发表机构
Nanchang University（南昌大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对大模型长期交互中记忆写入失控的问题，提出AMU框架，用结构化过滤与SLM引导的存储管理控制记忆准入与更新，实验证明其记忆更干净、更易检索。
AI 中文摘要
大型语言模型（LLMs）已成为个性化助手的基础，但在长期交互中维持持久的用户记忆仍然具有挑战性。现有的记忆系统通常侧重于存储、检索或整合，而记忆写入的控制仍显不足：瞬时请求、重复陈述和过时的用户状态可能进入记忆，并在后续被检索用于个性化。在本文中，我们提出了AMU：个性化对话的准入与记忆更新，一种由小语言模型（SLM）引导的结构化框架，用于写入时的记忆控制。AMU使用结构化记忆过滤来决定哪些内容应进入记忆，并通过SLM引导的存储管理来确定一条被准入的记录应被单独存储、作为重复项丢弃，还是作为更新进行融合。我们在受控的记忆写入与检索设置中评估了AMU。实验结果表明，AMU能够维护更干净且更易检索的个性化记忆。
英文摘要
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or consolidation, while memory writing remains less controlled: transient requests, duplicate statements, and outdated user states may enter memory and later be retrieved for personalization.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.
Comments14 pages, 2 figures. Source code and implementation are available at: https://github.com/UnicusT11/AMU-memory
