用图结构建模用户长期记忆，让大模型助手更懂你。
Graph-Based Personalized Memory for LLM Agents: Representation, Evolution, Retrieval, and Evaluation
- 用图结构显式表示用户偏好与关系，支持动态更新
- 构建从存储到检索的完整记忆生命周期框架
- 适合做长期个性化智能助手的研究者参考
大型语言模型（LLM）代理正从单次会话工具演变为需在任务、上下文和交互中持续适应个体用户的长期个人助理。这一转变使记忆成为个性化的核心需求，因为用户偏好、目标、约束、关系和过往经历是逐步积累且时常变化的。基于图的个性化记忆通过显式关系、时间上下文和证据链接，为建模此类用户信息提供了结构化方式。这类表示不仅能记录代理对用户的记忆，还能体现记忆间的关联、修订与检索过程，以支持个性化决策。然而，现有研究分散于个性化代理与通用图记忆框架之间，难以整体把握设计空间。本文提出面向生命周期的图基个性化记忆视角，围绕记忆表征、演化、检索与评估组织现有研究，比较关键设计选择，讨论当前评估实践，并揭示构建可靠长期个性化代理的开放挑战。本综述旨在阐明图基记忆如何支持自适应、可控制且以用户为中心的LLM代理。
原文摘要 · Abstract (English)
Large Language Model (LLM) agents are evolving from single-session tools toward long-term personal assistants that must adapt to individual users across tasks, contexts, and interactions. This shift makes memory a core requirement for personalization, since user preferences, goals, constraints, relationships, and past experiences are accumulated gradually and often change over time. Graph-based personalized memory provides a structured way to model such user information through explicit relations, temporal context, and evidence links. Such representations can model not only what an agent remembers about a user but also how memories are connected, revised, and retrieved to support personalized decisions. However, existing work remains fragmented across personalized agents and generic graph memory frameworks, making it difficult to understand the design space as a whole. This survey develops a lifecycle-oriented view of graph-based personalized memory for LLM agents. We organize existing studies around memory representation, memory evolution, memory retrieval, and memory evaluation. We further compare key design choices, discuss current evaluation practices, and open challenges in building reliable long-term personalized agents. This survey aims to clarify how graph-based memory can support adaptive, controllable, and user-centric LLM agents.
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