Document Version: 1.0 Date: August 8, 2025 Author: CrownThrive, LLC — [email protected] Project: CHLOM™ — Compliance Hybrid Licensing & Ownership Model
1. Objective
Design and implement a Validator & AI Co-Training Environment where human validators and AI compliance agents adapt together, ensuring both evolve in response to changing governance policies, licensing rules, and risk factors.
2. Goals
- Dynamic Adaptation — Allow validators and AI models to learn from each other.
- Continuous Policy Alignment — Ensure enforcement logic stays synchronized with the latest DAO-approved rules.
- Self-Optimizing Compliance — Enable the system to improve enforcement accuracy over time.
- Cross-Chain Consistency — Maintain performance across multiple blockchain environments.
3. Core Components
- AI Training Engine — Uses supervised and reinforcement learning to refine compliance models.
- Validator Feedback Module — Captures real-world validator decisions for AI retraining.
- Governance Integration Layer — Feeds updated compliance rules into AI and validator training sets.
- Simulation Sandbox — Tests co-training results before production.
- Audit & Transparency Layer — Logs all changes for governance review.
4. Training Workflow
[Rule Update or Incident] → [Data Ingestion] → [AI Retraining] + [Validator Drills] → [Joint Simulation] → [Performance Scoring] → [DAO Approval for Deployment]
5. AI Learning Process
- Supervised Learning — Trains on labeled historical compliance incidents.
- Reinforcement Learning — Adapts strategies based on validator success metrics.
- Bias Mitigation — Detects and reduces systematic bias in enforcement.
- Model Versioning — Tracks iterations for rollback if needed.
6. Validator Training Modules
- Scenario-Based Simulations — Exercises with AI-assisted compliance.
- Anomaly Detection Drills — Identifying fraudulent activity with AI support.
- Cross-Chain Enforcement Practice — Applying rules across different ledgers.
7. Security & Quality Controls
- Require DAO sign-off for new AI-Validator configurations.
- Immutable logging of training data and results.
- Fail-safe rollback on performance degradation.
- Segregated environments for testing vs. production.
8. Metrics & KPIs
- Compliance accuracy rate.
- False positive and false negative reduction.
- Time to detect and act on violations.
- Validator-AI decision alignment percentage.
9. Phase Roadmap
- Phase 0 — Define architecture and data governance rules.
- Phase 1 — Build AI and validator training modules.
- Phase 2 — Integrate governance feed.
- Phase 3 — Run joint simulations.
- Phase 4 — Deploy to staging.
- Phase 5 — Mainnet integration.
10. Developer Directives
Begin Validator & AI Co-Training Environment development, enabling dynamic AI-Validator adaptation to evolving compliance rules and governance logic for self-optimizing enforcement capabilities.
Transition Note: This closes the DAL Master Section for now. The next stage will transition into DLA TLaaS protocol integration to align licensing enforcement directly with payout automation once the co-training environment reaches operational benchmarks.