A new arXiv paper introduces PoQ-Judge, a framework for evaluating output quality in decentralized LLM inference networks.
The system trains reference-free judge models to score query-output pairs without ground-truth answers. It also uses cascade evaluation to reduce cost, reporting a large cost reduction with only modest quality loss.
That matters for decentralized inference because networks need proof-of-quality mechanisms that are lightweight enough to run at scale.