@inproceedings{yue-etal-2026-hypermem,
    title = "{H}yper{M}em: Hypergraph Memory for Long-Term Conversations",
    author = "Yue, Juwei  and
      Hu, Chuanrui  and
      Sheng, Jiawei  and
      Zhou, Zuyi  and
      Zhang, Wenyuan  and
      Liu, Tingwen  and
      Guo, Li  and
      Deng, Yafeng",
    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.1627/",
    pages = "35237--35254",
    ISBN = "979-8-89176-390-6",
    abstract = "Long-term memory is essential for conversational agents to maintain coherence, track persistent tasks, and provide personalized interactions across extended dialogues. However, existing approaches as Retrieval-Augmented Generation (RAG) and graph-based memory mostly rely on pairwise relations, which can hardly capture high-order associations, i.e., joint dependencies among multiple elements, causing fragmented retrieval. To this end, we propose \textbf{HyperMem}, a hypergraph-based hierarchical memory architecture that explicitly models such associations using hyperedges. Particularly, HyperMem structures memory into three levels: \textit{topics}, \textit{episodes}, and \textit{facts}, and groups related episodes and their facts via hyperedges, unifying scattered content into coherent units. Leveraging this structure, we design a hybrid lexical-semantic index and a coarse-to-fine retrieval strategy, supporting accurate and efficient retrieval of high-order associations. Experiments on the LoCoMo benchmark show that HyperMem achieves state-of-the-art performance with 92.73{\%} LLM-as-a-judge accuracy, demonstrating the effectiveness of HyperMem for long-term conversations."
}