Chain-of-Thought Reasoning

Systematically analyzing products for multiple defect types

CoT reasoning helps models methodically check for various defects rather than making snap judgments, reducing false negatives.

Example reasoning:

• "First, I'll check the surface for scratches... I see two small scratches on the left side."
• "Next, I'll examine the edges for chips... The top right corner has a small chip."
• "Finally, checking for discoloration... No discoloration detected."
• "Conclusion: This item has defects (scratches and chip) and should be rejected."

This systematic approach ensures thorough inspection coverage.

Accurately counting and categorizing items in complex warehouse scenes

When images contain many items or require counting by category, CoT reasoning breaks down the counting task systematically.

Example questions:

• "How many damaged boxes are on the top two shelves?"
• "Which shelf has the most items?"
• "Are there any items that appear to be misplaced?"

The model counts methodically rather than estimating, leading to more accurate inventory assessments.

Assisting with multi-step diagnostic reasoning

Healthcare applications benefit from transparent, step-by-step analysis that can be validated by medical professionals.

Example reasoning:

• "First, examining the indicated region... I observe an irregular mass approximately 2cm in diameter."
• "Second, checking the surrounding tissue... The borders appear irregular and not well-defined."
• "Third, comparing with typical presentations... These characteristics are consistent with concerning findings."
• "Recommendation: Further evaluation by a radiologist is advised."

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Important

Chain-of-Thought reasoning should assist, not replace, medical professionals. Always have qualified healthcare providers review and validate AI-generated analyses.

Making safe, verifiable decisions in complex environments

Robots can use CoT reasoning to make decisions that can be audited and validated, improving safety and reliability.

Example reasoning:

• "Checking if path is clear... I see a small object 2 meters ahead on the left side."
• "Identifying the object... It appears to be a cardboard box based on color and shape."
• "Assessing options... I can navigate around it on the right side where the path is clear."
• "Decision: Proceed forward and veer right to avoid the obstacle."

This transparency allows operators to understand and validate robot decisions.

Answering complex product questions that require multi-step reasoning

When customers ask questions that require comparing features or analyzing multiple aspects, CoT provides reliable answers.

Example questions:

• "Is this jacket suitable for both rain and cold weather?"
• "Does this furniture set fit the room dimensions shown in the image?"
• "Are all the components shown in the product image?"

The model breaks down each aspect of the question systematically before providing a comprehensive answer.

Understanding complex traffic scenarios

Self-driving systems can use CoT reasoning to make safer decisions by explicitly considering multiple factors.

Example reasoning:

• "Observing traffic light... Currently red."
• "Checking for pedestrians... One pedestrian approaching crosswalk from the right."
• "Monitoring other vehicles... No vehicles in intersection."
• "Anticipating changes... Light may turn green soon, but pedestrian will need to cross."
• "Decision: Remain stopped and prepare for pedestrian crossing when light changes."

Effective CoT prompts explicitly request step-by-step thinking:

✅ Good: "Let's approach this step-by-step. First count the red items, then count the blue items, then compare them."

❌ Poor: "How many red and blue items are there?"

✅ Good: "Before answering, explain your reasoning process."

❌ Poor: "Answer the question."

When configuring system prompts, include instructions like:**

• "Think step-by-step before providing your answer"
• "Break down complex questions into smaller parts"
• "Explain your reasoning for each step"

Learn how to configure system prompts →

Train your VQA model with annotated reasoning chains:

When creating training annotations, include examples where the answer demonstrates step-by-step reasoning:

Example annotation:

• Question: "Are there more than 5 defects visible?"
• Answer: "Let me count the defects systematically. Top section: 2 scratches. Middle section: 1 dent and 1 discoloration. Bottom section: 3 scratches. Total: 7 defects. Yes, there are more than 5 defects visible."

This teaches the model to reason explicitly rather than just providing final answers.

Structure questions to naturally require step-by-step thinking:

✅ Good: "First identify all safety equipment, then check if each person is wearing required items, then tell me if all requirements are met."

✅ Good: "Compare the number of items in each section and identify which section has the most."

❌ Poor: "Is this safe?" (Too vague, doesn't encourage reasoning)

❌ Poor: "What do you see?" (Doesn't require logical steps)

When evaluating CoT responses, check both reasoning and conclusions:

Don't just verify the final answer—check that each reasoning step is correct:

1. ✅ Verify observations: Are the visual details accurately described?
2. ✅ Check logic: Does each step follow logically from the previous?
3. ✅ Validate conclusion: Does the final answer follow from the reasoning?

This helps identify whether errors come from perception (seeing wrong) or reasoning (logical errors).

More reasoning steps aren't always better:

For very complex scenarios, overly detailed reasoning can become verbose without adding value.

Good balance:

• 2-4 reasoning steps for most questions
• More steps only when genuinely needed
• Each step should add meaningful information

Too much reasoning: "First I notice the image has lighting... The lighting is natural... Natural lighting suggests daytime... During daytime people work..." (Unnecessary detail)

Right amount: "I see 3 workers. Worker 1 wears a helmet and vest. Worker 2 wears a helmet only. Worker 3 has no safety gear. Therefore, not all workers meet safety requirements."

Yes, Chain-of-Thought reasoning takes longer because the model generates more tokens (the reasoning steps plus the answer).

Typical impact:

• Simple questions: 2-3x longer than direct answers
• Complex questions: May only be 1.5x longer (reasoning would happen internally anyway)

When speed matters:

• Use direct reasoning for simple questions
• Reserve CoT for complex questions where accuracy justifies the extra time
• Consider separating simple and complex questions in your application logic

Learn about model performance →

Yes! You can use Chain-of-Thought reasoning to make decisions about phrase grounding queries:

Example workflow:

1. Use CoT to analyze: "Let me check systematically. The defect region shows discoloration and surface irregularity on the upper right portion..."
2. Use phrase grounding to localize: "the defective area on the upper right"
3. Get precise bounding box for the identified defect

This combination provides both understanding and precise localization.

Learn about phrase grounding →

Evaluate both the reasoning process and the final answer:

Reasoning quality metrics:

• Logical consistency: Do steps follow logically?
• Visual accuracy: Are observations correct?
• Completeness: Are all necessary steps included?

Answer quality metrics:

• Correctness: Is the final answer right?
• Attribution: Does the answer follow from the reasoning?

You may want to create evaluation datasets specifically for testing reasoning quality.

Learn about evaluation →

Not necessarily, but it helps. Modern VLMs can perform CoT reasoning through prompting alone, but training with reasoning examples improves quality.

Option 1: Prompt-based CoT (No special training)

• Use system prompts that request reasoning
• Works with any trained VQA model
• Good for getting started

Option 2: Training with reasoning examples (Better results)

• Include reasoning steps in training annotations
• Model learns to reason naturally
• Produces more consistent, higher-quality reasoning

Start with prompt-based CoT, then add reasoning examples to your training data if you need better results.

This can happen when the model arrives at the correct answer through incorrect reasoning—a phenomenon called "right for the wrong reasons."

Example:

• Question: "How many red items?"
• Wrong reasoning: "I see 2 red boxes and 1 red book. Wait, that book might be orange. Let me say 2."
• Answer: "2 red items" (Correct, but reasoning showed confusion)

How to address this:

• Carefully review reasoning steps during model evaluation
• Include diverse examples in training to strengthen visual understanding
• If this happens frequently, the model may need more training data

Correct reasoning is important for reliability and trust, even when answers are correct.