AMU:Admission and Memory Update for Personalized Conversations---Structured Memory with SLM Guided Control
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Computer Science > Computation and Language
arXiv:2609.36976 (cs)
[Submitted on 29 Sep 2026]
Title:AMU:Admission and Memory Update for Personalized Conversations---Structured Memory with SLM Guided Control
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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or consolidation, while memory writing remains less controlled: transient requests, duplicate statements, and outdated user states may enter memory and later be retrieved for personalization.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.
| Comments: | 14 pages, 2 figures. Source code and implementation are available at: this https URL |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.36976 [cs.CL] |
| (or arXiv:2609.36976v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.36976
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled AMU:Admission and Memory Update for Personalized Conversations---Structured Memory with SLM Guided Control, by Tao Hwang and 1 other authors
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