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Graph-Based Personalized Memory for LLM Agents: Representation, Evolution, Retrieval, and Evaluation

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

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 remem

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