QUMem: Personalized Memory for Query-Conditioned User-State Inference in LLM Agents
Large language model (LLM) agents increasingly use external memory systems to support personalization by drawing on long and evolving interaction histories, in which user preferences may be distributed across time, change with context, and conflict with earlier evidence. However, existing systems face three limitations: fixed-turn, fixed-token, or session-based boundaries can mix unrelated dialogue or split an event from its causes, decisions, and outcomes; storing multiple pieces of user information from the same interaction as a single memory binds together items that serve different functions and should be independently retrievable; and treating the current task as a single top-$k$ ret...
| Authors | Heng Wang, Yifei Li, Lingling Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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