AI智能体时代中的记忆：形式、功能与动态综述

Core Insights - Memory is identified as a core capability for agents based on foundational models, facilitating long-term reasoning, continuous adaptation, and effective interaction with complex environments [1][11][15] - The field of agent memory research is rapidly expanding but is becoming increasingly fragmented, with significant differences in motivation, implementation, assumptions, and evaluation schemes [1][11][16] - Traditional classifications of memory, such as long-term and short-term memory, are insufficient to capture the diversity and dynamics of contemporary agent memory systems [1][11][16] Summary by Sections Introduction - Over the past two years, powerful large language models (LLMs) have evolved into robust AI agents, achieving significant progress across various fields such as deep research, software engineering, and scientific discovery [4][14] - There is a growing consensus in academia that agents require capabilities beyond just LLMs, including reasoning, planning, perception, memory, and tool usage [4][14][15] Importance of Memory - Memory is crucial for transforming static LLMs into adaptive agents capable of continuous adaptation through environmental interaction [5][15] - Various applications, including personalized chatbots, recommendation systems, social simulations, and financial investigations, depend on agents' ability to manage historical information actively [5][15] Need for New Classification - The increasing importance of agent memory systems necessitates a new perspective on contemporary agent memory research [6][16] - Existing classification systems are outdated and do not reflect the breadth and complexity of current research, highlighting the need for a coherent classification that unifies emerging concepts [6][16] Framework and Key Questions - The review aims to establish a systematic framework to reconcile existing definitions and connect emerging trends in agent memory [19] - Key questions addressed include the definition of agent memory, its relationship with related concepts, its forms, functions, and dynamics, as well as emerging research frontiers [19] Emerging Research Directions - The review identifies several promising research directions, including automated memory design, integration of reinforcement learning with memory systems, multimodal memory, shared memory in multi-agent systems, and issues of trustworthiness [20][12] Contributions of the Review - The review proposes a multidimensional classification of agent memory from a "form-function-dynamics" perspective, providing a structured view of current developments in the field [20] - It explores the applicability and interaction of different memory forms and functions, offering insights on aligning various memory types with different agent objectives [20] - A comprehensive resource collection, including benchmark tests and open-source frameworks, is compiled to support further exploration of agent memory systems [20]