arXiv Computation and Language September 4, 2026 By Benlu Wang, Yifan Zhang, Jiaqing Yu, Chin Siang Ong, Juncheng Huang, Zhuohao Li, Zhenyu Zhang, Arman Cohan, Hong Yu, Zonghai Yao

MedQA-MM: Shortcuts Behind Medical Visual Reasoning

Read the original on arXiv Computation and Language →

The paper introduces MedQA-MM, a benchmark that exposes shortcut reasoning in medical multimodal multiple-choice questions. By auditing prompts, images, and modalities, the authors show that models often rely on textual cues rather than visual evidence, with full-input accuracy at 62.63% but only 5.21% when restricted to text. The study highlights the need for route-level evidence to validate true medical image reasoning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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