MAGNET enables decentralized creation of specialized AI models on consumer hardware using autonomous research and energy-efficient BitNet training

Autoresearch pipeline automates the full ML workflow including dataset generation, hyperparameter tuning, and error-driven model improvements

BitNet b1.58 ternary training allows CPU-native inference without GPUs, democratizing AI deployment on commodity hardware

DiLoCo-based distributed merging efficiently aggregates multiple domain-specialist models with reduced communication overhead

Validation shows strong results: video safety classification reaching 98.51% accuracy, cryptocurrency prediction improving from 41% to 54.9% hit rate

On-chain contribution tracking via HOOTi EVM chain creates transparent incentives for decentralized model development