Inter-Model Arbitration Skill

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

| Mechanism | Use Case | Process | |-----------|----------|---------| | Weighted Consensus | Factual disputes | Weight by model expertise domain | | Human Escalation | Value conflicts | Defer to human operator | | Probabilistic Merge | Uncertain outcomes | Combine with confidence weights | | Precedent Lookup | Recurring conflicts | Apply previous rulings | | Third-Model Tiebreak | Binary deadlocks | Invoke neutral third model |

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

Neutrality: Arbitrator has no stake in outcome
Transparency: All parties see full reasoning
Consistency: Similar conflicts yield similar resolutions
Escalation Path: Human oversight always available
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

Agent Skills: Inter-Model Arbitration

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Skill Files