Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory
Large language model (LLM) agents require long-term user memory for consistent personalization, but limited context windows hinder tracking evolving preferences over long interactions. Existing memory systems mainly rely on static, hand-crafted update rules; although reinforcement learning (RL)-based agents learn memory updates, sparse outcome rewards provide weak supervision, resulting in unstable long-horizon optimization. Drawing on memory schema theory and the functional division between prefrontal regions and hippocampus regions, we introduce MemCoE, a cognition-inspired two-stage optimization framework that learns how memory should be organized and what information to update. In the...
| Authors | Derong Xu, Shuochen Liu, Pengfei Luo et al. |
|---|---|
| Primary category | cs.CL |
| Published | 1 May 2026 |
| Last updated at source | 1 May 2026 |
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