AI與大腦交匯：從認知神經科學到自主智能體的記憶系統

摘要

記憶作為連接過去與未來的關鍵樞紐，為人類和人工智慧系統提供了駕馭複雜任務的寶貴概念與經驗。近期自主智能體的研究日益借鑒認知神經科學，致力於設計高效的記憶工作流程。然而受學科壁壘制約，現有研究難以真正吸收人類記憶機制的精髓。為彌合這一鴻溝，我們系統性整合跨學科記憶知識，將認知神經科學的洞見與基於大語言模型的智能體相銜接。具體而言，我們首先沿著從認知神經科學到大語言模型再到智能體的遞進路徑，闡釋記憶的定義與功能；接著從生物與人工雙重視角，對記憶分類體系、存儲機制及完整管理生命週期展開對比分析；隨後評述評估智能體記憶的主流基準測試；此外從攻擊與防禦雙維度探討記憶安全性；最後展望多模態記憶系統與技能習得等重點研究方向。

English

Memory serves as the pivotal nexus bridging past and future, providing both humans and AI systems with invaluable concepts and experience to navigate complex tasks. Recent research on autonomous agents has increasingly focused on designing efficient memory workflows by drawing on cognitive neuroscience. However, constrained by interdisciplinary barriers, existing works struggle to assimilate the essence of human memory mechanisms. To bridge this gap, we systematically synthesizes interdisciplinary knowledge of memory, connecting insights from cognitive neuroscience with LLM-driven agents. Specifically, we first elucidate the definition and function of memory along a progressive trajectory from cognitive neuroscience through LLMs to agents. We then provide a comparative analysis of memory taxonomy, storage mechanisms, and the complete management lifecycle from both biological and artificial perspectives. Subsequently, we review the mainstream benchmarks for evaluating agent memory. Additionally, we explore memory security from dual perspectives of attack and defense. Finally, we envision future research directions, with a focus on multimodal memory systems and skill acquisition.

AI與大腦交匯：從認知神經科學到自主智能體的記憶系統

AI Meets Brain: Memory Systems from Cognitive Neuroscience to Autonomous Agents

摘要

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