从存储到经验：大语言模型智能体记忆机制演进的综述

摘要

基于大型语言模型（LLM）的智能体通过整合外部工具与规划能力，从根本上重塑了人工智能。尽管记忆机制已成为这些系统的架构基石，但当前研究仍零散分布，在操作系统工程与认知科学之间摇摆不定。这种理论割裂阻碍了对技术整合的统一认知与连贯演进视角的形成。为弥合这一鸿沟，本综述提出了一种面向LLM智能体记忆机制的新型演进框架，将发展过程形式化为三个阶段：存储（轨迹保留）、反思（轨迹精炼）与经验（轨迹抽象）。我们首先正式定义这三个阶段，继而分析驱动此演进的三大核心要素：长程一致性的必要性、动态环境中的挑战，以及持续学习的终极目标。此外，我们重点探讨前沿"经验"阶段中的两种变革性机制：主动探索与跨轨迹抽象。通过综合这些不同观点，本研究为下一代LLM智能体的开发提供了坚实的设计原则与清晰的路线图。

English

Large Language Model (LLM)-based agents have fundamentally reshaped artificial intelligence by integrating external tools and planning capabilities. While memory mechanisms have emerged as the architectural cornerstone of these systems, current research remains fragmented, oscillating between operating system engineering and cognitive science. This theoretical divide prevents a unified view of technological synthesis and a coherent evolutionary perspective. To bridge this gap, 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). We first formally define these three stages before analyzing the three core drivers of this evolution: the necessity for long-range consistency, the challenges in dynamic environments, and the ultimate goal of continual learning. Furthermore, we specifically explore two transformative mechanisms in the frontier Experience stage: proactive exploration and cross-trajectory abstraction. By synthesizing these disparate views, this work offers robust design principles and a clear roadmap for the development of next-generation LLM agents.

从存储到经验：大语言模型智能体记忆机制演进的综述

From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms

摘要

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