论文

本文系统梳理了 Agent 系统从 pre-LLM 时代到当前 LLM 驱动架构的演进，将其划分为软件型、物理型与自适应混合型，并突出展示了其在客户服务、软件开发、制造自动化、个性化教育、金融交易和医疗等领域的应用。This paper comprehensively examines the evolution of agent systems from the pre-LLM era to current LLM-powered architectures, categorizing agent systems into software-based, physical, and adaptive hybrid systems, highlighting applications across customer service, software development, manufacturing automation, personalized education, financial trading, and healthcare.

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.

本文首次全面综述了 LLM 元认知的研究现状，涵盖用于测量和评估 LLM 元认知能力的方法与 benchmark、激发、改进与应用 LLM 元认知的技术，以及当前研究的发现与启示。The first comprehensive overview of the current state of knowledge on metacognition for LLMs is presented, including methods and benchmarks to measure and evaluate LLMs'metacognitive abilities, techniques to elicit, improve, and apply metacognition in LLMs, and findings and implications of ongoing research.

本综述将现代具备自我改进能力的 Agent 视为将经验转化为持续能力增益的自适应系统，并提出一个系统级框架，将现代 Agent 建模为由基础模型与由 prompt、memory、工具及控制逻辑构成的运行支撑层相耦合的配置。This survey frames modern self-improving agents as adaptive systems that convert experience into accumulated capability gains, and offers a system-level framework that represents a modern agent as a configuration coupling a foundation model with an operational scaffold of prompts, memory, tools, and control logic.

一篇关于基于 LLM 的 Agent 的全面综述，追溯了 Agent 概念从其哲学起源到在 AI 中的发展历程，解释了为何 LLM 适合作为 Agent 的基础，并提出一个包含三个核心组件的通用框架：大脑、感知与行动。A comprehensive survey on LLM-based agents, tracing the concept of agents from its philosophical origins to its development in AI, and explaining why LLMs are suitable foundations for agents, and presenting a general framework, comprising three main components: brain, perception, and action.

本 tutorial 综述近年来面向对话式 AI 的神经方法，并综述 SOTA 神经方法，揭示神经方法与传统符号方法之间的联系。This tutorial surveys neural approaches to conversational AI that were developed in the last few years, and presents a review of state-of-the-art neural approaches, drawing the connection between neural approaches and traditional symbolic approaches.

提出 ExplainBench，一个自动评估 coding agent 解释的基准，基于信息性解释应能让 LLM 正确回答问题的直觉，实现 agent 之间解释质量的量化比较。This work proposes ExplainBench, a benchmark to automatically evaluate explanations from coding agents, based on the intuition that informative explanations should enable an LLM to correctly answer questions, allowing quantitative comparison of explanation quality between agents.

本文报告了一项完整的、有完整记录的案例研究：在规范优先协议下，由 AI 编码 Agent 对大规模架构进行重构，期间无人工代码审查、无预先存在的预言机来验证目标行为。该任务是在一个大型相互依赖的代码库中拆除核心不变量，作者评估认为通过增量重构基本上不可行，这类变更通常需要重写。本文所述协议下，Agent 成功完成了任务。该系统包含 717,725 行This paper reports a single, fully instrumented case study of a large-scale architectural refactoring by an AI coding agent under a specification-first protocol, with no human review of the generated code and no pre-existing oracle to validate the target behaviour. The task, dismantling a central invariant across a large interdependent codebase, was assessed by the author as effectively infeasible through incremental refactoring, the kind of change that conventionally calls for a rewrite instead. Under the protocol described here, the agent completed it successfully. The system is a 717,725-li