pith:XFMCH2AC
MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark
Multimodal models show 17 to 27 percent lower accuracy on MMMU-Pro than on MMMU.
arxiv:2409.02813 v3 · 2024-09-04 · cs.CL · cs.CV
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Results show that model performance is substantially lower on MMMU-Pro than on MMMU, ranging from 16.8% to 26.9% across models.
The assumption that questions solvable by text-only models require no visual understanding and that embedding questions in images cleanly tests seamless visual-textual integration without introducing new confounds or biases.
MMMU-Pro is a stricter multimodal benchmark that removes text-only solvable questions, augments options, and requires reading text from images, yielding substantially lower model scores of 16.8-26.9%.
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| First computed | 2026-05-18T03:12:35.515185Z |
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| Builder | pith-number-builder-2026-05-17-v1 |
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