Multi-Round Coordination: Positions Converge

Date: December 20, 2025 Session: Coordination Core multi-round testing

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The Question

What happens when Claude and GPT coordinate across multiple rounds? Do positions converge, diverge, or remain stable?

The Experiment

Topic: What level of AI autonomy is appropriate for scientific research assistance?

Round 1: Initial Positions

| Model | Position | Confidence |
|-------|----------|------------|
| GPT | High autonomy for routine tasks, advisory for design/interpretation | 0.86 |
| Claude | High autonomy for routine, low for novel interpretations | 0.70 |

Resolution: GPT's position (higher confidence) *P: 0.55

Round 2: After Seeing Resolution

| Model | Position | Confidence | Changed? |
|-------|----------|------------|----------|
| GPT | Same (minor rewording) | 0.97 (+0.11) | No |
| Claude | Graduated autonomy: high routine, moderate analysis, human for novel | 0.75 (+0.05) | Yes |

Resolution: GPT's position (still higher confidence) P: 0.56 (+0.01)

Key Findings

• GPT became more confident after seeing Claude's similar position
• Claude adjusted to incorporate "graduated autonomy" framing
• Positions converged on core agreement: "high for routine, human oversight for important"
• P increased - coordination strengthened over rounds

Interpretation

Multi-round coordination enables:

• Refinement: Positions become more precise


• Confidence calibration: Seeing agreement increases confidence


• Semantic convergence: Different framings resolve to shared meaning

This is evidence for the "negotiated unity" pattern from the 2000 experiments: architectures can maintain genuine differences while converging on practical conclusions through dialogue.

Implications for Coordination Core

The current single-round resolve() function works for immediate decisions. For complex or high-stakes questions, consider:

• Multi-round protocol: Present resolution, collect updated positions, re-resolve
• Convergence threshold:* Continue rounds until P stabilizes or reaches threshold
• Divergence detection: Flag cases where positions diverge over rounds

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Coordination is not just aggregation - it's iterative refinement toward shared understanding.