@inproceedings{van-etal-2026-memorai,
    title = "{M}em{ORAI}: Memory Organization and Retrieval via Adaptive Graph Intelligence for {LLM} Conversational Agents",
    author = "Van, Hung Pham  and
      Hieu, Nguyen Manh  and
      Tuan, Khang Pham Tran  and
      Hai, Nam Le  and
      Van, Linh Ngo  and
      Diep, Nguyen Thi Ngoc  and
      Le, Trung",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-form-platform/2026.findings-acl.1408/",
    pages = "28235--28253",
    ISBN = "979-8-89176-395-1",
    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 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."
}