SpatialMath: Spatial Comprehension-Infused Symbolic Reasoning for Mathematical Problem-Solving

• URL: http://arxiv.org/abs/2601.17489v1
• Date: Sat, 24 Jan 2026 15:31:20 GMT
• Title: SpatialMath: Spatial Comprehension-Infused Symbolic Reasoning for Mathematical Problem-Solving
• Authors: Ashutosh Bajpai, Akshat Bhandari, Akshay Nambi, Tanmoy Chakraborty,
• Abstract summary: Multimodal Small-to-Medium sized Language Models (MSLMs) have demonstrated strong capabilities in integrating visual and textual information.<n>Current models struggle to accurately decompose intricate visual inputs and connect geometric perception with structured reasoning.<n>We propose SpatialMath, a novel Spatial-Infused Reasoning Framework designed to integrate spatial representations into structured symbolic reasoning chains.
• Score: 17.304596904197204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal Small-to-Medium sized Language Models (MSLMs) have demonstrated strong capabilities in integrating visual and textual information but still face significant limitations in visual comprehension and mathematical reasoning, particularly in geometric problems with diverse levels of visual infusion. Current models struggle to accurately decompose intricate visual inputs and connect perception with structured reasoning, leading to suboptimal performance. To address these challenges, we propose SpatialMath, a novel Spatial Comprehension-Infused Symbolic Reasoning Framework designed to integrate spatial representations into structured symbolic reasoning chains. SpatialMath employs a specialized perception module to extract spatially-grounded representations from visual diagrams, capturing critical geometric structures and spatial relationships. These representations are then methodically infused into symbolic reasoning chains, facilitating visual comprehension-aware structured reasoning. To this end, we introduce MATHVERSE-PLUS, a novel dataset containing structured visual interpretations and step-by-step reasoning paths for vision-intensive mathematical problems. SpatialMath significantly outperforms strong multimodal baselines, achieving up to 10 percentage points improvement over supervised fine-tuning with data augmentation in vision-intensive settings. Robustness analysis reveals that enhanced spatial representations directly improve reasoning accuracy, reinforcing the need for structured perception-to-reasoning pipelines in MSLMs.

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