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This paper introduces Narrative Anchoring, a failure mode where clinical language models produce divergent diagnoses when identical clinical facts are expressed in different sociolinguistic registers. The authors release a USMLE-derived dataset and propose NarrativeShield, a three-agent pipeline that reduces the anchoring gap to near-zero.
Introduces MedMisBench to measure LLMs' ability to maintain correct medical reasoning under misleading context. Shows that accuracy drops sharply from 71.1% to 38.0% under adversarial conditions, with potential harm flagged by clinical panel.