Papers › Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset
Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset
Ke Wang, Junting Pan, Weikang Shi, Zimu Lu, Mingjie Zhan, Hongsheng Li
Recent advancements in Large Multimodal Models (LMMs) have shown promising results in mathematical reasoning within visual contexts, with models approaching human-level performance on existing benchmarks such as MathVista. However, we observe significant limitations in the diversity of questions and breadth of subjects covered by these benchmarks. To address this issue, we present the MATH-Vision (MATH-V) dataset, a meticulously curated collection of 3,040 high-quality mathematical problems with visual contexts sourced from real math competitions. Spanning 16 distinct mathematical disciplines and graded across 5 levels of difficulty, our dataset provides a comprehensive and diverse set of challenges for evaluating the mathematical reasoning abilities of LMMs. Through extensive experimentation, we unveil a notable performance gap between current LMMs and human performance on MATH-V, underscoring the imperative for further advancements in LMMs. Moreover, our detailed categorization allows for a thorough error analysis of LMMs, offering valuable insights to guide future research and development. The project is available at https://mathvision-cuhk.github.io
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multimodal Reasoning | MATH-V | GPT4V | Accuracy | 22.76 | #1 of 4 | Archive leaderboard | report |
| Multimodal Reasoning | MATH-V | Gemini Pro | Accuracy | 17.66 | #2 of 4 | Archive leaderboard | report |
| Multimodal Reasoning | MATH-V | Qwen-VL-Max | Accuracy | 15.59 | #3 of 4 | Archive leaderboard | report |
| Multimodal Reasoning | MATH-V | InternLM-XComposer2-VL | Accuracy | 14.54 | #4 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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