Model Control Protocol (MCP)
The Model Control Protocol (MCP) defines AIPaul’s standards for AI model lifecycle governance across proposal, deployment, monitoring, update, rollback, and retirement phases.
MCP guarantees transparency, security, community accountability, and verifiable trust in the operation of the prediction engine.
2. Core Objectives
Model Version Control
Unique version hash recorded on-chain
Community Governance
DAO voting required for model changes
On-Chain Verification
Deployment, evaluation, and rollback events logged on blockchain
Security Assurance
Mandatory third-party audits for major updates
Performance Monitoring
Continuous evaluation against benchmark datasets
3. MCP Operational Workflow
1. Model Proposal Submission
Submit model documentation, version hash, and benchmark performance data.
2. DAO Governance Voting
Initiate community voting via DAO. Token holders vote to approve or reject the model deployment.
3. Model Deployment and Registration
Upon approval, deploy the model.
Store model hash, deployment timestamp, proposer address, and initial benchmarks on-chain.
4. Live Monitoring and Performance Evaluation
Collect real-time metrics (accuracy, AUC, RMSE) to evaluate ongoing model performance.
5. Performance Threshold Checking
• If model performance meets or exceeds thresholds, operation continues normally.
• If severe degradation occurs, an Emergency DAO Vote is triggered.
6. Rollback to Previous Model
In case of critical issues, automatically revert to the last validated model version.
7. Continuous Update Cycle
New models can be proposed and voted on regularly.
4. Process Diagram (Text Form)
5. Security Practices
• Formal verification of critical models.
• Adversarial robustness testing.
• Future integration of Zero-Knowledge Proofs for model integrity verification.
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