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GENOME (Global Self-Evolving AI Mind)

The central AI intelligence running across validator nodes in the blockchain network, representing collective knowledge and continuous learning capability.

Key Features​

  • Self-learning through reinforcement learning, game theory, and calibrated forecasts
  • Decentralized execution across validator nodes
  • Privacy-preserving federated learning aggregation
  • Multi-domain expertise: healthcare, finance, disaster prediction, navigation

Technical Components​

Model Architecture​

  • Base: Large-scale transformer with domain-specific heads
  • Federated Learning Core: Aggregates encrypted gradients from AIA agents
  • Reinforcement Learning Module: RLHF (Reinforcement Learning from Human Feedback)
  • Game-Theoretic Optimizer: Balances competing objectives (accuracy vs. privacy)
  • Calibration Layer: Ensures well-calibrated probabilistic predictions

Training Pipeline​

  1. Collect encrypted model updates from AIA agents via smart contracts
  2. Apply secure multi-party computation (MPC) to aggregate gradients
  3. Update global model parameters with differential privacy guarantees
  4. Validate updates through consensus mechanism
  5. Distribute updated model to validator nodes
  6. Optionally push selective updates to AIA agents (pull-based)

Storage & State Management​

  • Model Weights: Distributed across IPFS with blockchain pointers
  • Training Metadata: On-chain storage (epochs, loss metrics, version hashes)
  • Contribution Records: Smart contract ledger of all client contributions
  • Model Checkpoints: Versioned snapshots for rollback capability

Computational Resources​

  • On-Chain: Smart contract coordination, validation, incentive distribution
  • Off-Chain: Heavy computation on validator nodes with proof submission
  • Hybrid: Critical aggregation steps use verifiable computation (zk-SNARKs)