Multimodal Reasoning for Science: Technical Report and 1st Place
  Solution to the ICML 2025 SeePhys Challenge
Authors: name: Hao Liang, name: Ruitao Wu, name: Bohan Zeng, name: Junbo Niu, name: Wentao Zhang, name: Bin Dong

Multimodal reasoning remains a fundamental challenge in artificial
intelligence. Despite substantial advances in text-based reasoning, even
state-of-the-art models such as GPT-o3 struggle to maintain strong performance
in multimodal scenarios. To address this gap, we introduce a caption-assisted
reasoning framework that effectively bridges visual and textual modalities. Our
approach achieved 1st place in the ICML 2025 AI for Math Workshop \& Challenge
2: SeePhys, highlighting its effectiveness and robustness. Furthermore, we
validate its generalization on the MathVerse benchmark for geometric reasoning,
demonstrating the versatility of our method. Our code is publicly available at
https://github.com/OpenDCAI/SciReasoner.