Human judges answer recursive AI debate leaf questions accurately enough to prevent systematic exploitation by debaters.

The claim is that, in recursive AI debate, the human judges who rule on the final narrow "leaf" questions are accurate enough that debaters cannot reliably exploit their mistakes. The theoretical case for debate assumes this. Formal results model the judge as an oracle whose answers define what counts as correct, so they show what debate achieves if leaf judgments can be trusted. They do not show that the judgments can be trusted.

The evidence weighs against the claim, though only indirectly. Random judge errors tend to cancel out. Systematic errors do not: a debater trained to win is rewarded for steering the debate toward leaves where judges are predictably wrong. Human judgment is known to be swayed by anchoring, order of presentation and fluent writing. Empirical debate studies have found accuracy gains with non-expert judges on narrow reading-comprehension tasks, but these involved weak debaters and no optimization against the judge. Reports from reinforcement-learning debate training describe debaters exploiting weak judges unless the setup constrains them.

Some protocol choices could make the claim true: keeping leaf questions narrow and simple, controlling which questions reach a human, training judges, using several judges, and cross-examination. No experiment has yet measured human accuracy at the leaves of a deep recursive debate against optimized debaters. Such a study would settle the question.