Research on Large Model Reasoning Optimization Framework Based on Monte Carlo Tree Search and Multimodal Fusion

This paper proposes a large model reasoning optimization framework based on Monte Carlo tree search (MCTS) and multimodal fusion. This framework significantly improves the reasoning efficiency and performance of large models through the original multimodal Monte Carlo tree search (MM-MCTS) algorithm and the cross-modal information fusion of multimodal data. The MM-MCTS algorithm expands the search space of traditional MCTS by introducing multimodal features as decision nodes, enabling it to explore the optimal reasoning path more efficiently. In the experimental part, this paper verifies the optimization framework on multiple standard data sets. Compared with the existing optimization methods, the reasoning time is shortened by 35.2 % on average and the model accuracy is improved by 4.8 percentage points. In addition, the multimodal fusion module further enhances the model's ability to understand complex scenes, making the optimization framework perform well in multimodal tasks. The research in this paper provides an innovative solution for efficient reasoning of large models and provides new ideas for the combination of multimodal fusion and optimization algorithms in the future.