LATIDIA · Ciberseguridad

Werracle: Oráculos reflejos de IA intra-bloque Sub-Cent y disyuntores de préstamo flash para contratos inteligentes EVM

arXiv: 2609.30719v1Tipo de anuncio: nuevo Resumen: La inteligencia artificial (IA) contemporánea en la cadena se encuentra con una memoria Von Neumann intratable y un muro de latencia. Almacenamiento estático de matrices de peso neuronal de punto flotante INSID

La noticia

arXiv:2609.30719v1 Announce Type: new Abstract: Contemporary on-chain artificial intelligence (AI) encounters an intractable Von Neumann memory and latency wall. Storing static floating-point neural weight matrices inside Ethereum Virtual Machine (EVM) storage costs millions of gas, rendering direct on-chain inference impossible. While Zero-Knowledge Machine Learning (ZK-ML) offloads matrix tensor multiplications to off-chain provers, it introduces fatal constraints: 10 to 300 seconds of SNARK proving latency and 250,000 to 500,000 gas per proof verification. Because decentralized finance (DeFi) exploits - such as uncollateralized flash-loan attacks, predatory sandwich MEV, and toxic loss-versus-rebalancing (LVR) flow - occur atomically inside a single block, ZK-ML oracles cannot react in time. Here, we present Werracle, a production-grade,