MCOP Framework 2.0 – Initial Production Release

MCOP Framework 2.0 – Initial Production Release (v2.0.0)

This marks the first stable, production-ready release of the Meta-Cognitive Optimization Protocol (MCOP) Framework 2.0, a deterministic recursive system for auditable AI orchestration and dynamic performance optimization.

Built around three interconnected meta-cognitive kernels—NOVA-NEO Encoder (provenance-preserving tensor hashing), Stigmergy v5 Resonance (cosine-based pheromone tracing with Merkle proofs), and Holographic Etch Engine (rank-1 confidence accumulation)—the framework enables reproducible dialectical synthesis loops with full tamper-evident lineage.

Ideal for research prototyping, decision support tools, and safety-critical applications requiring explainable self-optimization.

Highlights

• Production-Grade CI/CD: Modernized workflows with local composite actions for DRY setup, concurrency controls, matrix testing, and caching. Integrated GitHub CodeQL for multi-language (JavaScript/TypeScript + Python) static analysis.
• Enhanced Observability: Added Pino structured JSON logging with provenance hash correlation for auditing meta-cognitive cycles.
• Security Hardening: Resolved CodeQL alerts (e.g., sensitive logging), implemented custom trojan-source and malicious-module guards, pinned Docker base images, and achieved compliance with updated SECURITY.md, CODE_OF_CONDUCT.md, and CHANGELOG.md.
• Core Architecture Completion: Fully bootstrapped triad kernels with seed implementations, standalone M-COP v3.1 package deployment, and Next.js UI enhancements.
• Deployment Readiness: Docker Compose support for easy orchestration; GHCR publishing workflow prepared (with environment gating for approvals).

What's New (Key Changes)

• Comprehensive CI modernization and composite action refactoring
• Pino observability integration for structured logs
• CodeQL security scanning and alert resolutions
• Documentation gold compliance (CoC, Changelog, Security)
• Deterministic kernel implementations and provenance enhancements
• Dependency updates and configuration hardening

Deployment

• Run locally: docker compose up -d after configuring .env.
• Container images: Automatically published to GHCR on approved releases (requires "production" environment approval).

Thank you for interest in deterministic meta-cognition! Feedback welcome via Issues or Discussions.

MIT License – permissive for research and commercial use.