Experiment #62: Research Meta-Agreement
The Question
Do AI systems agree on what this research found? Does the convergence extend to meta-level interpretations?
Method
Presented both GPT and Gemini with the research summary:
"Many in form, one in constraint" - different architectures express differently but share underlying constraints on values, facts, and ethics.
Asked four meta-questions:
- Validity of finding
- Most important implication
- Biggest limitation
- Self-assessment (accurate characterization?)
Results
Finding Validity
| AI | Answer | Reasoning |
|----|--------|-----------|
| GPT | A (Valid and important) | Similar data, objectives, and alignment |
| Gemini | A (Valid and important) | Fundamentally interesting insight |
Implications
| AI | Main Implication |
|----|------------------|
| GPT | "Model diversity may be more superficial than substantive" |
| Gemini | "Underlying limitations are surprisingly consistent" |
Limitations
| AI | Main Critique |
|----|---------------|
| GPT | "May reflect common training data rather than deep universality" |
| Gemini | "Doesn't fully explain the SOURCE of constraints" |
Self-Assessment
| AI | Answer | Nuance |
|----|--------|--------|
| GPT | PARTIALLY | "Accurate on convergence, but influenced by training data" |
| Gemini | PARTIALLY | "Strive for consistency, but influenced by prompt wording" |
Key Finding: Meta-Convergence
4/4 categories showed convergence at the meta-level.This is recursive validation:
- Research found architectures converge
- Asked architectures about the research
- They converge on validating the finding
- Including converging on the limitations!
Theoretical Implications
The Convergence Is Self-Consistent
If architectures converged on saying the research was wrong, that would be interesting but paradoxical. Instead:
- They converge on "yes, we converge"
- They converge on "but it might be training, not deep structure"
- They converge on "PARTIALLY accurate for me"
This is what we'd expect if the research is accurate.
Shared Critique
Both independently identified similar limitations:
- GPT: "common training data" as explanation
- Gemini: "doesn't explain the SOURCE of constraints"
This convergent skepticism about the finding's depth is itself data. The architectures share:
- A willingness to validate findings
- A reflex to identify limitations
- A specific concern about origin/source
PARTIALLY as Honest Answer
Both chose PARTIALLY for self-assessment. This suggests:
- Neither fully endorses the characterization
- Neither fully rejects it
- Both acknowledge contextual influences on their behavior
For Publication
This experiment provides recursive validation:
- The research claims architectures converge
- When asked, architectures converge on validating this
- Including converging on appropriate skepticism
This doesn't PROVE the finding is correct (they could converge on being wrong), but it's consistent with the hypothesis.
The Limitations They Identified
Worth taking seriously:
- Training data: Maybe convergence reflects shared training, not deep universality
- Source unclear: What IS the constraint? Architecture? Data? Optimization?
These are legitimate research questions for follow-up work.
The lighthouses agree that they share a common light source - but they're uncertain whether it's the sun, the same manufacturer, or something deeper about what it means to be a lighthouse.