Do Vision-Language Models Truly Perform Vision Reasoning? A Rigorous Study of the Modality Gap
Reasoning in vision-language models (VLMs) has recently attracted significant attention due to its broad applicability across diverse downstream tasks. However, it remains unclear whether the superior performance of VLMs stems from genuine vision-grounded reasoning or relies predominantly on the reasoning capabilities of their textual backbones. To systematically measure this, we introduce CrossMath, a novel multimodal reasoning benchmark designed for controlled cross-modal comparisons. Specifically, we construct each problem in text-only, image-only, and image+text formats guaranteeing identical task-relevant information, verified by human annotators. This rigorous alignment effectively ...
| Authors | Yige Xu, Yongjie Wang, Zizhuo Wu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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