Reproduce “SpatialMath: Spatial Comprehension-Infused Symbolic Reasoning for Mathematical Problem-Solving” on OpenResearch
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About this paper
Ashutosh Bajpai, Akshat Bhandari, Akshay Nambi, Tanmoy Chakraborty
SpatialMath introduces a framework that enhances multimodal small-to-medium language models (MSLMs) for complex mathematical problem-solving by first generating structured textual spatial comprehension from visual inputs and then integrating this into symbolic reasoning. This approach improves accuracy by up to 10.2 percentage points on various geometric mathematical benchmarks while maintaining competitive computational efficiency.
Key insights
- Explicitly enhancing visual spatial comprehension and systematically infusing it into symbolic reasoning significantly boosts MSLMs' performance on vision-intensive mathematical problems.
- The quality of the generated spatial comprehension directly impacts the downstream reasoning core, highlighting the need for robust visual interpretation modules and quality control.
- A modular, two-stage approach allows for specialized optimization of visual perception and symbolic reasoning, offering a scalable solution for complex multimodal mathematical tasks.
Summary by alphaXiv.
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