Large language models (LLMs) have become the foundation of personalized assistants, but maintaining persistent user memory across long-term interactions remains challenging. Ex- isting memory systems often focus on stor- age, retrieval, or consolidation, while mem- ory writing remains less controlled: transient requests, duplicate statements, and outdated user states may enter memory and later be retrieved for personalization.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 mem- ory control. AMU uses structured memory filtering to decide what should enter memory and SLM-guided storage management to de- termine whether an admitted record should be stored separately, discarded as a duplicate, or fused as an update. We evaluate AMU in a con- trolled memory writing and retrieval setting. Experimental results show that AMU main- tains cleaner and more retrievable personalized memories.