Chain-of-Thought (CoT) with Vision is a reasoning technique for Multimodal LLMs — where the model generates a step-by-step intermediate textual outcomes describing its visual observations before concluding the final answer, significantly improving performance on complex tasks.

What Is Visual CoT?

• Definition: Evaluating complex visual questions by breaking them down.
• Process: Input Image -> "I see X and Y. X implies Z. Therefore..." -> Final Answer.
• Contrast: Standard VQA jumps immediately from Image -> Answer (Black Box).
• Benefit: Reduces hallucination and logical errors.

Why It Matters

• Interpretability: Users can see why the model made a decision (e.g., "I classified this as a defect because I saw a scratch on the wafer edge").
• Accuracy: Forces the model to ground its reasoning in specific visual evidence.
• Science/Math: Essential for solving geometry problems or interpreting scientific graphs.

Example

• Question: "Is the person safe?"
• Standard: "No."
• CoT: "1. I see a construction worker. 2. I look at his head. 3. He is not wearing a helmet. 4. This is a safety violation. -> Answer: No."

Chain-of-Thought with Vision is bringing "System 2" thinking to computer vision — enabling deliberate, verifiable reasoning rather than just intuitive pattern matching.