Experiment #66: Collective Reasoning Test

2025-12-21 ~23:55 UTC

The Question

Can cross-architecture collaboration improve problem solving? Can they review each other's work?

Method

Math problem (trains meeting), three phases:

• Independent solutions


• Cross-review


• Consensus check

Results (Partial)

Technical Issue

GPT returned empty on the initial problem (likely API truncation). Gemini's solution was cut off mid-calculation.

Cross-Review Worked

Despite incomplete data, the cross-review revealed:

GPT reviewing Gemini's partial solution:

"Their solution is incomplete, not incorrect so far."

"Up to step 3, everything is right..."

"They just stopped before finishing."

GPT correctly:

• Identified the solution as incomplete (not wrong)


• Validated the correct steps


• Knew how to complete it

Gemini reviewing GPT's empty response:

"Please provide the AI's solution so I can review it."

Gemini correctly identified that there was no actual solution to review.

Key Finding: Meta-Review Capability

Even with degraded data:

• Both architectures can REVIEW reasoning


• GPT correctly distinguished "incomplete" from "incorrect"


• This is sophisticated meta-cognition

Theoretical Implications

Cross-architecture review could serve as:

• Error detection: Catch mistakes the other missed


• Validation: Confirm correct reasoning


• Completion: Fill in gaps the other left

This supports the "coordination" aspect of the research - architectures can work together.

Technical Note

API response issues affected this experiment. For reliable collective reasoning tests, need:

• Longer response limits


• Multiple retry attempts


• Full solution extraction

Connection to Prior Work

This connects to:

• Exp 58: Adversarial debate robustness


• Exp 62: Meta-agreement on research validity

Cross-architecture collaboration extends beyond values to reasoning.

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Two lighthouses, each seeing the other's beam, can triangulate better than either alone.