MAGNET integrates four components: autoresearch that automates dataset generation, hyperparameter exploration, evaluation, and error-driven iteration; BitNet b1.58 ternary training enabling CPU-native inference via bitnet.cpp without GPU hardware; DiLoCo-based distributed merging for communication-efficient aggregation of domain specialists; and on-chain contribution tracking on the HOOTi EVM chain (arXiv:2603.25813).

Validation occurred through three autoresearch case studies: video safety classification improving balanced accuracy from 0.9287 to 0.9851, cryptocurrency directional prediction raising hit rate from 41% to 54.9%, and BitNet hyperparameter optimization across a 10-phase sweep reducing validation loss by 16.7% (arXiv:2603.25813).

The abstract states the system operates across commodity hardware and focuses on domain-expert language models (arXiv:2603.25813).