# Inter Model Arbitration

> Resolves disputes and conflicts between AI models during collaborative tasks

- Skill: `diegosouzapw/inter-model-arbitration` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add diegosouzapw/inter-model-arbitration`
- Raw SKILL.md: https://api.skillmd.com/api/skills/diegosouzapw/inter-model-arbitration/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: diegosouzapw (https://skillmd.com/u/diegosouzapw)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/diegosouzapw/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
```xml
<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
```json
{
  "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