arXiv:2606.02866v1 Announce Type: cross 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 weigh
The proliferation of multi-agent systems and the critical need for reliable data cleaning in AI development necessitates deeper understanding of their failure modes.
This research provides crucial insights into the limitations and effective applications of multi-agent debate in foundational AI processes like data cleaning, influencing future AI system design and deployment.
Our understanding of multi-agent debate's effectiveness in AI is refined, indicating that while it improves error detection, it can degrade generation quality, requiring more nuanced system architectures.
- · AI researchers focusing on robust agent design
- · Developers of AI debugging tools
- · Industries reliant on high-quality data input for AI
- · AI developers uncritically adopting multi-agent debate for all tasks
- · Systems susceptible to 'critique-induced confusion'
Increased focus on mechanisms to mitigate critique-induced confusion within multi-agent AI systems.
Development of specialized multi-agent architectures where debate is selectively applied for tasks like error detection but not generation.
Potential emergence of new AI safety considerations related to inter-agent communication and feedback loops.
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