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 MemORAI (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 provenance-enriched 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-the-art performance in memory retrieval and personalized response generation, demonstrating that selective storage, enriched representation, and adaptive retrieval are essential for coherent, personalized LLM agents.
@article{arxiv.2605.01386,
title = {MemORAI: Memory Organization and Retrieval via Adaptive Graph Intelligence for LLM Conversational Agents},
author = {Hung Pham Van and Nguyen Manh Hieu and Khang Pham Tran Tuan and Nam Le Hai and Linh Ngo Van and Nguyen Thi Ngoc Diep and Trung Le},
journal= {arXiv preprint arXiv:2605.01386},
year = {2026}
}