@inproceedings{jiang-etal-2026-synapse,
    title = "Synapse: Empowering {LLM} Agents with Episodic-Semantic Memory via Spreading Activation",
    author = "Jiang, Hanqi  and
      Chen, Junhao  and
      Pan, Yi  and
      Chen, Ling  and
      You, Weihang  and
      Zhou, Yifan  and
      Zhang, Ruidong  and
      Abate, Yohannes  and
      Liu, Tianming",
    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/ingest-acl/2026.findings-acl.1108/",
    pages = "22020--22036",
    ISBN = "979-8-89176-395-1",
    abstract = "While Large Language Models (LLMs) excel at generalized reasoning, standard retrieval-augmented approaches fail to address the disconnected nature of long-term agentic memory. To bridge this gap, we introduce Synapse (Synergistic Associative Processing Semantic Encoding), a unified memory architecture that transcends static vector similarity. Drawing from cognitive science, Synapse models memory as a dynamic graph where relevance emerges from spreading activation rather than pre-computed links. By integrating lateral inhibition and temporal decay, the system dynamically highlights relevant sub-graphs while filtering interference. We implement a Triple Hybrid Retrieval strategy that fuses geometric embeddings with activation-based graph traversal. Extensive evaluations on the LoCoMo benchmark show that Synapse significantly outperforms state-of-the-art methods in complex temporal and multi-hop reasoning tasks, offering a robust solution to the ``Contextual Tunneling'' problem."
}