@inproceedings{tang-etal-2026-mnemis,
    title = "Mnemis: Dual-Route Retrieval on Hierarchical Graphs for Long-Term {LLM} Memory",
    author = "Tang, Zihao  and
      Yu, Xin  and
      Xiao, Ziyu  and
      Wen, Zengxuan  and
      Li, Zelin  and
      Zhou, Jiaxi  and
      Wang, Hualei  and
      Wang, Haohua  and
      Huang, Haizhen  and
      Deng, Weiwei  and
      Sun, Feng  and
      Zhang, Qi",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1096/",
    pages = "23914--23928",
    ISBN = "979-8-89176-390-6",
    abstract = "AI Memory, specifically how models organizes and retrieves historical messages, becomes increasingly valuable to Large Language Models (LLMs), yet existing methods (RAG and Graph-RAG) primarily retrieve memory through similarity-based mechanisms. While efficient, such System-1-style retrieval struggles with scenarios that require global reasoning or comprehensive coverage of all relevant information. In this work, We propose Mnemis, a novel memory framework that integrates System-1 similarity search with a complementary System-2 mechanism, termed Global Selection. Mnemis organizes memory into a base graph for similarity retrieval and a hierarchical graph that enables top-down, deliberate traversal over semantic hierarchies. By combining the complementary strength from both retrieval routes, Mnemis retrieves memory items that are both semantically and structurally relevant. Mnemis achieves state-of-the-art performance across all compared methods on long-term memory benchmarks, scoring 93.9 on LoCoMo and 91.6 on LongMemEval-S using GPT-4.1-mini."
}