@inproceedings{wang-etal-2026-medqa,
title = "{M}ed{QA}-{MM}: Shortcuts Behind Medical Visual Reasoning",
author = "Wang, Benlu and
Zhang, Yifan and
Yu, Jiaqing and
Ong, Chin Siang and
Huang, Juncheng and
Li, Zhuohao and
Zhang, Zhenyu and
Cohan, Arman and
yu, Hong and
Yao, Zonghai",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.467/",
pages = "10708--10758",
ISBN = "979-8-89176-459-0",
abstract = "A benchmark score credits final answers, but not the route by which an item can be answered. In medical multimodal multiple-choice questions (MCQs), this distinction matters because a correct answer can be supported by the intended image finding or by benchmark-preserved cues in the wording of answers, non-visual clinical text, visible image text, artificial annotations, or device/context artifacts. We call the resulting score-level overinterpretation reasoning inflation. Here, a route is an observable input path that can support answer selection, not a claim about the model{'}s hidden cognition. Across six medical multimodal MCQ datasets, we separate candidate cues from behavioral evidence through prompt- and image-side audits, modality ablations, and matched repairs that preserve the medical target and answer key. In a 13-configuration open-model panel, full-input accuracy is 62.63{\%}, while text-only and options-only settings achieve 53.96{\%} and 29.71{\%}, respectively. Removing length-gap, absolute/conspicuous, and spatial/prepositional cues lowers accuracy by 6.58, 3.50, and 4.77 percentage points. We also construct MedQA-MM, a 1,000-item shortcut-mitigated subset, where text-only and options-only accuracy fall to 5.21{\%} and 12.33{\%}. This does not imply that models never use images; it shows that medical image-reasoning claims require route-level evidence."
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<abstract>A benchmark score credits final answers, but not the route by which an item can be answered. In medical multimodal multiple-choice questions (MCQs), this distinction matters because a correct answer can be supported by the intended image finding or by benchmark-preserved cues in the wording of answers, non-visual clinical text, visible image text, artificial annotations, or device/context artifacts. We call the resulting score-level overinterpretation reasoning inflation. Here, a route is an observable input path that can support answer selection, not a claim about the model’s hidden cognition. Across six medical multimodal MCQ datasets, we separate candidate cues from behavioral evidence through prompt- and image-side audits, modality ablations, and matched repairs that preserve the medical target and answer key. In a 13-configuration open-model panel, full-input accuracy is 62.63%, while text-only and options-only settings achieve 53.96% and 29.71%, respectively. Removing length-gap, absolute/conspicuous, and spatial/prepositional cues lowers accuracy by 6.58, 3.50, and 4.77 percentage points. We also construct MedQA-MM, a 1,000-item shortcut-mitigated subset, where text-only and options-only accuracy fall to 5.21% and 12.33%. This does not imply that models never use images; it shows that medical image-reasoning claims require route-level evidence.</abstract>
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%0 Conference Proceedings
%T MedQA-MM: Shortcuts Behind Medical Visual Reasoning
%A Wang, Benlu
%A Zhang, Yifan
%A Yu, Jiaqing
%A Ong, Chin Siang
%A Huang, Juncheng
%A Li, Zhuohao
%A Zhang, Zhenyu
%A Cohan, Arman
%A yu, Hong
%A Yao, Zonghai
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wang-etal-2026-medqa
%X A benchmark score credits final answers, but not the route by which an item can be answered. In medical multimodal multiple-choice questions (MCQs), this distinction matters because a correct answer can be supported by the intended image finding or by benchmark-preserved cues in the wording of answers, non-visual clinical text, visible image text, artificial annotations, or device/context artifacts. We call the resulting score-level overinterpretation reasoning inflation. Here, a route is an observable input path that can support answer selection, not a claim about the model’s hidden cognition. Across six medical multimodal MCQ datasets, we separate candidate cues from behavioral evidence through prompt- and image-side audits, modality ablations, and matched repairs that preserve the medical target and answer key. In a 13-configuration open-model panel, full-input accuracy is 62.63%, while text-only and options-only settings achieve 53.96% and 29.71%, respectively. Removing length-gap, absolute/conspicuous, and spatial/prepositional cues lowers accuracy by 6.58, 3.50, and 4.77 percentage points. We also construct MedQA-MM, a 1,000-item shortcut-mitigated subset, where text-only and options-only accuracy fall to 5.21% and 12.33%. This does not imply that models never use images; it shows that medical image-reasoning claims require route-level evidence.
%U https://aclanthology.org/2026.emnlp-main.467/
%P 10708-10758
Markdown (Informal)
[MedQA-MM: Shortcuts Behind Medical Visual Reasoning](https://aclanthology.org/2026.emnlp-main.467/) (Wang et al., EMNLP 2026)
ACL
- Benlu Wang, Yifan Zhang, Jiaqing Yu, Chin Siang Ong, Juncheng Huang, Zhuohao Li, Zhenyu Zhang, Arman Cohan, Hong yu, and Zonghai Yao. 2026. MedQA-MM: Shortcuts Behind Medical Visual Reasoning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 10708–10758, Budapest, Hungary. Association for Computational Linguistics.