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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.
This paper investigates how large language models maintain correct beliefs under adversarial pressure in clinical settings, proposing R-FT fine-tuning to improve epistemic resilience while balancing corrigibility, and demonstrating significant robustness gains on medical benchmarks.