@inproceedings{hou-etal-2025-synapticrag,
    title = "{S}ynaptic{RAG}: Enhancing Temporal Memory Retrieval in Large Language Models through Synaptic Mechanisms",
    author = "Hou, Yuki  and
      Tamoto, Haruki  and
      Zhao, Qinghua  and
      Miyashita, Homei",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.1048/",
    pages = "20422--20436",
    ISBN = "979-8-89176-256-5",
    abstract = "Existing retrieval methods in Large Language Models show degradation in accuracy when handling temporally distributed conversations, primarily due to their reliance on simple similarity-based retrieval. Unlike existing memory retrieval methods that rely solely on semantic similarity, we propose SynapticRAG, which uniquely combines temporal association triggers with biologically-inspired synaptic propagation mechanisms. Our approach uses temporal association triggers and synaptic-like stimulus propagation to identify relevant dialogue histories. A dynamic leaky integrate-and-fire mechanism then selects the most contextually appropriate memories. Experiments on four datasets of English, Chinese and Japanese show that compared to state-of-the-art memory retrieval methods, SynapticRAG achieves consistent improvements across multiple metrics up to 14.66{\%} points. This work bridges the gap between cognitive science and language model development, providing a new framework for memory management in conversational systems."
}