Bridging Intuitive Associations and Deliberate Recall: Empowering LLM Personal Assistant with Graph-Structured Long-term Memory

Large language models (LLMs)-based personal assistants may struggle to effectively utilize long-term conversational histories. Despite advances in long-term memory systems and dense retrieval methods, these assistants still fail to capture entity relationships and handle multiple intents effectively. To tackle above limitations, we propose Associa, a graph-structured memory framework that mimics human cognitive processes. Associa comprises an eventcentric memory graph and two collaborative components: Intuitive Association, which extracts evidence-rich subgraphs through Prize-Collecting Steiner Tree optimization, and Deliberating Recall, which iteratively refines queries for comprehensive evidence collection. Experiments show that Associa significantly outperforms existing methods in retrieval and QA (question and answering) tasks across longterm dialogue benchmarks, advancing the development of more human-like AI memory systems.