为大模型代理构建类生物记忆系统，实现长期知识持续学习
Position: Episodic Memory is the Missing Piece for Long-Term LLM Agents
- 引入五项核心特性构建情境化短期记忆框架
- 整合现有研究方向，推动长时记忆能力建设
- 适合关注智能体长期学习与记忆的科研人员
随着大语言模型从文本补全工具演变为在动态环境中运行的完整智能体，持续学习和长期知识保留成为关键挑战。许多生物系统通过情景记忆解决此类问题，支持对特定实例的单次学习。受此启发，本文提出一种面向大模型智能体的情景记忆框架，聚焦于支撑适应性与情境敏感行为的五个核心特性。尽管已有研究部分覆盖这些特性，本文主张现在是明确且集成地关注情景记忆的时机，以加速长时智能体的发展。为此，我们梳理了一条统一多个研究方向的路线图，旨在实现情景记忆的全部五项特性，从而提升大模型智能体的长期效率。
原文摘要 · Abstract (English)
As Large Language Models (LLMs) evolve from text-completion tools into fully fledged agents operating in dynamic environments, they must address the challenge of continually learning and retaining long-term knowledge. Many biological systems solve these challenges with episodic memory, which supports single-shot learning of instance-specific contexts. Inspired by this, we present an episodic memory framework for LLM agents, centered around five key properties of episodic memory that underlie adaptive and context-sensitive behavior. With various research efforts already partially covering these properties, this position paper argues that now is the right time for an explicit, integrated focus on episodic memory to catalyze the development of long-term agents. To this end, we outline a roadmap that unites several research directions under the goal to support all five properties of episodic memory for more efficient long-term LLM agents.
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