AStar: Boosting Multimodal Reasoning with Automated Structured Thinking

Understand

Multimodal large language models excel across diverse domains but struggle with complex visual reasoning tasks.

• To enhance their reasoning capabilities, current approaches typically rely on explicit search or post-training techniques.
• However, search-based methods suffer from computational inefficiency due to extensive solution space exploration, while post-training methods demand substantial data, computational resources, and often exhibit training instability.
• To address these challenges, we propose \textbf{AStar}, a training-free, \textbf{A}utomatic \textbf{S}tructured \textbf{t}hinking paradigm for multimod\textbf{a}l \textbf{r}easoning.