论文

实验表明，OpenComputer 的硬编码验证器比 LLM-as-judge 评估更贴合人类裁定，尤其当任务成败取决于细粒度应用状态时。Experiments show that OpenComputer's hard-coded verifiers align more closely with human adjudication than LLM-as-judge evaluation, especially when success depends on fine-grained application state.

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.

本文提出 π-Bench，一个用于评估主动式协助能力的基准，包含跨 5 个领域特定用户画像的 100 个多轮任务，用于评估 Agent 在长交互中预见并满足用户需求的能力，联合衡量长周期轨迹中的主动性与任务完成度，更贴近真实使用场景。$-Bench is introduced, a benchmark for proactive assistance comprising 100 multi-turn tasks across 5 domain-specific user personas that evaluates agents'ability to anticipate and address user needs over extended interactions, jointly measuring proactivity and task completion in long-horizon trajectories that better reflect real-world use.

Agents' Last Exam（ALE）是一个面向长时序、具有经济价值且结果可验证的真实任务的 AI Agent 评测基准，旨在弥合基准测试表现与 GDP 相关影响之间的差距，而非仅仅作为排行榜。Agents'Last Exam (ALE) is introduced, a benchmark designed to evaluate AI agents on long horizon, economically valuable, real world tasks with verifiable outcomes, intended not merely as another leaderboard, but as an instrument for closing the gap between benchmark success and GDP relevant impact.

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.

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.

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.

本文构建一种针对 GitHub issue 的持久会话评估方法，锚定在单一 base commit，对线性顺序探索与非线性、领域范围的并行 agentic 探索进行比较。This work constructs an approach for persistent-session evaluation of GitHub issues anchored at a single base commit, and compares linear sequential exploration against non-linear, domain-scoped parallel agentic exploration.

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.

本综述为 LLM Agent 记忆机制提出一种新的演化框架，将发展过程形式化为三个阶段：Storage（轨迹保存）、Reflection（轨迹精炼）与 Experience（轨迹抽象）。This survey proposes a novel evolutionary framework for LLM agent memory mechanisms, formalizing the development process into three stages: Storage (trajectory preservation), Reflection (trajectory refinement), and Experience (trajectory abstraction).

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.

介绍 MCP-Persona，这是首个专为评估 Agent 在真实场景、个性化 MCP 工具上的表现而设计的基准，并揭示了当前 Agent 在个性化工具使用上的显著不足，从而凸显该基准在发现并解决这些局限上的关键作用。MCP-Persona is introduced, the first benchmark specifically designed for evaluating agent performance on real-world, personalized MCP tools and demonstrates their significant struggles with personalized tool use, thereby highlighting the benchmark's crucial role in identifying and addressing these limitations.

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.

本文给出一份关于Agentic RAG系统的分析综述，梳理RAG范式的演进，提出基于Agent数量、控制结构、自主性与知识表示的Agentic RAG架构分类法，并对现有框架的设计权衡进行比较分析。An analytical survey of Agentic RAG systems is presented, which traces the evolution of RAG paradigms, introduces a principled taxonomy of Agentic RAG architectures based on agent cardinality, control structure, autonomy, and knowledge representation, and provides a comparative analysis of design trade-offs across existing frameworks.

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.

本文提出三种协议级原语以填补Model Context Protocol的空白：身份传递、自适应工具预算与结构化错误语义，并提出Structured Error Recovery Framework (SERF)，提供机器可读的失败语义以支持确定性的Agent自校正。Three protocol-level primitives are proposed to fill gaps in the Model Context Protocol: identity propagation, adaptive tool budgeting, and structured error semantics, and the Structured Error Recovery Framework (SERF), which provides machine-readable failure semantics that enable deterministic agent self-correction.

研究表明矛盾解析本质上是写入时并发控制，并将缺失的契约——一个在隔离性、模式与来源维度上被证明正确的写入时正确性规范——显式化，固定了每个生产启发式都默认假设、却没有任何已部署系统显式给出的保证。It is shown that contradiction resolution is write-time concurrency control and make the missing contract explicit, a write-time correctness specification, proved sound across isolation, schema, and provenance, pinning the guarantee every production heuristic assumes but no deployed system makes explicit.

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).

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.

本文提出一种分层隔离架构，结合策略感知的 ingestion、retrieval-time gating 与共享推理，并通过服务端 agentic 编排加以执行，在为多租户隔离提供天然强制点的同时，允许客户端框架保留对 agent 组合与延迟敏感操作的控制权。A layered isolation architecture combining policy-aware ingestion, retrieval-time gating, and shared inference, enforced through server-side agentic orchestration is introduced, creating natural enforcement points for multitenant isolation while allowing client-side frameworks to retain control over agent composition and latency-sensitive operations.

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.

本文系统梳理了基于 LLM 的现代智能体中记忆的设计、实现与评估方法，覆盖 2022 年至 2026 年初的相关工作，并将 Agent 记忆形式化为一个涵盖时间范围、表示基底与控制策略的三维分类体系。This survey offers a structured account of how memory is designed, implemented, and evaluated in modern LLM-based agents, covering work from 2022 through early 2026, and formalizes agent memory as a three-dimensional taxonomy spanning temporal scope, representational substrate, and control policy.

本文提出 AgenticRAGTracer，这是首个主要由大语言模型自动构建、专为支持逐步验证而设计的 Agentic RAG 基准。AgenticRAGTracer is introduced, the first Agentic RAG benchmark that is primarily constructed automatically by large language models and designed to support step-by-step validation, and is primarily constructed automatically by large language models and designed to support step-by-step validation.

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.