Not All Flips Are Conformity: Decomposing Stance Convergence in Multi-Agent LLM Debate
Xiqi Hao; Zengqing Wu; Yu-Xuan Qiu; Chuan Xiao; Ruiqi Xu; Shuyuan Zheng; Jianbin Qin · 2026 · arXiv
WASTE classifies this as Replication Failure · AI classification, approximate
A previously reported effect did not replicate here — verify it holds before you build on it.
Abstract (excerpt)
Multi-agent debate (MAD) is a promising strategy for improving LLM reasoning, but when agents converge on a shared answer, it is unclear whether that convergence reflects genuine deliberation or social compliance. We show that the conventional answer flip rate conflates three distinct mechanisms: spontaneous instability, stance-induced conformity, and reasoning-induced persuasion. Our three-source decomposition framework isolates each through controlled counterfactual conditions. In the primary MMLU-Pro setting, 37% of agent-question observations change under self-reflection alone, while robus
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Metadata source: arXiv