RGMem: Renormalization Group-inspired Memory Evolution for Language Agents 事件

RGMem: Renormalization Group-inspired Memory Evolution for Language Agents arXiv:2510.16392v3 Announce Type: replace Abstract: Personalized and continuous interactions are critical for LLM-based conversational agents, yet finite context windows and static parametric memory hinder the modeling of long-term, cross-session user states. Existing approaches, including retrieval-augmented generation and explicit memory systems, primarily operate at the fact level, making it difficult to distill stabl