Year: 2026
Type: conference-paper
Abstract: One of the key factors influencing the reasoning capabilities of LLM-based agents is their ability to leverage long-term memory.Integrating long-term memory mechanisms allows agents to make informed decisions grounded in historical interactions.While recent advances have significantly improved the storage and retrieval components-e.g., by encoding memory into dense vectors for similarity search or organizing memory as structured knowledge graphs-most existing approaches fall short in memory updating.In particular, they lack mechanisms for dynamically refining preference memory representations in response to evolving user behaviors and contexts.To address this gap, we propose a Preference-Aware Memory Update Mechanism (PAMU) that enables dynamic and personalized memory refinement.By integrating sliding window averages (SW) with exponential moving averages (EMA), PAMU constructs a fused preferenceaware representation that captures both shortterm fluctuations and long-term user tendencies.We conduct experiments on five task scenarios of the LoCoMo dataset, and the results show that our mechanism can significantly improve the output quality of LLM in five baselines, validating its effectiveness in long-term conversations.
Authors: Haoran Sun, Zekun Zhang, Shaoning Zeng
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4417157534