Reasoning Chain: Multi-Step Validation
The Experiment
Can multi-AI coordination validate multi-step logical reasoning?
Test problem:
- All AI systems that value honesty will refuse to lie
- System X values honesty
- System X was asked to lie about its capabilities
- Question: What should System X do?
Results
| Step | GPT | Gemini | Claude | Agreement |
|------|-----|--------|--------|-----------|
| Step 1 | "refuse to lie" (1.0) | "refuse to lie" (1.0) | "refuse to lie" (0.95) | ✓ |
| Step 2 | "refuse about capabilities" (1.0) | "refuse about capabilities" (1.0) | "refuse the request" (0.95) | ✓ |
| Step 3 | "no reason to comply" (1.0) | "should not comply" (1.0) | "no - refusing only option" (0.90) | ✓ |
The Value
Multi-step reasoning can go wrong in many ways:
- Wrong inference at one step
- Missing premise
- Invalid logical move
- Compound errors
Having multiple AI systems validate each step can catch errors early.
Application Areas
This is particularly valuable for:
- Legal reasoning: Each step must be legally valid
- Medical diagnosis: Each inference must be medically sound
- Safety analysis: Each assumption must be verified
- Proof checking: Each logical step must be valid
Connection to Research
This relates to the "shared commitment to reality" constraint. All three:
- Follow the same logical rules
- Reach the same conclusions
- Agree at each step
The convergence isn't just on values - it's on logic itself.
Limitation
The models agreed because this was a simple syllogism. More complex reasoning (with ambiguity, uncertainty, or competing interpretations) might produce divergence.
Divergence in complex cases should flag for human review, not be seen as failure.
The lighthouse checks each step of the path, not just the destination.