When does multi-agent debate help data cleaning, and when does it hurt? Across three benchmarks, four model families, and over 6,000 task-condition pairs, we find debate's effect reverses sign: it degrades generation across all four models (-1.6 to -15.5pp) through critique-induced confusion (CIC), hallucinated Critic feedback that the Generator accepts uncritically, yet improves error detection (+27.4pp F1, d=1.0). We derive a debate benefit condition: debate helps when the probability of rescuing a wrong output (Critic verification odds weighted by fixability) exceeds the probability of destroying a correct one. A factorial experiment proves adversarial separation is essential: self-verification with identical tools fails, while a separate Critic with code-execution grounding and evidence-gated generation produces the first debate configuration to significantly exceed single-agent on a generative task (+5.3pp, p<0.05). The condition correctly predicts all nine task types and generalizes with zero false positives across 19 published comparisons in seven domains.

翻译：在什么情况下，多智能体辩论有助于数据清洗，又在何时适得其反？通过在三个基准、四个模型家族和超过6000个任务-条件组合上的实验，我们发现辩论的效果发生了逆转：由于批评引发的混淆（CIC），即生成器不加批判地接受批评者提供的幻觉式反馈，导致所有四个模型的生成性能下降（-1.6至-15.5个百分点），然而错误检测能力却得到提升（F1分数提高27.4个百分点，d=1.0）。我们推导出一个有益辩论的条件：当挽救错误输出的概率（基于可修正性的批评者验证几率）超过破坏正确输出的概率时，辩论才有益。一项析因实验证明，对抗性分离至关重要：使用相同工具进行自我验证的效果不佳，而采用独立批评者结合代码执行依据和证据门控生成，则首次产生了在生成任务上显著优于单智能体的辩论配置（提高5.3个百分点，p<0.05）。该条件正确预测了所有九种任务类型，并在七个领域的19项已发表对比中实现了零假阳性的泛化效果。