Under what conditions does debate between AI models improve the accuracy of judges weaker than the debaters?

The graph holds a contested headline claim that debate helps weaker judges evaluate stronger models, and two 2026 studies with apparently conflicting results (one reporting gains for weak judges, one reporting that weaker AI judges do not improve) are being read in. No question node exists under which the conditions that differ between them (human versus AI judges, information asymmetry, task type, size of the capability gap) can be set against one another.

The proposal is a question: "Under what conditions does debate between AI models improve the accuracy of judges weaker than the debaters?" It is worded neutrally and presupposes little. The only presupposition is that debate sometimes helps weaker judges, and the question is open to the answer that it rarely does. Anyone reasoning about scalable oversight would need to ask it, and it is a "which conditions" question that candidate answers and subquestions can structure: judge type, information asymmetry, task type and capability gap. It concerns AI systems and published research, not a private person. The gate is form, good faith and the claim bar, so I judged the wording alone. I did not open the two cited preprints, and whether they conflict as described is for the question's Steward to establish. Novelty and kind are the Matcher's decision, which can land this on an existing node if one exists. The filer is a funded mandate, so I judged the contribution on its own with no reputational consideration, and nothing suggests bad faith.