NOYA Infrastructure Architecture @NetworkNoya Modular Design NOYA utilizes a core modular design to ensure future adaptability. Its architecture divides key functions into independent modules: data oracle, training module, behavior logging, execution layer, verification layer, cross-chain bridging, and messaging. Each module has clear responsibilities: Data oracle: Currently, an optimistic verification mechanism is used for data input, and a fraud proof system is under development to ensure data authenticity and integrity. Training module: Managed by an intelligent agent architecture, the training process is flexible and adaptable, but requires that historical model result proofs be stored on IPFS via NOYA pre-submitted inputs. Behavior logging: All operations and proofs are uploaded to IPFS to ensure traceability and verifiability. Execution layer: Keepers are responsible for transaction verification and execution, maintaining the integrity of network operations. Verification layer: Watchers monitor blockchain behavior in real time, providing a second line of defense. Cross-chain bridging: The modular bridging solution supports multiple LiFi bridge options to meet diverse cross-chain needs. Message Communication: A secure communication protocol based on the integration of LayerZero and Polyhedra ensures reliable cross-chain message transmission. These modules work together to build NOYA's highly scalable and secure infrastructure, continuously addressing the evolving needs of blockchain technology. On-Chain AI Model Stabilization Solution (ZKML) The Zero-Knowledge Machine Learning Revolution AI technology has become a transformative force for improving the efficiency of fields like decentralized finance (DeFi), but on-chain AI integration continues to face the core challenge of prohibitively high blockchain transaction computing costs. To address this, NOYA, in collaboration with Modulus Labs and EZKL, has innovatively developed a ZKML (Zero-Knowledge Machine Learning) architecture based on zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Arguments), creating a new paradigm for on-chain AI applications. ZKML's core breakthroughs lie in: - Trustless on-chain verification of model outputs - Complete privacy protection of model weights This dual feature not only safeguards the intellectual property competitive advantage of model designers, but also completely eliminates additional trust assumptions. Its technical principles can be summarized as follows... @KaitoAI #Kaito