将脑科学记忆机制融入AI agent，提升自主系统学习与适应能力
AI Meets Brain: Memory Systems from Cognitive Neuroscience to Autonomous Agents
- 从认知神经科学到大模型，梳理记忆的跨学科理论体系
- 对比生物与人工记忆的存储机制与管理生命周期
- 聚焦多模态记忆与技能习得，展望下一代智能体发展方向
记忆是连接过去与未来的关键枢纽，为人类和人工智能系统提供宝贵的经验与概念以应对复杂任务。近年来，自主智能体研究愈发关注借鉴认知神经科学设计高效的记忆流程。然而，受跨学科壁垒限制，现有工作难以真正吸收人类记忆机制的核心思想。为此，本文系统整合记忆领域的跨学科知识，将认知神经科学见解与大语言模型驱动的智能体相连接。首先，沿认知神经科学、大模型到智能体的演进路径，阐明记忆的定义与功能。其次，从生物与人工视角对比分析记忆分类、存储机制及全生命周期管理。接着，综述主流智能体记忆评估基准。此外，从攻防双重视角探讨记忆安全问题。最后，展望未来方向，重点关注多模态记忆系统与技能习得。
原文摘要 · Abstract (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.
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