{"paper":{"title":"MedVIGIL: Evaluating Trustworthy Medical VLMs Under Broken Visual Evidence","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"Medical vision-language models fail to refuse answers when visual evidence is broken, trailing radiologists by 14 points on a new composite score.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hanqi Jiang, Haozhen Gong, Hui Ren, Hyeokjae Kwon, Jinglei Lv, Junhao Chen, Lifeng Chen, Lin Zhao, Mingyu Kang, Quanzheng Li, Ruiyu Yan, Tianming Liu, Weihang You, Xiang Li, Yi Pan","submitted_at":"2026-05-08T15:55:30Z","abstract_excerpt":"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 introduce medvigil, a 300-case evaluation suite drawn from four public medical VQA sources, supervised end to e"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The independent radiologist scores MCS 83.3 at silent-failure rate 5.8%, leaving a 14.1-point composite headroom above the strongest audited model (Claude Opus 4.7 at 69.2).","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the four chosen perturbation types and the 300 clinician-authored cases sufficiently represent the range of broken visual evidence that occurs in real clinical practice.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"MedVIGIL introduces a clinician-supervised benchmark showing medical VLMs frequently give fluent answers on broken visual evidence, with top models 14 points below human radiologists on the composite score.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Medical vision-language models fail to refuse answers when visual evidence is broken, trailing radiologists by 14 points on a new composite score.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"5abf56945b417f28f53c287fd507a3ccfd3d0c308bedf4408748d780b5d4b2a2"},"source":{"id":"2605.07919","kind":"arxiv","version":2},"verdict":{"id":"40477877-05fe-4735-b79f-76bbcd099224","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-11T03:40:45.192215Z","strongest_claim":"The independent radiologist scores MCS 83.3 at silent-failure rate 5.8%, leaving a 14.1-point composite headroom above the strongest audited model (Claude Opus 4.7 at 69.2).","one_line_summary":"MedVIGIL introduces a clinician-supervised benchmark showing medical VLMs frequently give fluent answers on broken visual evidence, with top models 14 points below human radiologists on the composite score.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the four chosen perturbation types and the 300 clinician-authored cases sufficiently represent the range of broken visual evidence that occurs in real clinical practice.","pith_extraction_headline":"Medical vision-language models fail to refuse answers when visual evidence is broken, trailing radiologists by 14 points on a new composite score."},"integrity":{"clean":false,"summary":{"advisory":2,"critical":0,"by_detector":{"doi_compliance":{"total":2,"advisory":2,"critical":0,"informational":0}},"informational":0},"endpoint":"/pith/2605.07919/integrity.json","findings":[{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. 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