@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://aclanthology.org/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retrieval-centric: they treat memory as a static repository of past interactions, failing to resolve conflicting or superseded information when queried for the current patient state.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."
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<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.</abstract>
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%0 Conference Proceedings
%T Beyond Retrieval: Bi-Temporal State Arbitration for Longitudinal Healthcare Agents
%A Zhao, Jianing
%A Zhi, Xiaoquan
%A Yu, Xinqiang
%Y Chen, Canyu
%Y Zhang, Yuji
%Y Li, Zoey Sha
%Y Wang, Zihan
%Y Wang, Qineng
%Y Su, Jinyan
%Y Kargupta, Priyanka
%Y Marjanović, Sara Vera
%Y Pan, Jeff Z.
%Y Bansal, Mohit
%Y Augenstein, Isabelle
%Y Han, Jiawei
%Y Ji, Heng
%Y Li, Manling
%S Proceedings of the 4th Workshop on Towards Knowledgeable Foundation Models (KnowFM 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-403-3
%F zhao-etal-2026-beyond-retrieval
%X 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.
%U https://aclanthology.org/2026.knowfm-1.10/
%P 129-137
Markdown (Informal)
[Beyond Retrieval: Bi-Temporal State Arbitration for Longitudinal Healthcare Agents](https://aclanthology.org/2026.knowfm-1.10/) (Zhao et al., KnowFM 2026)
ACL