Meta-Optimized Continual Adaptation for coastal climate resilience planning with zero-trust governance guarantees

Short summary

Researcher presents MOCA, a framework combining meta-learning, continual learning, and cryptographic verification for coastal resilience planning. The system learns its own optimization strategy while preventing catastrophic forgetting and ensuring all model updates remain auditable. Prototype showed 200-300ms governance overhead on simulated 50km coastline scenarios.

• •MOCA framework solves three simultaneous challenges: continual adaptation to streaming data, meta-optimization of the learning process itself, and zero-trust cryptographic verification of all model updates
• •RNN-based meta-optimizer learns task-specific adaptation policies with memory for recurring patterns; EWC with learned importance weights prevents catastrophic forgetting
• •Decentralized governance via Merkle tree constraint verification ensures every update respects safety constraints (e.g., evacuation time limits) before deployment

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