han-meta-cognitive-self-optimization-model-v1

Humanoid Meta-Cognitive Self-Optimization Model

This model enables humanoid agents to evaluate and optimize their own internal reasoning strategies during runtime.

Instead of only making decisions, the model monitors how decisions are made, identifies inefficiencies, and dynamically refines reasoning pathways.

Objective

To introduce meta-cognitive awareness for continuous performance optimization in decentralized humanoid systems.

Architecture

• Primary Reasoning Core
• Meta-Evaluation Layer
• Strategy Efficiency Analyzer
• Adaptive Reweighting Engine
• Self-Optimization Feedback Loop

Capabilities

• Runtime reasoning evaluation
• Strategy performance scoring
• Dynamic parameter reweighting
• Inefficiency detection
• Continuous self-optimization

Operational Mode

• Decision execution
• Internal strategy monitoring
• Efficiency scoring
• Parameter adjustment
• Feedback reinforcement

Technical Foundation

• Meta-learning framework
• Adaptive gradient modulation
• Runtime strategy embedding comparison
• Self-referential performance scoring

Designed For

High-autonomy humanoid ecosystems requiring continuous improvement without external retraining.

Part of

Humanoid Network (HAN)

License

MIT