Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.08291 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915936973357056 |
|---|---|
| author | Yang, Tianyu Wu, Sihong Zhao, Yilun Liang, Zhenwen Dai, Lisen Zhao, Chen Cheng, Minhao Cohan, Arman Zhang, Xiangliang |
| author_facet | Yang, Tianyu Wu, Sihong Zhao, Yilun Liang, Zhenwen Dai, Lisen Zhao, Chen Cheng, Minhao Cohan, Arman Zhang, Xiangliang |
| contents | Multimodal Mathematical Reasoning (MMR) has recently attracted increasing attention for its capability to solve mathematical problems involving both textual and visual modalities. However, current models still face significant challenges in real-world visual math tasks, often misinterpreting diagrams, failing to align mathematical symbols with visual evidence, or producing inconsistent reasoning steps. Moreover, existing evaluations mainly focus on checking final answers rather than verifying the correctness or executability of each intermediate step. A growing body of recent research addresses these issues by integrating structured perception, explicit alignment, and verifiable reasoning within unified frameworks. To establish a clear roadmap for understanding and comparing different MMR approaches, we systematically review them around four fundamental questions: (1) What to extract from multimodal inputs, (2) How to represent and align textual and visual information, (3) How to perform the reasoning, and (4) How to evaluate the correctness of the overall reasoning process. Finally, we discuss open challenges and share our thoughts on future research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_08291 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Survey of Multimodal Mathematical Reasoning: From Perception, Alignment to Reasoning Yang, Tianyu Wu, Sihong Zhao, Yilun Liang, Zhenwen Dai, Lisen Zhao, Chen Cheng, Minhao Cohan, Arman Zhang, Xiangliang Artificial Intelligence Multimodal Mathematical Reasoning (MMR) has recently attracted increasing attention for its capability to solve mathematical problems involving both textual and visual modalities. However, current models still face significant challenges in real-world visual math tasks, often misinterpreting diagrams, failing to align mathematical symbols with visual evidence, or producing inconsistent reasoning steps. Moreover, existing evaluations mainly focus on checking final answers rather than verifying the correctness or executability of each intermediate step. A growing body of recent research addresses these issues by integrating structured perception, explicit alignment, and verifiable reasoning within unified frameworks. To establish a clear roadmap for understanding and comparing different MMR approaches, we systematically review them around four fundamental questions: (1) What to extract from multimodal inputs, (2) How to represent and align textual and visual information, (3) How to perform the reasoning, and (4) How to evaluate the correctness of the overall reasoning process. Finally, we discuss open challenges and share our thoughts on future research directions. |
| title | A Survey of Multimodal Mathematical Reasoning: From Perception, Alignment to Reasoning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.08291 |