AMU:Admission and Memory Update for Personalized Conversations---Structured Memory with SLM Guided Control
Large language models (LLMs) have become the foundation of personalized assistants, but maintaining persistent user memory across long-term interactions remains challenging. Existing memory systems often focus on storage, retrieval, or consolidation, while memory writing remains less controlled: transient requests, duplicate statements, and outdated user states may enter memory and later be retrieved for personalization. In this paper, we present AMU: Admission and Memory Update for Personalized Conversations, an SLM-guided (Small language model guided) structured framework for writing-time memory control. AMU uses structured memory filtering to decide what should enter memory and SLM-gui...
| Authors | Tao Hwang, Yishi Diao |
|---|---|
| Primary category | cs.CL |
| Published | 29 September 2026 |
| Last updated at source | 13 October 2026 |
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