本文来自PRO会员通讯内容，文末关注「机器之心PRO会员」，查看更多专题解读。 当前，智能体正经历范式转变，从高效的单任务执行模式，逐步向动态环境下的持续自适应、能力演化与经验积累模式转型。在此背景下，AI Memory 作为核心基石，赋能智 能体保持行为一致性、做出理性决策并实现高效协作。在长期探索中，AI Memory 已经分化为「Agent Memory」与「LLM Memory」两条截然不同的演进路径。 目录 01. OpenClaw 的「长效记忆」为何不代表「AI 拥有持久记忆」？ OpenClaw的记忆力表现属于哪种突破？LLM Memory 与 Agent Memory 有何区别？... 02 . AI Memory 的研究视角在如何变化？ 2025与2026的综述都在用什么视角解析AI Memory？如何理解 AI Memory 的「4W」分类？... 03 . 近期工作在如何探索 LLM Memory 和 Agent Memory？ 2026年的 LLM Memory 与 Agent Memory 研究都在解决哪些问题?... OpenClaw 的「长效记忆」为何不代表「AI 拥有持久记忆」 ...

Core Insights - The article discusses the evolution of memory systems in AI agents, emphasizing the transition from optional modules to essential infrastructure for various applications such as conversational assistants and code engineering [2] - A comprehensive survey titled "Memory in the Age of AI Agents: A Survey" has been published by leading academic institutions to provide a unified perspective on the rapidly expanding yet fragmented concept of "Agent Memory" [2] Forms of Memory - The survey categorizes agent memory into three main forms: token-level, parametric, and latent memory, focusing on how information is represented, stored, and accessed [16][24] - Token-level memory is defined as persistent, discrete units that are externally accessible and modifiable, making it the most explicit form of memory [18] - Parametric memory involves storing information within model parameters, which can lead to challenges in retrieval and updating due to its flat structure [22] - Latent memory exists in the model's internal states and can be continuously updated during inference, providing a compact representation of memory [24][26] Functions of Memory - The article identifies three core functions of agent memory: factual memory, experiential memory, and working memory [29] - Factual memory aims to provide a stable reference for knowledge acquired from user interactions and environmental facts, ensuring consistency across sessions [31] - Experiential memory focuses on accumulating knowledge from past interactions to enhance problem-solving capabilities, allowing agents to learn from experiences [32] - Working memory manages information within single task instances, addressing the challenge of processing large and complex inputs [35] Dynamics of Memory - The dynamics of memory encompass three stages: formation, evolution, and retrieval, which form a feedback loop [38] - The formation stage encodes raw context into more compact knowledge representations, addressing computational and memory constraints [40] - The evolution stage integrates new memories with existing ones, ensuring coherence and efficiency through mechanisms like pruning and conflict resolution [43] - The retrieval stage determines how memory can assist in decision-making, emphasizing the importance of effective querying strategies [41] Future Directions - The article suggests that future memory systems should be viewed as a core capability of agents rather than mere retrieval plugins, integrating memory management into decision-making processes [49][56] - There is a growing emphasis on automating memory management, allowing agents to self-manage their memory operations, which could lead to more robust and adaptable systems [54][62] - The integration of reinforcement learning into memory control is highlighted as a potential pathway for developing more sophisticated memory systems that can learn and optimize over time [58][60]