Cantor is a multimodal chain-of-thought (CoT) framework that leverages a perception-decision architecture to combine visual context acquisition with logical reasoning, effectively solving complex visual reasoning tasks. Acting as a decision generator, Cantor integrates visual input to analyze images and questions, ensuring tighter alignment with real-world scenarios. Furthermore, Cantor utilizes the advanced cognitive capabilities of large language models (LLMs) as multi-faceted experts to deduce higher-level information, enriching the CoT generation process. Extensive experiments on two challenging visual reasoning datasets demonstrate the effectiveness of the proposed framework. Notably, Cantor achieves significant improvements in multimodal CoT performance without requiring fine-tuning or real-world reasoning, surpassing existing baselines."