AI与大脑相遇：从认知神经科学到自主智能体的记忆系统

摘要

记忆作为连接过去与未来的关键枢纽，为人类和人工智能系统提供了应对复杂任务所需的宝贵概念与经验。当前自主智能体研究日益重视借鉴认知神经科学来设计高效记忆工作流，但受限于学科壁垒，现有研究难以真正吸收人类记忆机制的精髓。为弥合这一鸿沟，我们系统整合了跨学科记忆知识，将认知神经科学的洞见与基于大语言模型的智能体相衔接。具体而言，我们首先沿着从认知神经科学到大语言模型再到智能体的递进脉络，阐释记忆的定义与功能；继而从生物与人工双重视角对记忆分类体系、存储机制及完整管理生命周期展开对比分析；随后梳理评估智能体记忆的主流基准测试；此外还从攻击与防御双重维度探讨记忆安全性；最后展望以多模态记忆系统和技能习得为重点的未来研究方向。

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与大脑相遇：从认知神经科学到自主智能体的记忆系统

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

摘要

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