梳理大模型智能体记忆演进，提出存储-反思-体验三阶段框架。
From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms
- 按存储、反思、体验三阶段重构记忆机制演化路径。
- 揭示长期一致性、动态环境适应与持续学习三大驱动力。
- 聚焦主动探索与跨轨迹抽象，指导下一代智能体设计。
基于大语言模型的智能体通过整合外部工具与规划能力，深刻重塑了人工智能。记忆机制作为其架构基石，当前研究却分散于操作系统工程与认知科学之间，缺乏统一视角与演化脉络。为此，本文提出一种新型进化框架，将记忆机制发展划分为三个阶段：存储（轨迹保留）、反思（轨迹优化）与经验（轨迹抽象）。首先形式化定义三阶段，再分析推动演化的三大核心驱动力：长程一致性需求、动态环境挑战以及持续学习目标。进一步探讨经验阶段前沿的两项变革机制：主动探索与跨轨迹抽象。通过整合多元视角，本工作提供稳健的设计原则与下一代智能体的发展路线图。
原文摘要 · Abstract (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.
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