MV-MATH: Evaluating Multimodal Math Reasoning in Multi-Visual Contexts

• URL: http://arxiv.org/abs/2502.20808v4
• Date: Sun, 23 Mar 2025 14:55:02 GMT
• Title: MV-MATH: Evaluating Multimodal Math Reasoning in Multi-Visual Contexts
• Authors: Peijie Wang, Zhong-Zhi Li, Fei Yin, Xin Yang, Dekang Ran, Cheng-Lin Liu,
• Abstract summary: We introduce MV-MATH: a meticulously curated dataset of 2,009 high-quality mathematical problems.<n>Each problem integrates multiple images interleaved with text, derived from authentic K-12 scenarios, and enriched with detailed annotations.<n>MV-MATH includes multiple-choice, free-form, and multi-step questions, covering 11 subject areas across 3 difficulty levels.<n>We observe that MLLMs encounter substantial challenges in multi-visual math tasks, with a considerable performance gap relative to human capabilities on MV-MATH.
• Score: 34.972503583614674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal Large Language Models (MLLMs) have shown promising capabilities in mathematical reasoning within visual contexts across various datasets. However, most existing multimodal math benchmarks are limited to single-visual contexts, which diverges from the multi-visual scenarios commonly encountered in real-world mathematical applications. To address this gap, we introduce MV-MATH: a meticulously curated dataset of 2,009 high-quality mathematical problems. Each problem integrates multiple images interleaved with text, derived from authentic K-12 scenarios, and enriched with detailed annotations. MV-MATH includes multiple-choice, free-form, and multi-step questions, covering 11 subject areas across 3 difficulty levels, and serves as a comprehensive and rigorous benchmark for assessing MLLMs' mathematical reasoning in multi-visual contexts. Through extensive experimentation, we observe that MLLMs encounter substantial challenges in multi-visual math tasks, with a considerable performance gap relative to human capabilities on MV-MATH. Furthermore, we analyze the performance and error patterns of various models, providing insights into MLLMs' mathematical reasoning capabilities within multi-visual settings.

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