[2603.27910] GAAMA: Graph Augmented Associative Memory for Agents

Computer Science > Artificial Intelligence arXiv:2603.27910 (cs) [Submitted on 29 Mar 2026] Title:GAAMA: Graph Augmented Associative Memory for Agents Authors:Swarna Kamal Paul, Shubhendu Sharma, Nitin Sareen View a PDF of the paper titled GAAMA: Graph Augmented Associative Memory for Agents, by Swarna Kamal Paul and 2 other authors View PDF HTML (experimental) Abstract:AI agents that interact with users across multiple sessions require persistent long-term memory to maintain coherent, personalized behavior. Current approaches either rely on flat retrieval-augmented generation (RAG), which loses structural relationships between memories, or use memory compression and vector retrieval that cannot capture the associative structure of multi-session conversations. There are few graph based techniques proposed in the literature, however they still suffer from hub dominated retrieval and poor hierarchical reasoning over evolving memory. We propose GAAMA, a graph-augmented associative memory system that constructs a concept-mediated hierarchical knowledge graph through a three-step pipeline: (1)~verbatim episode preservation from raw conversations, (2)~LLM-based extraction of atomic facts and topic-level concept nodes, and (3)~synthesis of higher-order reflections. The resulting graph uses four node types (episode, fact, reflection, concept) connected by five structural edge types, with concept nodes providing cross-cutting traversal paths that complement semantic similarity. Ret...