arXiv: cs.AI preprintsInternational6 November 2026
MedGraph: Maintaining Patient State with Graph Memory for Long-Term Medical Dialogue
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arXiv:2611.08433v1 Announce Type: cross Abstract: LLM-based medical assistants that follow a patient across visits need a memory that keeps the patient's clinical state correct as it changes. We examine the errors of five memory systems on longitudinal medical dialogue and find three recurring causes: the retrieved evidence gives an outdated state, includes another person's health facts, or lacks a safety fact the answer depends on. We present MedGraph, a graph memory built for these three cases. Construction extracts dated facts from each session, attributes each fact to the person it describ
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