MAGNET：通过去中心化自研究和BitNet训练实现自主专家模型生成

MAGNET (Model Autonomously Growing Network) is a decentralized system designed for the autonomous generation, training, and serving of domain-expert language models on commodity hardware. It integrates four key components: (1) autoresearch, an autonomous ML research pipeline that automates dataset generation, hyperparameter exploration, evaluation, and error-driven iteration; (2) BitNet b1.58 ternary training, enabling CPU-native inference via bitnet.cpp without GPU hardware; (3) DiLoCo-based distributed merging for communication-efficient aggregation of domain specialists; and (4) on-chain contribution tracking on the HOOTi EVM chain. The effectiveness of autoresearch is validated through three case studies, including video safety classification with a balanced accuracy of 0.9287.