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).
论文
4 张论文卡片 · Agent 智能体 · 综述
条目A2：ACL 2026 Findings — LLM Agent记忆机制演进调查（arXiv:2605.06716）
② "Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG"（arXiv:2501.09136v4，2026-04更新）
② "Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG"（arXiv:2501.09136v4，2026-04更新）
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.
1️⃣ arXiv · Memory for Autonomous LLM Agents: Mechanisms, Evaluation, and Open Problems（⭐⭐⭐⭐⭐ 必读综述）
自主 LLM Agent 的记忆：机制、评估与开放问题
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.
Neural Approaches to Conversational AI
面向对话式 AI 的神经方法
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.