论文

本文提出 MAGE（Memory as Agent-Guided Exploration），一种将交互存储在分层状态树中的主动式执行状态管理器；实验显示，MAGE 将平均任务成功率较基线提升 7.8–20.4 个百分点，同时 token 消耗减少 55.1%。This work proposes MAGE (Memory as Agent-Guided Exploration), an active execution-state manager that stores interactions in a hierarchical state tree and shows that MAGE improves the average task success rate by 7.8--20.4 pp over baselines, while reducing token consumption by 55.1%.

本文提出 User as Code（UaC）范式，将 Agent 对用户的建模视为一个活的软件项目：类型化 Python 对象承载用户状态，普通 Python 函数编码其治理规则，从而在解释器可运行的同一媒介内完成对用户的表示与推理。This work introduces User as Code (UaC), a paradigm in which an agent's model of a user is a living software project: typed Python objects hold the user's state and ordinary Python functions encode the rules that govern it, so representing and reasoning about the user happen in one medium an interpreter can run.

综合结果表明，强大的 coding agent 通过使用工具、反馈和工作区状态来构建目标语言的可工作模型，从而适应不熟悉的语言。Together, these results show that strong coding agents adapt to unfamiliar languages by using tools, feedback, and workspace state to build a working model of the target language.

提出 agent harness 的操作性定义，配套统一词汇，可用于指导工程实践以及对 agentic 系统的科学比较。An operational definition of agent harness is proposed, with a shared vocabulary, able to guide engineering practice and the scientific comparison of agentic systems.

本文提出一个多 Agent 框架，由主 LLM 协调 grounding agent 定位与问题相关的片段，并协调 vision agent 提取目标文本观测，显著优于强非 Agent 基线，且强化学习进一步增强了已训练 Agent 的推理与规划能力。A multi-agent framework in which a master LLM coordinates a grounding agent to localize question-relevant segments and a vision agent to extract targeted textual observations is proposed, which significantly outperforms strong non-agent baselines and shows reinforcement learning further strengthens reasoning and planning for the trained agent.

本文提出 ReMemR1，将 memory retrieval 机制融入 memory update 过程，使 Agent 能够选择性回溯历史记忆以进行非线性推理，并设计多级奖励机制，将最终答案奖励与密集的 step 级信号相结合，引导对 memory 的有效使用。This work presents ReMemR1, which integrates the mechanism of memory retrieval into the memory update process, enabling the agent to selectively callback historical memories for non-linear reasoning, and proposes a multi-level reward design, which combines final-answer rewards with dense, step-level signals that guide effective memory use.

Decentralized Language Models (DeLM) 是一种 MAS 框架，通过并行 Agent、共享已验证上下文与任务队列实现去中心化协作，在软件工程 test-time scaling 与长上下文推理两方面均取得提升。Decentralized Language Models (DeLM), a MAS framework that decentralizes coordination through parallel agents, a shared verified context, and a task queue, improves both software-engineering test-time scaling and long-context reasoning.

提出一种自我演化的法律 Agent 框架，将 Model、Harness、Agent 角色、法律 Knowledge、确定性 Tools 与程序性 Skills 拆分为可审计的层面，用于来源可追溯性、日期与数字的事实校验、交付物合规性以及问题闭环。A self-evolving legal-agent framework that factors Model, Harness, Agent roles, legal Knowledge, deterministic Tools, and procedural Skills into auditable surfaces for source traceability, date and number grounding, deliverable compliance, and issue closure is introduced.

本文提出后确定性分布式系统（PDDS）作为一种研究与工程模型，用于协调确定性代码、随机模型与自主 Agent 共存的异构环境，并证明经典分布式计算模型是该参与者通用模型的零模糊特例。This paper introduces Post-Deterministic Distributed Systems (PDDS) as a research and engineering model for coordinating heterogeneous environments where deterministic code, stochastic models, and autonomous agents coexist, and shows that classical distributed computing models form a zero-ambiguity special case of this participant-general model.

提出 Stratum，一种统一系统基础设施，在 agentic pipeline 搜索期间将 pipeline 执行与规划和推理解耦，无缝集成现有 Python 库，将批量 pipeline 编译为优化的执行图，并通过基于 Rust 的新型 runtime 等异构后端高效执行。Stratum is proposed, a unified system infrastructure that decouples pipeline execution from planning and reasoning during agentic pipeline search, and integrates seamlessly with existing Python libraries, compiles batches of pipelines into optimized execution graphs, and efficiently executes them across heterogeneous backends, including a novel Rust-based runtime.

HERA 是一个分层框架，联合演化多 Agent 编排与角色特定的 Agent prompt，在多 Agent RAG 中实现针对性的、角色条件化的改进，并提供角色感知的 prompt 演化。HERA is a hierarchical framework that jointly evolves multi-agent orchestration and role-specific agent prompts, enabling targeted, role-conditioned improvements in multi-agent Retrieval-Augmented Generation and Role-Aware Prompt Evolution.

本文认为 UQ 研究必须转向面向交互式 Agent 的现实场景，需要一套面向 Agent 的新原则性框架，并首次提出了能涵盖现有 UQ 设置的 Agent UQ 通用形式化方法。This paper argues that UQ research must shift to realistic settings with interactive agents, and that a new principled framework for agent UQ is needed, and presents the first general formulation of agent UQ that subsumes broad classes of existing UQ setups.

文中认为，编码 Agent 已跨越能力门槛，传统的人工 code review 已不再是软件质量流水线的必要环节。It is argued that coding agents have crossed a threshold of capability at which traditional human code review is no longer a necessary component of a software quality pipeline.

该方法通过在现有企业搜索基础设施之上叠加一层轻量级 harness，为推理型 LLM 配备 search、find、open、summarize 等工具，使其能够自主地迭代检索信息、跨文档导航并分析证据，从而降低对单一环节的过度依赖。The approach reduces this overdependence by layering a lightweight harness on top of existing enterprise search infrastructure, equipping a reasoning LLM with search, find, open, and summarize tools enabling the model to iteratively retrieve information, navigate within documents, and analyze evidence autonomously.

本文围绕形式化框架与 Agent 生命周期，对面向 LLM-based Agent 的文本世界模型进行系统性综述，旨在整合这一快速发展的方向、厘清其设计空间，并指出未来研究的开放挑战。This work systematically review text world models for LLM-based agents, organized around a formal framework and the agent lifecycle, and aims to consolidate this rapidly developing area, clarify its design space, and highlight open challenges for future research.

本文提出 Beyond Rows to Reasoning (BRTR)，一种面向电子表格理解的多模态 Agentic 框架，将单轮检索替换为迭代式工具调用循环，支持从复杂分析到结构化编辑的端到端 Excel 工作流。This work introduces Beyond Rows to Reasoning (BRTR), a multimodal agentic framework for spreadsheet understanding that replaces single-pass retrieval with an iterative tool-calling loop, supporting end-to-end Excel workflows from complex analysis to structured editing.

介绍 Corpus2Skill：离线将文档语料蒸馏为分层 Skill 目录，服务时由 LLM Agent 自顶向下导航——从全局概览逐层深入到更细粒度的摘要再到具体文档，并在分支无效时回溯。Corpus2Skill is presented, which distills a document corpus offline into a hierarchical skill directory and lets an LLM agent navigate it at serve time, drilling from a bird's-eye view through progressively finer summaries down to documents, and backtracking when a branch is unproductive.

本研究对基于RAG的系统与工具增强的LLM Agent进行系统对比，特别是在Agent仅可访问基础关键词搜索工具的条件下，评估二者的检索机制与响应质量。This study conducted a systematic comparison between RAG-based systems and tool-augmented LLM agents, specifically evaluating their retrieval mechanisms and response quality when the agent only has access to basic keyword search tools.

本文提出FROAV（Framework for RAG Observation and Agent Verification），一个通过提供即插即用架构——结合可视化工作流编排、综合性评估框架与可扩展Python集成——来降低LLM Agent研究门槛的开源研究平台。FROAV (Framework for RAG Observation and Agent Verification), an open-source research platform that democratizes LLM agent research by providing a plug-and-play architecture combining visual workflow orchestration, a comprehensive evaluation framework, and extensible Python integration, is presented.

Bespoke-Card 在传统通用估计器与学习型估计器架构之外开辟了一条新的基数估计路径，它是一个 Agent 驱动的系统，将面向特定工作负载的基数估计器合成为可执行代码。Bespoke-Card is opening a new avenue for cardinality estimation next to classical generic estimators and learned estimator architectures, an agent-driven system that synthesizes workload-specific cardinality estimators as executable code.

本工作提出 QBugLM，一个多 Agent 框架，可自动化量子软件调试流水线，覆盖基于分类法的缺陷注入、基于 LLM 的检测与修复，直至基于仿真的验证，框架无关地支持 OpenQASM 3.0 程序。This work proposes QBugLM, a multi-agent framework that automates the quantum software debugging pipeline, from taxonomy-driven bug injection to LLM-based detection and repair, and finally to simulation-based validation, for framework-agnostic OpenQASM 3.0 programs.

PLENA 是一个软硬件协同设计的系统，采用三条核心优化路径，具备新颖的扁平化 systolic-array 架构以及支持非对称量化方案的高效计算与存储单元（路径 2）。PLENA is a hardware-software codesigned system that applies three core optimization pathways that features a novel flattened systolic-array architecture and efficient compute and memory units that support an asymmetric quantization scheme (Pathway 2).

GLM-5 在真实编码任务中展现出前所未有的能力，在端到端软件工程挑战的处理上超越既有基线，并提出了新颖的异步 Agent RL 算法，进一步提升了 RL 质量。GLM-5 demonstrates unprecedented capability in real-world coding tasks, surpassing previous baselines in handling end-to-end software engineering challenges and proposing novel asynchronous agent RL algorithms that further improve RL quality.

本教程综合了推动这些汇聚方向的算法、系统与设计原则，为数据科学与数据挖掘研究者提供统一视角，涵盖将 LLM、图数据管理、图挖掘、图 ML 与 agentic 计算融合到下一代 graph-native AI 系统中。This tutorial synthesizes the algorithms, systems, and design principles driving these converging directions, offering data science and data mining researchers a unified perspective on integrating LLMs, graph data management, graph mining, graph ML, and agentic computation into next-generation graph-native AI systems.

本文认为，AI agent——即以大语言模型作为主要推理引擎、动态生成与丢弃代码作为工具性资源的系统——的出现构成了对"软件"本身的根本性重构，而非渐进式的工具改进。This paper argues that the emergence of AI agents -- systems where large language models serve as the primary reasoning engine, dynamically generating and discarding code as an instrumental resource -- constitutes a fundamental restructuring of what software is, not an incremental tool improvement.

本文提出 Memanto，一种面向 agentic 人工智能的通用记忆层，挑战了"必须依赖知识图谱复杂度才能实现高保真 agent 记忆"的普遍假设，并取得 SOTA 准确率。Memanto is introduced, a universal memory layer for agentic artificial intelligence that challenges the prevailing assumption that knowledge graph complexity is necessary to achieve high fidelity agent memory and achieves state of the art accuracy scores.

Continuum，一个通过为 KV cache 保留引入 TTL 机制来优化多轮 Agent 工作负载任务完成时间的服务系统，能保持多轮连续性，并降低 Agent 工作流的延迟。Continuum, a serving system to optimize job completion time for multi-turn agent workloads by introducing time-to-live mechanism for KV cache retention, preserves multi-turn continuity, and reduces delay for agentic workflows.

本文对一个自 2026 年 3 月起持续运行的个人助理 Agent 运行时中的静默失败进行纵向研究，该系统包含约 40 个定时任务、8 个 LLM 提供商、一个工具治理代理以及一个知识库记忆层，由 4,286 个单元测试和 827 项治理检查守护。A longitudinal study of silent failures in a personal-assistant agent runtime in continuous production since March 2026, with roughly 40 scheduled jobs, 8 LLM providers, a tool-governance proxy, and a knowledge-base memory plane, defended by 4,286 unit tests and 827 governance checks is presented.

结果表明，纯视觉设置会降低准确率并增加 token 成本，因为 Agent 缺乏足够的符号化细节，需通过重复的视觉查询进行补偿；研究指向一种面向下一代编码 Agent 的实用文本与视觉混合设计。The results show that a strictly vision-only setup degrades accuracy and increases token cost, because agents lack sufficient symbolic detail and compensate with repeated visual queries, and point to a practical hybrid text-and-vision design for next-generation coding agents.