Bridging Intuitive Associations and Deliberate Recall: Empowering LLM Personal Assistant with Graph-Structured Long-term Memory (2025)

Abstract: 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 event-centric 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 long-term dialogue benchmarks, advancing the development of more human-like AI memory systems.

## introduction empowered by large language models (llms), lifelong personal assistants have demonstrated remarkable potential across various domains, including daily life @xcite , healthcare @xcite , and mental health counseling @xcite . these advancements show a significant opportunity to enhance quality-of-life and promote individual well-being through effective human-ai interaction @xcite . however, the personal assistants face a critical challenge: effectively maintaining and utilizing lifelong conversational histories @xcite . to tackle this challenge, researchers propose the long-term memory systems as a promising solution @xcite . these systems maintain the interaction history between assistants and users across multiple chat sessions @xcite , offering plug-and-play adaptability @xcite . notably, studies have demonstrated that such memory-augmented systems can substantially outperform native long-context llms by effectively combining short-context reasoning accuracy with extended information retention capabilities @xcite . current implementations typically employ pre-trained dense retrieval approaches, enhanced with various forms of information such as summaries @xcite , facts @xcite , or observations @xcite . however, these approaches exhibit significant deficiencies in their memory retrieval paradigms that hinder practical effectiveness @xcite . as shown in figure 1 , there is a mismatch between dense retrieval approaches and the actual process of gathering evidence from users' long-term memory (the memory retrieval approach). while dense retrievers are pre-trained to identify texts with the highest semantic similarity @xcite , the goal of long-term memory systems is to effectively assist users in "gathering evidence" @xcite . for instance, when a user poses the query: "what was the total reach of my facebook ad campaign and instagram influencer collaboration?," a pre-trained linear retrieval system might return historical user utterances related to "influencer marketing" or "social media." although dense retrieval search documents show significant semantic similarity and may even include matching entities, they still do not provide the key evidence needed to answer the query. this reflects the two gaps between dense retrieval approaches and memory retrieval approaches: @xcite the neglect of the association of entities. this instance denotes that the dense retriever ignores the binding relationship between "facebook" and "ad campaign," as well as between "instagram" and "influencer collaboration." in other words, utterances that include both of these entities will be considered stronger evidence. (2) the dense retriever may mix and blur different evidence-gathering intents. in this instance, the query contains two retrieval intents: "the reach of facebook ad campaign" and "the reach of instagram influencer collaboration." this overlap can cause the model to retrieve irrelevant information, undermining the accuracy of the results. inspired by the associative and deliberative nature of human long-term memory @xcite , we propose associa, a novel graph-structured longterm memory framework, to enhance the ability to extract "key evidences" from very long-term dialogues, ultimately improving the effectiveness of the personal assistant. specifically, to address gap (1), we first design an event-centric personal memory graph that incorporates multi-dimensional information. then, we introduce an "intuitive association" retrieval module. this module employs prize-collecting steiner tree (pcst) optimization @xcite with dynamic prize mechanisms, constructing a memory subgraph by maximizing the prizes of subgraph nodes and minimizing the costs of subgraph edges. through the subgraph, we retrieve utterances from long-term memory that are connected to the most "high-quality" nodes (such as events or entities). to address gap (2), we develop a "deliberating recall" module, which recursively assesses whether all evidence relevant to the user's intent has been fully collected. by instruction-tuning a specialized deliberating model, it collects missing clues and augments the user's query as feedback. these two modules work collaboratively to ensure comprehensive evidence gathering. our contributions are threefold: @xcite we propose associa, the first event-centric graph memory framework that systematically organizes long-term dialogue history by proposing a unified graph schema. the novel pcst-based subgraph retrieval mechanism enables associative memory retrieval in personal assistant systems, effectively addressing the information fragmentation challenge in extended conversations. ## the proposed framework: associa we propose associa, a novel graph-structured longterm memory framework that enhances evidence extraction from extended dialogue histories and improves the effectiveness of personal assistants. our framework introduces innovations in two crucial aspects: (1) an event-centric personal memory graph for memory management, and (2) a collaborative memory retrieval mechanism that combines associative memory retrieval with deliberation recall. ## conclusion this work addresses the critical challenge of longterm conversational memory utilization in llmbased personal assistants. we propose associa, a cognitively inspired framework that overcomes the limitations of dense retrieval through two key innovations: (1) an event-centric graph memory pre-