Look Light, Think Heavy: What Multimodal Chain-of-Thought Reasoning Can and Cannot Do
Chain-of-Thought (CoT) has become a standard method for improving reasoning capabilities in large language models (LLMs) by eliciting step-by-step thinking, but its effectiveness in multimodal tasks remains unclear. In this paper, we aim to systematically investigate the key question: What can multimodal Chain-of-Thought reasoning do, and where and why does it fall short? To this end, we evaluate 12 multimodal tasks across perception and reasoning categories using both 14 non-reasoning models and 8 reasoning models. Our analysis reveals several important findings: (1) CoT is not a free lunch and should be used selectively depending on the specific requirements of each task. For perception...
| Authors | Zhuoran Jin, Kejian Zhu, Hongbang Yuan et al. |
|---|---|
| Primary category | cs.CL |
| Published | 21 June 2026 |
| Last updated at source | 21 June 2026 |
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