论文

本文提出一种基于 Agentic Large Language Model (LLM) 的主动容错控制 (FTC) 框架，可将故障检测输出转化为基于特定工厂知识的、符合约束的恢复动作。该方法结合：(i) 将操作员职责分解为监测、规划、动作合成、仿真、验证与重新提示的多 Agent 工作流；(ii) 数字过程工厂孪生 (DPPT)，提供工厂数据、模型以及用于执行前测试的仿真服务；(iii) 基于 CPSMod 本体构建的 Graph Retrieval-Augmented Generation (Graph RAG) 层。We propose an agentic Large Language Model (LLM) framework for active Fault-Tolerant Control (FTC) that transforms fault detection outputs into constraint-aware recovery actions grounded in plant-specific knowledge. The approach couples (i) a multi-agent workflow that decomposes operator duties into monitoring, planning, action synthesis, simulation, validation, and reprompting; (ii) a Digital Process Plant Twin (DPPT) that exposes plant data, models, and a simulation service for pre-execution testing; and (iii) a Graph Retrieval-Augmented Generation (Graph RAG) layer built on the CPSMod ontol

本文提出 SHIFT，一种新颖的框架，将神经元级修改重构为可学习的门控调制，使 LLM 能够自适应地调节内部激活以解决知识冲突。SHIFT is introduced, a novel framework that reformulates neuron-level modification as learnable gate modulation, allowing LLMs to adaptively regulate internal activations for knowledge conflict resolution, allowing LLMs to adaptively regulate internal activations for knowledge conflict resolution.

本文将门控量子启发的 Kolmogorov-Arnold 网络快权重编程器用于直接多步 Abilene 流量矩阵预测，提出以经典慢速编程器搭配量子启发快速编程器的方案，作为面向资源受限场景的网络流量矩阵预测中一种兼顾精度与效率的有前景设计。This paper adapts gated quantum-inspired Kolmogorov-Arnold network fast-weight programmers to direct multi-step Abilene TM forecasting and identifies a classical slow programmer with a quantum-inspired fast programmer as a promising accuracy-efficiency design for resource-conscious network traffic-matrix forecasting.

提出 PerceptionRubrics——一个基于评分量表的评估框架，旨在弥合饱和的基准分数与真实场景脆弱性之间的差距，并验证了严格的感知保真是可靠生成的前提。PerceptionRubrics is introduced, a rubric-based evaluation framework that addresses the gap between saturated benchmark scores and real-world brittleness, validating that strict perceptual fidelity is the prerequisite for reliable generation.

提出 Simplified Sparse Attention——一种更简洁的稀疏注意力方案，无需任何架构改动，在检索增强生成中优于全注意力；并扩展为分层 gist-of-gist 变体，在高达 32× 的高压缩比下保持或提升精度，同时实现对数级解码复杂度。Simplified Sparse Attention is introduced, a simpler approach to sparse attention that requires no architectural changes that outperforms full attention in retrieval-augmented generation and extends to a hierarchical gist-of-gist variant that achieves log-linear decoding complexity while maintaining or improving accuracy at high compression ratios up to 32x.

提出 ZooClaw-FashionSigLIP2——一款面向时尚领域的专用 SigLIP2-base 模型，以简洁方案化解该权衡，性能上优于 LoRA、更大骨干网络以及外部训练数据。ZooClaw-FashionSigLIP2, a fashion-specialized SigLIP2-base model that resolves this tradeoff with a simple recipe and outperforms LoRA, larger backbones, and external training data, is presented.

提出 Gradient-Based Connections（GBC）——一种面向多智能体系统的细粒度归因与优化方法，可提升多智能体性能，超越强力的单智能体与多智能体基线；且归因质量越高，优化效果越显著。Gradient-Based Connections (GBC) is proposed, an approach for fine-grained attribution and optimization of multi-agent systems that improves multi-agent performance and outperforms strong single-agent and multi-agent baselines and higher attribution quality is associated with greater optimization effectiveness.

核心结论是"过时事实错误率"：在被要求作答时，RAG 有 15%–40% 的概率输出已被取代的旧值；MemStrata 将该比率降至约 0%，而这一类失效是 RAG 本身无法规避的。The central result is the stale-fact-error rate: when required to answer, RAG serves superseded values 15-40% of the time; MemStrata drives this to ~0%, a failure class RAG cannot avoid.

本文提出 PhysRAG——一条通过检索增强生成（RAG）提升视频生成物理感知的新流程，并基于 WISA-80K 数据集设计了两阶段数据过滤流程，最终筛选出 7K 高质量视频用于训练。This work introduces PhysRAG, a novel pipeline that enhances physical awareness in video generation through Retrieval-Augmented Generation (RAG), and designs a two-stage data filtering pipeline based on the WISA-80K dataset, resulting in a curated set of 7K high-quality videos for training.

提出一种面向 LCA 解释的视角条件化检索增强生成框架，在 AI 辅助的 LCA 中引入多视角检索与受控合成，以支持超出传统 LCA 研究的、面向落地的决策。A perspective-conditioned retrieval-augmented generation framework for LCA interpretation, where a multi-perspective retrieval and controlled synthesis is incorporated in the artificial intelligence (AI)-assisted LCA to support implementation-oriented decision-making beyond conventional LCA studies.

提出 MIRROR——一个统一的跨表层框架，在显式新颖性约束下以检索到的上下文为条件生成候选，并执行记忆引导的蒙特卡洛树搜索，使检索可影响搜索先验，同时避免提示词级别的复制。MIRROR is presented, a unified cross-surface framework that performs memory-guided Monte Carlo tree search while conditioning candidate generation on retrieved context under an explicit novelty constraint, allowing retrieval to inform search priors without enabling prompt copying.

本综述从数据中心化视角梳理 LLM 智能体的隐私问题，围绕智能体所接触的数据而非按攻击类型组织领域，并以"data agent"作为处理数据的 LLM 智能体的简称。This survey surveys the privacy of LLM agents from a data-centric view, organizing the field around the data an agent touches rather than by attack type, and uses data agent as shorthand for an LLM agent that works with data.

PAVE 是一种逐步式标注协议，利用来自故障注入的已知干预来重建因果传播路径；逐步式的因果真值正是可信的基于 LLM 的 RCA Agent 所缺失的关键一环。PAVE, a step-wise labeling protocol that leverages known interventions from fault injection to reconstruct causal propagation paths, is introduced, a step-wise causal ground truth is the missing piece for trustworthy LLM-based RCA agents.

提出 Erase-then-Delta Attention (EDA)，一种将"在哪里擦除"与"在哪里写入"解耦的内存更新规则；研究表明循环记忆模型不仅应决定写入什么，还应决定擦除哪些陈旧信息以及擦除的位置。Erase-then-Delta Attention (EDA), a memory update rule that decouples where to erase from where to write, is proposed, suggesting that recurrent memory models should decide not only what to write, but also what stale information to erase and where.

本工作将带外防御组织为经典完整性保护、引用监控与最小权限的具体实例，对它们覆盖与未覆盖的内容进行结构化对比；与该假设一致但尚未被证实的是：确定性的带外强制执行相比带内检测，是更难被自适应攻击者攻破的目标。This work organizes out-of-band defenses as instances of classical integrity protection, reference monitoring, and least privilege, yielding a structured comparison of what they do and do not cover, consistent with, but not established, the hypothesis that deterministic out-of-band enforcement is a harder target for an adaptive attacker than in-band detection.

提出 RedVox，一个基于真实人声构建的音频与语音多语言安全性与公平性基准，涵盖五种语言中的不安全与不公平的刻板请求；研究发现漏洞即使在非对抗条件下仍然存在，在非英语语言中更为严重，且在请求来自语音输入时会被进一步放大。RedVox is introduced, a multilingual safety and fairness benchmark for audio and speech built on real voices, covering unsafe and unfair stereotypical requests across five languages, finding that vulnerabilities persist even under non-adversarial conditions, worsen in non-English languages, and are amplified when the request comes from a spoken input.

一项针对基于 LLM 的程序修复中执行行为的双阶段实证研究揭示了一些模式，表明当前 Agent 不加区分地使用执行，在获益甚微的实例上仍承担其开销；执行应被视为具有明确成本效益权衡的资源。A two-stage empirical study over execution behavior in LLM-based program repair reveals patterns suggest that current agents apply execution indiscriminately, paying its cost on instances where it provides little benefit, and execution should be treated as a resource with an explicit cost-benefit tradeoff.

所得到的模型在大多数数据集-预测步长组合上优于先前的线性预测器，并在八个基准中的六个上超越 Transformer、MLP 和 CNN 基线；同时它还可作为对数据本身的诊断工具，揭示那些被更大模型默默吸收进其学习参数中的结构。The resulting models beat prior linear forecasters on most dataset-horizon entries and exceed Transformer, MLP, and CNN baselines on six of eight benchmarks, and serve as a diagnostic on the data itself, revealing structures that larger models absorb silently into their learned parameters.

水电隧道检测对基础设施完整性至关重要，但人工方式效率低下且具有危险性。本文提出 FLISP（Fast LiDAR-IMU Synchronized Path Planner），一种面向 UGV-UAV 协同检测的无地图规划框架。不同于传统基于地图的范式，FLISP 具有三项核心贡献：(1) 统一架构，由单套 UGV 搭载的 LiDAR-IMU 驱动两平台的同步路径生成；(2) 平台特定的求解器，采用增强型萤火虫算法用于 UGV 避障，以及动态迭代优化器用于 UAHydropower tunnel inspection is critical for infrastructure integrity yet remains inefficient and hazardous using manual methods. We propose FLISP (Fast LiDAR-IMU Synchronized Path Planner), a mapless planning framework for cooperative UGV-UAV inspection. Unlike traditional map-based paradigms, FLISP features three core contributions: (1) a unified architecture where a single UGV-mounted LiDAR-IMU suite drives synchronized path generation for both platforms; (2) platform-specific solvers utilizing an enhanced Firefly Algorithm for UGV obstacle avoidance and a dynamic iterative optimizer for UA

本工作通过强化学习循环改进视觉-语言-动作（VLA）策略，该循环预测成功、进展及若干任务相关的未来量，并驱动优势估计、实时失败检测与候选选择，在 LeHome Challenge 2026 中取得佳绩。The work improves a vision-language-action (VLA) policy with a reinforcement-learning loop that predicts success, progress, and a few task-relevant future quantities and drives advantage estimation, live failure detection, and candidate selection in the LeHome Challenge 2026.

提出 GauntletBench，一个用于评估 Agent 在挑战性场景中泛化能力的 Web 基准，聚焦于三种被低估的能力（时间感知、图形理解与 3D 推理），揭示了当前 Agent 能力与复杂真实场景所需能力之间的巨大差距。GauntletBench, a web-based benchmark for evaluating agent generalisation in challenging scenarios, focusing on three underexplored capabilities (temporal perception, graphical understanding, and 3D reasoning), is introduced, revealing the substantial gap between current agent capabilities and those required for complex real-world scenarios.

实验表明，LISA 不仅能持续加速训练收敛并提升最终合成结果，还能促使侧网络特征在条件建模中更加解耦，且几乎无额外训练成本，推理成本为零。Experiments demonstrated that LISA can not only consistently accelerate the training convergence and improve final synthetic results, but also encourage the side network's features to be more disentangled for conditional modeling with negligible additional training cost and zero extra inference cost.

本文从前瞻视角重新审视 token 重要性，提出衡量压缩 token 对未来上下文影响的新指标 Forward Influence，以及融合信息论信号的熵感知 KV cache 压缩框架 InfoKV。This paper revisits token importance from a forward-looking perspective and introduces Forward Influence, a metric that measures how compressed tokens affect future contexts, and proposes InfoKV, an entropy-aware KV cache compression framework that incorporates information-theoretic signals.

路由、投票、级联、融合与 Mixture-of-Agents 等多模型 LLM 系统常被用于超越单模型精度；研究表明其增益受限于一个该领域鲜少报告的量化指标，且在缺乏强查询级路由信号时，组合模型很少能胜过单一最佳模型。Multi-model LLM systems such as routing, voting, cascades, fusion, and mixture-of-agents are used to beat single-model accuracy, it is shown that their gain is capped by a quantity the field rarely reports, and combining models rarely beats the single best model without a strong query-level routing signal.

地球观测（EO）预报旨在依据变化的天气条件，从卫星观测预测未来地表动态。本文将其建模为部分可观测、天气驱动的世界建模问题，其中天气作为条件信号，而由于观测稀疏和未观测的陆面状态，预报本身具有不确定性。然而现有方法未能完整刻画这一设定：确定性模型将不确定性坍缩为单一未来预测，而基于扩散的方法通常将天气变量视作无条（原文此句截断）。Earth Observation (EO) forecasting aims to predict future Earth surface dynamics from satellite observations under changing meteorological conditions. In this paper, we view this task as a partially observed, weather-driven world modeling problem, in which weather acts as a conditioning signal, while forecasting remains uncertain due to sparse observations and unobserved land-surface states. However, existing methods do not fully capture this setting: deterministic models collapse uncertainty into a single future prediction, while diffusion-based methods typically treat weather variables as un

本文认为，将上下文学习（ICL）扩展至终身设置是 AI Agent 持续学习的实用方案；要在固定硬件预算下用 Transformer 理解终身上下文，需要注意力的参数化形式。It is argued that extending in-context learning to lifelong settings is a practical solution for continual learning in AI agents and that parametric forms of attention are needed to understand a lifetime of context with transformers on a fixed hardware budget.

所提系统为阿尔及利亚方言的端到端对话建模提供了可复现基线；实验结果显示各组件均表现优异：ASR 词错误率低，NLU 意图分类与实体识别得分高，语音合成质量稳定。The proposed system provides a reproducible baseline for end-to-end conversational modeling in Algerian Dialect, and experimental results show strong performance across all components, including low word error rate for ASR, high intent classification and entity recognition scores for NLU, and stable speech synthesis quality.

本文提出 TRACE——一种通过 token 影响归因追踪答案相关 token 来识别投毒攻击的轻量检测框架；该方法首先发现跨检索文档的反复出现的高影响关键词，再经二次验证确认其对模型预测的影响。TRACE is presented, a lightweight detection framework that identifies poisoning attacks by tracing answer-related tokens through token influence attribution, and first discovers recurrent high-influence keywords across retrieved documents and then performs a secondary verification to confirm their influence on model predictions.

本文展示了用于 IT-Grundschutz（IT-GS）认证部分自动化的多 Agent 系统（MAS）架构结合混合检索增强生成（HybridRAG）的技术实现与实证评估，并为强化合规严谨性引入两项新的 MAS 架构技术贡献。This paper presents the technical implementation and empirical evaluation of a Multi-Agent System (MAS) architecture combined with Hybrid Retrieval Augmented Generation (HybridRAG) for the partial automation of IT-GS certification and introduces two novel technical contributions to the MAS architecture to enforce the compliance rigor.

本文全面审视集中式、设备端、联邦与混合范式下 RAG 系统的隐私与安全挑战，并勾勒出构建可信、安全、韧性 RAG 系统的开放性研究挑战。A comprehensive examination of privacy and security challenges across RAG systems deployed in centralized, on-device, federated, and hybrid paradigms is provided and open research challenges toward building trustworthy, secure, and resilient RAG systems are outlined.

研究发现，强化学习（RL）与监督微调（SFT）交错训练可显著提升稳定性，但在格式与内容分布外（OOD）评测下性能下降；并展示了多样化监督信号如何引导探索式学习。It is found that interleaving supervised fine-tuning with RL substantially improves stability, but exhibits degraded performance under format and content out-of-distribution (OOD) evaluation, and how diverse supervisory signals can guide exploratory learning is demonstrated.

结果表明，工具使用评测应从函数调用准确率转向不可靠工具环境下的任务完成度，并建议工具使用评测应从函数调用准确率转向不可靠工具环境下的任务完成度。（注：原文末句疑似重复）Results suggest that tool-use evaluation should move beyond function-call accuracy toward task completion under unreliable tool environments, and suggest that tool-use evaluation should move beyond function-call accuracy toward task completion under unreliable tool environments.

提出 Transparent Two-Pass Execution，一种在推理时将工具执行与 schema 约束响应生成解耦的策略；实验结果表明该方法无需模型重新训练即可恢复工具调用能力，同时保持结构化输出保证。Transparent Two-Pass Execution is proposed, an inference-time strategy that decouples tool execution from schema-constrained response generation and experimental results show that this approach restores tool invocation while preserving structured output guarantees without requiring model retraining.

研究发现重建质量与表示质量是解耦的；在所考察的任务中，没有任何单一方法能够在所有任务上稳定取得最优表现。It is found that reconstruction and representation quality are decoupled, and no single method consistently performs best across the tasks considered here and no single method consistently performs best across the tasks considered here.

提出 Tatoxa，一种面向鞑靼语文本去毒的 SOTA 系统；对比实验表明，该方法在关键质量指标上优于现有开源及商用闭源 LLM。Tatoxa is presented, a novel state-of-the-art system for text detoxification in the Tatar language, and comparative experiments show that the proposed approach outperforms existing open source and proprietary commercial LLMs on key quality metrics.

本文表明强化学习（RL）后训练已具备实现有效 step-level 评分所需的要素，从而完全无需额外的奖励模型训练，并在通用随机 Markov 决策过程下推导出一种隐式 advantage，称为 progress advantage。This work shows that reinforcement learning (RL) post-training already provides the ingredients for effective step-level scoring, eliminating the need for dedicated reward model training altogether, and derives an implicit advantage under a general stochastic Markov decision process, which is term progress advantage.