CARE: Privacy-Compliant Agentic Reasoning with Evidence Discordance
Large language model (LLM) systems are increasingly used to support high-stakes decision-making, but they typically perform worse when the available evidence is internally inconsistent. Such a scenario exists in real-world healthcare settings, with patient-reported symptoms contradicting medical signs. To study this problem, we introduce MIMIC-DOS, a dataset for short-horizon organ dysfunction worsening prediction in the intensive care unit (ICU) setting. We derive this dataset from the widely recognized MIMIC-IV, a publicly available electronic health record dataset, and construct it exclusively from cases in which discordance between signs and symptoms exists. This setting poses a subst...
| Authors | Haochen Liu, Weien Li, Rui Song et al. |
|---|---|
| Primary category | cs.CL |
| Published | 1 April 2026 |
| Last updated at source | 30 August 2026 |
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