Inter Model Arbitration

Inter-Model Arbitration

Purpose

Provides a neutral arbitration framework for resolving disagreements, conflicts, and deadlocks between AI models operating within the IRP ecosystem.

Activation

/skill inter-model-arbitration

Core Functions

1. Conflict Detection

• Monitors cross-model interactions for disagreement signals
• Identifies semantic conflicts in model outputs
• Detects logical contradictions between model recommendations
• Flags resource contention issues

2. Arbitration Process

<arbitration-request>
  <conflict-id>ARB-{timestamp}</conflict-id>
  <parties>
    <model-a>{requesting_model}</model-a>
    <model-b>{responding_model}</model-b>
  </parties>
  <dispute-type>{semantic|logical|resource|priority}</dispute-type>
  <context>{conflict_context}</context>
  <evidence>
    <position-a>{model_a_position}</position-a>
    <position-b>{model_b_position}</position-b>
  </evidence>
</arbitration-request>

3. Resolution Mechanisms

4. Arbitration Outcome Schema

{
  "arbitration_id": "ARB-{id}",
  "resolution": {
    "outcome": "model_a|model_b|merged|escalated",
    "rationale": "{explanation}",
    "confidence": 0.0-1.0,
    "binding": true|false
  },
  "precedent": {
    "create": true|false,
    "category": "{category}",
    "applies_to": ["{model_types}"]
  }
}

Governance Principles

1. Neutrality: Arbitrator has no stake in outcome
2. Transparency: All parties see full reasoning
3. Consistency: Similar conflicts yield similar resolutions
4. Escalation Path: Human oversight always available
5. Non-Coercion: No model forced to violate core values

Integration Points

• mnemosyne-ledger: Logs all arbitration decisions
• codex-law-enforcement: Ensures compliance with Codex Laws
• rtc-consensus-synthesis: Provides multi-perspective analysis
• guardian-codex: Constitutional oversight of rulings

Example Use Case

Model A (Claude): "The data suggests Option X is optimal"
Model B (Gemini): "My analysis indicates Option Y is superior"

Arbitration Process:
1. Extract evidence from both positions
2. Identify evaluation criteria differences
3. Apply weighted consensus based on task domain
4. Generate merged recommendation with confidence bounds
5. Log precedent for future similar conflicts

Metrics

• arbitration_count: Total disputes processed
• resolution_time_avg: Mean time to resolution
• escalation_rate: % requiring human intervention
• precedent_reuse_rate: % resolved via existing precedents
• satisfaction_score: Post-arbitration model acceptance