人工智慧代理時代的記憶

摘要

記憶已成為並將持續作為基於基礎模型之智能體的核心能力。隨著智能體記憶研究快速擴展並吸引空前關注，該領域也日益呈現碎片化態勢。現有歸屬於智能體記憶範疇的研究工作在動機、實現方式和評估協議上往往存在顯著差異，而定義鬆散的記憶術語激增更進一步模糊了概念清晰度。傳統的長/短期記憶分類法已無法充分涵蓋當代智能體記憶系統的多樣性。本文旨在提供當前智能體記憶研究的最新全景圖。我們首先清晰界定智能體記憶的範疇，將其與大型語言模型記憶、檢索增強生成（RAG）及上下文工程等相關概念區分開來。接著我們透過形式、功能與動態性這三個統一視角審視智能體記憶：從形式視角識別出符記層級、參數化與潛在記憶這三種主流實現方式；從功能視角提出區分事實記憶、經驗記憶與工作記憶的細粒度分類框架；從動態性視角分析記憶如何隨時間形成、演進與檢索。為支持實際開發，我們彙編了完整的記憶基準測試與開源框架綜述。在整合現有成果基礎上，我們進一步闡述了對新興研究前沿的前瞻視角，包括記憶自動化、強化學習整合、多模態記憶、多智能體記憶及可信度問題。本研究不僅可作為現有工作的參考文獻，更希望為重新思考記憶作為未來智能體設計中一等公民的理論基礎提供概念支撐。

English

Memory has emerged, and will continue to remain, a core capability of foundation model-based agents. As research on agent memory rapidly expands and attracts unprecedented attention, the field has also become increasingly fragmented. Existing works that fall under the umbrella of agent memory often differ substantially in their motivations, implementations, and evaluation protocols, while the proliferation of loosely defined memory terminologies has further obscured conceptual clarity. Traditional taxonomies such as long/short-term memory have proven insufficient to capture the diversity of contemporary agent memory systems. This work aims to provide an up-to-date landscape of current agent memory research. We begin by clearly delineating the scope of agent memory and distinguishing it from related concepts such as LLM memory, retrieval augmented generation (RAG), and context engineering. We then examine agent memory through the unified lenses of forms, functions, and dynamics. From the perspective of forms, we identify three dominant realizations of agent memory, namely token-level, parametric, and latent memory. From the perspective of functions, we propose a finer-grained taxonomy that distinguishes factual, experiential, and working memory. From the perspective of dynamics, we analyze how memory is formed, evolved, and retrieved over time. To support practical development, we compile a comprehensive summary of memory benchmarks and open-source frameworks. Beyond consolidation, we articulate a forward-looking perspective on emerging research frontiers, including memory automation, reinforcement learning integration, multimodal memory, multi-agent memory, and trustworthiness issues. We hope this survey serves not only as a reference for existing work, but also as a conceptual foundation for rethinking memory as a first-class primitive in the design of future agentic intelligence.

人工智慧代理時代的記憶

Memory in the Age of AI Agents

摘要

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