@inproceedings{zhao-etal-2026-beyond-retrieval,
    title = "Beyond Retrieval: Bi-Temporal State Arbitration for Longitudinal Healthcare Agents",
    author = "Zhao, Jianing  and
      Zhi, Xiaoquan  and
      Yu, Xinqiang",
    editor = "Chen, Canyu  and
      Zhang, Yuji  and
      Li, Zoey Sha  and
      Wang, Zihan  and
      Wang, Qineng  and
      Su, Jinyan  and
      Kargupta, Priyanka  and
      Marjanovi{\'c}, Sara Vera  and
      Pan, Jeff Z.  and
      Bansal, Mohit  and
      Augenstein, Isabelle  and
      Han, Jiawei  and
      Ji, Heng  and
      Li, Manling",
    booktitle = "Proceedings of the 4th Workshop on Towards Knowledgeable Foundation Models ({K}now{FM} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.knowfm-1.10/",
    pages = "129--137",
    ISBN = "979-8-89176-403-3",
    abstract = "Longitudinal healthcare agents require persistent state tracking under temporal uncertainty. In domains like chronic disease management, patient states{---}medications, symptoms, and vital signs{---}evolve continuously over months. Existing memory architectures for Large Language Models (LLMs) are inherently retrieval-centric: they treat memory as a static repository of past interactions, failing to resolve conflicting or superseded information when queried for the current patient state. We propose a shift to state-centric memory. Our framework introduces (1) a bi-temporal state representation that decouples event time from ingestion time and tracks temporal validity windows, (2) an incremental state arbitration mechanism using four operators{---}SUPPORT, REFINE, SUPERSEDE, and BRANCH-CONFLICT{---}to handle evolving medical facts without destructive overwriting, and (3) a confidence-thresholded evidence escalation layer for robust, efficient memory access. Evaluated on a longitudinal diabetes management suite as a representative biomedical state tracking task, our method achieves a Unique-F1 of 0.85 and Conflict-F1 of 0.98, substantially improves upon long-context LLMs (0.38 / 0.89) and standard vector memory (0.30 / 0.60), demonstrating that agentic AI in longitudinal biomedical settings requires continuous, evidence-grounded arbitration rather than simple retrieval."
}