打破学科壁垒！400篇参考文献重磅综述，统一调查「人脑×Agent」记忆系统

Core Viewpoint - The article discusses a significant review paper titled "AI Meets Brain," which bridges cognitive neuroscience and artificial intelligence, focusing on how human memory mechanisms can inform the development of human-like memory systems in agents [2][6]. Summary by Sections Memory Definition - Memory is redefined as not just data storage but as a cognitive link that connects past experiences with future decisions, involving a two-stage process in the human brain [6]. Perspectives on Memory - From a cognitive neuroscience perspective, memory serves as a bridge between past and future [6]. - For large language models (LLMs), memory exists in three forms: parametric memory, working memory, and explicit external memory [7]. - Agent memory transcends simple storage, functioning as a dynamic cognitive architecture that integrates experiences and environmental feedback [8]. Importance of Memory - Memory plays a crucial role in enhancing agent capabilities by overcoming context window limitations, building long-term personalized profiles, and driving experience-based reasoning [12][13]. Memory Classification - The review categorizes memory based on cognitive neuroscience definitions, distinguishing between short-term and long-term memory, with long-term memory further divided into episodic and semantic memory [15][21]. Memory Storage Mechanisms - Memory storage in the human brain involves dynamic cooperation across brain regions, while agent memory systems are explicitly engineered to optimize data structure selection for computational efficiency [31][32]. Memory Management - Memory management in agents is a continuous process involving extraction, updating, retrieval, and application, contrasting with the static nature of traditional memory systems [33][34]. Future Directions - Future agent memory systems should aim for omni-modal capabilities, integrating various data types beyond text, and facilitating skill transfer across different agents [49][50].