AI RESEARCH
MedVIGIL: Evaluating Trustworthy Medical VLMs Under Broken Visual Evidence
arXiv CS.CV
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ArXi:2605.07919v1 Announce Type: new Medical vision--language models (VLMs) are usually evaluated on intact image--question pairs, but trustworthy clinical use requires a stronger property: a model must recognise when the evidential basis for an answer has failed. We study this through silent failures under perturbed evidence, where a vision-required medical question is paired with a false premise, wording perturbation, knowledge-only rewrite, or ROI-corrupted image, yet the model returns a fluent non-refusal answer. We.