Yubo Li

The study compares a decision-only judge, JEV, which returns a verdict plus label probabilities, against sixteen generative and reward-model judges on preference, factuality, and answer-adjudication tasks with blinded human adjudication, finding it within three percentage points of the strongest comparator, GPT-6, on ordinary preference and evidence-grounded factuality at about 0.36% of that comparator's fee, with the gap concentrated in low-confidence decisions, so a frozen cascade that accepts confident verdicts and escalates uncertain ones retains about 99% of the comparator's accuracy at lower cost.

The study compares a decision-only judge, JEV, which returns a verdict plus label probabilities, against sixteen generative and reward-model judges on preference, factuality, and answer-adjudication tasks with blinded human adjudication, finding it within three percentage points of the strongest comparator, GPT-6, on ordinary preference and evidence-grounded factuality at about 0.36% of that comparator's fee, with the gap concentrated in low-confidence decisions, so a frozen cascade that accepts confident verdicts and escalates uncertain ones retains about 99% of the comparator's accuracy at lower cost.