论文

提出了用于复杂图像创建与编辑的大规模多模态工具调用数据集 CanvasCraft，以及通过多轮交互学习编排异构视觉工具的工具增强多模态 Agent CanvasAgent。CanvasCraft, a large-scale multimodal tool-use dataset for complex image creation and editing, and CanvasAgent, a tool-augmented multimodal agent that learns to orchestrate heterogeneous visual tools through multi-turn interaction are introduced.

结果表明，显式且可编辑的搜索工作流为将文献搜索智能体与复杂科学意图对齐提供了有效且可控的接口。The results show that explicit, editable search workflows provide an effective and controllable interface for aligning literature search agents with complex scientific intent.

在资源受限环境中，专业癫痫专家稀缺，使基于 LLM 的决策支持对管理纵向治疗的一线临床医生具有吸引力。此类系统必须适应当地处方实践并知道何时转诊。我们在乌干达儿科癫痫诊疗中研究该问题，基于纵向非结构化门诊记录预测抗癫痫用药方案。标准提示与医生处方取得了一定程度的一致性，但神经科医生审查显示许多错误反映的是分布失校的处方默认值而非失败。Specialist epilepsy expertise is scarce in resource-constrained settings, making LLM-based decision support attractive for frontline clinicians managing longitudinal treatment. Such systems must adapt to local prescribing practice and know when to defer. We study this problem in Ugandan pediatric epilepsy care, predicting anti-seizure medication regimens from longitudinal unstructured clinic notes. Standard prompting achieves non-trivial agreement with physician prescriptions, but neurologist review shows that many errors reflect distribution-miscalibrated prescribing defaults rather than fail

提出 ATMA：在现有记忆系统之上的状态感知叠加层，保留被替换记录与过渡记录，为查询所需的"目标状态视图"构建证据包，并向问答模块暴露当前、历史与过渡三类标签。This work proposes ATMA, a state aware overlay for existing memory systems, which keeps superseded and transition records in the bank, builds evidence packets for the query's requested state view, and exposes current, historical, and transition labels to QA.

提出了一种 Agent 设计和一套经过验证、可复用的方法，用于研究显式记忆层如何影响长周期 LLM Agent 决策，并给出了一种替代的有界契约。An agent design and a validated, reusable methodology for studying how explicit memory layers shape long-horizon LLM-agent decisions, as well as an alternative bounded contract, are introduced.

提出 TRACE，一个针对演化会话数据在时序证据图上的查询处理框架，将词汇召回与证据重建分离，从而在长会话历史中实现有界的查询时推理。TRACE is presented, a query processing framework over temporal evidence graphs for evolving conversational data that separates lexical recall from evidence reconstruction, enabling bounded query-time reasoning over long conversational histories.

本文将 PSP 形式化为逆向规划问题，并提出 SPIRE，一个通过有意破坏干净幻灯片的视觉结构来近似求解 PSP 的原则性框架，从而构建一个可验证的去噪任务。This work formulates PSP as an inverse planning problem, and proposes SPIRE, a principled framework to solve PSP approximately, by intentionally corrupting the visual structures of clean slides, which creates a verifiable task to denoise the corruption.

实验结果表明，所得参数集可生成可区分的个性化换道行为，同时 RAG 始终提升偏好理解效果，尤其对隐式指令效果显著，表明将基于 LLM 的自然语言交互与 Apollo 集成以支持个性化换道行为生成具有潜力。Experimental results show that the derived parameter sets generate distinguishable personalized lane-change behaviors, while RAG consistently improves preference interpretation, particularly for implicit commands, indicating the potential of integrating LLM-based natural-language interaction with Apollo to support personalized lane-change behavior generation.

本文提出 AI-Infra-Guard，一个围绕单一观测组织 AI 红队测试的开源框架，是目前唯一覆盖所有层面（包括对日益扩展 AI Agent 能力的 Agent Skills 供应链审计）的开源框架。AI-Infra-Guard is presented, an open-source framework that organizes AI red teaming around a single observation, and is the only open-source framework to span all of these, including supply-chain auditing of the agent skills that increasingly extend AI agents.

本文探索多轮视觉推理，观察到 MLLM 反复无法定位目标，导致冗长的推理轨迹，进而提出 PixelEyes，一种将推理与感知显式解耦的多轮视觉推理 Agent。This paper explores multi-turn visual reasoning and observes that MLLMs repeatedly fail to localize the target, leading to long, redundant trajectories, and proposes PixelEyes, a multi-turn visual reasoning agent that explicitly decouples reasoning from perception.

本文提出 DuoMem，一种双空间蒸馏框架，可将程序化问题求解能力从大型教师模型迁移到紧凑学生模型，并适用于实时边缘部署，而这一点对教师模型而言颇具挑战。DuoMem is introduced, a dual-space distillation framework that transfers procedural problem-solving ability from a large teacher model to compact student models and is viable for real-time edge deployment, which would be challenging for the teacher.

研究发现，模型规模、推理能力与 Agent 脚手架以不同方式影响弃答行为，能力更强或更大的模型有时反而在及时弃答上表现更差。It is found that model scale, reasoning, and agent scaffolding affect abstention in different ways, where larger or more capable models sometimes perform worse at timely abstention.

本文探讨了具身集体智能 (ECI) 这一未来多机器人范式，其中机器人团队将世界上下文、任务进度与技能经验作为共享资源进行累积与利用。This article explores Embodied Collective Intelligence (ECI), a future multi-robot paradigm in which a robot team accumulates and uses world context, task progress, and skill experience as shared resources.

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

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.

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.

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.

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.

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.

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.

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.

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.

提出 SkillHone，一个基于持久决策历史实现 Agent Skill 持续进化的 harness；在内部工具辅助的分析场景中提升了准确率，并在未预先集成搜索栈的情况下优于商业支持的深度研究 Agent。SkillHone is introduced, a harness for continual agent skill evolution grounded in persistent decision history that improves accuracy on internal tool-mediated analysis scenarios and outperforms commercially backed deep-research agents without a pre-integrated search stack.

提出 conversational infill：让一个小型 talker 模型在外部 reasoner 模型产生结果前即时生成上下文相关的回复以掩盖延迟，并在推理过程中将 reasoner 流式输出的知识流畅地融合到回复中。Conversational infill is introduced, where a small talker model both immediately generates contextually grounded responses to hide the latency of an external reasoner model and fluently integrates streamed reasoner knowledge into its responses during inference.

AOHP 的核心设计原则是将 Agent 视为 OS 中的一等公民，从而支持自适应用户界面以及对 Agent 友好的运行时环境；在任务完成度、执行成本和安全策略合规性方面均展现出明显优势。The core design principle of AOHP is to treat agents as first-class OS actors, enabling adaptive user interfaces and agent-friendly runtime environments, and shows clear advantages in task completion, execution cost, and security-policy compliance.

本研究收集了使用主流 AI Agent 开发的大量真实应用语料，并设计了结合 Agent 辅助代码审计与人工验证的漏洞分析框架，揭示出与传统软件开发流程中常见的漏洞模式不同的反复出现的漏洞规律。This study collects a large corpus of real-world applications developed using popular AI agents and designs a vulnerability analysis framework that combines agent-assisted code auditing with human validation, and reveals recurring vulnerability patterns that differ from those commonly observed in conventional software development workflows.

对话式与工具使用的 LLM Agent 在上下文窗口中同时从多个方向被填充，而必须在多轮之间回忆信息的 Agent（即 memory 的典型场景）恰恰是 recency 截断失效的地方。Conversational and tool-using LLM agents operate over a context window that fills from several directions simultaneously, and agents that must recall information across many turns, the defining case for memory, are precisely where recency truncation fails.

提出 S-Agent，一种面向连续多视图图像与视频理解与推理的空间工具使用 Agent 范式，以无需训练的方式持续提升开源与闭源 VLM。This work introduces S-Agent, a spatial tool-use agentic paradigm for understanding and reasoning over continuous multi-view images and videos, and consistently improves both open-source and closed-source VLMs in a training-free manner.

揭示了指导的生成方式才是决定性变量，并提出 probe-and-refine tuning（探测-微调）：通过合成 bug 修复探测任务，利用单次 LLM 调用迭代诊断并修补仓库的指导文件，调优过程中不涉及 Agent 循环或工具调用。It is shown that how the guidance is produced is the decisive variable, and probe-and-refine tuning is introduced, a procedure that uses synthetic bug-fix probes to iteratively diagnose and patch a repository's guidance file through single-shot LLM calls, with no agent loop or tool use during tuning.

提出一种特权感知的训练后防御方法，教导 Agent 优先选用足够的低权限工具，仅在必要时升级；该方法在保留通用能力的同时大幅减少了不必要的高权限工具使用。A privilege-aware post-training defense that teaches agents to prefer sufficient lower-privilege tools and escalate only when necessary is introduced, showing that this defense substantially reduces unnecessary high-privilege tool use while preserving general capabilities.

提出 Qwen-RobotNav，一种基于 Qwen-RobotNav 构建的可扩展导航模型，通过参数化接口沿两个互补维度应对该问题：用于选择导航行为的多种任务模式，以及用于控制视觉历史编码方式的可控观测参数。Qwen-RobotNav is presented, a scalable navigation model built on Qwen-RobotNav that addresses it through a parameterised interface with two complementary dimensions: multiple task modes that select the navigation behaviour, and controllable observation parameters that govern how visual history is encoded.

提出 GateMem，一个面向多主体共享内存 Agent 的基准，联合评估合法长程请求及其状态更新的效用、跨上下文授权边界的访问控制，以及 Agent 在收到显式删除请求后的主动遗忘能力。GateMem is introduced, a benchmark for multi-principal shared-memory agents that jointly evaluates utility for legitimate long-horizon requests with state updates, access control across contextual authorization boundaries, and agent-facing active forgetting after explicit deletion requests.

C-Trace 提出一个验证框架，将 GDPR 的部分要求（包括同意、目的限制、数据最小化与被遗忘权）表达为针对 Agent 执行轨迹的正式策略谓词。C-Trace (Compliance Trace based Runtime Agent Conformance Enforcement), a verification framework that expresses a subset of GDPR requirements, including consent, purpose limitation, data minimization, and the right to erasure, as formal policy predicates over agent execution traces is proposed.