Multi-agent discussion gains less when dissent is withheld
Multi-agent systems of LLMs add discussion to majority voting and are therefore expected to be more capable. However, empirical reports conflict on whether discussion improves accuracy or leads to an incorrect consensus. Here, we introduce a parsimonious model that explains when discussion improves accuracy and when it ends in an incorrect consensus, built from four behaviors repeatedly observed in LLM agents: (1) withholding dissent, (2) internalizing a stated answer, (3) reconsidering after seeing dissent, and (4) correcting toward the correct answer. The model shows that discussion can overturn an incorrect initial majority only when the withholding rate $c$ is below a critical rate $c...
| Authors | Chand Sahil Mansuri, Xin Wang, Mengying Li et al. |
|---|---|
| Primary category | physics.soc-ph |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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