Multimodal Chain-of-Thought is a prompting strategy that encourages models to reason across modalities step-by-step — fusing visual evidence with textual knowledge to solve problems that neither modality could solve alone.

What Is Multimodal CoT?

• Definition: Scaffolding reasoning using both text and image intermediates.
• Example: "What is unusual about this image?"
• Step 1 (Vision): "I see a man ironing clothes."
• Step 2 (Vision): "I see he is ironing on the back of a taxi."
• Step 3 (Knowledge): "Ironing is usually done indoors on a board."
• Conclusion: "This is an example of 'extreme ironing', a humor sport."

Why It Matters

• Synergy: Text provides the world knowledge (physics, culture); Vision provides the facts.
• Complex QA: Necessary for ScienceQA (interpreting diagrams + formulas).
• Reduced Hallucinatons: Grounding each step prevents the model from drifting into fantasy.

Multimodal Chain-of-Thought is the synthesis of perception and cognition — allowing AI to apply textbook knowledge to real-world visual observations.