Graph-Based Personalized Memory for LLM Agents: Representation, Evolution, Retrieval, and Evaluation
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 personal...
| Authors | Dac Duy Anh Nguyen, Zhangchi Qiu, Shigeng Chen et al. |
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| Primary category | cs.AI |
| Published | 8 September 2026 |
| Last updated at source | 8 September 2026 |
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