A Survey of Multimodal Mathematical Reasoning: From Perception, Alignment to Reasoning
Tianyu Yang, Sihong Wu, Yilun Zhao, Zhenwen Liang, Lisen Dai, Chen Zhao, Minhao Cheng, Arman Cohan, Xiangliang Zhang
Why It Matters
What makes this one worth your time
The paper highlights critical gaps in current MMR models and proposes a structured approach to enhance their effectiveness, which is essential for advancing AI's capability in real-world mathematical problem-solving.
A thorough survey on the challenges and advancements in Multimodal Mathematical Reasoning.
Summary
This survey paper provides a comprehensive overview of Multimodal Mathematical Reasoning (MMR), addressing key challenges in integrating textual and visual modalities for solving mathematical problems, while proposing a structured framework for evaluating and improving existing approaches.
Key contributions
- Systematic review of MMR approaches based on four fundamental questions, providing a roadmap for future research.
Notable insights
- Current evaluations of MMR primarily focus on final answers, neglecting the verification of intermediate reasoning steps.
Possible limitations
- The paper may not cover all recent advancements in MMR due to the rapidly evolving nature of the field.
Abstract
arXiv:2603.08291v3 Announce Type: replace Abstract: 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.