When Helping Hurts and How to Fix It: Multi-Agent Debate for Data Cleaning

Abstract: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.

27 pages, 4 figures, 12 tables. Includes appendix with full experimental results, prompt templates, and dataset statistics

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)

arXiv:2606.02866 [cs.AI]

(or arXiv:2606.02866v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.02866 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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