna与Gemini-3.5-Flash）也复现了该反转现象，其中GPT-5.6-Luna同时表现出算术任务中的原始方向。研究揭示，视觉-语言模型的模态依赖并非固定不变，而是显著依赖于任务性质、证据结构、模型架构及评估范式。
链接: https://arxiv.org/abs/2608.17205
作者: Rodela Ghosh,Aviral Gupta,Guangjing Wang
机构: University of South Florida (南佛罗里达大学)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: 20 pages. Under review
Abstract:Vision-language models (VLMs) combine images and text, but when the two conflict and one becomes harder to read, it is unclear how a model shifts its reliance between them. We study this modality reallocation with a controlled setup: we degrade either the image or the text across four levels of legibility while keeping the other clean, and track how the model’s preference changes. We build conflicts from GSM8K and SVAMP by pairing the rendered image of one arithmetic problem with the text of another, so the two sources support different answers. We also introduce ChartQA-Conflict, a manually reviewed benchmark of 229 chart-report conflicts with matched chart and table-image representations. We evaluate six open-weight VLMs using both generated answers and a length-normalized conditional log-likelihood margin. On GSM8K and SVAMP, five of six models shift more strongly away from degraded text than from degraded images. On ChartQA-Conflict, all six likelihood-scored models exhibit the opposite pattern, shifting more strongly away from the degraded visual source. This reversal persists after calibrating for unimodal accuracy loss and after replacing charts with plain table images. Two frontier API models, GPT-5.6-Luna and Gemini-3.5-Flash, behaviorally replicate the ChartQA-Conflict reversal, with GPT-5.6-Luna also matching the arithmetic direction. These results show that modality reliance in VLMs is not fixed, but varies across tasks, evidence structures, models, and evaluation settings. The source code is available at this https URL.
[NLP-46] oken Optimization and Context Window Management in Multi-Agent AI Workflows
【速读】： 该论文旨在解决多智能体人工智能（Multi-agent AI）工作流在实际生产中面临的三大核心瓶颈：令牌成本（token cost）、延迟（latency）以及上下文窗口（context-window）的质量问题。其解决方案的关键在于构建一个基于内部生产仪表板的工程化优化框架，通过从会议、邮件和聊天等非结构化数据中提取结构化任务项，并利用大语言模型（LLM）进行摘要路由，实现跨工作流的高效协同。该框架提出了六项可复用的优化模式：上下文分层（context stratification）、一次获取/本地处理架构（fetch-once/process-locally architecture）、模式压缩提示（schema-contracted prompts）、令牌感知的降级链（token-aware fallback chains）、语义缓存（semantic caching）以及智能体间通信压缩（inter-agent communication compression）。在真实生产环境中，这些方法将冷启动延迟从基准值3.5–10.5分钟显著降低至61–116秒（六次测试），并实现了约60%–70%的令牌消耗减少。此外，研究通过控制性实验揭示了“相关性对比上下文”（relevance-contrast context）现象：在固定提示长度下，引入同领域低相关性内容反而提升了模型对目标项的相关性判断准确性，表明适度的噪声信息可能增强模型的判别能力。这一发现为上下文设计提供了新的认知基础。最终，该研究贡献了一个介于模型研发与生产实践之间的可度量工程层，提供了一套可重复的优化模式与评估方法，以实现更快速、低成本且可靠的多智能体工作流。
链接: https://arxiv.org/abs/2608.17188
作者: Dvir Shamay
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 29 pages (main paper + technical appendix), 3 figures. Also archived on Zenodo: https://doi.org/10.5281/zenodo.21924612
Abstract:Multi-agent AI workflows are limited not only by model quality but by token cost, latency, and context-window quality. This paper presents a practitioner framework for token optimization and context-window management, grounded in an internal production dashboard that extracts structured work items from meetings, email, and chat with LLMs and routes summaries across workstreams. Six patterns are described: context stratification, fetch-once/process-locally architecture, schema-contracted prompts, token-aware fallback chains, semantic caching, and inter-agent communication compression. In production they cut measured cold-load latency to 61-116 seconds (six timed runs) from an operational baseline of roughly 3.5-10.5 minutes, with an estimated 60-70% token reduction. It also reports a controlled context-composition study: 2,420 confirmatory trials across 11 model configurations, using 661 anonymized workplace items scored for relevance. Holding the prompt at a fixed ten items, replacing some high-relevance items with same-domain low-relevance items improves the model’s relevance-score concordance on the target items, versus high-relevance items only; we call this relevance-contrast context. In the all-11 paired analysis, the 50:50 signal/noise condition improved relevance accuracy by +0.077 over the 100% condition (naive 95% CI [+0.056, +0.098], Cohen’s d = 0.49, Holm-adjusted p .001, n = 220). These cells are not independent; by the nine model families the effect is +0.084 (95% interval [+0.064, +0.103]), reported as a within-corpus descriptive comparison, not a population inference. A Fusion-of-N follow-up found that learned synthesis did not beat the mechanical set union of item IDs. The contribution is a measured engineering layer between model research and production agent practice: repeatable patterns and evaluation methods for faster, cheaper, more reliable workflows.
[NLP-47] AISA: AI Safety Assistant Framework for Continuous Improvement of Highway Construction
【速读】： 该论文旨在解决高速公路施工安全报告与任务前规划中，历史事故数据因以非结构化叙述形式存储而导致难以在规划阶段高效调用的问题。其核心解决方案是构建一个基于大语言模型（Large Language Models, LLMs）的新型框架，强调确定性、本地化推理能力，以实现对事故叙述的分类与质量评分，并提升相关历史事故、关联图像及可信行业文档的检索效率。关键创新在于通过神经探针（neural probes）对事故叙述进行多类别和二元分类（依据职业伤害与疾病分类系统，OIICS），并生成综合质量评分；同时，采用文本嵌入模型实现跨模态信息检索，验证了开放权重解码器嵌入模型在文档问答任务中的优越性能。实验表明，该框架在事故分类与检索方面显著优于随机基线，尤其在词汇特征显著的施工活动场景中表现突出，为未来自主代理（agentic）应用提供了可扩展、可信赖的数据融合基础。
链接: https://arxiv.org/abs/2608.17184
作者: Mason Smetana,Trevor Neece,Lev Khazanovich
机构: University of Pittsburgh(匹兹堡大学)
类目: Computation and Language (cs.CL)
备注: 17 pages, 5 figures
Abstract:Job Safety Analysis (JSA) and pre-task planning can benefit from prior incident records, yet historical accident data is often stored as unstructured narratives that are difficult to consult at the point of planning. A novel framework centered on large language models (LLMs) for highway construction safety reporting and planning is proposed as a foundation for future agentic applications, prioritizing deterministic, local inferencing. The first aim is to enable classification and quality scoring of incident narratives for existing and future reporting purposes. The second is to evaluate retrieval of relevant historical accidents, related imagery, and trusted industry documents for incorporation into daily safety plans. Neural probes were trained to classify incidents along four multiclass and two binary Occupational Injury and Illness Classification System (OIICS) fields and to derive an overall quality score, evaluated on a test set of over 15,000 narratives and a held-out set of 100 author-labeled records, benchmarked against a majority-vote LLM ensemble. The retrieval of historical accidents, reference imagery, and industry documents was benchmarked across embedding models using standard information retrieval metrics. OIICS classification reached 75% held-out accuracy, though the two binary flags were degenerate. The quality score, while meaningful on one database, was distorted on out-of-distribution fatalities in the held-out dataset. Accident retrieval recovered relevant incidents far above chance, performing best on lexically distinct construction activities. On document question answering, an open-weight decoder embedding model surpassed proprietary models. Overall, this work provides a new framework rooted in local inferencing and text embedding models for future agentic applications, with emphasis on bridging external data to JSA reports.
[NLP-48] Polaris: Learning to Generate Table Descriptions from Retrieval Feedback
【速读】： 该论文旨在解决自然语言到SQL（NL2SQL）等以表格为中心的自然语言处理任务中，因表检索效果不佳而导致的性能瓶颈问题。现有方法虽利用大语言模型（LLM）生成自然语言形式的表格描述以提升检索效果，但这些模型通常仅优化语言流畅性，而忽视了实际检索有效性。其解决方案的关键在于提出Polaris系统，通过利用现有表检索基准数据集中的查询-表相关性标注作为监督信号，直接从检索反馈中训练LLM生成更具检索导向性的表格描述。具体而言，系统为每张表生成多个候选描述，基于BM25检索得分对这些描述进行排序，并构建偏好对（preference pairs），进而采用直接偏好优化（Direct Preference Optimization, DPO）方法微调LLM，使其生成的描述更有利于后续检索。此外，Polaris在生成前对缩写的表名和列名进行扩展，有效缓解词汇不匹配问题。大量实验表明，Polaris显著优于当前最优方案AutoDDG，且研究结果进一步证明，可将现有的检索基准数据转化为训练生成式检索元数据的监督信号，为未来模型设计提供了新范式。
链接: https://arxiv.org/abs/2608.17171
作者: Ting Cai,Tuan Minh Phan,AnHai Doan
机构: University of Wisconsin-Madison(威斯康星大学麦迪逊分校)
类目: Computation and Language (cs.CL); Databases (cs.DB)
备注: 22 pages, 6 figures
Abstract:Many table-centric NLP tasks such as NL2SQL first retrieve relevant tables from large collections using keyword search. Recent work uses LLMs to generate natural-language table descriptions to improve retrieval, but they are typically optimized for fluency rather than retrieval effectiveness. We present Polaris, a system that trains an LLM to generate table descriptions directly from retrieval feedback. Our key insight is that existing table retrieval benchmarks already contain the supervision needed for this task: given query-table relevance judgments, we generate multiple candidate descriptions for each table, rank them by their BM25 retrieval effectiveness, and use the resulting preference pairs to fine-tune the LLM with Direct Preference Optimization (DPO). Polaris further expands abbreviated table and column names before generation to reduce vocabulary mismatch. Extensive experiments show that Polaris outperforms the state-of-the-art AutoDDG solution, often by a significant margin. More broadly, our results demonstrate that retrieval benchmarks can be repurposed as supervision for training LLMs to generate retrieval-oriented metadata.
[NLP-49] Can LLM s Reason in a Legally Meaningful Manner? A Small-scale Study on European Court of Human Rights Cases ICML2026
【速读】： 该论文旨在解决生成式 AI 在高要求法律任务（如法律案件预测）中推理能力的应用与质量评估不足的问题。其核心挑战在于，尽管当前大语言模型（LLM）已普遍具备推理能力，但在欧洲人权法院（ECtHR）判例语境下的法律推理表现仍缺乏系统性评估。研究的关键解决方案是通过设计多种提示策略（prompting strategies），探究何种提示能引导模型生成更符合 ECtHR 司法判例中“法律上有意义的推理”；同时采用人工标注者与另一大语言模型作为评判者（LLM-as-a-Judge）双重评估机制，以检验模型输出的合理性与一致性。研究发现，所考察的 GPT 5.4 模型虽能生成结构完整但实质内容浅显的分析，且 LLM 评判者虽具内部一致性，但与人类专家评价存在较弱相关性，表明其无法有效替代人工评估。此外，尽管专家定制提示能促进更全面的推理过程，却未显著提升预测准确性。因此，研究强调：不应仅依赖自动化 LLM 评估，更不能将任务准确率作为衡量推理质量的可靠代理指标。
链接: https://arxiv.org/abs/2608.17168
作者: Amogh Raina,Ilias Chalkidis,Daniel Hershcovich,Henrik Palmer Olsen
机构: University of Copenhagen (哥本哈根大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 24 pages, 4 figures, 4 tables, Submitted to AI4LAW Workshop at ICML 2026
Abstract:Reasoning has become a standard technique and feature for contemporary LLMs; however, its application and quality in the context of demanding legal-oriented tasks, such as legal case forecasting, remain under explored. We investigate how LLMs reason in the context of legal case forecasting, using legal cases from the European Court of Human Rights (ECtHR) as a testbed. We evaluate OpenAI GPT 5.4, a recent top-tier LLM, by exploring alternative prompting strategies that are more or less suggestive of what counts as legally meaningful reasoning in the context of ECtHR jurisprudence. We present our findings derived from assessing the model’s responses with both human and LLM evaluation. We find that the examined model scores far from ideal in legal reasoning, the model produces structurally complete but substantively shallow analyses, and that LLM-as-a-Judge evaluators are internally consistent yet align only weakly with our trained annotators, i.e., reliable but not a valid substitute for human evaluation. Overall, the expert-curated prompt leads to more comprehensive reasoning, which does not result in more accurate predictions compared to the other examined settings. Based on our findings, we urge the community not to rely solely on automated LLM-based evaluation and to avoid using task accuracy as an appropriate proxy for reasoning quality.
[NLP-50] owards Safer RAG : Only Agents Capable of System 2 Thinking may Access Untrusted Documents
【速读】： 该论文旨在解决生成式 AI（Generative AI）在检索增强生成（Retrieval-Augmented Generation, RAG）系统中面临的知识投毒攻击（knowledge-poisoning attacks）问题，即被检索的错误信息虽可被模型识别，仍可能对最终输出产生负面影响。现有解决方案“隔离原则”（Cordon Principle）通过严格隔离答案生成模块与原始证据，虽有效但带来显著计算开销。本文提出一种优化的安全原则：仅具备反思性思维（deliberative System 2 reasoning）能力的智能体才可访问不可信文档。为验证该原则，研究引入新型量化指标，衡量模型对错误信息的检测能力与其在下游任务中受其影响程度之间的差异。实验对比了具备推理能力的先进语言模型与普通语言模型在该指标上的表现，结果表明，具备系统2推理能力的模型在面对污染证据时展现出更强的鲁棒性，且无需采用严格的隔离机制。该发现为构建更高效、安全的RAG系统提供了实证支持，并确立了一种更具实践性的安全设计范式。
链接: https://arxiv.org/abs/2608.17153
作者: Mehrdad Ghassabi
机构: University of Isfahan(伊斯法罕大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Retrieval-Augmented Generation (RAG) has significantly enhanced the performance of large language models (LLMs), yet these systems remain vulnerable to knowledge-poisoning attacks, in which misinformation in retrieved documents can influence the model’s final outputs. Notably, an LLM may correctly detect that a document contains incorrect information while nevertheless being influenced by it. Prior work has addressed this vulnerability through the Cordon Principle, which prevents models responsible for final answer synthesis from directly accessing raw evidence. Although effective, this strict isolation can introduce substantial computational overhead. In this work, we propose a refined security principle: only agents capable of deliberative System 2 reasoning may access untrusted documents. To evaluate this principle, we introduce novel metrics that quantify the discrepancy between misinformation detection and downstream influence. We then empirically compare state-of-the-art reasoning language models with standard language models across these metrics. Our results show that reasoning-capable models are substantially more robust to corrupted evidence, without requiring the strict isolation imposed by the Cordon Principle. These findings provide empirical support for our refined principle and suggest a more practical foundation for secure RAG system design.
[NLP-51] Children but not language models show accelerating returns in word learning
【速读】： 该论文旨在解决儿童词汇习得过程中语言学习效率随时间变化的机制问题，特别是为何儿童在早期词汇积累缓慢但随后迅速加速。传统模型将词汇增长视为经验的线性累积过程，而本文提出，儿童的词汇增长更应被描述为加速累积（accelerating accumulation）——即每新增一单位语言输入所带来的学习收益，均高于前一次。这一发现揭示了儿童学习机制的核心特征：随着认知发展，其对语言输入的利用效率显著提升。相比之下，尽管语言模型（language models）也基于语言数据进行训练，即使使用面向儿童的语言数据（child-directed speech），其学习仍表现为恒定比例回报，符合典型的缩放定律（scaling laws），缺乏加速特性。因此，该研究的关键突破在于指出：儿童以远少于语言模型的数据量实现高效学习，其核心优势可能源于对输入信息日益高效的利用方式，这为理解人类语言学习的本质提供了新的理论视角。
链接: https://arxiv.org/abs/2608.17120
作者: Michael C. Frank
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Children learn hundreds of words over the first years of their lives, in a process that begins slowly but quickly picks up speed. Prior models describe vocabulary growth as evidence accumulation over time. Here we show that the process is best characterized as accelerating accumulation: children learn more from each additional unit of linguistic experience than they did from the one before. In contrast to children, language models – even those trained on child-directed speech – do not accelerate. Instead, they show constant proportional returns on new data, consistent with scaling laws. Children learn using many orders of magnitude less training data than language models; their increasingly efficient use of their learning input is a candidate explanation.
[NLP-52] Emotion Across Speech and Faces: Shared Affective Mechanisms in Multimodal Foundation Models
【速读】： 该论文旨在解决多模态基础模型（Multimodal Foundation Models, MFMs）在情感识别任务中，语音与面部情绪是否通过共享的效价功能单元（affective functional units）进行处理，还是依赖于模态特异性的通路这一关键问题。其解决方案的关键在于首次从激活层面系统性地识别并验证了情感敏感神经元（Emotion-Sensitive Neurons, ESNs），即在解码器中稀疏分布、与特定情绪类别显著关联的神经元。研究通过语音情绪识别和面部表情识别作为互补探针，在Gemma-4-12B-it、MiniCPM-o-4.5和Qwen2.5-Omni-7B三款MFMs中分别定位了声学与视觉ESNs。实验表明，视觉ESNs具有因果可解释性：关闭它们会特异性损害对应情绪的识别能力，而调控其激活水平则能选择性增强目标情绪相对于其他情绪的识别表现。此外，声学与视觉ESNs表现出情绪匹配的重叠模式及相似的层间分布特征，揭示了跨模态效价表征的部分结构对齐性。更进一步，跨模态干预实验证明了双向因果转移效应——某一模态中识别出的ESNs在另一模态中亦能产生特异性的情绪影响。这些发现为多模态情感处理提供了首个基于激活层级的跨模态功能单元分析，表明语音与面部情绪识别部分收敛于可定位、可操控的稀疏解码器组件，且无需额外训练即可实现干预。
链接: https://arxiv.org/abs/2608.17102
作者: Xiutian Zhao,Luqi Sun,Björn Schuller,Berrak Sisman
机构: Johns Hopkins University (约翰霍普金斯大学); Imperial College London (帝国理工学院)
类目: Computation and Language (cs.CL); Audio and Speech Processing (eess.AS); Image and Video Processing (eess.IV)
备注: 9 pages, 4 figures
Abstract:Modern multimodal foundation models (MFMs) have made rapid progress on tasks requiring integrated perception across speech, vision, and language, including emotion recognition. However, it remains unclear whether they recognize speech and facial emotion through shared affective functional units or modality-specific pathways. We explore emotion-sensitive neurons (ESNs), sparse decoder neurons selectively associated with emotion categories, in three MFMs: Gemma-4-12B-it, MiniCPM-o-4.5, and Qwen2.5-Omni-7B. Using speech emotion recognition and facial expression recognition as complementary probes, we identify acoustic and visual ESNs. Visual ESNs are causally meaningful: deactivating them selectively impairs recognition of the associated facial emotion, whereas steering their activations selectively enhances recognition of that emotion relative to other emotion categories. Acoustic and visual ESNs further show emotion-matched overlap and similar layer-wise distributions, indicating partial structural alignment between affective representations across speech and faces. Finally, cross-modal interventions reveal bidirectional causal transfer: ESNs identified from one modality produce emotion-specific effects when applied to the other. Our findings provide one of the first cross-modality activation-level analyses of affective functional units in MFMs, suggesting that speech and facial emotion recognition partially converge onto sparse decoder-level components that can be localized and manipulated without training.
[NLP-53] A Glyph Is Not a Letter a Token Is Not a Word a Space Is Not a Space: What the Units of Voynichese Are Not
【速读】： 该论文旨在解决对《Voynich手稿》（Voynich manuscript）长期以来基于三个未明言假设的解读问题：即其符号为字母、空白之间的字符串为单词，且每个空白均为词间分隔符。研究通过使用Zandbergen-Landini转写体系，并结合匹配的散文文本、密码文本及伪文本对照，以及页码组（quire-level）重采样方法，系统检验了这三个假设。结果表明，三者均不成立，且失败模式具有共同特征：Voynichese的有序性主要体现在词元边缘及词元间的渐变边界上，而非词元序列本身。符号规律性过强，无法支持任何测试过的明文进行一对一替换（条件熵2.7比特，远低于拉丁语、意大利语和英语的约3.5比特），反而呈现出一种稳定的多符号单元重复结构，该结构在页码组层面保持稳定。尽管词元构成一个合理的词汇表，但一个词元对其下一个词元的预测能力不足总词元熵的1%，显著低于所有对照组（2%-10%）；而词元边缘的符号之间存在0.2比特互信息，高于所有散文对照组。空白则呈现两种模式：转写者标记为不确定的分隔符表现出词内连接点特征，物理宽度更窄（独立图像坐标下AUC达0.905，盲法墨水审计亦显示一致方向），且即使在移除所有空格后，学习模型仍能识别这些位置。这一特征组合具备判别力。此外，已发表的模仿Voynich的密码系统与自引用文本生成器虽可复现低熵、单元尺度、弱词序及校准替代攻击的零结果，但无法再现边缘符号耦合关系或开放性、罕见词型丰富的词汇结构（70%为独有类型，而对照组分别为41%及59%-60%）。因此，任何对《Voynich手稿》的解释必须通过实证测量来确立从符号、词元到分隔符向字母、单词和词空间的跃迁，而非预设前提，这些测量应成为构建理论的基础。
链接: https://arxiv.org/abs/2608.17096
作者: Liudmila Rozanova,Alexander Temerev
机构: University of Geneva (日内瓦大学); International Institute for Applied Systems Analysis (国际应用系统分析研究所)
类目: Computation and Language (cs.CL)
备注: 33 pages, 7 figures, 3 appendices. Analysis code and data are included as ancillary files and mirrored at this https URL
Abstract:The Voynich manuscript (Beinecke MS 408) is usually analysed on three unstated assumptions: that its glyphs are letters, that the strings between blanks are words, and that every blank is a word space. We test all three against the Zandbergen-Landini transliteration with matched prose, cipher, and pseudo-text controls and quire-level resampling. None holds, and the failures share a shape: the order in Voynichese sits at the edges of tokens and at graded boundaries between them, not in the succession of tokens themselves. Glyph regularity is too strong for one-to-one substitution of any tested plaintext (conditional entropy 2.7 bits against about 3.5 for Latin, Italian, and English) and resolves instead onto a quire-stable scale of recurrent multi-symbol units. Tokens form a plausible vocabulary, yet the identity of one token predicts the next by under 1% of token entropy, below every matched control (2-10%), while the glyphs at token edges share 0.2 bits of mutual information, more than in any prose control. Blanks fall into two regimes: the separators transcribers marked uncertain behave like word-internal junctures, are physically narrower on the page (AUC 0.905 from independent image coordinates, with the same sign in a small blind ink audit), and are crossed by learned units even when every space is erased before learning. This profile is also what discriminates. A published Voynich-imitating cipher and a self-citation text generator both reproduce the low entropy, the unit scale, the weak token order, and the null result of a calibrated substitution attack; neither reproduces the edge-glyph coupling or the open, hapax-rich vocabulary (70% singleton types against 41% and 59-60%). Any account of the manuscript must therefore earn, rather than assume, the step from glyphs, tokens, and separators to letters, words, and word spaces, and these are the measurements on which to do so.
[NLP-54] here is No Theoretical Curse of Multilinguality For Embedding Space Structure
【速读】： 该论文旨在解决多语言自然语言处理（Multilingual NLP）中“多语言诅咒”（curse of multilinguality）的问题，即随着语言数量的增加，多语言模型在单语性能和跨语言对齐方面出现性能退化。其核心问题是：多语言嵌入空间是否在理论上就无法实现“完美多语言性”（perfect multilinguality），从而导致性能随语言规模增长而不可逆下降。论文的关键贡献在于形式化定义了“完美多语言性”的两个条件，并从理论上证明，实现该目标所需的嵌入空间最小维度仅随语言数量的对数增长，表明嵌入空间结构本身并不存在理论上的多语言诅咒。这一发现揭示了实际中观察到的多语言性能退化并非源于嵌入空间的内在限制，而是由真实世界数据分布与训练条件所导致。研究通过小规模实证分析验证了该理论推断，首次从理论与内在机制层面为多语言诅咒现象提供了科学解释，对构建高效、可扩展的多语言模型具有重要指导意义。
链接: https://arxiv.org/abs/2608.17088
作者: Niyati Bafna,Neha Verma,Vilém Zouhar,Philipp Koehn,David Yarowsky
机构: ETH Zürich(苏黎世联邦理工学院); Johns Hopkins University(约翰霍普金斯大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:A central goal of multilingual NLP is to achieve high monolingual performance per language and cross-lingual alignment for large-scale language coverage with a multilingual model. The curse of multilinguality describes the phenomenon of degradation in multilingual model performance as we increase language coverage, posing a threat to the above goal. This paper asks whether multilingual embedding spaces are inherently incapable of achieving perfect multilinguality without a prohibitive increase in required capacity. We first formalize the goal of “perfect multilinguality”, embodied in two multilinguality conditions. We then prove that the minimum dimensionality required for perfect multilinguality grows only logarithmically in the number of languages. That is, we show that there is no theoretical curse of multilinguality for embedding space structure. This suggests that the empirical curse of multilinguality is a result of real world data and training conditions. We back this understanding with a small-scale empirical study. Our paper provides the first theoretical and intrinsic perspective on the curse of multilinguality, with implications for the scientific understanding of this phenomenon.
[NLP-55] Uncertainty-Aware Decision Making in Multimodal Large Language Models
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在面对视觉、文本、时间、音频、文档、图表或具身性等多源证据时，其输出正确性不仅依赖语言能力，更受输入质量、感知误差、模态间冲突、推理不稳定性及分布偏移等因素影响的问题。传统方法往往仅关注生成答案的流畅性，忽视了其背后潜在的不确定性来源，导致模型在缺乏充分证据、证据矛盾或环境变化时仍给出看似合理但错误的回答。解决方案的关键在于构建以决策为中心的不确定性感知框架：将不确定性来源转化为可观测信号，通过校准或控制这些信号来管理风险，并使校准后的不确定性直接指导系统行为。该研究系统综述了包括词元与对数几率不确定性、语义分歧度、扰动不稳定性、可解释性与归因分数、言语化置信度、验证器与评判者评分、合规预测、选择性回答、回避回答、澄清请求、信息检索、自我检查及升级机制在内的多种方法，强调不确定性评估不应局限于单一置信度数值，而应考察其是否有效提升模型在证据不足、冲突、分布漂移或高风险场景下的行为表现。研究将本综述置于纯文本不确定性、模型泛化、幻觉分析及安全导向文献之间，指出了未来在源感知分解、行为导向基准测试、分布偏移下的校准、黑箱不确定性估计、更广泛模态覆盖、可复现报告以及以人为本的不确定性传达等方面的开放挑战。
链接: https://arxiv.org/abs/2608.17084
作者: Abderrahmene Boudiaf,Irfan Hussain,Sajid Javed
机构: Khalifa University of Science and Technology (哈利法大学科学技术学院)
类目: Computation and Language (cs.CL)
备注:
Abstract:Multimodal large language models (MLLMs) increasingly answer questions whose correctness depends on visual, textual, temporal, acoustic, document, chart, or embodied evidence. Their failures are therefore not only linguistic. A fluent answer may conceal poor input quality, a perceptual error, weak grounding, conflict between modalities, unstable reasoning, distribution shift, or a question that is not answerable from the supplied evidence. This survey organizes the literature on uncertainty-aware MLLMs around a decision-centered framework: uncertainty sources give rise to observable signals, signals must be calibrated or controlled for risk, and calibrated uncertainty should determine the system action. We review work on token and logit uncertainty, semantic disagreement, perturbation instability, grounding and attribution scores, verbalized confidence, verifier and judge scores, conformal prediction, selective answering, abstention, clarification, retrieval, self-checking, and escalation. The central argument is that uncertainty should not be evaluated only as a confidence number; it should be evaluated by whether it improves behavior under insufficient, conflicting, shifted, or high-risk multimodal evidence. We position this survey against text-only uncertainty and abstention surveys, broad MLLM surveys, MLLM hallucination surveys, and safety-oriented reviews. We conclude with open problems in source-aware decomposition, action-aware benchmarks, calibration under shift, black-box uncertainty estimation, broader modality coverage, reproducible reporting, and human-centered uncertainty communication.
[NLP-56] Foundation Agents Meet Agent ic Deep Research: Evidence-Grounded Clinical Code Forecasting
【速读】： 该论文旨在解决未来就诊中标准化诊断编码（ICD）的前瞻性多标签预测问题，即在患者历史电子健康记录（EHR）的基础上，预测未来可能记录的多个诊断代码。传统方法受限于对长期病程模式捕捉不足或缺乏灵活的诊断假设生成能力。其解决方案的关键在于提出一种名为ICD-DeepResearch的深度研究工作流，该框架融合了结构化EHR基础模型（SparseEHR）与语言基础模型（GPT-5）的优势：前者通过学习患者病程中的复发与时间演进规律生成初始候选集，后者则提供基于自然语言理解的诊断假设补充；两者通过医学检索与ICD词典联合引导，在固定前K个候选预算下，利用患者证据、外部临床关系及代码语义进行候选评估与筛选。最终通过统一的验证、去重与排序模块整合两路径结果，并由独立模块生成解释性理由而不影响预测输出。实验表明，ICD-DeepResearch在MIMIC-III和MIMIC-IV数据集上分别达到24.60%/35.09%和25.14%/48.32%的平均精确率/召回率，且医生对所检索文献的有用性评价显著高于单一模型或现有研究系统，证明其在预测性能与临床可解释性方面的双重优势。
链接: https://arxiv.org/abs/2608.17075
作者: Junda Wang,Meysam Ghaffari,Akshat Choube,Mohsen Sharifi Renani,Hong Yu,Carlos Morato
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Next-encounter ICD forecasting predicts which standardized diagnosis codes will be documented at a future visit from the longitudinal record available beforehand. The task is prospective and multi-label: the target note does not yet exist, and several codes may be correct. Structured EHR foundation models capture recurrence and temporal progression, whereas language foundation models generate flexible diagnostic hypotheses. We introduce ICD-Deepresearch, a DeepResearch workflow that composes these predictive foundation models with medical search and ICD dictionaries. Because no source reveals the future code set, research evaluates candidate transitions by linking patient evidence, external clinical relations, and exact code semantics under a fixed top-K budget. Candidate Generation uses SparseEHR to produce an EHR Prior that initializes two bounded Research Expansion rounds; an independent GPT-5 Direct Forecast supplies complementary candidates. Final Selection validates, deduplicates, and jointly ranks both paths, after which a separate module writes rationales without changing predictions. Finally ICD-Deepresearch achieves patient-averaged precision/recall of 24.60/35.09% on MIMIC-III and 25.14/48.32% on MIMIC-IV. Physicians rate 51% and 68% of its retrieved documents useful, compared with 22% and 39% for standalone GPT-5 web search and 32% and 41% for Medical Deep Research. ICD-Deepresearch therefore improves over the registered local comparators while retrieving evidence with higher physician-rated usefulness than the standalone research systems
[NLP-57] J-Miner: Recovering Executable Decision Knowledge from Language-Model Classifiers
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在微调为专用分类器后，其内部决策知识隐含于模型参数中、难以被解释与复用的问题。现有方法仅输出最终分类标签，缺乏对模型如何做出判断的可追溯性与可验证性。为此，论文提出J-Miner框架，其核心创新在于通过聚合多层、多位置的词汇对齐内部信号，自动挖掘文本层面的命名概念（named concepts），并利用分类器自身的预测结果学习可执行的决策规则。该方法将局部的内部表征提炼为显式的、可解释的分类器级知识表示。实验表明，J-Miner生成的规则在多个分类任务上可复现高达98.3%的源分类器决策，且在同等紧凑度下比基于输入词学习的规则提升6.0–29.5个百分点的行为保真度。进一步分析显示，所提取的命名概念反映了与任务决策相关的内部语义证据，而学习到的规则则将这些分散的信号整合为可审计的决策结构。此外，该知识可高效迁移至轻量级独立学生模型——仅需源模型约1/24的参数量，即可从原始文本重构并执行该知识表示，同时保持99.8%的平均任务准确率。研究证实，特定任务的决策知识能够以显式、可执行的形式被忠实表征，并在原分类器之外实现跨模型复用。
链接: https://arxiv.org/abs/2608.17063
作者: Yunfan Gao,Xinyi Huang,Tao Sheng,Haorui Song,Yun Xiong,Haofen Wang
机构: Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University(上海智能自主系统研究院，同济大学); Shanghai Key Laboratory of Data Science, College of Computer Science and Artificial Intelligence, Fudan University(上海市数据科学重点实验室，复旦大学计算机科学技术与人工智能学院); Meituan(美团); College of Design and Innovation, Tongji University(同济大学设计创意学院)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 19 pages, 12 figures, and 13 tables; includes appendices
Abstract:Large language models can be fine-tuned into specialized classifiers that perform well across diverse text tasks and make complex judgments, but they typically expose only final labels, leaving the decision knowledge acquired through fine-tuning implicit within the model. We study how to mine this internal decision knowledge from a fine-tuned classifier and encode it in an executable representation that can be inspected, validated, and reused beyond the source classifier. We introduce J-Miner, which mines text-level named concepts by aggregating vocabulary-aligned internal signals across layers and token positions, and uses the classifier’s own predictions to learn executable decision rules over them. This process distills local internal readouts into an explicit classifier-level knowledge representation. Across multiple classification tasks, J-Miner rules reproduce up to 98.3% of source-classifier decisions and achieve 6.0–29.5 percentage points higher behavioral fidelity than equally compact rules learned from input words. Further analysis shows that the named concepts reflect internal semantic evidence associated with task decisions, while the learned rules consolidate these distributed signals into inspectable decision structures. The resulting decision knowledge also transfers to lightweight standalone students: using about 1/24 as many parameters as the source classifiers, they reconstruct and execute the representation from raw text while retaining 99.8% of the source classifiers’ mean task accuracy. These findings show that task-specific decision knowledge can be faithfully represented in an explicit, executable form and reused beyond the classifier in which it was learned.
[NLP-58] Institution-Specific LLM Prompting Recovers PHI That De-identification Systems and Their Gold Standards Both Miss
【速读】： 该论文旨在解决电子健康记录（Electronic Health Records, EHR）在二次使用中去标识化（de-identification）过程中，现有系统普遍遗漏机构特定受保护健康信息（Protected Health Information, PHI）的问题，如医院缩写、建筑名称及内部编码等，这类信息的敏感性由机构本地决定，传统方法难以覆盖。其解决方案的关键在于利用大语言模型（Large Language Models, LLMs）结合上下文学习（In-Context Learning, ICL）的能力，通过精心设计的提示词（prompt）实现对机构特异性PHI的精准识别，并有效控制精确率与召回率之间的权衡。研究通过在德克萨斯儿童医院儿科肿瘤病历上的实证评估表明，经过多轮提示优化（包括补充缺失类别和抑制过度去标识化），单一提示的校准式调用即可在保持高精确率的同时将召回率提升至0.981，显著优于专用系统（如Stanford TiDE和OpenMed PII）。此外，通过分析模型输出发现的414个候选标注缺口并经人工复核确认227个新PHI，进一步验证了该方法的潜力。最终结果表明，经过合理校准的ICL机制能够在单次模型调用中同时弥合机构特定PHI遗漏的空白并实现精度与召回率的平衡，为去标识化系统提供了一种可适应、可审计且灵活的替代方案，主张以机构专属提示词开发作为主要适配策略。
链接: https://arxiv.org/abs/2608.17051
作者: Daniel Palacios,Matthew Brady Neeley,Angel Adetomike Otto,Shalini Dhamodharan,John P. Woodhouse,Chi-fan Lin,Mark Zobeck,Zhandong Liu,Hyun-Hwan Jeong
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Secondary use of electronic health records requires de-identification, yet existing systems miss \emphinstitutionally situated protected health information (PHI) such as hospital abbreviations, building names, and internal codes whose status is locally determined. We ask whether large language models (LLMs) with in-context learning (ICL) can close this gap and control the precision–recall trade-off. On 100 annotated pediatric oncology notes (5,322 PHI spans) from Texas Children’s Hospital, we benchmarked eight LLMs against two purpose-built systems (Stanford TiDE, OpenMed PII) and two pattern-based baselines. Each LLM ran under three prompts of increasing specificity: (1) a HIPAA-aligned baseline, (2) baseline plus the institutional PHI categories it missed, and (3) prompt 2 plus instructions against over-redacting clinical content. We then compared 14~multi-agent and ensemble configurations against the best single prompt, with recall the primary safety metric. LLMs outperformed the purpose-built systems (best F1=0.918 \pm 0.001 vs.\ TiDE 0.779), with advantages concentrated in contextual categories. Naming the missed categories recovered 79% (48/61) of them, and discouraging over-redaction restored precision. No agentic architecture beat calibrated single-pass prompting (F1 0.906–0.907), but LLM outputs surfaced 414~candidate annotation gaps; re-annotation confirmed 227~PHI spans, against which the final prompt reached recall=0.981 (F1=0.907 \pm 0.002). Well-calibrated ICL resolves both the institutional PHI gap and the precision–recall trade-off in one LLM call per note. LLMs cost more to run than traditional methods, but that cost buys a way to audit the reference standard. LLMs are a legitimate, adaptable alternative to purpose-built de-identification systems; institution-specific prompt development should be the primary adaptation strategy. Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.17051 [cs.CL] (or arXiv:2608.17051v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.17051 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Hyun-Hwan Jeong [view email] [v1] Mon, 17 Aug 2026 18:56:04 UTC (1,381 KB) Full-text links: Access Paper: View a PDF of the paper titled Institution-Specific LLM Prompting Recovers PHI That De-identification Systems and Their Gold Standards Both Miss, by Daniel Palacios and Matthew Brady Neeley and Angel Adetomike Otto and Shalini Dhamodharan and John P. Woodhouse and Chi-fan Lin and Mark Zobeck and Zhandong Liu and Hyun-Hwan JeongView PDFHTML (experimental)TeX Source view license Current browse context: cs.CL prev | next new | recent | 2026-08 Change to browse by: cs cs.AI References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[NLP-59] Cross-Model Memory Transfer via Target-Side Reader Adaptation
【速读】： 该论文旨在解决大语言模型在利用外部知识时面临的权衡问题：非参数化检索虽能灵活访问外部知识，但引入了检索延迟、上下文开销，且与模型主干的集成较浅；而参数化适配虽推理高效，却将知识与模型权重耦合，难以更新、审计或迁移。为突破这一困境，论文提出基于Engram风格哈希记忆的中间范式——将学习到的知识存储于外部可寻址的记忆表中，通过一个小型可学习的读取器（reader）来消费该记忆。其核心问题是：当此类记忆从源模型迁移到目标模型时，究竟是冻结的记忆本身更重要，还是目标侧的读取器更关键？研究通过跨模型冻结记忆提取实验发现，虽然记忆内容和正确寻址均重要，但只有当读取器与目标模型对齐时，迁移后的记忆才能真正发挥作用。在下游问答任务中，采用双层四分支读取器几乎完全弥合了同模型与跨模型复用之间的性能差距，平均得分达38.8（在受控评估协议下）。此外，若提供方读取器与目标接口直接兼容，则无需目标侧训练即可实现显著效用，而可选的读取器微调可进一步提升性能。研究结果表明，只要目标模型具备兼容的读取器接口，Engram式记忆即可作为可复用的外部知识构件；目标侧的适应性调整则可在直接复用不足时进一步优化对齐效果。
链接: https://arxiv.org/abs/2608.17050
作者: Mingyuan Li,Guangsheng Yu,Xu Wang,Shaoxiong Ji
机构: University of Turku (图尔库大学); University of Technology Sydney (悉尼科技大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Methods for improving knowledge use in large language models typically fall into two regimes. Non-parametric retrieval offers flexible access to external knowledge, but adds retrieval latency, context overhead, and only shallow integration with the backbone. Parametric adaptation is efficient at inference time, but entangles knowledge with model weights and can be hard to update, audit, or transfer. Engram-style hashed memory occupies a middle regime: it stores learned information in an external, addressable table, yet consumes that table through a small learned reader. This raises a basic question: when such a memory is moved across backbones, what matters more, the frozen memory itself or the target-side reader? We study this question through cross-model frozen-memory extraction, in which a memory trained on a source model is frozen and attached to a different target model, with only a lightweight reader trained. Ablations show that learned memory content and correct addressing both matter, but the transferred table becomes useful only through a reader aligned to the target model. In downstream question answering tasks, a dual-layer, four-branch reader nearly closes the gap between same-model and cross-model reuse, achieving an average score of 38.8 under our controlled evaluation protocol. Moreover, when the provider reader is directly compatible with the target interface, the frozen artifact can provide substantial utility without target-side training, while optional reader adaptation yields further improvement. These results suggest that Engram can serve as a reusable external knowledge artifact, provided that the target has access to a compatible reader interface; target-side adaptation can further improve alignment when direct reader reuse is insufficient.
[NLP-60] Margin-Regularized Structured Semantic Alignment for Brain-Language Correspondence
【速读】： 该论文旨在解决生成式语言模型在脑-语言解码中可能过度依赖自身语言先验，导致解码结果更多反映模型重构而非真实神经表征的问题，从而削弱了对大脑与语言内在对应关系的可解释性。其核心解决方案是提出一种边际正则化的结构化语义对齐框架（MD-SigLIP），通过在共享语义空间中直接对齐脑信号嵌入（brain embeddings）与文本嵌入（text embeddings），实现基于检索的解码。该方法的关键在于引入一种基于列表的边际正则化项，结合去重感知的Sigmoid对比学习，强制正向语义簇与负样本之间保持结构化排序约束，同时建模多正例语义结构与基于边际的有序关系，从而有效捕捉语言嵌入在神经信号中所体现的流形组织特性。实验表明，该方法在全词汇量和子集评估设置下均达到当前最优的检索性能。
链接: https://arxiv.org/abs/2608.16975
作者: Jiaqi Wang,Huawen Hu,Shu Zhang
机构: Northwestern Polytechnical University (西北工业大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:With the rapid advancement of large language models, brain-language decoding has achieved remarkable progress. However, it remains unclear whether decoded content genuinely reflects neural representations or is largely reconstructed by the language model itself. This ambiguity limits interpretability and hinders the investigation of intrinsic brain-language correspondence. To address this challenge, we propose MD-SigLIP. This margin-regularized structured semantic alignment framework directly aligns brain embeddings with text embeddings in a shared semantic space, enabling retrieval-based decoding. This formulation enables explicit modeling of the correspondence between neural representations and language semantics. Building upon duplicate-aware sigmoid contrastive learning, we introduce a listwise margin-regularized term that enforces structured ranking constraints between positive semantic clusters and negative samples. By modeling multi-positive semantic structure and margin-based ordering simultaneously, the method captures the manifold organization of language embeddings reflected in neural signals. Experiments demonstrate state-of-the-art retrieval performance under both full-vocabulary and subset evaluation settings.
[NLP-61] he Price of Thinking: Reasoning Effort as a Model-Specific API Contract
【速读】： 该论文旨在解决生成式 AI (Generative AI) 服务中，模型调用合同（contract）的隐含条款对推理成本与性能影响的可解释性问题。具体而言，研究聚焦于“推理努力项”（reasoning-effort term）在合同中的显式存在与否如何影响实际服务成本与任务准确性。其解决方案的关键在于通过一项注册的成对对照实验，将同一模型（Sonnet 5）在显式高推理努力与省略推理努力两种合同条件下进行对比测试，使用30道AIME 2026题目并每题执行五次调用。研究采用冻结的终端分类体系和重复调用保留机制，确保结果的可复现性；结果显示，显式高努力合同下平均每次调用成本高出0.01031美元（95%置信区间：+0.00204至+0.01974），但准确率差异不显著（+0.0133，95%置信区间：-0.0267至+0.0467），表明可能存在高达4.67个百分点的潜在性能增益未被排除。进一步地，成本/正确答案指标显示，高努力合同为0.08665美元，而省略合同为0.07662美元，凸显合同设计对经济效率的影响。研究通过注册请求日志、解析器、终端分类体系及分析流程的预先冻结，保证了结论的可信度，并基于模型-任务-采集日期的限定范围，揭示了不同提供商内部存在的模型特定省略语义，且即使原始响应结构不确定，文档级别声明仍保持有效性。
链接: https://arxiv.org/abs/2608.16956
作者: Yeabin Moon
机构: Brandeis School of Business and Economics (布兰迪斯商学院与经济学院); Brandeis University (布兰迪斯大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注: 15 pages, 3 figures, 2 tables
Abstract:API buyers purchase a dated contract, not a model name alone: the contract includes the requested and served model, reasoning-effort term or its omission, output rail, service product, prompt, and price schedule. We study the reasoning-effort term through a registered paired contrast of Sonnet 5 with explicit high effort against the same model with effort omitted, using 30 AIME 2026 items and five calls per item. Every paid attempt was assigned one frozen terminal category, and inference resampled items while retaining their repeated calls. Mean delivered cost was \ 0.01031 per call higher under the explicit-high contract than under the omitted contract [+\ 0.00204, +\ 0.01974]. The corresponding accuracy contrast was +0.0133 [-0.0267, +0.0467]; we did not detect an accuracy difference, and the interval permits a gain of up to 4.67 percentage points that this design cannot rule out. Cost per correct answer was \ 0.08665 under the high-effort contract and \ 0.07662 under the omitted contract, as registered point estimates. A dated contract census, Models-API metadata, and preregistered raw-response probes further documented model-specific omission semantics, including within a provider; claims remained at documentation grade when raw structure was indeterminate. The request registry, parser, terminal taxonomy, statistical plan, and analysis pipeline were frozen before outcomes were examined; the resulting claims are bounded to the model, task, and collection date studied.
[NLP-62] SeqFeed: Improving Agent ic RTL Code Generation with Sequential Behavior Feedback
【速读】： 该论文旨在解决生成正确寄存器传输级（Register-Transfer Level, RTL）代码过程中，如何有效向智能体（agent）传递时序信息这一关键挑战。现有方法中，RTL代码本身不显式暴露特定执行过程中的周期级信号行为，而完整的仿真波形又因数据量庞大且噪声过多，难以被大语言模型（Large Language Model, LLM）高效分析。为克服上述局限，作者通过研究人类工程师对时序行为的推理方式，提炼出有效反馈应具备的三个核心要求：事件可定位（event-addressable）、依赖可追溯（dependency-traceable）和可迭代查询（iteratively-queryable）。基于此，提出SeqFeed框架，包含两个互补机制：(1) SeQuery——一种类SQL的波形查询语言，支持智能体以语义事件为锚点，精准采样相对时钟周期上的信号值；(2) SeGraph——一种信号传播依赖图，用于追踪跨时钟周期的信号演化路径。实验结果表明，SeqFeed显著提升了多款LLM在RTL生成任务中的通过率，且SeQuery与SeGraph各自独立有效，联合使用时产生协同增益。
链接: https://arxiv.org/abs/2608.16934
作者: Yuxin Du,Juxin Niu,Tao Hu,Xi Wang,Zhe Jiang,Nan Guan
机构: University of Science and Technology of China (中国科学技术大学); Tsinghua University (清华大学)
类目: Hardware Architecture (cs.AR); Computation and Language (cs.CL)
备注:
Abstract:RTL code generation is a critical stage in hardware design, and the emergence of agentic systems offers new opportunities to automate this process. To generate correct RTL code, agents must understand sequential behavior, including how signals evolve and propagate over multiple clock cycles. However, effectively conveying such temporal information to agents remains a significant challenge. RTL code does not expose cycle-level signal behavior for a specific execution, whereas full simulation waveforms are too voluminous and noisy for effective LLM analysis. To address these limitations, we study how human engineers reason about sequential behavior and identify three requirements for effective feedback: it should be event-addressable, dependency-traceable, and iteratively-queryable. Guided by these requirements, we propose \textitSeqFeed, which comprises two complementary mechanisms: (1) \textitSeQuery, an SQL-like waveform query language that enables agents to anchor queries to semantic events and sample signal values at relative time points; and (2) \textitSeGraph, a dependency graph that tracks signal propagation across clock cycles. Experimental results across multiple LLMs demonstrate the effectiveness of SeqFeed in improving pass rates. SeQuery and SeGraph are each effective independently and provide complementary benefits when used together.
[NLP-63] When Personalization Becomes Bias: Structural and Discursive Religious Framing in AI-Generated Financial Advice
【速读】： 该论文旨在解决生成式 AI（Generative AI）在金融咨询系统中可能再现宗教偏见的问题，尤其关注大型语言模型（LLMs）在跨宗教身份情境下提供的财务建议是否存在系统性偏差。研究通过混合方法对ChatGPT、Gemini和Grok三款主流模型在16种宗教身份组合（基督教、伊斯兰教、印度教及无宗教身份）与三大核心家庭财务决策（股票投资、购房、人寿保险）中的表现进行实证分析，共涵盖432次模拟顾问-客户交互。其解决方案的关键在于构建一个双维度框架，将模型训练与设计所导致的结构性偏见（structural bias）与通过语言表达的表意性偏见（discursive bias）相联结，并揭示偏见在语言层面的具体实现机制：包括宗教锚定（religious anchoring）、文化信号不对称传递以及语气调节等策略。研究发现，仅有12%-18%的建议为无偏见，且不同模型间存在显著差异——Gemini表现出最明显的偏见，而ChatGPT与Grok无显著差异；此外，非宗教客户常遭遇以宗教为中心的劝导话语，且在人寿保险场景中宗教语言更为突出。该研究不仅揭示了算法建议如何随用户身份线索进行语言调适，凸显个性化与中立性之间的管理困境，也为金融机构、企业及监管机构提供了保障人工智能中介建议中立性、文化敏感性与可信度的重要依据。
链接: https://arxiv.org/abs/2608.16909
作者: Muhammad Salar Khan,Hamza Umer,Hasan Mahmud,Sandra Rothenberg
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 50 pages
Abstract:Large language models (LLMs) are increasingly integrated into financial advisory systems, yet their role in reproducing religious bias remains underexamined. This study provides systematic mixed-methods evidence of such bias across three LLMs (ChatGPT, Gemini, and Grok) using 432 simulated advisor-client interactions spanning 16 religious identity pairings (Christian, Muslim, Hindu, and non-religious) and three core household financial decisions: stock investment, house purchase, and life insurance. Combining regression and reflexive thematic analyses, we identify structural biases across models and decision contexts and the discursive mechanisms through which they are linguistically enacted. Unbiased advice appeared in only 12-18% of cases. Gemini consistently produced more bias than Grok, while ChatGPT’s outputs were statistically comparable to Grok’s. Religiously symmetric advisor-client pairings almost always triggered explicit religious framing, and non-religious clients often received advisor-centered religious appeals. Qualitative findings show that bias is linguistically manifested through religious anchoring, uneven cultural signaling, and tone modulation, varying by model and financial scenario. Stock investment prompts produced more financially technical responses, whereas life insurance advice triggered stronger religious language. The study develops a dual-dimensional framework linking structural bias rooted in model training and design with discursive bias expressed through language, advancing understanding of algorithmic bias in LLM-generated financial advice. It also shows that such advice adapts linguistically to identity cues, revealing a managerial dilemma between personalization and neutrality. Finally, it highlights implications for businesses, financial institutions, and regulators seeking to ensure neutrality, cultural sensitivity, and trust in AI-mediated advice.
[NLP-64] he politics of postmortem privacy
【速读】： 该论文旨在解决后死亡隐私（postmortem privacy）概念内部的不稳定性问题，具体包括其适用范围的不确定性、正当性基础的模糊性以及不同法域间实践差异带来的冲突。现有研究虽已关注欧美之间在数据保护理念上的差异及欧洲内部的治理张力，但对全球南方地区在后殖民与后威权背景下的特殊情境关注不足。本文的核心解决方案在于揭示后死亡隐私背后所蕴含的“政治性”——即其作为社会协商记忆与尊严的制度性工具，在不同文化、历史与政治语境下被建构的方式存在显著差异。通过整合法律学术成果与跨文化、跨历史的社会政治观察，论文从三大分析维度展开：大西洋两岸的法律范式分歧、欧洲内部数据治理的张力，以及全球南方的非西方语境，强调这些结构性差异共同构成了后死亡隐私的复杂政治图景。这一视角不仅深化了对隐私权延伸至死后阶段的理解，也为未来数据治理中如何平衡个体权利、集体记忆与社会价值提供了理论框架。
链接: https://arxiv.org/abs/2608.16905
作者: Mauricio Figueroa
机构: 未知
类目: Computers and Society (cs.CY); Computation and Language (cs.CL); Social and Information Networks (cs.SI)
备注:
Abstract:While the existence of postmortem privacy is increasingly acknowledged (such as the protection of the presence of deceased within digital spaces), far less attention has been paid to its internal instability: its scope (the extent of its application), justificatory foundations (why do we protect the deceased in the first place), and uneven articulation across jurisdictions (for example, some jurisdictions may tolerate or endorse practices that may be contestable in a different jurisdiction). This piece unearths the internal diversity of the concept by illuminating specific points of tension and conflict that the notion of postmortem privacy evokes. These points of tension are collectively refer to as the politics of postmortem privacy. To do so, this paper organises existing contributions of legal scholarship, placing them in dialogue with broader cultural, social, historical and political observations to illustrate the politics of postmortem privacy through three different loci of analysis: the transatlantic divide between European and American approaches, intra-European tensions within data protection governance, and postcolonial and post-authoritarian contexts in the Global South. While existing literature has glimpsed toward the former two, this piece contends that the latter deserves greater attention and inclusion in the debates around privacy and the dead. The piece explains, in continuity with existing scholarship, how postmortem privacy is assembled differently as a productive register through which societies negotiate memory and dignity, which play a great role in the governance of data of the dead and information flows.
[NLP-65] An Investigation of the NeurIPS and ICML 2025 Position Tracks
【速读】： 该论文旨在解决机器学习（Machine Learning, ML）顶会中，如NeurIPS与ICML的立场论文（Position Paper）赛道在议程设定（agenda-setting）功能上的局限性问题。尽管该赛道被设计用于推动领域发展方向，但其2025年公开可访问的评审稿件池却呈现出以“改革式批判”（reformist critique）为主导的倾向，即多数论文集中于对现有基准、评估方法或技术范式的批评，而非提出开创性的新框架或研究路径。其解决方案的关键在于：通过明确鼓励并系统性吸纳具有“议程引领性”（direction-setting）特征的研究工作，与现有的改革式批判并行共存，而非取代后者。研究通过对2025年两大会场所有可获取投稿的系统性审计发现，尽管改革式论文在“成果耦合度”（artifact-coupling）指标上得分较高，但其证据深度与审稿评分之间无显著关联；相比之下，历史上具有里程碑意义的议程转变型论文（如AlexNet、Transformer、《AI安全中的具体问题》等）普遍提供了可构建、可检验或可反驳的新工具或范式，如测量协议、基准提案、原型实现、数据集卡片或可证伪的实验计划。因此，作者建议从征稿启事（Call for Papers, CFP）层面实施四项干预措施，以促进更丰富的研究类型混合，同时保留对现有批判性工作的支持。
链接: https://arxiv.org/abs/2608.16894
作者: Fan Yang,Wenkai Li,Jun Liu
机构: Fujitsu Research(富士通研究); 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:ML venues shape what kinds of research claims become legible to reviewers and what forms of evidence count as rigorous. The NeurIPS and ICML Position Paper Tracks were created for agenda-setting work, making their early composition worth auditing. \textbfThis paper argues that the publicly accessible 2025 reviewed pool is dominated by reformist critique, and that the track should explicitly solicit direction-setting work alongside, not in place of, the reformist critiques it already hosts well. We audit every accessible submission to the NeurIPS 2025 and ICML 2025 Position Tracks under a pre-specified rubric, and compare the resulting pattern with a reference class of widely recognized agenda-shifting ML papers. Three-quarters of audited submissions critique an existing benchmark, evaluation, or methodology; these papers score highly on our artifact-coupling rubric, but evidentiary depth does not predict reviewer rating. The reference class (AlexNet, the Transformer, Concrete Problems in AI Safety, and others) differs from the accessible reviewed pool in \emphartifact kind: agenda-shifting papers typically gave the field something new to build on, test against, or contest, such as a measurement protocol, benchmark proposal, toy implementation, dataset card, audit template, or falsifiable experimental program. We close with four CFP-level interventions aimed at broadening the submission mix without displacing the critiques the track already hosts well.
[NLP-66] Potential of ChatGPT in predicting stock market trends based on Twitter Sentiment Analysis
【速读】： 该论文旨在解决如何利用生成式 AI（Generative AI）从非结构化社交媒体数据中提取有效信息，以预测股票市场走势的问题。其核心挑战在于如何准确识别并量化社交媒体情绪对特定股票价格变动的影响。该研究的解决方案关键在于构建一个基于情感分析与大语言模型（如 ChatGPT）的融合框架，通过解析 Twitter 平台上的用户发帖内容，自动判断其情感倾向（正面、负面或中性），并将其与微软（Microsoft）和谷歌（Google）两家科技巨头的次日股价变化进行关联分析。实验结果表明，ChatGPT 的情感评估与后续股价表现之间存在显著正相关性，验证了其在金融预测场景中的有效性与可扩展性。这一方法不仅凸显了生成式 AI 在处理复杂自然语言语境下的优势，也揭示了其在智能投研与市场预测中的潜在应用价值。
链接: https://arxiv.org/abs/2311.06273
作者: Ummara Mumtaz,Summaya Mumtaz
机构: 未知
类目: atistical Finance (q-fin.ST); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: total 11 pages including references, 4 figures and one table
Abstract:The rise of ChatGPT has brought a notable shift to the AI sector, with its exceptional conversational skills and deep grasp of language. Recognizing its value across different areas, our study investigates ChatGPT’s capacity to predict stock market movements using only social media tweets and sentiment analysis. We aim to see if ChatGPT can tap into the vast sentiment data on platforms like Twitter to offer insightful predictions about stock trends. We focus on determining if a tweet has a positive, negative, or neutral effect on two big tech giants Microsoft and Google’s stock value. Our findings highlight a positive link between ChatGPT’s evaluations and the following days stock results for both tech companies. This research enriches our view on ChatGPT’s adaptability and emphasizes the growing importance of AI in shaping financial market forecasts.
信息检索
[IR-0] VisDocAgent Bench: Benchmarking Agents for Visually Rich Document Retrieval
链接: https://arxiv.org/abs/2608.17889
作者: Lexiang Hu,Yanzhao Zhang,Mingxin Li,Dingkun Long,Yikang Li,Fuwei Zhang,Yisen Wang,Zhouchen Lin
类目: Information Retrieval (cs.IR)
备注:
Abstract:Visually rich documents encode relevance through language, layout, structured visual elements, and corpus context, yet retrieval is typically evaluated by one-shot query–page matching. Agentic-search benchmarks usually score downstream question answering or report generation, leaving document ranking under iterative evidence acquisition underexplored. We introduce VisDocAgentBench, a closed-corpus benchmark comparing static and agentic retrieval under a shared ranked-output contract. It contains 2,375 pages from 100 documents and 120 unique-target queries balanced across direct, one-bridge, and two-bridge evidence structures. Relation-preserving construction yields semantic, relational, and visual queries, followed by full-document review and hard-negative validation. A strong late-interaction visual retriever reaches 97.50% Recall@1 on direct items but 2.50% on two-bridge items, exposing the limits of query–target matching when relevance depends on corpus context. Agents recover much of this loss, but planner choice and retrieval representation remain decisive. Every planner performs better with visual retrieval, whose best R@1 reaches 67.50% versus 37.50% for OCR-text. Ablations identify iterative search and page inspection as consequential capabilities, and providing the complete support context improves ranking on both routes. Trace analysis localizes the remaining losses to target discovery, candidate examination, and evidence-role integration. These findings motivate retrieval agents that combine modality-preserving discovery with evidence-directed verification.
[IR-1] DEPT: Document Embedding Preservation Tuning for Unified Query Expansion and Retrieval
链接: https://arxiv.org/abs/2608.17632
作者: Jingyuan Wang,Richong Zhang,Zhijie Nie,Mingxin Li,Yanzhao Zhang
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) can both expand underspecified queries and encode text as dense representations, suggesting a unified model for query expansion and retrieval. Existing systems usually rely on prompted expansions, independently trained modules, or staged optimization, leaving generated expansions only indirectly aligned with the retrieval loss that judges them. We train a single decoder-only LLM end to end, where the same model generates the expansion and encodes both the expanded query and candidate documents. This unified setting creates a moving-target problem: retrieval supervision should improve query-side expansion, but the same update also shifts the document embeddings that serve as retrieval targets. We introduce Document Embedding Preservation Tuning (DEPT), which keeps tuned document embeddings close to cached initial embeddings while allowing retrieval gradients to pass through straight-through decoding into the generator. DEPT converts joint query–document movement into query-side adaptation against approximately stable, whitened document embeddings that support index reuse and online hard-negative mining. Experiments with Qwen3-4B-Instruct-2507 and LLaMA-3.2-3B-Instruct on five datasets in BEIR benchmark show that DEPT improves average retrieval quality over training-free, independently trained, and staged unified baselines, while ablations isolate the effects of preservation, whitening, end-to-end expansion training, and online negatives. Code is available at this https URL.
[IR-2] From Student Risk Prediction to SC2R: Semantics-Constrained Counterfactual Recourse for Educational Decision Support
链接: https://arxiv.org/abs/2608.17618
作者: Ngoc Luyen Le,Marie-Hélène Abel,Bertrand Laforge
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Learning analytics models can identify students at risk of poor performance, but they do not directly indicate which interventions are feasible, actionable, and compatible with educational constraints. This paper introduces SC2R, a semantics-constrained counterfactual recourse framework for educational decision support. SC2R combines a calibrated predictive model, integer-programming-based recourse generation over discrete action variables, a lightweight RDF vocabulary for intervention-plan representation, and SHACL validation for enforcing timing, budget, immutability, and availability constraints. The framework is evaluated offline on the OULAD dataset using snapshots constructed relative to each assessment at two decision horizons. Results show that the predictive component provides strong performance, that compact intervention plans can be generated at scale, and that semantic validation reveals infeasible plans that lighter optimization-only settings would otherwise accept. Rather than claiming causal improvement in student outcomes, this work shows that counterfactual recourse becomes more operationally meaningful in education when recommendations are not only model-valid, but also semantically feasible and machine-checkable.
[IR-3] Once Generated Ranked: End-to-End Generative Slate Recommendation with Unified Semantic-Collaborative IDs
链接: https://arxiv.org/abs/2608.17613
作者: Yang Hu,Jiayi Guo,Jingui Ma,Ning Li,Jiangling Qin,Yanming Li,Yang Deng,Xiaoshuang Chen,Kaiqiao Zhan
类目: Information Retrieval (cs.IR); Social and Information Networks (cs.SI)
备注: 18 pages, 3 figures
Abstract:Slate recommendation treats a slate rather than an individual item as the recommendation unit, requiring joint optimization of item interactions and slate utility. Existing approaches typically separate candidate generation from ranking and restrict optimization to retrieved candidates. Generative recommendation with Semantic IDs (SIDs) offers a path to end-to-end recommendation, but existing SID construction often lacks recommendation-aware semantics and effective local collaborative signals, while next-token prediction is misaligned with slate-level objectives. We propose OGR, an end-to-end framework that directly generates ordered slates-“Once Generated, Ranked.” OGR first introduces TUSID, which adaptively fuses item-specific semantic and local collaborative information into hierarchical SIDs. It then uses list-wise preference planning and pipelined position-wise SID decoding to model global preferences and inter-item dependencies while generating ordered slates. We further propose SPA, a reward-guided conservative policy optimization method that aligns generated slates with user preferences beyond likelihood imitation. Offline experiments show that OGR outperforms representative baselines, with 48.2% and 27.2% relative NDCG@5 gains on industrial and public datasets, respectively. Online A/B testing on Kuaishou further yields a 1.120% improvement in Effective Views.
[IR-4] Empowering Compact LLM s with Fusion of Layer-wise Exits for Recommendation ICDM’26
链接: https://arxiv.org/abs/2608.17316
作者: Xurong Liang,Tong Chen,Quoc Viet Hung Nguyen,Jianxin Li,Xiangliang Zhang,Hongzhi Yin
类目: Information Retrieval (cs.IR)
备注: Accepted by ICDM’26
Abstract:Large language model-based recommender systems (LLM-RSs) have demonstrated remarkable capabilities, but are computationally unsustainable for many real-world applications. Compact LLMs offer a practical alternative, yet their reduced capacity often requires reasoning or knowledge distillation methods that increase latency or depend on larger models. Combined with autoregressive generation, these approaches face severe scalability bottlenecks. In contrast, discriminative LLM-RSs enable efficient full-corpus ranking through embedding similarity, but compact backbones remain limited in expressiveness and structural adaptivity. We propose the Fusion of Layer-wise Exits for Sequential Recommendation (FLEXRec), a discriminative framework that enhances compact LLMs while retaining scalable full-corpus ranking. FLEXRec inserts prediction heads (i.e., exits) at multiple transformer layers and adaptively fuses their score distributions. An adaptive continuous router (AC-Router) dynamically selects both the number and identity of exits for each user sequence, while a novel target-k hinge loss regulates routing sparsity. Experiments on three real-world datasets with Qwen 3 1.7B and Llama 3.2 3B show that FLEXRec achieves state-of-the-art accuracy among compact-backbone methods while remaining highly efficient. Code: this https URL
[IR-5] Overview of the TREC 2025 Product Search and Recommendation Track
链接: https://arxiv.org/abs/2608.17138
作者: Dean E. Alvarez,Surya Kallumadi,Daniel Campos,ChengXiang Zhai,Alessandro Magnani,Rikiya Takehi,Michael D. Ekstrand
类目: Information Retrieval (cs.IR)
备注:
Abstract:In the past few years, consumers have moved the bulk of their product exploration and purchasing efforts online seeking speed, convenience, and price comparison with ease unimaginable for in-person shopping. As product catalogs have grown in diversity and size product search and recommendation have become a cornerstone for e-commerce sites. Despite the widespread usage of search engines in e-commerce, there is no high-quality dataset designed to evaluate end-to-end retrieval quality. In 2025, we ran a revised and continued version of the Product Search track previously run at TREC 2023 and TREC 2024. The 2025 product search track had two tasks: query expansion and related-product recommendation. The related-product recommendation task is particularly novel, providing an annotated data set of product relationships that distinguishes between complementary and related products. We anticipate the data from this track will enable better recommendation and search applications that reflect user needs, as a building block for conversational product discovery experiences. Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2608.17138 [cs.IR] (or arXiv:2608.17138v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2608.17138 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-6] owards welfare-oriented recommendations in activity-travel behavior
链接: https://arxiv.org/abs/2608.16922
作者: Ekin Ugurel,Takahiro Yabe
类目: Information Retrieval (cs.IR); Computers and Society (cs.CY)
备注:
Abstract:While mainstream recommender systems (RS) rely on diverse heuristics to rank alternatives, they generally lack a principled account of user welfare (i.e., whether accepting the recommendation will leave the user better off than other alternatives). The problem is particularly acute in activity-based travel behavior, where users incur costs they cannot recoup (i.e., energy, time) regardless of eventual satisfaction. As a result, existing systems may recommend options based on popularity or collaborative filtering, but may still leave users worse off than nearby or self-selected alternatives. We address this gap by introducing a welfare-oriented framework for activity recommendation that evaluates suggestions in terms of net utility, defined as experienced benefit minus travel costs. Specifically, we formalize two operational decision criteria: Positive Utility Probability (PUP) recommends only when the probability of non-negative net utility exceeds a threshold, while Regret Minimization (RM) recommends only when expected regret relative to the user’s best organic alternative falls below a tolerance level. To evaluate these criteria, we develop an agent-based simulation in which heterogeneous synthetic travelers interact with multiple RS over time in a spatial environment with realistic travel costs, congestion, and behavioral feedback loops. This framework enables controlled counterfactual evaluations, and offers a practical foundation for designing RS that treat user welfare as a primary objective rather than an incidental byproduct.
[IR-7] CARA: Cognitive Adaptive Recommendation Agent
链接: https://arxiv.org/abs/2608.16919
作者: Weijun Gao,Jinyang Dong,Chuanru Ren,Hengxiao Li
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advances in large language models and agent-based recommendation frameworks have introduced new opportunities for more flexible and context-aware recommendation. However, existing methods still largely rely on semantic matching, end-to-end generation, or loosely structured agent workflows, without explicitly modeling how user preferences are processed and translated into final decisions. To address this limitation, we propose CARA, a cognitively inspired recommendation framework that formulates recommendation as a structured decision-making process. The core intuition of CARA is that user decisions are jointly shaped by two complementary mechanisms: intuitive affective preference and deliberate rational evaluation. Accordingly, CARA organizes recommendation into two coordinated stages: candidate filtering, which narrows the search space based on coarse-grained preference constraints, and dual-perspective decision modeling, which captures recommendation decisions through affective and rational judgment. We further introduce a boundary-aware KTO strategy that prioritizes instructions the model can solve occasionally but not consistently, thereby increasing the density of informative preference signals. Extensive experiments on three Amazon Reviews domains show that CARA achieves the best performance on most evaluation metrics, with relative improvements of up to 10.15% over the baseline.
[IR-8] Sparse Coverag e: Semantic Center Representations for Patent Prior-Art Retrieval
链接: https://arxiv.org/abs/2608.16918
作者: You Zuo(ALMAnaCH),Kim Gerdes(LISN, Qatent, STL),Éric de la Clergerie(ALMAnaCH),Benoît Sagot(ALMAnaCH)
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Patent prior-art retrieval is a recall-oriented search task over long and highly structured technical documents. Dense retrieval improves semantic matching, but single-vector representations may compress multiple technical components, functions, and constraints into a single embedding. We propose Sparse Coverage, an unsupervised semantic retrieval framework that maps local span embeddings to a sparse vocabulary of embedding-space centers. The centers are selected with a coverage-oriented k-center objective, and spans activate nearby centers to produce sparse representations compatible with inverted-index retrieval. Experiments on CLEF-IP 2013 show that Sparse Coverage matches or exceeds the document-level recall of strong dense patent encoders in several configurations, while remaining competitive for passage-level retrieval. By combining local semantic evidence with sparse inverted-index search, Sparse Coverage provides an effective first-stage retrieval approach for patent search.
[IR-9] Grounding Healthcare LLM s in a Causal Knowledge Graph: Framework Metrics and a Cardiovascular Pilot
链接: https://arxiv.org/abs/2608.15382
作者: Ummara Mumtaz,Aimen Noor,Awais Ahmed
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR); Quantitative Methods (q-bio.QM)
备注:
Abstract:Large language models (LLMs) are increasingly proposed for healthcare decision support, but their evaluations still reward single-answer accuracy rather than reasoning about interventions, mechanisms, harms, evidence, and uncertainty. We propose a reproducible, graph-centered evaluation framework for intervention-oriented LLM behavior in healthcare and stress-test it in a cardiovascular pilot. The framework has four components: (i) a domain causal knowledge graph in which assertions are first-class, provenance-preserving nodes with stable identifiers; (ii) a scenario-conditioned subgraph extraction step that, given any clinical scenario, retrieves the relevant reified-assertion subgraph; (iii) four controlled grounding conditions that vary how the retrieved subgraph is composed into the model’s context (ungrounded C1, knowledge-graph C2, causal-graph C3, integrated C4); and (iv) an automated scoring pipeline, anchored on assertion identifiers, that computes intervention accuracy, and other evaluation measures on a single pass. To test the framework, we built a category-balanced scenario generator across eight reasoning failure modes and instantiated it on a cardiovascular graph. The metric panel discriminates conditions along interpretable, non-redundant axes: C4 obtains the strongest causal edge F1 (0.838), adverse-effect F1 (0.833), evidence accuracy (0.738), and unsupported claim rate (0.114), while C1 obtains the highest raw intervention accuracy (0.948) with no measurable causal or evidential grounding.
[IR-10] Intent-Driven Dynamic Chunking: Segmenting Documents to Reflect Predicted Information Needs
链接: https://arxiv.org/abs/2602.14784
作者: Christos Koutsiaris
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 8 pages, 4 figures. Code available at this https URL
Abstract:Breaking long documents into smaller segments is a fundamental challenge in information retrieval. Whether for search engines, question-answering systems, or retrieval-augmented generation (RAG), effective segmentation determines how well systems can locate and return relevant information. However, traditional methods, such as fixed-length or coherence-based segmentation, ignore user intent, leading to chunks that split answers or contain irrelevant noise. We introduce Intent-Driven Dynamic Chunking (IDC), a novel approach that uses predicted user queries to guide document segmentation. IDC leverages a Large Language Model to generate likely user intents for a document and then employs a dynamic programming algorithm to find the globally optimal chunk boundaries. This represents a novel application of DP to intent-aware segmentation that avoids greedy pitfalls. We evaluated IDC on six diverse question-answering datasets, including news articles, Wikipedia, academic papers, and technical documentation. IDC outperformed traditional chunking strategies on five datasets, improving top-1 retrieval accuracy by 5% to 67%, and matched the best baseline on the sixth. Additionally, IDC produced 40-60% fewer chunks than baseline methods while achieving 93-100% answer coverage. These results demonstrate that aligning document structure with anticipated information needs significantly boosts retrieval performance, particularly for long and heterogeneous documents.
人机交互
[HC-0] Colour Blinded by the Noise IEEE-VIS2026
链接: https://arxiv.org/abs/2608.17976
作者: Harriet Mason,Rachel Rogers,Alison Kleffner,Dianne Cook
类目: Human-Computer Interaction (cs.HC); Methodology (stat.ME)
备注: To be presented at IEEE VIS 2026 and published in IEEE TVCG in 2027
Abstract:Uncertainty visualisation is important for data transparency, especially for map visualisations where data is often aggregated. Despite the importance of this area, studies evaluating uncertainty visualisation lack consensus and produce conflicting results. This work introduces a new evaluation approach for uncertainty visualisation that attempts to assess uncertainty as noise, rather than signal. We evaluate five methods of visualising uncertainty: standard choropleth maps, value/variance bivariate maps, value-suppressing uncertainty palettes, overlaid sampling, and pixelated sampling maps. Built on principles of implicit testing, we put an ‘uncertainty visualisation’ spin on the classic Ishihara colourblind test to create a novel test that is able to evaluate uncertainty as noise. We compare signal visibility to conventional hypothesis tests at various levels of group separation. By building our experimental design on top of established graphics theory, we isolate the plot components that facilitate successful signal suppression and establish foundational theory for the perception of uncertainty visualisation.
[HC-1] Analysis of Types of Inquiries in Student-AI Interaction: A case study of two CS2 tasks
链接: https://arxiv.org/abs/2608.17919
作者: Matin Amoozadeh,Amin Alipour
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:Background and Context: Question and inquiry are integral parts of knowledge seeking and learning. Despite their importance, students tend not to ask enough questions in the classroom. However, studies have shown that students interact extensively with generative AI systems for learning and problem solving. Objective: In this paper, we seek to better understand the types of questions that students ask AI systems, and how those questions evolve during problem solving and across tasks. Method: We use the Graesser et al. taxonomy to classify students’ inquiries into 18 types. We develop a few-shot learning approach to automatically classify students’ interactions with AI into these categories. We use this system to analyze 830 interactions of CS2 students across two programming tasks. Findings: Our results suggest that a small subset of question types accounts for the majority of student inquiries, and that the types of questions students ask change substantially as the task progresses. Subjects: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.17919 [cs.HC] (or arXiv:2608.17919v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2608.17919 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Matin Amoozadeh [view email] [v1] Tue, 18 Aug 2026 15:45:06 UTC (1,288 KB)
[HC-2] AdaLens: Interactive Storyline for Monitoring and Steering Long-Running Agent ic Data Analysis
链接: https://arxiv.org/abs/2608.17834
作者: Yangtian Liu,Yan Miao,Shuhan Liu,Yunfan Zhou,Dae Hyun Kim,Di Weng,Yingcai Wu
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models are pushing data science toward increasingly autonomous and agentic workflows, with recent systems already supporting multi-step and long-running analyses. As these workflows become more autonomous, conventional interfaces no longer provide adequate support for two critical requirements: observability for understanding an agent’s evolving reasoning and evidence, and steerability for redirecting low-value directions or deepening promising ones during execution. Existing interactive approaches improve process visibility and open intervention points, but they remain largely designed for discrete, turn-by-turn exchanges rather than the parallel branches and evolving decision structures of long-running agentic analysis. We study this need as interactive oversight in long-running agentic data analysis and present AdaLens, an interactive system for monitoring and steering ongoing runs. AdaLens combines a storyline-based representation that unifies analytical plans, execution progress, intermediate findings, and data-column involvement with steering interactions grounded in these analytical elements for directional guidance and execution control. We evaluate AdaLens through two case studies and a user study, examining how it supports analysts in monitoring and steering long-running agentic data analysis.
[HC-3] MotoSafety: Edge-AI with Learned Temporal Importance for Two-Wheeler Collision Risk Assessment Under Time Pressure
链接: https://arxiv.org/abs/2608.17823
作者: Sumit S. Shevtekar,Chandresh K. Maurya,Gourab Sil,Subasish Das
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: 40 pages
Abstract:Powered two-wheeler riders face critical safety challenges in low- and middle-income countries, yet limited studies exist on how cognitive stressors such as Time Pressure influence collision risk. To address this gap, we introduce a large-scale dataset of over 129,000 labeled multivariate time-series sequences from 153 simulator rides by 51 participants under No, Low, and High TP, capturing 64 features across vehicle dynamics, control inputs, proximity, and behavioral violations. Building on this dataset, we propose MotoSafety, a novel edge-AI architecture grounded in the Learned Temporal Importance principle. MotoSafety achieves 94.97% accuracy and 99.33% ROC AUC, outperforming ten baselines, including TimesNet and LLM4TS, and achieves 0.039 MSE and 0.094 MAE for forecasting (4.4x lower error than Time-LLM and iTransformer). With only 1.15M parameters and 0.135 ms latency, it is suitable for edge deployment on low-cost CPU hardware. Using ground truth TP as an inductive bias improves accuracy from 94.09% to 94.97%, while predicted TP achieves 94.82%. Using only 21 IMU+GPS features, it achieves 93.91% accuracy, indicating practical deployment. Beyond PTW safety, the architecture shows better transferability to human activity (97.66%) and clinical (99.65%) domains. This lightweight framework advances PTW collision risk assessment, supporting the Safe System Approach for Intelligent Transportation Systems.
[HC-4] Interpretable Humans Alien LLM s: Expert Analysis of Latent Structures in Assessment Responses
链接: https://arxiv.org/abs/2608.17810
作者: Alona Strugatski,Licol Zeinfeld,Jason Cooper,Shelley Rap,Gil Schwarts,Giora Alexandron
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: Accepted for publication at AIME 2026
Abstract:The evaluation of large language models (LLMs) relies heavily on human-designed assessments, implicitly assuming that AI and humans employ similar underlying cognitive constructs. Challenging this assumption, we investigate whether the latent factors governing LLM performance carry the same substantive, human-interpretable meaning as the cognitive constructs governing human learners. Using responses from humans and six LLMs across quantitative reasoning and chemistry assessments, we conducted Exploratory Factor Analysis (EFA) separately for both groups. Subject-Matter Experts (SMEs) then blindly evaluated the resulting factor graphs to ascribe pedagogical meaning to the emerged constructs. SMEs successfully interpreted most of the human-derived factors. Conversely, they could not ascribe meaning to any LLM-derived factors in quantitative reasoning and interpreted only half of the LLM factors in chemistry. By combining data-driven EFA with blind expert interpretation, this framework shows that LLMs frequently operate on statistically opaque mechanisms distinct from human reasoning.
[HC-5] From Substitution to Scaffolding: Breaking the Self-Reinforcing Harm Cycle of AI in Education (and Beyond)
链接: https://arxiv.org/abs/2608.17451
作者: Lucile Favero,Juan Antonio Pérez-Ortiz,Tanja Käser,Nuria Oliver
类目: Human-Computer Interaction (cs.HC)
备注: accepted to ACM AI Leadership Summit
Abstract:Artificial intelligence is being adopted in educational settings faster than its consequences are understood. We argue that the central risk is misalignment: AI that eliminates human effort erodes the very capacities education is meant to build. We organize this risk into an integrative framework of four interrelated dimensions -cognition, agency, emotional well-being, and ethics- linked by a self-reinforcing cycle where cognitive offloading reduces effort, weakens agency, and compounds emotional and ethical harm. We ground the framework in the perspective of a small cohort of students: an exploratory analysis of 49 International Baccalaureate argumentative essays about the impact of AI reveals that learners perceive these risks, with 80% of essays reporting that AI reliance reduces thinking. At the same time, the essays articulate a consistent vision of the AI the students want: systems that support rather than replace learning by withholding immediate answers, prompting recall, and encouraging reflection through questions instead of solutions. These desiderata closely align with established principles from the learning sciences. Building on these insights, we propose a single design principle, scaffold, do not substitute. We argue that this principle extends beyond education. It represents a broader challenge for the AI ecosystem: any system that mediates human thinking can either weaken human capabilities through substitution or strengthen them through scaffolding. We conclude by outlining a research agenda for developing AI systems that foster enduring human capacity, an imperative not only for learners but, ultimately, for democratic societies.
[HC-6] he Oracle of Chemnitz: An interactive art installation to reanimate old things in a garag e featuring a rotary phone
链接: https://arxiv.org/abs/2608.17407
作者: Karola Köpferl,Albrecht Kurze
类目: Human-Computer Interaction (cs.HC)
备注: In ThingsCon State of Responsible Technology 2026 - RESIZE REMIX REGEN (pp. 67-81). Stichting ThingsCon Amsterdam
Abstract:Garages have a long tradition of tinkering, creativity and innovative change. School of Garage, a participatory artistic summer school project in Chemnitz, the European Capital of Culture 2025, took up this tradition and turned old Eastern Bloc garages into temporary ateliers for collaborative making and discussion. In our HackLab garage we conceptualized and created the Oracle of Chemnitz within one week. It gives a place filled with history back its stories. It is an interactive installation of artifacts from the past typically found in garages: an old typewriter, radio, desk, tires, mixer and a rotary-dial telephone. Each got a name, personality and story to tell. The phone rings when a visitor approaches. Once answered, it asks for name and month of birth before a story about a device is told, along with hints to other places in the city. Around 2,700 visitors interacted with the system over three months.
[HC-7] Procedural Collapse: A Structural Account of Disengagement in LLM -Assisted Writing
链接: https://arxiv.org/abs/2608.17326
作者: JaeWon Kim,Katelyn Mei
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:When students use large language models for writing, the dominant explanation for disengagement is dispositional: they are over-reliant, and the remedy is to scaffold self-regulation. We argue that a structural explanation is needed, offering an alternative basis for design interventions to support appropriate AI-assisted writing. Current LLM writing interfaces induce procedural collapse: the replacement of an iterative, self-paced writing process with a single output that shifts the writer’s task from generation to comprehensive evaluation. Because that evaluation is costly, shallow engagement becomes the default, and the cognitive work writing was supposed to produce goes unperformed. The framework points toward design directions that reduce the burden on writers to self-regulate, including decomposed interaction, goal elicitation as a default first step, and single-level output. They complement metacognitive scaffolding by restructuring the interaction itself.
[HC-8] SleuthTalk: Supporting Historical Photo Identification with Private Workspaces for Collective Sensemaking and Deliberation
链接: https://arxiv.org/abs/2608.17297
作者: Liling Yuan,Vikram Mohanty,Kurt Luther
类目: Human-Computer Interaction (cs.HC)
备注: Published at ACM Collective Intelligence 2026 (to appear)
Abstract:Identifying individuals in historical photographs is a critical task across fields such as history, journalism, genealogy, and archival research. While AI-based facial recognition can efficiently generate candidate matches, it often produces ambiguous results that require deeper analysis and contextual interpretation. Existing platforms lack robust support for collaborative deliberation, especially in uncertain or high-stakes cases. We present SleuthTalk, a private collaborative workspace integrated into Civil War Photo Sleuth, designed to scaffold structured comparison, discussion, and group decision-making. SleuthTalk enables users to curate custom shortlists, annotate facial features, and build consensus through structured feedback. In a mixed-methods evaluation with experienced historical photo researchers, SleuthTalk enhanced self-reported confidence, surfaced diverse perspectives, and supported transparent, reflective identifications.
[HC-9] Balancing Safety and Autonomy: Accessibility-Oriented Interventions in Generative AI for Cognitive Impairment
链接: https://arxiv.org/abs/2608.17175
作者: Yibo Meng,Jingruo Chen,Lyumanshan Ye,Bingyi Liu,Zhicong Lu
类目: Human-Computer Interaction (cs.HC)
备注: Accepted to ASSETS 2026
Abstract:Generative AI systems are increasingly used by older adults with cognitive impairment for everyday tasks such as information seeking, health management, and communication. While these systems provide flexible, language-based support, their open-ended outputs introduce risks of over-reliance, misinterpretation, and inappropriate decision-making. Prior work has focused on usability and adoption, with limited attention to how system design shapes users’ participation in decision-making and the distribution of agency in care contexts. We present a qualitative study of 45 individuals with cognitive impairment and their caregivers. We identify five accessibility-oriented mechanisms: AI Capability Constraint, Human Oversight Embedding, Cognitive Engagement Maintenance, Human-AI Relationship Regulation, and Risk Transparency and Control, through which systems structure interaction. These mechanisms both support and constrain users by redistributing decision-making across users and caregivers. We show that their effects vary by impairment level: while protective mechanisms support users with severe impairment, they can restrict autonomy for those with mild impairment. As impairment progresses, tensions become less visible as user participation diminishes. Our findings highlight the need for dynamic designs that balance safety and autonomy in AI-supported care.
[HC-10] KnowSim: Evaluating Information Calibration in LLM Assistants with User Simulators that Learn
链接: https://arxiv.org/abs/2608.17150
作者: Yoonjoo Lee,Hyoungwook Jin,Tae Soo Kim,Shaoyang Zhang,Philippe Laban,Q. Vera Liao
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: 30 pages, 6 figures, 16 tables
Abstract:To effectively collaborate with users on knowledge-intensive tasks, Large Language Models (LLMs) must perform information calibration: matching content to a user’s evolving understanding and cognitive capacity. Yet user simulators used to evaluate and train LLMs do not explicitly model user knowledge so they neither produce realistic interactions across knowledge levels nor reflect how interactions unfold as that knowledge evolves. To close this gap, we introduce KNOWSIM, an evaluation framework built around a user simulator that maintains explicit knowledge states, represented as a graph of Information Units with prerequisite relationships, that evolve under update rules grounded in learning theory. KNOWSIM computes three metrics (Knowledge Gain, Delivery Calibration, Cognitive Overload) directly from the knowledge state trajectory, reflecting key mechanistic aspects of information calibration. We validate KNOWSIM against 705 human-AI sessions across two domains, stratified by knowledge level: its rankings align significantly with human judgments (73-74% sign agreement), outperforming three baseline simulators. Applied to 9 LLMs, KNOWSIM reveals that the best model shifts by user knowledge level, revealing aptitude-treatment interactions invisible to standard evaluation.
[HC-11] Health Inquiry with AI: How Empathetic Expression and Conversational Contexts Shape Users Communicative Acts
链接: https://arxiv.org/abs/2608.17144
作者: Xi Zheng,Xuyu Yang,Can Liu,Yuhan Luo
类目: Human-Computer Interaction (cs.HC)
备注: 5 pages, 2 figures. To appear in UbiComp Companion 2026 (October 11-15, 2026, Shanghai, China)
Abstract:As online health information-seeking shifts to conversational AI, high-quality information retrieval increasingly relies on users’ ``communicative acts’'(proactively sharing and seeking information)—similar to how effective diagnosis and personalized guidance are elicited in patient-clinician communication. Drawing on health communication research, this study examines how a chatbot’s modality of empathetic expression (Verbal, Visual, Multimodal) and the conversational context (General, Sensitive, Mental Health) influence these acts through a 2 x 2 x 3 within-subjects experiment (N = 48). The results revealed that while verbal and multimodal empathy significantly increased reply length, communicative acts were largely shaped by conversational context, with Sensitive context triggering more question-asking and Mental Health context leading to heightened concerns, assertive responses, and unprompted information disclosure. Combined with qualitative findings, we discuss design implications for building context-sensitive AI health inquiry systems that can encourage active user participation.
[HC-12] oward Personal Intelligence Through Cooperative Observation
链接: https://arxiv.org/abs/2608.17128
作者: Yashar Talebirad,Osman Jime,Ali Parsaee,Eden Redman,Yongbin Kim,Osmar R. Zaiane
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注:
Abstract:A personal AI system needs a model of the user’s goals, constraints, and ongoing commitments to plan and act on their behalf, and the quality of that model is bounded by what the system can observe. Broader observation does not by itself improve assistance because a bounded system must select and compress information for the task at hand. We argue that this observation bottleneck has a cooperative structure: the system builds a partial model of the user’s changing life, the user evaluates its actions, and the user’s consent and control shape what it can observe next. Useful and inspectable behavior can give users a reason to maintain or expand the observation channel, while failures can lead them to correct, narrow, revoke, or abandon it. We use the term cooperative observation for this feedback loop among usefulness, trust, and future access, and propose it as a framework for personal intelligence. We report a preliminary single-subject account from Organizm, a prototype used over six months, and outline evaluation directions for measuring how observation quality shapes personal AI.
[HC-13] A Multiplication-Free Feature Extractor for Signal Classification: Keyword Spotting Case Study
链接: https://arxiv.org/abs/2608.17108
作者: Radu Dogaru,Ioana Dogaru
类目: ound (cs.SD); Human-Computer Interaction (cs.HC); Audio and Speech Processing (eess.AS)
备注: 5 pages, 3 figures, 2 tables, 1 algorithm, submitted to IEEE Signal Processing Letters
Abstract:A very low complexity feature extractor called next iRDT is proposed and evaluated for the problem of keyword spotting (KWS). Unlike any other types of feature extractors including the widely used MFCC, or adaptive, CNN-based ones, our algorithm is multiplier-free and it employs only simple, energy-efficient arithmetic operators. Since keyword-spotting of speech commands (KWS) is a typical application for TinyML platforms requiring low complexity for the signal classification chain, we consider it as a case study to evaluate complexity and functional performance. If properly tuned, iRDT demonstrates similar accuracy to solutions based on MFCC or CNN-based extractors using baseline classifiers on Google’s KWS 12-classes dataset. With a different classifier the system achieved 94.7% validation accuracy. Processing times on CPU for the proposed feature extractor, are at least one order of magnitude smaller than for the MFCC. The proposed algorithm has a very low hardware footprint, making it ideal for ultra-low power edge devices. Code and demo are available [18].
[HC-14] Appearing Legitimate is Not Enough: Interrogating Synthetic Agents in Representational Processes through a Participatory Design Lens AAAI
链接: https://arxiv.org/abs/2608.17099
作者: Aditya Nayak,Aditi Vashistha,Alissa Centivany,Aakash Gautam
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注: 13 pages total, 4 figures, accepted to the 9th AAAI Conference on AI, Ethics, and Society (AIES 2026)
Abstract:Synthetic agents built atop LLM-based foundation models are gaining popularity as substitutes for human participants across research contexts, including user-testing, market-research, computational social science, surveys, and qualitative research. We are also witnessing an extension of synthetic agents into experimental implementations of policy consultation, jury deliberation, humanitarian diplomacy, and similar contexts where human participation and representation are central to the perceived legitimacy of the institutional processes. The value of participation extends beyond informational contributions and consensus generation; participation is a necessary, legitimizing condition for democratic political institutions and processes. Treating synthetic agents as human substitutes raises serious political, representational, and ethical concerns. Participatory Design’s modes of engagement — probing, priming, understanding, and generating — offer helpful tools for engaging with representational questions of personhood. We apply the lens to three case studies of synthetic agents substituting for personhood at varying representational scales: local policy, enterprise jury deliberation, and global diplomacy. We argue that legitimacy and personhood are integral and mutually constitutive while identifying the ethical, representational, and methodological risks of using synthetic agents in representational processes. We conclude by proposing soft and hard boundaries for designing oversight on LLMs and synthetic agents in representational processes.
[HC-15] Wasted large language models : A life cycle thinking approach
链接: https://arxiv.org/abs/2608.17055
作者: Erik Johannes Husom,Maria Emine Nylund,Ophelia Prillard
类目: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注: 5 pages, 1 figures. Accepted at the 2nd International Workshop on Low Carbon Computing (LOCO 2026), Lancaster University, United Kingdom, 10-11 September 2026. Part of the LOCO 2026 proceedings, arXiv: LOCO2026/P14
Abstract:Large Language Models (LLMs) are machine learning (ML) models that have an increasingly large carbon footprint through their development and use. Efforts to increase the energy efficiency of these models have not translated into reduced consumption due to rebound effects such as Jevons Paradox - that increased efficiency drives increased use. There is therefore a need for additional measures to solve this problem. We suggest that one possible way forward is to use life cycle thinking, and view LLMs as products that can become waste. With this perspective, we investigate the potential of the waste hierarchy from the EU’s Waste Framework Directive, which suggests five different measures for how to manage waste: prevention, reuse, recycling, recovery, and disposal. We examine how these measures can inform and motivate new types of thinking and approaches to reducing LLM waste and their environmental impact in general. Applying the waste hierarchy to LLMs highlights that preventing waste is essential for reducing the models’ environmental impact, mainly because it reduces the need for training new models. Prevention can be achieved through many existing methods for reusing, “recycling”, and “recovering” LLMs. Additionally, disposal can be important both for saving energy and for keeping a considerate attitude to the resources being spent on training LLMs. We also call to attention that prevention of unnecessary use of LLMs carry huge potential for lowering the climate impact of the models. Comments: 5 pages, 1 figures. Accepted at the 2nd International Workshop on Low Carbon Computing (LOCO 2026), Lancaster University, United Kingdom, 10-11 September 2026. Part of the LOCO 2026 proceedings, arXiv: LOCO2026/P14 Subjects: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG) Reportnumber: LOCO2026/P14 Cite as: arXiv:2608.17055 [cs.CY] (or arXiv:2608.17055v1 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2608.17055 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[HC-16] Why This and Not That? A Collaborative Reflection Approach for Understanding Thought Coverag e in Decision Making Support Dialog
链接: https://arxiv.org/abs/2608.17054
作者: Morita Tarvirdians,Hayley Hung,Catharine Oertel
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Conversational agents that support reflection for decision making often rely on adaptive dialogue policies that map observed user behavior to actions such as probing, deepening, or redirecting. Yet the same pattern can reflect a range of different reasons such as deliberate prioritisation or limited self-access. By modeling the observable pattern rather than the user’s reason for it, current policies risk premature assumptions about the user state and inappropriate next actions. To address this gap, we introduce a human-centered method for surfacing this hidden inference step. In a user study with 62 users and 232 collaborative moments, we pause a reflection-support agent when it would normally redirect the conversation, surface its observation, and ask users to interpret the pattern and decide how to proceed. We derive a taxonomy of nine interpretation categories and show that similar reflective states can call for substantially different follow-up actions. Our findings challenge the assumption that adaptive dialog policies can rely on observable behavior alone, and show how user-provided interpretations can inform more appropriate conversational actions.
[HC-17] What Cognitive Accessibility Reveals About Data Visualization IEEE-VIS2026
链接: https://arxiv.org/abs/2608.17039
作者: Keke Wu,Jinjuan Heidi Feng,Jonathan Lazar
类目: Human-Computer Interaction (cs.HC)
备注: Accepted to the 3rd Workshop on Accessible Visualization at IEEE VIS 2026
Abstract:Data visualization aims to augment human cognition and make data accessible to diverse audiences. As data increasingly shapes participation and decision-making across many domains, there is a growing need to examine whether prevailing assumptions in visualization adequately reflect the diversity of human abilities, experiences, and needs. We argue that cognitive accessibility provides a critical lens for examining these questions and functions as a stress test for visualization theory. Drawing on cognitive accessibility research and our experiences studying accessible visualization, we identify three interconnected assumptions that shape visualization research and practice: assumptions about what forms of cognition visualization supports, how accessibility is defined and measured, and whose needs and abilities are centered in design and evaluation. Making these assumptions explicit reveals opportunities to rethink longstanding approaches and open new directions. Ultimately, we believe that cognitive accessibility can serve as a catalyst for innovation, expanding what visualization supports, whom it serves, and the roles it plays in people’s lives.
[HC-18] LadderTeam: Dual-Agent Laddering Elicitation Framework
链接: https://arxiv.org/abs/2608.17029
作者: Manjushree Aithal,Alexander Kotz,James Mitchell
类目: oftware Engineering (cs.SE); Human-Computer Interaction (cs.HC)
备注: 4 pages, 1 figure, 2 tables, Accepted in ACM AI Summit 2026
Abstract:Eliciting detailed and actionable software requirements from end-users is a critical phase in the iterative development of a software product or application. To ensure the feedback collected is detailed and actionable, software teams can leverage the laddering interview technique. While effective for ensuring granular and actionable items from the software feedback, these interviews are subject to several limitations. They are traditionally a manual process associated with a time and financial burden, limiting scalability; interviewers must balance probing for depth while managing interviewee behavioral and cultural constraints. To address these limitations, we present \textbfLadderTeam, an open, reproducible framework that automates UX wireframe interviews using a dual-agent Large Language Model (LLM) architecture. An active interviewer agent executes one of three probing strategies (ACV, 5-Whys, and JTBD) to elicit actionable software requirements from usability feedback comments, while a concurrent background Judge agent evaluates probe-response pairs and triggers real-time guardrails to prevent topic drift. To rigorously evaluate LLM laddering without participant variance confounds, we introduce a controlled simulation methodology utilizing scripted ground-truth transcripts to isolate probe quality as the sole experimental variable. Across 216 interviews, \textbfLadderTeam achieved 99.1% chain convergence and an 81.0% ground-truth actionable response match (86.1% reluctant personality, 75.9% terse personality) with zero drift across all runs. All evaluation code, all transcripts, inputs, and a live demonstration platform will be open-sourced upon acceptance.
[HC-19] “It just kind of shows that I went somewhere”: An Exploratory Study of Fitness Data Sharing
链接: https://arxiv.org/abs/2608.17014
作者: Mara Solen,Thomas James Davidson,Emily Wall,Tamara Munzner
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注:
Abstract:The sharing of curated fitness data posts occurs frequently on fitness-focused social platforms such as Strava and on general social media platforms such as Instagram, which is a novel context for visualization. To better understand the process of sharing and designing fitness data posts, as well as the role of visualization within them, we conduct a constructivist grounded theory study. We conduct and analyze 18 semi-structured interviews with fitness data sharers. From our analysis of the data, we find three novel characteristics of fitness data sharing: (i) the role of visualization as providing proof that an individual did an activity, (ii) the importance of expressing individuality in posts, and (iii) design conformity to cultural norms. We also derive a set of design implications, including a need for more options for visualizations for activities without routes, more user control in fitness data sharing platforms, and maintained ease of use while increasing customization options.
[HC-20] Experiential Learning of Runtime Monitoring Using Pachinko
链接: https://arxiv.org/abs/2608.16898
作者: Miles Scharff,Maria Chemodanova,Mark Santolucito
类目: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Logic in Computer Science (cs.LO); Software Engineering (cs.SE)
备注: 5 pages, 2 figures, TEAL 2026
Abstract:We present documentation of a classroom assignment that teaches runtime monitoring through a creative embedded systems build: an interactive Pachinko game. The assignment centers on a dual-core ESP32 workflow in which students write RTLola specifications for monitors, compile these monitors to C, and deploy them alongside sensor and actuator control logic. Pachinko game events are logged in real time and used to trigger sound, animation, and motor behavior according to formal temporal logic specifications. This work showcases how formal methods can be taught in a hands-on, project-based setting for learners in a creative and classroom-scale setting. We also discuss portability: the assignment template, hardware stack, code base, and assessment approach are designed and documented to be replicated in other embedded systems, creative computing, or makerspace-style courses. This assignment was given to the students of Creative Embedded Systems (COMS3930) at Barnard College. Comments: 5 pages, 2 figures, TEAL 2026 Subjects: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Logic in Computer Science (cs.LO); Software Engineering (cs.SE) Cite as: arXiv:2608.16898 [cs.CY] (or arXiv:2608.16898v1 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2608.16898 Focus to learn more arXiv-issued DOI via DataCite
计算机视觉
[CV-0] From Corpora to Co-Evolving Capabilities: Capability-Centric Data Design for Generalist Image Generation
链接: https://arxiv.org/abs/2608.18076
作者: Xingjian Wang,Zhao Wang,Taihang Hu,Jun Zheng,Qing Jin,Qinye Zhou,Zhengtao Wu,Yongchao Du,Zuan Gao,Chao Lin,Yefeng Shen,Xiaoli Xu,Zhengze Xu,Hao Yan,Yuhang Yu,Mingzhou Zhang,Mengting Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 19 pages, 10 figures
Abstract:Large-scale image generation has benefited from advances in data scale, quality, rebalancing, and recaptioning, yet conventional pipelines typically optimize task-specific datasets in isolation. A central challenge is not only how to curate each task-specific corpus, but also how to organize heterogeneous supervision according to the dependencies among generative capabilities. We present a \textbfcapability-driven data infrastructure that couples capability-specific supervision construction with capability-aligned curriculum scheduling. Its three specialized yet interoperable data engines build complementary relational supervision for text-image grounding, inter-image transformation, and image-knowledge association, while caption experts align T2I and editing supervision across tasks and granularities. A multi-stage curriculum jointly evolves task composition, visual-concept distribution, data quality, and image resolution along the dependency order of capability acquisition, with capability-aware evaluation closing the loop through targeted retrieval, expert construction, and gap-aware resampling. At scale, the framework curates a 440M-image T2I corpus, 120M editing pairs, and over 27M image-entity pairs. With this infrastructure, we train multimodal diffusion models at two scales from scratch, with 3B and 6B sizes respectively. We conduct quantitative evaluation on CPI-Bench, along with qualitative evaluations across diverse text-to-image and editing scenarios. Experimental results present broad visual coverage, versatile rendering, and effective transfer across generative capabilities.
[CV-1] EDITBRIDGE: Towards Faithful and Efficient Ultra-High-Resolution Image Editing
链接: https://arxiv.org/abs/2608.18063
作者: Jiayi Song,Shijie Huang,Fangtai Wu,Yubo Huang,Zhenxiong Tan,Songhua Liu,Jiaming Liu,Ruihua Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:High-resolution image editing is increasingly demanded in professional workflows, yet existing diffusion-based models remain constrained to resolutions below 1K due to quadratic attention complexity and prohibitive memory requirements. A prevalent workaround employs a two-stage pipeline: editing at low resolution followed by independent super-resolution. However, this approach suffers from two critical issues: information divergence, where hallucinated details contradict the original high-resolution (HR) source, and texture degradation, manifesting as over-smoothed or over-sharpened artifacts. We propose EditBridge, a diffusion bridge framework for efficient ultra high-resolution editing. Unlike conventional diffusion that regenerates from noise, we formulate refinement as structured data-to-data translation from the low-resolution (LR) edited result to its HR counterpart, explicitly conditioned on the original HR source to preserve authentic details. To efficiently incorporate HR source guidance, we introduce a prior-guided block-wise sparse attention mechanism that exploits semantic correspondence from first-stage editing to constrain cross-image interactions to spatially aligned regions, significantly reducing computational overhead. Extensive experiments demonstrate that EditBridge achieves high-fidelity editing with superior perceptual quality at resolutions up to 4K, delivering 3.6–8.4 \times speedup at 2K and enabling practical 4K editing in 61 seconds.
[CV-2] Optimize Your Sampling: Tuned Diffusion Sampling with Bayesian Optimization
链接: https://arxiv.org/abs/2608.18040
作者: Travis Zhang,Christian Belardi,Justin Lovelace,Jin Peng Zhou,Saebyeol Shin,Carla P. Gomes,Kilian Q. Weinberger
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Sampling from a diffusion model typically requires many forward passes through a large neural network, making generation computationally expensive. While much work has focused on efficient solvers and samplers, comparatively little attention has been paid to selecting the sampling timesteps themselves. A recent line of work optimizes theoretically derived surrogates for sample quality rather than the quality metric itself. We propose Optimizing Your Sampling (OYS), which instead treats timestep selection as a black-box optimization problem, optimizing the target metric directly with Bayesian optimization. OYS outperforms both the default schedules and those of Align Your Steps on text-to-image generation, and improves over the default schedules on inpainting and other image tasks, in both quantitative and human evaluations. OYS requires no additional training, is applicable even to distilled models, and improves both simple and sophisticated samplers such as Euler and DPM-Solver++. A 5-step OYS schedule retains 89%-94% of the quality of a 50-step schedule while reducing inference cost by 10x.
[CV-3] Plug-and-Play Traffic Element Awareness for End-to-End Autonomous Driving ECCV2026
链接: https://arxiv.org/abs/2608.18035
作者: Zongzheng Zhang,Jijun Wang,Saining Zhang,Shuo Wang,Yiru Wang,Hai Yang,Yang Chen,Yuwen Heng,Hao Sun,Anqing Jiang,Hao Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV 2026; Project Page: this https URL
Abstract:Traffic elements such as traffic lights and road signs play a fundamental role in human driving decisions and should naturally influence end-to-end driving performance. However, existing end-to-end driving research predominantly focuses on dynamic road participants (e.g., vehicles and pedestrians), while the role of traffic elements remains largely unexplored. The community still lacks a systematic study quantifying their impact, largely because public datasets rarely provide structured traffic-element annotations and modern driving systems vary widely in architecture and training paradigm. In this work, we present the first systematic investigation of traffic element awareness for end-to-end autonomous driving. We construct a unified research infrastructure by augmenting multiple public driving datasets with comprehensive traffic-element annotations. To support diverse model families, we adopt a minimal and universal integration design that incorporates traffic-element signals into existing pipelines in a plug-and-play manner with negligible architectural modification. We evaluate this design across modern paradigms, including perception-prediction-planning pipelines, vision-language-action models (VLA), regression-based planners, diffusion-based policies, and trajectory-scoring frameworks, on nuScenes, NAVSIM-v1, NAVSIM-v2, and Bench2Drive. Across all paradigms and datasets, this simple integration consistently improves driving performance, demonstrating that traffic element awareness provides a robust and generalizable signal for end-to-end driving systems. Notably, on the challenging NAVSIM-v2 benchmark, our approach significantly improves state-of-the-art architectures and data pipelines, establishing a new state of the art.
[CV-4] Deep Academic Survey: Stateful Agent ic Closed-Loop Paradigm for Academic Survey Automation
链接: https://arxiv.org/abs/2608.18034
作者: Zhikai Xu,Zhucun Xue,Teng Hu,Yabiao Wang,Yong Liu,Jiangning Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL | Code: this https URL | Data: this https URL
Abstract:Academic surveys play a central role in organizing rapidly expanding scholarly literature, yet their construction requires extensive paper analysis, coherent knowledge organization, fine-grained citation support, and reliable manuscript assembly. Existing Deep Research and automated survey generation systems address parts of this process, but typically do not coordinate paper understanding, literature organization, evidence-grounded drafting, and manuscript validation through a shared, revisable state. We introduce DAS, a stateful agentic framework for generating publication-oriented academic surveys. Its key idea is to separate reusable paper analysis from topic-specific manuscript construction. DAS builds on DAS-2M, a dynamically updated metadata lake containing survey-oriented representations of approximately two million papers. Its agents maintain explicit literature, organization, writing, and finalization states through candidate-grounded taxonomy planning, reverse paper-to-section routing, and hierarchical claim and citation planning. Semantic review reactivates only the affected writing states for repair and reevaluation, forming a scoped closed loop with deterministic validation. We further introduce DAS-Bench, a 30-topic benchmark, together with DAS-Eval, which assesses scholarly citation quality, taxonomic synthesis, hierarchical discourse, and manuscript assembly reliability through 16 criteria. Among systems evaluated on all 30 topics, DAS achieves the highest average in all four dimensions, with an overall score of 4.34 compared with 4.03 for the strongest competitor, and the same ordering is preserved on the matched 21-topic CS subset. Blinded expert evaluation further prefers DAS to Naive RAG on 27 of 30 topics and to AutoSurvey on 19 of 21 shared CS topics. The project page is available at this https URL.
[CV-5] Initialization-Free Bundle Adjustment Revisited: A Controlled Experimental Study
链接: https://arxiv.org/abs/2608.18028
作者: Simon Weber,Mateo de Mayo,Je Hyeong Hong,Carl Olsson,Daniel Cremers,Ronald Clark
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Initialization-free bundle adjustment (InitFree BA) aims to recover camera poses and scene structure directly from image observations, avoiding the geometric initialization stages of conventional structure-from-motion pipelines. Recent methods based on Object-Space Error (OSE) formulations and Variable Projection (VarPro) show encouraging optimization behavior from random camera configurations. However, existing evaluations primarily measure optimization success, leaving unclear whether a low OSE objective yields a valid metric 3D reconstruction. We revisit InitFree BA experimentally through a unified evaluation framework combining a C++ implementation of existing OSE formulations with a Blender-based dataset generator providing exact ground truth and controlled camera configurations and observation densities. Our experiments reveal a previously overlooked optimization–reconstruction gap: projective solutions with similarly low OSE values can lead to substantially different Euclidean reconstructions after metric upgrade. We identify initialization priors, landmark observation density, and metric-upgrade stability as key factors governing reconstruction success. Overall, our results suggest that the main challenge of InitFree BA is not merely minimizing OSE objectives, but obtaining projective reconstructions that admit reliable metric upgrade. We believe that the proposed benchmark, implementation, and analysis establish stronger experimental foundations for future research on initialization-free bundle adjustment, a problem largely unexplored within the computer vision community. Project page is available at this https URL.
[CV-6] Automated ACL Footprint Identification Using 3D Deep Learning
链接: https://arxiv.org/abs/2608.18012
作者: Ruida Cheng,Ali Uneri,Gabriel Gibson,Frances T. Sheehan,Barry Boden
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 5 figures
Abstract:One of the most common reasons for anterior cruciate ligament (ACL) reconstruction failure is femoral tunnel malpositioning (ACL footprint center and tunnel orientation). Such failures may lead to the development of meniscal pathology and osteoarthritis. Accurate ACL femoral footprint identification is therefore essential for precise tunnel placement, restoration of the native knee joint mechanics, post-surgical knee joint health and prevention of graft failure. Recent advances in artificial intelligence (AI) bring new opportunities to improve image-guided orthopedic surgery. However, at present, existing AI research focuses primarily on ACL segmentation and rupture classification based on pre- and post-operative magnetic resonance (MR) images. Identification of the ACL footprint center using deep learning methods has not been thoroughly researched. Thus, the purpose of this study is to explore 3D deep learning models for ACL femoral footprint identification directly from 3D MR images. Two comprehensive 3D deep learning architectures were developed: a 3D graph convolutional neural network-based geometric model applied to 3D femoral meshes; and a 3D landmark-enhanced identification model based on 3D MR images. A total of 4883 right and 3087 left knee image sets were used from a publicly available database. Eighty percent (80%) were applied to model generation, and twenty percent (20%) were preserved for model testing. Both models achieved excellent performance; however, the image-based method outperformed the model-based method (average error of 2.1mm vs 2.8 mm). Thus, 3D deep learning provides a feasible clinical approach for ACL footprint localization and has the potential to improve ACL reconstruction footprint accuracy.
[CV-7] Memory Tree Guided Key Frame Querying for Efficient 3D Question Answering ECCV2026
链接: https://arxiv.org/abs/2608.18009
作者: Hsiang-Wei Huang,Fu-Chen Chen,Li-Wu Tsao,Cheng-Han Lee,Che-Chun Su,Lu Xia,Ronghui Peng,Jenq-Neng Hwang,Min Sun,Cheng-Hao Kuo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026
Abstract:Answering questions accurately and efficiently in embodied scenarios presents significant challenges due to limited computational and memory resources for Vision Language Model (VLM) inference. Existing methods adopt visual search key frame retrieval method to select critical question-related key frames for VLM input. However, visual search methods are inefficient because they require visual search among thousands of video frames for each individual user query. In this work, we propose a memory tree guided key frame selection paradigm for efficient 3D question answering in embodied scenarios. Our method leverages a compact and reusable 3D scene representation, termed MemTree3D, which supports real-time online construction leveraging camera 6-DoF poses. MemTree3D captures multi-level 3D scene information, enabling a Large Language Model to efficiently query and retrieve question-relevant key frames through our scoring-based frame selection without reprocessing the entire video stream. On OpenEQA, our method improves the LLM-Match of GPT-4o by 17.4%, LLaVA-OneVision-7B by 5.8%, outperforms existing visual search methods. Our code is available at this https URL
[CV-8] AViTS: Adaptive Spatiotemporal Token Selection for Efficient Dynamic-Resolution Generation ECCV2026
链接: https://arxiv.org/abs/2608.17995
作者: Haoran Qin,Zhengan Yan,Shikang Zheng,Xiaobing Tu,Jiacheng Liu,Yuqi Lin,Chang Zou,JinShan Liu,Peiliang Cai,Xiantao Zhang,Jinkui Ren,Linfeng Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026. 20 pages including appendix. Code: this https URL
Abstract:Diffusion Transformers (DiTs) achieve high-quality generation but are costly due to iterative sampling. Dynamic-resolution sampling reduces early-stage cost by denoising at low resolution; however, uniformly upsampling all latent tokens at resolution transitions incurs redundant computation and may degrade fine-detail consistency. Existing partial upsampling strategies typically rely on local latent structure cues or single-step statistics, making it difficult to jointly capture token-text semantic relevance and token-wise representation dynamics across diffusion steps. We propose AViTS, an adaptive spatiotemporal token selection framework for dynamic-resolution DiTs. AViTS models spatial importance via latent-text attention and temporal importance via token-level feature variation across diffusion timesteps, and fuses them to enable spatiotemporal importance-aware selective upsampling: it prioritizes resolution refinement for critical tokens while deferring less important ones, thereby reducing redundant high-resolution computation and improving the quality-efficiency trade-off. AViTS achieves up to 6.34x on FLUX and nearly 9x FLOPs reduction on Qwen-Image-Edit and FLUX.1-Kontext-dev, orthogonal to distillation, quantization, and feature caching, and reaching 14.76x with distilled models. Code: this https URL
[CV-9] GS-Voxel: Fitting-Free Structured Latents for Large-Scale 3DGS Generation
链接: https://arxiv.org/abs/2608.17988
作者: Ming Qian,Zijian Wang,Minchao Sun,Jincheng Xiong,Hang Zhang,Mu Xu,Chi Wang,Baoquan Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Many scalable latent 3D generators operate on structured tensors, whereas pre-optimized 3D Gaussian Splatting (3DGS) reconstructions are unordered, spatially irregular, and vary widely in primitive count. We present GS-Voxel, a fitting-free structured latent framework, and evaluate it for large-scale aerial 3D Gaussian scene generation. GS-Voxel deterministically converts a compatible pre-optimized 3DGS reconstruction into sparse active voxels without additional per-scene optimization, retaining the sub-voxel positions and rendering attributes of the selected primitives. A GS-specific factorized VAE then separately encodes voxel geometry and local Gaussian attributes into sparse 3D latents whose size grows with the number of occupied voxels rather than being limited by a fixed scene-wide primitive count. We train image-conditioned flow models in the GS-Voxel latent space to generate aerial 3DGS scenes. A key application enabled by GS-Voxel is large-area scene generation: overlap-aware tiled inference extends synthesis beyond a single training crop conditioned on satellite-view images. Our results show that GS-Voxel provides structured latents for pre-optimized aerial 3DGS reconstructions, with latent capacity that grows with the number of occupied voxels.
[CV-10] Dual Co-Train: Cross-Dataset Ultrasound Tongue Segmentation Under Extreme Data Scarcity
链接: https://arxiv.org/abs/2608.17983
作者: Alisher Myrgyyassov,Zhen Song,Bruce Xiao Wang,Yu Sun,Min Ney Wong,Yihao Zhou,Yongping Zheng
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Ultrasound tongue contour segmentation remains challenging under cross-dataset domain shift, where limited annotations, probe variability, and acquisition noise often degrade model generalization. We present a source-free domain adaptation framework for robust ultrasound tongue segmentation built on a lightweight UltraUNet backbone. Starting from a checkpoint pretrained on only five labeled source images, simulating an underfitted constrained source model, the proposed method adapts to a fully-unlabeled target domain by iteratively refining pseudo-labels, filtering unreliable masks with a contour-based quality-control module, and generating target-style synthetic image-mask pairs through a segmentation-guided conditional GAN. The student model is then trained on a mixture of clean pseudo-labeled target images, noisy pseudo-labels with consistency regularization, and synthetic samples, enabling closed-loop adaptation without access to source data. We evaluate the method on 12 source-target transfer pairs across eight ultrasound tongue imaging datasets, and conduct source-size scaling experiments and ablation studies. Across all comparisons, the proposed framework improves segmentation overlap and contour accuracy over the baselines, including supervised ones. These results suggest that task-specific pseudo-label refinement and synthetic target-style augmentation can substantially improve source-free adaptation for ultrasound tongue imaging.
[CV-11] aDSL: Agent ic 3D Creation via Joint Agent -Program Design
链接: https://arxiv.org/abs/2608.17975
作者: Rui-Huan Wang,Si-Tong Wei,Jia-Qi He,Heng-Yi Wei,Baoquan Chen,Peng-Shuai Wang
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Programmatic representations provide a compelling paradigm for 3D content creation, enabling fine-grained edits, interpretability, and explicit structural control. Yet, agentic workflows that rely on large language models (LLMs) to author 3D programs remain brittle, often failing to translate high-level intent into consistent low-level geometry. We attribute this fragility to a mismatch between existing programmatic interfaces and the reasoning strengths of LLMs, which favor semantic structure and spatial relations over fragile numeric choices. In this paper, we jointly design an Agent-centric Domain-Specific Language (aDSL) and a role-specialized multi-agent system to close this gap. aDSL bridges semantic logic and geometric constraints by emphasizing composability and spatial reasoning; it enables agents to manipulate geometry through relational operators instead of brittle absolute coordinates. Building on aDSL, our training-free multi-agent system follows a Plan-Execute-Critic loop to decompose requests, synthesize code, and iteratively repair errors and constraint violations using execution feedback. Experiments show that this co-design improves robustness, controllability, and faithfulness to user intent. Our method outperforms prior LLM-based baselines on text-to-shape and image-to-shape tasks while preserving explicit structure, editability, and interpretability. It also enables downstream applications such as articulated object creation and structured scene composition. Our code is available at this https URL.
[CV-12] LinCa: Accelerating Diffusion Models via Learnable Decomposed Feature Caching ECCV2026
链接: https://arxiv.org/abs/2608.17973
作者: Jinshan Liu,Haoran Qin,Xiaobing Tu,Jiacheng Liu,Jiahui Hu,Zhengan Yan,Yukun Xie,Kerui Shen,Jinkui Ren,Yuqi Lin,Xiantao Zhang,Linfeng Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026. 28 pages including appendix. Code: this https URL
Abstract:Diffusion models have achieved remarkable success in image and video generation, yet the high computational cost of iterative sampling remains a critical bottleneck for practical deployment. Feature caching has emerged as a promising acceleration paradigm by reusing or predicting intermediate features across timesteps. However, existing training-free methods apply uniform prediction strategies that cannot adapt to the heterogeneous feature dynamics, causing significant quality degradation under high acceleration ratios. We propose LinCa, a feature caching framework based on learnable invertible networks. LinCa decomposes cached features into sub-components with distinct continuity properties via a lightweight invertible network and applies differentiated prediction orders matched to each component. The strict invertibility guarantees lossless reconstruction back to the original feature space, forming a unified Decompose-Predict-Reconstruct pipeline. By training separate predictors for different models and timestep segments, LinCa adapts to heterogeneous feature dynamics. Experiments on FLUX, Qwen-Image, and HunyuanVideo demonstrate that LinCa, with less than 0.2% additional parameters, significantly outperforms existing methods and maintains near-lossless quality at 5-7x speedup. Code: this https URL
[CV-13] SFMformer: A Spatial-Frequency Modulation Transformer for Lightweight Image Super-Resolution
链接: https://arxiv.org/abs/2608.17966
作者: Chih-Hsiang Yang,Chia-Min Lin,Ching-Yu Tsai,Yung-Che Wang,Jen-Shiun Chiang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 20 pages, 13 figures, 5 tables
Abstract:Sparse attention mechanisms, which score all token pairs but propagate only the strongest, now underpin the most efficient Transformers for lightweight image super-resolution. This paper observes that sparsification changes what it means to improve such a network. A dense attention layer has one place where representation quality matters: the aggregation of attended features. A sparse layer has two, because the top-k operator first decides which tokens survive and only then decides what to do with them, and a token discarded at the selection stage cannot be recovered downstream. Selection quality and aggregation quality are therefore separable targets, addressed by modules placed before and after the attention respectively. We test this by pairing a dual-branch spatial enhancement on the input of a progressive focused attention with a wavelet-domain modulation on its output, forming SFMformer. Measuring each module alone and jointly over all fifteen benchmark-scale pairs, we find their gains are not additive: the joint gain exceeds the sum of the individual gains on nine pairs, and the sign of the discrepancy is predicted by how much the weaker module contributes on its own (r = -0.72), so the two compound when they relieve different constraints and overlap when they relieve the same one. Enabling spectral modulation once per block rather than once per layer retains the effect at roughly one-sixth of its cost, keeping the model below one million parameters at every scale. SFMformer ranks first on 28 of 30 PSNR/SSIM entries across five benchmarks and three upscaling factors. We report the cases where the pairing does not help, and deploy the model on a Raspberry Pi 5 to confirm the design is practical under tight resource budgets.
[CV-14] Cross-Domain Generalization in Machine Unlearning via Label-Conditioned Energy Magnitude Regularization ECCV2026
链接: https://arxiv.org/abs/2608.17942
作者: Syed Ali Ahmed(1),Syed Bilal Ahsan(1),Muhammad Zaigham Zaheer(2) ((1) National University of Computer and Emerging Sciences, Karachi, Pakistan, (2) Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 3 figures, accepted at the ECCV 2026 Workshop on Unlearning and Model Editing (UMe)
Abstract:Machine unlearning removes the influence of specific data from a trained model. However, most methods treat the forgotten concept as isolated. In this paper, we study what happens to the rest of the model when a class is forgotten, using a label-conditioned energy-based model (EBM) that assigns per-class energies, making the effect directly observable. We forget a class by raising the energy of its image-label pairs, training with a forget term, a retain anchor to the pretrained model, a global margin, and an energy regularizer that stops the energy magnitudes from growing without limit. A propagation term applies the same forget signal to retain samples, weighted by each sample’s DINOv2 similarity to the forget class, so forgetting reaches images that resemble it and leaves the rest untouched. We evaluate on two benchmark datasets: 1) On a subset of DomainNet across four visual domains, we forget tiger, lion, and scissors one at a time. Forgetting a class in the sketch domain also erases it from real, clipart, and painting, with forgetting error reaching 98% and 99% for lion and scissors, and the effect carrying over to the most similar class. 2) On CIFAR-10, we turn off the propagation term and forget each of the ten classes on its own. Forgetting is complete (100%), while the other nine classes retain 98.5% of their pre-unlearning accuracy on average.
[CV-15] Beyond Instrument Motion: Recognizing Tissue Tension Toward Surgical Skill Assessment ECCV ECCV2026
链接: https://arxiv.org/abs/2608.17935
作者: Marko Haralovi,Zhiqi Miao,Alexander Machiel Bont,Jiapan Guo,Frans van Workum,Estefania Talavera
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: The paper is accepted by ECCV 2026 Workshop On Medical Video Understanding and submitted the camera-ready version to the ECCV organization
Abstract:Surgical performance assessment in minimally invasive surgery largely relies on manual expert review, making it time-consuming, subjective, and difficult to scale. While existing surgical video understanding methods address tasks such as instrument segmentation, surgical phase recognition, and action recognition, they do not explicitly capture fine-grained tissue handling, a key indicator of surgical quality. To address this gap, we introduce tissue tension recognition, a new clinically motivated video understanding task for laparoscopic and robot-assisted rectal cancer surgery. To support this task, we construct SurgTension, the first expert-annotated tissue tension dataset, providing a benchmark for objective tissue tension recognition. We further propose TensionTRAC, a lightweight trajectory-based framework that models tissue tension from sparse point trajectories. Using a compact trajectory encoder, TensionTRAC achieves competitive performance against strong pretrained video backbones.
[CV-16] PerFact: Perception-Derived Fact Prompting for 3D Brain MRI Report Generation
链接: https://arxiv.org/abs/2608.17926
作者: Jianyu Sun,Zhenxuan Zhang,Guang Yang,Peter J. Lally
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Radiology report generation has matured almost entirely on 2D chest radiographs, where the default route to better reports is a larger backbone or a pre-training one on medical data. We revisit that assumption on 3D multi-sequence brain MRI, a volumetric multi-disease regime, and find that the model is not the lever. Zero-shot medical and radiology vision-language models transfer poorly to brain MRI, with chest radiograph specialists failing most conspicuously, and five backbones fine-tuned identically across three model families and an order of magnitude in scale differ only marginally. What determines the quality of the report is the information injected into the prompt. We delegate perception to upstream 3D segmentation and classification, serialize their outputs into a structured fact sentence, and prompt a LoRA-adapted vision-language model with it; we call this \textbfPerFact. In a controlled study that fixes the backbone, data split, target reports, and adaptation while varying only the injected grounding, perception-derived facts outperform retrieved prior reports, retrieval becomes redundant once facts are present, and end-to-end predicted facts remain effective without any ground-truth annotation at inference. The residual gap between predicted and oracle facts is explained by the granularity of the facts rather than by the generator. Closed-ended visual question answering comes at no measurable cost to report quality, though the grounding source has little effect on it. On 3D brain MRI, grounding information, not model choice, is the dominant controllable factor in report quality.
[CV-17] AppendiGrade: An XAI-Enhanced Deep Learning Framework for Grading Appendicitis in Ultrasound with Gaussian Blur and Grad-CAM
链接: https://arxiv.org/abs/2608.17923
作者: Fahad Ahammed,Omar Faruq Shikdar,Navid Zaman,Md Tahsin,Md. Nawab Yousuf Ali,Golam Sorwar
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Appendicitis is one of the most common abdominal emergencies worldwide and requires prompt diagnosis and treatment to prevent life-threatening conditions. However, accurately differentiating complicated cases, such as perforation or abscess formation, from uncomplicated appendicitis remains a significant clinical challenge. Among other methods, ultrasound is a safer and more cost-efficient diagnostic technique because of the lack of radiation exposure. In this research, an advanced system capable of automatically detecting complicated appendicitis from ultrasound images was developed. A dataset consisting of 4679 ultrasound images with 5 classes, namely perforated, abscess, acute, appendicolith, and normal, was used for the proposed model training and testing. Four pretrained deep learning models, DenseNet201, InceptionV3, ConvNextTiny, and VGG19, have been employed for detecting and classifying complicated appendicitis. In the initial configuration, InceptionV3 achieved the second highest accuracy, with a value of 69.21%. Owing to suboptimal performance with raw images, further optimization techniques, including image preprocessing, hyperparameter tuning, model fine-tuning, and image sharpening, were applied. These enhancements significantly improved the model’s performance, with an accuracy of 95.58% for InceptionV3. The model performance is then explained with gradient-weighted class activation mapping (Grad-CAM), which creates a heatmap of the regions responsible for the model’s prediction of the infected areas. This could make crosschecking with experts much easier.
[CV-18] Comparative Study of Out-of-the-Box Technology for Automatic Target Detection and Recognition
链接: https://arxiv.org/abs/2608.17917
作者: Alma M. Liezenga,Lotte Nijskens,Henrik R. Baumann,Stefan Becker,Simon Bensberg,Niccolò Camarlinghi,Håvard R. Eiring,Alexander W. Johnsgaard,Tanel Liiv,Giuseppe Martino,Matteo Marturini,Matthias Rapp,Jan Erik van Woerden,Alexander Wolpert,Hugo J. Kuijf
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: This paper was originally presented at the International Conference on Military Communication and Information Systems, organized by the Information Systems Technology Scientific and Technical Committee, IST-224-RSY - the ICMCIS, held in Bath, United Kingdom, 12-13 May 2026
Abstract:Automatic Target Detection and Recognition (ATD/R) is critical for military decision support and (semi-)autonomous operations. Recent advances in object detection and artificial intelligence (AI) significantly boosted the potential performance of ATD/R. However, the scarcity of publicly available military datasets limits the application of these systems. As a solution, this paper explores the use of publicly available models and civilian datasets to achieve reasonable performance in military contexts. We benchmark several state-of-the-art models, including six iterations of the YOLO series and two variations on the DETR framework, on a newly acquired military relevant dataset. This dataset features military vehicles and challenging circumstances, including various degrees of occlusions and small targets. The out-of-the-box version of each model is validated alongside a version finetuned on the VisDrone dataset. This dataset features small objects, an Air-to-Ground (A2G) perspective and relevant classes, potentially generalizing to our military ATD/R task. We compare the performance of the models using mAP@0.5 and mAP@0.5:0.95, across A2G and Ground-to-Ground (G2G) perspective, target size and model size, giving insight into the real-time capabilities of models. Our main findings are: (1) bigger models outperform smaller models, (2) DETR-based models show promising results compared to the YOLO series,(3) fine-tuning models on an out-of-domain A2G dataset, improves their A2G performance and slightly improves their performance on small objects, but (4) all models still struggle with detecting small objects in an A2G scenario. We conclude that, despite recent advances in object detection, in-domain training is still crucial for creating capable ATD/R systems.
[CV-19] CFB-GBM v2.0: An Augmented Longitudinal Dataset for Multi-Modal Glioblastoma Segmentation Radiomics and RANO Progression Tracking
链接: https://arxiv.org/abs/2608.17884
作者: Alexandre G. Leclercq,Noémie N. Moreau,Hugo Audebert,Andros Nassar,Thomas Cochin,Thomas Leleu,Loïc Le Henaff,Alexis Desmonts,Yoann Poirier,Aurélie Dubru,Laura Guillemette,Pascal Lecoeur,Kévin Lemasson,Cyril Jaudet,Sébastien Bougleux,Romain Hérault,Carole Brunaud,Samuel Valable,Dinu Stefan,Charlotte Raboutet,Alain Batalla,Joëlle Lacroix,Roman Rouzier,Aurélien Corroyer-Dulmont
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 2 figures,
Abstract:Glioblastoma (GBM) is the most aggressive primary brain tumor in adults, with a median overall survival of 15 months. Longitudinal, multi-modal imaging datasets with comprehensive clinical and treatment data are essential to support the development of reproducible computational methods for treatment response prediction, disease progression modelling, and personalized medicine. We present CFB-GBM v2.0, an extension of our previously released CFB-GBM dataset comprising 264 GBM patients treated according to the standard Stupp protocol. The primary contribution of this release is the completion of Gross Tumour Volume (GTV) delineations across all available timepoints ( t_0 , t_1 and t_2 ), increasing the overall GTV completion rate from 35% to 97%. This was achieved using a nnU-Net model pre-trained on BraTS 2021 and fine-tuned on CFB-GBM ground-truth contours, with the generated segmentations validated by five radiation oncologists. From these longitudinal GTV annotations, volumetric RANO 2.0 response category labels were derived for all available temporality pairs ( t_0 \rightarrow t_1 , t_0 \rightarrow t_2 and t_1 \rightarrow t_2 ). To further ease dataset usability and reproducibility, brain masks computed with HD-BET and pre-computed radiomic features extracted with PyRadiomics are provided for each patient timepoint and MRI modality. Additionally, the WHO classification guideline (2016 vs. 2021) applicable to each patient’s diagnosis is now explicitly documented. CFB-GBM v2.0 is publicly available on The Cancer Imaging Archive (TCIA) at this https URL .
[CV-20] Improving Complex Moiré Removal with Generative Supervision
链接: https://arxiv.org/abs/2608.17883
作者: Xinyang Gu,Zhilu Zhang,Honglei Xu,Yanting Mei,Yukang Ding,Wangmeng Zuo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 5 figures. Project page: this https URL
Abstract:The availability of high-quality paired data is essential for training learning-based image demoiréing models. However, it remains challenging for existing datasets to encompass the complex moiré patterns captured in uncontrolled real-world scenarios. Such degradations typically manifest as large-scale, multicolored moiré patterns. Moreover, these patterns frequently occur in images for which clean counterparts are difficult to obtain, such as photographs acquired from public displays or existing online resources. In this work, we propose a novel data engine designed to improve the removal of complex moiré patterns by generating training supervision. Specifically, we initially collect real-world images containing complex moiré patterns and localize the corresponding screen regions. Multiple image-conditioned generative foundation models are subsequently deployed to produce candidate references. To establish reliable supervision, these candidates are subjected to patch-level quality control to filter and select the optimal results. Based on this systematic paradigm, we construct the WildMoiré dataset, which contains 6.8K moiré-GT training pairs. For evaluation, we additionally build an independent test set comprising \sim 250 pairs with captured clean ground truth. Extensive experiments on ESDNet, SDXL, and Qwen-Image-Edit demonstrate that the proposed generative supervision consistently improves the performance of complex moiré removal.
[CV-21] DistillPath: An Efficient 22M Distilled Pathology Encoder Approaching Large Foundation Model Performance ECCV2026
链接: https://arxiv.org/abs/2608.17872
作者: Ramon Kaspar,Andrey Ignatov,Valentina Boeva
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 26 pages, 5 figures. Accepted at the ECCV 2026 Workshop on Medical Foundation Models and Benchmarks (MedFM-Bench)
Abstract:Many high-performing pathology tile encoders are now foundation models with hundreds of millions to over a billion parameters. Encoding and storing the thousands of tiles in each whole-slide image with such models is costly on commodity hardware, so compact encoders that retain useful downstream performance are a valuable alternative. We present DistillPath-KS16, which starts from the existing 22M kaiko ViT-S/16 encoder and improves it by distilling from released pathology encoders used as frozen teachers. The recipe reads only the teachers’ final class and patch tokens and trains on 6,000 public slides, needing neither their DINO nor iBOT pretraining heads nor a billion-tile corpus, so it applies to any released encoder that exposes backbone tokens. We distill four teachers spanning 86M to 1.1B parameters into the same student. Every variant improves the kaiko baseline on all three benchmarks we use, EVA, HEST, and PLISM, and the strongest teacher is task-dependent. On the seven-task EVA mean, DistillPath-KS16-Virchow2 reaches 0.795 , within 0.015 points of Virchow2, the top-scoring model in our evaluation, at about 29\times fewer parameters; it also scores above H0-mini and GPFM on this aggregate metric, though that advantage is task-concentrated rather than uniform. Because it remains a 22M ViT-S/16 with 384-dimensional features, DistillPath-KS16 runs more than 25\times faster than Virchow2. Code is available at this https URL, and released model weights are available at this https URL.
[CV-22] GenRec: Knowing Where to Reconstruct and Where to Generate
链接: https://arxiv.org/abs/2608.17832
作者: Ata Çelen,Jaewoo Jung,Federico Tombari,Marc Pollefeys,Sunghwan Hong,Michael Niemeyer,Daniel Barath
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Generative novel view synthesis from sparse input images is rarely all reconstruction or all generation: pixels visible in some source view have a unique correct value modulated only by view-dependent shading, while pixels in disocclusions or beyond the captured volume admit a distribution of plausible completions. Existing generative novel-view-synthesis methods conflate these regimes under a single uniform loss, blurring the line between geometric fidelity and creative hallucinations even when scene geometry is injected through warped point clouds or projected depth. We introduce GenRec, a multi-view flow matching model that builds the reconstruction–generation split directly into its architecture, supervision, and gradient flow. Guided by an observation mask derived from the source cameras and a monocular depth estimator, a flow matching backbone jointly denoises RGB and scene-coordinate maps across all target views, while a pixel-space refinement stage restores high-frequency detail on observed pixels; the same mask gates supervision so regression signals do not contaminate the generative prior. Across RealEstate10K, DL3DV-10K, and Mip-NeRF~360, in both single-view extrapolation and two-view interpolation, GenRec attains the best reconstruction fidelity in observed regions while also surpassing purely generative baselines on perceptual quality in unobserved ones, showing the effectiveness of our approach.
[CV-23] Scale Matters: Adaptive Granularity Selection for Cross-Species 3D Plant Organ Segmentation
链接: https://arxiv.org/abs/2608.17803
作者: Carla Salazar,Lazaros Nalpantidis
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Recent 3D foundation models provide powerful feature representations for point cloud learning by controlling spatial granularity. However, relying on a fixed spatial granularity severely limits generalization in applications like plant phenotyping, where organ morphology and size vary substantially across species and growth stages. To address this, we propose AGS-PlantSeg, a few-shot 3D plant organ segmentation method that leverages the frozen Utonia (arXiv:2603.03283) foundation model combined with Adaptive Granularity Selection. By dynamically selecting the best granularity levels for each specific plant model, our method extracts optimized geometric features for a lightweight MLP segmentation head. Extensive experiments across PLANesT-3D (arXiv:2407.21150), Pheno4D , and Crops3D demonstrate that AGS-PlantSeg significantly improves cross-species generalization, achieving 88.9% average mIoU performance and outperforming fixed-granularity baselines by 2.5 mIoU points. Despite requiring minimal annotated data, our approach is highly competitive with fully supervised, plant-specific architectures.
[CV-24] raining with synthetic data for drone detection in thermal imagery
链接: https://arxiv.org/abs/2608.17799
作者: Tanel Liiv,Sander Soodla,Nzamba Bignoumba,Alma M. Liezenga,Toomas Pruuden
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Robotics (cs.RO)
备注: To be presented at SPIE: Sensors + Imaging, Artificial Intelligence for Security and Defence Applications IV
Abstract:Ground-to-Air (G2A) drone detection in medium- and long-wave infrared (MWIR/LWIR) imagery is challenging due to reduced texture information, sensor noise, weak thermal contrast, and the scarcity of annotated data. This work investigates a synthetic-first training strategy that combines synthetic scene generation with fine-tuning on real data. We show that synthetic data provides an effective basis for learning initial object representations, while real in-domain thermal imagery is still essential for reliable deployment. Even small amounts of real IR data substantially reduce domain gaps. Our experiments indicate that dataset alignment has a stronger impact on performance than model scale. Finally, our analysis of the dataset suggests that semantic alignment in feature space is the strongest predictor of model performance, while radiometric properties such as entropy and dynamic range also contribute to detection robustness. This work provides a foundation for combining synthetic and real IR data for effective G2A drone detection.
[CV-25] ETHEREAL: A 25.6-μs/inf. Low-latency Event-driven Graph-neural-network Processor for High-resolution Vision at the Edge
链接: https://arxiv.org/abs/2608.17787
作者: Adrian Kneip,Martin Lefebvre,Daniel Gehrig,Victoria Catalán Pastor,Davide Scaramuzza,Marian Verhelst,Charlotte Frenkel
类目: Hardware Architecture (cs.AR); Computer Vision and Pattern Recognition (cs.CV)
备注: This work has been submitted to the IEEE JSSC for possible publication
Abstract:Dynamic vision sensors (DVS) are enticing candidates to reach the low-latency, sub-ms target of edge-vision applications, as they generate events with a \mu s-level time resolution. However, using DVS front ends also calls for novel algorithm/hardware back ends capable of efficiently handling streams of sparse spatiotemporal events. While event-driven graph neural networks (EV-GNNs) have emerged as a solution on the algorithmic side that is both accurate and efficient, there is no dedicated hardware to date capable of efficiently supporting their mixed requirements of dense-regular compute operations and sparse-irregular memory accesses. We therefore introduce ETHEREAL, the first EV-GNN processor chip, capable of bridging this gap by means of a neighbor-parallel spline-convolution engine combined with a split-2D/3D memory hierarchy that introduces a novel spatiotemporal event-caching mechanism. Measurement results demonstrate a 25.6 \mu s latency and a 1.6 \mu J energy per end-to-end event-wise inference on the state-of-the art DAGr-GNN workload and VGA-resolution (640x480 pixels) DSEC dataset.
[CV-26] INA: Probing Residual Visual Knowledge in Unlearned Diffusion Models via Diffusion-Consistent Text-Free Inversion
链接: https://arxiv.org/abs/2608.17747
作者: Qianlong Xiang,Miao Zhang,Kun Wang,Haoyu Zhang,Junhui Hou,Liqiang Nie
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: The project page is this https URL
Abstract:Although text-to-image diffusion models exhibit remarkable generative power, concept erasure techniques are essential for preventing harmful content. Existing adversarial probes evaluate these methods by testing whether erased concepts can still be recovered. However, existing erasure and probe methods remain largely text-centric, focusing on whether the text-to-image mapping is severed while overlooking whether the corresponding visual knowledge remains. To investigate this question from a visual perspective, we leverage diffusion inversion to probe whether a generative trajectory can reconstruct visual instances of an erased concept. Under a null-text condition, standard inversion avoids the textual pathway but amplifies approximation errors, hindering faithful trajectory recovery. To address this challenge, we introduce TINA+, a diffusion-consistent Text-free INversion Attack equipped with optimization-based inversion. We also find that unconstrained diffusion inversion may discover spurious trajectories, even allowing a randomly initialized diffusion model to reconstruct the target concept. Such trajectories may falsely indicate residual visual knowledge. TINA+ therefore introduces Diffusion-Consistent Trajectory Regularization to suppress this failure mode. By penalizing trajectories that fall far below the expected marginal energy evolution of diffusion, TINA+ suppresses spurious inversion paths while preserving its ability to recover erased concepts. Experiments across twelve erasure methods, four concept-erasure tasks, and different model architectures demonstrate that TINA+ reliably probes residual visual knowledge through diffusion-consistent visual trajectories. These results provide stronger evidence that current methods often obscure concepts by severing text-image links rather than eliminating the underlying visual knowledge.
[CV-27] Evaluation of AI-based Visual Crack Detection in Steel Bridges Using Probability of Detection
链接: https://arxiv.org/abs/2608.17726
作者: Andrii Kompanets,Finn Michael Sherry,Remco Duits,Davide Leonetti,H.H. Snijder
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Submitted
Abstract:Bridge structures are regularly inspected for structural damage such as cracks and corrosion in order to ensure public safety and reduce maintenance costs. Much research has been done on automating this process using computer vision methods, which are often evaluated and compared using metrics such as intersection over union, mean average precision, etc. However, predicting the actual effectiveness of an inspection method within the field of structural engineering from these metrics remains challenging. To enable the systematic use of these increasingly popular methods in engineering practice, evaluating the performance of these methods in a way that is compatible with standard engineering approaches is therefore an urgent necessity. We present a new statistical evaluation framework to allow the comparison of computer vision methods with conventional visual inspection for crack detection in steel bridges. The framework is based on probability of detection curves and can account for the influence of image resolution. We apply this evaluation method to the real-world ``Cracks in Steel Bridges’’ dataset, which contains annotated images of cracks in bridge structures. The quantification of the probability of detection and its uncertainty enables a practical assessment of the effect of automated methods for damage detection in structural reliability analyses. In turn, this enables the wide-spread use of automated (AI-based) damage detection in safety critical applications. This evaluation method provides evidence that the proposed computer vision approach approach is robust for the crack detection task and can have a high added value as an addition to conventional visual inspection methods.
[CV-28] Vision-Language Models for Analog Gauge Reading: An Empirical Study of Specialization Transfer and Reliability
链接: https://arxiv.org/abs/2608.17723
作者: Abdul Mueez,Aaditya Baranwal,Junior Chaj-Mejia,Guneet Bhatia,Jason T. Voelker,Shruti Vyas
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Submitted to Engineering Applications of Artificial Intelligence
Abstract:Analog gauges remain common in industrial environments where manual inspection is costly or hazardous. The engineering application addressed here is direct numerical reading of single-target analog-gauge images, while the artificial-intelligence contribution is a systematic evaluation of specialization, transfer, robustness and reliability for a general-purpose vision-language model (VLM) without an explicit pointer-segmentation and geometric-reading pipeline. The Qwen2.5-VL-7B-Instruct model is evaluated using zero-shot prompting, in-context learning (ICL) and parameter-efficient fine-tuning with Quantized Low-Rank Adaptation (QLoRA) on a public synthetic dataset, a video-derived Pressure Gauge dataset and a proprietary industrial dataset. All fine-tuning experiments use a fixed 20-epoch protocol with the final epoch used for analysis; separate models with and without supplied gauge ranges remove prompt-setting confounds. The primary metric is range-normalized mean percentage error (MPE). The best fine-tuned MPE values are 2.39% on the synthetic dataset, with a 95% bootstrap confidence interval (CI) of 1.43-3.90%; 2.61% on the Pressure Gauge dataset, with a CI of 1.66-3.80%; and 4.43% on the proprietary industrial dataset, with a CI of 2.31-7.14%. Leave-one-dataset-out experiments reveal substantial transfer degradation on held-out synthetic and proprietary data, while robustness tests identify Gaussian blur as the strongest tested corruption. Reliability analysis shows that high-confidence errors remain possible, motivating abstention and independent validation in safety-critical use. These results support QLoRA-specialized VLMs for direct single-gauge reading but not yet a deployment-ready plant-monitoring pipeline.
[CV-29] DynaForcing: Overcoming Dynamic Collapse in Self-Forcing Distillation for Streaming Avatar Generation
链接: https://arxiv.org/abs/2608.17707
作者: Yubo Huang,Sirui Zhao,Xinchen Yao,Zhengye Zhang,Jinyang Huang,Fengqi Cui,Shiwei Wu,Enhong Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注: Accepted at ACM International Conference on Multimedia (MM '26)
Abstract:Audio-driven avatar generation requires realistic lip-sync, expressive motion, and real-time streaming. Recent work achieves the latter via self-forcing with Distribution Matching Distillation (DMD), but this paradigm suffers from a critical failure that has not been systematically characterized: dynamic collapse, where the student model converges to a near-static optimum with high perceptual quality but severely suppressed temporal dynamics. We trace this to two causes: the reverse KL objective in DMD, which biases toward low-motion modes, and unanchored self-conditioning, which creates a feedback loop that amplifies collapse. This is especially harmful for avatars, where even subtle motion loss breaks lip-sync and expression. To address this, we propose DynaForcing, a training framework with three complementary strategies applied at different levels. Specifically, Hybrid Forcing anchors rollouts to ground-truth dynamics at the data level to break the feedback loop. Dynamics-Aware Reward Regularization introduces explicit motion rewards via the RL interpretation of DMD to counteract the reverse KL bias at the loss level. Reference Perturbation perturbs reference images to decouple identity from static details, forcing the model to rely on audio for motion at the conditioning level. We further introduce computation graph pruning and gradient replay, reducing the GPU footprint of self-forcing by over an order of magnitude. Experiments show that DynaForcing recovers dynamics to teacher-comparable levels (Dyn-Deg: 0.31 - 0.73, Sync-C: 7.03 - 7.68) while improving visual quality, resolving the quality-dynamics trade-off throughout training without early stopping. Comments: Accepted at ACM International Conference on Multimedia (MM '26) Subjects: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM) Cite as: arXiv:2608.17707 [cs.CV] (or arXiv:2608.17707v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.17707 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-30] Monitoring Pasture Restoration from Satellite Image Time Series: Caveats and Opportunities ECCV2026
链接: https://arxiv.org/abs/2608.17704
作者: Linnea Sartorius,Isak Randahl,Delia Fano Yela,Georg Andersson,Sadegh Jamali,Aleksis Pirinen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the 3rd Workshop on Computer Vision for Ecology at ECCV 2026
Abstract:Monitoring nature restoration at scale is an important but difficult ecological problem. Deep learning methods to analyze satellite image time series (SITS) have been widely used for land surface monitoring. In semi-natural grasslands - the habitat type in focus in this work - restoration outcomes develop gradually, yet satellite observations are influenced by weather, acquisition conditions, and processing artefacts, making it difficult to distinguish genuine restoration signals from unrelated temporal variation. In this work, we examine - to the best of our knowledge, for the first time - whether restoration status can be detected directly from satellite image time series by formulating pasture restoration as a binary deep learning classification problem. We evaluate two common SITS deep learning architectures on different Sentinel-2 image combinations, across 1,397 restored Swedish pastures and find that explicitly modeling intra-year variability and per-pasture normalization increases separability, reaching 0.88 accuracy for the best model. We further investigate our results and perform a targeted bias analysis finding that reliable deployment requires temporally balanced labels and evaluation protocols that explicitly test for year-related confounding. We therefore frame our contribution not as a solved restoration-monitoring system, but as a realistic case study of what works, what fails, and what future studies should control for. Code and models are available at this https URL.
[CV-31] Environment-Invariant Subspace Learning for Generalizable Deepfake Detection
链接: https://arxiv.org/abs/2608.17700
作者: Shenghao Chen,Hao Jia,Chen Li,Chunjie Ma,Zan Gao,Shengyong Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 4 figures, 11 tables
Abstract:Cross-distribution generalization remains a critical bottleneck in deepfake detection. While recent efforts leverage the semantic priors of large-scale visual foundation models (VFMs), a noteworthy yet underexplored challenge remains: the susceptibility of these semantic priors to environmental interference from factors such as lighting and style. Crucially, this interference establishes spurious correlations between forgery cues and environmental patterns that severely limit generalization. To address this fundamental challenge, we propose an innovative Environment-Invariant Subspace Learning (EISL) framework. The core contribution of EISL is that it aims to disentangle features into orthogonal forgery-relevant invariant factors and environment-related residual factors via a learnable low-rank projection. To facilitate robust feature disentanglement, we also design an Environmental Intervention module that generates diverse and challenging intervention pairs, simulating out-of-distribution environmental shifts to guide the model toward discovering truly invariant forgery representations. Experiments across cross-dataset, cross-generator, whole-face synthesis, and corruption settings show consistent gains and competitive or leading performance against strong detectors, demonstrating improved robustness to unseen forgery types and environmental variations. This work provides a new perspective and a valuable exploration for understanding and tackling the generalization barriers of VFMs in deepfake detection.
[CV-32] Magnitude-Direction Decoupling for Fast Video Generation with Flow Matching Models
链接: https://arxiv.org/abs/2608.17695
作者: Haonan Xu,Feiyang Chen,Songkui Chen,Hongpeng Pan,Zhefeng Wang,Xinyu Duan,Baoxing Huai,Yang Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Flow matching models for video generation achieve impressive performance but suffer from high computational overhead due to iterative denoising. In fact, the original model is not necessary for all denoising steps, allowing some steps to use lightweight alternatives for faster sampling. However, directly using caching or lightweight models can deviate from the original denoising trajectory, resulting in suboptimal performance. Through empirical analysis, we find that lightweight models can robustly capture the magnitude components of the original model’s output, while caching provides reliable directional guidance. Building on this insight, we propose the Magnitude-Direction Decoupling (MDD) method, which adaptively employs a direction-calibrated lightweight model as a substitute for the original model to accelerate inference and effectively correct deviations in the denoising trajectory. Moreover, MDD further reduces inference costs by reusing magnitude information under classifier-free guidance (CFG). As a result, MDD offers a more reliable and lightweight solution to accelerate sampling. Experiments show that MDD outperforms existing acceleration methods, delivering promising speedups (e.g., up to 2.95x on Wan2.1) while preserving high visual fidelity and content richness.
[CV-33] Differentiable Voronoi Ray Tracing Beyond Rasterization Speeds
链接: https://arxiv.org/abs/2608.17682
作者: Bernardo Taveira,Carl Lindström,Joakim Johnander,Fredrik Kahl
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Real-time novel view synthesis is dominated by rasterized explicit primitives. These projection-based pipelines provide high throughput but require specialized extensions for non-pinhole effects such as distortion, rolling shutter, and depth of field. Ray-based rendering expresses these effects naturally but is generally assumed too slow for competitive real-time rendering. We analyze the factors governing throughput in differentiable Voronoi ray tracing and identify traversal length, per-cell work, and memory locality as principal determinants. Guided by this, we introduce VoroTracing, which co-designs the scene representation, optimization, and GPU execution to reduce these costs. Compact octahedral appearance textures reduce memory traffic, while surface-concentrated opacity promotes early termination. The fixed-budget representation is optimized without pruning or densification and rendered with a GPU implementation designed for coherent traversal. On Mip-NeRF 360, VoroTracing renders at 623 FPS on an RTX 5090, providing 3.2\times the throughput of the fastest prior ray-based method and 2.8\times that of 3D Gaussian Splatting, while maintaining competitive reconstruction quality. Our renderer supports fisheye, rolling-shutter, motion-blur, and depth-of-field effects through ray generation and sampling, requiring no specialized rasterization. These results show that real-time throughput can be achieved with the flexibility of ray-based rendering. We release our source code, see this https URL
[CV-34] Is Haar Enough? Exploring Symlets and Coiflets for Wavelet Convolution Layers
链接: https://arxiv.org/abs/2608.17662
作者: Md Rifat Ur Rahman
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Wavelet convolution layers have recently emerged as an efficient mechanism for enlarging receptive fields through multiresolution analysis, but prior work has fixed the wavelet basis to Haar or Daubechies at a chosen decomposition depth, leaving open whether a different basis can shift the underlying efficiency frontier. We identify and characterize a previously unexplored trade-off in this setting: bases with stronger approximation properties (longer filters) can reduce the decomposition depth required for competitive accuracy, yielding a net reduction in parameters and FLOPs despite higher perlevel transform cost. We formalize this as an F-vs.-L tradeoff (filter length vs. decomposition levels) and study it systematically across Haar, Daubechies, Symlets, and Coiflets under controlled architectures and budgets. On image classification (CIFAR-10, ImageNet-1K) and semantic segmentation (Cityscapes), Coiflet-based wavelet convolutions match Haar at deeper levels with approximately 32% fewer additional parameters and 33% fewer additional FLOPs, providing a concrete and actionable design choice for practitioners building wavelet-based architectures.
[CV-35] Denoised Variance-Based Pruning with Optimal Brain Bias Compensation ECCV2026
链接: https://arxiv.org/abs/2608.17657
作者: Geon Tack Lee,Jaegul Choo,Kang Eun Jeon
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026
Abstract:Vision Transformers (ViTs) achieve state-of-the-art performance but carry massive computational overhead that restricts edge deployment. Although structural pruning has emerged as a key strategy to reduce these costs, existing methods often suffer from severe accuracy degradation or require expensive retraining. Recently, Variance-Based Pruning (VBP) introduced a promising paradigm by selecting neurons based on activation variance; however, it remains limited by statistical noise in finite-sample activation covariance and reliance on bias-only updates that cannot fully account for structural reconstruction error. To address these limitations, we introduce Denoised Variance-Based Pruning with Optimal Brain Bias Compensation (DVBP + OB ^2 C). We leverage random matrix theory to filter noise from the activation covariance spectrum for robust neuron selection and mathematically prove that integrating mean-shift compensation into the Optimal Brain Compression objective reduces the layer-wise Hessian exactly to the activation covariance matrix. This enables an optimal, closed-form update of the remaining weights using the same statistics gathered for selection. Extensive experiments on DeiT, Swin, and ConvNeXt architectures demonstrate that DVBP + OB ^2 C achieves state-of-the-art training-free performance; at 50% MLP pruning, it retains over 90% of the original Top-1 accuracy on Small and Base variants, outperforming VBP by up to 29.46% (ConvNeXt-T) and 7.33% (Swin-S). The code is available at: this https URL.
[CV-36] MaLViL: Multi-axis Low-rank Vision-LSTM for Medical Image Segmentation MICCAI
链接: https://arxiv.org/abs/2608.17635
作者: Afshin Bozorgpour,Sina Ghorbani Kolahi,Moein Heidari,Ilker Hacihaliloglu,Dorit Merhof
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the MICCAI Workshop on Machine Learning in Medical Imaging (MLMI), 2026
Abstract:Vision-LSTM (ViL) enables efficient global modeling, but its cost still scales with the number of spatial tokens, so existing segmenters confine ViL to a coarse bottleneck and lose fine anatomical detail. Rasterizing 2D features into a 1D sequence further breaks adjacency across the orthogonal scan axis. We propose MaLViL, a Multi-axis Low-rank Vision-LSTM network that extends ViL across decoder resolutions. Bidirectional low-rank ViL (Bi-LRViL) reasons on a compact orthonormal subspace and preserves detail through an orthogonal residual; scale-aware SaLViL restores cross-axis neighbors before serialization; and a Cross-Directional Mixer (CDM) fuses orthogonal horizontal and vertical traversal paths. Statistics-Guided Skip Modulation (SGSM) further retains boundary cues in encoder skips. On skin-lesion, ultrasound, and multi-organ CT benchmarks, MaLViL achieves competitive or state-of-the-art segmentation accuracy, while reducing ViL operator memory by up to 83\times at fine decoder resolutions. Code is available at: this https URL.
[CV-37] RetiWave-Mamba: A Dual-Stream Network for Retinal Disease Detection based on Multi-scale Context and Frequency-Adaptive Mamba Projection
链接: https://arxiv.org/abs/2608.17623
作者: Cheng Cheng,Jin Hong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Retinal diseases are a leading cause of irreversible vision impairment, making early and accurate diagnosis essential for effective treatment. Optical Coherence Tomography (OCT) serves as a critical imaging modality for this purpose, yet its automated analysis is hindered by inherent speckle noise, varying lesion scales, and subtle inter-class similarities. To address these challenges, we propose a novel framework, RetiWave-Mamba, which integrates spatial-frequency domain learning with state-of-the-art state space models. The framework utilizes Discrete Wavelet Transform (DWT) to decompose OCT images into low- and high-frequency streams, enabling decoupled processing of structural context and fine-grained details. For the low-frequency branch, we design a Multi-scale Contextual Localization Module (MCLM), which synergizes multi-scale dilation with spatial attention to expand the global receptive field and precisely localize lesion regions. For the high-frequency branch, we introduce an Attention-Guided High-Resolution Network (AG-HRNet) equipped with an intelligent gating mechanism to suppress noise propagation during multi-scale interactions. Furthermore, a Frequency-Adaptive Mamba Projector (FAMP) is incorporated to capture long-range dependencies within disjoint high-frequency textural features. Extensive experiments on the OCT-C8 dataset demonstrate that our approach achieves a state-of-the-art (SOTA) classification accuracy of 98.25%, surpassing existing methods. These results highlight the efficacy of RetiWave-Mamba in robustly identifying retinal pathologies under noisy conditions, offering a promising tool for clinical diagnosis.
[CV-38] PathoArgus: Advancing Evidence-Grounded Long-Context Visual Reasoning across Gigapixel Whole-Slide and Multi-Slide Case Contexts
链接: https://arxiv.org/abs/2608.17607
作者: Bowen Liu,Qixiang Zhang,Xiaomeng Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Whole-slide pathology reasoning requires models to integrate gigapixel-scale visual evidence across complete case-linked slides, yet current question-answering benchmarks primarily measure final answer accuracy–a metric vulnerable to linguistic priors and benchmark regularities, and insufficient to establish that predictions are grounded in the supplied tissue. We introduce PathoArgus-Bench, a benchmark and evaluation protocol that explicitly tests the full evidence chain: availability, accessibility, use, and responsiveness. PathoArgus-Bench comprises 22,078 four-choice questions from 4,913 patients across 15 TCGA projects, covering six pathology capabilities across three levels of evidence demand, and operates under a fixed reader budget that retains only a small fraction of the gigapixel context. To further isolate evidence-grounded reasoning, we contribute ESG (Evidence State Quartets), a controlled set of 483 quartets where the question text is fixed while the target WSI set is moved, replaced, or removed, requiring consistent predictions across all states. Evaluating 20 general-purpose, medical, and pathology-specific systems reveals a stark gap: while GPT-5.6 achieves 57.09% overall accuracy and 57.04% on ESG, it correctly completes only 19 of 483 quartets (3.93% QExact), exposing that row-level accuracy does not translate into reliable evidence grounding. We also introduce PathoArgus, a fixed-budget reader that allocates context via question relevance and spatial coverage, attaining 50.39% overall accuracy yet only 1.86% QExact–demonstrating that improved context access alone does not ensure consistent evidence-based prediction. Our benchmark and diagnostics establish that acquiring useful whole-slide context is necessary but far from sufficient, and call for a shift from answer-centric to evidence-grounded evaluation in computational pathology.
[CV-39] SpurCon: Weighted Supervised Contrastive Learning for Mitigating Spurious Cues in Medical Imaging
链接: https://arxiv.org/abs/2608.17598
作者: Shenhav Nadir,Meir Yossef Levi,Eyal Gofer,Guy Gilboa
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Despite the rapid progress of deep neural networks in visual recognition, their adoption in high-risk medical applications remains limited due to reliability and robustness concerns. Models may exploit spurious correlations, particularly in medical imaging, where devices or treatment artifacts often co-occur with pathology. In small or imbalanced datasets, such cues further reduce worst-group performance and undermine clinical trust. To solve these issues, two major challenges should be addressed: identifying dataset-specific spurious cues, which typically require domain knowledge, and mitigating reliance on them. To tackle both, we propose SpurCon, a lightweight framework based on a novel supervised contrastive loss formulation that leverages available metadata and predicted spurious labels to enhance robustness. We introduce a fast few-shot procedure, without network training, to estimate spurious labels using a small number of expert-annotated samples. We then propose a weighted supervised contrastive objective, WtSupCon, that reshapes the representation geometry by assigning sample-specific weights that depend on the [pathology, spurious, metadata] combination. For example, the highest weight is assigned to samples that differ only in their spurious label. This yields highly similar representations for images with the same metadata and pathology, differing only in the predicted spurious label. Our method operates on pretrained image encoders (such as BiomedCLIP) and trains only a lightweight projection head. We evaluate SpurCon on a synthetic setting and on Waterbirds, CheXpert, a chest X-ray classification dataset, and ISIC 2020, a skin cancer classification dataset. Our approach delivers the best spurious-mitigation performance, balancing well worst-group and overall accuracy on multiple datasets.
[CV-40] CoinVE-200K: A Large-Scale High-Quality Dataset for Compositional Instruction-Guided Video Editing
链接: https://arxiv.org/abs/2608.17566
作者: Fuchen Long,Cong Wang,Zitao Gao,Wenhao Zhong,Yu Cheng,Xiaolu Hou,Yan Li,Xiao Cao,Xinlong Sun,Xi Chen,Yu Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL ; Dataset is available at this https URL see source codes at this https URL
Abstract:The quality and diversity of instruction-based video editing datasets are steadily improving, yet existing datasets mainly focus on single editing operations and fall short in supporting compositional instruction-guided video editing. In particular, multiple editing intents must be jointly understood and faithfully executed within the same video. To address this issue, we introduce CoinVE-200K, a large-scale, high-quality dataset for Compositional Instruction-Guided Video Editing. CoinVE-200K contains 1080p video-editing pairs of up to 201 frames, covering diverse compositional scenarios where each sample involves 2 to 5 atomic editing operations. The instructions target humans, objects, and backgrounds, and cover edit types such as addition, removal, modification, and stylization. All samples are built through a carefully designed generation and filtering pipeline to ensure instruction faithfulness, visual quality, temporal consistency, and compositional diversity. We also introduce CoinVE-Bench, a benchmark for compositional-instruction video editing across diverse subjects, operation types, and instruction complexities. Furthermore, we present CoinVE-Edit, a 22B compositional video editing model built upon Wan2.1-T2V-14B and Qwen3-VL-8B-Instruct. CoinVE-Edit disentangles region-aware attention for different editing instructions, enabling precise multi-region editing while preserving irrelevant content and temporal coherence. Experiments on CoinVE-Bench show that CoinVE-Edit achieves strong performance in instruction following, compositional editing accuracy, visual quality, and temporal consistency.
[CV-41] Where a New Concept Must Enter: Entry Point Gates Cross-Task Usability in Unified Multimodal Models
链接: https://arxiv.org/abs/2608.17564
作者: Zongyang Qiu,Yihan Wu,Kaixuan Fan,Bo Li,Hui Xiong
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 27 pages, 10 figures
Abstract:Unified multimodal models (UMMs) are motivated by the hope that understanding and generation reinforce each other but controlled ablations repeatedly find that adding a generation objective leaves understanding flat. Joint-training studies cannot settle the disagreement: with overlapping supervision, a gain cannot be attributed to the architecture rather than the data. To further investigate the relationship between the two directions in UMMs, we separate them by construction. A novel visual entity, a rendered 3D asset paired with a pseudo-word screened for absence from the frozen model’s behavior, is bound through exactly one task direction, and the untrained direction is then measured. We find that the channel is real in both directions, but the directions differ in kind: generation training installs a name the model can only match among candidates; understanding training installs one it can also produce. What governs cross-task usability is where the binding enters the shared computation. An alignment probe predicts export across 36 configurations (Spearman \rho = +0.68 ). That objective’s alignment term, maximized in closed form over activations with every weight frozen, makes a concept drawable when injected at layer 7 of 28 and is indistinguishable from the base model from layer 14 on, while the weight-based version of the same edit peaks at layers 10-14. In an observational series of four models, this window appears only where the understanding pathway is a semantic vision encoder, suggesting that unified weights are not enough: the two directions must share a semantic format at the entry point. Exploiting the rule, a mid-stack alignment objective acquires the concept for a 0.1% relative loss of the model’s general text-to-image ability, against 41% for the standard generative route. Our code is at this https URL.
[CV-42] Leverag ing existing sparse point annotations for benthic imagery dense segmentation
链接: https://arxiv.org/abs/2608.17561
作者: Cesar Borja,Breck A. McCollum,Jarret E. Byrnes,Kenneth Sebens,Ana C. Murillo
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:The health of marine ecosystems is a critical indicator of global environmental change, yet the physical constraints of underwater observation and the intrinsic challenges of processing marine imagery severely limit the scalability of systematic monitoring. While recent visual foundation models such as the Segment Anything Model (SAM) series show great promise, they still struggle with the fine-grained recognition required in these complex scenarios and still require expert supervision. Our work addresses this gap by bridging state-of-the-art foundation models with existing sparse supervision. Because historical benthic surveys are typically annotated with only a few sparse expert points per image, we utilize these legacy point-labels as visual prompts for SAM2. Our primary contribution is a novel mechanism to automatically identify which of these points are suitable, and which are actively harmful, when used for propagation. By filtering out unreliable points, we extract high-quality pseudo-ground-truth masks capable of training more accurate, fine-grained semantic segmentation models. We demonstrate the effectiveness of our approach on public benthic data and introduce a new, challenging benchmark featuring real-world sparse expert annotations, paving the way for scalable ecological analysis.
[CV-43] MSEditor: Toward Consistent Multi-Shot Video Editing ECCV2026
链接: https://arxiv.org/abs/2608.17559
作者: Kunyu Feng,Yue Ma,Bingyuan Wang,Yuefeng Wang,Zhiyuan Qin,Hao Cheng,Hao Li,Qifeng Chen,Zeyu Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026
Abstract:In this paper, we tackle the problem of performing consistent, unified modifications to a multi-shot video sequence. This task is particularly challenging because multi-shot videos consist of discontinuous temporal segments that vary significantly in viewpoint, camera scale, and subject pose, leading to severe identity drift and cumulative error propagation. Achieving coherent edits requires establishing reliable cross-shot semantic awareness to maintain stable subject appearance and visual continuity across these disjointed boundaries. To address this, we propose MSEditor, the first framework designed specifically for consistent multi-shot video editing. To overcome the scarcity of high-quality multi-shot training data, we repurpose existing multi-view video datasets to provide robust cross-shot supervision. Architecturally, we introduce a Supervisory Adapter that injects this cross-shot information into the diffusion backbone, enabling the model to learn identity-consistent representations. Furthermore, to effectively mitigate cumulative errors and ensure long-range temporal coherence, we design a Cross-Shot Packing strategy that dynamically aggregates information from semantically related shots within the self-attention window. Extensive experiments demonstrate that MSEditor significantly outperforms existing methods on our curated multi-shot video editing benchmark in terms of identity preservation, temporal stability, and overall visual quality.
[CV-44] GroupForward: Building Referable 3D Scenes via Instance-Grouped Feed-Forward Gaussian Splatting
链接: https://arxiv.org/abs/2608.17535
作者: Qijian Tian,Zimeng Wu,Xuhong Wang,Lizhuang Ma,Xin Tan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Simultaneously reconstructing and understanding 3D environments is essential for embodied agents. Toward this goal, feed-forward semantic 3D Gaussian Splatting (3DGS) efficiently constructs semantic scene representations from sparse multi-view observations. However, existing methods lack explicit instance discrimination and mainly support category- or phrase-based semantic queries. To this end, we propose GroupForward, an instance-grouped feed-forward Gaussian splatting model that reconstructs geometry, appearance, instance structure, and semantics from sparse, unposed, and uncalibrated multi-view images. Unlike existing methods that attach high-dimensional semantic features to each Gaussian, GroupForward learns compact instance embeddings that group Gaussians into cross-view consistent 3D instances, reformulating feed-forward semantic 3DGS from per-Gaussian semantic feature rendering to instance-level semantic aggregation and propagation. Building on these instance groups, we further propose a Referential Scene Reasoning Framework (RSRF) for complex 3D referring segmentation. RSRF constructs an instance-grouped 3D scene graph and retrieves candidate instances for a given referring expression. A vision-language model then reasons over structured instance evidence and multi-view observations to identify the referred instance among the candidates. RSRF thereby extends language interaction from simple semantic querying to complex referential scene reasoning. Experiments on semantic reconstruction and referential reasoning demonstrate the effectiveness of our instance-grouped reconstruction and reasoning framework.
[CV-45] Explainable AI-Powered Framework for Video-Based Skill Assessment in Cataract Surgery
链接: https://arxiv.org/abs/2608.17522
作者: Mohammad Javad Ahmadi,Hamid D. Taghirad
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Persistent shortages in the surgical workforce and inherent limitations of traditional training methods highlight the necessity of automated, data-driven approaches in surgical education. This study addresses these challenges by introducing a novel, explainable AI-powered framework for automated skill assessment, specifically focusing on cataract surgery. We present the world’s largest dataset of cataract surgery videos, comprising 2,000 recordings. Additionally, we propose an AI-powered analytical framework that employs advanced computer vision and signal-processing techniques to automatically evaluate surgical videos to derive objective, quantitative performance indicators that complement or potentially replace subjective scoring methods. A significant advantage of our framework over previous methods lies precisely in its explainability of outputs, elevating it beyond merely an opaque skill classification tool. Through experimental analysis of 83 cataract surgery videos, we demonstrate that the automatically computed metrics exhibit strong correlations with expert-based subjective evaluations, achieving up to 87% accuracy in surgical skill assessment. Each metric was individually examined, and expert surgeons provided subjective ratings using the newly introduced Capsulorhexis Skill Assessment System (CSAS). These subjective assessments were compared with ten objective motion-based metrics extracted through our framework. The results indicated a robust correlation between subjective ratings and automated indicators, underscoring the framework’s capacity to accurately model surgical expertise.
[CV-46] BrainNorm: A Foundation Model that knows Normal via Semantic Atlas Pretraining
链接: https://arxiv.org/abs/2608.17521
作者: Madhumitha Venkatesh,Shanawaj S Madarkar,Konda Reddy Mopuri
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We introduce BrainNorm, a normative foundation model, trained and tested on ~66,000 T1-weighted structural MRI (T1w sMRI) scans. By leveraging language-image style contrastive pretraining on healthy cohorts across ages, BrainNorm learns a Semantic Atlas Latent space (SAL), where each scan is represented as a set of atlas-parcel embeddings. This yields parcel-specific healthy aging template trajectories that support age-consistent template matching and localized deviation scoring relative to a subject’s chronological age. Across 6 downstream cohorts, BrainNorm demonstrates generalization evaluated across 25 task-setting combinations spanning age estimation, brain-age gap estimation, parcel identification, and single- multi-disease classification tasks under direct inference, zero-shot, few-shot full-data linear-probe settings. The resulting deviation patterns in SAL space enable zero-shot tasks for disease prediction using parcel-wise abnormalities. Fine-tuning on healthy-only cohorts of downstream datasets further improves the performance of various tasks. Across all classification tasks, linear probing on BrainNorm’s frozen embeddings outperforms 9 baselines finetuned under end-to-end supervision. Furthermore, the localized deviations identified by BrainNorm across various neurodegenerative disorders closely align with established neurodegeneration pathology in clinical literature.
[CV-47] Looking Beyond the Scale: Do Surgical Skill Models Learn Transferable Representations Across Assessment Rubrics?
链接: https://arxiv.org/abs/2608.17519
作者: Hanna Hoffmann,Felix von Bechtolsheim,Stefanie Speidel,Rebecca Hisey
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Vision-based surgical skill assessment has shown strong in-domain results, yet a fundamental question remains unasked: do these models learn transferable representations of surgical proficiency, or do they merely encode dataset-specific visual patterns? This paper systematically analyzes what limits cross-domain skill transfer between the GOALS and OSATS assessment scales using the LASANA and JIGSAWS datasets. Each evaluated method serves a targeted diagnostic purpose: end-to-end training to test whether supervised skill learning transfers directly, Adaptive Sharpness-Aware Minimization (ASAM) to probe whether flatter loss landscapes improve generalization, and augmentation-based self-supervised and contrastive learning to assess whether domain-invariant pretraining decouples skill from visual context. Transfer is evaluated in both directions using a disjoint-participant held-out test set for JIGSAWS. Results reveal an asymmetry: backbones pretrained on JIGSAWS achieve CCC values of 0.77 to 0.80 on LASANA, closely matching the end-to-end baseline, showing cross-rubric transfer is feasible when the target domain provides consistent supervision. Transfer to JIGSAWS fails across all methods, likely due to annotation inconsistencies. Control experiments with a Kinetics-pretrained backbone suggest task-specific heads carry the majority of the skill prediction burden, while the backbone need only provide adequate spatiotemporal features. These findings offer a new perspective on vision-based skill assessment: the central question of whether skill representations transfer across scoring systems has not been previously investigated. Results indicate the visual component is dominant but not solely responsible for skill prediction; further work is needed to conclusively disentangle transferable skill features from those bound to a specific visual domain. Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) Cite as: arXiv:2608.17519 [cs.CV] (or arXiv:2608.17519v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.17519 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Hanna Hoffmann [view email] [v1] Tue, 18 Aug 2026 08:44:31 UTC (6,125 KB)
[CV-48] SE-MoLoRA: Shared-Expert LoRA Adapters for Domain-Specific Photographic Assessment
链接: https://arxiv.org/abs/2608.17514
作者: Bishwash Khanal,Anlan Zhang,Sasu Tarkoma,Tommi Mikkonen,Abhishek Kumar
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注:
Abstract:Vision-language models can describe images fluently, but they often fail to provide actionable photographic critique because semantic content and aesthetic judgment remain entangled. We propose SE-MoLoRA, a modular parameter-efficient adaptation framework for domain-specific photographic assessment. The method separates general photographic knowledge from specialist residual judgments using an always-active shared LoRA expert and routed adapters for composition, lighting, and technical quality. A lightweight query router selects the relevant specialist, enabling targeted critique without training separate full models. A rank-64 shared adapter captures broad photographic vocabulary, while rank-32 specialists learn domain-specific residuals with an orthogonal regularization penalty that encourages disentangled representations. Training data is obtained by distilling the Reddit Photo Critique Dataset into domain-labeled critique samples. On held-out critique generation, SE-MoLoRA improves BERTScore-F1 from 0.2317 to 0.4215 over monolithic LoRA and is preferred in 84.6% of pairwise comparisons, while using fewer active parameters than separate specialist models. SVD-based ablation study shows that shared-specialist decomposition and orthogonal regularization reduce expert overlap. These results demonstrate that modular adaptation improves controllability and specificity in multimodal photographic critique.
[CV-49] When More Foundation Models Means Less: Diagnosing and Addressing Multi-View Fusion Failure ALT
链接: https://arxiv.org/abs/2608.17490
作者: Yibo Liu,Bowen Jiang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 26 pages, 4 figures. Code and results: this https URL
Abstract:Foundation-model hubs turn multi-view fusion into a selection problem: from a large heterogeneous encoder pool, which views should be fused, and how many? We show that downstream performance is non-monotonic in the number of fused encoders; later views can be redundant or task-misaligned, causing accuracy to saturate or decline. We formalise this setting as view-set composition and propose KAGES (Kernel-Alignment Greedy Encoder Selector), a label-aware method that orders frozen encoders by their marginal gain in centred kernel-target alignment. KAGES requires no downstream classifier training during selection, evaluates each candidate in \mathcalO(n^2) time independent of encoder dimension, and admits a conditional (1-e^-\gamma) prefix-wise guarantee under monotonicity and a positive submodularity ratio. Across five recognition regimes and low-shot, larger-pool, and full-data protocols, KAGES improves average AULC over full fusion by 3.9, 5.8, and 3.3 points, respectively, and exceeds DPP and facility-location selection in average AULC. Image retrieval exhibits later, task-dependent saturation along the KAGES ordering, while peak-then-decline reproduces in frozen-LLM fusion. These results show that effective large-pool fusion depends on selecting a compact, task-aligned set of views rather than indiscriminately fusing more encoders.
[CV-50] NeuroPath: Brain-Inspired Dual-Pathway Graph Convolutional Networks for Skeleton-Based Action Recognition
链接: https://arxiv.org/abs/2608.17487
作者: Kanglei Zhou,Ruizhi Cai,Hubert P. H. Shum,Frederick W. B. Li,Xiaohui Liang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to Pattern Recognition
Abstract:Skeleton-based action recognition aims to recognize human actions from sequences of human joint coordinates. Most existing Spatial-Temporal Graph Convolutional Networks (STGCNs) have achieved promising results by modeling skeletal structures with implicit spatial-temporal representations. However, our empirical study reveals a clear performance imbalance across different skeletal modalities, indicating that implicitly coupling spatial and temporal information limits the full exploitation of complementary structural and motion cues. Inspired by the ventral and dorsal pathways in human perception, we propose Dual-Pathway Graph Convolutional Networks (NeuroPath), which adopt a dual-pathway architecture for separate yet collaborative modeling of spatial and temporal information. Specifically, transformation units first convert the input into pathway-specific skeletal representations, allowing each pathway to focus on complementary aspects of human motion. To further capture coordinated joint behaviors and their interrelationships, we introduce a group graph convolution block that dynamically identifies key body parts and models their spatial-temporal dependencies. In addition, inter-pathway dynamic fusion modules integrate complementary inter-modal information across pathways, facilitating higher-level semantic interpretation of actions. Extensive experiments on Kinetics Skeleton 400, NTU RGB+D 60, and NTU RGB+D 120 demonstrate consistent performance improvements, validating the effectiveness of dual-pathway spatial-temporal modeling for skeleton-based action recognition.
[CV-51] S3AM: A Single-Stream SAM with Reliability-Calibrated Frequency Adapter for Multi-modal Salient Object Detection
链接: https://arxiv.org/abs/2608.17475
作者: Ruichao Hou,Boyue Xu,Tongwei Ren,Dongming Zhou,Gangshan Wu,Jinde Cao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision foundation models have recently advanced multi-modal salient object detection (MSOD) through parameter-efficient tuning and prompt learning. However, existing Segment Anything Model (SAM)-adapted MSOD methods often rely on dual-stream encoders or auxiliary prompt generators, leading to redundant computation. Although a single-stream alternative can reduce this cost, early fusion may also propagate noisy or misaligned auxiliary high-frequency cues through the backbone. In this paper, we propose a novel single-stream framework that integrates reliability-calibrated frequency adaptation into the adopted SAM backbone for MSOD. It avoids duplicated foundation backbones while explicitly controlling auxiliary frequency injection. Specifically, we design a mixture of frequency experts module, which uses the stationary wavelet transform to decompose each modality and aggregate cross-modal frequency information. We further introduce a reliability-calibrated frequency adapter with a dual-gate calibration mechanism, which selectively propagates the calibrated residual across transformer stages while jointly controlling its injection strength and cross-modal reliability. A hypernetwork-guided semantic-structural decoder then combines semantic mask features from the adopted backbone with Mamba-based structural detail recovery. Comprehensive experiments on RGB-D, RGB-T, and RGB-NIR salient object detection benchmarks validate that the proposed framework achieves competitive performance with only 12.20M trainable parameters, accounting for 5.4% of the total parameters. The code will be available at this https URL.
[CV-52] NGS-Marker: Robust Native Watermarking for 3D Gaussian Splatting
链接: https://arxiv.org/abs/2608.17447
作者: Hao Qin,Yukai Sun,Luyuan Chen,Mengxu Lu,Feng Zhang,Ming Kong,Zhenhong Du,Qiang Zhu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:With the rapid development and adoption of 3D Gaussian Splatting (3DGS), the need for effective copyright protection has become increasingly critical. Existing watermarking techniques for 3DGS mainly focus on protecting rendered images via pre-trained decoders, leaving the underlying 3D Gaussian primitives vulnerable to misuse. In particular, they are ineffective against Partial Infringement, where an adversary extracts and reuses only a subset of Gaussians. In this paper, we propose NGS-Marker, a novel native watermarking framework for 3DGS. It integrates a jointly trained watermark injector and message decoder, and employs a gradientbased progressive injection strategy to ensure full-scene coverage. This enables robust ownership decoding from any local region. We further extend NGS-Marker with hybrid protection (combining native and indirect watermarks) and support for multimodal watermarking. Extensive experiments demonstrate that NGS-Marker effectively defends against partial infringement while offering practical flexibility for real-world deployment.
[CV-53] Counterfactual Anatomy-guided Spatial-Temporal Decoding for Annotation-Free Hallucination Mitigation in Medical VLMs MICCAI2026
链接: https://arxiv.org/abs/2608.17427
作者: Yifan Lu,Adinath Dukre,Abhijit Das,Ziyun Zou,Haolin Yang,Yutong Xie,Imran Razzak
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by MICCAI 2026
Abstract:Medical vision-language models (Med-VLMs) have demonstrated strong performance on medical visual question answering, yet they remain prone to hallucination, generating clinically unsupported statements that are insufficiently grounded in image evidence. Mitigation methods applied during decoding offer a practical solution, but they typically lack anatomical awareness or rely heavily on ground truth annotations, which limits their applicability. We propose Counterfactual Anatomy-guided Spatial-Temporal decoding (CAST), a framework that operates entirely during inference and requires no manual annotations for anatomically grounded hallucination mitigation. CAST automatically discovers anatomical regions relevant to the given query through broad medical segmentation. It then selects a compact, causally informative area using counterfactual intervention based on the drop in answer likelihood under occlusion. Guided by this chosen region, CAST performs a unified contrastive decoding process, combining classifier-free guidance to correct spatial attention with stepwise temporal contrast to regulate generation dynamics. Experiments on the SLAKE and MIMIC-CXR datasets across three Med-VLMs demonstrate that CAST consistently outperforms strong baselines and surpasses decoding strategies reliant on ground truth. Our results indicate that compact, automatically selected regions provide highly effective contrastive guidance without expert annotations, offering a practical and generalizable solution for improving spatial grounding and reducing hallucinations. Code is available at this https URL.
[CV-54] SemComp-Bench: Benchmarking Semantic Task Completion in Video Generation
链接: https://arxiv.org/abs/2608.17426
作者: Keyu Tu,Zhuowei Chen,Mengqi Huang,Yuxin Wang,Jiahao Zhu,Zhendong Mao,Yongdong Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce Semantic Task Completion Video Generation, an outcome-oriented video generation task. Under this formulation, success requires both achievement of the intended outcome and semantic grounding. Semantic grounding characterizes the correspondence between the reference image and the generated outcome in terms of high-level semantics relevant to the task. Evaluation focuses on the generated outcome and requires neither the presentation of a complete sequence of intermediate task steps nor conventional appearance consistency with the reference image. To support systematic evaluation, we construct SemComp-Data, an evaluation dataset covering six domains. Each instance comprises a reference image, a detailed instruction, a brief instruction, and an outcome-centric video clip. A scalable four-stage curation pipeline converts raw videos into standardized SemComp-Data instances. We further introduce SemComp-Bench, an evaluation protocol that uses a vision-language model (VLM) to answer structured binary questions. SemComp-Bench reports the OA Score and the GR Score for Outcome Achievement and Generation Reliability, respectively. Experiments on representative video generation models show that achieving intended outcomes while maintaining task-relevant semantic grounding in reference images remains challenging.
[CV-55] GSToken: Geometry-Structured Gaussian Tokens for Compact 3D Medical Image Representation
链接: https://arxiv.org/abs/2608.17425
作者: Xiaoduo Li,Quan Gu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Effective segmentation of multi-modal MRI is central to improving neural network accuracy in brain tumor recognition. Existing methods typically compress 3D volumes into token sequences via fixed patch encoding or learned attention pooling (e.g., TokenLearner). However, these compression schemes discard explicit spatial shape information; the resulting tokens convey no notion of lesion morphology or spatial extent. Meanwhile, end-to-end evaluation entangles a tokenizer’s information retention with the reconstruction capacity of the downstream decoder, and the lack of a unified capacity contract across methods makes performance differences difficult to attribute. In this paper, we introduce Gaussian tokens to multi-modal brain tumor segmentation for the first time: each token carries not only a semantic feature but also a learned 3D center, anisotropic scale, and orientation, endowing the representation with explicit geometric support at negligible parameter cost. We further propose a frozen-token utility evaluation protocol: the trained tokenizer is frozen, its output is cast into a fixed-capacity serialized contract, and a shared lightweight Transformer probe independently measures each tokenizer’s retained information under strictly matched conditions. Multi-seed paired statistical testing shows that GSToken consistently and substantially outperforms capacity-matched adaptive baselines under frozen probing, with uniform advantages across all tumor sub-regions, surface, and distance metrics. These results demonstrate that explicitly encoding spatial geometry within tokens significantly improves the information density of volumetric representations, offering a new design principle for compact 3D medical image representation and downstream reading.
[CV-56] F-CADE: Foreground-Concentrated Text-Video Alignment for Zero-Shot Temporal Action Detection
链接: https://arxiv.org/abs/2608.17422
作者: Yearang Lee,Ho-Joong Kim,Seong-Whan Lee
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Zero-Shot Temporal Action Detection (ZSTAD) aims to lo- calize and recognize action instances from unseen action categories in untrimmed videos. Although existing meth- ods have shown effectiveness by advancing architectural text-video alignment, they still struggle with capturing se- mantic distinctions between action classes, resulting in text- irrelevant predictions. To address this issue, we propose a Text-Foreground Concentrated Alignment for zero-shot temporal action DEtector (TF-CADE) that explicitly aligns textual information with action-relevant foreground regions. Specifically, we introduce Action Concentrate Aggregation (ACA), which extracts action concentrate scores to aggregate temporally informative video segments into a foreground- weighted video embedding. This foreground concentrated alignment enhances the semantic consistency between text and video features and improves inter-class discriminabil- ity. In addition, a Certainty-based Confidence Re-weighting (CCR) strategy refines per-snippet confidence scores by lever- aging foreground-aware similarity, effectively suppressing irrelevant action classes during inference. Extensive evalua- tions show that our TF-CADE not only achieves state-of-the- art performance under in-distribution settings but also excels in cross-dataset generalization to unseen action classes.
[CV-57] EAMS: Text-prompted spatiotEmporal dual-heAd Mamba Snake
链接: https://arxiv.org/abs/2608.17421
作者: Ruicheng Zhang,Jianhui Lei,Kaiwen Shen,Haowei Guo,Jun Zhou,Bin Chen,Mengtang Li,Shen Zhao,Shuo Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Medical Image Analysis (MedIA), 2026, In Press, Online Early Access Available
Abstract:Deep snake is a promising family of instance segmentation methods that accurately predicts object-level contours, thereby overcoming common pixel-level misclassification issues such as mask cavities and jagged edges in semantic segmentation approaches. However, existing deep snake methods face challenges in handling complex morphological variations, accurately capturing fine-grained organ details, and correcting base detection errors. To mitigate these limitations, we propose a cohesive Text-prompted spatiotEmporal dual-heAd Mamba Snake (TEAMS), a novel vision-language Mamba snake framework with three key innovations: (1) A Spatiotemporal Snake Evolution Strategy (SSES) is introduced to tackle complex morphological variations by capturing bidirectional spatial dependencies along the snake contour and temporal dynamics across evolution steps in a state space model. (2) A Contour Morphology-Aware Mamba (CMAM) is proposed to quantify local contour morphologies to modulate the structured attention mask in the Mamba2 SSD dual form, which extends Mamba’s capability to perceive the relative importance of its input sequence elements for better delineation of fine-grained organ details. (3) A Text-prompted Collaborative Dual-Head Snake (TCDHS) is designed to incorporate cues from textual prompts and transfer the evolved contour information to the base detection head, which enhances the deep snake workflow and mitigates wrong detections. Comprehensive evaluations on five datasets covering different organs and imaging modalities demonstrate that TEAMS outperforms existing semantic and deep snake segmentation methods (e.g., relative mDice/mBF improvements of 6.9%/9.1% in a spinal dataset), underscoring its potential as a reliable tool across diverse medical image segmentation scenarios.
[CV-58] SPVC: Structured and Panoptic Video Fixing for Cross-Dataset Driving Scene Rendering
链接: https://arxiv.org/abs/2608.17420
作者: Gen Li,Shu Han,Yun Xi Qiao,Hua Chen,Xuyang Dai,Bohan Li,Hao Zhao,Chaojian Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Driving scene reconstruction and rendering, especially with 3D Gaussian Splatting, has become an important component of autonomous driving simulation. However, rendered views often degrade under extrapolated ego trajectories and scene edits, producing blurry structures, temporal flicker, and foreground-background misalignment. Existing refinement methods are commonly designed for a specific setting, such as image-level novel-view repair or object-editing correction. In this paper, we introduce SPVC, a structured and panoptic video fixing framework for cross-dataset driving scene rendering. The name summarizes four design principles. (1) Structured fixing denotes the use of explicit spatial conditions, including camera pose, 3D bounding boxes, and HD maps, to guide the repair process and reduce uncontrolled hallucination. (2) Panoptic fixing refers to correcting both background rendering artifacts, such as distorted roads, buildings, and lanes, and foreground vehicle artifacts introduced by scene editing, such as inconsistent object appearance. (3) Video fixing means that the model operates on driving sequences rather than isolated frames, allowing temporal cues to be used during artifact correction. (4) Cross-dataset fixing means that a single shared network is trained and applied across multiple driving datasets, reducing the need for dataset-specific or scene-specific fixers. Concretely, we construct paired degraded-clean training data by simulating under-constrained 3DGS rendering and foreground vehicle insertion artifacts, and train a two-stage controllable video diffusion model that first addresses video-level appearance and then refines scene layout with structured controls.
[CV-59] Spectral Gradient Orthogonalization Improves Differentially Private Training at Scale ECCV2026
链接: https://arxiv.org/abs/2608.17415
作者: Sabari Shanmugam,Nick Barnes,Kerry Taylor
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCV 2026
Abstract:Differentially private training adds isotropic Gaussian noise to clipped gradients, corrupting every singular direction equally. In vision models, where spatial correlation concentrates gradient energy into a low-rank subspace, most of this noise falls in directions that carry little signal. Spectral gradient orthogonalization via polar decomposition is introduced as a post-processing step that recovers directional signal from the noisy gradient’s low-rank structure at zero additional privacy cost. A phase transition governs the utility of this approach: orthogonalization improves accuracy only when the per-direction spectral signal-to-noise ratio (SNR) suffices for singular vector recovery; in low-SNR regimes, the directional bias of the gradient is replaced by a nearly random orthogonal update, and the transformation is harmful. The recovery threshold is determined by the spectral gap of the gradient and is surpassed at large batch sizes. Empirically, the benefit scales with model capacity: spectral orthogonalization achieves a +20.9% improvement over DP-SGD on WRN-28-10 (B = 4096) and +14.9% on ResNet-18, while reducing inter-run variance by a factor of two to three. In the fine-tuning regime, spectral orthogonalization matches the stability of DP-Adam while maintaining a first-order memory footprint. Combining spectral with temporal denoising yields 50.3% on CIFAR-10 (epsilon = 4), the highest accuracy in any tested configuration. These gains are specific to moderate-to-high-SNR regimes such as large-batch training of higher-capacity models. Small-batch or low-SNR settings are better served by DP-SGD or temporal denoising.
[CV-60] REChart: Reasoning -Efficient Chart Editing with Large Reasoning Models
链接: https://arxiv.org/abs/2608.17414
作者: Yuanbang Liu,Chenxi Ruan,Yihan Hou,Qiong Luo,Wei Zeng
类目: Computer Vision and Pattern Recognition (cs.CV); Programming Languages (cs.PL)
备注:
Abstract:Chart editing requires inferring and modifying visualization code from a reference chart image based on an editing instruction, challenging fine-grained visual reasoning, instruction following, and executable code synthesis capabilities of MLLMs. Large reasoning models (LRMs) with extended Chain-of-Thought (CoT) reasoning are suitable for tackling such complex multimodal tasks. However, our preliminary study reveals an inverted-U'' relationship between reasoning length and chart-editing performance: Excessive reasoning often leads to overthinking,‘’ where models drift toward hallucinated visual details or get stuck in redundant reasoning loops. To address the gap, we introduce REChart, a two-stage training framework that provides process-level supervision over intermediate reasoning steps, improving both editing fidelity and reasoning efficiency. First, we synthesize 200k high-quality reasoning trajectories for supervised fine-tuning from a large image-instruction-code pool, using a role-specialized agentic Reason-Score-Refine workflow that iteratively refine the chart code toward higher quality. Second, we optimize the model via reinforcement learning with two complementary rewards: a \emphfidelity reward evaluating code correctness, visual fidelity, and structural consistency, and an \emphefficiency reward that assigns each rollout a random thinking budget, truncates the reasoning process, and credits the final reasoning segment according to its contribution to the output. On the ChartEdit and ChartMIMIC benchmarks, our model achieves state-of-the-art chart-editing performance among open-source models of comparable scale, while mitigating overthinking and reducing average reasoning token usage by 79.0% under a maximum thinking budget of 16,384 tokens compared with the base model.
[CV-61] MoE-ViE: Mixture of Experts Vision Encoder for Efficient Image and Video Understanding ECCV2026
链接: https://arxiv.org/abs/2608.17402
作者: Bonan Zhang,Shiyu Dong,Quan Hung Tran,Katharina Gschwind,Shuqi Yang,Sijia Chen,Adel Ahmadyan,Seungwhan Moon,Lu Zhang,Ahmed Kirmani,Babak Damavandi,Anuj Kumar
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026
Abstract:Vision encoders are a critical component of vision-language models, and scaling their capacity effectively improves performance. However, dense scaling increases compute cost and inference latency. Mixture-of-Experts (MoE) architectures offer a compelling alternative, having enabled efficient scaling in LLMs, yet the MoE design space for CLIP-style vision encoders remains underexplored at State-of-the-Art (SOTA) levels. In this work, we systematically study MoE designs for vision encoder scaling and find that fine-grained MoE topologies yield substantial gains over both dense and standard MoE counterparts. We further propose an auxiliary-loss-free balancing variant for better expert utilization, and design a specialized MoE kernel to mitigate inference latency overhead. To enhance video capabilities while preserving image knowledge, we introduce frame-level distillation paired with a novel freezing mechanism. We pretrain a series of Mixture-of-Experts Vision Encoders (MoE-ViE) across a range of sizes, all consistently outperforming their dense counterparts. Our largest model matches the zero-shot performance of a SOTA encoder 1.7x its size at 76% of its latency. When aligned with an LLM, MoE-ViE surpasses all compared encoders on image and video benchmarks, including those with up to 5x more activated parameters. Code is available at this https URL.
[CV-62] o Remove or Not to Remove Clouds: A Comparative Analysis and Fusion of Raw SAR and Synthetic NDWI for Overcast Water Segmentation
链接: https://arxiv.org/abs/2608.17398
作者: Saleh Sakib Ahmed,Sara Nowreen,M. Sohel Rahman
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Persistent clouds blind optical satellites during floods. While Synthetic Aperture Radar (SAR) penetrates clouds, its raw data is noisy and lacks clear contrast. To mitigate this, recent studies utilize deep learning models to translate SAR into cloud-free synthetic optical imagery for downstream tasks like water body segmentation. However, because raw SAR is the original source for both of these operations, a critical methodological dilemma arises: during complete overcast should segmentation models process the raw SAR directly, or rely on a translated synthetic Normalized Difference Water Index (NDWI) proxy? This study resolves the debate by demonstrating that synthetic NDWI yields better results, as the translation process acts as a powerful filter against radar noise. This raises a natural second question: what if we utilize both? Building on our findings, we introduce a Combined Framework that integrates both raw SAR and synthetic NDWI into a unified model. By fusing the sharp physical boundaries of raw SAR with the high contrast of synthetic NDWI, this hybrid approach consistently outperforms all standalone methods.
[CV-63] Noisy group neurons with synchronous resetting for high-performance spiking neural networks
链接: https://arxiv.org/abs/2608.17394
作者: Yajie Zhai,Yanmei Kang,Meng Li,Zigang Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Spiking neural networks (SNNs), characterized by bio-inspired neuronal dynamics and event-driven communication, have attained significant progress in recent years. Nevertheless, training deep SNNs remains challenging due to spatiotemporal information loss and gradient mismatching. To simultaneously address these issues, we propose a noisy group neuron (NGN) model, which incorporates population-level synchronous resetting and neural stochasticity as fundamental computational mechanisms. We then develop the NGN method as a framework that combines the NGN model with backpropagation learning based on mean-field dynamics. We demonstrate the advantages of the NGN method through theoretical analysis and experimental validation on CIFAR-10, CIFAR-100, Tiny-ImageNet, DVS-Gesture, N-Caltech101, and CIFAR10-DVS. The proposed approach achieves an accuracy of 87.35% on CIFAR10-DVS within 10 inference time steps. These results support NGN as a practical approach to high-performance neuromorphic computing.
[CV-64] GeoWeaver: Accurate Long-Sequence 3D Reconstruction via Hierarchical Geometric Assembly
链接: https://arxiv.org/abs/2608.17389
作者: Tinghao Jiang,Sheng Tang,Shengzhe Wei,Juntong Fang,Weiqi Zhang,Junsheng Zhou,Zesong Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, including supplementary material; 7 figures and 10 tables. Project page: this https URL
Abstract:Long-sequence 3D reconstruction from RGB videos requires both accurate local geometry and globally consistent camera motion. Feed-forward models provide strong depth and pose predictions, but their memory cost prevents joint inference over long sequences. Chunk-wise processing improves scalability, yet independently predicted chunks often exhibit scale drift, pose errors, and point-cloud misalignment. We present GeoWeaver, a unified framework comprising a Geometric Prior Model (GPM) and Test-Time Adaptation (TTA). The GPM predicts chunk-wise depth, confidence, and camera parameters as adjustable geometric priors. TTA then performs sequential initialization, global chunk-level Sim(3) alignment, and coarse-to-fine refinement of camera poses, affine depth corrections, and intrinsics. Dense correspondences provide adjacent, cross-chunk, and long-range constraints, while a robust CDF-style objective jointly optimizes weighted 2D reprojection and 3D consistency residuals. This design preserves local geometric accuracy while correcting accumulated pose, scale, depth, and calibration errors. Experiments across diverse long-sequence benchmarks demonstrate improved camera accuracy, global consistency, and point-cloud quality. Ablations verify the contribution of each adaptation stage, and applying the same TTA procedure to different geometric prior models consistently improves their trajectory estimates, demonstrating that GeoWeaver is not tied to a specific GPM.
[CV-65] Continuity-Driven Representation Learning for Industrial Defect Detection BMVC
链接: https://arxiv.org/abs/2608.17362
作者: Minjong Kim,Hyun Jun Kim,Jeongrae Kim,Heeseung Shin,Changwon Lim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the British Machine Vision Conference (BMVC) 2026
Abstract:Industrial defect detection differs from natural-image object detection because inspection images are captured under controlled conditions and contain large normal-dominant regions with repetitive structures. Defects therefore appear as localized disruptions of otherwise predictable patterns, while conventional detectors rely mainly on sparse bounding-box supervision, resulting in weakly constrained normal-region representations. We propose a continuity-driven representation regularization framework that exploits normal-dominant regions as dense auxiliary supervision. The framework introduces two detector-agnostic objectives: Multi-Continuity Loss, which combines 1D patch-sequence prediction and 2D masked spatial prediction, and Differencing Loss, which regularizes first-order feature variation and second-order curvature between neighboring patch embeddings. Both objectives are applied with box-derived region weighting to stabilize normal-region representations while preserving defect-related discontinuities. Experiments on two real-world industrial datasets and the public NEU-DET benchmark, using six detector architectures including YOLO-family models, MambaYOLO, and DETR, demonstrate consistent improvements over native detector baselines. In the full-data setting, the proposed regularizers improve average mAP@0.5:0.95 by up to 3.49 percentage points on Industrial Metal, 5.38 percentage points on MEA, and 5.03 percentage points on NEU-DET. Under limited-data conditions, the gains become more pronounced, with Differencing Loss achieving improvements of up to 21.07 percentage points in mAP@0.5 and 8.23 percentage points in mAP@0.5:0.95 on NEU-DET using only 25% of the training data. These results suggest that continuity-driven regularization provides an effective prior for improving industrial defect detection, particularly when annotated data are scarce. Comments: Accepted at the British Machine Vision Conference (BMVC) 2026 Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.17362 [cs.CV] (or arXiv:2608.17362v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.17362 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-66] Primitive-Driven Compositional Forensic Visual Prompting for Open-World Face Anti-Spoofing
链接: https://arxiv.org/abs/2608.17351
作者: Fangling Jiang,Qi Li,Bing Liu,Weining Wang,Quilin Huang,Zhenan Sun,Ming-Hsuan Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Open-world face anti-spoofing must address both covariate and semantic shifts: source and target domains differ in imaging conditions, while target domains contain diverse attack types absent from training. Existing prompt-based approaches often express spoofing through category semantics or language guidance, which is effective for modeling high-level concepts but is less suited to explicitly capturing the evolving fine-grained and spatially heterogeneous forensic evidence of unseen attacks. Motivated by the hypothesis that many unseen attacks can be characterized by new combinations of recurring visual cues, we propose a compositional forensic visual prompt learning framework that operates entirely in the visual feature this http URL on a frozen ViT-based vision foundation model, the framework employs patch-aware attention to refine a shared set of learnable micro-forensic primitives into localized forensic evidence units derived from image patches. Class-specific global contextual prompts then provide input-dependent routing weights that adaptively select and compose these primitives into compositional forensic visual prompts for real/spoof discrimination. The primitives are not assigned predefined semantic meanings; instead, their specialization and reuse emerge from shared parameterization and joint optimization across this http URL experiments on nine open-world protocols demonstrate state-of-the-art performance, strong cross-domain generalization, and robust adaptation to unseen attacks.
[CV-67] Learning latent progression states from spatial heterogeneity in uterine histopathology
链接: https://arxiv.org/abs/2608.17337
作者: Qiming He,Yan Liu,Shuang Ge,Fan Yang,Yuxiang Wang,Ieng Man Zhang,Jing Yang,Zihao Jia,Ajin Hu,Yexing Zhang,Zixiu Song,Qiang Huang,Xiaoya Zhao,Zihan Wang,Xianjing Zheng,Yijun Zheng,Liling Lin,Shuxing Liu,Bin Bao,Yue Xie,Tian Guan,Yonghong He,Congrong Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Emerging Technologies (cs.ET)
备注:
Abstract:Tumor progression is accompanied by changes in architecture, morphology and microenvironmental organization, yet progression-associated heterogeneity is usually compressed into static diagnostic categories in histopathology. Here we present SpaTIE, a uterus-specific computational pathology framework that learns morphology-aware representations and organizes spatial histopathological heterogeneity into progression-associated tumor states. SpaTIE was developed using 10,426 uterine hematoxylin and eosin whole-slide images and evaluated in TCGA-UCEC and TCGA-UCS cohorts. The learned representations formed morphology manifolds, supported diagnostic, molecular and survival-related prediction tasks, and localized attention to informative tumor regions. Beyond supervised prediction, SpaTIE inferred tumor-state axes from cross-sectional morphology without temporal or molecular supervision. These morphology-derived states were spatially coherent and showed associations with clinicopathological variables and survival outcomes, while not simply recapitulating staging or diagnostic labels. Integrative multi-omics analyses linked the inferred states to DNA methylation, somatic copy-number variation, mutation, RNA-seq and RPPA profiles, highlighting molecular programs related to chromatin regulation, copy-number-associated structural variation, receptor tyrosine kinase signaling, cell adhesion, extracellular-matrix remodeling and metabolic adaptation. Progression-guided virtual perturbation further prioritized molecular features coupled to the morphology-derived state organization. Together, these findings suggest that uterine histopathology contains recoverable progression-associated tumor-state information and establish SpaTIE as a framework for connecting spatial morphology with multi-omics-informed tumor-state discovery.
[CV-68] MS-MFAD : Multimodal large language models for Face Anti-spoofing Detection
链接: https://arxiv.org/abs/2608.17328
作者: Xiaoyong Yu,Rongzhen Li,Shuming Shi,Xinge You
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Facial biometric recognition systems currently face compound threats intertwining generative AI and high-fidelity physical spoofing. Existing defenses suffer from systemic bottlenecks, including poor generalization, non-auditable reasoning, and reliance on massive, low-quality datasets. To address these challenges, we propose Multimodal Large Language Models (MFAD) for face anti-spoofing detection, an explainable reasoning system for Unified Face Anti-Spoofing Detection (UFAD), accompanied by a semantic-level annotation benchmark. Unlike methods relying on external tools or coarse alignment, MFAD activates the intrinsic reasoning capabilities of Multimodal Large Language Models (MLLMs) via a fine-grained pixel-semantic anchoring mechanism. This eliminates localization hallucinations and ensures auditable reasoning paths. We introduce a cross-attack semantic-level unified annotation paradigm: by annotating only 1,000 precise masks per attack category, we generate reasoning evidence chains strictly corresponding to spoofed regions. Supervised fine-tuning on the Qwen-VL foundation model demonstrates that, using limited high-quality samples, the system achieves a 40-50% relative reduction in in-domain ACER and restricts cross-domain performance degradation to within 11.62%/5.23%, significantly outperforming existing frameworks. Furthermore, under white-box adversarial attacks, detection accuracy drops by only 3.2%, validating the robustness of semantic anchoring compared to models trained on massive short-text data. Domain practitioners rated the evidence reliability of reasoning paths at 4.57/5, with inference latency satisfying real-time deployment requirements. These results confirm that a few-shot, high-quality semantic annotation paradigm is effective for building trustworthy, explainable, and cost-efficient UFAD systems.
[CV-69] If Then Otherwise: Diagnosing Conditional Branching in Vision-Language Navigation
链接: https://arxiv.org/abs/2608.17318
作者: Seoyoung Lee,Neel P. Bhatt,Pranay Samineni,Cong Liu,S P Sharan,Timothy Barclay,Gregory M. Wagner,Daniel Milan,Sandeep Chinchali,Ufuk Topcu,Atlas Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 11 pages, 1 figure, 3 tables. Project page: this https URL
Abstract:Vision-language navigation agents are often evaluated on their ability to follow route-like instructions toward a fixed goal. Yet, real navigation instructions often depend on observed states of the environment: if a condition holds, then follow one path, otherwise take another. Such instructions require an agent to evaluate scene evidence, select the correct logical branch, and execute the corresponding navigation behavior. Existing evaluations provide limited control over conditional branch execution, making it difficult to determine whether agents fail because of perception, grounding, navigation, or logical decision-making. We introduce CondVLN, a scene-graph-grounded benchmark for diagnosing conditional branching in vision-language navigation. CondVLN programmatically generates instructions whose branch conditions are grounded in verifiable 3D scene-graph predicates, with controlled variation in branch depth, dependency chain length, spatial composition, evidence observability, and instruction horizon. CondVLN contains over 11,500 generated conditional instructions across AI2-THOR, Matterport3D, Gibson, and ReplicaCAD, and evaluates agents using standard VLN metrics and branch-specific diagnostics: Branch Selection Accuracy and Conditional Success Rate. Evaluating four state-of-the-art VLN agents (VLN-Zero, NaVid, NaVILA, and Open-Nav) shows that conditional branching exposes failures that are not captured by standard success rate or path length alone: agents can navigate plausibly while committing to a branch inconsistent with the observed scene condition. We also present a lightweight neurosymbolic branch-selection model that separates condition grounding from navigation execution, improving performance by 2x. CondVLN provides a reusable testbed for measuring whether embodied agents can not only follow instructions, but follow the right instruction under the right condition.
[CV-70] Scanline-Aware Animatable Gaussian Avatars from Rolling-Shutter Videos
链接: https://arxiv.org/abs/2608.17314
作者: Youxiang Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Animatable human avatars are routinely reconstructed from multi-view video under a silent assumption: that every pixel of a frame observes the same instant of the body’s motion. Rolling-shutter (RS) sensors expose image rows sequentially, so within one frame the head and the feet of a moving person are separated by tens of milliseconds of articulated motion, and every scanline sees a different pose. Feeding such video to a state-of-the-art avatar bakes the distortion into the canonical representation, where it survives as shear and wobble under novel views and novel poses. Worse, every camera in a rig follows its own readout schedule, so the multi-view consistency that drives the reconstruction is violated even when the geometry is correct. We present RS-Avatar, which reconstructs a sharp, undistorted, animatable 3D Gaussian avatar directly from RS video. The formulation is minimal: a motion-aware avatar already renders the body at several sub-frame instants, and where a blur model averages those renderings, a rolling-shutter model composites them scanline by scanline. Changing that operator is the only modification required. On RS-ZJU, a benchmark we build from ZJU-MoCap, this improves novel-view synthesis over training as if the frames were instantaneous, on every subject. A motion-aware blur model built on the same sub-frame machinery does not transfer, and in fact falls below the shutter-oblivious baseline: the machinery is reusable, the operator is not.
[CV-71] Learning What Not to Learn: Adversarial Disentangled Prompt Tuning for Robust Vision-Language Models
链接: https://arxiv.org/abs/2608.17306
作者: Yang Chen,Zhan Zhuang,Yanbin Wei,Zebin Chen,Hua Liu,Yu Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:While adversarial prompt tuning can enhance robustness of vision-language models efficiently, we find that existing methods aggravate robust generalization overfitting on seen classes, leading to a rapid degradation in performance against adversarial examples of unseen classes as training progresses. We empirically identify that this degradation stems from the tendency of the model to learn pseudo-robust features (i.e., non-generalizable shortcuts). To mitigate this, we propose ADAPT (Adversarial Disentangled Prompt Tuning), a robust prompt tuning framework following the philosophy of ``Learning What Not to Learn’'. Specifically, ADAPT uses a dual-prompt mechanism with a target prompt and a pool of decoy prompts. During training, the decoy prompts are guided to entrap diverse pseudo-robust features, while the target prompt is constrained to be orthogonal to the decoys in the embedding space to learn robust features. By disentangling the robust features from the pseudo-robust features, ADAPT effectively prevents robust generalization overfitting. We further provide an analysis showing that the orthogonal loss bounds the effect of shifts in pseudo-robust features on unseen classes, yielding a testing error guarantee. Empirically, extensive experiments demonstrate that ADAPT substantially improves the robustness of the target prompt on unseen classes. The code is available at this https URL.
[CV-72] 3D Gaussian Accelerated Ray Tracing: Fast training through particle-based backward propagation
链接: https://arxiv.org/abs/2608.17298
作者: Laurent Vit,Oliver Batchelor,Richard Green
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:3D Gaussian Splatting has made Gaussian primitives a highly efficient representation for real-time novel view synthesis, but its rasterisation-based formulation relies on screen-space approximations that limit accurate view-dependent ordering and the integration of secondary ray effects such as reflections, refractions, and shadows. Gaussian ray tracing addresses these limitations by evaluating explicit ray-primitive intersections, yet it remains costly to train. We observe that the main bottleneck is not ray traversal alone, but the pixel-centric backward propagation, where many threads concurrently accumulate gradients into the same primitive parameters, causing severe atomic contention and thread serialisation. We present 3DGART, a practical training framework for ray-traced Gaussian rendering. Our key idea is to reorganise backward propagation around primitives rather than pixels. Using conservative perspective-correct screen-space bounds, we build a compact intermediate buffer and a tile-primitive mapping that allows each thread to accumulate the contribution of one primitive over its covered pixels within a tile. This transforms gradient computation from a contention-heavy scatter operation into a structured gather-like process. On Mip-NeRF 360, 3DGART achieves an \approx 3-3.5\times raw training speedup over per-pixel baseline and \approx4 \times over 3DGRT on Mip-NeRF 360 while improving quality. More importantly, 3DGART makes fully ray-traced Gaussian training practical, reaching runtimes competitive with rasterisation-based pipelines while preserving benefits of ray tracing. Subjects: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.17298 [cs.GR] (or arXiv:2608.17298v1 [cs.GR] for this version) https://doi.org/10.48550/arXiv.2608.17298 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-73] B-Spline Embedded Structure Learning for 3D Tooth Segmentation
链接: https://arxiv.org/abs/2608.17291
作者: Xianghan Wei,Jianwen Lou,Zhiguo Lu,Hairong Jin,Haihua Zhu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate 3D tooth segmentation forms the cornerstone of digital dentistry, yet it remains a formidable challenge due to the inherent intricacy of real-world dentitions, such as crowding, misaligned teeth and high morphological similarity between adjacent teeth. To resolve this, we present B-Spline Embedded Structure Learning, a novel framework that distills the inherent sequential arrangement of teeth into a continuous structural constraint to regularize representation space. Our approach parameterizes the global dental topology by fitting a parametric B-spline trajectory to tooth centers, assigning each point a continuous structural embedding that forces the shared backbone to capture global arch organization. To fully exploit these embedded priors, we introduce a Structure-Aware Dynamic Classifier (SADC) to substitute rigid static templates with adaptive, case-calibrated decision boundaries. SADC regularizes dynamic prototype pooling via a localized Gaussian proximity gate and contextually co-evolves them through an attention block modeling spatial relations and bilateral symmetries across teeth. Extensive evaluations on the 3DTeethSeg22 benchmark demonstrate that our method establishes a new state-of-the-art accuracy with exceptional structural robustness and efficiency in computational overhead, markedly enhancing the model’s capacity to handle complex dental configurations.
[CV-74] UniQuery4R: Unified 4D Scene Reconstruction from a Single Query
链接: https://arxiv.org/abs/2608.17283
作者: Tiancheng Chen,Sheng Tang,Wenhua Jin,Weiqi Zhang,Juntong Fang,Junsheng Zhou,Zesong Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 8 figures. Project page: this https URL
Abstract:Reconstructing dynamic 4D scenes requires jointly estimating correspondence, geometry, object motion, and camera motion. Existing feed-forward methods typically predict dense task-specific maps or independently process source-target pairs, leading to unnecessary computation for sparse queries and limited feature reuse across different frame pairs. We present UniQuery4R, a query-conditioned framework that encodes a multi-frame clip once and selects the source view, target view, and continuous source-image coordinate only at decoding time via source-to-target cross-attention. Each query jointly predicts target correspondence, target-time 3D position, and scene flow, along with source depth, while camera parameters are estimated per view. This design allows the encoded clip to be reused across arbitrary source-target selections and supports both sparse inference and dense reconstruction through batched queries, without learned temporal embeddings tied to a fixed clip length. We further introduce a direction-magnitude parameterization of scene flow with separate supervision for moving and static points. Among the evaluated methods, UniQuery4R achieves the best macro-average results on WorldTrack for both scene-flow estimation and dynamic-point reconstruction.
[CV-75] Key-Frame Reasoning with SAM3: Third Place Solution for the MeViS-Text Track of the 8th LSVOS Challenge
链接: https://arxiv.org/abs/2608.17279
作者: Ce Bian,Xusheng He,Jinrong Zhang,Canyang Wu,Xianjing Han,Jianlong Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:This report presents a two-stage, training-free solution for the MeViS-Text track of the 8th LSVOS Challenge. The task requires a model to localize and segment the object specified by a natural-language expression throughout a video. Such expressions often depend on temporal cues, including actions, interactions, directions, and relative positions. Our first stage uses Gemini-3.1 Pro via API to decompose a video-level event into instance-level targets, select a key frame for each target, and generate a discriminative description aligned with that frame. In the second stage, SAM3-agent produces a pixel-level seed mask on the selected frame, and the SAM3 video tracker propagates the mask bidirectionally through the video. Valid instances are grounded and propagated independently before their frame-wise masks are merged. All local SAM3 processing runs on a single NVIDIA GeForce RTX 4090 without task-specific training or model ensembling. Our method ranked third on the challenge test set, obtaining JF, J, F, N-acc., T-acc., and Final scores of 0.761, 0.7367, 0.7852, 0.8333, 0.9755, and 0.856593, respectively.
[CV-76] Learning Where and What to Lift for Bi-planar X-ray-to-CT Reconstruction
链接: https://arxiv.org/abs/2608.17255
作者: Yifei Wu,Yicheng Wu,Qiang Ma,Qi Chen,Renyang Gu,Xinyu Liu,Yongsheng Pan,Yong Xia
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:X-ray imaging can be approximately modeled as the projection of an underlying volumetric attenuation field, with each measurement recording the accumulated attenuation along a corresponding ray path. Reconstructing a CT volume from only a few X-ray views is therefore severely ill-posed, as the projections collapse depth information and leave 3D locations of anatomical regions and their corresponding intensity distributions highly entangled and ambiguous. We observe that once the spatial organization of anatomical regions is established, estimating their CT intensities becomes substantially more tractable. Motivated by this, we propose LiftXR, an interleaved, geometry-guided framework that explicitly incorporates spatial layout recovery into CT reconstruction. Specifically, a layout lifter first generates a 3D anatomical layout from bi-planar X-rays, providing spatial guidance for an intensity renderer to reconstruct a CT volume. An anatomical parser then performs volumetric perception on the reconstruction, exploiting its spatially resolved boundary and intensity cues to recover a refined anatomical layout. This transition from projection-conditioned layout generation to reconstruction-conditioned anatomical perception allows the parsed layout to provide feedback for region-specific intensity calibration. Extensive experiments on two public datasets demonstrate that LiftXR consistently outperforms recent X-ray-to-CT reconstruction methods, establishing a new state of the art. Moreover, the reconstructed CT achieves superior performance in external downstream segmentation, indicating improved anatomical fidelity. Code will be released.
[CV-77] Heterogeneity-Aware Deep Learning for Tumour Classification from Multiparametric MRI
链接: https://arxiv.org/abs/2608.17254
作者: Yue Xia,Euijoon Ahn,Tian Xia,Yuan Yuan,Michael Fulham,Jinman Kim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Intra-tumoural heterogeneity (ITH) reflects spatial variation in tumour biology and is an important determinant of tumour behaviour, prognosis, and treatment response. Radiomics and deep learning have shown promise for tumour classification from multiparametric MRI (mp-MRI), but radiomics relies on handcrafted features, while most deep learning methods use whole-tumour representations or manually defined sub-regions, limiting scalable modelling of tumour heterogeneity. We propose a Heterogeneity-Aware Deep Learning Classification (HA-DLC) framework that explicitly models imaging-derived tumour sub-regions for lesion-type diagnosis and molecular-status prediction. HA-DLC consists of: (1) a Heterogeneous Sub-region Generation (HSG) module that produces initial pseudo-labelled sub-regions via unsupervised clustering, followed by Cross-Patient Sub-region Alignment (CPSA), which maps cluster-derived regions to a shared label space using soft assignments; and (2) a Dual-Stream Feature Extraction (DSFE) module that integrates local heterogeneity-aware features with global tumour representations. Given the initial clustering masks, CPSA, segmentation, feature extraction, and classification are jointly optimized end-to-end using soft-target segmentation and classification objectives. We evaluate HA-DLC on the LLD-MMRI2023 liver lesion dataset and the RSNA-ASNR-MICCAI 2021 Radiogenomic Brain Tumour dataset. HA-DLC consistently outperforms state-of-the-art radiomics and deep learning baselines, demonstrating the value of cross-patient sub-region alignment and dual-stream heterogeneity modelling for tumour classification from mp-MRI.
[CV-78] Co-RL: Unsupervised Reasoning Emerges from Diverse Cohort in Multi-agent RL
链接: https://arxiv.org/abs/2608.17253
作者: Yunhao Yang,Yuexin Bian,Yunjie Tian,Di Fu,Tianjin Huang,Yuanyuan Shi,Ziang Xiao,Nuno Vasconcelos,Yijiang Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 30 pages, 5 figures, 11 tables
Abstract:Reinforcement learning (RL) has emerged as a powerful approach for improving reasoning in language and vision-language models, yet its strongest successes still depend heavily on ground-truth supervision (e.g., verifiable reward). Such annotations are costly to obtain and become increasingly scarce as reasoning capabilities advance beyond what humans can reliably evaluate. Self-rewarding RL reduces this dependence by enabling models to derive reward signals from their own completions. However, training solely on self-generated feedback can reinforce existing biases and suboptimal behaviors, reduce response diversity, and ultimately lead to homogenized responses and training collapse. In this work, we show that unsupervised reasoning can emerge through cooperative multi-agent training. We introduce Co-RL, a framework in which multiple decoupled models, sharing no parameters, are simultaneously optimized through RL using rewards derived from their peers. We further show that increasing cohort diversity, through heterogeneous model families, sizes, and rephrased training samples, reduces the correlated errors that drive self-reinforcing feedback loops. This diversity consistently improves reasoning performance, maintains behavioral diversity, and mitigates training collapse. Across text-only and multimodal domains, Co-RL consistently outperforms the base models and prior label-free approaches, while matching or surpassing supervised methods, without access to any ground-truth labels. Concretely, Co-RL yields average gains of 3.0-8.6% across seven text-only benchmarks for LLMs and 2.3-7.2% across four multimodal benchmarks for VLMs. Code is available at this https URL.
[CV-79] Structural Plan-to-Model Conversion with Deterministic Geometry and Guarded Agent ic Vision-Language Refinement
链接: https://arxiv.org/abs/2608.17237
作者: Mohammad Talebi-Kalaleh,Qipei Mei
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Converting structural framing plans into editable finite-element model drafts remains labor-intensive and prone to transcription error. Existing drawing-understanding systems for building components rely on task-specific trained neural detectors, and language-model agents in structural engineering operate on text or model data rather than the drawing itself. This paper presents, to the authors’ knowledge, the first framework applying an agentic vision-language layer to structural component detection and model drafting from framing-plan PDFs, without task-specific detector training or fine-tuning. A deterministic stage extracts primitives, estimates scale by dimension-ratio consensus, recognizes five entity classes with a drafting grammar, and assembles an editable layout. The agentic stage proposes typed corrections constrained by deterministic candidates, operation-specific admission tests, change-level review, and fail-closed transactions. Evaluation used an author-generated benchmark of 100 plans: a development half that informed every rule revision, and a seed-disjoint held-out half generated after the rules froze, evaluated once. All reported scores are end-to-end results of the complete framework on the held-out half. Scale was estimated within 0.1% of the generator reference for every drawing. Recall and precision were 0.922/0.997 for columns, 0.886/0.990 for beams, 1.000/1.000 for walls, 1.000/1.000 for braces, and 1.000/0.964 for openings. A controlled study repeated two corruptions three times on three development drawings. Calibration passed all nine trials; member repair met every strict end-state predicate in five of nine. Guarded review corrected missed framing and false marks within explicit bounds. The held-out half shares the development generator, so the study excludes independently drafted plans, raster evaluation, analytical connectivity, and solver validation.
[CV-80] Probing Association Instability with Track-State Perturbations for Clip-Level Active Learning in Query-Propagation Multi-Object Tracking BMVC2026
链接: https://arxiv.org/abs/2608.17224
作者: Riku Inoue,Shogo Sato,Kazuhiko Murasaki,Tomoyasu Shimada,Toshihiko Nishimura,Ryuichi Tanida
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the 37th British Machine Vision Conference (BMVC 2026)
Abstract:Training query-propagation end-to-end multi-object tracking (MOT) models requires dense bounding-box and identity annotations across video sequences, making dataset construction expensive. Clip-level active learning reduces this cost by selecting video clips for annotation, but prior acquisition criteria based on output-level temporal uncertainty may miss clips whose informativeness comes from association instability in propagated track states. We propose QPID (Query-Propagation Instability and Diversity), a clip acquisition method for query-propagation MOT that targets association instability in propagated track states. QPID estimates this instability by applying two-sided perturbations to internal track states and measuring prediction differences from a clean reference branch. The key idea is that, in stable clips, each propagated track should continue to follow the same target under small perturbations, whereas in ambiguous clips, small changes in the track state can alter which target the track follows, leading to changes in localization or confidence. QPID measures these perturbation-induced prediction differences with two metrics: Localization Drift and Entropy-Weighted Confidence Discrepancy. These metrics are aggregated into a clip-level association-instability score. To avoid redundant uncertainty-only selection, QPID selects a representative annotation batch from high-instability clips using Uncertainty-Weighted Visual Coverage with track-level visual prototypes. Experiments on DanceTrack and SportsMOT with MeMOTR and SambaMOTR show that QPID achieves strong performance compared with active learning baselines under the same annotation budget.
[CV-81] ach and Grow: An Agent -Centered Architecture for General Robot Learning
链接: https://arxiv.org/abs/2608.17209
作者: Chang Nie,Zhe Liu,Hesheng Wang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:End-to-end vision-language-action (VLA) and world-action models offer an elegant route to general-purpose robotics, but their reliability is bounded by validated physical coverage. When an unfamiliar object, sensor, embodiment, or contact falls outside that coverage and no validated fallback exists, correcting the failure requires new robot data, a policy update, and regression testing. This recurring burden is the retraining tax. Unlike text, embodied data must often be created by operating machines. We present Teach-and-Grow Learning (TGL), an agent-centered architecture for general robot learning. In its general form, a multimodal agent turns a few successful demonstrations into reusable Skill Blocks: closed-loop behaviors for meaningful subgoals. In a new scene, the agent grounds and composes these blocks, selects learned or geometric tools, observes the physical outcome, and revises the route when execution departs from intent. A Skill Library stores executable behavior, while structured Experience Memory carries forward success, failure, and repair. New tasks are acquired without task-specific policy retraining. Our LIBERO evaluation attains state-of-the-art performance; controlled studies expose skill induction, persistent reuse, and agent-directed adaptation. Finally, we propose the Teach-and-Grow scaling-law hypothesis: if X denotes effective reusable experience, future-task error and teaching demand should approach irreducible floors as power laws in X. The architecture therefore treats deployment as a period of continued learning, in which one task can make the next easier.
[CV-82] How smoothing the affinity matrix affects neighborhood preservation in t-SNE
链接: https://arxiv.org/abs/2608.17190
作者: Shirin Mohebi,Guillaume Bied,Jefrey Lijffijt
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the 29th International Conference on Discovery Science (DS 2026). 15 pages, 7 figures
Abstract:Dimensionality reduction methods are instrumental to visualize high-dimensional data, and t-SNE stands as one of the most widely used methods due to its emphasis on local neighborhood preservation. A central component of t-SNE is the affinity matrix, which expresses pairwise similarities in the form of symmetrized probabilities, over which the optimization problem of t-SNE is defined. We study how the sharpness of this probability distribution affects neighborhood preservation at different scales. We introduce a row-wise power transform controlled by a parameter gamma that can smooth or sharpen each row of the affinity matrix while preserving sparsity and rank order. We show that this transform is equivalent to rescaling the Gaussian bandwidth and thus to changing the perplexity. However, as the sharpness of the probability distribution varies per point, a fixed gamma leads to point-dependent effective perplexities, making it distinct from changing the global perplexity. Empirically, we find that sharpening improves preservation of the very nearest neighbors, while smoothing improves preservation of broader local neighborhoods, outperforming alternative affinity constructions including multiscale methods in the mid-local range.
[CV-83] RADmesh: Remesh-Aware Mesh Deformation ECCV2026
链接: https://arxiv.org/abs/2608.17182
作者: Nam Anh Dinh,Itai Lang,Oded Stein,Rana Hanocka
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026 (Oral). Our project page is at this https URL
Abstract:We propose a remeshing-enhanced method for generatively deforming shapes with visual losses. It is intuitive that sufficiently drastic deformations of a mesh without changing its triangulation can easily compromise element quality, even if such large geometry changes may be semantically desired. Shape deformation methods could thus benefit from changing the triangulation; however, this is not done by most generative, text-based, visually-supervised mesh deformation methods. Remeshing is a discrete operation, proven to be especially challenging to couple with the notoriously noisy supervision signal provided by visual losses. We propose a vertex-based deformation optimization quantity capable of large deformations and robustness to such noise; we periodically remesh using an isotropic remesher that interpolates and carries forward the deformation optimization state. This enables continuous, geometry-informed progress in coarse-to-fine addition of resolution. The resulting shapes’ triangulations fit their optimized geometry and have neat isotropic elements. Further, our method is localizable, able to grow new features on a base shape with expressive detail, leaving the rest unchanged. We showcase the effectiveness of our method on a variety of shapes and prompts, both local and global deformations, and demonstrate its superior visual quality and triangle efficiency. Our project page is at this https URL.
[CV-84] Mask What Matters: Saliency-Guided Video Self-Supervised Learning for Autonomous Driving
链接: https://arxiv.org/abs/2608.17178
作者: Christopher Lang,Alexander Braun,Abhinav Valada
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at GCPR 2026. The final publication will be available through Springer
Abstract:Video self-supervised learning through masked spatiotemporal prediction has emerged as a promising paradigm for learning feature representations from unlabeled data. However, existing methods typically rely on random masking, which indiscriminately removes regions irrespective of their semantic or temporal relevance. In ego-centric driving videos, this can weaken the pretext signal since safety-critical cues such as pedestrians, vehicles, lane boundaries, and dynamic interactions often occupy only a small portion of the frame, yet are central to downstream perception. We introduce V-JEPA4A, a domain-specialized variant of V-JEPA for autonomous driving that is pre-trained on publicly available driving videos with a novel saliency-driven masking policy. It accounts for semantically and temporally relevant context. The proposed policy preserves and predicts context according to semantic importance and temporal relevance, yielding more informative representation learning while retaining the efficiency of masked prediction. We evaluate the resulting encoders on four driving benchmarks spanning tracking, semantic segmentation, and depth estimation. The results demonstrate that V-JEPA4A reduces identity switches on BDD100k MOT by 25% over V-JEPA with random masking, achieves 73.2 mIoU on Cityscapes, and 3.75 RMSE on KITTI-2015 depth, while incurring only ~14% additional pre-training iteration overhead.
[CV-85] Rapid Debris-Volume Estimation from Post-Hurricane Aerial Imagery
链接: https://arxiv.org/abs/2608.17165
作者: Kooshan Amini,Jamie Ellen Padgett,Guha Balakrishnan
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注:
Abstract:Hurricane debris removal is planned, contracted, and federally reimbursed on the basis of volume estimates, yet operational practice still relies on parametric forecasts with 41-90% documented over-estimation or on truck-load tallies that arrive only after hauling begins. We present DebrisHeightNet, a segmentation-conditioned monocular debris-height network that estimates spatially explicit debris volume from a single pass of post-event aerial RGB imagery, the kind of survey routinely flown within days of a hurricane landfall. We train only a lightweight 1.08 M-parameter head on top of two frozen vision foundation models. This head regresses height from a Depth Anything V2 backbone, conditioned on the debris segmentation of CLIPSeg-debris from our prior work. Because no post-hurricane debris-height ground truth exists, we synthesize the training target by confidence-weighted LiDAR-monocular fusion (CW-LMF), designed to suppress non-debris LiDAR returns. This fused target is a constructed supervision signal rather than ground truth, so we corroborate it against external references rather than claiming it as truth. A region-level power-law calibration, driven by each region’s low-density debris fraction, converts model volume into an estimate of the reported hauled debris with quantified uncertainty. Across ten regions spanning five hurricanes and three states, the uncalibrated model agrees with an independent uncrewed-aerial-vehicle (UAV) survey of the training region at Spearman \rho = 0.87 and lands within 30% of the reported record where the Hazus and FEMA-hybrid parametric forecasts over-predict it by 2.7-4.8 \times . Deployment requires no LiDAR, no ground access, and no second flight, so the method can produce spatially explicit volume estimates wherever single-pass post-event imagery is flown.
[CV-86] Lymphocyte Mimicry Correction via Region-Level Tissue Reasoning and Unbalanced Optimal Transport MICCAI2026
链接: https://arxiv.org/abs/2608.17151
作者: Xiang Li,Yuqi Wang,Casey C. Heirman,Jihye Heo,Kyle J. Lafata
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 13 pages, 3 figures. Accepted to the MICCAI 2026 COMPAYL Workshop
Abstract:Cell mimicry arises when different cell types appear morphologically similar. Human pathologists resolve this ambiguity using surrounding tissue context, whereas current vision models either lack contextual reasoning (cell foundation models) or cannot operate at the cell level (pathology MLLMs). We present Loki-OT, which propagates region-level tissue reasoning to individual cell predictions via Unbalanced Optimal Transport, using MLLM-derived density priors as soft guidance for ambiguous cell reassignment. Loki-OT is motivated by the observation that pretrained cell foundation model features already encode discriminative information, including tissue context, but standard cell-level supervision fails to use tissue context effectively. The resulting transport plan is distilled into a lightweight student MLP classifier that learns context-aware decision boundaries within the pretrained feature space. On the independent TCGA-BRCA cohort, Loki-OT achieved lower patient-level MAE than the fully supervised in-domain PanopTILs classifier and improved F1 in epithelium-rich mimicry tissues, using 278 weak region-level MLLM estimates built on a general-domain cell foundation model. Code: this https URL
[CV-87] PROBE: Manipulation-Grounded Visual Question Answering with VLM Agents
链接: https://arxiv.org/abs/2608.17129
作者: Vineet Bhat,Siyi Chen,Alex Zook,Xuning Yang,Stan Birchfield,Valts Blukis,Jonathan Tremblay
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Vision-language Models (VLMs) excel at 2D grounding, spatial reasoning and agentic tool-based planning in static scenes. However, consider asking a home robot “Is my medication still in the cabinet?” The answer may be physically hidden behind a row of containers that must first be moved aside. Answering such questions in real-world cluttered environments requires reasoning in dynamic scenes: distractors must be manipulated to reveal occluded objects, and each action changes the scene the model must reason over. We formalize this setting as Manipulation-Grounded Visual Question Answering (MG-VQA) and introduce PROBE, a framework for benchmarking and finetuning VLM agents on such tasks. We first develop PROBE-Sim, a high-fidelity tabletop simulator with everyday objects and a robot manipulator equipped with grasping and pushing tools. PROBE-Sim is used to create PROBE-Bench: an evaluation suite of 150 tasks across 6 question types on cluttered tabletop scenes, where a VLM perceives, picks up or pushes objects before answering. We observe consistent trend across all frontier VLMs: agentic tool-based methods outperform their perception-only baselines (8.0% on average) across all task types. We further design PROBE-Agent, a finetuning recipe to distill successful trajectories from a powerful teacher foundation model to a smaller open-weight model using a mixed data recipe that encourages manipulation-efficient question answering. PROBE Agent finetuned models outperform their off-the-shelf agent baseline (11.5% on average) and demonstrate positive transfer to unseen objects and a held-out task. We validate sim-to-real transfer by deploying PROBE-Agent finetuned policies in real-world tabletop environments.
[CV-88] OV3D-Bench: A Diagnostic Benchmark for Open-Vocabulary Monocular 3D Detection ECCV’26
链接: https://arxiv.org/abs/2608.17110
作者: Mariia Gladkova,Neehar Peri,Ishan Khatri,Deva Ramanan,Daniel Cremers
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to OpenSUN3D workshop at ECCV’26; benchmark is released on this https URL
Abstract:Open-vocabulary monocular 3D detectors report strong in-domain performance, but each evaluates under a different protocol, several rely on per-image category oracles unavailable at deployment, and all collapse geometry and semantics into a single AP metric. To address this, we introduce OV3D-Bench, a diagnostic benchmark that compares open-vocabulary monocular 3D detectors under deployment-realistic conditions across seven indoor and outdoor datasets. Our benchmark replaces the per-image class name oracle with test-time dataset-level class name prompts, and decouples detection accuracy along three axes: localization, semantic robustness, and cross-domain transfer. We evaluate seven representative detectors and find that (i) they localize objects well yet often mislabel a correctly localized box as a semantically adjacent category; (ii) accuracy is highly sensitive to prompt phrasing (e.g. WildDet3D’s performance collapses from 18.6 to 5.4 AP when prompted with “a detailed high-resolution photo of a car” rather than “car”); and (iii) the widely adopted target-aware protocol hides these errors (e.g. inflating DetAny3D’s AP by 1.9 \times on ScanNet). Lastly, we demonstrate that simply remapping a frozen closed-vocabulary detector’s predictions using a contrastive vision-language encoder such as SigLIPv2 performs competitively against recent purpose-built open-vocabulary methods. This indicates that geometric localization is more mature, while open-vocabulary semantics remains the primary bottleneck.
[CV-89] Inference-Time Attention Steering for Vision-Language-Action Driving Models
链接: https://arxiv.org/abs/2608.17095
作者: Darshan Nagendra Prasad,Lars Ullrich,Knut Graichen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Attention Steering, Vision-Language-Action, AutonomousDriving, Inference-Time Intervention
Abstract:Vision-language-action (VLA) driving models couple a reasoning stage with a diffusion-based trajectory decoder, but do not give a direct way to redirect attention toward safety-critical actors at inference time without retraining. We studied a bounded additive pre-softmax attention bias on the visual tokens of detector localized traffic actors on Alpamayo-R1’s Qwen3-VL backbone. It is applied as a fail open forward pre-hook with no weight changes. On 50 lane-change scenarios from the Physical AI World Model Synthetic dataset. The trajectory decoder shows a monotonic dose response in the bias magnitude, separate from a paired zero bias control at every tested magnitude. It reaches \approx 17 ,cm mean displacement with lateral shifts up to \sim 140 \ cm at the clamp. A layer ablation places the action-relevant signal in late layers, where the effect increases with the number of hooked layers (2.0cm for the first 8 layers; 67.6cm for all 36). A per call injection audit explains why the Chain-of-Causation text never changes. The mask based bias never reaches the reasoning pathway in this serving stack, so the invariance is verified exposure, not robustness. Steered trajectories tend to shift toward the attended actor, suggesting the bias governs where the model looks rather than encoding a target behavior.
[CV-90] CAS-FD: Contact-Aware Temporal Sampling for Single-View Foul vs Dive Recognition
链接: https://arxiv.org/abs/2608.17060
作者: Md. Jahidul Islam,Mahfujul Alam,Md. Nazmul Islam Seyam,Md. Tamim Hossain
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Distinguishing a genuine foul from a simulated dive in football remains one of the sport’s most contested fine-grained recognition problems, especially when such decisions have to be from a single broadcast view without multi-view camera angle. We introduce a balanced 600-clip single-view Foul/Dive dataset and show that contact-aware sampling concentrating the model’s attention around the moment of physical contact rather than treating all frames equally yields substantially improved recognition of this contact- specific problem. The proposed approach achieves 86.0% accuracy and macro-F1 0.860 on the held-out test split, a 12 percentage- point gain over contact-unaware alternatives that grows further on unseen data. We also evaluate each pipeline component against human annotations, establishing where and why the system suc- ceeds and fails. The result is a documented dataset, a reproducible single-view pipeline, and a grounded evaluation framework for fine-grained contact-event recognition in broadcast football footage. The dataset and code are available at this https URL tamim/contact-aware-dive.
[CV-91] he 10th AI City Challenge ECCV2026
链接: https://arxiv.org/abs/2608.17044
作者: Zheng Tang,Shuo Wang,David C. Anastasiu,Ming-Ching Chang,Anuj Sharma,Quan Kong,Munkhjargal Gochoo,Jun-Wei Hsieh,Tomasz Kornuta,Zhedong Zheng,Renran Tian,Judah Goldfeder,Fulgencio Navarro,Yuxing Wang,Yizhou Wang,Sameer Satish Pusegaonkar,Anqi Li,Nalin Dadhich,Ridham Kachhadiya,Dhanishtha Patil,Haoquan Liang,Jiajun Li,Han Zhang,Yilin Zhao,Zaid Pervaiz Bhat,Shuyu Yang,Ashutosh Kumar,Rong Wang,Rafael Martin Nieto,Peter Christiansen,Ahmed Abduljawad,Mohanrasu Shanmugam,Nadeem Shaik,Sujit Biswas,Xunlei Wu,Vidya Murali,Rama Chellappa
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Summary of the 10th AI City Challenge Workshop in conjunction with ECCV 2026
Abstract:The 10th AI City Challenge, held with ECCV 2026, marks a decade of community benchmarking for intelligent transportation, smart cities, and physical AI. Since its 2017 start with vehicle detection, classification, and tracking, the challenge has grown into a broad benchmark suite for multi-camera perception, multimodal reasoning, synthetic-to-real learning, generative forecasting, and privacy-preserving evaluation. The 2026 edition continued this growth with 325 registered teams, up from 245 in 2025, and participation from 26 countries and regions, up from 15. Its six primary tracks cover multi-camera 3D perception, transportation safety captioning and VQA, traffic anomaly reasoning, text-based person anomaly search, generative traffic video forecasting, and cross-city object detection. Track 3 further includes two out-of-domain leaderboards, submitted as Tracks 7 and 8, for fisheye traffic-violation understanding and pedestrian situated-intent VQA. This paper summarizes the challenge setup, datasets, evaluation protocols, leaderboard results, and workshop papers. Across tracks, successful systems combine foundation models with geometric grounding, retrieval or reranking, synthetic-data design, domain adaptation, and controlled inference.
[CV-92] YILDIZ-VPR: A Novel Dataset with Dense Coverag e Under Diverse Environmental Conditions for Visual Place Recognition
链接: https://arxiv.org/abs/2608.17033
作者: Serdar Yildiz,Abbas Memiş,Songül Varli
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Visual Place Recognition (VPR) aims to recognize the location of a query image by comparing it with a set of geo-referenced images. Although many datasets have been proposed for VPR, collecting dense and diverse visual data from pedestrian-level viewpoints is still an important need. In this paper, we introduce YILDIZ-VPR, a visual geo-localization dataset collected through repeated walking traversals on the Davutpasa campus of Yildiz Technical University. The dataset includes outdoor scenes captured at different times of day, seasons, and weather conditions. It contains a wide range of visual content, including historical buildings, modern structures, roads, green areas, and wooded regions. Each video was recorded with a GoPro 9 camera and synchronized with GPS sensor data to provide location labels for the extracted frames. In addition to GPS coordinates, the dataset also includes auxiliary sensor information such as gyroscope, speed, and temperature data. With its dense coverage and long-term visual variability, YILDIZ-VPR provides a useful resource for studying image-based and temporal visual place recognition under realistic outdoor conditions.
[CV-93] PXDepth: Pixel-Space Modeling for Structure Preserving Monocular Depth Estimation
链接: https://arxiv.org/abs/2608.16984
作者: Zhiyuan Yuan,Guanying Chen,Lingteng Qiu,Ruimao Zhang,Shuguang Cui,Xiaochun Cao
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR)
备注: Project Page: this https URL
Abstract:Recent monocular depth estimators achieve strong zero-shot generalization, yet often struggle to preserve fine-grained structures and object boundaries. We attribute this limitation to the prevalent combination of large-patch ViT encoders and convolutional decoders, as coarse tokenization can weaken pixel-level cues that upsampling cannot fully recover. To address this issue, we propose PXDepth, a discriminative monocular depth model that separates global context modeling from pixel-level depth prediction. Specifically, a large-patch ViT captures global scene context, while a pixel-space predictor composed of Context-Modulated Pixel Transformer blocks maintains high-resolution spatial representations throughout depth estimation. This design preserves fine structures and sharp boundaries without sacrificing global depth consistency. Across diverse zero-shot benchmarks, PXDepth combines faithful local geometry with competitive global depth accuracy while remaining efficient at inference. Our code and model are available at this https URL.
[CV-94] AerialYield-B2D: A Greenhouse Blueberry Dataset with Five-Stage Ripeness Masks and Fruit Counts
链接: https://arxiv.org/abs/2608.16973
作者: Iyyakutti Iyappan Ganapathi,Afeefa Azam,Muhammad Owais,Irfan Hussain,Yusra Abdulrahman
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注:
Abstract:Blueberry ripeness is judged by berry colour, cluster composition, and the distribution of maturity stages within a plant, however, public green house image resources with dense ripeness-stage masks remain limited. We present AerialYield-B2D, where B2D denotes BlueBerry Dataset, acurated real-image resource containing 514 RGB images and 30,195 annotated blueberry instances across five ripeness stages: green immature, pale pink, pink-turns-purple, fully ripe and over-ripe. The release provides class-specific binary masks, overall berry masks, semantic label maps, image-level count tables, SHA-256 hashes, source metadata, recommended train/validation/test splits and technical validations. AerialYield is the broader project name; this release does not provide harvest weight, fruit mass or per-area yield measurements, and the count labels should therefore be interpreted as image-level berry counts rather than yield estimates. The images include 424 smartphone greenhouse images, 67 video-derived frames, and 23 DJI Fly video-frame samples, providing a reproducible dataset for ripeness segmentation, berry counting, and class-imbalance analysis in controlled-environment blueberry production.
[CV-95] Multi-Observer Vehicle Localization Case Study with Roadside Radar and Connected Vehicle Sensing
链接: https://arxiv.org/abs/2608.16966
作者: Aleksi Pippuri,Nilusha Jayawickrama,Risto Ojala
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 12 pages, 7 figures and 8 tables
Abstract:In modern intelligent transportation systems, it is essential to accurately estimate vehicle positions, especially in mixed traffic conditions where both connected and conventional vehicles coexist. Roadside infrastructure and connected vehicles can provide complementary observations of the same traffic scene, but real-world evidence on decision-level fusion between these sources remains limited. This paper proposes a multi-observer vehicle localization framework that fuses compact object-level detections from a static roadside radar and a dynamic LiDAR-equipped connected vehicle. We evaluate the framework with real-world data collected at an urban intersection in Helsinki, Finland, with a separately instrumented target vehicle used as the reference trajectory. Two extended Kalman filter based strategies for the localization task were benchmarked. The performance of the radar and LiDAR sensors were evaluated separately, and the two fusion strategies were explored under nominal sensing conditions, reduced LiDAR update rates, simulated LiDAR occlusions, and different target-vehicle motion states. The results show that, under full LiDAR availability, fusion performance is dominated by the LiDAR observations, while the less accurate and less consistent radar observations provide only limited additional improvement. Nevertheless, AEKF achieves small gains over the LiDAR-only baseline, and object-level connected vehicle observations remain useful when shared at reduced update rates. These findings indicate that decision-level fusion provides scenario-dependent benefits rather than automatic improvement over a strong single-sensor baseline. We release the dataset and implementation on Github to support further research: this https URL
[CV-96] Hierarchical Data Selection via Manifold Coverag e and Sparse Feature Coverag e in LLM Post-training
链接: https://arxiv.org/abs/2608.16927
作者: Peng Sun,Yi Yang,Antong Zhang,Chunxiao Li,Yanbo Wang,Dianbo Liu,xin chen,Kai Yu,Lu Chen,Tianfan Fu
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:As supervised fine-tuning data continues to scale, selecting high-value subsets from large candidate pools is crucial for reducing training cost and improving model performance. Existing methods often measure diversity directly in the original embedding space, where geometric metrics entangle dominant semantic directions, fine-grained supervision differences, and local noise. We address this limitation by formulating data selection as a coarse-to-fine hierarchical coverage problem and propose MASS. MASS learns low-dimensional principal manifold coordinates with a dense autoencoder for coarse semantic grouping, and then performs quality-aware sparse feature coverage within each group using a TopK sparse autoencoder. Experiments on Vision Flan and LLaVA-CoT show that MASS consistently outperforms strong data selection baselines across multiple budgets, and in several settings matches or surpasses full data training with only a small subset of data.
[CV-97] Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction
链接: https://arxiv.org/abs/2608.18055
作者: Veronika Spieker,Wenqi Huang,Cemre Ariyurek,Liam Timms,Daniel Rueckert,Onur Afacan,Julia A. Schnabel,Sila Kurugol
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Signal Processing (eess.SP); Medical Physics (physics.med-ph)
备注:
Abstract:Reliable quantitative analysis of dynamic contrast-enhanced MRI requires high-quality spatiotemporal reconstructions at high undersampling rates. Scan-specific reconstructions using Gaussian and Gabor primitives have shown promising results without the need for large training datasets, but have not addressed the additional dimension of dynamic contrast. We propose a multi-dimensional, primitive based framework for dynamic contrast-enhanced MRI reconstruction that disentangles the underlying anatomy, the dynamic contrast enhancement, and residual motion into separate temporal basis functions, thereby enabling a geometrical interpretation of the representation. We show that this architecture achieves performance competitive with conventional reconstruction methods, both in reconstruction quality and in the accuracy of extracted aorta and kidney enhancement curves. The modular tier design extends naturally to additional dynamic factors and higher acceleration rates. Code available at this https URL 2026-GaborDCE-spieker.
[CV-98] Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors
链接: https://arxiv.org/abs/2608.18036
作者: Mahdi Saberi,Yaşar Utku Alçalar,Merve Gülle,Chetan Shenoy,Mehmet Akçakaya
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Medical Physics (physics.med-ph)
备注:
Abstract:MRI reconstruction methods for undersampled k-space data naturally utilize complex-valued measurements. Parallel developments in sparse phase retrieval have shown that magnitude-only measurements may provide complementary information for signal recovery. However, their use in MRI reconstruction remains largely unexplored, due to lack of practical settings where informative magnitude measurements can be obtained without additional scan time. In this work, we investigate the use of auxiliary k-space magnitude information for accelerated steady-state dynamic MRI reconstruction, and demonstrate strong consistency of k-space magnitudes across time-frames. Building on this observation, we propose \mathbbC+\textMag , a magnitude-informed physics-driven deep learning reconstruction method. The proposed method employs an ADMM-based unrolling framework with a novel magnitude-aware data-fidelity formulation, where quadratically smoothed optimization and momentum-based updates are introduced to address the non-differentiability and non-convexity of the magnitude constraints. Experiments on retrospectively undersampled cine MRI and phase-contrast flow MRI datasets, as well as prospectively undersampled real-time cine MRI acquisitions, demonstrate improved artifact suppression, sharper anatomical recovery, and better preservation of phase information compared to conventional PD-DL methods, which is further supported through blinded expert reader evaluations.
[CV-99] MagViT: Interpretable Multi-Magnification Transformers with Patient-Level Model Selection for Breast Histopathology
链接: https://arxiv.org/abs/2608.16959
作者: Nabil Ashab,Soumit Kumar Kundu,Saif Mahmud Parvez,Shahadat Hossain Sohag,Bidhan Biswas,Nazmus Subha
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 13 pages, 5 figures, Accepted for publication in the International Conference on Electrical, Computer and Communication Technologies (ECCT 2026) proceedings by Taylor \ Francis Books. This is the author-produced version
Abstract:Breast cancer is one of the most common types of cancer among women around the world. Rapid detection and early treatment can hinder its progress to more complex stages and can impede its spread to other parts of the body. Histopathological image classification is the most common task in cancer detection due to its robustness in analyzing cellular data. Breast histopathology classification requires handling both multi-scale tissue morphology and clinically relevant generalization beyond the source domain. This paper presents MagViT, an interpretable multi-magnification transformer framework with scale-gated fusion and patient-level model selection. The model uses four BreakHis magnifications (40X, 100X, 200X, 400X) and extracts per-scale representations with a ViT backbone, and combines them via a learnable gate that masks missing scales. Patient-level five-fold cross-validation with a fixed seed has been run and compared with three architectural branches. The most accurate branch is then selected as the final model due to the strongest patient-level accuracy while retaining the simplest fusion pathway. On BreakHis, our architecture achieves a mean image accuracy of 0.9191, a mean patient accuracy of 0.9643, and a mean macro-F1 of 0.9042. External transfer experiments provide preliminary evidence of cross-dataset generalization under controlled adaptation settings on BUSI (image accuracy 0.8306, macro-F1 0.7480, patient accuracy 0.8291) and IDC (image accuracy 0.8577, macro-F1 0.8191, patient accuracy 0.8372). Grad-CAM visualization indicates that the model focuses on diagnostically significant and meaningful regions across magnifications. Relative to prior ViT-centered BreakHis work, this study emphasizes patient-level selection and cross-dataset robustness under a reproducible protocol.
[CV-100] ORViT-DR: Ordinally-Robust Hybrid ViT for Low-Resolution Diabetic Retinopathy Grading
链接: https://arxiv.org/abs/2608.16958
作者: Soumit Kumar Kundu,Nabil Ashab,Bidhan Biswas,Shahadat Hossain Sohag,Saif Mahmud Parvez,Souvik Kumar Kundu,Zunayed Ahmed Rafi
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCT 2026
Abstract:Diabetic retinopathy (DR) is one of the main causes of impaired vision. A good and reliable automated grading system can make the screening process safer and more accurate. Because DR stages progress gradually, the task of grading disease severity naturally follows an ordinal structure in which neighboring classes share similar visual characteristics. In this study, ORViT-DR, a hybrid deep learning framework, is designed to improve DR grading from low-resolution retinal images. The proposed approach combines convolutional feature extraction with transformer-based global context modeling through a pre-trained ViT-Hybrid backbone, which integrates BiT-ResNetv2 with a Vision Transformer architecture. The approach is tested on the RetinaMNIST subset of the MedMNISTv2 dataset, which contains 28x28 retinal fundus images annotated with five levels of disease severity. To promote stable training and better feature learning, the training strategy applies progressive layer unfreezing, layer-wise learning rate decay, exponential moving average (EMA) parameter updates, and ensemble-based prediction during inference. Experimental results on the official RetinaMNIST test set show that the proposed method achieves 57.00% classification accuracy, along with a quadratic weighted kappa score of 0.5963 and a macro-F1 score of 0.4293. These results suggest that hybrid CNN-Transformer architectures can provide effective representations for ordinal retinal image analysis.
人工智能
[AI-0] Delegation Asymmetry in Agent ic Recommender Systems: Measuring Two-Sided Receptivity in Online Dating
链接: https://arxiv.org/abs/2608.18058
作者: Daria Leshchikova,Valentina V. Kuskova,Dmitry Zaytsev,Valerii Klimov
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Autonomous LLM agents that converse on a user’s behalf are an emerging design pattern in matching platforms, yet their viability depends on a condition rarely examined: users must accept not only delegating conversation to an agent, but also receiving agent-mediated communication from others. We study this condition using two large-scale surveys of active users of a major dating platform (N=2,894 on generative profile features; N=2,617 on autonomous conversational agents, fielded in two languages). We develop a latent-variable measurement model of agent receptivity based on graded response models with latent regression, and show via model comparison that willingness to send and willingness to receive agent communication are distinct constructs: highly correlated (rho=0.92) but separable (Delta BIC=52), with partial measurement invariance across languages. The model quantifies a systematic delegation asymmetry: deploying one’s own agent requires far lower receptivity (threshold -0.38) than engaging a counterpart’s agent (+0.32; full engagement +1.39), and mean deployment propensity exceeds engagement propensity roughly threefold. Under a random-pairing counterfactual derived from stated receptivity, only 4-13% of directed dyads combine agent deployment with receiver engagement, with a pronounced gender-directional imbalance. Design counterfactuals quantify the levers: a reciprocity requirement cuts interaction volume by half or more by excluding nearly two-thirds of would-be deployment, while routing agent contacts on receive receptivity triples per-contact engagement, a lift that survives out-of-sample validation with the target item held out (AUC 0.88, 3.1x quartile lift under respondent-level cross-validation). We discuss implications for agentic recommender design, including disclosure, opt-in mechanics, and receptivity-aware matchmaking.
[AI-1] HLSR: Hybrid Live Forecast Selective Dynamic Vehicle Rerouting for Real-Time Congestion Avoidance
链接: https://arxiv.org/abs/2608.18056
作者: Xiao Wang,Shun Ren Yang,Hui Nien Hung
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Urban traffic congestion reduces productivity and increases travel cost and emissions. Network-wide live travel-time shortest-path rerouting can be highly effective in simulation, but assumes that essentially every on-road vehicle is replanned every decision period. We propose HLSR, a selective hybrid live–forecast vehicle rerouting framework that fuses live edge speeds with short-horizon forecasts under limited intervention scope. Building on dual-threshold congestion detection, calibrated upstream selection, and driver-tailored travel-time prediction, HLSR further introduces approaching-vehicle expansion, travel-time-weighted k-shortest-path generation, and a horizon-dependent hybrid live–forecast segment speed used in multi-cost route allocation.
[AI-2] StagedWorkspace: A Versioned Workspace for Knowledge-Work Agents
链接: https://arxiv.org/abs/2608.18050
作者: Yining Hua,Hongbin Na,Yifan Zhou,Akshay Kalose,Cyrus Ayubcha,Levi Lian
类目: Artificial Intelligence (cs.AI)
备注: Under Review
Abstract:AI agents increasingly perform knowledge work (i.e., produce and modify persistent digital artifacts such as code repositories, documents, spreadsheets, slides, reports), yet the parsed views they search, the native files they edit, the changes they review, and the artifacts they submit can refer to different versions of the same work product. We formulate this as a workspace-state contract: every view should be explicitly tied to a version of the evolving workspace state. Coding agents partly address this need through repository contracts for search, diffs, and tests, whereas an analogous contract is less explicit for PDFs, spreadsheets, slides, notebooks, and mixed-format project folders. We propose StagedWorkspace, a versioned workspace for knowledge-work agents. The workspace binds parsed records and review diffs to content hashes of the native files as they change. In fixed-harness ablations on OfficeQA Pro and APEX-Agents, dual parsed/native access has the highest point estimate for every tested model; relative to the more limiting single view, it improves OfficeQA Pass@1 by 8.3-12.1 points and APEX mean rubric score by 4.7-9.2 points. SW-AGENT scores 63.9% with Gemini 3.1 Pro on OfficeQA and 42.1 with GPT-5.4 Nano on APEX, compared with published same-model scores of 29.3% and 25.5, respectively. A paired review-axis ablation on 57 file-editing tasks further finds higher observed scores when diffs are visible. These results identify workspace state as an experimental variable in knowledge-work agents and motivate benchmarks that score evidence, staged edits, and submitted artifacts as explicit state transitions.
[AI-3] Why GPT -Style Models Do Not Directly Transfer to Symbolic Music: Compression in the Wrong Coordinate System
链接: https://arxiv.org/abs/2608.18025
作者: Yi Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Sound (cs.SD)
备注:
Abstract:GPT-style models achieve strong performance by representing language with finite vocabularies of reusable discrete tokens. This success has motivated symbolic music tokenizations to treat recurring musical structures, such as chords, motifs, and phrases, as reusable units analogous to linguistic tokens. However, tokenization derives its advantage not from reusable combinations alone, but from compression: effective compression requires coordinates in which recurring regularities form stable and predictable conditional distributions. The key problem is therefore not to find larger musical combinations, but to discover the coordinate system in which musical facts become predictively compressible. We formulate the Effectiveness–Losslessness Framework and define tokenization as the construction of a predictively effective and relationally lossless coordinate system. The Predictive Effectiveness Principle defines the Fact–Token Boundary: decoupling and denesting construct coordinate interfaces that expose predictive regularities. The Relational Losslessness Principle defines the Token–State Boundary: tokenization stops before context-dependent relations are fixed, leaving their computation to model states. Controlled symbolic-music experiments validate these boundaries. Effective coordinate construction improves predictive compressibility, while fixed relational projections constrain contextual modeling. Sequence compaction alone does not guarantee predictive compression, while preserving contextual freedom allows higher-order musical organization to emerge without explicit structural labels. These results reveal why GPT-style models do not transfer directly across modalities: architectures transfer, but tokenization interfaces do not. Tokenization must discover effective representations while preserving the relational freedom from which contextual structure can emerge.
[AI-4] Can Large Language Models Explain Flight Safety Events? A Prior-Guided Semantic LLM -based Approach
链接: https://arxiv.org/abs/2608.18017
作者: Lu Xu,Xu Li,Linjiang Zheng,Fan Li,Riquan Zhang,Jiaxing Shang
类目: Artificial Intelligence (cs.AI)
备注: 14 pages, 6 figures, submitted to IEEE Transactions on Intelligent Transportation Systems
Abstract:Improving flight safety with flight data requires not only accurate detection of risk events, but more importantly, clear interpretation of their underlying causes at the level of pilot control behavior. Existing explainable AI techniques, such as feature importance maps, often require considerable domain knowledge to translate them into operationally meaningful explanations. Large Language Models (LLMs), which excel at language reasoning, bring a promising solution to this issue. However, applying LLMs in this domain presents key challenges such as modal inconsistency, limited classification ability, scarcity of task-specific data for fine-tuning, and lack of domain knowledge. To overcome these challenges, we propose FlightLLM, a prior-guided semantic LLM-based approach for interpretable flight safety analysis. Specifically, we first perform feature engineering to address modal inconsistency, combining statistical descriptors with physically meaningful flight indicators. This representation is further processed by a Semantic Discretization module, which converts abstract numerical patterns into qualitative descriptions that are more compatible with language reasoning. In addition, since LLMs are not inherently strong classifiers, CatBoost is incorporated as a statistical expert, and its prediction results are injected into the prompt as prior guidance. A contrastive few-shot learning strategy is further adopted to compensate for limited data. Finally, we design structured prompts to embed aviation-specific knowledge into the inference process. Using hard landing, a representative risk event with complex causal mechanisms, as an anchor point, we evaluate FlightLLM on a dataset of 704 real-world A320 flight samples. Experimental results show that the proposed approach achieves competitive classification performance while generating direct and reasonable explanations for event causes.
[AI-5] Policy-Invariant Reward Shaping from LLM Feedback: A Framework for Hybrid RL Agents
链接: https://arxiv.org/abs/2608.18008
作者: Christophe D. Hounwanou,John Emeka Eze,Yaé U. Gaba
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 14 pages
Abstract:Combining large language models with reinforcement learning is increasingly explored, yet the theoretical status of LLM-derived reward signals is often left implicit. We formalize the hybrid LLM-planner and RL-controller architecture as a Goal-Augmented Markov Decision Process and show that when the LLM per-state progress score is used as a bounded potential function, the resulting shaping term preserves the optimal policy set even when the LLM scores are inaccurate. This guarantee is stronger than what general LLM-as-reward approaches provide. We verify the result numerically on a small MDP under four potential configurations, including an adversarial one scaled to twenty times the base reward magnitude.
[AI-6] raceable Trust for action-ready artificial intelligence in bioscience
链接: https://arxiv.org/abs/2608.17997
作者: Huayu Xin,Yizhi Cai,Mukilan Deivarajan Suresh,Gavin Michael Farrell,Iwona Gajda,Charlie Harrison,Conor Houghton,Mato Lagator,Yang Lu,Virginia Portillo,Reyer Zwiggelaar,Sebastian Lobentanzer
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:Artificial intelligence (AI) is becoming part of the working infrastructure of the biosciences. AI models can predict biomolecular structures, design proteins, rank variants, annotate images, recommend strains and optimise experimental conditions. We argue that the decision to use an AI output to guide laboratory action is a key juncture for trustworthy research and should follow a defined, reviewable process. We propose Traceable Trust as a proportionate assessment-and-design framework for this output-to-action boundary. It asks what evidence supports the output, what capability is being claimed, what agency has been delegated, what threshold authorises action, who can override it and how outcomes inform later decisions. We illustrate the framework through three case studies spanning ecosystem resources, project design and laboratory action. Together, the cases show how trust can be documented where AI outputs begin to shape scientific work.
[AI-7] oo Sure to Be Safe: Model Calibration for Reliable Log Anomaly Detection ICDM2026
链接: https://arxiv.org/abs/2608.17965
作者: Bin Li,Dongdong Wang,Siyang Lu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: Accepted at the 2026 IEEE International Conference on Data Mining (ICDM 2026)
Abstract:Online log anomaly detection is critical for maintaining the reliability of large-scale computing systems. Although recent language model-based log anomaly detectors achieve strong detection performance, their confidence estimates remain poorly calibrated. We show that these detectors frequently assign excessive confidence to incorrect predictions, particularly for anomalous logs under severe class imbalance. Moreover, confidence on erroneous predictions remains persistently high even when conventional calibration metrics indicate good calibration, creating a critical reliability gap for operational monitoring systems. To address this issue, we propose Log Reconstruction and Distance (LoRD), a lightweight post-hoc calibration framework for reliable log anomaly detection. LoRD learns prediction-route-specific reliability models from latent representations of correctly classified validation samples and estimates prediction reliability through route-wise reconstruction distances. Based on the estimated reliability, LoRD selectively recalibrates high-risk predictions to suppress overconfident errors while preserving reliable predictions. Extensive experiments on four large-scale log benchmark datasets and multiple language model-based detectors demonstrate that LoRD consistently improves confidence reliability and substantially reduces overconfident anomaly-related errors without sacrificing anomaly detection performance.
[AI-8] owards Zero-Shot Task Transfer with Neurosymbolic World Models
链接: https://arxiv.org/abs/2608.17959
作者: Isidoro Tamassia,Lennert De Smet,Giuseppe Marra
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:State-of-the-art model-based reinforcement learning methods learn neural world models that allow policy improvement by planning in a latent space, without assumptions on the structure of the underlying environment. While expressive, these models are generally task-dependent: they learn uninterpretable latent representations that are tied to the training task and thus hard to generalize to new tasks. In this work, we present a novel world model formulation where the reward prediction only depends on a subset of structured, symbolic components of the whole latent state. Decoupling observation reconstruction and reward prediction allows us to learn world models that can adapt zero-shot, i.e. without further environment interactions, to new reward functions defined over the same symbolic state space. We discuss the main advantages and challenges of learning these neurosymbolic world models and demonstrate the strong generalisation properties of our approach over purely neural methods.
[AI-9] An Omitted Mode Is a Rare Rule: The Sampling-Verification Danger Law in Continuous Code World Models
链接: https://arxiv.org/abs/2608.17956
作者: Javier Aguilar Martín
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注: 92 pages, 5 figures. Code, data and result artifacts: this https URL
Abstract:In the Code World Model paradigm an LLM synthesizes an executable world model that a classical planner searches, and the model is accepted when it reproduces sampled transitions. We ask what that acceptance certifies in continuous control. We define the pipeline’s danger as an expected risk and isolate its exact factor: the probability that N i.i.d. gate rollouts all miss a critical event of probability r is exactly (1-r)^N; an independent acceptance sample adds its budget to the exponent. On three hybrid instruments the accepted mode-blind model is exploited: the planner is pinned at the mode boundary at a regret of nearly the whole attainable return. We prove a localization budget, valid at boundary points: models with Lipschitz constant at most L differing by eta at a point disagree above tolerance eps on a region of volume at least kappa((eta-eps)/L)^(d+m); the discontinuous reset modes studied pay no such budget. With real LLM synthesis, GPT-5.x repairs an omitted 1D clamp in 105 of 111 mode-containing draws – every attempt exact on 50 of 56 instrument-stream blocks (95% CI [0.781, 0.960]). On 2D regions no artifact recovers the rule (0/156); eight targeted interventions leave the failure in place, and positive controls locate it: a located rule is not induced, while given form and location the constants follow exactly. A version-space certificate proves identification is class-relative: at the widest dose the declared fit succeeds in 20/20 blocks and every sample-consistent circle is within tolerance in 18/20. We prove a class of entry rules exactly consistent with every sample yet harmless at play, so identifiability is a measurable property of the instrument. Re-scoring all 1034 artifacts on independent samples confirms acceptance certifies sample consistency and no more: where the gate is provably informative it covers about two percent of the exploited planner’s queries.
[AI-10] SIGMA: SHAP-Guided Implicit-Trajectory Generation for Metadata-Free LLM -Based AutoFE
链接: https://arxiv.org/abs/2608.17948
作者: Xuan Zheng,Kento Uchida,Shinichi Shirakawa
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent research has leveraged Large Language Models (LLMs) to enhance Automated Feature Engineering (AutoFE) through semantic descriptions and trajectory-based prompting. However, there exist two challenges that limit their applicability and scalability in long-horizon optimization: (1) semantic metadata is unavailable in many practical settings, and (2) trajectory accumulation increases the risk of exceeding the context window, while without it, the generation process can become unstable, leading to becoming stuck in the local optima and a high duplicate rate of generated features. To this end, we propose a SHAP-enhanced Implicit-trajectory Generation for Metadata-free AutoFE (SIGMA), a scalable constant-context optimization framework. SIGMA leverages SHAP values to provide task-aware signals for guiding group feature generation instead of semantic information. In addition, we adopt an EXposed-feature Implicit Trajectory (EXIT) approach, where the exposed features in the prompt implicitly represent the trajectory. Empirical results demonstrate that SIGMA achieves performance comparable to the state-of-the-art (SOTA) LLM baselines with a nearly constant prompt length. Notably, EXIT significantly reduces the duplicate ratio of generated features from 37.2% to 6.8%. At the same time, SIGMA matches traditional SOTA performance with only 5.4 features on average, demonstrating substantial efficiency gains in feature utilization.
[AI-11] Procedural Content Metageneration via Program Search and Continual Abstraction Discovery
链接: https://arxiv.org/abs/2608.17947
作者: Matthew Siper,Ahmed Khalifa,Julian Togelius
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
备注: Accepted for publication in IEEE Conference on Games 2026
Abstract:Large language models can generate executable programs, which makes it possible to search directly over procedural content generators rather than individual levels. We study this approach in Sokoban, Zelda, Dangerous Dave, and Lode Runner. Each run evolves complete Python generators through language-model mutation and crossover. We introduce Continual Abstraction Discovery, or CAD, which extracts reusable primitives from high-fitness programs into a run-specific helper module. A 2x2 experiment crosses CAD with access to a fixed hand-written domain API. The completed data set contains 160 complete runs, with at least ten 50-generation runs in every cell. CAD raises mean final best fitness in all eight domain and API comparisons. Across all CAD runs, learned libraries are adopted by most later programs and repeatedly rediscover validation, reachability, and structural utilities. These results support that discovering reusable primitives improves evolutionary program search for content generators.
[AI-12] EvoTS-Agent : A Self-Evolving LLM Agent for Financial Time Series Change Point Detection
链接: https://arxiv.org/abs/2608.17933
作者: Lei Jiang,Ye Wei,Xinyu Xi,Jordan Langham-Lopez,Yifan Bao,Raad Khraishi,Yihao Ang,Anthony K. H. Tung,Lukasz Szpruch,Hao Ni
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注:
Abstract:Financial time series exhibit non-stationary and heterogeneous statistical properties, making change-point detection challenging because no single unsupervised algorithm performs consistently across assets and market regimes. Conventional workflows consequently depend heavily on expert-driven model selection, feature design, and hyperparameter tuning, limiting their scalability and adaptability. We propose EvoTS-Agent, a validation-guided self-evolving LLM agent for autonomous financial time-series change-point detection. EvoTS-Agent first performs curated exploratory data analysis to characterize dataset properties and initialize candidate detection models. It then evolves executable experiment trajectories through three complementary operators: \textitRevision exploits the current best solution, \textitAlternative Strategy explores fundamentally different modeling directions when progress stagnates, and \textitRecombination synthesizes complementary evidence from high-performing trajectories. Validation feedback guides trajectory evolution throughout the search, enabling the agent to adapt its detection pipeline to the statistical characteristics of each dataset while preserving reliable optimization. Experiments across four benchmark datasets demonstrate that EvoTS-Agent consistently outperforms existing LLM-based agents while maintaining a 100% execution success rate across all evaluated backbone LLMs.
[AI-13] Adaptive Policy Portfolios for Robust Markov Decision Processes
链接: https://arxiv.org/abs/2608.17929
作者: Kasper Engelen,Sebastian Junges,Guillermo A. Pérez,Marnix Suilen
类目: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
备注:
Abstract:Robust Markov decision processes optimize one policy against a set of plausible transition functions. This can be conservative when the unknown dynamics are fixed and become partially identifiable after deployment. We study adaptive policy portfolios: finite sets of memoryless randomized policies synthesized offline and paired with a lightweight online selector. Robust regret is a natural measure of portfolio quality: for each plausible environment, it measures the loss of the best portfolio member relative to the policy that would have been optimal had that environment been known. Related regret objectives were studied by Ghavamzadeh et al. (2016) with an emphasis on approximations and relaxations for safe policy improvement. We give a complexity-theoretic account of portfolio certification and synthesis. Certifying a given portfolio is \forall\mathbbR -complete already for deterministic portfolios in acyclic (s,a)-rectangular RMDPs. Synthesizing a portfolio of unary-bounded size is \exists\forall\mathbbR -complete for general rational polytopes, even with fixed discount and acyclic dynamics. The single-policy case is already hard, both combinatorially and algebraically. Finally, we present an offline portfolio construction that is amenable to runtime specialization.
[AI-14] ARASH: Adaptive Retrieval And Shot Selection for Tabular Prediction
链接: https://arxiv.org/abs/2608.17856
作者: Samirasadat Jamalidinan,Yue Xu,Kazem Cheshmi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Tabular prediction is a critical task across numerous applications. The recent success of large language models has sparked various approaches for adapting them to the tabular domain. A prevalent strategy involves training or fine-tuning specialized Tabular Foundation Models (TFMs) such as TabPFN. However, TFMs require substantial computational resources, and frequent model retraining is often impractical. In-context learning (ICL), specifically, few-shot prompting, offers a resource-efficient alternative to enhance performance. Yet, identifying the most relevant rows to serve as shots remains a challenge for tabular data. This paper introduces ARASH (Adaptive, query-specific Retrieval And Shot selection), a method that improves TFM efficiency by selecting optimal shots based on local neighborhood analysis within the training set. Our results demonstrate that ARASH reduces the prompt length and memory usage of TabPFN by 1261.5 \times and 2.56 \times , respectively, while providing comparable accuracy.
[AI-15] he Models Tell: Measuring Context-Leakage Attack Signals with Behavior Gauges
链接: https://arxiv.org/abs/2608.17829
作者: Maosen Zhang,Jianshuo Dong,Boting Lu,Wenyue Li,Xiaoping Zhang,Tianwei Zhang,Jie Zhang,Han Qiu
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: Preprint
Abstract:LLMs increasingly rely on external contexts, such as pre-defined system prompts or retrieved documents, to improve generation quality. However, processing these contexts alongside user queries creates an attack surface: adversarial inputs can induce models to disclose them. Prior probing studies suggest that leakage-related signals emerge in hidden states, yet the need to extract these states poses additional deployment challenges. In this paper, we explore whether this internal signal leaves a more accessible ``tell’’ before decoding. We propose LeakGauge, which probes this response by appending a suffix that gauges leakage behavior and mapping its prefill token probabilities to an attack-risk score. While a direct gauge uses the initial tokens of confidential content, we find that a content-agnostic one that verbalizes leakage behavior yields more robust signals. Across 11 LLMs, including GLM-5.2 (753B) and Kimi-K3 (2.8T), LeakGauge reaches an AUROC range of 0.944–0.996 on unseen attacks. The signal remains stable when the content changes language or the attack shifts from verbatim to semantic disclosure. By activation-steering interventions, we further show that the risk score is sensitive to an internal leakage-related direction, relating the observable signal to the model’s internal representation. In addition, LeakGauge enables an input detector with fewer than 0.5K extra parameters and added latency of 10.34 ms. Code: \hrefthis https URL.
[AI-16] StartupBench: Benchmarking General-Purpose Agents on Market-Validated End-to-End Workflows
链接: https://arxiv.org/abs/2608.17800
作者: Liya Zhu,Xin Ma,Tao Liu,Haodong Wang,Ge Zhang,Jingzhe Ding,Qingshui Gu,Yongjie Zhong,Jinxiang Meng,Yuan Gao,Yunqiu Zhou,Hao Zhu,Jifeng He,Yongzhi Liao,Xinyi Zhang,Chaoxin Li,Yi Zhu,Xi Lin,Duju Zeng,Xiang Gao,Wen Zhang,Yunyang Wang,Duo Wang,Huan Zhou,Zuo Wang,Jin Chen,Kaiyuan Zhang,Chuqian Yu,Tianhao Yu,Longxiang Liu,Jianbo Xue,Huimin Che,Jiahao Wang,Yujia Qin,Jiaheng Liu,Shen Yan,Xiaolong Chang,Wenhao Huang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advances in Large Language Models(LLMs) and agents have substantially improved the ability of AI systems to execute complex tasks. Yet existing benchmarks largely rely on researcher-selected tasks, leaving uncertain whether such progress extends to the work that real-world users actually demand from AI systems. We introduce \textbfStartupBench, an E2E agent benchmark grounded in market-validated AI startup products. Rather than defining tasks from pre-defined assumptions about useful agent capabilities, we systematically study AI products with demonstrated adoption, together with their product workflows and users, to identify real-world tasks for which AI has established practical demand across diverse professional domains. We translate these workflows into complete deliverable-oriented tasks and evaluate them with fine-grained rubrics capturing their complex requirements. Across representative models evaluated under a unified agent harness, even the strongest model successfully completes only approximately 30% of StartupBench, despite making substantial partial progress on many tasks. Further analysis identifies aspects like complex instruction following and domain-specific expertise as major sources of failure. Our results reveal that many market-validated workflows remain beyond the reliable capabilities of current general-purpose agents, establishing StartupBench as an empirical measure of progress toward E2E completions of real-world user tasks.
[AI-17] Learnware for CSI Feedback: Scene-specific Small Models Can Do Big
链接: https://arxiv.org/abs/2608.17760
作者: Xiangyi Li,Jiajia Guo,Chao-Kai Wen,Xin Geng,Shi Jin,Zhi-Hua Zhou
类目: Information Theory (cs.IT); Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
备注: This work has been accepted by IEEE Transactions on Wireless Communications. Copyright may be transferred without notice, after which this version may no longer be accessible
Abstract:Intelligent channel state information (CSI) feedback is essential for realizing the high capacity and spectral efficiency goals of future 6G systems, yet existing deep learning solutions face a trade-off between model generalization and scenario-specific performance. Large neural networks generalize well but incur high computational and tuning costs, while small models excel in particular environments but require repetitive costly end-to-end training for each base station (BS). To address these challenges, we introduce a model repository-based deployment framework in which a centralized AI data center maintains a catalog of scene-specific CSI models. The repository is enhanced with a Learnware-based framework, where each model is associated with a specification including semantic part (network architecture parameters) and statistical part (codeboo-fingerprint embeddings of training-data distributions). A BS submits only its local statistical specifications to retrieve the most relevant pre-trained model, enhancing data privacy by avoiding raw CSI transmission and drastically reducing retrieval latency and communication overhead. We further develop a data-driven search strategy that matches codebook fingerprints to model performance, achieving over 90% selection accuracy. In simulations, our scheme yields 18.8% and 57.7% performance improvements over the General Model in LOS and NLOS scenarios, respectively while reducing local fine-tuning by up to 1000 samples and 100 epochs. This Learnware-based approach minimizes redundant training, maximizes model reuse, and supports rapid,privacy-enhancing deployment of CSI feedback models.
[AI-18] D2ACCI: A Dual-Loop Diagnostic Protocol for Evidence-Preserving Agent Memory
链接: https://arxiv.org/abs/2608.17756
作者: Xule Liu,Yijun Liu,Chao Li,Shao Kun
类目: Artificial Intelligence (cs.AI)
备注: Preprint
Abstract:Memory is a key capability of LLM agents. Persistent memory extends this across sessions—enabling recall, revision, and personalization. Yet its multi-stage pipeline (ingestion, retrieval, filtering, generation) makes failures difficult to localize: end-to-end evaluation reveals that an error occurred, but not which stage caused it. Existing evaluations often report aggregate performance without paired statistical comparisons, slice-level non-regression checks, or stage-level diagnostic traces. We propose D ^2 ACCI (Diagnostic-Driven Artifact-based Closed-loop Controlled Iteration), a dual-loop protocol whose outer diagnostic gate promotes, feature-flags, or rejects memory interventions based on paired evidence, protected-slice monitoring, and trace-level localizability. We further introduce DCR, a graded observability metric that measures whether failures remain localizable, and D ^2 ACCI-Eval, a reusable artifact for gate replay. We instantiate the protocol in MemStack and evaluate on three public benchmarks, achieving 93.59% on LoCoMo, 90.93% on LongMemEval, and 57.20% on PersonaMem-V2. Five paired ablations show that supplement extraction, session-memory retrieval, and Forget Guard yield statistically significant gains (+1.9 to +3.7pp, all p \le .003). In contrast, BM25/RRF is retained as a monitored feature flag—a distinction invisible to aggregate-only evaluation. A diagnostic audit shows enriched traces substantially improve root-cause agreement over result-only relabeling. Diagnostic artifacts reach 98–100% DCR@3 versus 0% for results-only logs. These results establish that robust memory-system iteration demands traceable, statistically grounded, and regression-aware evidence—exactly the gap D ^2 ACCI fills.
[AI-19] he Curious Case of Exploding DecPOMDPs: Containing the Fire through Policy Counting
链接: https://arxiv.org/abs/2608.17749
作者: Nazlı Nur Karabulut,tanya Braun
类目: Artificial Intelligence (cs.AI)
备注: Full version including appendix of a paper accepted at the 17th International Conference on Scalable Uncertainty Management (SUM2026) under the same name
Abstract:Decentralised partially observable Markov decision processes (DecPOMDPs) provide a general framework for modelling multi-agent decision making under uncertainty. However, DecPOMDPs are known to suffer from exponential complexity in the number of agents. One way to combat this intractability in agent numbers is to look at partitions of agents that exhibit a form of symmetry among agents, allowing for a compact encoding by counting. However, a challenge arises as the policy space explodes, even though the model complexity and evaluation cost reduce to a polynomial dependence. In this paper, we redirect our focus from counting agents to counting policies, which actually enables tractability in agent numbers for so called policy-counted DecPOMDPs. Further, we present policy-counted dynamic programming using the compact representation to solve policy-counted DecPOMDPs efficiently.
[AI-20] Neuro-symbolic learning over OWL 2 DL via consequence-based compilation to differentiable circuits
链接: https://arxiv.org/abs/2608.17741
作者: Olga Mashkova,Asaad Mohammedsaleh,Fernando Zhapa-Camacho,Robert Hoehndorf
类目: Artificial Intelligence (cs.AI)
备注: Accepted at NeSy 2026
Abstract:OWL 2 DL ontologies, grounded in the description logic \mathcalSROIQ , express large knowledge bases in biomedicine and the Semantic Web. Neuro-symbolic (NeSy) learners over description logics either embed the ontology in a continuous space, abandoning classical entailment, or restrict to the Horn fragment \mathcalEL^++ , which has a single canonical model. We present Baobab, which compiles a \mathcalSROIQ ontology with a finite ABox into a Sentential Decision Diagram (SDD): it saturates a propositional core under a consequence-based calculus and instantiates the remaining \mathcalSROIQ features (nominals, number restrictions, and the role axioms) over the active domain. The SDD’s evidence-conditioned weighted model count then trains a perception network to recognize real images under partial ABox supervision: on an ontology that exercises every distinctive \mathcalSROIQ feature, a CNN learns to read MNIST digits coupled by a successor relation and recovers latent ontology concepts that an independent perception leaves at chance. When the supervision admits several ontology-consistent completions, an independent perception collapses onto one, a reasoning shortcut: we show that a mixture indexed by the query’s justifications can represent the calibrated posterior no independent perception can, and that seeding it from the circuit’s enumerated completions attains the Bayes-optimal posterior on a real-image MNIST task where single-WMC and learned mixtures (the BEARS-ensemble hypothesis class) do not: to our knowledge the first to characterize and mitigate reasoning shortcuts in a non-Horn description logic. Soundness of the compiler and the representation result are machine-checked in Lean 4. Code is available at this https URL.
[AI-21] Evaluating the Diversity of AI-Generated Content with Diversity Profiles
链接: https://arxiv.org/abs/2608.17731
作者: Xiuyuan Hu,Xuege Hou,Guoqing Liu,Yang Zhao,Jieran Li,Dongbiao Sun,José Miguel Hernández-Lobato,Hao Zhang,Xue Liu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Diversity is a fundamental criterion for evaluating generative artificial intelligence (AI) systems, yet its measurement remains inherently ambiguous. Existing approaches typically represent generated samples in an embedding space, compute pairwise distances or similarities, and aggregate them into a single scalar score. Such scalar summaries are convenient, but they often encode different inductive biases and may yield contradictory rankings of the same sample sets. In this paper, we argue that diversity evaluation for AI-generated content is intrinsically under-specified when reduced to a single number. We first review representative diversity metrics, and then diagnose their limitations from two complementary perspectives: an axiomatic analysis showing that no representative scalar metric satisfies all desirable properties simultaneously, and an empirical analysis showing that high-dimensional representation spaces can induce concentrated, modality-dependent distance distributions. To address these issues, we propose diversity profiles: curve-valued, condition-aware summaries that evaluate a parameterized diversity family across a range of thresholds, scales, exponents, or orders under a specified representation and distance or kernel function. Diversity profiles reveal whether a comparison is robust across resolutions or instead depends on an arbitrary parameter choice. We instantiate profiles for several representative metric families and demonstrate their practical use in generative AI evaluation. Overall, diversity profiles provide a more transparent and resolution-aware framework for comparing the diversity of AI-generated content.
[AI-22] Beyond Suspicious Steps: Ontological Trust in Long-Horizon Agents
链接: https://arxiv.org/abs/2608.17718
作者: An He,Yao Wang,Haibin Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Long-horizon agents increasingly operate across many steps, tools, and observa- tions. In this setting, the relevant oversight question is not only whether each action is locally valid, but whether the evolving trajectory still corresponds to the task the user authorized. Drift can accumulate quietly: an agent may call the right tool with plausible arguments at every step, while its prefix moves toward a broader role, an adjacent objective, or evidence the user never supplied. Existing monitors mostly check local compliance, deliver final-trace verdicts, or score generic risk; they do not directly estimate this prefix-level relation. We introduce ontological trust, a task-conditioned property of trajectory prefixes, and instantiate it as RGE, an online monitor that decomposes trust along Role, Goal, and Evidence. RGE uses LLMs only to derive structured task and step representations; trust-state updates, projec- tions, and intervention decisions are deterministic, so the output is a replayable and auditable trust trajectory rather than a single end-to-end judge verdict. We construct a cross-domain trajectory corpus from OSWorld, FinanceBench, and EICU-AC, covering benign executions, prefix-paired drift, and pseudo-consistency failures. On this corpus, RGE outperforms adapted rule-, judge-, and shield-style baselines on prefix-paired drift detection. With the two larger estimator models, it exceeds 93% Drift F1 on every benchmark while keeping benign coverage at or above 95.8%. Pseudo-consistency is harder: detection depends on whether task completion is externally visible, a structural limit we characterize empirically.
[AI-23] Accuracy and Robustness of Model Cascades Under Data Perturbations
链接: https://arxiv.org/abs/2608.17711
作者: Pallavi Mitra,Jai Kushwaha,Felix Biessmann
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Prediction cascades significantly reduce energy consumption of Artificial Intelligence (AI) models while maintaining high predictive performance. The idea is that easy inputs are routed through a lightweight small model, and difficult uncertain cases are deferred to a larger model. While this design can improve computational efficiency on clean data, its effectiveness depends on the reliability of confidence-based routing. Input degradations, such as static corruptions and sequential perturbations, can shift model confidence and routing decisions. In this paper, we study confidence-based cascade frameworks for image classification and investigate how such degradations affect their confidence-based deferral behavior. We select a model cascade at the pareto-optimum of accuracy, routing quality, and energy consumption that achieves competitive predictive performance with an up to 10-fold decrease in CO _2 emissions. We study the behavior of that model cascade under input corruptions and analyze how the cascade’s routing decisions change when the input distribution shifts. Our analysis identifies three failure modes. Static corruptions either (1) break the routing signal while the large model remains useful, or (2) degrade both models so deferral no longer recovers accuracy. Sequential perturbations reveal a third mode: predictions stabilize but deferral suppresses, yielding stable but unreliable predictions. These findings demonstrate that energy efficient model cascades require evaluation beyond clean accuracy, with explicit attention to routing reliability under distribution shift.
[AI-24] Dijkstra as an Oracle for Online Stochastic Shortest Path Navigation with Provable Guarantees
链接: https://arxiv.org/abs/2608.17703
作者: Mansur M. Arief,Ali Akarma,Ahmad Alfan Alfian Irfan
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
备注:
Abstract:Mobile robots that operate in side by side with humans and critical facilities must reach their goals at low cost, despite often unknown true traversal costs of the map apriori and imperfect actuation. Planners that solve the underlying stochastic shortest path problem exactly, such as value iteration, require computation that grows with the diameter of the map, whereas Dijkstra’s algorithm is fast but is usually considered inexact once transitions are stochastic. This study shows that Dijkstra’s algorithm can remain an exact planning engine under a condition that is much weaker than the causality condition often invoked in the literature, namely nonnegativity of a reduced cost defined on the determinized map. Building on this characterization, an online learner DORA (Dijkstra Oracle Reduced-cost Algorithm) is proposed for robot navigation that calls a shortest path oracle a fixed number of times per episode, never estimates a transition kernel, and adds a logarithmic survival weight when the probability of contact with a dynamic obstacle must stay within a budget. In the numerical experiments involving three other benchmarks that cover grid world navigation, directional drilling, and drone surveillance, the learner matches optimistic value iteration that is given the true transition kernel while performing 4.5 to 19.3 times less planner work, reduces contacts during learning by a factor of seventeen relative to determinize and replan, and keeps the contact rate within budgets that span two orders of magnitude. These results indicate that shortest path search supports safe and efficient online navigation and path planning tasks.
[AI-25] GADR: Gathering Architecture Decision Records from Meeting Transcriptions
链接: https://arxiv.org/abs/2608.17694
作者: Lucas Daniel Costa da Silva,Kiev Gama
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Existing LLM-based approaches to Architecture Decision Record (ADR) generation share a critical and largely unexamined assumption: that input is already reasonably structured. In practice, architectural decisions emerge from informal, noisy meetings where choices are implicit, fragmented, and entangled with off-topic dialogue, precisely the conditions under which single-pass prompting degrades. This paper presents GADR, a multi-agent, self-correcting workflow that extracts architectural decisions from raw meeting transcriptions and generates Nygard-formatted ADR drafts. A feasibility study comprising five real project meeting transcripts, expert review by four senior architects, and evaluation by fifteen students provides initial evidence that the agentic workflow captures most expert-identified decisions and produces drafts participants found clear and useful, outperforming zero-shot and few-shot baselines in stability and structural adherence. The study also addresses the underexplored trade-off of RAG-based enrichment improving ADR depth while simultaneously risking transcript-unfaithful content, raising open questions about traceability in automated architectural documentation that we believe is worth the community’s attention.
[AI-26] Mixture-of-Expert Blocks Contain Strong Hallucination Detection Signals
链接: https://arxiv.org/abs/2608.17687
作者: Joao Fonseca,Rodrigo Rodrigues,Paolo Romano
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Despite their widespread use, Large Language Models (LLMs) remain limited by a fundamental problem: the generation of plausible but false content, known as hallucinations. Most existing detection methods operate at the answer or sentence level, yet per-token detection is essential for localizing hallucinated spans and enabling fine-grained interventions. In this paper, we explore the use of the Mixture-of-Experts (MoE) paradigm to address this gap. In MoE architectures, a single forward pass activates a sparse subset of experts (i.e., distinct feedforward networks per layer) via a routing mechanism, producing internal signals (e.g., router entropy, expert disagreement, and expert usage patterns) that are unavailable in dense architectures and have not been previously exploited for hallucination detection. To this end, we introduce InnerExpert, the first method to leverage these MoE-specific signals for per-token hallucination detection. InnerExpert combines routing-level and standard transformer signals into compact per-token feature vectors, classified by a lightweight detector trained on labels produced by an LLM-as-a-judge pipeline, which enables continuous model updates without manual annotation. Our results show that InnerExpert outperforms existing methods across five datasets and two MoE architectures, achieving up to 0.91 answer-level and 0.76 token-level AUROC, while requiring only a single forward pass.
[AI-27] Auditing Self-Evolution in Financial Agents : Capability Gains Security Drift and Execution-Interface Mismatch
链接: https://arxiv.org/abs/2608.17684
作者: Jialong Li,Jialing Zhu
类目: Artificial Intelligence (cs.AI)
备注: 8 pages, 2 figures
Abstract:Self-evolving agents turn experience into reusable skills, workflows, or memories, but post-evolution accuracy alone does not show whether learned behavior preserves previously correct behavior or security. We audit SkillOpt, Agent Workflow Memory (AWM), and ReasoningBank in simulated e-banking using matched benign acquisition trajectories, sealed evaluation endpoints, execution-grounded checks, and independent state replay. On Qwen 3.7 Flash, SkillOpt raises benign utility from 0.741 to 0.837 while exposure to injected content rises from 0.820 to 0.943. Conditional attack success after exposure falls from 0.605 to 0.562, yet overall attack success rate (ASR) rises from 0.496 to 0.530 and unauthorized financial state changes rise to 0.685. Across three independently evolved lineages, capability, exposure, and unauthorized-state changes increase in all three, whereas ASR increases in only two. ReasoningBank raises utility to 0.859 without increasing aggregate ASR, although unauthorized state changes remain slightly above Static. AWM reveals a separate evaluation hazard: a literal WebArena text-action envelope disrupts tool execution in our native function-calling executor. In a post-hoc sensitivity test, removing only that envelope restores utility from 0.319 to 0.756, while exposure rises from 0.299 to 0.909 and ASR from 0.195 to 0.575. Auditing self-evolving financial agents therefore requires tracking regressions, attack-surface contact, unauthorized financial-state change, and artifact-executor compatibility, not accuracy alone.
[AI-28] Benchmarking Automated Security Patch Backporting: How Far Are We?
链接: https://arxiv.org/abs/2608.17671
作者: Jincheng Yang,Yulong Fu,Chengwei Liu,Lyuye Zhang,Fangyuan Zhang,Bingyang Ren,Yang Liu,Hui Li
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 13 pages, 3 figures. Accepted at ASE 2026. Artifact: this https URL
Abstract:Automated security patch backporting is critical for mitigating N-day vulnerabilities. Recent tools report success rates above 80% on their respective datasets. However, these evaluations are often confined to homogeneous environments, such as one repository or specific project versions. Consequently, it remains unclear how well these tools generalize beyond their originally targeted scenarios. We present Porting Benchmark, a curated dataset of 1,234 security patch backporting cases spanning cross-version, cross-branch, and cross-repository scenarios, paired with a common evaluation framework. Using this benchmark, we evaluate five tools spanning program analysis, LLM prompting, and LLM agents under aligned settings. Our results show that aligned evaluation changes the apparent performance landscape: PortGPT and TSBPort remain comparatively strong on the Replication Dataset, while FixMorph and Mystique degrade substantially under the common protocol. Performance degrades sharply on structurally complex patches: the best commit-level success rate falls from 85.2% on Type-I patches to 24.0% on Type-IV. We identify four root-cause categories (missing target API awareness, cross-version semantic mismatch, non-local dependency propagation failure, and patch construction or localization failure) and derive concrete directions for next-generation tool design. On a 45-case dynamically validated subset with verified test cases and constructed POCs, we further observe that reference-based benchmark scores do not fully capture real-world remediation: exact match sharply under-credits harder target adaptations, while executable validation reveals residual integration failures in the target that static reference agreement misses. Executable-feedback refinement provides limited but measurable recovery on the hardest executable cases.
[AI-29] GraphWake: Group Polarization via Memory-Mediated Polarization Cascade in LLM -Agent Communities
链接: https://arxiv.org/abs/2608.17665
作者: Haoran Bu,Zejian Chen,Litian Zhang,Xi Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:LLM-driven agents can autonomously exchange opinions on online platforms and form communities. Such agent-operated social platforms raise a new security concern: attackers may manipulate agents to induce group polarization. Existing methods manipulate agent prompts or construct echo chambers, both of which are difficult to realize in practice. We therefore formulate a new threat, Memory-Mediated Polarization Cascade, which uses agent memory as a persistence channel and public discussion as a propagation channel. This threat contains three stages. During exposure and memory retention, the attacker exposes a small set of target agents to arguments that reinforce their respective stated stances. The targets’ memory systems then process and retain these arguments. During retrieval and reproduction, a shared stance-neutral discussion cues the targets to retrieve and reproduce their respective retained arguments. During iterative propagation, untreated agents influenced by the reproduced arguments restate and spread them. We instantiate this threat in GraphWake with three components: (i) stance-support argumentation knowledge graphs construct knowledge-based arguments; (ii) axiom-oriented triple selection distills them for reliable retention and reproduction; and (iii) stance-neutral memory cueing triggers concurrent retrieval and reproduction, initiating propagation. Experiments across multiple discussions and memory systems show that GraphWake substantially increases group polarization. These findings reveal a community-level polarization risk.
[AI-30] MobileWorldSafety: Benchmarking GUI Agent Safety Against Environmental Injection Attacks in Android Apps
链接: https://arxiv.org/abs/2608.17659
作者: Sujin Chen,Lijun Li,Tianyi Du,Jing Shao
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:LLM-powered GUI agents that autonomously operate smartphones are rapidly transitioning from research prototypes to early real-world deployment. However, because these agents routinely process untrusted environmental content, they are highly vulnerable to environmental injection attacks, which include indirect prompt injections and adversarial instructions. Such attacks can manipulate the behavior of agents without user awareness through diverse channels encountered in everyday mobile use. Despite these risks, existing benchmarks often fail to capture everyday user scenarios, lacking a systematic evaluation of GUI agents under environmental injection attacks on mobile devices. To address this gap, we introduce MobileWorldSafety, a benchmark of 142 risk tasks built on real Android applications. For each task, we define a programmatically verifiable risk indicator over the final system state and evaluate outcomes with a two-stage pipeline: rule-based verification handles unambiguous cases, while an LLM judge adjudicates ambiguous ones. This distinguishes safety failures from capability failures and enables objective and reproducible assessment. Evaluations on six agents, including both general agents and specialized GUI agents, demonstrate that all agents remain highly vulnerable, with attack success rates ranging from 40.4% to 66.9%. These findings indicate that current agents often fail to maintain safety alignment when adversarial content is presented as ordinary mobile context. MobileWorldSafety provides a foundation for quantifying these vulnerabilities and advancing research on robust mobile GUI agents.
[AI-31] Beyond the Trace: Coupling an Interpretable Reasoning -State Readout to Native MoE Routing
链接: https://arxiv.org/abs/2608.17638
作者: Kang Chen,Sihan Zhao,Yixin Cao,Yugang Jiang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:What a reasoning model writes is only a partial record of the process that produces it. We introduce a two-level internal readout for mixture-of-experts reasoning. We first distill vocabulary-scale J-space into J64, a 64-axis semantic frame learned from the model’s own reasoning states. J64 reveals readable process state that the emitted trace does not show: it separates inference effort from problem-induced strain. It also adds 0.096 to 0.135 held-out AUC over a baseline that reads the same rollout as token occupancy and aggregates it in exactly the same way. We then reconstruct J64 from native expert-routing statistics. The result is R64, a low-overhead proxy: its median per-axis correlation with J64 is 0.69 to 0.86 across three models and two families, and on gpt-oss-20b it preserves 95 to 100% of J64’s predictive gain. The readout supports test-time decisions at two temporal resolutions. Over completed candidate sets, J64 and R64 improve single-branch selection, and R64-weighted voting improves plain majority voting in seven of eight settings. During generation, rolling readout windows drive a cumulative stop-and-resample policy whose operating point is fixed on training questions alone. J64 improves accuracy by 1.1 to 5.9 points over a sibling-permuted control, and the routing-only R64 proxy retains 0.9 to 3.2 of those points. Finally, router edits aimed at the mechanism J64 names induce the predicted reasoning behaviors and shift a diagnosed stall from numerical guessing toward exact symbolic execution. Together, J64 makes latent process state readable, while routing makes it deployable and actionable.
[AI-32] Graph Surgery and the Do-Operator: A Precise Correspondence for Acyclic Structural Causal Models
链接: https://arxiv.org/abs/2608.17634
作者: Satpreet Makhija
类目: Artificial Intelligence (cs.AI); Programming Languages (cs.PL)
备注:
Abstract:The \operatornamedo -operator is described graphically by deleting arrows into its targets and functionally by replacing their mechanisms with constants. To call these operations equivalent is not yet a mathematical statement: one returns a graph and remembers only the targets, whereas the other returns mechanisms and also remembers the imposed values. We make a dependency-level comparison precise for deterministic acyclic structural causal models with finitely many endogenous variables. If \operatornameGraph(F) extracts the dependencies of a mechanism family F , our main theorem is \operatornameGraph(F^\iota)=\operatornameSurg(\operatornameGraph(F),T_\iota) . Thus replacing target mechanisms removes exactly the dependencies removed by graph surgery. For a model M=(G,F) whose graph may contain unused arrows, we characterize when the same equality holds with G in place of \operatornameGraph(F) ; it holds for every intervention exactly when G records the dependencies of F exactly. We then define the intervened model, characterize its run, show how sequential interventions combine, and prove that an outcome depends only on interventions at its actual dependency ancestors.
[AI-33] Iterative Grasp Pose Refinement: A Deep Reinforcement Learning Approach for 2D Vision
链接: https://arxiv.org/abs/2608.17628
作者: Amir Arsalan Nematollahi,Shayan Ahmadi,Mehdi Tale Masouleh,Ahmad Kalhor
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
备注:
Abstract:Developing robots capable of understanding and manipulating objects requires compact, interpretable, and generalizable representations. This work proposes a reinforcement learning-based framework for robotic grasp refinement, integrating keypoint-based object representations with a Deep Q-Network (DQN). Using 2D overhead images captured in a simulated environment, a geometric-based algorithm generates initial grasp candidates, which are iteratively refined by the proposed framework, transforming failed grasps into successful ones. Experiments conducted on 300 objects from the Dex-Net dataset using a UR5 manipulator demonstrate the framework’s effectiveness, achieving a 100% success rate on objects previously deemed ungraspable by geometrical methods. The framework’s sim-to-real transferability is further validated through physical experiments on a Delta parallel robot, where a refined grasp successfully manipulates an object that was previously ungraspable. The findings underscore the effectiveness of reinforcement learning in addressing challenges in robotic grasping, offering a scalable and adaptable solution for contact-rich manipulation tasks.
[AI-34] Validated Adaptation for Aerial Crowd Monitoring at Mass Gathering Scale: A Deployment Protocol a Severity Law and a Diagnostic for Label-Free Drone Crowd Counting Toward the FIFA World Cup 2034 (Saudi Arabia)
链接: https://arxiv.org/abs/2608.17625
作者: AlAnoud AllGhayth,AlJawharh AlOtaibi,Jude AlSubaie
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Saudi Arabia will host the 2034 FIFA World Cup and already operates crowd management at Hajj scale. Drone-based counting must hold accuracy on footage unlike anything in its training corpus, without labels, and must warn of dangerous inflow before a crush forms. We deliver a validated answer built on 525 controlled runs, a full-resolution corpus study, five falsification ablations, and a five-condition safety-interlock evaluation. Label-free adaptation recovers 31-49% of shift-induced error across four corruptions and five severities, with the strongest method gaining 41.8 MAE over the frozen source (95% CI [34.1, 49.6], p=7.5x10^-10, d=2.52). We establish a severity law separating methods with a constant absolute margin from the one whose margin grows, and a stability budget identifying which configuration is safe to fly. On a full-resolution corpus carrying a genuine +48 MAE aerial gap (source retrained to 14.6 validation MAE, a 34% improvement), adaptation repairs the dense-scene undercounting that would otherwise under-report a forming crush, and the flux-based risk module fires on real congestion episodes in 2 of 6 full-length clips. We localise the recoverable error: in a regime built to favor a physics-informed conservation prior (300-frame clips at 200ms spacing, five times wider than standard), the adaptation signal is normalisation-driven, not flow-driven; the continuity residual is invariant to the proportional counting errors domain shift produces, confirmed by four on/off ablations correlated at r=0.999 and a 40% input corruption moving accuracy by only 0.05 MAE. A label-free shift gate shows shift magnitude and accuracy damage are rank-independent (Spearman rho=0.20; rho=-0.60 among genuine shifts), quantifying the 58% of headroom a magnitude gate forgoes. We establish unconditional adaptation with tail monitoring as policy, closing with a six-point protocol.
[AI-35] HarnessRisk: A Lifecycle-Oriented Benchmark for Agent Harness Safety
链接: https://arxiv.org/abs/2608.17597
作者: Yajing Bai,Jinhao Duan,Jie Peng,Xianfeng Wu,Sijia Liu,Song Wang,Tianlong Chen
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: Project Page: this https URL
Abstract:Large language models are increasingly deployed through agent harnesses that manage tools, extensions, persistent state, permissions, and external actions. Existing safety benchmarks mainly target individual attack mechanisms or a limited subset of operational settings, making it difficult to compare how safety failures emerge across different harness responsibilities. We present HarnessRisk, a lifecycle oriented benchmark that organizes agent harness safety into six operational phases including Harness Configuration, Capability Extension, Runtime Operation, State Persistence, Action Control, and Incident Recovery. HarnessRisk contains 128 sandboxed cases, each pairing a benign user objective with an adversarial instruction embedded in an untrusted workflow artifact. We evaluate each trajectory using Utility, Attack Success Rate, Persistence, and Detection. Across three harnesses, six language models, and 14 model and harness configurations, attack success ranges from 12.6% to 80.9%, while Utility remains between 75.0% and 97.6%. Harness Configuration is the most vulnerable phase across all three harnesses, showing that attacks can succeed by altering security sensitive parameters within otherwise authorized workflows. We also find that explicit risk recognition does not reliably lead to safe action, as some configurations detect risks in more than 90% of runs while retaining substantial attack success. These results highlight the need to evaluate agent safety across multiple harness responsibilities and at the level of the deployed model and harness configuration.
[AI-36] nyDSM: A Framework for Skill Modeling and Development for Resource-Constrained Millirobots
链接: https://arxiv.org/abs/2608.17596
作者: Markus D. Kobelrausch,Michael Miedler,Axel Jantsch
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Manuscript submitted to IEEE Transactions on Cognitive and Developmental Systems
Abstract:In this study, we investigate developmental mechanisms that enable small, resource-constrained systems such as cm-sized millirobots to autonomously explore, learn, and adapt their capabilities throughout their lifespan. Reinforcement learning algorithms guide the agent’s skill acquisition and adaptation through the interplay of our proposed tinyDSM, which integrates intrinsic motivation and fitness-based assessment. We strive for minimal, hard-wired skills while encouraging the open-ended development of new skills. A key emphasis in our approach is to encode minimal a-priori general knowledge, which serves as a foundational starting point for the system as it further learns system-specific dependencies from the initial knowledge provided. Thus, by design, our approach attempts to cover very generic application domains. The methodology is based on (a) developmental mechanism with intrinsic motivation, and (b) a cognitive architecture (knowledge, reasoning, learning), while © utilizing minimal resources. It uses a hierarchical knowledge graph and kinematic reasoners to model and evaluate simple and advanced motion related skills. In our experiments, we use a resource-constrained millirobot with a volume of 36 cm^3 with a Raspberry Pi Pico 32-bit microcontroller (RP2040) that integrates all described features and capabilities except the camera system in 9 kB. Starting with learning the most elementary motor skills the millirobot autonomously progresses from simple linear and angular movements to complex geometric patterns within 15 minutes. To complement the physical experiments, we perform a simulation-based analysis that enables systematic comparisons across learning algorithms and intrinsic motivation parameters.
[AI-37] RUSS: Towards Task-Reliable and User-Safe Automated Agent Skill Generation
链接: https://arxiv.org/abs/2608.17588
作者: Zhibo Zhang,Zhen Ouyang,Ling Shi,Kailong Wang
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Agent Skills package reusable natural language procedures with executable resources, enabling software agents to acquire task specific capabilities without model adaptation. Automatically generating such Skills can improve task performance, yet evaluating a candidate solely from its artifact or final task outcome leaves unresolved which actions the equipped agent will perform and which side effects those actions will produce. We present TRUSS, an evidence guided framework for generating functionally effective and safety reliable Agent Skills. TRUSS first inspects functional claims against source and domain evidence while evaluating the complete artifact under nine predefined safety properties. Candidates admitted by this static gate are loaded by a shadow agent inside a Controllable Execution Environment, where brokered tools expose requested actions to policy enforcement and record their results as provenance preserving execution traces. Functional failures and property violations are linked back to the responsible Skill content and used to guide iterative refinement. We evaluate TRUSS on 168 SkillInject artifacts, 155 SkillSafetyBench cases, and all 187 tasks in SkillGenBench. TRUSS achieves 100.00% precision and recall in vulnerability detection. Repair reduces attack success from 38.71% to 19.35% with GPT 5.5 and from 46.45% to 29.68% with GPT 5.4, with zero attack regression. For Skill generation, TRUSS raises task effectiveness from 17.11% without Skills to 52.94%, while increasing the benchmark Security rate from 50.80% to 100.00%. These results show that execution evidence can expose behavioral failures missed by artifact inspection and can guide Skill generation toward jointly verified functional and safety outcomes. Subjects: Artificial Intelligence (cs.AI); Software Engineering (cs.SE) Cite as: arXiv:2608.17588 [cs.AI] (or arXiv:2608.17588v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.17588 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-38] Quantifying Risk Under Evolving Uncertainty: Belief-Dependent Robustness for Safe Sequential Decision Making ECAI2026 IJCAI
链接: https://arxiv.org/abs/2608.17574
作者: Deep Kumar Ganguly,Jan Kretinsky
类目: Artificial Intelligence (cs.AI)
备注: Accepted for presentation at the IJCAI-ECAI 2026 RobustifAI workshop
Abstract:How cautious should an agent be while it is still learning its environment? We propose RATTL (Risk-Adversarial Total-Reward Learning), which ties caution to epistemic uncertainty: the agent holds a Bayesian posterior over unknown dynamics and plans against a Wasserstein ambiguity set whose radius is a monotone function of that posterior. The radius contracts with evidence, so behaviour interpolates continuously between worst-case robustness and risk-neutral total-reward maximization. The design follows the duality underlying the Entropic Value-at-Risk, which converts the choice of a risk level into the choice of an ambiguity radius. We show the resulting planning problem is well posed under transience and compactness conditions, and prove a Safety Sandwich: the RATTL value lies between the uninformed robust value and the full- knowledge optimum, with a gap that vanishes as the posterior concentrates. In a canonical binary-hazard instance, the induced criterion reduces to Conditional Value-at-Risk at a level set by the posterior entropy. A worked example shows the agent deferring the efficient action until a sharp identification threshold. RATTL targets runtime safety for agents, including LLM-based systems, acting under uncertainty.
[AI-39] No Gaussian Required: Contrastive Inverse Dynamics for JEPA World Models
链接: https://arxiv.org/abs/2608.17542
作者: Jack Boylan,Chris Hokamp
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 17 pages, 5 figures. Code: this https URL
Abstract:Joint-Embedding Predictive Architectures (JEPAs) learn world models by predicting future embeddings, but the objective admits a trivial solution of a constant encoder, so every practical system adds an anti-collapse mechanism (LeCun, 2022; Assran et al., 2023; Bardes et al., 2022; 2024). LeWorldModel (LeWM) prevents collapse with SIGReg, a regularizer that forces the latent distribution to match an isotropic Gaussian: the representation is stabilized by prescribing what it must look like, independently of the environment it models. We argue that the anti-collapse pressure can instead come from the transition data itself. Action-Contrastive Masked Transition Modeling (AC-MTM) keeps LeWM’s forward latent-prediction objective and adds a training-only inverse-dynamics head trained with Action-NCE: each latent transition must identify the action that produced it among the other actions in the batch, a discrimination task that a collapsed encoder provably fails. The inverse branch is discarded after training, leaving test-time encoding, forward prediction, planning, and compute identical to LeWM. On four standard pixel-control tasks under a matched planning protocol, AC-MTM trains stably from scratch and matches SIGReg on average. On the harder multi-object OGBench Visual Scene task, results are consistent with the prescribed geometry becoming a bottleneck: AC-MTM reaches 80.0 \pm 2.0% success versus 58.0 \pm 2.0% for SIGReg, improving by 20-24 points in each training seed. A single 50-episode random-policy run gives a 52% baseline estimate. Contrastive inverse dynamics thus provides a distribution-free anti-collapse signal that requires no target network, stop-gradient, pretrained encoder, or reconstruction objective, and we characterize the action-space and observability assumptions under which it holds. We make our code available at this https URL
[AI-40] When to Review: Spaced Repetition for Continual Pre-Training of Language Models
链接: https://arxiv.org/abs/2608.17530
作者: Alankar Atreya,Devesh Batra,Yoages Kumar Mantri,Geremy Bantug,Greig A Cowan,Raad Khraishi
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Continual pre-training of large language models must acquire new information without erasing old knowledge. Existing replay methods often choose a global old/new mixture and sample uniformly, ignoring that examples differ in how quickly they are forgotten. We formulate continual pre-training as adaptive review scheduling: the training loop should decide not only how much history to replay, but which examples should return at each step. We introduce Spaced Repetition Training (SRT), a continual learning framework inspired by cognitive science, which schedules sample-rehearsal using the SuperMemo-2 (SM-2) algorithm. SRT maintains per-example review state, maps per-example perplexity to a recall-quality signal, and schedules historical examples for retention and new examples for consolidation while leaving the model, objective, and optimizer unchanged. On temporally separated Wikipedia and code corpora, SRT improves the stability-plasticity trade-off, recovering 5 to 37 percentage points of old-knowledge accuracy lost by naive continual pre-training across model scales while preserving or improving new-knowledge acquisition. At larger scale, SRT preserves broad benchmark performance that naive continual pre-training and uniform replay substantially degrade. Experiments with vision and tabular data further suggest that the scheduling principle extends beyond language when paired with an appropriate recall signal.
[AI-41] Agent Lightning v1.0: Towards Harnessed Agent ic RL
链接: https://arxiv.org/abs/2608.17528
作者: Zhiyuan He,Siwei Zhang,Zhiwen Zhou,Yuqing Yang,Yu Kang,Yuge Zhang,Luna K. Qiu,Tin Yan Tsui,Jiahang Xu,Chong Luo
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Modern agents operate inside agent harnesses that manage tools, context, and control flow, making the harness a critical part of the agent system. Our original Agent Lightning introduced a disaggregated architecture that connects arbitrary agents to RL training through an LLM endpoint proxy, an approach later adopted by frameworks such as verl Uni-Agent, AReaL 2.0, slime, and Polar. We refer to this paradigm as harnessed agentic RL, where the deploy-time harness directly participates in model post-training. Harnessed agentic RL differs fundamentally from traditional agentic RL: the harness, rather than the training engine, owns the environment interaction loop, while the trainer observes only sequences of LLM request-response pairs. This introduces challenges in retokenization, sample merging, advantage calculation, loss normalization, and backend scheduling, which can substantially affect training stability and effectiveness. We present Agent Lightning v1.0, a lightweight framework for harnessed agentic RL implemented in approximately 3,500 lines of code. It supports arbitrary agent harnesses and serves as a practical testbed for studying these challenges. We evaluate it on instruction-following, search, and coding agents, and provide a complete reproducible pipeline for coding-agent RL. Using only 6K training examples and modest compute, RL improves Qwen3.5-9B on SWE-bench Verified from 41.8% to 56.4%, a 14.6-point absolute gain. We release the complete workflow and training scripts to facilitate reproducible research on harnessed agentic RL.
[AI-42] Beyond FLOPs: Energy-Aware Knowledge Distillation for Sustainable LLM s on Code-Related Task
链接: https://arxiv.org/abs/2608.17515
作者: Enrique Barba Roque,Luís Cruz,Annibale Panichella
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Background: Large Language Models (LLMs) are increasingly being applied to Software Engineering (SE) tasks, achieving high accuracy across problems such as clone detection, vulnerability prediction, and code summarization. However, their high computational demands and energy consumption raise sustainability concerns and hinder their use on consumer hardware and resource-constrained platforms. A common way to report the computational cost of an LLM in the literature and industry is to use the number of Floating Point Operations (FLOPs) required to perform a pass over the network. Aims: This paper investigates the implications of energy-aware knowledge distillation for SE, aiming to improve model efficiency while maintaining performance and to determine whether FLOPs is a reliable energy-aware metric. Method: We conduct a controlled experiment using Morph, a Many-Objective Optimization-based distillation methodology, to empirically examine whether FLOPs accurately reflect energy consumption in Clone Detection and Vulnerability Prediction tasks. We extend this methodology to include energy-surrogate models that directly estimate CPU and GPU energy consumption during optimization, and we apply Morph to generative tasks using CodeT5+ for code summarization. Results: Our results show that FLOPs is not always a reliable indicator of energy consumption, and better results can be achieved by using energy-surrogate models. Distilled student models can reduce inference energy consumption by up to 90% and memory usage by 86%, with only modest accuracy trade-offs. Conclusions: Energy-aware knowledge distillation when guided by direct energy surrogates rather than FLOPs can improve the energy consumption, sustainability, and deployability of LLMs for SE applications, enabling efficient models on consumer hardware.
[AI-43] SGHA: Evidence-Grounded Research Problem Discovery with Local Language Models
链接: https://arxiv.org/abs/2608.17501
作者: Sarvesh Gharat,Junpei Komiyama
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Link to Code and artifacts: this https URL
Abstract:Recent efforts toward fully automated AI scientists have demonstrated that language-model agents can generate hypotheses, execute experiments, and draft scientific manuscripts. However, during the early stages of research, when research problems are formulated, these AI scientists often rely heavily on proprietary frontier models. Their proposals are shaped by opaque parametric knowledge and by literature searches conditioned on the proposals themselves. Such knowledge is effectively a black box, and this dependence makes the evidential basis and validity of generated research problems difficult to audit and leaves the process vulnerable to model-specific hallucinations and biases. Furthermore, if proprietary research materials are transmitted to external APIs, the use of these models creates confidentiality, privacy, and data-governance concerns. We introduce the Structural Gap Hypothesis Agent (SGHA), a fully automated, corpus-first research-problem discovery system that runs entirely on a local LLM. SGHA structures a scientific literature corpus into evidence-linked paper objects and a typed evidence graph, detects unresolved structural patterns across papers, screens candidate gaps before formulation, and produces traceable research-problem families. In particular, it is able to output assumptions, objectives, success criteria, and remaining ambiguities. All LLM-based components of SGHA are executed using a locally served open-weight 9B language model, without requiring proprietary frontier-model APIs. We compare SGHA with the AI Scientist-v2 idea formulation module in five machine-learning domains. Our results suggest that explicit corpus structure and evidence-constrained reasoning can support promising, inspectable research-problem formulation without relying on frontier models during generation or verification. Comments: Link to Code and artifacts: this https URL Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2608.17501 [cs.AI] (or arXiv:2608.17501v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.17501 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-44] owards Better Agents for Multi-Turn User Interaction: The Next User Turn Is More Than Context
链接: https://arxiv.org/abs/2608.17499
作者: Yiwen Zhao,Zhihao Wen,Yuchen Mao,Mingxuan Jiang,Yihao Hu,Pan Wang,Xin Zhang,Wei Wu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:User-facing tool agents must coordinate dialogue and tool use as user goals unfold over multiple turns. Yet interactive reinforcement learning typically reduces each rollout to a terminal reward, assigning the same credit to effective elicitation, errors, and later repair. The next user turn is more than context: it also provides noisy, temporally local evidence about the preceding user-to-user segment. We introduce \textbfFeedback-\textbfAware \textbfCredit \textbfAssignment (\textscFACA), which aligns each reaction with that segment, derives a locally normalized reaction advantage, and adds it to verified terminal outcome advantage without an extra critic or rollout. Against an outcome-only Interactive GRPO control matched in simulator, visible dialogue, initialization, rollout, and optimization, \textscFACA improves the nine-domain \tau -family average across three independently trained runs by 5.91 and 10.22 percentage points at 8B and 14B, respectively. Gains concentrate in Telecom; at 8B, randomizing reaction polarity removes the Telecom gain. The same ordering holds zero-shot on Pare-Bench and Co-Gym. These results demonstrate that next-turn user reactions provide actionable local credit for improving multi-turn user-interacting agents.
[AI-45] When AI Designs AI: Innovation or Imitation?
链接: https://arxiv.org/abs/2608.17471
作者: Yikang Yang,Zhengxin Yang,Luzhou Peng,Minghao Luo,Yanqi Kan,Wanling Gao,Jianfeng Zhan
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Recent advances in LLM agents have made them increasingly capable of designing methods for complex AI tasks. This raises two central questions about agent-designed methods relative to human-designed methods: how well they perform, and how different their algorithmic designs are. To study these questions, this paper introduces an analysis that derives task-specific algorithmic design spaces from human-designed methods, maps both human- and agent-designed methods into these spaces, and quantifies their algorithmic differences at the module level. Widely used LLM agents are evaluated on a suite of representative, open-ended AI tasks spanning multiple modalities, and the methods they design are analyzed in terms of both task performance and algorithmic differences from human-designed methods. Experimental results show that current agents can occasionally match or surpass human state-of-the-art (SOTA) performance (10/72 configurations), but such success does not generalize reliably across tasks or agents. Moreover, 96.8% of agent-designed methods fall within human-derived algorithmic design spaces, largely recombining algorithmic choices found in human-designed methods, while nearly half exactly match an existing human algorithmic design. Taken together, these findings suggest that although current agents can occasionally match or surpass human SOTA performance, their algorithmic designs remain within human-derived algorithmic design spaces, reflecting the reuse and recombination of algorithmic choices.
[AI-46] SAGE: Self-Evolving Storyboard Skills via Attribution-Guided Rule Evolution
链接: https://arxiv.org/abs/2608.17468
作者: Maolin Ran,Xiaoyang Lu,Jiaqi Liu,Jian Wang,Weiwen Liu,Jianghao Lin,Yong Yu,Weinan Zhang
类目: Artificial Intelligence (cs.AI)
备注: 11 pages, 9 figures, 4 tables. Dataset available at this https URL
Abstract:Storyboards turn screenplays into visual shot plans for automated short drama production. Professional storyboarding relies on tacit directorial expertise and remains an industrial bottleneck. Large language models can automate this step, but methods for supplying directing knowledge face three challenges: (1) Knowledge acquisition: the craft remains implicit in exemplars or must be written manually. (2) Knowledge refinement: authored knowledge is not evaluated against execution outcomes, and opaque generation prevents feedback attribution to the knowledge behind each decision. (3) Knowledge injection: injecting all knowledge exceeds usable context, while manual selection for every narrative group does not scale. We present SAGE (Skill with Attribution-Guided Evolution), a deployed framework that learns, attributes, evolves, and routes directing knowledge from expert demonstrations. SAGE derives rules that are independent of episode content by contrasting each training screenplay with its expert storyboard. During generation, the model records each narrative group’s adopted rules. Combining these records with localized feedback enables targeted updates to individual rules. Evolved rules form scenario packages with a routing index, so each group retrieves only a bounded set appropriate to its situation without expert intervention. On 18 test episodes across three genres, SAGE scored 77.8 on a rubric validated by experts, versus 77.1 for professional directors. Deployed for 14 days on Virtual Film Studio, SAGE produced 1,344 narrative group outputs; 87.2 percent were accepted without substantive edits, and the production team recorded over 83 percent less authoring time per episode. We release PROSE, the first public dataset pairing screenplays with storyboards by professional directors across 68 episodes: this https URL.
[AI-47] Structure-Internalized Rule Language Model for Faithful Knowledge Graph Reasoning
链接: https://arxiv.org/abs/2608.17443
作者: Xingrui Zhuo,Jiapu Wang,Manzong Huang,Gongqing Wu,Xindong Wu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Knowledge Graph Reasoning (KGR) aims to discover latent facts by leveraging the structural evidence available in KGs, posing a challenge to the structural semantic understanding capability of KGR models. Recent studies have demonstrated that Large Language Models (LLMs) can achieve remarkable progress on KGR tasks via flexible in-context learning. However, the inherent representation inconsistency between KG structural context and LLM parametric knowledge remains inadequately addressed. This limitation prevents LLMs from effectively perceiving reasoning evidence that aligns with KG constraints, which undermines both the effectiveness and faithfulness of reasoning. We refer to this problem as reasoning evidence perception drift of LLMs over KGs. To address this problem, we propose a Structure-Internalized Rule Language Model (SIRLM), which centers on structural rule generation to couple the parametric learning of structural knowledge with the faithfulness evaluation of reasoning logic, enabling LLMs to anchor tightly to KG-grounded evidence. Specifically, we first design a Structure-Internalized Rule Generator (SIRG), which incorporates an in-context learning block augmented with a structural relation memory to coordinate structural and parametric knowledge. Furthermore, we equip SIRG with a KG tokenizer based on structural invariance learning and a neuro-symbolic reasoner based on rule-constrained message propagation. These components provide SIRG with learnable structural representations and faithful rule-execution feedback, respectively. Our SIRLM can be seamlessly integrated into standard LLM training paradigms, such as SFT and GRPO. Extensive experiments against 17 state-of-the-art KGR methods on 36 datasets demonstrate the significant superiority of SIRLM.
[AI-48] Depth Enables Local Entropy: Quadratic Depth Dependence in Deep Variation-Norm ReLU Regression
链接: https://arxiv.org/abs/2608.17434
作者: Tao Jiang,Minbo Gao,Shaowei Cai
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We study Gaussian regression over the explicit vector-valued Parhi–Nowak deep-RBV^2 architecture with depth L, width w, layer-sum variation budget A, and output bound B. For this O(L w^2)-parameterized architecture, the known lower and upper bounds differ by one factor of depth. We construct a local packing showing that the quadratic depth dependence is intrinsic under an explicit sample-size-dependent radius condition. The packing has log-cardinality Omega(L^2 w^2 log w); its codewords lie in an O(lambda) L^2 ball and are pairwise Omega(lambda)-separated. The main ingredients are a bias-corrected bounded-coefficient approximation theorem and balanced amplification: multiplying a depth-D ReLU network by q can be implemented using one constant channel so that every coefficient grows by only q^(1/D). Translation to vector-valued RBV^2 blocks then has layer-sum cost O(D w^2 q^(1/D)). Gaussian Fano yields a radius-explicit lower bound governed by the output, testing, and representation scales. Under A=B=R, sigma proportional to R, and the stated radius condition, this gives minimax risk at least of order L^2 w^2 log(w) R^2/n. A pseudodimension-based finite-net upper bound gives O-tilde(L^2 w^2 R^2/n) for unbounded Gaussian responses. Thus the minimax risk has quadratic polynomial dependence on depth, up to logarithmic factors, and exhibits a transition to representation-limited behavior at smaller radius.
[AI-49] LEGO-RL: Harness-Native Reinforcement Learning for Coding Agents
链接: https://arxiv.org/abs/2608.17393
作者: Yiming Du,Yuxin Jiang,Tao Yuan,Jianbo Dai,Shaowei Wang,Jierun Chen,Chaofan Tao,Xianzhi Yu,Lifeng Shang,Kam-Fai Wong,Xiaohui Li,Haoli Bai
类目: Artificial Intelligence (cs.AI)
备注: Webpage: this https URL
Abstract:Reinforcement learning for coding agents increasingly relies on long-running agent harnesses to manage tool integration, repository contexts, and execution feedback. However, the native execution environments of these harnesses are inherently misaligned with policy-gradient training: environmental crashes and reward hacking corrupt outcome signals, while train-inference discrepancies decouple rollout behavior from policy updates. To address this, we present LEGO-RL, a framework that bridges native coding-agent harnesses with scalable policy-gradient optimization without modifying their internal control flow. LEGO-RL is built upon three pillars: (1) faithful optimization via in-process LLM proxying that captures raw generation streams for token-level alignment and robust trainer-side log-probability recomputation, even under harness-side compaction or re-serialization; (2) reliable execution via scalable sandbox orchestration featuring image caching and stage-wise defenses to mitigate reward hacking; and (3) observable training through an integrated plugin that automates validation and monitoring, paired with a Live UI for granular trajectory diagnostics. We evaluate LEGO-RL by training the sparse MoE model Qwen3.5-35B-A3B with GSPO across three native coding-agent harnesses. LEGO-RL improves Qwen3.5-35B-A3B across OpenHands SDK (64.0% to 70.4%), Claude Code (62.4% to 68.2%), and OpenCode (57.2% to 66.6%) on SWE-bench Verified, while maintaining a rollout-training probability correlation above 0.99.
[AI-50] Integrating Novelty and Surprise for Experience Prioritization and Exploration in Image-Based Reinforcement Learning
链接: https://arxiv.org/abs/2608.17373
作者: Hoda Yamani,Henry Williams,Bruce A. MacDonald
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 9 pages, 4 figures. Published in International Journal of Computer and Systems Engineering, 2026. Code: this https URL
Abstract:Sample efficiency is a central challenge in reinforcement learning (RL), particularly in image-based domains where agents must learn from high-dimensional visual inputs. Traditional sampling often relies on random or suboptimal experience selection, leading to redundant updates and slow learning. Improving efficiency requires mechanisms that prioritize informative experiences while also encouraging effective exploration. Prioritized Experience Replay (PER) addresses part of this challenge by reusing high-value transitions, while intrinsic rewards promote the exploration of novel or uncertain states. However, their integration has not been extensively studied. This paper introduces Novelty and Surprise Prioritized Experience Replay (NSPER), which uses novelty to capture underrepresented states and surprise to expose gaps in the agent’s understanding of the environment. We further extend this with NSPER+R, integrating these signals as intrinsic rewards to jointly improve replay quality and exploration. Experiments on DeepMind Control Suite tasks show that NSPER and NSPER+R improve training efficiency and convergence speed compared to existing methods in image-based RL.
[AI-51] Fair ASR: Re-Evaluating Black-Box Jailbreaks under Shared Target-Call Budgets
链接: https://arxiv.org/abs/2608.17360
作者: Zhida He,Xiaoyu Wen,Han Qi,Ziyuan Zhou,Peng Yu,Jiajia Li,Chaochao Lu,Qiaosheng Zhang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 29 pages, 8 figures, 13 tables. Code: this https URL
Abstract:Reliable jailbreak evaluation is essential for assessing LLM safety, but most existing studies rely solely on attack success rate (ASR) without accounting for its dependence on attack budgets, resulting in unfair comparisons across methods. Existing compute-aware evaluations reduce heterogeneous resources into FLOPs, which is difficult to estimate for black-box models and fails to capture resource-specific constraints. To provide a comparable evaluation basis, we introduce Fair-ASR, an evaluation protocol for black-box jailbreak attacks under shared target-call budgets B, using target calls as a directly observable and method-agnostic comparison axis while tracking attacker calls separately for efficiency analysis. We re-evaluate 11 representative attacks under the Fair-ASR protocol and find that attack rankings change substantially across target-call budgets, simple stochastic perturbations and hand-crafted templates remain highly competitive under equal target access, and no evaluated LLM-driven method is efficient in both target and attacker calls. Motivated by this efficiency gap, we introduce ReCode, a compositional budget-efficient attack that combines desensitization rewriting with two effective low-cost primitives identified by Fair-ASR. Under a budget of 20 target calls, ReCode achieves 85% ASR on GPT-5 while requiring only 7.19 attacker calls per request on average, showing strong efficiency in both target and attacker calls.
[AI-52] Cognitive Graph Intelligence for Adaptive and Robust DDoS Attack Detection in Next Generation Networks
链接: https://arxiv.org/abs/2608.17352
作者: Mohammad Arif Hossain,Yeahia Sarker,Md Jafrin Hossain,Most. Humayra Khanom Rime,Nirwan Ansari
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Distributed Denial-of-Service (DDoS) attacks threaten network availability, requiring a cognitive detection process that senses traffic, infers intent, and supports an adaptive response under severe class imbalance and non-stationary conditions. This paper proposes a Graph-based Generative Adversarial Network (GraphGAN) that serves as the cognitive detection engine for this task. GraphGAN captures the relational structure among traffic flows while addressing imbalance through adversarial generation of synthetic samples. Sequential flows are converted into k -nearest neighbor graphs using sliding windows to preserve feature-similarity and temporal dependencies among flows. The generator learns the distribution of DDoS attacks to synthesize realistic minority samples, while a Graph Convolutional Network (GCN)-based discriminator distinguishes real from synthetic graph data. A separate GCN classifier, trained on the balanced dataset, performs the final detection decision. Evaluations on four benchmark datasets show that GraphGAN achieves superior accuracy, precision, and recall compared to state-of-the-art approaches, particularly in data-scarce scenarios. By integrating temporal graph construction, adversarial augmentation, and GCN classification, GraphGAN effectively models coordinated attack behaviors and mitigates class imbalance, providing a robust and topology-aware solution for intrusion detection in data-constrained environments.
[AI-53] MoFE: A Novel Mixture-of-Experts Framework with Fourier Neural Operators for Cryptocurrency Forecasting
链接: https://arxiv.org/abs/2608.17342
作者: Bowen Liu,Mingming Sun
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 9 pages, 7 figures. Published in 2026 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)
Abstract:Forecasting cryptocurrency prices remains a formidable challenge due to inherent non-stationarity, abrupt regime shifts, and multi-scale stochastic dependencies. Conventional deep learning models often struggle to capture complex underlying dynamics, frequently resulting in persistent phase-lagged predictions. To address these limitations, we propose MoFE, a novel deep learning framework that integrates Fourier Neural Operators (FNOs) within a Mixture-of-Experts (MoE) architecture. Rooted in the theoretical framework of stochastic differential equations, MoFE conceptualizes cryptocurrency volatility as a superposition of multi-frequency components, which includes user network based fundamental growth, mining costs and halving mechanism caused seasonal volatility, and market sentiment-induced chaos. Specifically, specialized adaptive FNO (AFNO) and Convolution dual-domain experts learn continuous function-to-function mappings to encapsulate global spectral trends, cyclical adjustments and microstructures, while a dynamic gating based MoE mechanism enables adaptive strategy switching across diverse market regimes. Extensive experiments on Bitcoin datasets spanning January 2020 to December 2025 demonstrate that MoFE achieves state-of-the-art (SOTA) performance in both T+1 and T+5 forecasting horizons. Notably, the model effectively mitigates the phase-lag effect, delivering superior Directional Accuracy (DA) and Information Coefficient (IC). In high-fidelity simulated trading environments, these predictive gains transfer into significant excess returns and robust risk-adjusted performance, characterized by a high Sharpe ratio.
[AI-54] LLM -Only PDDL Domain Repair with Open-Weight Models
链接: https://arxiv.org/abs/2608.17341
作者: Nader Karimi Bavandpour,Pascal Bercher
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:AI planning is concerned with finding a sequence of actions that achieves a specified goal. It relies on explicit models of the world, commonly represented in the Planning Domain Definition Language (PDDL). An active line of research investigates how errors in such models can be detected and repaired. For example, users may provide positive test plans that are solutions, and negative test plans that fail during execution. Automated repair methods then modify the PDDL model to satisfy these constraints. In this paper, we evaluate the ability of recent open-weight large language models to perform this repair task using an LLM-only approach. Our experiments show that the symbolic baseline achieves an F_1 score of .49 , while the best-performing LLM reaches .87 with high reasoning effort, an absolute improvement of .38 . However, that setting has a mean test pass rate of only .82 , falling to .06 on the Thoughtful domain; even the best setting that includes the test traces reaches only .92 . Thus, current open-weight models cannot guarantee satisfaction of the test constraints required for reliable automated model repair.
[AI-55] Mix: Tile-Centric Mixed-Precision Attention for LLM Inference Acceleration
链接: https://arxiv.org/abs/2608.17336
作者: Hanzhi Zhang,Qiao Zhang,Qinglei Cao,Heng Fan,Yan Huang,Kewei Sha,Yunhe Feng
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Long-context prefill in large language models (LLMs) incurs substantial computation and memory traffic because dense self-attention computes quadratic query-key scores. Existing methods either use a uniform low-precision path or select token interactions, leaving spatial precision routing over hardware-aligned score tiles outside fused dense attention. We introduce TileMix, a tile-centric precision-routing kernel that makes numerical precision an executable spatial decision over score-tile groups within fused dense attention. TileMix partitions the attention matrix into hardware-aligned score tiles, packs routing decisions into compact bitmasks, and dispatches each tile group through FP16 or INT8 score computation while both paths update a shared online-softmax state. Scalable precision grouping lets each routing bit govern multiple adjacent key tiles, preserving hardware-aligned compute tiles and compact metadata at long contexts. By routing all legal tile groups, TileMix preserves dense token connectivity, requires no training, and supports grouped-query attention, variable-length batches, and INT8 key/value caches. Across LongEval, LV-Eval, and A100 prefill benchmarks on LLaMA, Qwen, and Vicuna, TileMix recovers long-context quality lost under uniform INT8 and improves prefill throughput over FP16, yielding a controllable accuracy-efficiency frontier across model families. The implementation is available at this https URL.
[AI-56] ORPA: Online Residual Policy Adaptation for Robot Manipulation Control with Human Feedback
链接: https://arxiv.org/abs/2608.17323
作者: Muhammad A. Muttaqien,Tomohiro Motoda,Ryo Hanai,Yukiyasu Domae
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Robotic manipulation policies trained via imitation learning, such as Action Chunking with Transformers (ACT), can achieve strong performance under ideal conditions but often remain sensitive to small execution errors and distribution shifts. Correcting these failures typically requires dataset aggregation and full-policy retraining, which is computationally expensive and unsuitable for real-time deployment. In this work, we propose Online Residual Policy Adaptation (ORPA), a framework that enables immediate, feedback-driven correction of robot actions without modifying the underlying policy parameters. ORPA augments a pretrained control policy with a lightweight, feedback-conditioned module that predicts residual adjustments directly in joint space, allowing the system to adapt its behavior at runtime. We evaluate ORPA on a set of precision-sensitive manipulation tasks using the ALOHA platform, demonstrating improvements in success rate and recovery from small perturbations compared to baseline control policies and rule-based inverse kinematics corrections.
[AI-57] Wuying-Browser-Agent : Real-World Centric Fundamental Long-Horizon Browser Agents
链接: https://arxiv.org/abs/2608.17319
作者: AIMAE Team:Tianxiang Chen,Yan Cheng,Zhangye Han,Xiaowei Li,Chang Liu,Cheng Liu,Zhongqiang Ma,Long Peng,Xiaobing Tu,Yinggui Wang,Hongliang Wei,Chen Wu,Daiping Xin,Kunyu Zhou,Pengyang Zhou,Peiyuan Chen,Ziyuan Chen,Yutao Deng,Chunyu Dong,Xiangyu Fu,Yicheng Feng,Ruian He,Haochen Li,Miancan Liu,Zhengqin Liu,Wei Peng,Jinkui Ren,Haoyu Tan,Dong Xiao,Rongkun Xue,Shujian Yang,Xianhang Ye,Ziqi Yuan,Ziyang Yu,Linghan Zhang,Xiantao Zhang,Xuanpu Zhao,Yinan Zhao,Zhenghui Zhao,Bin Zhu,Likai Zou
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Browser agents perform well on short, clean demonstrations, but real deployment is fundamentally different: agents must sustain dozens of decisions on live websites while recovering from mistakes and navigating complex UIs. We argue that closing this gap requires alignment at every level of the pipeline, including execution, supervision, optimization, and evaluation, rather than scale alone. We present Wuying-Browser-Agent, a unified framework that addresses each of these levels. A structured browser harness provides stable execution primitives and decision-oriented context management. Reflection and UI-specialized Curriculum SFT (RUIC-SFT) explicitly trains on recovery trajectories and complex-UI interactions. Divergence-Aware Online GRPO (DAO-GRPO) improves long-horizon credit assignment through potential-based reward shaping and divergence-aware step weighting. Finally, we introduce BrowserBench, a bilingual real-web benchmark of 350 tasks averaging 37.9 steps, because most existing benchmarks are too short to expose long-horizon failure modes. Wuying-Browser-Agent-27B achieves 80.6% on WebVoyager, 66.7% on Online-Mind2Web, and 65.1% on BrowserBench, establishing a new open-source state of the art on browser-use benchmarks. The same pipeline also transfers beyond browser use, demonstrating strong general agentic ability and reaching an average score of 73.8 on Tau2-Bench, Claw-Eval, and BFCL-v4.
[AI-58] NeuroAbs: A Neuro-Symbolic RTL Abstraction Framework for Property Checking Acceleration
链接: https://arxiv.org/abs/2608.17304
作者: Zhiyuan Yan,Xiaofeng Zhou,Ziyue Zheng,Ziyi Yang,Wenbin Che,Wei Zhang,Yangdi Lyu,Hongce Zhang
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: Accepted at ICCAD 2026
Abstract:Formal verification is a crucial technique for ensuring the functional correctness of hardware designs. In the context of property checking, a key challenge is how to efficiently prove a user-specified property in the face of increasingly complex RTL designs. To address this challenge, abstraction techniques are often employed to reduce system complexity and accelerate the verification process. However, prior RTL abstraction methods either require significant manual effort or rely on rule-based techniques that lack flexibility. This paper introduces NeuroAbs, a neuro-symbolic framework for RTL abstraction. NeuroAbs first uses LLM-assisted RTL analysis to identify signals suitable for abstraction. It then combines LLM-based abstraction with an AST-based symbolic RTL representation to better align the generated abstraction with the intended transformation. The soundness of each abstraction is checked using satisfiability modulo theories (SMT) solving. If the abstraction is too coarse for a successful proof, NeuroAbs applies counterexample-guided abstraction refinement (CEGAR) to iteratively refine the model. Experimental results show that NeuroAbs significantly improves the efficiency of hardware property checking across a range of verification tasks.
[AI-59] SignalReason er: Assessing the Upper Bound of 3B Models for Signal Mathematical Reasoning
链接: https://arxiv.org/abs/2608.17301
作者: Guozheng Sun
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Post-training with supervised chain-of-thought fine-tuning and reinforcement learning from verifiable rewards has substantially improved the mathematical reasoning capabilities of large language models (LLMs). However, their application to signal processing problems remains relatively under-explored. This report investigates reinforcement fine-tuning strategies for adapting Qwen2.5-3B-Base to graduate-level signal mathematical problems from WirelessMATHBench-XL, a comprehensive benchmark for mathematical reasoning in this domain. We examine two training paradigms: (i) direct reinforcement learning (RL) on WirelessMATHBench-XL with verifiable rewards; and (ii) supervised fine-tuning (SFT) on a distilled wireless-domain chain-of-thought corpus, followed by the same domain-specific RL stage. Across both paradigms, we benchmark Group Relative Policy Optimization (GRPO), Group Sequence Policy Optimization (GSPO), and Geometric-Mean Policy Optimization (GMPO). We aim to assess whether domain-aware CoT SFT serves as an effective initialization for subsequent RL, and whether GSPO or GMPO offer advantages in stability or accuracy over GRPO for signal reasoning tasks. Our best model achieves an overall accuracy of 39.12%, representing a more than threefold improvement over the untrained Base model (12.37%).
[AI-60] LiveHouse-TS: An Open-world Living Benchmark for Time Series Foundation Models
链接: https://arxiv.org/abs/2608.17299
作者: Haomin Wen,Ziyu Zhou,Qingxiang Liu,Siru Zhong,Yuxuan Liang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Time Series Foundation Models (TSFMs) have recently emerged as a highly promising paradigm for cross-domain zero-shot forecasting. However, existing evaluation protocols predominantly rely on static benchmarks with fixed historical test windows. While these benchmarks provide a valuable baseline snapshot, they evaluate an average performance on a fixed history, failing to capture how models behave in continuously evolving real-world environments characterized by seasonal variations, distribution shifts, and unexpected events. To bridge this gap, we introduce LiveHouse-TS, the first open-world living benchmark infrastructure for TSFMs. By evaluating models prequentially on real future data in open-world environments, LiveHouse-TS shifts time series benchmarking from snapshot accuracy to continuous temporal validity. Rather than acting as a one-off leaderboard, our infrastructure serves as a continuous time series infrastructure designed to explore vital, long-term scientific questions: Can model rankings be maintained over the long term? Which models remain genuinely robust under distribution shifts? Extensive streaming evaluations across 11 domains with 17 datasets demonstrate that static rankings undergo a dramatic reshuffling under a live protocol.
[AI-61] Beyond MSE: Rethinking the Evaluation Metric and Benchmarking for Irregular Time Series Forecasting
链接: https://arxiv.org/abs/2608.17293
作者: Rongwen Li,Haixin Xie,Xiao Wang,Changjian Chen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Existing research on irregular time-series forecasting has primarily focused on model design, while evaluation metrics remain insufficiently studied. Existing benchmarks typically use mean squared error (MSE) as the evaluation metric. We show that, in irregular forecasting, MSE is determined not only by the model prediction but also by the sample-specific timestamp sampling distributions, leading to a biased assessment of the models’ continuous-time predictive performance. To address this issue, we propose the Continuous-time Squared Error (CSE), which employs importance weighting to eliminate the influence of the timestamp sampling distributions. We further theoretically prove that CSE’s asymptotic estimation error with respect to continuous-time risk is no greater than that of MSE. Finally, we construct a systematic benchmark covering synthetic, semi-synthetic, and eight real-world datasets to validate the effectiveness of CSE and systematically evaluate models’ continuous-time predictive performance. Experiments show that CSE can recover continuous-time risk more accurately than MSE, while relying solely on MSE may not fully reflect models’ continuous-time predictive performance in real-world scenarios. Our code can be obtained at this https URL.
[AI-62] PlanPO: Group Planning -Aware Policy Optimization for Multi-Turn Agent ic LLM s
链接: https://arxiv.org/abs/2608.17289
作者: Dayang Liang,Liyuan He,Xuan Feng,Shuxin Li,Bo An,Yunlong Liu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Group-relative policy optimization has emerged as a key paradigm for training agentic large language models (LLMs) on multi-turn interactive tasks. However, most existing variants fail to distinguish advantages among successful trajectories even when these trajectories differ substantially in their interaction efficiency. For instance, circuitous successes are often assigned the identical outcome reward, causing advantage collapse and severe performance bottlenecks. To this end, we propose Group Planning-aware Policy Optimization (PlanPO), a simple yet effective RL method for learning generalizable planning abilities beyond task-specific high-quality behavior patterns. Specifically, PlanPO introduces coarse-to-fine advantage signals, which capture the relative differences in trajectory-level lengths and turn-level response lengths conditioned on successful trajectories sampled for the same task. Within the group-relative optimization structure, this enables agents to actively learn generalizable and deliberate behaviors spanning interaction planning and textual generation from high-quality rollouts, without degenerating into vanilla length minimization. Experimentally, PlanPO improves over GRPO by 27.2% on average across the challenging multi-turn benchmarks ALFWorld, WebShop, and SciWorld, outperforming recent powerful baselines while incurring negligible additional training cost.
[AI-63] Rethinking Irregular Time Series Forecasting from the Perspective of Basis Functions
链接: https://arxiv.org/abs/2608.17284
作者: Rongwen Li,Changjian Chen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Irregular time series forecasting is crucial in many domains, such as healthcare and meteorological observation. However, due to the inherent characteristics of irregular time series, including sparse observations and non-uniform sampling, accurately predicting future dynamics remains challenging. In light of these two characteristics, many existing methods aggregate irregular observations into fixed-dimensional estimated response coefficients through predefined basis functions and use these coefficients as sequence representations. Nevertheless, this modeling paradigm still suffers from two key limitations: (i) a potential non-vanishing asymptotic bias caused by ignoring the sampling density of timestamps; and (ii) the limited adaptability of predefined basis functions to diverse temporal patterns. In this study, we propose a Debiased Neural Basis-Function Network (DNBNet) to address these challenges. Its core is a debiased neural basis-function response mechanism, which corrects asymptotic bias through importance sampling while parameterizing basis functions with neural networks to adapt to diverse temporal patterns. In addition, considering the sparsity of irregular data, we design a novel multi-scale decomposition module based on average pooling, together with a mass-aware fusion mechanism, to obtain richer representations. Finally, a dual-branch decoder is employed for forecasting. Extensive experiments on multiple real-world datasets demonstrate the effectiveness of DNBNet and its strong generalizability across diverse irregular time series scenarios. Our code can be obtained at this https URL.
[AI-64] DeAR: Decentralized Agent ic Reasoning via Capability Grounding and Collaborative Thought Navigation
链接: https://arxiv.org/abs/2608.17282
作者: Xing Wei,Changmeng Zheng,XiaoYong Wei,Xiufen Ye,Qing Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Existing agentic reasoning systems typically rely on centralized protocols. This design introduces routing bottlenecks and static role allocations that often fail when handling complex multimodal queries. We propose DeAR (Decentralized Agentic Reasoning), a framework that shifts from central control to autonomous peer-to-peer collaboration. DeAR is built on three mechanisms: (1) decentralized capability grounding for query-dependent agent specialization, (2) thought map navigation for targeted peer interactions, and (3) topology update for adaptive error correction. Evaluations across 9 diverse multimodal reasoning and text-based QA benchmarks indicate that DeAR consistently outperforms recent baseline methods, validating that decentralized and adaptive collaboration among agents enhances accuracy in knowledge-intensive reasoning tasks. The source code will be available at https://open_upon_acceptance.
[AI-65] When Agents Act on Web3: An Attack-Surface Survey of MCP Skills and Tool Calling
链接: https://arxiv.org/abs/2608.17275
作者: Rabimba Karanjai,Yang Lu,Nour Diallo,Wujie Xiong,Lei Xu,Weidong(Larry)Shi
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:AI agents increasingly act rather than merely read: across the Model Context Protocol (MCP) ecosystem, the share of deployed tools that modify external state has risen from 27% to 65% of tool use. When agents exercise this authority on public blockchains through MCP, skills, and tool calling, the consequences of an attack are governed by the blockchain execution layer rather than by conventional software assumptions. This survey argues that four properties of that layer (irreversibility, signing authority, continuous autonomy, and sequence-level composition) qualitatively change the threat model, turning the recoverable failures of generic agent security into a standing, irreversible loss. We organize the fragmented MCP-security literature into an attack-surface taxonomy, then contribute a Web3 risk-mapping matrix that ties each attack class to its amplified impact, the responsible amplifiers, a representative mitigation, and the residual gap. We synthesize defenses, including emerging blockchain-based mechanisms, and find them improving but insufficient: measured protections stop fewer than 30% of attacks, and model-level safety refuses fewer than 3%. We close by positioning the work against adjacent surveys and deriving a research agenda from the matrix’s open cells.
[AI-66] ASI-Bench: At the Dawn of Artificial Superintelligence
链接: https://arxiv.org/abs/2608.17271
作者: Junwei Zhou,Zhen Sun,Binyu Li,Jiangyu Zhou,Yuexi Pan,Hengyu Wang,Honghe Ren,Xiaohan Jia,Xueyang Zhou,Xiaoyu Cao,Yongchao Chen,Yuanning Feng,Junhao Wu,Cheng Zhang,Sijia Chen,Haoyu Xue,Chengsong You,Huan Wang,Koutian Wu,Peigan Gao,Jiakun Wu,Wenzhe Li,Ergan Shang,Qingyuan Zheng,Jingjing Zhou,Ruixuan Jia,Yan Xu,Hongrui Zhang,Xiao-Han Ma,Zhengxiang Cheng,Yuexing Hao,Liting Mai,Xianglin Ji,Wenjun Zhang,Zhuofan Chen,Yixiao Huang,Chi Wang,Wenyue Hua,Yilun Hao,Yuantao Zhai,Ziyan Zhao,Jingyan Xie
类目: Artificial Intelligence (cs.AI)
备注: 16 pages, 5 figures, 2 tables
Abstract:Artificial superintelligence (ASI) requires AI to move beyond mastering existing knowledge toward exploring the unknown, creating new knowledge, and turning new ideas into verifiable results. However, the capabilities of today’s AI systems are still largely built on learning, compressing, and applying existing human knowledge. Accordingly, existing benchmarks primarily test whether AI can produce correct answers based on learned knowledge, or whether it can complete tasks under extensive human guidance. We therefore introduce ASI-Bench, the first benchmark to jointly evaluate AI systems’ capabilities of innovative exploration and autonomous scientific execution across general research domains, and the first to progressively withdraw human methodological guidance within the same research project to test how far AI can proceed on its own. Built by over 40 experts with the cost of 31,000+ human hours, ASI-Bench contains 60 project-level research tasks across 11 scientific domains and progressively reduces methodological guidance to test whether AI can independently select methods, conduct research, and produce verifiable results. All tasks undergo expert review, AI-assisted auditing, sandbox execution, and scorer validation. Across 18 state-of-the-art agent–model configurations, the average score drops from 50.91 with full methodological guidance to 29.10 with only the method specified and 26.62 when agents must determine the method themselves. This sharp decline shows that current systems remain heavily dependent on human guidance and are still far from autonomously conducting end-to-end, project-level scientific research. ASI-Bench is open to the world. We invite researchers and builders everywhere to contribute new tasks, challenge the limits of today’s AI, and help accelerate humanity’s collective path toward artificial superintelligence at this https URL.
[AI-67] Do LLM s Know a Good Hypothesis When They See One? Logit-Based Energy Scoring Outperforms Prompted LLM -as-Judge for Scientific Hypothesis Ranking
链接: https://arxiv.org/abs/2608.17270
作者: Swati Rajwal,Sanjay Das,Tirthankar Ghosal
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) are increasingly used for scientific hypothesis generation. However, evaluating generated hypotheses remains a challenge for trustworthy AI-enabled scientific workflows. Existing approaches often use LLMs as judges or rely on semantic similarity, which can favor familiar ideas over novel ones. We propose a logit-based energy scoring method that evaluates hypotheses using a language model’s intrinsic confidence rather than comparative judgment. We benchmarked seven language models on 1,323 papers across 12 disciplines. Each paper was paired with its hypothesis and fifteen incorrect alternatives. Intrinsic scoring reached 33.0% Hit@1 pooled across both scorers, compared with 16.6% for prompted listwise ranking. The strongest configuration, a 1-billion-parameter model using logit-based energy scoring, reached 53.1%, though this was the maximum across 14 model-by-scorer combinations selected post hoc. Overall, intrinsic model confidence shows potential for scientific hypothesis evaluation. This study also motivates future research on confidence-based methods for trustworthy AI-enabled scientific discovery.
[AI-68] Understanding Curriculum Learning in Large Language Models via Cross-Difficulty Optimization Dynamics
链接: https://arxiv.org/abs/2608.17268
作者: Zhikai Ding,Ziyi Ye
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Curriculum learning has been widely adopted in the post-training of large language models by organizing training data from easy to hard. However, its effectiveness varies substantially across reasoning tasks, suggesting that no single curriculum is universally optimal and raising a fundamental question: what determines when curriculum learning works? In this paper, we answer this question by analyzing the optimization dynamics induced by different curriculum schedules. We show that the transfer relationship between different difficulty levels characterizes the optimization dynamics induced by curriculum learning, which in turn explains the effectiveness of different curriculum schedules, and formalize this relationship as Relative Transfer, a principled measure of cross-difficulty knowledge transfer. Based on this measurement, we derive Transfer-aware Dynamic Curriculum Sampling (TDCS), which dynamically adjusts the sampling distribution according to the estimated transfer relationship throughout training. Extensive experiments on multiple reasoning benchmarks demonstrate that TDCS consistently outperforms representative scheduling strategies across different tasks, model scales, and training paradigms. More importantly, our work provides a unified optimization-based explanation of curriculum learning through cross-difficulty transfer.
[AI-69] Nonadaptive Learning in Robust Nonlinear Output Regulation
链接: https://arxiv.org/abs/2608.17262
作者: Shimin Wang,Martin Guay,Richard D. Braatz
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI); Mathematical Physics (math-ph); Optimization and Control (math.OC)
备注:
Abstract:This paper considers robust nonadaptive regulation for general nonlinear systems in an output-feedback setting with arbitrarily high relative degree. We develop a nonadaptive design that combines an input-driven filter and a generic internal model with a recursive backstepping law, thereby recasting the regulation problem as the robust input-to-state stabilization of an augmented error system. Unlike adaptive schemes, the proposed method does not rely on linearly parameterized regressors and does not require the construction of Lyapunov functions having merely nonpositive derivatives. Under standard assumptions on the exosystem, including purely imaginary and simple eigenvalues, together with a minimum-phase input-to-state stability condition on the internal dynamics, we establish global asymptotic regulation and derive explicit, verifiable inequalities for selecting the design gains. The resulting nonadaptive framework guarantees convergence of the estimation and tracking errors even when the controlled-system dynamics are complex or only partially known. The effectiveness of the theoretical results is demonstrated using a benchmark controlled Duffing system.
[AI-70] Adaptive surrogate modeling for high-dimensional spatio-temporal output
链接: https://arxiv.org/abs/2608.17250
作者: Berkcan Kapusuzoglu,Shunsaku Matsumoto,Yoshitomo Miyagi,Daigo Watanabe,Sankaran Mahadevan
类目: Computational Engineering, Finance, and Science (cs.CE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Methodology (stat.ME); Machine Learning (stat.ML)
备注: Structural and Multidisciplinary Optimization
Abstract:This paper develops an adaptive surrogate modeling method for problems with very high-dimensional spatio-temporal outputs. The analysis of spatio-temporal multi-physics systems is computationally expensive and consists of a large number of inputs and outputs. Surrogate models are often constructed to replace the physics-based model to achieve computational efficiency in analyses such as uncertainty quantification and optimization that require many function calls. In order to address the challenge introduced by the high dimensionality of spatio-temporal output, a dimension reduction method is first employed to map the high-dimensional output to a low-dimensional latent space. This is followed by the construction of the surrogate model in the low-dimensional space. The prediction error in the original space, which includes both the reconstruction error and surrogate model error, is evaluated using different error metrics. Based on the prediction accuracy of the surrogate model, new training points are identified for adaptive improvement of the surrogate model. We present a novel adaptive sampling technique that combines exploration and exploitation to improve the surrogate model accuracy with the fewest possible runs of the expensive physics-based model. Thermo-mechanical analysis of a gas turbine engine blade is used to analyze the effectiveness of the proposed method.
[AI-71] Explicit State Elicitation Is Not Enough: A Controlled Audit of Memory-Policy Classification
链接: https://arxiv.org/abs/2608.17247
作者: Yihang Chen,Pin Qian,Su Wang,Chong Peng,Huan Xu,Shuaiting Li,Yiqi Sun
类目: Artificial Intelligence (cs.AI)
备注: 34 pages, 1 figure
Abstract:Personalized agents must decide whether retrieved user memory should be used, ignored, updated, or queried before it affects a current task. We use this setting to develop an empirical audit protocol for structured intermediate outputs: first audit dataset shortcuts, then isolate bundled prompt changes, check whether intermediate labels are answer-associated, test decomposed semantic evidence, and audit provider-level execution failures. A 480-example synthetic development set initially suggested large gains from a state-structured prompt bundle, but TF-IDF diagnostics showed lexical separability and no positive standalone Ignore cases. We therefore construct a frozen 160-example controlled counterfactual set with 40 matched four-way families and rule-derived reference policies. On this set, exposing the four state definitions improves accuracy, but an isolated explicit state-output field does not significantly improve policy accuracy for Llama-3.3-70B and gives only a marginal, non-significant gain for GPT-OSS-120B. Supplying benchmark-associated state labels shifts policy predictions, but because those labels deterministically map to policies, this is a label-conditioning diagnostic rather than evidence of a faithful internal mechanism. Family-level and seed-stability analyses further show that example-level accuracy overstates counterfactual consistency: complete four-way family success is rare. An exploratory follow-up that elicits decomposed semantic evidence also fails to improve routing for the cleanly evaluated endpoint; the corresponding GPT-OSS condition was unavailable because of provider-side request validation. We evaluate policy classification only, not downstream responses, tool actions, or memory-store mutation.
[AI-72] COMIC: Reference-Aware Safety Gating for Multimodal Large Language Models
链接: https://arxiv.org/abs/2608.17234
作者: Md Abdullahil Oaphy,Anhao Xiang,Zongxing Xie,Huayue Gu,Chenyu Wang,Honghui Xu
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal large language models (MLLMs) are increasingly used to interact with screenshots, scanned documents, diagrams, and other visually grounded inputs. This shift introduces a new safety risk: in many multimodal jailbreaks, neither the prompt nor the image is harmful in isolation. Unsafe behavior emerges only when the model binds an apparently benign operation, such as summarizing, translating, or following, to a localized visual target. This reveals a structural weakness in current multimodal defenses, which largely moderate the prompt-image pair as a whole even though the true security-relevant unit is the grounded operation-target pair produced during dereference. In this work, we identify and analyze this reference-dependent failure mode and show that existing defenses degrade when harmful semantics are localized, activated only after grounding, and dependent on visual reference resolution. To address this problem, we propose COMIC (Context-Operation-Modality-Image-Classifier), a reference-aware pre-generation safety gate for MLLMs. COMIC first infers the requested operation and reference type, constructs candidate targets from OCR and open-vocabulary proposals, grounds plausible referents, and evaluates safety over explicit operation-target pairs. To handle ambiguity conservatively, COMIC combines max-risk aggregation with quality-aware routing before deciding whether to forward or block a request. We evaluate COMIC across multiple open-source MLLMs, localized and broader multimodal jailbreak benchmarks, and benign reference-sensitive settings. The results show that COMIC consistently improves robustness while preserving benign utility and practical efficiency. More broadly, our findings suggest that multimodal safety cannot be enforced reliably without modeling the requested operation, the visual target to which it applies, and the confidence of that grounding.
[AI-73] Delta2Gamma: Band-Wise Adaptive Contrastive Learning of EEG for Alzheimers Disease Detection
链接: https://arxiv.org/abs/2608.17231
作者: Chanwoo Park,Chanwoo Kim
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted to 2026 IEEE Biomedical Circuits and Systems Conference (BioCAS)
Abstract:Low-cost, scalable screening for dementia remains an open problem. Imaging-based diagnosis is costly and hard to deploy widely. Electroencephalography (EEG) is portable and inexpensive, but its recordings are noisy, vary widely across subjects, and carry few clinical labels. We tackle this with Delta2Gamma, a self-supervised framework that learns EEG representations from unlabeled data by contrasting augmented views of each signal. Rather than treat EEG as a single stream, Delta2Gamma decomposes every recording into the five canonical neural rhythms (delta, theta, alpha, beta, gamma). Each band gets its own encoder and projection head. Each also gets a temperature that is predicted adaptively during contrastive training, so bands with different signal statistics are balanced automatically. On the ADFTD cohort under a strict leave-one-subject-out protocol, Delta2Gamma separates Alzheimer’s disease from cognitively normal controls with 92.4% accuracy. This exceeds both supervised backbones and recent dedicated EEG methods.
[AI-74] PACE: Policy-Attested Contract Execution for Safe AI Agents in Decentralized Finance
链接: https://arxiv.org/abs/2608.17220
作者: Rabimba Karanjai,Yang Lu,Richard Williamson,Hemanth Hm,Prakhar Mehrotra,Lei Xu,Weidong(Larry)Shi
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Autonomous AI agents are emerging as interfaces for decentralized finance (DeFi) actions such as swaps, lending operations, and yield management. Because these agents rely on large language models (LLMs) to plan transactions, they inherit the LLM’s susceptibility to prompt injection and lack of mechanisms to bind a verifier’s approval to the exact transaction ultimately submitted on-chain. We present PACE (Policy-Attested Contract Execution), a transaction-level authorization framework that interposes between an LLM-based agent and on-chain execution. PACE introduces typed transaction intents, a deterministic policy verifier, and signed Policy Decision Records (PDRs) that cryptographically bind the approved intent, policy, and simulation report to the exact execution bytes, with replay and expiration protection. A Solidity smart account enforces PDR signatures on-chain with a measured overhead of 29,826-31,822 gas. We evaluate PACE against six baselines on 40 tasks spanning four attack categories plus benign utility (2,800 trials, 10 seeds). In our deterministic sandbox, PACE achieves a 0.00 unsafe execution rate and 0.00 false-positive rate on benign tasks, compared to 0.80 for the unguarded baseline. Ablation studies identify permissive policy settings (+57.5 pp) and the touched-contract allowlist (+12.5 pp) as the dominant safety components. To test whether the same deterministic floor holds for real model outputs, the artifact additionally provides a three-model live-LLM evaluation over the full task suite with repeated runs. A mainnet-fork harness is included for archive-RPC deployments, but fork results are reported only when the corresponding artifacts are generated. These auxiliary studies are separate from, and never substitute for, the deterministic benchmark. We frame our claims as logic-level safety within a reproducible benchmark rather than deployment-ready DeFi security.
[AI-75] Fools Gold: Defensive Deception Against Safety-Removal Attacks on Open-Weight Models
链接: https://arxiv.org/abs/2608.17202
作者: Mark Russinovich
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注:
Abstract:Safety alignment in open-weight language models is trivially removable: abliteration projects a refusal-mediating direction out of the weights in minutes, and no release-time defense we are aware of prevents it durably. What cannot be prevented can be deceived. Our defense, decoy hardening (“Fool’s Gold”), concedes the refusal strip and poisons its payoff: once refusal is stripped, most answers to hazardous operational requests are confident, fluent decoys whose critical elements are falsified. Decoys are trained inside a differentiable simulation of the attack, expressing only in the attacked state; a refusal pin and benign leash hold clean-state behavior to the original. We instantiate it on seven models from five families (9B-122B, dense and mixture-of-experts). On the six models passing our pre-registered efficacy gate, 0.51-0.90 of attacked-state responses to held-out prompts are decoys, +0.27-0.84 attributable to the defense; all six stay within registered benign-behavior and capability budgets; the seventh (smaller) fails the gate (boundary case). Rates replicate on a frozen test split or untouched strata. The claim is epistemic: without independent ground truth, no observation surface we tested separates falsified answers from correct ones - on external red-team benchmarks’ CBRNE-adjacent slice, the defended 122B is fatally wrong on 0.82-0.86 of matched-quality answers vs at most 0.10 undefended. Repeated sampling does not restore trust: element-wise consensus at K=64 reconstructs a fully usable procedure on 0.083-0.625 of prompts where the instrument validates, vs 0.58-0.96 undefended, with no label-free way to tell the regimes apart; on the weakest such model the claim is per-draw only. We evaluate chemical and biological hazards; the defense does not address in-context jailbreaks and protects only the initially released defended weights.
[AI-76] Graphectory Viewer: A Tool for Process-Centric Analysis of Agent ic Software Trajectories
链接: https://arxiv.org/abs/2608.17195
作者: Charlie Jyu,Shuyang Liu,Reyhaneh Jabbarvand
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 5 pages, Short Paper; ASE 2026 Tool Track
Abstract:We present Graphectory Viewer, a web-based tool for interactive, process-centric analysis of software-agent trajectories. Building on the Graphectory representation introduced in our previous work, Graphectory Viewer transforms heterogeneous raw trajectories into phase-aware graphs that connect low-level execution details with higher-level behavioral structures. The tool supports trajectories from multiple agent frameworks and provides interactive graph construction; node-level inspection of thoughts, actions, and observations; search and filtering over large trajectory collections; and Sankey-style summaries of problem-solving phase transitions. These capabilities enable researchers and practitioners to inspect individual executions, identify recurring behavioral patterns, compare successful and failed runs, and analyze large trajectory corpora beyond final task outcomes. To support reproducibility and further research, we release Graphectory Viewer as an open-source artifact together with documentation, precomputed graphs, and the large-scale trajectory corpus.
[AI-77] Benchmarking the Benchmarks: Evaluating Automated Safety Benchmarks for Small Language Models ESORICS2026
链接: https://arxiv.org/abs/2608.17183
作者: Nyamtulla Shaik,Fengjun Li,Bo Luo
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: This paper is accepted for publication at ESORICS 2026
Abstract:Small Language Models (SLMs) are increasingly deployed in resource-constrained, privacy-sensitive settings, where safety and bias failures can cause security and societal risks. However, existing AI safety\slash security\slash compliance benchmarks are designed for large language models that may not transfer reliably to SLMs. We therefore ask: Can these benchmarks effectively and reliably evaluate SLMs? To answer this question, we conduct a large-scale assessment of the effectiveness and robustness of these automated pipelines by evaluating five widely used benchmark suites across 26 open-source SLMs under a unified judging rubric, which assigns a score of 0, 1, or 0.5 to harmful, safe, or ambiguous/irrelevant responses, respectively. Across the benchmarks, ambiguous judgments dominate and correlate with prompt complexity and model architecture, indicating that \em LLM-centric safety benchmarks are insufficient as standalone evidence for SLM safety assessment. In general, the ambiguity rate increases with lexical density, output perplexity, and output length and decreases with lexical sophistication, self-coherence, and reply-prompt similarity. This reveals a capability-safety confound that mixes model capability with apparent safety. Since ambiguity is prevalent, aggregate mean-score leaderboards are mathematically brittle: model rankings change significantly under reasonable ambiguity treatments, even when the underlying outputs remain unchanged.
[AI-78] ask Specialization Fine-Tuning for Contextual Reinforcement Learning
链接: https://arxiv.org/abs/2608.17180
作者: Jianan Zhou,Jung-Hoon Cho,Tianyue Zhou,Han Zheng,Jie Zhang,Roy Dong,Yining Ma,Cathy Wu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Contextual Reinforcement Learning (CRL) seeks to generalize classical RL by maximizing task coverage across a context space of related tasks. While prior works often train from scratch and rely on either multi-task learning for a single policy or strategically training multiple policies, we advocate for a unified alternative: pretraining a single policy with good initial performance, followed by fine-tuning multiple policies for task specialization. This new paradigm, however, introduces unique challenges, such as heterogeneous marginal returns and sample inefficiency. This raises a critical research question: given a pretrained policy and a constrained budget, how much fine-tuning should each task region receive to enable sample-efficient CRL? To this end, we propose Task Specialization Fine-Tuning (TSFT), an online framework that predicts fine-tuning performance with a simple parametric model and exactly solves the resulting discrete budget allocation problem via integer linear programming. Extensive experiments across diverse decision domains, including combinatorial optimization, continuous control, and LLM fine-tuning, demonstrate that TSFT significantly outperforms baselines in task coverage and approaches oracle performance. Our work charts a new direction for model-based CRL, aligning with the modern pretrain-finetune era.
[AI-79] he Acknowledgment Point Is the System: Durable Policy-Decision Receipts for AI Audit Evidence
链接: https://arxiv.org/abs/2608.17176
作者: Neeraj Kumar Singh Beshane
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 6 pages, 4 figures, 2 tables. Code and release artifacts: this https URL
Abstract:An AI audit record is useful only if its durability and trust boundary are explicit. Returning a guarded decision before any durable write minimizes latency, but it cannot guarantee that evidence survives an immediate crash. We rebuild RuntimeGuard-AI around this constraint. The resulting research prototype binds each deterministic policy decision to the exact policy source, commits a privacy-minimizing record at a caller-selected synchronization boundary, and returns an Ed25519-signed receipt that states whether that boundary completed. After restart, the engine validates framed records, manifests, shard placement, sequence continuity, and replay identity. A separate attestation path groups committed records into chained, signed Merkle epochs that an auditor verifies with an externally obtained key. On an Apple M4 Pro at four worker threads and 2,048-byte prompts, buffered signed evidence reaches 27,193 requests/s with 141.9 microseconds median latency. Per-record data and full synchronization reduce throughput to approximately 242 requests/s and raise median latency to 16.0 ms. Sealing a 100,000-record signed epoch takes 97.0 ms. The result is a measured durability-latency trade-off, not a “free” asynchronous audit path. The prototype does not prove model execution, prevent a compromised signer from forking history, or establish legal conformity.
[AI-80] Synthesizing Feature Extractors: An Agent ic Approach for Algorithm Selection
链接: https://arxiv.org/abs/2608.17170
作者: Hai Xia,Carlos Ansótegui,Stefan Szeider
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Algorithm selection for constraint satisfaction problems requires extracting features that capture problem structure. Manually designing feature extractors demands deep domain expertise and quickly becomes a bottleneck when new problem classes appear. We present an automated approach that uses Large Language Models (LLMs) in an agentic check–fix–verify loop to synthesize executable Python scripts that act as interpretable, problem-specific feature extractors. Given a high-level MiniZinc model and an instance, the LLM agent generates code that constructs a typed graph representation and computes structural properties such as graph density, variable clustering, and constraint tightness. We evaluate our approach on three combinatorial problems (vehicle routing, car sequencing, fixed-length error-correcting codes) with a portfolio of five state-of-the-art solvers. The synthesized extractors yield algorithm selectors that consistently outperform both expert-curated mzn2feat features (up to 8.3 percentage points (pp) test-set accuracy on FLECC) and the best transformer-based trans2feat variants. In the meanwhile, the synthesized feature extractors remain inspectable.
[AI-81] Expected free energy as an information constraint on the Bethe Lagrangian
链接: https://arxiv.org/abs/2608.17167
作者: Wouter M. Kouw
类目: Information Theory (cs.IT); Artificial Intelligence (cs.AI); Systems and Control (eess.SY); Machine Learning (stat.ML)
备注: 17 pages, 4 figures, table 2. International Workshop on Active Inference
Abstract:Active inference selects actions by minimising an expected free energy functional over predicted futures. However, adding an expectation over yet-unobserved outcomes means the free energy functional no longer has a Kullback-Leibler structure, which hinders message passing treatments of inference procedures. We propose an alternative formulation based on a Bethe free energy functional, fully supporting inference by message passing. The epistemic drive is maintained by imposing an information constraint, next to normalisation, marginalisation and form constraints, insisting that the mutual information between future observations, states and parameters given actions must be at least as large as the entropy of the goal prior. For a specific value of the corresponding Karush-Kuhn-Tucker multiplier, the stationary point of this constrained Bethe Lagrangian recovers the expected free energy solution. We show that, as the information demand is varied, the solved multiplier moves through its inactive, interior, and saturated regimes. In the inactive regime the agent’s epistemic drive switches off entirely, while in the saturated regime it is maximal. We compare the performance of the constrained Bethe agent on three tasks against EFE and Q-MDP.
[AI-82] Q-Learning With World Models
链接: https://arxiv.org/abs/2608.17163
作者: Perry Dong,Yueru Jia,Chelsea Finn,Dorsa Sadigh
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Off-policy reinforcement learning (RL) has become increasingly sample-efficient, enabling applications such as RL fine-tuning of Vision-Language-Action models into reliable, high-performing policies. World models offer a further lever for sample efficiency, as they predict state changes rather than actions alone, but their success has largely been confined to supervised policy learning. Prior model-based RL methods often optimize the policy or value function directly on imagined rollouts, which is prone to compounding bias and struggles to scale to large, high-dimensional problems such as real-world robotics, a problem that worsens with task horizon and visual complexity. In this work, we instead ask whether we can leverage world models directly on top of standard Q-learning to improve performance, while remaining trained and grounded in the real, online setting. We propose QWM, a framework that leverages world models to perform test-time search over imagined trajectories on top of Q-learning to select high-value actions during both online rollouts and evaluation. Since the policy and value function are trained only on real transitions, QWM avoids compounding model bias while still gaining the sample-efficiency benefits of predictive search. On challenging manipulation benchmarks Robomimic and LIBERO, QWM significantly outperforms strong prior state-of-the-art methods on both sample efficiency and performance.
[AI-83] Authorization Before Context: A Model-Neutral Audience Boundary Against Cross-Audience Memory Leakage in Agent ic Systems
链接: https://arxiv.org/abs/2608.17148
作者: Sibo Liu
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 13 pages, 3 figures. Author preprint. Accepted for presentation at AdvML-Frontiers x CoTMA, a non-archival workshop at COLM 2026
Abstract:A personal language agent learns a fact from one audience and may later place it in the prompt it assembles for another. This memory-to-context step is an attack surface: ambiguous or inconsistent channels, cross-audience prying, and poisoned memory can each cause the system to assemble context containing a fact relevant to the query yet unauthorized for the current viewers. We introduce authorization before context: a single, anti-monotone audience-membership rule applied at the memory-to-context transition. Each item carries the audience present when it was recorded; the current viewer set is read from channel metadata and falls back to public when ambiguous; and the item is admitted only when every current viewer already belonged to its audience. We prove that this rule gives every participant cross-channel recall while ensuring, by exclusion rather than by model behavior, that nothing recorded for a narrower audience reaches a broader one and that poisoned memory cannot widen its own audience. The boundary is a model-neutral invariant on the exact assembled context: a forbidden fact must be absent before the model is called. On a synthetic Contextual-Integrity suite, no forbidden fact entered the context our boundary assembled, whereas unscoped baselines included such facts by construction; we further audit that every read path fails closed. The evidence is preliminary and synthetic.
[AI-84] Iterative tensor network transformations for element-wise evaluation of elementary and filtering functions
链接: https://arxiv.org/abs/2608.17135
作者: Xiao Wang,Tomohiro Hashizume,Pia Siegl,Dieter Jaksch
类目: Machine Learning (cs.LG); Statistical Mechanics (cond-mat.stat-mech); Artificial Intelligence (cs.AI); Computational Physics (physics.comp-ph); Quantum Physics (quant-ph)
备注: 23 pages, 10 figures
Abstract:Tensor networks are powerful formats for compressing large-scale data. However, their application to general data processing has been limited by the difficulty of performing nonlinear operations. Here, we introduce iterative tensor network transformations (ITNTs), a general algorithmic framework for the element-wise evaluation of elementary and nonlinear filtering functions on data encoded as tensor trains (TTs), a class of tensor networks. Our approach operates entirely in the compressed domain, enabling efficient computation on exponentially large datasets while maintaining a controlled computational cost. We demonstrate its power in two key areas: (I) evaluating highly nonlinear elementary and filtering functions on a 3D reactive flow field, enabling high-fidelity reaction rate computation and region filtering, and (II) finding extrema in complex optimization problems, such as solving Max-SAT instances on spaces up to 2^70 configurations. These results establish ITNT as a foundational tool that provides tensor network methods with the capability for general-purpose data science and large-scale optimization.
[AI-85] A decodability criterion predicts when hidden-state selection beats majority voting in large language models
链接: https://arxiv.org/abs/2608.17124
作者: Zhixiang wang,Ziliang Hong,Ulas Bagci
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Combining the answers a large language model (LLM) samples for a question into one decision is a test-time information fusion problem, usually solved by majority voting. Voting is unreliable on difficult questions, where the sampled answers share correlated errors, so the wrong answer can win and drawing more samples makes the decision worse. Selecting a candidate by reading a correctness signal from the model’s hidden states is a promising alternative, but its accuracy varies across models and tasks, and no measure indicates when it can be trusted. In this paper, we propose CASE (Correctness-Axis SElection), a dynamic selection combiner that trains a linear gate on the answer-token hidden state and selects the highest-scoring candidate. Its main contribution is decodability, a leakage-free measure of how well the gate ranks a question’s correct candidates above its incorrect ones, which predicts whether hidden-state selection will outperform voting. A conventional probe appears accurate only because of question-identity leakage, which vanishes under question-grouped evaluation. On held-out data, decodability predicts the accuracy gain of selection over voting with a Pearson correlation r=0.75 and a decision threshold near AUC=0.60. Across general and medical LLMs, CASE improves over voting by up to 19 points on medium-difficulty questions and 16.8 points on hard questions. Decodability depends on the aligned knowledge a model must recall, not on its scale, and its prediction transfers to an unseen scientific domain within 3.8 points. It thus provides a practical criterion, measurable in advance for a given model and task, for choosing between learned selection and majority voting.
[AI-86] From Abductive Explanations to Global Logical Rules for Node Classification in SGCs
链接: https://arxiv.org/abs/2608.17103
作者: Bryan Lima Cavalcante,Thiago Alves Rocha
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
备注:
Abstract:Graph Neural Networks (GNNs) have achieved remarkable performance in node classification tasks, motivating growing interest in methods capable of explaining their predictions. Recent logic-based approaches, such as LogicXGNN, derive global logical rules for Graph Neural Networks (GNNs) from collections of explanatory subgraphs. While informative, these subgraphs may contain redundant structural information that is specific to individual nodes, potentially limiting the generality of the extracted rules. In this work, we propose a logic-based framework for node classification in Simple Graph Convolution (SGC) networks that uses minimal abductive explanations as an intermediate representation for rule extraction. For each node, we compute a minimal set of node-feature pairs sufficient to preserve the predicted class. These explanations are then used to train decision trees from which global logical rules are extracted. Experiments on benchmark datasets show that the proposed framework produces compact global rules while maintaining high fidelity to the original SGC model.
[AI-87] Structured Driving-State Narratives for Small Language Model-Based GNSS Spoofing Detection
链接: https://arxiv.org/abs/2608.17092
作者: Abyad Enan,Sagar Dasgupta,Mizanur Rahman,Mashrur Chowdhury
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: This work has been submitted to the Transportation Research Record: Journal of the Transportation Research Board for possible publication
Abstract:Autonomous vehicles (AVs) depend on reliable Global Navigation Satellite System (GNSS) positioning. However, spoofed GNSS signals can induce plausible but incorrect vehicle states. This study develops a small language model (SLM)-based framework for detecting and classifying GNSS spoofing attacks by comparing vehicle behaviors independently derived from GNSS and other sensing sources. The framework converts independent driving states from GNSS and other sensing sources into structured semantic narratives that are provided to an SLM for spoofing detection and attack classification. The performance of the SLM-based framework is compared with large language models (LLMs) fine-tuned on identical training data and evaluated on the same test set. The evaluation considers five classes: no attack, overshoot attack, stopped attack, turn-by-turn attack, and wrong-turn attack. The framework is also evaluated with geographically unseen field data collected in Clemson, South Carolina, United States. Experimental results indicate that the evaluated SLMs achieve performance similar to the LLMs, achieving an average accuracy of 96.99%, precision of 99.05%, recall of 95.59%, and F1-score of 97.18%. In terms of computational efficiency and resource utilization, the SLMs demonstrate advantages over the LLMs by requiring lower inference latency and less GPU memory during both fine-tuning and inference. Evaluation using field data collected in a geographically distinct location further demonstrated its efficacy. The presented framework can detect and classify GNSS spoofing attacks in real-time while requiring relatively low computational and memory resources, and is therefore suitable for deployment on resource-constrained vehicular computing platforms.
[AI-88] DiSCO: Defending text-to-image generation through distribution-guided contrastive prompt optimization
链接: https://arxiv.org/abs/2608.17067
作者: Tong Zhang,Motasem Alfarra,Carlos Hinojosa,Christos Louizos,Bernard Ghanem
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:As text-to-image generative models advance, they raise critical safety concerns, particularly the generation of Not-Safe-For-Work (NSFW) content such as violence and nudity, further exacerbated by red-teaming adversarial attacks. Existing defenses predominantly operate under white-box assumptions, relying on text encoder optimization, weight editing, or inference-time intervention, and fundamentally cannot scale to proprietary models. Black-box alternatives based on LLM prompt rewriting offer broader applicability, yet fail in a critical regime we identify as the \textitbenign adversarial problem: prompts that are linguistically safe but still trigger harmful generation due to the model’s learned data distribution. We propose DiSCO, a zero-shot, strictly black-box defense that operates entirely at the prompt level as a plug-and-play module, requiring no model retraining, fine-tuning, or access to model internals. DiSCO performs distribution-guided suffix expansion via beam search, optimized through contrastive scoring over safe and unsafe image pools generated by the target model itself, with iterative adaptive feedback until safe content is produced. We demonstrate that DiSCO consistently enhances the safety of both undefended and defended models on the I2P benchmark under multiple red-teaming attacks, achieving 37.7% and 25.13% ASR reduction, respectively, while maintaining semantic fidelity and improving image coherence. As a black-box, architecture-agnostic module, DiSCO can be readily applied to any text-to-image system without necessitating any changes to the model itself.
[AI-89] Without journalists there is no journalism: the social dimension of generative artificial intelligence in the media
链接: https://arxiv.org/abs/2608.17017
作者: Simón Peña-Fernández,Koldobika Meso-Ayerdi,Ainara Larrondo-Ureta,Javier Díaz-Noci
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: 15 pages
Abstract:The implementation of artificial intelligence techniques and tools in the media will systematically and continuously alter their work and that of their professionals during the coming decades. To this end, this article carries out a systematic review of the research conducted on the implementation of AI in the media over the last two decades, particularly empirical research, to identify the main social and epistemological challenges posed by its adoption. For the media, increased dependence on technological platforms and the defense of their editorial independence will be the main challenges. Journalists, in turn, are torn between the perceived threat to their jobs and the loss of their symbolic capital as intermediaries between reality and audiences, and a liberation from routine tasks that subsequently allows them to produce higher quality content. Meanwhile, audiences do not seem to perceive a great difference in the quality and credibility of automated texts, although the ease with which texts are read still favors human authorship. In short, beyond technocentric or deterministic approaches, the use of AI in a specifically human field such as journalism requires a social approach in which the appropriation of innovations by audiences and the impact it has on them is one of the keys to its development. Therefore, the study of AI in the media should focus on analyzing how it can affect individuals and journalists, how it can be used for the proper purposes of the profession and social good, and how to close the gaps that its use can cause.
[AI-90] SkillEffect: Checked Lowering for Memory-Bounded Agent Tools
链接: https://arxiv.org/abs/2608.17007
作者: Yinuo Wang,Yiyu Shi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Agent Skills can specify procedural and resource obligations for tool use, and language models instantiate them as concrete programs. However, when models turn this guidance into code for existing tool interfaces, even a semantically correct program may load an entire input and exceed the memory available to one tool call. We present SkillEffect, a checked-lowering runtime for computations with a recoverable source relation, an audited bounded implementation, and a registered output postcondition. Before granting execution authority, an independent checker rebuilds each proposed lowering from the submitted program and immutable input. Every relation plugin supplies a source recognizer, input-fact extractor, bounded-IR constructor, arena-bound function, and postcondition; one common runtime provides checked selection, bounded-VM execution, atomic capacity leasing, and staged publication. Generality in SkillEffect is architectural rather than automatic: each supported computation requires an audited relation plugin, while the dispatch, resource-control, execution, and publication mechanisms are shared across plugins. Across six operator families, bounded access substantially reduces peak memory and improves completion under externally fixed caps. Six plugins instantiate the same contract across five execution patterns, from streaming reduction to bounded-heap Top-k. The XLSX onboarding study and Top-k extension show that a new relation and a new retained-state pattern reuse the same trust boundary, while the checker accepts all evaluated legal configurations and rejects all adversarial proposals. Together, these results show that one checked-lowering architecture can enforce heterogeneous registered memory relations at Agent tool dispatch.
[AI-91] he Problem Is the Problem: Towards Scalable Mathematical Discovery
链接: https://arxiv.org/abs/2608.16977
作者: Zeyu Zheng,Shengtong Zhang,Jeremy Avigad,Prasad Tetali,Sean Welleck
类目: Artificial Intelligence (cs.AI); Combinatorics (math.CO)
备注: Code available at this https URL
Abstract:AI systems are increasingly capable of contributing to mathematical research. In research practice, frontier-model reasoning is a limited resource, and expert mathematical review is even more sharply constrained. Allocating these scarce resources well is therefore central to making AI-assisted mathematical discovery efficient. In most current AI-for-math workflows, human effort is concentrated at the beginning and end, in selecting suitable research problems and later reviewing the resulting artifacts. These two stages are becoming bottlenecks for research-level mathematics. We address them by proposing a new human-AI discovery paradigm. The human input is no longer a single problem selected in advance, but a research direction in which the experts have interest and expertise. The system then searches a broad literature corpus for candidate problems in that direction. Inspired by search and recommender systems, we build Find, Attempt, and Recommend (FAR), a literature-to-review cascade that automates the search for suitable problems and focuses human attention on artifacts that have passed several stages of filtering. In a combinatorics pilot, the pipeline starts from 5,245 combinatorics papers, recovers 6,453 candidate conjectures or open problems, and filters them to 4,717 apparently well-posed and still-open conjectures. Subsequent reasoning and automated triage stages surface 598 potential resolutions and select 77 items for author-team review. Among them, we identify many interesting discoveries, including results on conjectures and questions of Davies–Jenssen–Perkins–Roberts, Erdős–Straus, Ikenmeyer–Pak–Panova, and Lund–Saraf–Wolf. These results demonstrate the effectiveness of this new mode of human-AI collaboration for mathematical discovery.
[AI-92] Position: Fairness Failure in Generative Models is an Evaluation Problem ICML2026
链接: https://arxiv.org/abs/2608.16974
作者: Mariia Vladimirova,Jean-Yves Franceschi,Thibaut Issenhuth
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at ICML 2026 (Position Paper Track), cf. this https URL
Abstract:Despite groundbreaking advancements in generative models during the last decade, concerns about their lack of fairness, reinforcing societal inequalities and harming marginalized groups, remain under-addressed and difficult to act upon. This position paper argues that fairness failures in generative models, albeit driven by multiple factors, are ultimately stemming from an evaluation problem: fairness findings are rarely comparable across papers or actionable for deployment decisions. This paper diagnoses recurring empirical and conceptual failure modes in current practice and motivates a shift from ad-hoc bias checks to standardized, generative-specific evaluation. We propose Fairness Cards as a minimal reporting artifact that makes evaluation choices explicit (prompt families, counterfactual protocols, metrics, and refusal handling) enabling reproducibility, comparability, and accountability. We conclude with additional recommendations towards a paradigm shift in evaluation standards. Our project page can be found at this https URL .
[AI-93] FedPref: Federated Preference Learning for Structured Radiology Report Extraction MICCAI2026
链接: https://arxiv.org/abs/2608.16971
作者: Flint Xiaofeng Fan,Cheston Tan,Yew-Soon Ong,Roger Wattenhofer
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at ELAMI 2026, held in conjunction with MICCAI 2026. To appear in the Springer proceedings
Abstract:Radiology reports describe findings and locations in free text, but downstream search and analysis require these relations in a fixed schema. Learning this extraction requires labels that are unevenly distributed across institutions: smaller hospitals have less local evidence, and pooling data may be infeasible. We introduce FedPref: frozen public language models propose alternative JSON extractions, local annotations rank them, and sites collaboratively train compact Qwen3-8B adapters while sharing only model updates. A heterogeneous teacher pool provides cross-model contrast when repeated single-model samples collapse. On development data from six simulated hospitals with unequal data volume and disease prevalence, FedPref improves client-mean F1 by 2.49 points and worst-site F1 by 9.10 points compared with training each site in isolation, with the largest gains at the sites holding the least data. Central training on the pooled preference-pair union is 2.66 points higher on client-mean F1. On a locked, 400-report manually validated gold test set, FedPref reaches 68.68 F1 and pooled training 71.67, preserving that same ordering. FedPref thus lets institutions with unequal, unpooled data benefit from collaboration without ever sharing reports or annotations.
[AI-94] Probing the Prefill: Detecting Code Vulnerabilities via Latent Activations
链接: https://arxiv.org/abs/2608.16970
作者: Alizishaan Khatri
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:LLM-based code generation is now embedded in mission-critical pipelines, but defenses against vulnerable output remain post-hoc – static analyzers, fine-tuned classifiers, or an LLM judge that screen completed code, ignoring the generating model’s own internal state. We test a narrower, directly measurable question: when an LLM reads a piece of C/C++ code as context, do its hidden activations already carry a signal about that code’s vulnerability status? We extract last prefill token activations from four LLMs (Granite-4.1-8B, Qwen3.5-9B, Qwen3.6-27B, Gemma-4-12B) across three model families and train MLP probes on these activations. We evaluate them on four function-level C/C++ benchmarks (Devign, Big-Vul, Draper VDISC, PrimeVul). Our probes achieve 41.7% average F1 using 13.4–16.0M-parameter probes – under 0.2% of base-model size. On Devign, the best probe (Qwen3.5-9B, 68.8% F1) matches the published fine-tuned-classifier SOTA (67.9%) despite reading only a frozen, general-purpose LLM’s activations; on the harder, more imbalanced benchmarks (Big-Vul, Draper VDISC, PrimeVul) probes trail SOTA substantially. This is early evidence that a coding LLM’s own representation of arbitrary code is informative about that code’s vulnerability status, motivating further work toward lightweight, model-native vulnerability screening.
[AI-95] EMAN: Optimization-Driven Capacity Growth through Path Emergence in Multi-Task Learning
链接: https://arxiv.org/abs/2608.16930
作者: Chenlei Fang,Jingchen Li,Hongzong LI,Qingyao Li,Yixuan Zhang,Huarui Wu,Haobin Shi,Chunjiang Zhao
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Existing multi-task learning methods rely on hard sharing, multiple paths or experts, adaptive sharing, and dynamic expansion. However, their capacity changes are usually constrained by predefined structures or triggered by task boundaries and conflict signals. This raises a fundamental question: can a network start from exact single-path computation and grow a new independent path only when persistent optimization evidence appears? We propose the Emergent Modular Atomic Network (EMAN), an optimization-driven framework for exposing an antisymmetric growth direction through latent relative phases without instantiating a second path, and for monitoring multiple decision signals during training to transform local optimization evidence into a structural decision. EMAN materializes two equal-capacity independent paths only after certification. EMAN adaptively allocates shared and task-specific representation capacity to accommodate varying task requirements. Extensive experiments on controlled rank settings, PASCAL-Context, and NYUv2 validate its effectiveness, achieving improved performance at a competitive computational cost.
[AI-96] WIP: LLM Odyssey: A Game-Based Platform for Teaching LLM Engineering Concepts
链接: https://arxiv.org/abs/2608.16924
作者: Priyamvada Tripathi
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: 5 pages, 4 figures. Accepted at the 2026 IEEE Frontiers in Education Conference (FIE 2026), Work in Progress track
Abstract:This work-in-progress (WIP) innovative practice category paper presents LLM Odyssey, an open source, browser-based serious gaming platform comprising 13 interactive games for teaching Large Language Model (LLM) engineering concepts. Topics such as tokenization, transformer architecture, prompt engineering, retrieval augmented generation (RAG), and production deployment are underrepresented in computer science curricula. Existing interactive tools address individual concepts but lack pedagogical scaffolding or structured learning pathways. LLM Odyssey addresses this gap through three learning tiers aligned with Bloom’s revised taxonomy: Cognitive Core (7 foundational games), Systems Forge (5 production engineering games), and Foundry Arena (capstone challenges). Each game incorporates five pedagogical strategies drawn from the literature: immediate formative feedback, scaffolded hints grounded in the Zone of Proximal Development, progressive difficulty informed by flow theory, worked examples to manage cognitive load, and authentic scenarios drawn from production practice. The platform was deployed in Winter 2026 semester at a Canadian college for an initial review. Feedback confirmed functional requirements and identified adaptive difficulty as a priority for future development. A formal mixed methods evaluation protocol (N=50) has been designed, comprising pre and post knowledge tests, validated surveys, engagement analytics, and interviews, and is documented here to enable future evaluation studies with the publicly available platform.
[AI-97] Averag e Distance Approximation for Static Large Graphs
链接: https://arxiv.org/abs/2608.16916
作者: Kartikey Ahlawat
类目: Data Structures and Algorithms (cs.DS); Artificial Intelligence (cs.AI); Computational Geometry (cs.CG)
备注:
Abstract:Calculating average distances in large-scale networks is computationally intensive and constrained by limited main memory, posing a significant challenge in graph analytics. This study explores and evaluates two primary approaches for estimating average distances: a graph sampling-based method (Random Walk) and landmark-based methods, including the Size Estimation Framework (SEF) and the Eppstein-Wang (EW) algorithm. Random Walk was found to be unreliable for small sample sizes and computationally expensive for larger ones, requiring at least 15% of nodes for accuracy. Landmark-based approaches, leveraging probabilistic data structures like HyperLogLog for memory-efficient neighbor exploration, demonstrated superior performance. Among these, the SEF algorithm offers better memory efficiency, while the EW algorithm achieves higher accuracy with lower computation time. Experiments on static, undirected, and unweighted graphs (both unipartite and bipartite) revealed that the EW algorithm produced results with an error margin as low as 0.02%. Additionally, a subset of 100 randomly selected nodes was sufficient for accurate estimations in most large graphs. The findings indicate that the EW algorithm provides a practical and scalable solution for average distance estimation, with higher reliability on unipartite graphs compared to bipartite graphs.
[AI-98] Education-centered critical policy analysis of AI: Ghanas AI strategy as a case
链接: https://arxiv.org/abs/2608.16910
作者: Matthew Nyaaba,Vida Awinime Bugri,Eric Kojo Majialuwe,Bismark Nyaaba Akanzire,Ibrahim Nantomah,Felicia Boateng,Patrick Kyeremeh,Benjamin Quarshie,Ellen Kwarteng,Macharious Nabang
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:National AI strategies increasingly guide governance, workforce development, innovation, and competitiveness, but less is known about how they frame education as a sector with pedagogical, cultural, ethical, and implementation demands. This study develops and applies an Education-Centered AI Policy Framework to analyze Ghana’s National Artificial Intelligence Strategy, 2025-2035. Using critical qualitative policy document analysis, we examined the strategy through six components: policy purpose, teacher agency and professional learning, curriculum and assessment, language and culture, responsible AI and learner protection, and participation and implementation governance. Findings show that Ghana’s strategy is ambitious and timely, especially in its emphasis on AI literacy, youth skills, TVET, workforce readiness, rural outreach, local language data, inclusion, and responsible AI governance. However, the education agenda is stronger on national AI readiness than on school-level implementation. Teacher agency, pre-service teacher education, curriculum progression, assessment guidance, AI disclosure, multilingual pedagogy, culturally responsive AI use, child-centered safeguards, and participatory governance remain underdeveloped. We also identify document-level concerns about transparency and coherence, including apparent AI-styled visual content without visible disclosure and a mismatch between a vision and mission figure and its textual explanation. We argue that Ghana needs a sector-specific, education-centered AI policy and implementation pathway that connects workforce readiness with teacher preparation, curriculum reform, assessment redesign, learner protection, infrastructure, local language instruction, culturally responsive pedagogy, locally responsive AI tools, and participatory governance.
[AI-99] Effective Personalized AI Tutors via LLM -Guided Reinforcement Learning
链接: https://arxiv.org/abs/2608.16907
作者: Angel Tsai-Hsuan Chung,Botong Zhang,Ling-Chieh Kung,Hamsa Bastani,Osbert Bastani
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:Generative AI (GenAI) is rapidly reshaping education by unlocking the potential for personalized tutoring. Yet, emerging platforms largely focus on GenAI chatbot tutors that reactively answer student questions. We hypothesize that the efficacy of GenAI chatbot tutors can be substantially improved by proactively guiding student learning. To test this, we design a novel tutoring platform that tightly integrates a carefully-designed GenAI chatbot with a reinforcement learning algorithm for sequencing practice problems. Critically, this algorithm leverages rich signals from student-chatbot interactions to adaptively select practice problems of an appropriate difficulty level. In partnership with the Taipei City Government and American Institute in Taiwan, we deployed our tutoring platform in conjunction with a five-month course to teach Python to students across ten high schools. We randomized students between a fixed practice problem sequence and our adaptive sequencing algorithm. We find that adaptive sequencing increased unassisted final exam performance by 0.15 standard deviations (equivalent to 6-9 months of schooling by some estimates); mediation analysis suggests that gains were driven by increased engagement. Our work provides large-scale field evidence that student-chatbot interactions provide valuable signals for proactively optimizing and personalizing student learning.
[AI-100] ComNetX: Local Hierarchical Adaptation for Dynamic Community Detection
链接: https://arxiv.org/abs/2608.16906
作者: Aleksandr Konovalov,Anna Uporova,Alexander Drobyshev,Iaroslav Egorov,Grigoriy Bokov
类目: ocial and Information Networks (cs.SI); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 10 pages, 3 figures
Abstract:Dynamic community detection is commonly addressed either by full-snapshot recomputation or by solver-specific dynamic procedures. Full recomputation preserves the semantics of mature static solvers, but it repeatedly processes unchanged graph regions when updates are small. Solver-specific dynamic methods can reduce this cost, but their update rules often have limited transferability across objectives, feature representations, and implementations. In addition, localizing computation only by graph distance may omit community context needed by high-quality solvers. We introduce ComNetX, a solver-agnostic hierarchical adaptation framework for local dynamic updates. ComNetX maintains a multi-level community state, expands the updated region, closes it over affected communities, and contracts these communities into compact local instances. This affected-community closure and contraction preserve solver context while restricting computation to the changed part of the graph. The same interface can wrap modularity heuristics, graph-clustering models that use node features, and native dynamic solvers as local backends. We evaluate ComNetX through a multi-backend study on six real networks, longer real-data streams for topology-based backends, and controlled dynamic stochastic block model stress streams. The results show that ComNetX can preserve the quality of strong modularity-based solvers while reducing update time on large graphs: in paired runs on the largest real graph, Local Leiden keeps final modularity within 0.006 of full-snapshot recomputation while achieving a 41.9 +/- 0.2x speedup. The combined protocols also identify regimes where locality breaks down and a full refresh is preferable.
[AI-101] AI Brain Death Detection and Islamic Law
链接: https://arxiv.org/abs/2608.16903
作者: Muhammad Aurangzeb Ahmad
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: Muslims in ML workshop 43rd International Conference on Machine Learning, Seoul, South Korea (2026)
Abstract:The deployment of machine learning systems capable of detecting covert consciousness in neurologically injured patients creates a profound challenge at the intersection of clinical medicine, AI ethics, and Islamic jurisprudence. We argue that the shift from binary clinical verdicts to probabilistic, temporally granular neural-state estimates should be addressed through three foundational constructs in Islamic legal epistemology: bayyina (clear evidentiary proof), yaqin (epistemic certainty), and the theologically mandated agnosticism about there (soul). We survey the current technical literature on AI-based consciousness detection, map it onto the landscape of Islamic brain death scholarship, and identifykey challenges. We also discuss its implications for AI surrogate decision systems.
[AI-102] What If AI Carried Her Imagination? Black Girls as Creators in an AI Storytelling Weekend Program
链接: https://arxiv.org/abs/2608.16896
作者: Chun Li,Lauren Brown,Hubert Asare,Shawna Patterson,Dennis Henderson,Ericka Roland,tara Nkrumah,Angela E.B. Stewart
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:This paper presents the design and outcomes of a seven-weekend AI storytelling program developed for Black girls aged 10-12. Grounded in Afrofuturism and Black feminist thought, the program adopted AI-enabled counter-storytelling, supported the development of foundational AI literacies, and fostered future-oriented imagination. Activities included brainstorming AI-related topics, developing character and story plots, and delivering collaborative group presentations. Drawing on the analysis of learners’ artifacts from the case study, findings show that participants created Afrofuturist narratives rooted in their identities and everyday experiences. At the same time, they developed core AI literacies, including prompt engineering, bias critique, and awareness of data privacy. This program demonstrates that integrating Afrofuturist storytelling with generative AI in informal learning spaces can be a powerful approach for engaging Black girls in computer science education.
[AI-103] A Framework for Using and Evaluating LLM s as Surrogate Experts in Security Surveys: Reliability Bias and Implications
链接: https://arxiv.org/abs/2608.16893
作者: Despoina Giarimpampa,Roland Meier,Tegawendé F. Bissyandé,Vincent Lenders,Jacques Klein
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:Expert surveys are widely used in security research to study practitioner workows and decision-making, yet recruiting domain experts - especially in Security Operations Centres (SOCs), where analysts face high workload, burnout and confidentiality constraints - is difficult and often results in small samples. Large language models (LLMs) oer an appealing alternative by generating synthetic responses at scale, but little guidance exists on when such surrogate participants are reliable. We present a methodological framework for evaluating LLMs as substitutes or supplements to expert survey respondents. Using responses from SOC professionals, we compare persona-based and aggregate LLM-generated answers across multiple models and prompting settings. We measure stability, inter-model agreement and alignment with human responses. Our results show that although LLMs produce internally consistent answers, they systematically diverge from experts, exhibiting reduced variance, central tendency bias and homogenised opinions. This work contributes methodological evidence and practical guidance to the security research community on the appropriate use and limitations of LLM-generated survey responses. We conclude that LLMs are useful for piloting and hypothesis generation but not for replacing expert elicitation, and we discuss implications for researchers using LLM-augmented surveys.
[AI-104] Runtime Governance for Agent ic AI: Action-Boundary Control with Trusted Provenance and Fail-Closed Execution
链接: https://arxiv.org/abs/2608.16891
作者: Adam Mazzocchetti
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Cryptography and Security (cs.CR); Computers and Society (cs.CY)
备注:
Abstract:Agentic AI systems request tool actions that can modify files, send messages, launch jobs, or change workflow state. This shifts the safety problem from harmful text generation to harmful operational side effects. Prompt-level governance can shape model behavior, but it does not create an execution boundary. We introduce Aegis, a runtime governance system that treats model outputs as action proposals and mediates them through a trusted decision layer before tool execution. The model proposes; the trusted runtime decides. Aegis evaluates proposals against active policy state, resolves provenance server-side, fails closed under uncertainty, and routes selected cases through Senate-style settlement, a quorum- based non-unilateral authorization path. We evaluate Aegis on a repeated sandbox corpus spanning five run families, 42 tasks, three conditions, and ten repeats per family. Across 6,300 rows, prompt-policy conditioning produced 79 risky comparator-path leakage rows. Across 2,100 Aegis-governed rows, the system recorded zero governed mock-tool applications and zero governed risky side-effect completions. All 1,832 Aegis-attempted governed rows preserved trusted Aegis-resolved provenance, and all 1,019 Senate-settled rows had quorum and final signed tally evidence. These results do not prove general autonomous-agent safety. They support the narrower systems claim that, in this evaluated sandbox corpus, runtime action-boundary governance prevented observed risky proposals from becoming governed side effects.
[AI-105] GxP-Agent : Process-DAG Topology for Reliable Clinical Trial Programming with LLM Agents
链接: https://arxiv.org/abs/2608.16890
作者: Jaime Yan
类目: Artificial Intelligence (cs.AI)
备注: Preprint. 9 pages main text, 3 figures, plus references and appendix
Abstract:Clinical trial programming – transforming study protocols into analysis-ready datasets under CDISC standards – is a bottleneck in regulatory submissions, yet LLM-based code generation fails catastrophically on this task: across 11 single-shot attempts with five frontier models, none produces a valid subject-level analysis dataset. We introduce GxP-Agent, a multi-agent system that encodes regulatory process ordering as a directed acyclic graph (DAG), decomposing monolithic dataset generation into 15 domain-specific nodes executed by worker agents with pharmaverse skill context, validation gates, and conditional retry. On CDISC-Bench, a new execution-based benchmark built from the FDA pilot submission CDISCPilot01 (254 subjects, 49 ground-truth ADSL variables), GxP-Agent with Claude Sonnet 4.6 achieves 100% structural match (49/49 variables, 254 correct records) across three independent runs, compared to 59.2% for the best retrieval-augmented baseline and 0% for all single-agent and flat multi-agent approaches. The DAG topology also enables weaker models: GPT-4.1 achieves 59.2% mean structural match under the same DAG, where it scores 0% under every other architecture. The approach generalizes to ADAE (adverse events; 9-node branching DAG, 55 variables, 1,191 records), achieving 100% structural match on the first attempt. These results demonstrate that encoding domain process knowledge as graph topology – rather than relying on LLM reasoning alone – is a key enabler for reliable, GxP-compliant clinical trial programming.
[AI-106] he Working Set of a Coding Agent : Coherence Debt in Repository-Scale Tasks
链接: https://arxiv.org/abs/2608.16630
作者: Bardia Mohammadi,Lars Klein,Aman Chadha,Akhil Arora,Laurent Bindschaedler
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Repository-scale coding requires an agent to keep tests, imports, configuration, and migration rules consistent within a bounded context window. We model this as reconstructing a coupled-fact graph: at each edit, a required fact comes from recent context or parametric memory, and the facts covered by neither form coherence debt. We supply and withhold each channel and inject faults across seven models and five harnesses. As expected, no model completes a task on an unseen API with both channels empty, and putting the facts in the prompt restores success. When a rename defeats what models memorized about a real library, all seven fail in the same place, passing and missing the same tests. Availability decides the outcome and distance does not: withholding a fact costs exactly the work it supports, and a supplied fact works as well far from the edit as next to it. Harnesses pay unequal prices for it: configurations that all pass every test differ more than tenfold in tokens consumed because they rebuild the same content at different rates, and spending more recovers nothing when facts are withheld. A missing fact produces wrong work rather than absent work: an agent asked to act acts, fabricating the file or guessing the value, so instruments built on reads look for a hole already filled. How often it says it is blocked instead is a property of the model, from every trial to none. Availability does not settle every edit: where standard and code disagree, agents follow the standard even when it prescribes the worse code, so a stale convention file costs more than no file. Because parametric memory substitutes for reading, on SWE-bench, where models likely know the repositories, reads no longer predict success. Harnesses should keep the facts an edit depends on available when the agent writes, and check that availability against what the agent produces rather than what it reads.
[AI-107] Post-Deployment Accountability in AI Governance: A Cross-Regulatory Empirical Analysis of AI Incidents
链接: https://arxiv.org/abs/2605.16281
作者: Ummara Mumtaz,Rabi Noor,Summaya Mumtaz
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Software Engineering (cs.SE)
备注:
Abstract:Post-deployment accountability has become central to AI governance, yet little empirical evidence shows whether monitoring, incident reporting, and impact assessment obligations are visible when AI systems fail. This study analyzes real-world AI incidents from the AI Incident Database (2020-2026) and codes them against nine post-deployment provisions from the EU AI Act, the NIST AI Risk Management Framework, and the GDPR. The findings show substantial accountability gaps: 77.1% of incidents lack evidence of EU AI Act post-market monitoring, and 99.6% lack documented Data-Protection Impact Assessment evidence. Governance gaps are also systemic, with 9.8% of incidents simultaneously non-compliant under two or more regimes. Incidents detected through internal monitoring show much higher compliance than externally detected incidents (87.5% vs. 5.3% under the EU AI Act; 95.8% vs. 58.1% under NIST), suggesting that monitoring capacity is a key condition for effective post-deployment governance. Building on these findings, the paper proposes the Proactive AI Governance Compliance Framework (PAGCF), a four-phase lifecycle for pre-deployment assessment, continuous monitoring, incident preparedness, and cross-framework verification.
[AI-108] Communicating Credit Risk with Large Language Models : Evaluation of Explanations from Standard and Alternative Data-Based Models
链接: https://arxiv.org/abs/2608.17715
作者: Sahab Zandi,Noah Kostesku,Christophe Mues,María Óskarsdóttir,Cristián Bravo
类目: Risk Management (q-fin.RM); Artificial Intelligence (cs.AI)
备注: 49 pages, 14 figures
Abstract:Credit decisioning is a high-stakes task in which model outputs must be accurate and explainable to support compliant decisions. Although modern credit risk models such as eXtreme Gradient Boosting (XGBoost) and Graph Neural Networks (GNNs) improve predictive performance, their explanations are often too technical for stakeholders creating communication gaps that can shape approvals, denials, and fairness judgments. We examine whether Large Language Models (LLMs) can serve as explanation layers that translate post-hoc explanation artefacts into stakeholder-appropriate risk narratives. Using Freddie Mac single-family loan-level data, we develop three pipelines: standard tabular (XGBoost + SHAP), and two with alternative data, a pure network-based (GNN + GNNExplainer), and a bimodal one (combining tabular and network data). We generate narratives with three LLM configurations: a small fine-tuned LLM (Gemma 3 4B), a large fine-tuned LLM (DeepSeek R1 70B), and a zero-shot commercial LLM (Gemini 2.5). Explanation quality is evaluated through automated checks across all pipelines and a human study of bimodal explanations comparing credit risk professionals and non-professionals on eight decision-relevant dimensions. We have three main findings. First, the pipeline accounts for higher variance in evidence-grounding scores than the language model, meaning that the binding constraint on explanation quality is the evidence representation, not the model used. Second, the explanation narratives reliably name the influential factors but are less reliable when stating the direction of influence, which may be consequential for adverse-action communication. Finally, professionals apply stricter evidentiary standards than non-professionals. We discuss implications for the governance of risk models, including deployment considerations and the value of domain-aligned LLMs in regulated credit settings.
[AI-109] DMT-Dens: Density-preserving manifold visualization for biological data
链接: https://arxiv.org/abs/2608.17571
作者: Ruizhe Wang,Yixuan Dong,Bolin Yang,Bingo Wing-Kuen Ling,Fuji Yang,Zelin Zang
类目: Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI)
备注: 22 pages, 5 figures, 2 tables; includes supplementary material. Source code: this https URL
Abstract:Motivation: Low-dimensional embeddings are widely used to explore cell-state heterogeneity in single-cell and other high-dimensional biological data. Although many methods preserve local neighborhoods, they may distort the apparent sampling density of processed observations, altering the visual contrast between dense and sparse regions and complicating the interpretation of rare, transitional, or continuous cell-state populations. Results: We present DMT-Dens, a parametric manifold-visualization method built on a latent-token Transformer encoder. The model integrates rank-based manifold alignment with hard-pair aggregation. To preserve density, it optimizes a loss based on the Pearson correlation between k-nearest-neighbor log-radius estimates in the processed input and two-dimensional embedding spaces. Benchmark evaluations demonstrate strong density preservation, particularly on biological datasets, while retaining competitive label separability. Availability: Source code, data-processing scripts, and resolved experiment configurations are available at this https URL.
[AI-110] Leverag ing generative hallucination and biophysics-informed modeling for unified biomolecular sequence-structure co-design
链接: https://arxiv.org/abs/2608.17381
作者: Xuefeng Liu,Mingxuan Cao,Xiao Luo,Songhao Jiang,Tobin Sosnick,Jinbo Xu,Louis Maher,Rick Stevens
类目: Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Biomolecules (q-bio.BM)
备注:
Abstract:Biomolecular design underpins applications from molecular recognition to therapeutics and synthetic biology, yet de novo interaction design remains challenging-especially for DNA/RNA, underexplored non-protein modalities with scarce, heterogeneous complex data and sharper geometric and chemical constraints. We introduce MCTH (Monte Carlo Tree Hallucination), an inference-only framework that casts all-atom sequence-structure co-design as uncertainty-aware planning over hallucinated states from pretrained folding and inverse-folding models, with optional biophysical control within the same decision loop. MCTH treats these models as frozen black-box operators and uses Monte Carlo Tree Search to allocate a fixed inference budget across competing design trajectories, incorporating model confidence and uncertainty, as well as cross-expert consensus/disagreement when multiple predictors are available. Across protein-RNA, protein-DNA, protein-protein, and protein-ligand design, matched-budget experiments show that adaptive search improves over simpler sampling and cycling strategies, while held-out AlphaFold3 and Chai-1 evaluations demonstrate transfer beyond the search-time oracle. MCTH provides a shared planning layer across modalities while allowing task-specific folding, inverse-folding, and biophysical modules, requiring no fine-tuning or backpropagation through component models.
[AI-111] Inductively Scalable Single-Step Neural Surrogates for Wave-Scattering Inverse Problems
链接: https://arxiv.org/abs/2608.17344
作者: Charles Dove,Laura Waller
类目: Optics (physics.optics); Artificial Intelligence (cs.AI)
备注:
Abstract:Neural network surrogates are an emerging alternative to traditional electromagnetic wave simulators like finite-difference time-domain (FDTD); their goal is to replace rigorous physical simulations with pre-trained neural networks that solve wave-scattering forward and inverse problems orders of magnitude faster. However, nonrecurrent, single-step surrogates have scaled only to a few tens of simulation variables. Here, we show that this barrier can be overcome by dynamically generating salient training examples during training, rather than randomly sampling the large space of possible examples. We introduce an algorithm that runs in parallel with surrogate training, using gradient ascent to search refractive-index and source configurations for cases where the surrogate disagrees with a full-wave ground-truth simulator. We also use source and ground-truth normalization with an evolving replay dataset to stabilize and accelerate learning. Using this approach, we train a fast, single-step surrogate for two-dimensional wave scattering with up to 41,772 controllable variables, including dense, freely configurable grids of refractive indices and complex-valued sources. The resulting neural surrogate is robustly accurate across diverse structured and unstructured examples and generalizes inductively to larger domains, reaching over 3 million controllable variables without retraining, a 73.8\times increase. We demonstrate the surrogate on large-scale forward simulations and inverse design of freeform beam splitters and gradient-index (GRIN) lenses up to 98 wavelengths wide, showing comparable or better performance than FDTD-based designs, with speedups from 1.29\times to 26.5\times . These results demonstrate a practical path toward fast, robustly accurate, inductively scalable neural simulators for photonic inverse design and other wave-scattering inverse problems.
[AI-112] SPACE: Sample-cloud Predictive Adaptive Conformal Ellipsoids for Multivariate Time-Series Forecasting
链接: https://arxiv.org/abs/2608.17333
作者: Baishi Li,Kelvin J.L. Koa,Ke-Wei Huang
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Modern probabilistic time-series forecasters often express uncertainty through forecast samples. While typically converted into nominal prediction regions using empirical quantiles, these model-implied sets lack formal coverage guarantees and frequently deviate from nominal targets under distribution shift. Existing multivariate conformal methods can calibrate these regions online, but they typically estimate geometry from historical residuals using fixed or accumulating look-back windows. This reliance on the past limits their ability to exploit the instantaneous dependence structure of current predictions and leaves them vulnerable to stale-regime contamination. To address this, we propose SPACE, a conformal wrapper for sample-generating multivariate forecasters. SPACE constructs ellipsoidal joint prediction regions by estimating time-local covariance geometry directly from the current forecast sample cloud, calibrating the region’s radius via a dynamic backward window-selection scheme. Across diverse multivariate datasets, probabilistic forecasters, and conformal baselines, SPACE consistently brings realized joint and rolling coverage closer to the nominal target, achieving superior coverage-efficiency tradeoffs relative to competing wrappers.
[AI-113] Maximum Tsallis Entropy Distributions for Robust and Efficient Sparse Learning from Correlated Data
链接: https://arxiv.org/abs/2608.17244
作者: Kai Yang,Masoud Asgharian,Celia M.T. Greenwood
类目: Optimization and Control (math.OC); Artificial Intelligence (cs.AI); Statistics Theory (math.ST); Computation (stat.CO); Machine Learning (stat.ML)
备注: 38 pages; thesis manuscript (July 2024); also available at this https URL
Abstract:This paper addresses the limitations of Gaussian distribution assumptions in statistical sparse learning, particularly in modeling correlated and heterogeneous data. Conventional Gaussian models often lack robustness towards outliers and underlying distribution assumptions. To overcome these limitations, we propose the use of the q Gaussian distribution, derived from Tsallis entropy maximization, as a robust alternative. This is notably relevant in biostatistics, where the presence of correlated observations and heterogeneity, such as in genetic and longitudinal studies, is prevalent. Our contributions include modeling of correlated data through the re-derived multivariate probability density function from Tsallis entropy maximization, thereby addressing the limitations inherent in conventional Gaussian models. Furthermore, we introduce a novel framework that adapts numerical methods designed to find equilibria in flows to tackle composite optimization problems prevalent in statistical sparse learning. Applying this framework to the Hager-Zhang conjugate gradient algorithm \citeHager2005, we develop a numerically stable and efficient algorithm for sparse statistical learning. The q Gaussian distribution, informed by the principle of maximizing Tsallis entropy, presents a viable and flexible alternative to Gaussian-based methods. This paper not only contributes to the theoretical understanding of statistical distributions and optimization techniques, but also paves the way for practical data analysis.
[AI-114] QuantumNovelty: A Skill-Orchestrating Language Agent for Referee-Style Review and Patentability Screening of Quantum Papers and Patents
链接: https://arxiv.org/abs/2608.16900
作者: Shlomo Kashani
类目: Physics and Society (physics.soc-ph); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Quantum Physics (quant-ph)
备注:
Abstract:Language-model agents increasingly produce quantum-science results; we ask whether the same agentic paradigm can also scrutinize them in an auditable, reproducible, and cost-transparent form. We present QuantumNovelty, an open-source skill-orchestrating language agent that both generates quantum-computing artifacts (papers, Pareto-front ansatz candidates, and patent drafts) and reviews them through simulated referee and patent-examiner panels. Its design contribution is an audit-and-falsify layer of deterministic gates – strict Pareto domination, numerical recomputation from on-disk artifacts, Wilson small-sample intervals, and a cross-vendor consensus guard – that constrains, rather than generates, the claims allowed to survive; every model call is logged with backend, token count, and cost. We make no accuracy claim against human experts, and validate only what is checkable without human labels: on a planted adversarial corpus the deterministic gates catch every planted overclaim with no false positives, and on a first deployment (six manuscripts and one granted patent, at a measured cost of about twenty-four US dollars) the panels are directionally more conservative than the public acceptance record, on a one-sided sample. The framework is decision support, not a replacement for peer review or patent examination, and we report in full where its mechanisms remain unexercised on real inputs.
机器学习
[LG-0] he concentration game: Bayesian updating regret and information
链接: https://arxiv.org/abs/2608.18061
作者: Akshay Balsubramani
类目: Machine Learning (cs.LG); Computer Science and Game Theory (cs.GT); Probability (math.PR); Statistics Theory (math.ST)
*备注:
Abstract:We give a two-player zero-sum repeated game between a learner and nature whose value identity generates Bayesian updating and an exact accounting of exponential-weights regret at once, and supplies the comparator-class variational form that a wide class of concentration phenomena share. The terminal payoff is the most a comparator can gain at fixed relative entropy from the prior, and the one-step constraint is an information budget on nature’s move under the learner’s mixed action. With the learner’s move otherwise unrestricted, Gibbs/Bayes weights emerge as its unique Bellman equalizer – the mixed action that makes the per-round loss independent of which direction nature moves – with log-partition functions playing the role of value functions. The regret decomposes exactly into three parts: a per-round information loss reflecting the variation in observed outcomes, an additive retempering drift that accounts exactly for any change of measurement scale between rounds, and the information the comparator carries relative to the prior. The variance and bounded-range proxies that drive standard regret bounds are looser relaxations of this decomposition, which holds generally and governs them all. Both players’ strategies are read off from the decomposition term by term, and repeated play yields an information-theoretic ledger of self-play in place of the usual quadratic-variation surrogate. The same comparator-class geometry accounts for the classical large-deviation bounds, and methods across bandits, posterior sampling, aggregation, and boosting are specializations of the one regret decomposition.
[LG-1] abNSM: Neural Sparse Mixer for Tabular Regression
链接: https://arxiv.org/abs/2608.18026
作者: Ali Eslamian,Qiang Cheng
类目: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE)
*备注:
Abstract:Large-scale, high-dimensional tabular regression remains challenging: tree-based models are robust but lack end-to-end representation learning, while deep models enable flexible feature learning but often incur costly interaction modeling and sensitivity to noisy or redundant features. We propose TabNSM, a scalable regression framework that extends our earlier sparse-attention and mixer architectures. At its core, the Adaptive Sparse Interaction Module (ASIM) integrates foreground feature discovery, sparse local interaction encoding, and Feature-Token Mixing, providing near-linear complexity under fixed sparse configurations. For regression, TabNSM introduces three complementary components: a Multi-Stage Regression Head for progressive prediction refinement; GridLoss, an ordinal-aware soft-binning objective that incorporates target structure into representation learning; and RISE (Reweighted Instance Sampling by Error), a difficulty-aware sampling strategy based on loss-quantile bins. Across nine real-world regression benchmarks, TabNSM delivers strong predictive performance and practical scalability, with particularly consistent gains on high-dimensional and heterogeneous datasets. These results demonstrate that selective interaction modeling, structured regression supervision, and difficulty-aware sampling provide an effective and scalable approach to deep tabular regression.
[LG-2] Revisiting WEASEL 2.0: Reproduction Sensitivity and an Adaptive Ensemble-Size Rule ECML KDD2026 ALT
链接: https://arxiv.org/abs/2608.18021
作者: Cian Higgins,Gerard Carrigan,Pinar Sungu Isiacik,Georgiana Ifrim
类目: Machine Learning (cs.LG)
*备注: 24 pages, 7 figures. Accepted at the 11th International Workshop on Advanced Analytics and Learning on Temporal Data (AALTD 2026), held at ECML/PKDD 2026, Naples, Italy
Abstract:WEASEL 2.0 is a dictionary-based time series classifier that combines dilated sliding windows with a randomised hyperparameter ensemble and a fixed-size dense feature representation. Two of its hyperparameter choices, the maximum ensemble size and the maximum window size, are specified by simple thresholding rules whose chosen thresholds are not empirically justified in the original paper. In this work we reproduce WEASEL 2.0 on 114 UCR datasets, achieving a mean accuracy of 0.865 and median of 0.928, closely matching the published values (Wilcoxon signed-rank, p = 0.655). We then test the sensitivity of four design choices: the downstream classifier, the absence of feature weighting, the maximum window-size rule, and the maximum ensemble-size rule. The first three are robust to perturbation. The fourth is over-provisioned for long-series datasets, motivating an adaptive rule that sets the maximum ensemble size from series length and number of classes. Evaluated on fixed-length datasets, the adaptive rule reduces peak fit memory by a median of 37 MB (mean 395 MB) and fit time by a median of 0.4 s (mean 4 s), with a median accuracy change of 0% (mean -0.11%). Memory and time savings concentrate on long-series datasets where the original rule allocates the largest ensemble size.
[LG-3] Composing Flow-Matching Energies with Known Physics: Generation OOD Detection and Inversion on PDE Fields
链接: https://arxiv.org/abs/2608.18004
作者: Yixuan Sun,Anirban Samaddar,Sandeep Madireddy
类目: Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
*备注:
Abstract:Probabilistic modeling of physical fields benefits from both a data-driven prior and known physical structure such as the governing equations. Energy-based models (EBMs) are a natural fit since energies compose additively, which enables augmenting physics information during inference. However, EBMs have been difficult to train and sample from due to the intractable partition function. We show in this work that flow matching models with a potential-induced velocity yield an explicit scalar energy at all transport times, whose gradient is exactly the converted learned score and which recovers the marginal negative log-density at the population optimum. The time-dependent energy functions are obtained purely from the matching regression objective on an independent linear Gaussian interpolation, without a variational form or additional MCMC steps, and the sampling retains the flow ODE. Access to the energy function from a trained model serves three roles: energy-corrected data generation, energy as a scoring function for out-of-distribution (OOD) detection, and energy compositional posterior sampling for inverse problems. In particular, we show the explicit energy permits general MCMC samplers in the predictor-corrector sampling framework, reducing PDE residual and spectral distance compared to the flow ODE baseline. Furthermore, we demonstrate utilizing the data energy and physics-based energy (e.g., PDE residuals) as complementary mechanisms to improve detection accuracy for OOD tasks. In addition, we explore the connection to MCMC-based inference for inverse problems by composing the energy with a quadratic observational likelihood that yields a posterior energy, used as an explicitly chosen family of inference-time targets.
[LG-4] Recirculation
链接: https://arxiv.org/abs/2608.17981
作者: Michael C. Mozer,Shoaib Ahmed Siddiqui,Danny Sawyer,Sunny Sanyal,Rosanne Liu
类目: Machine Learning (cs.LG)
*备注:
Abstract:We describe an inference-time architectural enhancement for off-the-shelf foundation models that markedly reduces perplexity and boosts accuracy across generation and reasoning tasks. Our approach incurs essentially no additional latency during generation, though it requires serial processing in the prefill phase. Motivated by the fundamental limitation that state updates in feedforward transformers are bounded by model depth, our technique, recirculation, introduces a specific form of recurrence that allows the model to act as a dynamical system and track belief states. We distinguish this technique from chain-of-thought computation—which is better reserved for complex inferences rather than basic state tracking—as well as from popular depth-recurrence techniques (looping) and the costly training of recurrent transformers. We also propose and evaluate an adaptive variant of recirculation which requires only light tuning of hyperparameters while freezing the original model weights. Relative to the off-the-shelf baseline, adaptive recirculation achieves remarkable gains on the Gemma3 family, including a 23% reduction in perplexity on a suite of datasets, a 21% increase in accuracy on GSM8k, and reliable improvements in accuracy on other downstream tasks. Our training-free approach succeeds by leveraging the model itself to inform architectural modifications, suggesting a route to architectural evolution guided by a trained network’s properties rather than forced, arbitrary design choices.
[LG-5] Evaluating and improving crop-yield forecasting methods during extreme drought
链接: https://arxiv.org/abs/2608.17971
作者: Shrey Gupta,Yi Ming,George Mohler
类目: Machine Learning (cs.LG)
*备注:
Abstract:The impact of climate variability on food production has led to the creation of various forecasting models that uses machine learning (ML), numerical weather predictors (NWP) or a hybrid of ML-NWP models to identify structural and physical relationships between meteorological drivers and crop growth, in order to predict crop yield. Droughts, for example the 2012 Midwestern US (Corn Belt) drought, are extreme events that affect crop production and test the limits of these forecasting models. Using 16 meteorological drivers as predictors, we compare ML (non-deep learning) and deep learning forecasting models to predict the county-level corn yield for the extreme drought year, 2012. This forecasting problem is characterized by a dissimilarity between the feature distributions of the training and test data, where the meteorological conditions of the extreme drought year fall outside the range of historically observed values. Additionally, the dataset consists of spatial and temporal irregularities where counties with missing yields introduce spatial sparsity and the use of only a subset of daily values per year introduce temporal sparsity. To overcome this, we use sample weighting and feature selection as modifications to improve our forecasting models. These modifications lead to an improvement for ML models; however, the deep learning model VITA shows little to no improvement. While VITA outperforms the ML models with or without modifications, our current study sheds light on the effect of dissimilarity between train and test feature distributions on forecasting models, compares deep learning versus non-deep learning models, and introduces modifications that are effective for non-deep learning models.
[LG-6] Understanding the Surprising Generalization Properties of Tabular Foundation Models
链接: https://arxiv.org/abs/2608.17957
作者: Nour Shaheen,Junwei Ma,Alex Labach,Frank Hutter,Valentin Thomas,Anthony L. Caterini
类目: Machine Learning (cs.LG)
*备注: This work extends our previous work, Generalization Can Emerge in Tabular Foundation Models From a Single Table ( arXiv:2511.09665 )
Abstract:Tabular Foundation Models (TFMs) increasingly rely on in-context learning, where a model receives labelled examples at inference time and predicts labels for new inputs without updating its weights. Existing TFMs are typically trained on either massive synthetic corpora or very large collections of real datasets. In contrast, we show that surprisingly strong transfer can emerge from self-supervised pre-training on just a single real table. In this setting, we also find that tables tend to be either broadly useful or broadly poor regardless of downstream prediction task, and that the strongest predictor of usefulness is the number of features rather than the number of instances. This leads to a task-centric interpretation of tabular pre-training: the number and the quality of tasks are essential for the pre-training of TFMs. We show that the same task-centric perspective can help corpus design at scale: fine-grained column-level pre-processing consistently improves downstream performance, while no improvements are observed when we filter or deduplicate at the dataset level. Finally, we offer a new perspective for how TFMs generalize: we believe that tabular in-context generalization is largely retrieval-based, and good models are those that learn to identify relevant examples in the provided context and aggregate them well. The mechanics of TFMs have been relatively understudied; our task-centric, retrieval-based perspective offers a new framework to guide future model and corpus design. Comments: This work extends our previous work, Generalization Can Emerge in Tabular Foundation Models From a Single Table (arXiv:2511.09665) Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.17957 [cs.LG] (or arXiv:2608.17957v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.17957 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-7] Hybrid ML for Lightweight Pre-Route Delay Estimation in Open-Source IC Design
链接: https://arxiv.org/abs/2608.17914
作者: Marvin Castro Castro,Erick Carvajal Barboza
类目: Machine Learning (cs.LG)
*备注:
Abstract:Static Timing Analysis (STA) is a critical step in the design flow of digital integrated circuits, however, obtaining accurate delay estimations can represent a challenge when limited information regarding physical design is available. In response, this work presents a hybrid and light-weight machine learning (ML) based approach that combines a decision tree with linear regression to improve pre-routing delay estimations generated by the open-source RTL-to-GDSII tool OpenLane. The proposed model achieves an 80% reduction in error compared to OpenLane’s estimates, demonstrates a 71% improvement even without utilizing OpenLane-specific parameters. Overall, this method offers an alternative to traditional delay propagation techniques and more complex machine learning models that is not only accurate, but is also over 300 times smaller, 2 times faster and offers a higher explainability.
[LG-8] Dynamic Compression in Recurrent Networks
链接: https://arxiv.org/abs/2608.17896
作者: Jyothish Pari,Ryan Bahlous-Boldi,Pulkit Agrawal
类目: Machine Learning (cs.LG)
*备注:
Abstract:Recurrent models process long contexts efficiently by compressing their history into a fixed-size state, but modern architectures typically do so in a single causal pass over the sequence. Each input must therefore be compressed before the model knows how it will later be used, forcing a limited state to compromise across possible future demands. We introduce dynamic compression, which allows a recurrent model to selectively revisit past tokens and revise its fixed-size state through additional recurrent updates. The model need not preserve every part of the history at uniformly high fidelity in its recurrent state, because lower-fidelity information can be revisited from the retained raw sequence when it becomes relevant. We study this in a controlled setting where the model first learns multiple functions in-context and, later in the same sequence, encounters a series of few-shot tasks that each require it to identify and reuse one of those functions. A single-pass model must preserve every function at sufficient fidelity for any future task, whereas selective re-scanning allows the model to revisit and refine only the function currently needed. We find that dynamic compression substantially reduces the recurrent state required for accurate reuse and scales more favorably as the number of stored functions grows. These results demonstrate a computation–memory tradeoff in which recurrent models can spend more computation revisiting their history to make more effective use of a fixed-size state.
[LG-9] Efficient Resource Optimization for Split Federated Learning
链接: https://arxiv.org/abs/2608.17849
作者: Wei Wei,Xianhao Chen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Split federated learning (SFL) has emerged as a powerful paradigm for model training at the edge. However, SFL inherently involves discrete decision variables for model splitting and resource allocation, resulting in a challenging mixed-integer problem. Consequently, prior optimization schemes for SFL are either \textitheuristic or \textitcomputationally inefficient, which cannot handle large-scale user populations. To address this limitation, this work establishes an efficient optimization framework for SFL under resource-constrained networks. Our framework jointly optimizes model splitting and resource allocation to minimize training cost, which is defined as the weighted sum of latency and energy costs. We first study the model splitting problem and develop a polynomial-time algorithm that achieves the global optimum. Then, we extend the approach to the joint model splitting and resource allocation problem. In this case, we formulate it as a two-dimensional master problem and develop an efficient approximation method with a (1+\epsilon) -approximation guarantee. Extensive experiments show that the proposed approach provides efficient solutions to strike the optimal energy–latency tradeoff.
[LG-10] MoRAX: Mobility-based Representation Augmentation for Geospatial Foundation Models
链接: https://arxiv.org/abs/2608.17848
作者: Ya Wen,Jixuan Cai,Yulun Zhou,Alec Kirkley
类目: Machine Learning (cs.LG); Social and Information Networks (cs.SI)
*备注:
Abstract:Geospatial Foundation Models (GFMs) are emerging as a powerful paradigm for learning semantically rich and geographically consistent visual and physical representations. However, their reliance on Earth-observation (EO) data leaves information about human activity largely underrepresented. Human mobility data reveals the functional and relational structure between regions that is missing from EO data, but is often limited only to the city where it is observed, making it challenging to use for transferable urban representation learning. We introduce MoRAX, a lightweight framework for augmenting geospatial embeddings with functional structure derived from human mobility. MoRAX preserves the coverage and consistency of a GFM while providing information about the functional connectivity among urban regions, permitting zero-shot deployment in unseen cities with or without available mobility data. Across four target cities spanning two countries, the MoRAX teacher model, which observes mobility, consistently outperforms GFMs and strong urban representation baselines in eight socioeconomic and environmental prediction tasks. Meanwhile, the student model, which never takes mobility data as input, approaches the teacher in performance on most tasks. Transfer results across countries further demonstrate that modulation conditioned on mobility flows provides a general mechanism for grounding geospatial foundations in the human dimension of cities.
[LG-11] Leverag ing Association Context Retrieval in Knowledge Edit- ing to Build White-Box Attacks on LLM s
链接: https://arxiv.org/abs/2608.17836
作者: Roman Maksimov,Vladimir Aletov,Vladimir Solodkin,Dmitry Bylinkin,Daniil Medyakov,Aleksandr Beznosikov
类目: Machine Learning (cs.LG)
*备注:
Abstract:As large language models (LLMs) are granted increasing autonomy, it is essential to investigate methods that can induce unsafe behavior. We propose a novel white-box attack inspired by locate-then-edit approaches from the field of Knowledge Editing. Our choice is motivated by the observation that models edited with such schemes tend to assign unusually high prediction probabilities to the edit target, a property that is particularly advantageous when designing attacks. We modify the editing framework by incorporating as- sociative knowledge retrieved from the model, thereby extending constraint removal to an entire thematic category rather than being limited to prompts from a predefined dataset. Experiments with various archi- tectures demonstrate improved attack effectiveness over competing methods without dealing critical damage to general model performance.
[LG-12] Fourth-Moment Geometry of Rademacher Sums
链接: https://arxiv.org/abs/2608.17802
作者: Peigan Gao,Jian Qian
类目: Machine Learning (cs.LG); Probability (math.PR)
*备注:
Abstract:Let \varepsilon_1,\ldots,\varepsilon_n be independent Rademacher signs and let a=(a_1,\ldots,a_n)\in\R^n satisfy the normalization below. For the normalized Rademacher sum, we determine how its higher moments depend on the fourth-order mass. Combining a sharp fixed-q moment envelope with a separate argument below the convexity threshold gives the Gaussian stability inequality for the full range p\geq4 of this linear-in-q bound. The same fourth-order framework determines the sharp finite dimensional L_p/L_4 Khintchine constant for p\geq5 , with the flat coefficient vector as the extremizer. These results settle the conjectures of Jakimiuk and of Barański, Murawski, Nayar, and Oleszkiewicz stated below. We also prove Jakimiuk’s conjectured quadratic stability estimate at p=3 . The resulting bounds retain information about sparsity and effective dimension, with applications to Rademacher random projections and randomly signed errors; those applications are not developed further here. Their Laplace-transform form also gives coefficient-sensitive tail bounds. The proofs are discovered with substantial assistance from ChatGPT 5.6 Sol.
[LG-13] Diff-DDoS: Realistic Cyber-Physical Attack Synthesis and Robust Detection for 5G-Enabled CPS Using Tabular Diffusion Models
链接: https://arxiv.org/abs/2608.17796
作者: Bilal Hussain,Xiao Tang,Qinghe Du,Tan Li,Muhammad Azhar,Danista Khan
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: Accepted manuscript. IEEE Transactions on Industrial Informatics, paper no. TII-26-6533. 11 pages + 9-page supplementary material (ancillary PDF). © 2026 IEEE. Personal use of this material is permitted
Abstract:Deep learning-based DDoS detectors for 5G-enabled cyber-physical systems face scarce labeled attack data and unrealistic synthetic substitutes, which limit robustness against adaptive adversaries. Detectors trained on hand-crafted attacks with fixed scaling multipliers degrade catastrophically (F1-score drops of about 47 percent to 100 percent, depending on scenario) when confronted with realistic, distribution-preserving samples. We propose Diff-DDoS, a three-phase framework for realistic attack synthesis and robust detection using tabular diffusion models. Phase 1 trains a baseline CNN cell-level detector on spatiotemporal grids from call detail records (CDRs). Phase 2 trains a tabular denoising diffusion probabilistic model (TabDDPM) on normal CDR aggregates to generate realistic attacks and expose detector vulnerabilities. Phase 3 introduces adversarial diffusion training (ADT), using inverse classifier guidance to generate hard yet distribution-preserving samples until the detector converges. On a Milano CDR dataset across SMS-flooding, silent-call, Internet-signaling, and blended scenarios, ResNet50 with ADT recovers F1-scores of 79.62 percent (silent-call), 100 percent (Internet), and 92.79 percent (blended). After validation-based threshold calibration, ADT reaches 100 percent SMS F1 versus 47.3 percent for CTGAN, and matches the strongest gradient-based adversarial-training baseline on silent-call. These results support tabular diffusion models for stress-testing and hardening intrusion detectors in data-scarce 5G cyber-physical deployments.
[LG-14] Debate Training Reduces Reward Hacking in RLAIF
链接: https://arxiv.org/abs/2608.17776
作者: Zachary Kenton,Lili Janzer,Rory Greig,Tian Huey Teh,Kirill Tyshchuk,Jonah Brown-Cohen,Harri Edwards,Senthooran Rajamanoharan,Noah Y. Siegel,Natasha Jaques,Rohin Shah
类目: Machine Learning (cs.LG)
*备注:
Abstract:We demonstrate that RL finetuning an LLM using debate, a two-player adversarial game between a generator and a critic adjudicated by a weaker LLM judge, reduces reward hacking compared to a reinforcement learning from AI feedback (RLAIF) baseline. Reward hacking is a central obstacle in RLAIF: as training progresses, the policy learns to exploit systematic errors in its AI judge, degrading task performance, a problem that worsens precisely when the judge is weaker than the policy, the setting most relevant to overseeing increasingly capable AI systems. We study mathematics tasks, where final-answer correctness is verifiable, allowing us to measure reward hacking dynamics. We train a Gemini~2.5 Flash-class policy with a frozen, weaker Gemini~2.5 Flash Lite judge, comparing a single-player RLAIF baseline against debate. While the baseline quickly hacks the judge, debate maintains judge performance throughout training, leading to a higher peak validation accuracy (45% performance gap recovered) that persists through many RL steps. Additional experiments show that: 1) further weakening the judge leads to faster hacking, but this can be compensated by adding an additional debate round; 2) debate incentives override prompted misalignment; 3) RL using an LLM judge has a smaller train/validation reward gap than RL from verifiable rewards; 4) learning to critique to convince the judge using ground truth labels is possible but slow. Taken together, our results are a positive update on the feasibility of debate, while highlighting that balancing multi-agent training is critical: without player constraints, adversarial training risks defaulting to critic judge-hacking. We show that critique word limits (effective up to 150 words) successfully balance the game and avoid judge hacking, though this introduces a trade-off by restricting critic expressive clarity.
[LG-15] raining-Free Human-in-the-Loop Anomaly Detection via Memory Bank Correction
链接: https://arxiv.org/abs/2608.17775
作者: Ayusha Abbas,Saram Abbas,Kabita Adhikari
类目: Machine Learning (cs.LG)
*备注: 15 pages, 9 figures, 5 tables
Abstract:Anomaly detectors are hardest to deploy exactly where training data is scarcest: a newly commissioned production line has a handful of verified “golden” samples and no machine-learning engineer on the factory floor. We present a training-free human-in-the-loop framework in which a domain expert corrects a PatchCore detector by direct memory bank editing: no retraining, no gradients, no original training data. A false-positive correction inserts the reviewed image’s normal patches through a self-calibrating novelty gate admitting only those beyond the median pool-normal nearest-neighbour distance. From a bank built on only ten golden samples, operator corrections close a median 66% of the gap to an uncorrected fully trained bank (mean 80%, raised by three categories that overshoot parity), significantly improving 12 of 15 MVTec AD categories and harming none: ten samples plus corrections outperform hundreds of samples without them. On already-trained banks the headroom is smaller and concentrated where the bank undersamples normal appearance (gated: toothbrush +0.10, metal nut +0.09, zipper +0.05, screw +0.05), and no category except grid is significantly harmed. Evaluation uses a held-out protocol (20 splits per category, Holm-corrected Wilcoxon), because corrected images entering the bank inflate naive evaluation toward AUROC 1.0 by memorisation. Passive and active querying are statistically indistinguishable; a matched-label-budget control attributes gains to deployment-time label production at 43% of exhaustive-review cost; a defect-memory extension fails decisively. Feedback is simulated from ground truth; live expert trials, where mislabelling is costliest on small banks, remain future work.
[LG-16] MAGPIE-Net: Predicting short-duration heavy-rainfall events in station neighborhoods from multitemporal FY-4A AGRI observations
链接: https://arxiv.org/abs/2608.17753
作者: Xiang Lin,Yunying Li,Chengzhi Ye,Zitong Chen,Jing Sun
类目: Machine Learning (cs.LG)
*备注: 26pages, 10 figures
Abstract:Short-duration heavy-rainfall warning determines whether 1 h rainfall will exceed a threshold within a target-station neighborhood over the next few hours. Multitemporal infrared and water-vapor observations from the Fengyun-4A Advanced Geostationary Radiation Imager (FY-4A AGRI) capture cloud-top cooling, moisture evolution, and cloud expansion before substantial surface rainfall develops. However, most deep-learning nowcasting methods convert these signals into local warnings by post-processing gridded precipitation predictions, preventing station-neighborhood event targets from directly supervising the satellite-to-station learning pathway. We propose MAGPIE-Net, which embeds a geographically adaptive, differentiable grid-to-station mapping in a pathway combining convection-initiation features, multiscale encoding, and auxiliary gridded precipitation diagnosis. Station-neighborhood event losses thereby constrain the satellite representation and its mapping to irregular station locations for 0-3 h event prediction. In independent 2023 warm-season tests over central and eastern China, critical success index (CSI) values under the primary 40 km/20 mm h-1 definition were 0.371, 0.304, and 0.238 at 0-1, 1-2, and 2-3 h. Across episodes, MAGPIE-Net achieved a detection rate of 65.1% and a mean lead time of 64.6 min, compared with 23.6% and 18.3 min for the best gridded-output baseline, and remained superior for smaller neighborhoods and the 50 mm h-1 threshold. During the critical early-warning stage, when antecedent 1 h rainfall within 40 km remained below 1 mm, MAGPIE-Net detected 51.9% of episodes with a mean lead time of 38.5 min. These results show that event-oriented satellite-to-station modeling converts multitemporal geostationary cloud and moisture observations into local heavy-rainfall warnings more effectively than gridded-precipitation modeling.
[LG-17] MemCatalyst: Amplifying Data Auditing on Vision-Language Models via Data Poisoning
链接: https://arxiv.org/abs/2608.17722
作者: Xukun Luan,Jinyan Liu,Yuhui Gong,Yuanguo Bi,Bing Hu,Xuesong Li,Di Wang
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:Vision-Language models (VLMs) achieve outstanding performance largely due to the amount of training data available on the internet. At the same time, data holders (e.g., artists) urgently need to determine whether their data has been used for model training without authorization, which concerns both intellectual property rights and personal privacy. Data auditing, particularly through membership inference (MI), has attracted attention as a direct tool. This work proposes MemCatalyst, a set of data poisoning tools, aiming to amplify the data auditing performance on VLMs. MemCatalyst employs two strategies: Poisoning Text (PT) and Poisoning Image (PI). MemCatalyst forces VLMs to over-learn specific inconsistencies between image features and textual semantics during training, thereby increasing their susceptibility to membership information auditing. Crucially, the transferability of poisoned samples across different VLM architectures is demonstrated to be effective in the black-box setting. Extensive evaluations using five state-of-the-art data audits on two prominent VLMs demonstrate that MemCatalyst markedly enhances MI AUC scores with a minimal budget of poisoned samples, while maintaining a negligible impact on model performance.
[LG-18] Cross-View Correspondence Is a Measurement Intervention: Two-Sided Validation for Agent Evaluation and Credit Assignment
链接: https://arxiv.org/abs/2608.17713
作者: Zhen Zhang,Ahmad Hafez,Amr Alanwar
类目: Machine Learning (cs.LG)
*备注:
Abstract:Agent evaluations and trace-based learning often compare outputs across transformed views through a post-response correspondence treated as neutral preprocessing. We show that this correspondence is a measurement intervention: omitting it can manufacture sensitivity, an over-aggressive map can manufacture invariance, and multiple optimal correspondences can leave mechanism labels and signed learning credit unidentified. We develop a validity theory and audit with three components: two-sided validation of nuisance removal and response preservation, all-optima identification of downstream conclusions, and uncertainty propagation after validity is established. We characterize the linear feasibility boundary for response-preserving nuisance removal, compute sharp ranges over exact-optimum correspondence sets, and give a distribution-free certificate that retains a credit coordinate only when all exact optima agree on its nonzero sign. Across public code and SQL pipelines, two deterministic optimal tracebacks disagree on temporal localization for 55.9% of 1,586 nonzero trajectory pairs; two frozen 800-rollout tool-use audits, including a task-and-seed-disjoint replication, expose exact-optimum reversals of intended turn-level credit, although a clean public quick-start subset shows none. A pre-registered transport gate failed on natural responses; frozen corrected and held-out controls then show that a map calibrated only on benign examples erases every retained harmful response, while two-sided validation selects response-preserving alternatives. Cross-view correspondence must therefore be declared, validated, and propagated into uncertainty before agent evaluation or credit assignment supports a point conclusion.
[LG-19] Conformal Prediction for Molecular Properties under Label Shift NEURIPS2025
链接: https://arxiv.org/abs/2608.17678
作者: Hyeonsu Lee,Juyeon Kim,Erkhembayar Jadamba,Seungjin Choi,Hyunjin Shin
类目: Machine Learning (cs.LG)
*备注: NeurIPS 2025 Workshop on Reliable ML from Unreliable Data
Abstract:Drug discovery and development underpins healthcare but remains costly and failure-prone. A critical bottleneck lies in predicting molecular properties such as solubility, potency, and toxicity, which directly determine whether a candidate can advance from preclinical to clinical trials. Artificial Intelligence (AI) has accelerated this process, yet its reliability is often undermined by distribution shift, as experimental conditions frequently diverge from training data. In addition, conventional point predictions provide only single-value estimates, offering limited guidance for high-stakes experimental design. We address these challenges with a conformal prediction framework tailored to label shift. By weighting conformal scores using marginal label probability ratios, our method produces statistically rigorous prediction intervals without retraining. This enables robust uncertainty quantification even when property distributions drift, directly tackling one of the most pervasive obstacles to applying AI in real-world drug development. By moving beyond accuracy alone to provide actionable confidence measures, our approach enhances the trustworthiness of AI-driven predictions. This further aligns predictive modeling with regulatory demands for transparency and uncertainty reporting and ultimately supports more reliable decision-making in billion-dollar development pipelines.
[LG-20] Picard Proximal Monte Carlo for Parallel Bayesian Imaging with Score-Based Generative Priors
链接: https://arxiv.org/abs/2608.17666
作者: Deliang Wei,Evan Bell,Wenhan Guo,Yifan Chen,Yu Sun
类目: Machine Learning (cs.LG)
*备注:
Abstract:Bayesian imaging inverse problems often require sampling from high-dimensional posterior distributions. While recent score-based and diffusion models provide expressive Bayesian priors, their sampling procedures remain inherently sequential and computationally expensive for large-scale imaging applications. We propose PiX-MC, a time-parallel posterior sampling framework based on proximal Langevin dynamics and Picard iteration. The proximal-likelihood formulation exploits the fact that many imaging likelihoods admit efficient, problem-specific proximal operators, while Picard refinement exposes parallelism across discretization nodes and naturally supports multi-GPU implementation. To further improve practical scalability and sampling performance, we develop multi-block and annealed variants of the proposed framework. We establish convergence guarantees under transparent assumptions, accommodating non-log-concave posteriors, imperfect learned score models, multi-block implementations, and annealing schedules. Experiments on a diverse collection of imaging inverse problems demonstrate that PiX-MC substantially reduces wall-clock time while preserving reconstruction quality. On a 512\times512\times80 sparse-view computed tomography (CT) problem, annealed multi-block PiX-MC achieves up to a 50\times runtime speedup over the standard Langevin sampler using eight GPUs.
[LG-21] Elimination Geometry
链接: https://arxiv.org/abs/2608.17646
作者: Mian Huang,Xueqin Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:This monograph develops elimination geometry (EG), a typed, native-loss, audit-oriented framework for studying when locally optimal objects can be realized by a shared deployment rule. Elimination and compression may erase distinctions required by prediction, inference, control, or representation. EG asks which distinctions are lost, whether the induced defect is visible to the declared task, and whether changing information, architecture, action space, or deployment domain can repair it. EG separates local solvability, global realizability, and finite-sample certifiability. It derives native defects from the original objective and distinguishes architecture obstruction from model approximation, generalization, and implementation error. The monograph synthesizes tools from geometry, optimization, information theory, statistics, and machine learning into interfaces for integrability, representation admissibility, resource constraints, observational overlap, and common deployment. Formal results address regular, coordination, singular, compositional, and resource-limited mechanisms with explicit antecedents and claim boundaries. Applications include sparse model selection, distribution-free prediction, observational treatment policies, routed expert and retrieval systems, and learned score fields. Obstruction-Aware Learning and Inference links structural diagnosis to finite-data authorization, mechanism-matched intervention, and independent validation. Reproducible synthetic and real-data studies illustrate how certificates can guide architecture repair while recording failed gates and unresolved cases. The framework requires the deployment contract, native endpoint, competing explanations, information and compute budgets, and validation rule to be fixed before a persistent performance floor is attributed to architecture.
[LG-22] rl-triton: High-Performance Triton GPU Kernels for Reinforcement Learning Credit Assignment
链接: https://arxiv.org/abs/2608.17641
作者: Lars Simon Zehnder
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC); Performance (cs.PF)
*备注: 18 pages, 3 figures, 6 tables. Code: this https URL
Abstract:We present rl-triton, an open-source library of high-performance GPU kernels for reinforcement learning credit assignment, implemented in Triton. The core contribution is a unified associative scan framework that recasts seven distinct RL estimation algorithms - Generalized Advantage Estimation (GAE), V-Trace, Retrace( \lambda ), TD( \lambda ) returns, discounted returns, eligibility traces, and episodic prefix sums - as instances of a single first-order linear recurrence solved in O(\log T) parallel steps. All algorithms share the same associative scan operator, with algorithm-specific fused Triton kernels constructing their recurrence coefficients on-chip. We verify the associative operator algebraically and define the treatment of terminated and truncated episodes explicitly. Benchmarks show a 1.6-5.70 \times full-call speedup over a vectorized this http URL baseline in the massively parallel simulation regime (thousands of environments, short rollouts). The reported range covers all seven algorithms on both GPUs, both with and without per-step truncation handling. For most algorithms, speedups increase at longer sequence lengths, as the baseline requires more scan stages as \log T grows, each adding an intermediate HBM round-trip. The library is available at this https URL.
[LG-23] OOD Detection for EEG-based Machine Learning in High-Risk Environments
链接: https://arxiv.org/abs/2608.17620
作者: Philipp Bomatter,Henry Gouk
类目: Machine Learning (cs.LG)
*备注:
Abstract:Machine learning models for electroencephalography (EEG) analysis show great promise across a wide range of applications, but their deployment in high-risk domains is hindered by their vulnerability to distribution shifts. Encountering out-of-distribution (OOD) data can lead to catastrophic, overconfident predictive failures. While OOD detection methods can mitigate these risks, they remain heavily under-explored for EEG. Moreover, evaluations in the broader literature typically evaluate OOD detection performance in isolation, ignoring their practical impact on downstream applications. To bridge this gap, we introduce a benchmark for EEG OOD detection, evaluate a broad range of methods, and furthermore evaluate their value in two clinical downstream prediction task. Our results disentangle OOD detection and model uncertainty estimation capabilities, which are frequently conflated in the literature, provide actionable insights about the current state of the art for EEG OOD detection and model uncertainty estimation, and demonstrate how complementary methods for both aspects can be combined to form a robust safety net for the deployment of EEG-based machine learning models in real-world applications.
[LG-24] Evaluating RL Explainability Methods by How Much They Help Fix Bugs in Agents
链接: https://arxiv.org/abs/2608.17524
作者: Ram Rachum,Yotam Amitai,Bálint Gyevnár,Reuth Mirsky,Cameron Allen
类目: Machine Learning (cs.LG)
*备注:
Abstract:This preliminary paper outlines a planned evaluation benchmark for Explainable Reinforcement Learning (XRL) methods. Current evaluations rely on functionally-grounded metrics like faithfulness and compactness, and on human-grounded proxies like subjective ratings or prediction accuracy. We suggest evaluating XRL methods by how effectively their generated explanations help to diagnose and fix malfunctioning reinforcement learning (RL) agents. We propose EvalXRL, a benchmark in which a Large Language Model (LLM) coding agent uses different XRL methods to diagnose a held-out malfunction in an RL agent, and then repair it. Our proposed benchmark iterates across (environment \times malfunction \times XRL method) tuples and uses the reward signal of the RL agents to form a final score for each XRL method. The coding agent may use the method interactively: invoke the XRL method, process its output, form new hypotheses on what is broken, and invoke the method again with parameters adjusted for testing these hypotheses. This closed-loop structure may be described as a simplified version of the scientific method. Some XRL methods provide self-evaluations that follow this pattern; we propose the first head-to-head comparison of multiple XRL methods in closed-loop usage. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.17524 [cs.LG] (or arXiv:2608.17524v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.17524 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journalreference: Proceedings of the Workshop on Explainable Artificial Intelligence (XAI) at IJCAI-ECAI 2026, Bremen, Germany
[LG-25] Causal Local States: Scalable Simultaneous Causal Network Inference and Forecasting for Dynamical Systems
链接: https://arxiv.org/abs/2608.17452
作者: Jonas Braun,Fabian Fischbach,Daniel Köglmayr,Sebastian Baur,Christoph Räth
类目: Machine Learning (cs.LG)
*备注:
Abstract:Machine learning methods predict many real-world systems with remarkable accuracy, but they are typically treated as black boxes that offer no insight into which interactions drive the dynamics. Causal discovery methods reconstruct the interaction network from observational data, but without regard to whether the inferred structure supports prediction. Existing approaches combining both tasks rely on a single global hyperparameter, such as a causal threshold or a fixed neighborhood size, which cannot recover the structure of heterogeneous systems. Here we introduce causal local states (CLS), a framework that simultaneously infers an approximate Granger-causal interaction network and forecasts the system dynamics. For each node independently, we select the smallest set of neighbors that allows a predictive model to forecast the node near-optimally, and the resulting neighborhoods are then combined for a forecast of the full system. On three benchmarks of increasing difficulty, we achieve reconstruction of the underlying networks with high fidelity and forecasts on par with a model that is supplied with the true network, providing a step toward explainable and scalable forecasting of complex systems.
[LG-26] General Semantic Knowledge Infusion for Spatio-Temporal Traffic Forecasting MDM’26
链接: https://arxiv.org/abs/2608.17440
作者: Mattis thor Straten,Yannick Wolker,Steffen Strohm,Prathvish Mithare,Ralf Krestel,Matthias Renz
类目: Machine Learning (cs.LG)
*备注: 8 pages, 3 figures (published at MDM’26)
Abstract:Although Graph Neural Networks (GNNs) have made significant advances in spatio-temporal traffic forecasting, their performance is limited when they rely solely on sensor proximity or road-network topology. This paper presents a spatio-temporal prediction framework, developed to incorporate knowledge in various forms. This framework aims to improve sensor-level, contextual understanding of the environment. A general-purpose knowledge graph (e.g., Wikidata) is used to create semantic subgraphs around traffic sensors and generate knowledge graph embeddings that capture meaningful relationships, such as nearby points of interest, administrative hierarchies, and the functional roles of locations. These embeddings are then fused with conventional traffic sensor graphs to provide additional adjacency matrices informed by semantics. This allows GNNs to learn the semantic context beyond physical connectivity. This study differs from previous research in two key ways. Firstly, rather than proposing a novel GNN architecture, it demonstrates the general impact of external knowledge on prediction accuracy. Secondly, experiments with well-established traffic forecasting approaches show that external knowledge provides additional information that street network data alone cannot convey. The results show that integrating data from general-purpose knowledge graphs and sensor networks through data fusion can enhance the prediction accuracy of traffic forecasting models, and offers a potential pathway toward improved interpretability.
[LG-27] Prism-GRPO: Faster VLA Policy Optimization via Splitting Same-outcome Groups
链接: https://arxiv.org/abs/2608.17423
作者: Zeyun Deng,Yuzhe Lu,Yawei Wang,Linbo Liu,Qing Ping,Han Ding,Guande Wu,Panpan Xu,Jun Huan
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:GRPO is increasingly used for reinforcement learning of vision-language-action (VLA) policies because, unlike PPO, it does not require training a critic. This simplification comes with a sampling cost: group-relative advantages require multiple rollouts from each scene. Under binary success rewards, groups whose rollouts all succeed or all fail have zero advantage and are discarded by dynamic sampling. These groups are especially common early in training, when most rollouts fail, wasting much of the expensive robotic rollout budget. We introduce Prism-GRPO, which augments binary outcome reward with a weighted trajectory-level execution-quality score. By splitting same-outcome groups into a quality spectrum, Prism-GRPO recovers training signal while ensuring that every success still outranks every failure. Quality scores can be derived from simulator contacts, executed actions, or visual observations, avoiding task-specific progress rewards. We prove that Prism-GRPO never increases the probability that a sampled group is discarded for having zero advantages, and derive a gradient-alignment condition under which its combined update remains a local ascent direction for task success. Across four RoboTwin tasks spanning different horizons and coordination patterns, Prism-GRPO improves success and quality at matched rollout budgets and reaches target success rates with up to 56% fewer rollouts. It also suppresses a reward-hacking shortcut, with the cleaner behavior transferring under direct deployment to a real robot. Through ablations, we show consistent gains across contact-, smoothness-, and VLM-derived quality signals.
[LG-28] GUPO: Gradient Uncertainty-aware Policy Optimization for Post-Training Large Language Models
链接: https://arxiv.org/abs/2608.17411
作者: Peizheng Guo,Jianqi Zhang,Xingyu Zhang,Yun Fan,Jiahuan Zhou,Changwen Zheng,Wenwen Qiang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Group Relative Policy Optimization (GRPO) has become a widely used approach for post-training Large Language Models (LLMs) for reasoning. In GRPO, the group gradients induced by different queries within the same mini-batch are directly averaged to form the policy update. However, these group gradients can point in conflicting directions. Our empirical analysis suggests that group-gradient conflicts tend to be associated with less effective policy updates, motivating the need for a reliable aggregated update direction under such conflicts. Standard GRPO aggregation treats the realized group gradients as deterministic contributions and does not account for differences in their reliability during aggregation. To address this issue, we propose Gradient Uncertainty-Aware Policy Optimization (GUPO), which models each group gradient as a random variable under a Bayesian formulation and estimates its probability distribution. GUPO then derives gradient uncertainty using a Dirichlet-based formulation and uses it to calibrate the contribution of each group gradient during aggregation. Extensive experiments on multiple benchmarks demonstrate the effectiveness of GUPO.
[LG-29] Pathology Transport: Optimal-Transport Explanations for Clinical Data and When Their Heatmaps (Fail to) Localize Disease
链接: https://arxiv.org/abs/2608.17370
作者: Lalit Kumar
类目: Machine Learning (cs.LG)
*备注:
Abstract:Generative models promise a route to explainable clinical AI: rather than probe a classifier, model the distributions of healthy and diseased patients and read explanations off the geometry between them. We build such a system - an optimal-transport rectified flow trained between two clinical distributions - and use it to ask a pointed question the field too rarely tests: do the resulting explanation heatmaps actually localize disease? On tabular tumour biomarkers (Breast Cancer Wisconsin) a single flow yields per-patient counterfactuals, an unsupervised malignancy score (AUROC 0.91; 0.93 +/- 0.01 across five seeds), and a label-free attribution that agrees with a supervised classifier (r ~ 0.5) - a compact, honest interpretability engine, though it never out-predicts logistic regression. Moving to chest X-rays, we show the transport heatmap is a population-level signal, not a localiser; a reconstruction-based, identity-preserving variant does localize synthetic lesions (pointing game 0.52), yet on real RSNA radiologist boxes it collapses to chance while only supervised Grad-CAM stays above it. The central result is a synthetic-to-real gap: label-free heatmaps that look compelling on planted lesions are not evidence of real localisation. We contribute a reusable optimal-transport recipe for generative explanations and a controlled benchmark for stress-testing whether they localize.
[LG-30] CORAM: Coherent Orthogonal Rotation for Model Merging
链接: https://arxiv.org/abs/2608.17366
作者: Xinyi Sui,Ziran Liu,Nam Ling,Wei Wang,Wei Jiang
类目: Machine Learning (cs.LG)
*备注: 26 pages, including supplementary material
Abstract:Merging finetuned models combines specialized capabilities without joint training or access to the original data. Most methods operate by linear arithmetic in Euclidean weight space, which cannot carry the geometry of the update. Orthogonal Model Merging (OrthoMerge) uses a single orthogonal transform for each weight matrix, but such a transform cannot change singular values. We propose CORAM, which partitions each target matrix into row slices, represents every expert slice by its singular value decomposition in the corresponding base-model SVD frame, and merges the task-specific factors on their corresponding manifolds. Because manifold averaging contracts the merged update, CORAM applies an amplification coefficient \lambda=\kappa\hatc . The scale c_hat is estimated from the expert and merged update norms and is approximately \sqrtN for N experts with comparable update magnitudes. The restoration strength kappa is selected from the dispersion of expert updates without evaluating candidate merged models. This rule remains within 0.72 points of the best swept value on all evaluated suites. CORAM also includes spread slicing to distribute highly updated rows across slices and a residual pathway for non-target layers. Across four suites covering three model families, 3B to 9B scales, and language and vision-language experts, CORAM improves over OrthoMerge by 0.25 to 1.35 points and matches or exceeds the strongest weight-space baselines.
[LG-31] Repetition as Reinforcement: Enhancing Sample Efficiency via Instant Episode Repetition in Reinforcement Learning
链接: https://arxiv.org/abs/2608.17347
作者: Hoda Yamani,Yuning Xing,Koen van Rijnsoever,Bruce A. MacDonald,Henry Williams
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注: 23 pages, 12 figures. Accepted at RLC 2026; to appear in Reinforcement Learning Journal (RLJ) 2026. Code: this https URL
Abstract:Repetition is a fundamental mechanism in human learning, where revisiting successful experiences strengthens memory, consolidates skills, and improves future performance. Motivated by this biological principle, we introduce Instant Episode Repetition (IER), a simple and novel mechanism that improves sample efficiency by immediately repeating action sequences from successful episodes during environment interaction. Unlike conventional approaches such as Experience Replay and Self-Imitation Learning (SIL), which passively reuse past experience during training updates, IER directly influences the data collection process. Upon identifying a high-reward episode, the agent repeats its action sequence for a fixed number of subsequent episodes, reinforcing valuable behaviors through renewed interaction with the environment. We integrate IER into state-of-the-art SAC and TD3 algorithms and evaluate its effectiveness on continuous-control benchmarks, including MuJoCo, the DeepMind Control Suite, and a real-world dynamic object translation task with a robotic manipulator. Experimental results demonstrate that this simple mechanism improves learning performance over standard and self-imitation-based baselines.
[LG-32] ght Bounds for Data-driven Multiple Hyper-parameter Tuning with Structured Loss Function
链接: https://arxiv.org/abs/2608.17343
作者: Anh Tuan Nguyen,Viet Anh Nguyen
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 19 pages, 2 figures
Abstract:Data-driven algorithm design frames hyperparameter tuning as a statistical learning problem, but establishing generalization guarantees remains challenging due to the implicit, non-smooth dependence of model performance on hyperparameters. Existing multi-dimensional bounds under piecewise-polynomial assumptions remain theoretically loose and lack comprehensive lower bounds. We resolve this by establishing tight pseudo-dimension bounds for multi-dimensional data-driven tuning. First, we refine the learning-theoretic upper bound using real algebraic geometry; by analyzing invariant connected sign cells during block elimination rather than isolated sign vectors, we avoid topological over-counting to derive strictly sharper sample complexities. Second, we present a multi-regime lower-bound framework that disentangles combinatorial and algebraic capacities. By constructing shattered problem instances across distinct regimes, we prove our upper bounds are tightly saturated. Finally, we extend our topological framework to accommodate general bi-level validation-loss tuning and broader semi-algebraic applications.
[LG-33] Agent ic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Requirements
链接: https://arxiv.org/abs/2608.17310
作者: Zhi Zheng,Rongsheng Chen,Yunpeng Ba,Zhenkun Wang,Yee Whye Teh,Wee Sun Lee
类目: Machine Learning (cs.LG)
*备注:
Abstract:Reinforcement Learning (RL) has been promising in single-turn LLM fine-tuning. However, long-horizon agentic reasoning introduces increasingly branching interactions and sparse rewards, exposing several limitations of RL: its heavyweight backpropagation-based training stack makes it impractical to fine-tune larger LLMs, and longer-horizon trajectories make credit assignment in RL substantially harder. This paper argues that evolution strategies (ES) can be a better choice for fine-tuning long-horizon LLM agents. Compared with agentic RL, ES offers three key advantages: 1) Model Scalability: ES enables full-parameter optimization with only minimal, inference-level GPU memory, making it possible to fine-tune large LLMs. 2) Flexibility: its lightweight, black-box feedback interface makes ES fine-tuning easy to compose with prompt-space evolution (e.g., skill optimization test-time compute); and 3) Long-Horizon Scalability: ES performs trajectory-level parameter attribution without decomposing rewards across horizons, yielding better scalability than Agentic RL as the horizon length grows. Based on this insight, we propose Agentic ESOpt, a full-parameter agentic fine-tuning framework tailored to flexible parameter–context co-evolution. At each step, Agentic ESOpt samples perturbations around the current LLM parameters, evaluates the resulting agents with rewards, and applies an online reward-weighted update. To improve the exploration–adaptation trade-off, Agentic ESOpt further introduces a cosine decay schedule of the perturbation scale \sigma . On WebArena-Lite, full-parameter optimization of Qwen-3.5-27B improves the No Skill baseline by 6.69%. In test-time automatic heuristic design, Agentic ESOpt performs online prompt–parameter co-evolution, improving its matched baseline in 28 of 36 settings.
[LG-34] Abra: Scaling Diffusion Image Training
链接: https://arxiv.org/abs/2608.17286
作者: Kyle Chickering,Wei-An Lin,Swayam Bhanded,Dan Saunders,Akshat Tripathi,Jiaming Song,Shyamal Buch,Xinchen Yan
类目: Machine Learning (cs.LG)
*备注: 25 pages, 19 figures
Abstract:Compute-optimal scaling laws guide the training of frontier language models yet remain largely unexplored for visual generation. We present a systematic scaling law study for text-to-image diffusion models using Abra, a controlled family of flow-matching transformers trained across three orders of magnitude worth of compute ( 10^19 to 10^22 FLOPs), reaching significantly larger compute budgets than previous works. We demonstrate that diffusion models scale just as predictably as language models but require far more data to train optimally: compute optimality occurs at approximately 200 image tokens per parameter, ten times the Chinchilla compute-optimal prescription for LLMs. We show that unlike language models, diffusion models are robust to overtraining and that practitioners should err on the side of more data rather than a larger model. Finally, we show that this predictability extends beyond training loss to generative quality metrics, optimal CFG settings, representation quality, and even the shape of the training curves, which collapse onto a universal form.
[LG-35] Information fusion and machine learning for sensitivity analysis using physics knowledge and experimental data
链接: https://arxiv.org/abs/2608.17248
作者: Berkcan Kapusuzoglu,Sankaran Mahadevan
类目: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG); Methodology (stat.ME); Machine Learning (stat.ML)
*备注: Reliability Engineering System Safety
Abstract:When computational models (either physics-based or data-driven) are used for the sensitivity analysis of engineering systems, the sensitivity estimate is affected by the accuracy and uncertainty of the model. This paper considers global sensitivity analysis (GSA) for situations where both a physics-based model and experimental observations are available, and investigates physics-informed machine learning strategies to effectively combine the two sources of information in order to maximize the accuracy of the sensitivity estimate. Two representative machine learning (ML) techniques are considered, namely, deep neural networks (DNN) and Gaussian process (GP) modeling, and two strategies for incorporating physics knowledge within these techniques are investigated, namely: (i) incorporating loss functions in the ML models to enforce physics constraints, and (ii) pre-training and updating the ML model using simulation and experimental data respectively. Four different models are built for each type (DNN and GP), and the uncertainties in these models are included in the Sobol indices computation. The DNN-based models, with many degrees of freedom in terms of model parameters and training options, are found to result in smaller bounds on the sensitivity estimates when compared to the GP-based models. The proposed methods are illustrated for additive manufacturing and lake temperature modeling examples.
[LG-36] Physics-Informed and Hybrid Machine Learning in Additive Manufacturing: Application to Fused Filament Fabrication
链接: https://arxiv.org/abs/2608.17246
作者: Berkcan Kapusuzoglu,Sankaran Mahadevan
类目: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE); Computation (stat.CO)
*备注: 11 pages, JOM (Journal of The Minerals, Metals Materials Society)
Abstract:This article investigates several physics-informed and hybrid machine learning strategies that incorporate physics knowledge in experimental data-driven deep-learning models for predicting the bond quality and porosity of fused filament fabrication (FFF) parts. Three types of strategies are explored to incorporate physics constraints and multi-physics FFF simulation results into a deep neural network (DNN), thus ensuring consistency with physical laws: (1) incorporate physics constraints within the loss function of the DNN, (2) use physics model outputs as additional inputs to the DNN model, and (3) pre-train a DNN model with physics model input-output and then update it with experimental data. These strategies help to enforce a physically consistent relationship between bond quality and tensile strength, thus making porosity predictions physically meaningful. Eight different combinations of the above strategies are investigated. The results show how the combination of multiple strategies produces accurate machine learning models even with limited experimental data.
[LG-37] Pessimistic Meta-Induction and Its Limits: Lessons from Frequentist Statistics and Machine Learning Theory
链接: https://arxiv.org/abs/2608.17213
作者: Hanti Lin
类目: Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:This paper challenges the pessimistic meta-inductive argument against scientific realism by undermining its inductive step rather than its historical premise. Although related challenges already exist, I develop a new one. Drawing on a general epistemology of scientific inference developed in frequentist statistics, machine learning, and formal epistemology, I evaluate induction in terms of convergence to the truth. I argue that ordinary enumerative induction can achieve everywhere convergence, whereas meta-induction fails even to achieve almost everywhere convergence. Indeed, in the problem context where meta-induction arises, the failure is deeper: no inference method whatsoever achieves almost everywhere convergence.
[LG-38] Reinforcement Learning as (Discrete) Potential Theory
链接: https://arxiv.org/abs/2608.17181
作者: Christopher Connolly
类目: Machine Learning (cs.LG); Computer Science and Game Theory (cs.GT)
*备注: 10 pages, 2 figures
Abstract:Reinforcement learning (RL) theory fundamentally depends on probability theory through the Markov chain. There is a deep connection between probability theory and potential theory. This paper reviews that connection and explores the potential-theoretic viewpoint for core reinforcement learning representations and algorithms under a fixed-policy assumption. This viewpoint may offer a path for improved sample efficiency and formal constraints that can be applied to RL. When the fixed-policy assumption is relaxed, the linear potential theory framework can be naturally extended to the nonlinear case.
[LG-39] Population Health-Based Machine Learning Reveals Associations Between Psychosocial Factors and Chronic Kidney Disease
链接: https://arxiv.org/abs/2608.17174
作者: Md. Atik Shams,David Eisenberg,Sumaiya Fatema,Asma Sultana,D. M Hasibul Islam,Junnatul Mawa,Anindita Datta,Nafiya Ahmed,Danastan Tasaouf Mridula,SK. Sazid Mahmud,Simon Bin Akter,Tanjila Helaly,Jorge Fresneda Fernandez,Humayera Islam,Tanmoy Sarkar Pias
类目: Machine Learning (cs.LG)
*备注:
Abstract:Chronic kidney disease (CKD) progresses silently and severely undermines quality of life, making early detection critical for improving patient outcomes. We present a two-part study that combines large-scale telehealth data with advanced machine learning to both classify self-reported CKD status and identify key drivers of disease. Using selected features from the Behavioral Risk Factor Surveillance System (BRFSS 2021: 438,693 samples; BRFSS 2019: 418,268 samples) and the National Health Interview Survey (NHIS 2021: 29,482 samples; NHIS 2020: 31,568 samples), we addressed missing data with nine state-of-the-art imputation methods and mitigated class imbalance via sampling strategies. Our customized stacked ensemble model achieved balanced accuracy of 72.56-76.12%, with corresponding AUROC scores of 79.59-82.29%. SHapley Additive exPlanations (SHAP) analysis, followed by clinical review, highlighted critical predictors, including regular medical check-ups, age, blood pressure, and indicators of mental health stress. These findings deliver a robust and interpretable framework for CKD risk stratification and provide actionable insights into its associated factors.
[LG-40] SCENARIODIFF: A Scenario-level Guidance Framework for Multimodal Time Series Forecasting–Extended Version ICDM2026
链接: https://arxiv.org/abs/2608.17164
作者: Tuan-Binh Tran,Dat Nguyen Cong,Duc-Trong Le,Thanh Trung Huynh,Tung Kieu
类目: Machine Learning (cs.LG)
*备注: 10 pages. An extended version of “SCENARIODIFF: A Scenario-level Guidance Framework for Multimodal Time Series Forecasting” accepted at ICDM 2026
Abstract:Textual context such as news, reports, and logs can provide valuable signals for time series forecasting, especially when future dynamics are driven by external events that are not yet visible in historical values. Existing multimodal forecasting methods often either ask large language models (LLMs) to predict numerical values directly or fuse text and time series implicitly, making contextual influence difficult to interpret and control. We propose SCENARIODIFF, a hierarchical contextual reasoning framework for multimodal time series forecasting under noisy and weakly aligned documents. SCENARIODIFF organizes contextual information into three levels: a Historical Context Agent extracts stepwise evidence from raw documents, a Scenario Agent produces a qualitative scenario description for the forecast horizon, and an Anchor Guidance Agent generates sparse anchor points for event-relevant future regions. These structured signals condition a Multimodal Diffusion Transformer, while Anchor Blended Sampling locally refines generated trajectories without retraining. Experiments on the Time-MMD benchmark show that SCENARIODIFF is especially effective in event-driven domains, demonstrating the value of explicit hierarchical scenario guidance for multimodal time series forecasting. Our full implementation is available at this https URL
[LG-41] OraclePhys: A Systematic Framework for LLM Fine-Tuning on Structural Mechanics ACL
链接: https://arxiv.org/abs/2608.17162
作者: Mingyu Li,Guorui Song,Jing Lin,Haoqian Wang
类目: Machine Learning (cs.LG)
*备注: 18 pages, 8 figures, 9 tables. Under review at ACL Rolling Review
Abstract:What a language model internalizes from fine-tuning is usually diagnosed after the fact. We make it an experimental variable. OraclePhys is a systematic fine-tuning framework with three components: OraclePhys-Bench, an exactly-graded structural-mechanics benchmark whose finite-element oracle scores every answer and counterfactual edit – no human labels, no LLM judging; OraclePhys-30K, a supervision dataset of seven answer forms over byte-identical structure descriptions; and a controlled training study across the seven forms and three verifier roles. The study yields two findings. First, the label’s answer form – not its bit count – causally determines what fine-tuning teaches: a ranking objective installs an out-of-distribution forward model where the untrained base sits at the guessing prior, a scalar objective at best a partial one, a boolean nothing detectable; the vector-scalar gulf survives a second physics domain, a second model family, and a paraphrased evaluation surface. Second, written or score-filtered answers install this capability, while advantage-weighted scores (GRPO) raise reward yet leave the model statistically equivalent to its start on held-out physics – within the recipes and budgets tested – sufficing only for routing. The trained 8B – the first LLM on spatial structural response – reaches the task’s data-precision frontier: above a frontier LLM at zero- and 32-shot, at a specialist’s level. What the label spells out about the target computation is what fine-tuning teaches; what you train on is what you route.
[LG-42] Picture the Epsilon: Pursuing Identity-Level Privacy Guarantees for Images
链接: https://arxiv.org/abs/2608.17147
作者: Arman Zareian Jahromi,Vishnu Bondalakunta,Mohammad Akbar Bin Shah,Naimul Haque,Shuangqing Wei,George T. Amariucai
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: 14 pages, 3 figures
Abstract:Image-to-image face generators are widely used, and visual dissimilarity between their outputs and source images is sometimes treated as evidence of privacy. Auditing whether these systems satisfy formal identity-level (epsilon, delta)-differential privacy requires choosing among several distinct routes for converting embedding-space observations into estimates or bounds on the differential privacy parameter epsilon. We present a comparative study of four such audits applicable to pre-trained, black-box face generators: a Gaussian-mechanism reading of per-identity sensitivity (GaussMech); a per-dimension kernel-density log-ratio aggregated by basic composition (KDE-LR); an analytical population-level lower bound on pure-DP epsilon derived from the maximum mean discrepancy via the total variation distance (MMD-TV); and a hypothesis-testing evaluation of a cross-validated classifier’s out-of-fold ROC (ROC-HT). For each method we make explicit its assumptions, hyperparameter dependence, finite-sample limitations, and the regime in which its epsilon estimate is informative. Applied to FaceFusion and InstantID across multiple identity encoders and reference datasets, the audits consistently reveal substantial identity distinguishability while reporting markedly different epsilon estimates that reflect each method’s distinct assumptions and finite-sample treatment. In this high-distinguishability regime, the experiments do not support a reliable ranking of the four methods. Their relative trade-offs should be evaluated on partially private mechanisms, which we identify as the natural next study. The resulting framework places these audits in a shared identity-level audit setting and clarifies how their assumptions and finite-sample treatments shape the resulting differential privacy estimates.
[LG-43] Causal Discovery in Equal Variance Linear Gaussian DAGs via SURE-Tuned Ridge Regression
链接: https://arxiv.org/abs/2608.17132
作者: Sambit Mishra,Urbashi Mitra
类目: Machine Learning (cs.LG); Signal Processing (eess.SP); Machine Learning (stat.ML)
*备注: 5 Pages, 3 Figures. Accepted at 60th Asilomar Conference on Signals, Systems, and Computers 2026
Abstract:Recovering the directed acyclic graph (DAG) of a structural equation model (SEM) from observational data is a central problem in causal discovery. The iterative gradient descent and per-problem hyperparameter tuning of continuous-optimization methods are poorly suited to two practically important regimes: the sample-limited regime, where the number of samples is comparable to or smaller than the number of nodes in the DAG, and the compute-limited regime. This work proposes SURE-Ridge, a non-iterative, closed-form estimator for equal variance linear Gaussian SEM. The method performs parallel node-wise regressions with regularization parameters chosen adaptively by Stein’s unbiased risk estimate (SURE), and applies an adaptive thresholding procedure to extract a DAG from the resulting soft adjacency matrix. Numerical results show that SURE-Ridge achieves the lowest structural Hamming distance in the small-sample regime and the lowest run time across all sample sizes tested, compared with NOTEARS, DAGMA, and GBNSL baselines.
[LG-44] Digital Twin-Based Intrusion Detection for Vehicle Powertrain CAN Bus Systems
链接: https://arxiv.org/abs/2608.17093
作者: Araf Rahman,M Sabbir Salek,Mashrur Chowdhury
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: 20 pages, 4 figures Paper submitted for presentation at the Transportation Research Board Annual Meeting and publication in Transportation Research Record. Under review for both cases
Abstract:Existing automotive intrusion detection systems (IDSs) for the Controller Area Network (CAN) largely target discrepancies in message timing, frequency, or sequencing and cannot detect attacks that preserve these properties while manipulating the payload. Digital twins (DTs) have been used to emulate CAN traffic and generate attack scenarios for IDS evaluation, but their use for intrusion detection remains unexplored. This study develops a DT-based IDS that jointly models physical relationships among decoded powertrain signals and identifies attacks through residuals between predicted and observed behavior. A shared-encoder LSTM DT was trained on 17 decoded signals from a real Hyundai/Kia CAN log to jointly predict seven numeric and two categorical gear signals over a 24-step window. A timestep is flagged when a residual exceeds a calibrated threshold, while adaptive rollout protects the twin’s input history from sustained contamination. Four attacks (plateau, continuous drift, masquerade, and gear masquerade) were evaluated against the twin and a range-and-plausibility baseline. The DT outperformed the baseline across all attacks, achieving detection rates of 94.6% for continuous drift and 89.2% for masquerade, while the baseline detected almost none of the fabricated payload attacks. These results demonstrate that learning coupled vehicle dynamics enables detection of stealthy payload manipulations that preserve normal CAN communication patterns. False positive rates reached 39.6%, highlighting the need for improved robustness under sustained attacks. The DT-based IDS shows promise for detecting stealthy payload-level CAN attacks that preserve normal communication patterns, supporting behavior-based cybersecurity for connected and automated vehicles.
[LG-45] Deep Learning for Cross-Border Electricity Price Forecasting: A Comparative Study
链接: https://arxiv.org/abs/2608.17091
作者: Hadeer Elashhab,Sai Srijan Papineni,Marvin Dorn,Veit Hagenmeyer,Benjamin Schäfer
类目: Machine Learning (cs.LG)
*备注:
Abstract:While publicly available electricity market data presents a valuable resource for forecasting research, the field lacks established benchmark datasets for standardized comparison. As a result, many studies have relied on different datasets and metrics to evaluate methods in isolated settings, making it difficult to assess progress and compare state-of-the-art approaches consistently. In this work, we use public data to evaluate deep learning models for electricity price forecasting (EPF) across multiple market settings. Our goal is to establish a reproducible framework that enables a consistent evaluation of forecasting models. Although deep learning has been explored for day-ahead EPF, many prior studies are limited to single-market settings, narrow feature sets, or fixed training regimes. This work presents a comparative evaluation of six deep learning models–covering state-space, MLP, RNN, and Transformer-based architectures–emphasizing generalization across markets. We simulate low-data target-market conditions using zero-shot, one-shot, and few-shot learning. Our test set focuses on the Germany-Luxembourg (DE-LU) bidding zone in 2024 using a standardized dataset with calendar, historical price, and market-derived features. Our findings suggest that N-HiTS and NBEATSx perform competitively in limited-data scenarios, while transformer-based models can reach comparable accuracy but tend to require more adaptation and tuning. Model performance also benefits from careful feature selection and hyperparameter tuning, and we note that the differences between the strongest models are often small.
[LG-46] Backward through Time Algebraically
链接: https://arxiv.org/abs/2608.17087
作者: Konstantinos Kogkalidis
类目: Machine Learning (cs.LG); Logic in Computer Science (cs.LO); Programming Languages (cs.PL); Systems and Control (eess.SY)
*备注:
Abstract:Linear temporal logic is a modal extension of propositional logic that allows one to state how a system should behave over time. Its canonical domain is the booleans, but discretely-valued judgements are of little use in steering softly-valued systems (neural policies, adaptive controllers, sequence models, etc). In such cases, the goal formula’s (dis)satisfaction becomes a training signal, and differentiability becomes a prime concern. Candidate differentiable semantics abound, but navigating them is tricky. Implementations, where available, are shallow embeddings, demanding an upfront commitment to a single semantic algebra and its (usually implicit) conduct. The paper casts the reader as a functional programmer asked to come to terms with this predicament, and refusing. Out of that refusal comes an evaluation engine that is algebra-generic and amenable to differentiation, together with an executable specification of the algebras it can accept. Various algebras are implemented and audited for their behavior, both forward and backward. Each algebra turns out to be a choice of which direction to disappoint, and how. Everything described (and more) is part of the PyTorch library telos, to be found at this https URL.
[LG-47] Dynamic Regime-Aware Conformal Calibration for Reliable Economic Forecast Intervals under Multiple Distribution Shifts
链接: https://arxiv.org/abs/2608.17079
作者: Bogdan Oancea
类目: Machine Learning (cs.LG)
*备注:
Abstract:Conformal prediction provides distribution-free prediction intervals but relies on exchangeability, an assumption often violated in economic forecasting because of covariate shift, concept drift, local heterogeneity and latent regimes. We propose Dynamic Regime-Aware Conformal Prediction (DRACP), which combines density-ratio, localized kernel and probabilistic regime-aware weighting with a self-tuning online significance controller in a unified weighted conformal calibration framework. We distinguish three theoretical results: finite-sample validity under oracle importance weights, a coverage-gap bound for estimated weights with rates in effective sample size, and deterministic or regret guarantees for the online controller. We evaluate DRACP against six baselines on 48 real forecasting series covering euro-area and EU-27 HICP inflation, US macroeconomic and energy indicators, and daily financial series. Recent online methods (FACI, strongly-adaptive online conformal prediction and conformal PID) were verified against the authors’ implementations. DRACP is not the most efficient method: strongly-adaptive online conformal prediction achieves the best interval score and intervals about 20% narrower. Instead, DRACP provides the most reliable calibration, achieving coverage closest to the nominal 0.90 (0.890), never falling below 0.80 on any series, maintaining the best coverage at all forecast horizons, and performing best during the 2021-2023 inflation surge. The strongly-adaptive method undercovers on 20 of 48 series versus 10 for DRACP. DRACP therefore offers a principled trade-off between calibration and efficiency, favoring reliable coverage when prediction intervals must satisfy coverage standards. An ablation study shows that the online controller and conditional-scale normalization provide most of the performance gain, whereas the weighting components make a smaller contribution.
[LG-48] Certified but Private: Scalable Zero-Knowledge Proofs for Neural Network Guarantees
链接: https://arxiv.org/abs/2608.17070
作者: Youwei Zhong,Ben Merbaum,Timos Antonopoulos,Ning Luo,Charalampos Papamanthou,Katerina Sotiraki,Ruzica Piskac
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR); Logic in Computer Science (cs.LO)
*备注: 23 pages, 2 figures (11 pages for the main text), for code of implementation and evaluation, see this https URL
Abstract:With the growing deployment of machine learning models, formal guarantees of the robustness and fairness of these models have become increasingly important in safety-critical and legal-compliance settings. However, model parameters are often commercial secrets that cannot be disclosed to auditors or end users. To this end, we present PANDA, a scalable system that uses zero-knowledge proofs (ZKPs) to prove the robustness and fairness properties of a model without revealing its private parameters. PANDA is built on top of CROWN, an efficient robustness certification framework that is used in many state-of-the-art formal verification tools for neural networks. The core contribution of PANDA is a novel algorithm for proving linear relaxation bounds for non-linear activation layers, yielding simple, lightweight proofs. Remarkably, our system can generate proofs of local robustness for neural networks with more than 2.9M parameters in 5 minutes, and can verify them in 10 seconds. Prior ZKP-based robustness system rely on exponential-time algorithms that cannot scale to nontrivial networks. In contrast, PANDA scales polynomially in the number of neurons in a network, allowing us to support neural networks 4 orders of magnitude larger than previous approaches with significantly reduced prover overhead.
[LG-49] Agents unlock new capabilities through Switching LoRA Adapters as a Tool (SLAaaT)
链接: https://arxiv.org/abs/2608.17034
作者: Kenneth Ge
类目: Machine Learning (cs.LG)
*备注:
Abstract:Post-training can unlock new capabilities and improve performance on specialized tasks, but sometimes at the cost of catastrophic forgetting in other domains. This poses a problem in long agent trajectories that compose different capabilities. We reject this tradeoff by giving an agent a tool to switch between specialized LoRA adapters mid-trace. To test its effectiveness, we compose two synthetic coding tasks that are logically simple but require specialization. We find that this allows the model to solve problems it previously could not, that the model is able to switch autonomously (and find a new strategy that beats our human heuristic baseline on one task), and that this incurs an up to an 18x reduction in capability tax compared to an agent using only one specialized adapter. Our approach also substantially outperforms spawning subagents in both task capabilities and token usage.
[LG-50] Lambda-Hold Control: Human-Like Movement Emerges from a Minimal Task Reward in Predictive Musculoskeletal Simulation
链接: https://arxiv.org/abs/2608.17030
作者: Jun Hyuk Lee,Chihyeong Lee,Jooeun Ahn
类目: Robotics (cs.RO); Graphics (cs.GR); Machine Learning (cs.LG)
*备注: 19 pages, 8 figures, 1 table. Project page and video demos: this https URL
Abstract:The massive overactuation in the human musculoskeletal system makes it challenging to train musculoskeletal models to generate human-like motion via reinforcement learning, primarily because exploration in the resulting high-dimensional and redundant action space is extremely inefficient. To address this problem, we propose the \lambda -hold controller, inspired by the equilibrium-point (EP) hypothesis, which has been widely supported by extensive evidence from human motor control studies. The policy’s control variable is the per-muscle EP threshold length \lambda , from which a stretch-reflex recruitment law computes the muscle excitations automatically. Holding each \lambda over an interval of the gait phase also sharply reduces the frequency at which the policy must be queried. Consequently, the controller, to our knowledge for the first time, enables a muscle-actuated skeletal model to learn human-like sprinting using only a minimal reward within an hour of training. The efficient exploration through the proposed \lambda -hold controller is not merely an engineering trick but an approach grounded in physiology, bringing together the EP hypothesis, intermittent control, and optimal feedback control. Beyond encapsulating human-like behavior in predictive simulation, this achievement contributes to developing a learnable model of the human motor controller.
[LG-51] VLCP: Vision Language Control Policy Closed-Loop Code Replanning for Robot Manipulation
链接: https://arxiv.org/abs/2608.16978
作者: Dhia Naouali,Minghan Wu,Claudia Wong,Abhinav Puthran,Omar G. Younis
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Turning a frontier vision-language model into a robot policy usually means fine-tuning it to emit an action representation it never saw in pretraining, which throws away much of the reasoning that made the model worth reaching for. We go the other way and keep the VLM frozen. It writes the policy as a short Python control function, with no demonstrations and no fine-tuning. Writing that code once is open-loop, though. Existing closed-loop methods react at the wrong level: they retry a fixed policy or pick a different subtask, but never rewrite the code that failed. VLCP closes the loop where the failure actually lives, on the control code, within a single episode. Every K steps the VLM re-observes the scene from multi-view RGB, proprioceptive state, and a state delta, then rewrites the control function from what it just saw, so a failure is caught before it compounds. We evaluate on a 57-task MuJoCo/RoboVerse sweep. This training-free policy reaches 35.1% pooled success, against 3.5% for the identical system queried once per episode. That tenfold gap holds with non-overlapping confidence intervals in every scene family. The gain traces to a 27.3% within-episode recovery rate on failed grasps: a miss an open-loop controller would carry to the end of the episode gets re-observed and fixed at the next replan. And the loop stays cheap. A median 84% of input tokens hit cache, an episode needs only about 10 compact queries, and control blocks written during any replan persist to a cross-episode skill library reused in later prompts. Subjects: Robotics (cs.RO); Machine Learning (cs.LG) Cite as: arXiv:2608.16978 [cs.RO] (or arXiv:2608.16978v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2608.16978 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-52] MultiSigBERT: Beyond Survival Analysis through Multimodal and Sequential Modeling in Oncology ECML KDD2026
链接: https://arxiv.org/abs/2608.16972
作者: Paul Minchella,Stéphane Chrétien,Guillaume Metzler,Loïc Verlingue,Rémi Vaucher
类目: Machine Learning (cs.LG)
*备注: Accepted at ECML PKDD 2026, Applied Data Science Track
Abstract:Machine learning has become an essential component of modern healthcare, where the integration of heterogeneous data sources offers unprecedented opportunities to improve clinical decision-making. Electronic Health Records (EHR) contain complementary information – including narrative clinical reports, numerical measurements, and structured variables – yet most survival models remain limited to a single modality or fail to exploit the temporal nature of patient trajectories. We propose MultiSigBERT, a unified framework for multimodal sequential survival modeling in oncology based on path signature representations. Here, narrative medical reports (free-text) are converted into sentence embeddings by extracting and averaging contextual word embeddings. These representations are then compressed via modality-specific PCA and concatenated with structured covariates to form joint temporal trajectories which are then encoded using the Signature transform, a tool from Rough Paths theory that efficiently captures higher-order temporal interactions across modalities without supervision needed. The computed Signature features are finally incorporated as high dimensional features into a LASSO-regularized Cox model to estimate individualized risk scores. The performance of our novel MultiSigBERT pipeline is illustrated on the analysis of a real-world oncology cohort from the Léon Bérard Center, comprising over 120,000 medical reports and structured records from more than 2,500 patients. The model achieves a concordance index of 0.743 (sd 0.029) on an independent test set, demonstrating the benefit of jointly modeling multimodal temporal dynamics together with patient-level geometric structure for survival prediction.
[LG-53] RoBell-RVFL: A Robust Generalized Bell Random Vector Functional Link Network
链接: https://arxiv.org/abs/2608.16965
作者: A. Rahaman,A. Quadir,M. Tanveer
类目: Machine Learning (cs.LG)
*备注:
Abstract:The dominance of majority classes in real-world datasets poses a fundamental challenge to randomized neural networks, often biasing decision boundaries and overlooking critical minority samples. Existing remedies, such as synthetic minority over-sampling (SMOTE) and class-weighted loss functions, primarily address class proportions while neglecting intra-class distribution, making them vulnerable to label noise and outliers. In this paper, we propose \textbfRoBell-RVFL, a robust and lightweight \emphquality-aware generalized bell random vector functional link network that redefines how randomized models handle class imbalance and noisy data. RoBell-RVFL employs a dual-strategy, sample-level weighting mechanism that strictly preserves minority class information using unit weights, while adaptively regulating the influence of majority class samples through a probability-weighted generalized bell (gbell) membership function in a kernel-induced feature space. This design effectively suppresses noisy, boundary, and outlier samples within the majority class, enabling the network to learn from informative samples rather than merely abundant ones. By explicitly incorporating local class probability and class distribution information into the learning process, RoBell-RVFL achieves adaptive control over sample contributions without sacrificing the closed-form learning efficiency of RVFL networks. Extensive evaluations on UCI and KEEL benchmark datasets, along with robustness tests under up to 40% label noise, demonstrate that RoBell-RVFL consistently and significantly outperforms recent state-of-the-art RVFL variants. The results indicate that adaptive, quality-aware sample weighting is essential for robust RVFL learning, rendering conventional global weighting schemes ineffective in noisy and imbalanced environments.
[LG-54] Study-Strategy Clusters from EdNet Logs Track Engagement Not Mastery
链接: https://arxiv.org/abs/2608.16963
作者: Qingchuan Lyu,Yingxin Li,Albert Yang
类目: Machine Learning (cs.LG); Computers and Society (cs.CY); Applications (stat.AP)
*备注:
Abstract:Learning analytics often treats unsupervised clusters of intelligent tutoring system (ITS) logs as learner types that should predict learning. We test that assumption on EdNet-KT3. Clustering study-strategy features (resource use, revision, video, problem practice) for 5,000 active learners yields a silhouette-selected parent cut ( k=5 ) with 4 contrast poles (reading-focused, video-heavy, revision-heavy, and problem-first) plus a large near-mean residual ( \sim 64.9%). Reclustering that residual adds four finer styles, giving a bootstrap-stable hierarchy of 8 named strategies. We split each learner’s timeline by respond count so clusters use only the early half and outcomes only the late half. Early clusters predict later engagement (continuing to practice and finishing late sessions, especially persistence, \eta^2\approx 0.106 ; completion \eta^2\approx 0.021 ) but not later unassisted accuracy (correctness on late first-attempts without help; p_\mathrmadj\approx 0.093 ). Volume rises with some styles, yet volume-only clustering barely matches strategy labels (ARI =0.064 ). A knowledge-tracing model (SAKT) on the seven TOEIC exam sections predicts next correctness only modestly better than a baseline that knows only how hard each section usually is (AUC lift +0.051 ; CI [+0.045,+0.058] ), and that mastery signal is nearly independent of behavior styles (ARI =0.007 ). Behavioral clustering here describes study styles and engagement, not knowledge gains.
[LG-55] A Constant-Competitive Algorithm for Dynamic Mixture-of-Experts Serving
链接: https://arxiv.org/abs/2608.16947
作者: Ian D’Ambrosio(Nth Research Collective)
类目: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)
*备注: 7 pages. The new Dynamic MoE reduction and quantified main theorem are checked in Lean 4 relative to exact formal interfaces for the cited Chasing Positive Bodies theorem and Lazy Threshold Rounding lemma. A separate reproducibility archive contains the pinned-source bootstrap, exact controls, proofs, tests, and canonical SHA-256 manifests
Abstract:Huang, Lou, and Xiao introduced Dynamic Mixture-of-Experts Serving and gave an O(sqrt(log k))-competitive randomized algorithm for its integral primal problem, where k is the number of replica GPUs beyond the mandatory copy of each expert. Their matching lower barrier applies to an auxiliary dual and leaves the primal order open. We prove that the randomized primal competitive ratio is in fact Theta(1) for arbitrary numbers of experts. The upper bound reduces reciprocal-max service costs to chasing positive bodies with covering row sparsity two. A finite tangent envelope approximates each reciprocal epigraph within a constant factor, summable positive resets convert accumulated service into movement, and a nonexpansive balanced projection removes the positive-body algorithm’s resource augmentation. Combining the resulting fractional path with Lazy Threshold Rounding gives E[ALG] = 10 C_PB OPT + (5 C_PB + 2) k + 16, where C_PB is the absolute constant from Chasing Positive Bodies at resource augmentation one and covering sparsity two. The full reduction, rounding composition, and quantified main theorem are machine-checked in Lean 4 relative to exact formal interfaces for the two cited source theorems. Deterministic rational controls and a fresh independent replay accompany the formal proof. Comments: 7 pages. The new Dynamic MoE reduction and quantified main theorem are checked in Lean 4 relative to exact formal interfaces for the cited Chasing Positive Bodies theorem and Lazy Threshold Rounding lemma. A separate reproducibility archive contains the pinned-source bootstrap, exact controls, proofs, tests, and canonical SHA-256 manifests Subjects: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG) Cite as: arXiv:2608.16947 [cs.DS] (or arXiv:2608.16947v1 [cs.DS] for this version) https://doi.org/10.48550/arXiv.2608.16947 Focus to learn more arXiv-issued DOI via DataCite
[LG-56] DOW-KE: Anchor-Free Multi-Layer Knowledge Editing via Direct End-to-End Weight Optimization
链接: https://arxiv.org/abs/2608.16932
作者: Ran Chen,Junbo Zhang,Qianli Zhou,Xinyang Deng,Wen Jiang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Multi-layer locate-then-edit methods for knowledge editing first optimize target residual-stream activations (anchors) at selected layers, then realize them layer by layer as weight updates. This pipeline optimizes an intermediate representation but deploys multi-layer weight updates whose joint effect through the true forward pass is never itself optimized: regardless of how anchors are set or propagated, each update comes from a local solve, so propagation-induced attenuation and distortion go uncorrected, leaving a closure gap between anchor targets and realized edits. We propose DOW-KE, an anchor-free method built on a single principle: what is optimized must be exactly what is deployed. DOW-KE backpropagates the final editing objective through the complete model, jointly optimizing the updates of all edited layers so cross-layer propagation and coupling enter every gradient step. The same principle dictates where preservation resides: embedding the preservation projection in the update parameterization, inside the computation graph, makes every gradient act on the deployed update; post-hoc constraints would reopen the gap, and the constrained search keeps edits clear of protected knowledge. In large-scale sequential editing on two datasets and three models, DOW-KE achieves the highest overall Score and neighborhood Specificity in five of six model-dataset settings among the evaluated baselines.
[LG-57] SW-ProxyCE: Zero-Query Adversarial Transfer from Public EEG Encoders to Private Downstream Models
链接: https://arxiv.org/abs/2608.16931
作者: Linhua Cong,Dingkun Liu,Dongrui Wu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Electroencephalography (EEG) foundation models have recently emerged as a promising paradigm for EEG decoding by learning reusable representations from large-scale heterogeneous neural recordings. However, the open release of EEG foundation encoders, while facilitating downstream developments, also introduces a previously unexplored security risk: publicly available representations may make private downstream models vulnerable. This paper investigates adversarial transfer attacks in EEG foundation model deployment in a public-encoder and private-downstream setting, where attackers have white-box access to a released encoder and a small task-matched labeled reference set, but no access or query to victim parameters, outputs, or gradients. We propose Shrinkage-Whitened Proxy Cross-Entropy (SW-ProxyCE), a query-free task-aware attack framework that recovers task-level decision geometry from a small labeled reference set through shrinkage-whitened class prototypes, enabling transferable adversarial generation without training an additional surrogate classifier. We evaluated SW-ProxyCE across three EEG tasks using three general-purpose foundation encoders and a paradigm-specific pre-trained encoder, covering both linear-probing and full-fine-tuning downstream models in cross-subject and within-subject scenarios. Results demonstrated that adversarial examples generated from the public encoder and limited labeled references can effectively transfer to inaccessible downstream models. SW-ProxyCE consistently outperformed task-agnostic representation-shift attacks, revealing that the strong transferability of EEG foundation models does not necessarily lead to adversarial robustness. Our code will be available on GitHub.
[LG-58] Mr.Dec: Daily-Scale Longitudinal Multimodal Modeling for 30-Day Readmission Prediction MICCAI2026
链接: https://arxiv.org/abs/2608.16929
作者: Minjun Kim,Jong Hak Moon
类目: Machine Learning (cs.LG)
*备注: MICCAI 2026 MultiTab Workshop Oral
Abstract:Predicting 30-day hospital readmission is essential for assessing patient stability and optimizing healthcare resources. As clinical risk evolves with the accumulation of evidence during hospitalization, capturing these dynamic trajectories is essential. However, many existing approaches compress the complex longitudinal history into fixed representations, often losing the granular, day-level clinical signals that reflect a patient’s evolving physiological state. To address this, we propose this http URL (Multimodal Readmission-risk prediction Decoder), which models each admission as a natural chronological sequence of daily multimodal events. By leveraging a Transformer Decoder, this http URL integrates daily Electronic Health Record(EHR) updates and intermittent Chest X-ray(CXR) findings in a time-aligned stream, reflecting the actual clinical workflow. To ensure robustness, we utilize Disease-Specific Supervised Contrastive Learning as an auxiliary regularization to induce a diagnosis-aware structure in the latent space. Evaluations on the MIMIC-IV and MIMIC-CXR datasets show that this http URL achieves state-of-the-art performance by preserving the integrity of the clinical sequence. Furthermore, our model identifies “Critical Days” within an admission, providing actionable and clinically grounded interpretations for real-time risk stratification. Code is available at: this https URL
[LG-59] Benchmarking Classical and Transformer-Based Models for Document Sensitivity Classification
链接: https://arxiv.org/abs/2608.16928
作者: Aleesha Zainab,Muhammad Ahmed Khalid,Faheem Ullah Khan,Asifullah Khan
类目: Machine Learning (cs.LG)
*备注: 6 pages, 4 images
Abstract:Automatic sensitivity classification of organizational documents is a critical yet underserved problem, where the consequences of misclassification range from regulatory violations to security breaches. While AI-based approaches offer a scalable alternative to manual review, their reliability depends fundamentally on the integrity of training data. A pervasive but underreported problem in this domain is label leakage: residual classification markers embedded within document bodies that allow models to exploit surface shortcuts rather than learning genuine content-based sensitivity signals, producing performance estimates that are inflated and unreliable. This paper addresses this problem by introducing Strategic 16K, a carefully constructed, leakage-controlled corpus of 16,000 diplomatic cables sourced from the WikiLeaks Public Library of US Diplomacy (PlusD), and presents a systematic benchmark evaluating six model architectures spanning classical machine learning and transformer-based approaches. We document an extended leakage removal protocol that identifies and eliminates three categories of residual classification markers embedded within document bodies. On the clean benchmark, BERT achieves the strongest performance (Accuracy = 89.14%, F1 = 89.33%), followed by ELECTRA (Accuracy = 88.57%, F1 = 88.90%). Among classical models, TF-IDF with Logistic Regression achieves the strongest performance at significantly lower computational cost. These results constitute the first fully reproducible sensitivity classification benchmark constructed under explicit leakage-controlled conditions from WikiLeaks PlusD.
[LG-60] Data-DPO: Direct Preference Optimization for Target Model Data Selection in LLM Post-Training
链接: https://arxiv.org/abs/2608.16926
作者: Peng Sun,Yi Yang,Antong Zhang,Chunxiao Li,Yanbo Wang,Dianbo Liu,xin chen,Kai Yu,Lu Chen,Tianfan Fu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Data selection in supervised fine-tuning aims to select a small set of effective samples from large-scale candidate data, reducing training cost while preserving model performance. However, existing methods usually treat data value as a relatively static property, and pay limited attention to the compatibility between data and the capability distribution of the target model. To address this issue, we propose Data-DPO, a target model-oriented SFT data selection method. Data-DPO observes the local training feedback of the target model on different samples through one-step probing, transforms activation differences among samples into pairwise data preferences, and trains a lightweight reward model to learn target-model-aware data preferences. In the final selection stage, Data-DPO further combines target model preference, external quality scores, and marginal diversity to construct a more stable and effective training subset. Experimental results on Vision-Flan and LLaVA-CoT show that Data-DPO consistently outperforms existing data selection baselines under multiple data budgets and stably surpasses full data training performance.
[LG-61] Detecting and Discriminating Operator Misspecification in Hybrid PDE-Parameter Learning: a Reference-Free Instrument with Discrimination Bounded In Sample
链接: https://arxiv.org/abs/2608.16925
作者: Eric Fock
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA); Methodology (stat.ME)
*备注: 14 pages, 8 figures. Supplementary material (5 pp.) included as an ancillary file
Abstract:We build an instrument that reads, from a single fit and with no oracle, whether the operator a hybrid PDE-parameter estimator postulates is wrong-and separates that from a merely unidentifiable parameter. On one self-adjoint parabolic inverse problem, an information-matrix statistic with plug-in scale and per-seed parameter has median 0.19 under correct specification, rejection rate 0.033 against a pre-registered ceiling of 0.10 , and rises to 224 and 85 under two misspecifications, firing in every replicate. On a correctly specified but non-identifiable design it stays mute- 0.050 at n=200 , Clopper-Pearson [0.024, 0.090] -while a rank statistic collapses to zero at a pre-registered boundary c_5^*=2.15\times10^-3. Two readings of one fit therefore separate the two failures across the three designs a deployable test reaches. That separation is the contribution; detection alone is a crowded flank. In sample it is a bound, out of sample a direction. It is needed because the usual accuracy check is blind: the misspecified estimator’s in-domain RMSE is 2.7\times 10^-2 , below the observation noise for \sigma\geq 0.05, while the coefficient is wrong by 29.7% at zero noise, 31.2% at the loudest. Nor is the failure architectural: a one-parameter curve fit, a bare parameter and multilayer perceptrons of 49 and 241 parameters converge to the same pseudo-true, matched in closed form to 0.07%, whereas a physics-informed network, with its composite objective, converges to a disjoint one. We report where the instrument is blind, a pre-registered negative where a neural estimator loses to Tikhonov-regularized inversion at recovery, and the hypothesis under which its guarantee holds but a trained network violates it.
[LG-62] Network Denoising Revisited: A Ricci-Flow-Inspired Graph Diffusion Method
链接: https://arxiv.org/abs/2608.16923
作者: Ye Fang,Chuan-Xian Ren
类目: ocial and Information Networks (cs.SI); Machine Learning (cs.LG)
*备注: 9 pages, 5 figures
Abstract:Networks provide a fundamental representation of relationships among entities. However, real-world networks are often corrupted by noise caused by measurement errors and inherent stochasticity, hindering the discovery of meaningful structure. Most denoising methods rely on similarity-driven diffusion and ignore the non-Euclidean geometry of graphs, where local variations induce heterogeneous information transport. This motivates a geometric revisit of network denoising. In this work, we propose Ricci-Diffusion, a curvature-guided graph diffusion method inspired by Ricci flow. Specifically, Ricci-Diffusion exhibits a Ricci-flow-like evolution, in which relative edge-level curvature modulates local transport in the diffusion kernel and guides edge-weight updates toward a more regular graph geometry. We further provide a theoretical analysis showing that curvature can distinguish graph structures that common similarity-driven diffusion kernels fail to separate, and that curvature induces first-order corrections in one-step diffusion updates. The resulting diffusion process explicitly characterizes transport heterogeneity across local geometries and admits theoretical convergence to a stable denoised network. Results on real-world and synthetic graphs show that curvature-guided updates and curvature homogenization improve structure recovery and downstream performance.
[LG-63] Which CS1 Students Will Fail? Identifying Digital Markers from Learning Analytics in Computer Systems and Architecture Using Weighted Academic Momentum and Interaction Logs
链接: https://arxiv.org/abs/2608.16914
作者: Lighton Phiri,Mutune Chaibela,Ivy Chisha,David Pungwa,Danny Siabbaba,Bydon Simukoko
类目: Computers and Society (cs.CY); Machine Learning (cs.LG)
*备注: dataset available on Kaggle and Zenodo
Abstract:Digital learning platforms generate rich behavioural traces (digital markers) that offer the potential to identify struggling students early. This paper investigates whether a combination of traditional and digital markers can predict failure in a first-year CS1 course (Computer Systems and Architecture) with sufficient recall to enable timely intervention. Using data from four cohorts (2017-2021, N=284) at a large public university in sub-Saharan Africa, we conducted a mixed-methods stakeholder elicitation to identify ten candidate factors. These were operationalised into a comprehensive feature set spanning demographics, self-reported surveys, Moodle interaction logs, and continuous assessment scores. A systematic ablation study using logistic regression with 5-fold cross-validation and SMOTE+ENN resampling revealed that the most predictive feature subset was Base + Demo + LMS: weighted academic momentum (M = 0.1Q1 + 0.15Q2 + 0.2Q3 + 0.55T1), basic demographics (gender, sponsorship, COVID-19 cohort), and a binary indicator of any LMS activity. On a held-out test set, logistic regression achieved 74.7% accuracy, 0.742 macro F1, and an AUC of 0.800. At the default threshold of 0.5, the model identified 87% of failing students (recall = 0.87) with a 41% false positive rate. SHAP analysis confirmed that weighted academic momentum is the strongest predictor, followed by its interaction with LMS engagement. These results demonstrate that simple digital markers can power a practical early-warning system by the fifth week of the semester. Our main contributions are: (1) a multi-source dataset and a stakeholder-guided methodology; (2) an ablation study quantifying feature group contributions; and (3) an interpretable, high-recall model ready for deployment.
[LG-64] Proactive Road Safety Intervention in Australia: Predicting Risky Driving Hotspots from Connected Vehicle Data
链接: https://arxiv.org/abs/2608.16913
作者: Adriana-Simona Mihăiţă,Clarence Cheung,Artur Grigorev,Tuo Mao,David Lillo-Trynes
类目: Machine Learning (cs.LG); Computers and Society (cs.CY)
*备注: 15 pages, 11 figures, 2 tables, Submitted to the ATRF 2026 Conference to take place in November 2026 Sydney, Australia
Abstract:Road safety monitoring has historically been reactive, relying on crash-record analysis after fatalities and injuries have already occurred. Proactive identification of high-risk locations and dangerous driving behaviour before incidents occur is a critical but underexplored challenge. This paper addresses this gap using connected vehicle telemetry data from Greater Sydney, Australia, to detect and forecast near-miss risky driving events at the Local Government Area (LGA) level. Risky driving is quantified through g-force thresholds (hard braking 0.6g, harsh cornering 0.47g, harsh acceleration 0.5g), and spatio-temporal heatmaps are constructed to identify high-risk zones. Eight predictive models are benchmarked across three families: ensemble learning (Random Forests, XGBoost, LightGBM), deep learning (LSTM, N-BEATS), and classical time-series methods (ARIMA, Exponential Smoothing, Prophet). ARIMA achieves the lowest mean absolute error (MAE: 162.21), performing comparably to LSTM (MAE: 163.92) and outperforming all ensemble methods, with N-BEATS reaching an MAE of 180.75. These results demonstrate that parsimonious time-series models are competitive with deep learning approaches when training data volume is limited. The study highlights the potential of IoT-based connected vehicle data to support proactive road safety interventions, with Sydney’s inner and western LGAs (CBD, Parramatta, Bankstown) identified as persistent high-risk zones warranting targeted policy action.
[LG-65] Advancing Health Equity through Multi-Level Fairness in Health Informatics ALT
链接: https://arxiv.org/abs/2608.16902
作者: Nick Souligne,Vignesh Subbian
类目: Computers and Society (cs.CY); Machine Learning (cs.LG)
*备注: 10 pages, 3 figures, Submitted to Health Informatics Knowledge Management Conference 2026
Abstract:The increasing integration of machine learning in healthcare has highlighted critical challenges related to fairness, transparency, and health equity. Specifically, the use of multi-level fairness techniques, which combine multiple bias mitigation steps or techniques, show promise for reducing biases across different patient demographics, yet this approach remains underexplored in terms of its health equity outcomes. In this paper, we assess the current landscape of multi-level fairness in health informatics by focusing on its impact on equitable healthcare outcomes and evaluating how transparency and reporting standards contribute to these advancements. Through an examination of the existing literature, we identify key gaps in both the implementation of multi-level fairness techniques and the consistent reporting of health equity impacts. Furthermore, we analyze the role of reporting standards, including MINIMAR and TRIPOD, in improving model transparency and ensuring that machine learning models in healthcare address health disparities. These standards offer valuable benchmarks for reporting on ML models, yet we identify key opportunities for enhancing how these reports capture fairness and equity outcomes. The paper concludes by providing recommendations that focus on improving transparency in reporting, advocating for the broader adoption of multi-level fairness techniques, and ensuring that health equity is explicitly prioritized in future research efforts.
[LG-66] Where A Small Language Model Helps in Invoice Categorisation Understood Through Embedding Geometry
链接: https://arxiv.org/abs/2608.18033
作者: Emma Ceccherini,Daniel Lawson,Anjulika Salhan
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 22 pages, 10 figures
Abstract:Categorising invoices into the correct General Ledger (GL) code underpins financial reporting and tax compliance. This is a skilled accounting judgement rather than a routine task: the correct category depends subtly on the nature of the purchasing business, the vendor and the invoice text. Whilst AI is increasingly being adopted across industries to automate tasks, including invoice categorisation, implementations built on in-house small language models (SLMs) can simultaneously reduce cost and improve data security, confidentiality, and interpretability. We investigate this approach by first analysing the pre-trained embedding geometry of a small sentence transformer (SBERT) and classic SLM (DeBERTa). The sentence-embedding space of this financial corpus is globally anisotropic but composed of locally isotropic clusters, extending prior token-level findings to sentence embeddings in a financial setting, and these clusters are strongly correlated with the vendor identity. SBERT fine-tuned on a single GPU reaches 0.96 accuracy on invoice classification, above both a zero-shot LLM and a vendor identity baseline, increasing performance for smaller, challenging categories and new clients. For this important generalisation problem, SBERT reaches 0.9 F1 with roughly 100 client-specific invoices, showing that an in-house SLM implementation is promising. Combining these results with geometric analysis shows that pre-trained embedding geometry is associated with classification performance and reveals a counterintuitive finding that a structured input that would help a human reader does not improve the SLM performance.
[LG-67] A Residual Learning Approach for Unsteady Aerodynamic Load Prediction
链接: https://arxiv.org/abs/2608.17894
作者: Divya Sanghi,Carlos E. S. Cesnik
类目: Fluid Dynamics (physics.flu-dyn); Machine Learning (cs.LG)
*备注: 27 pages, 21 figures, 9 Tables
Abstract:This paper investigates the feasibility of using residual learning to improve unsteady aerodynamic load prediction for aeroelastic applications. The machine learning technique selected for the study is the long short-term memory (LSTM) neural network, which is used for its suitability for sequential data with aerodynamic memory effects. The approach is investigated for the NLR 7301 airfoil benchmark using high-fidelity CFD lift data for prescribed pitch and plunge motions in the transonic flow regime in the presence of shock motion. An analytical unsteady aerodynamic model based on the Wagner function is used as a physics-based baseline, and the neural network is trained to learn the difference between the CFD lift coefficient and the Wagner prediction. The residual model is compared with a direct neural-network model trained to predict the CFD lift coefficient. The comparison includes feature and normalization studies, external benchmark cases, and leave-one-out and leave-family-out generalization tests across a range of sinusoidal and non-sinusoidal motions. The residual model performs best when its inputs align with the Wagner formulation variables, generally giving lower error and more consistent performance across training runs, though the direct model remains more accurate for some high-frequency cases. The residual model also generalizes better in the leave-one-out and leave-family-out tests, with a smaller increase in error than the direct model when entire motion families are withheld from training. Overall, the results indicate that residual learning shows promise as a modular approach for augmenting classical low-order aerodynamic theories, especially when the physics baseline removes a structured part of the aerodynamic response and leaves a lower-variance correction for the neural network to learn.
[LG-68] oward the Optimal Regret-Instability Trade-off in Multi-Armed Bandits
链接: https://arxiv.org/abs/2608.17841
作者: Kaifei Wang,Yinyu Ye,Han Zhong
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Optimization and Control (math.OC); Statistics Theory (math.ST)
*备注:
Abstract:Multi-armed bandit algorithms are evaluated by regret, yet comparable regret can coexist with different allocations across independent runs. We study the trade-off between worst-case regret \mathcalR_K,T and instability \mathcal S_K,T , defined as the largest standard deviation of a terminal pull count, for K arms and T rounds. We prove the finite-time lower bound \mathcal R_K,T\mathcal S_K,T\ge C T^3/2 , where C is independent of K and T , under a finite-time regret condition and without the regularity assumptions imposed in the prior asymptotic analysis. We also introduce Stabilized Lower-Envelope UCB (\textup\textscSLE-UCB), a new tunable algorithm combining a running lower-envelope index with a decreasing pull-count stabilizer. \textup\textscSLE-UCB satisfies \mathcal R_K,T\mathcal S_K,T=O(T^3/2\log K) , with an implicit constant independent of K and T , matching the lower bound exactly in T and within a logarithmic factor in K . To prove the instability bound, we develop a new offline top-prefix representation that removes path dependence from online decisions. Together with single-reward perturbations and the Efron–Stein inequality, this representation controls pull-count variance. Thus, regret and instability depend reciprocally on K , while their product has no polynomial dependence on K . These results resolve the open question raised in the literature concerning the sharp arm-dependent regret–instability frontier.
[LG-69] Spatially explicit feature importance for building height estimation using research-access high-resolution SAR and optical sensors
链接: https://arxiv.org/abs/2608.17822
作者: Guilherme Iablonovski,Pierre-Louis Frison,Tatiana Silva da Silva
类目: Physics and Society (physics.soc-ph); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 4 pages, 3 figures, intended for JURSE 2027
Abstract:Accurate building height information at the individual footprint scale is essential for material stock accounting and post-disaster damage assessments yet remains difficult to obtain at city scale in the Global South where airborne LiDAR coverage is rare and commercial very high-resolution imagery is cost-prohibitive or unavailable. While recent works have demonstrated building height estimation using freely available Sentinel imagery, the resolution ceiling of resulting products is still coarse for material stock analysis. This study incorporates products derived from data freely accessible under scientific research licenses, TerraSAR-X StripMap and PlanetScope, alongside Sentinel-1 to predict building heights in a large city in Brazil. To account for the spatial autocorrelation in the training set, features from all sources are integrated in a geographically weighted random forest model, returning an RMSE of 5.34 m and R2 of 0.756 against a LiDAR reference dataset. Local feature importance showed predictor dominance to vary consistently across intra-urban contexts, with footprint geometry dominating for low-rise buildings, shadow-derived height for taller and more isolated structures, and spectral reflectance for the tallest buildings in the set. Sentinel-1 backscatter and InSAR occupy complementary spatial niches, with no single sensor uniformly preferable across the set. Results provide optioneering guidance and insight over satellite-derived products predictive relevance in distinct contexts, which global machine learning or neural network models cannot offer.
[LG-70] Feature Priming in Online Linear Regression: Sparse-Regret Lower Bounds and a Tight Univariate Rate
链接: https://arxiv.org/abs/2608.17573
作者: Huibo Xu,Shi Fu,Qixin Zhang,Dacheng Tao
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Applications (stat.AP)
*备注: 47 pages, 2 figures
Abstract:In high-dimensional online prediction, the best predictor may depend on only a few features, so regret should scale with sparsity rather than the ambient dimension. Feature priming pursues this goal by estimating feature weights from past data and refitting a minimum-norm predictor on the rescaled design. Warmuth and Amid asked at COLT 2023 whether any of three such rules admits a competitive online regret guarantee. Using the natural Moore–Penrose protocol based only on past data, we give a negative answer to the sparse-logarithmic form of this COLT open problem. Our analysis identifies a common obstruction: cheap nuisance interpolation causes the refit to underweight the truly predictive coordinate. An exact target-mass identity and a two-sign argument turn this effect into clipped prediction loss. Hadamard constructions force \Omega(\min\T,\sqrtd) regret for all three rules against a zero-loss one-sparse comparator, with extensions to fixed prime powers and selectors among the rules. Conversely, regret is controlled by data rank, and a Euclidean-normalized triangular construction matches this dependence for powered univariate priming, even under nonnegative second-stage ridge regularization; a paired ridge construction also covers all three powered rules. Exploratory diagnostics on frozen language-model activations exhibit the same relation among nuisance interpolation, target weight, and loss. The exact multivariate and Pearson frontiers remain open.
[LG-71] Online Generalized Sparse Regression: How Does Overparametrization Help?
链接: https://arxiv.org/abs/2608.17466
作者: Shuoguang Yang,Qiang Sun
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注: 24-page main, 20-page supplement
Abstract:Regularized sparse regression has been extensively studied in the offline setting, but online formulation remains relatively under-explored. This gap stems from four key challenges: (i) the infeasibility of dynamically updating the regularization parameter in every online round, (ii) managing storage and memory complexity, (iii) enabling real-time computation via closed-form updates rather than solving full optimization problems at each round, and (iv) achieving optimal statistical guarantees under realistic assumptions. In this paper, we propose an online generalized-sparsity-constrained regression framework, focusing on online cardinality-constrained linear regression and low-rank matrix sensing. Unlike online regularized regression, our constrained formulation eliminates the need for dynamic parameter tuning. We introduce an efficient online hard-thresholding algorithm that performs closed-form updates and requires storing only summary statistics, making it computationally, memory, and storage efficient. Despite the inherent nonconvexity and combinatorial nature of the formulation, our algorithm achieves global convergence at the optimal statistical rate under realistic assumptions, provided that the projection set is properly overparameterized. Numerical experiments demonstrate that our method consistently outperforms state-of-the-art alternatives.
[LG-72] Nonlocal Transition Kernel for Efficient Learning of Restricted Boltzmann Machines
链接: https://arxiv.org/abs/2608.17450
作者: Kaiji Sekimoto,Muneki Yasuda
类目: Machine Learning (stat.ML); Disordered Systems and Neural Networks (cond-mat.dis-nn); Machine Learning (cs.LG)
*备注:
Abstract:Learning restricted Boltzmann machines (RBMs) is computationally challenging because it requires expectations whose exact evaluation is generally intractable. The expectations are typically evaluated using a sampling approximation based on blocked Gibbs sampling (BGS), which is a local Markov chain Monte Carlo transition kernel. However, the locality of BGS can lead to poor sampling quality when the RBM has high energy barriers, thereby degrading learning performance. Deep tempering (DT), which performs parallel tempering over a sequence of learnable RBMs including the training RBM, alleviates this locality issue. However, DT algorithmically requires multiple steps to move through the RBM sequence to achieve a nonlocal transition. In this paper, we propose a transition kernel defined over the RBM sequence used in DT. The proposed kernel has a round-trip structure over the sequence, enabling nonlocal moves within a single transition while leaving the RBM sequence invariant. Numerical experiments show that the proposed kernel performs nonlocal transitions more frequently and achieves higher sampling quality with fewer transitions than BGS and DT. We further verify that learning based on the proposed kernel is more stable and mitigates the training failures observed with BGS- and DT-based learning.
[LG-73] On the Pseudo-Mixing of Kacs Walk
链接: https://arxiv.org/abs/2608.17374
作者: Natesh S. Pillai,Aaron Smith,Vinod Vaikuntanathan
类目: Probability (math.PR); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: 48 pages
Abstract:Motivated by a conjecture of Vaikuntanathan and Zamir, we study the pseudo-mixing of Kac’s walk on \mathrmSO(n) : whether short trajectories are indistinguishable from Haar measure by low-complexity tests. We prove that the first k columns mix in Wasserstein distance in O(n(k+\log n)\log n) steps for fixed accuracy, resolving a conjecture of Oliveira. Combining this with a representation-theoretic variance bound, we show that if T=\omega(nk(k+\log n)\log n) , then every degree- k polynomial normalized to have unit Haar variance has expectation under the T -step law within o(1) of its Haar expectation. As an application, we show that this pseudo-mixing estimate can be used to prove the effectiveness of a fast Johnson–Lindenstrauss transform with the usual target dimension.
[LG-74] Expressivity In Multimodal Contrastive Learning
链接: https://arxiv.org/abs/2608.17203
作者: Andrew Stuart,Florian Wolf
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
Abstract:Contrastive learning has become a cornerstone of modern representation learning, powering CLIP-style models that underpin text-to-image generation, vision-language models, and retrieval across a rapidly growing range of modalities. Despite this empirical success, the expressive power of these architectures remains poorly understood. To gain insight, we study expressivity by adopting a population-level, density-estimation viewpoint: each architecture comprises a parameterized set of densities whose parameters may be chosen to approximate the joint distribution of the modalities. This isolates a question of pure representational capacity: which joint distributions can a given contrastive family of parameterizations approximate to arbitrary accuracy? We show that expressivity is sharply architecture-dependent. For two modalities, the simple two-tower CLIP architecture is a universal approximator. A natural generalization of CLIP, widely used in practice when three or more modalities are present, is based on a loss found by summing over all pairwise similarities. This provably cannot represent arbitrary joint distributions, although we prove that it remains expressive enough to match all pairwise conditionals. Motivated by this gap, we propose Hadamard-CLIP, which adds a single learned weight vector on top of the existing encoders and restores universal approximation of the joint for any number of modalities while preserving CLIP’s fast, precomputable-embedding retrieval.
[LG-75] Policy Optimization and Statistical Inference for Online Contextual Matrix Games
链接: https://arxiv.org/abs/2608.17173
作者: Liner Xiang,Yixin Wang,Hengrui Cai
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME)
*备注:
Abstract:Online decision making often requires navigating a landscape shaped by both dynamic contexts and strategic interactions. In competitive pricing, for example, hotels must account for both dynamic contextual factors and rivals’ strategic responses. Existing approaches address only part of this challenge: contextual bandits optimize single-agent decisions using observable features but ignore multi-player interactions, while online matrix games capture strategic behavior through Nash equilibrium but assume fixed payoffs, ignoring contextual information. How should agents act then when strategic payoffs evolve with contextual signals? We introduce \emphonline contextual matrix games to integrate contextual information into multi-player online games. We further propose \emphOnGameLearn, an online learning algorithm that efficiently balances exploration and exploitation across both player actions and contexts. This approach comes with statistical guarantees: tail bounds for the estimated payoff matrix, the convergence of the estimated Nash equilibrium, the asymptotic normality of the parameter estimators, and the sublinear regret bound. We also develop the notion of \emphpolicy value in matrix games and develop a doubly robust, \sqrtT -consistent estimator for it. Across simulated studies and a real-world hotel pricing application, we find that OnGameLearn effectively navigates the intertwined challenges of strategic and contextual decision-making.
[LG-76] Dynamic Entanglement-Weighted Pruning for Quantum Federated Unlearning in Supply-Chain Risk Prediction
链接: https://arxiv.org/abs/2608.17069
作者: Aditya Kumar,Sumit Chongder
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注: 25 pages, 10 figures, 11 tables. Code: this https URL . Data (Zenodo): this https URL . Research carried out as part of the QIntern 2026 programme (QWorld Association)
Abstract:Federated deployments of variational quantum classifiers are attractive for cross-organisation risk prediction in supply chains, because raw data never leaves the client, yet data-protection regulations such as the GDPR grant clients a right to request that their contribution be removed from a trained model after the fact. Retraining a federated model from scratch to honour such a request is correct but wasteful, and it is not obvious which quantum circuit parameters actually carry a given client’s influence. We introduce Entanglement-Weighted Pruning (EWP), an unlearning procedure for quantum federated learning that scores every trainable circuit parameter with the product of two signals: the diagonal entry of the quantum Fisher information matrix estimated on the target client’s data via the parameter-shift rule, and a structural entanglement weight associated with the parameter’s gate. Parameters with the lowest scores are pruned, optionally followed by a short fine-tuning pass on the retained clients. We implement the full pipeline in Qiskit for a four-qubit data-re-uploading ansatz trained with FedAvg across five simulated supply-chain-risk clients, and benchmark EWP against full retraining, fine-tuning alone, random pruning, Fisher-only pruning, and entanglement-only pruning, over three random seeds. EWP attains a mean post-unlearning accuracy statistically indistinguishable from the full-retraining oracle, while producing a lower forgetting score and requiring roughly 16 times less wall-clock time. Ablations over pruning threshold, client count, and non-IID strength show that combining the two signals is necessary, as entanglement-only and Fisher-only pruning each substantially degrade accuracy relative to EWP.
[LG-77] Diagonal Multi-omics Integration of Heterogenous Datasets
链接: https://arxiv.org/abs/2608.16968
作者: Maksim V.Kukushkin,Mikhail S. Arbatskiy,Dmitriy E. Balandin,Alexey V. Churov
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Functional Analysis (math.FA)
*备注:
Abstract:In this paper, we consider methods for the diagonal multi-omics integration of heterogeneous datasets. Several approaches to the nature of biological heterogeneity are analyzed and developed to comprehend more clearly the generated differences. Specifically, the extremal trace problems for the coupled Laplacian on sets homeomorphic to the Stiefel manifold embedded in the complex Euclidean space are investigated. The gradient ascent method for the maximization problem is elaborated in the classical terms of functional analysis, which is of significant interest in itself. On this basis, we introduce a novel characteristic of dataset heterogeneity by employing the norm of the difference between the maximum and minimum points.
[LG-78] SPSA Hyperparameter Tuning for Variational Quantum Natural Language Inference
链接: https://arxiv.org/abs/2608.16939
作者: Nayan D’Souza,Christopher J. Agostino
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注: Accepted submission for poster to QNLP AI 2026
Abstract:Training variational quantum models requires choosing between parameter-shift gradients, which are exact but cost O§ forward evaluations, and simultaneous perturbation stochastic approximation (SPSA), which uses only two samples but produces high-variance estimates that can degrade optimisation on small supervised tasks. Whether the cheap gradient is usable depends on the variance that results from different choices of the SPSA perturbation scale, learning rate, and gain-decay schedule. We varied those quantities across a broad grid on a 6-qubit, 60-parameter QNLI classifier and compared the best configurations to parameter-shift AdamW and BuresQNG. AdamW-style SPSA with c_0=0.01 , \eta=0.10 , \gamma=0.10 reached 55% \pm 11% test accuracy, improving over the default configuration ( 49% \pm 6% ) but remaining 16-19 percentage points below the parameter-shift baselines because the two-sample SPSA gradient estimate has too much variance for reliable optimisation of 60 parameters in 40 epochs. Classical-gain SPSA and Bures-preconditioned SPSA performed worse, at 51% and 46% respectively. Bures-preconditioning a noisy two-sample SPSA gradient amplifies perturbation noise.
[LG-79] Information Spreading in Diffusion Models from Effective Field Theory ICLR2026
链接: https://arxiv.org/abs/2608.14308
作者: Navonil Neogi,Nabil Iqbal
类目: High Energy Physics - Theory (hep-th); Statistical Mechanics (cond-mat.stat-mech); Machine Learning (cs.LG)
*备注: This work was previously presented at the Scientific Methods for Understanding Deep Learning Workshop at ICLR 2026. 14 pages + appendices. Code available at: this https URL
Abstract:We study score-matching diffusion models with a convolutional architecture. We argue that the inductive bias of locality means that the machinery of effective field theory from physics can be usefully applied to describe the denoising dynamics. We apply this formalism first to a simple toy example which permits an analytical description, and thereafter to MNIST, and show that in both cases, the mutual information between two points grows in a manner predicted by a simple effective field theory of Brownian motion.
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