Boosting Multimodal Reasoning with Automated Structured Thinking

Multimodal large language models excel across diverse domains but struggle with complex visual reasoning tasks. Current approaches aim to incorporate structured thinking via two strategies: explicit search methods and post-training techniques. However, both approaches face significant limitations: Search-based methods suffer from computational inefficiency due to extensive solution space exploration, while post-training methods require substantial data, computational resources, and often encounter training instability. To address these limitations, we propose AStar, an \textbf{A}utomated \textbf{S}tructured \textbf{t}hinking paradigm for multimod\textbf{a}l \textbf{r}easoning. Our method introduces "thought cards", a lightweight library of high-level reasoning patterns abstracted from 500 prior samples using Monte Carlo Tree Search. For each test problem, AStar adaptively retrieves the optimal thought cards and seamlessly integrates these external explicit guidelines with the model's internal implicit reasoning capabilities. Extensive experiments demonstrate AStar's effectiveness and efficiency: using only 500 prior samples and a 7B backbone, our training-free framework achieves 53.9$\%$ accuracy on MathVerse (surpassing GPT-4o's 50.2%) and 32.7% on MathVision (versus GPT-4o's 30.4%). Further analysis reveals that AStar generalizes beyond multimodal reasoning to visual perception and understanding domains, and serves as a plug-and-play test-time inference method compatible with mainstream post-training techniques like GRPO.

@article{wu2025_2502.02339,
  title={ Boosting Multimodal Reasoning with Automated Structured Thinking },
  author={ Jinyang Wu and Mingkuan Feng and Shuai Zhang and Fangrui Lv and Ruihan Jin and Feihu Che and Zengqi Wen and Jianhua Tao },
  journal={arXiv preprint arXiv:2502.02339},
  year={ 2025 }
}