MemORAI: Memory Organization and Retrieval via Adaptive Graph Intelligence for LLM Conversational Agents
Year: 2026
Type: conference-paper
Abstract: Large Language Models (LLMs) lack persistent memory for long-term personalized conversations.Existing graph-based memory systems suffer from information dilution, absent provenance tracking, and uniform retrieval that ignores query context.We introduce MemO-RAI (Memory Organization and Retrieval via Adaptive Graph Intelligence), a framework that integrates three innovations: selective memory filtering with dual-layer compression to retain user-persona-relevant content, a provenanceenriched multi-relational graph tracking factual origins at the turn level, and query-adaptive subgraph retrieval with Dynamic Weighted PageRank that applies query-conditioned edge weighting.Evaluated on LOCOMO and LongMemEval benchmarks, MemORAI achieves state-of-theart performance in memory retrieval and personalized response generation, demonstrating that selective storage, enriched representation, and adaptive retrieval are essential for coherent, personalized LLM agents.
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166826552