AMU:Admission and Memory Update for Personalized Conversations---Structured Memory with SLM Guided Control

Abstract: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or consolidation, while memory writing remains less controlled: transient requests, duplicate statements, and outdated user states may enter memory and later be retrieved for personalization.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-guided storage management to determine whether an admitted record should be stored separately, discarded as a duplicate, or fused as an update. We evaluate AMU in a controlled memory writing and retrieval setting. Experimental results show that AMU maintains cleaner and more retrievable personalized memories.

14 pages, 2 figures. Source code and implementation are available at: this https URL

Computation and Language (cs.CL)

arXiv:2609.36976 [cs.CL]

(or arXiv:2609.36976v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.36976 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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