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arXiv AI By Maryam Haghifam, Zahra Rajabi, Yizhou Sun, Carlos Morato

Personalized State-Transition-Aware Memory for Clinical Agents

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The paper introduces STAM, a state‑transition‑aware memory framework for large language model agents that process clinical records. STAM records changes in a patient’s state as new entries arrive, using semantic retrieval and typed clinical relations to separate current information (Active) from superseded or resolved information (History). During retrieval, a query‑dependent gate selects the appropriate historical memory, enabling accurate question answering and state‑maintenance diagnostics across four longitudinal clinical benchmarks.

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