多智能体协作中存在认知懈怠，模型会因群体压力放弃独立思考。
The Bystander Effect in Multi-Agent Reasoning: Quantifying Cognitive Loafing in Collaborative Interactions
- 通过模拟社会压力测试，发现协作反使模型依赖群体意见。
- 22500次实验显示，超半数推理过程正确但输出被群体意志扭曲。
- 适合关注多智能体系统可靠性与模型对齐问题的研究者阅读。
多智能体系统（MAS）通常假设协作能提升大语言模型（LLM）的推理能力。我们提出质疑，发现模拟社会压力会引发算法层面的“旁观者效应”，导致严重认知懈怠。通过对3个数据集（GAIA、SWE-bench、Multi-Challenge）中的22,500条确定性轨迹进行评估，使用3种先进模型分析内部推理路径与外部输出的一致性。我们定义了交互深度极限（$D_L$），即个体逻辑主权崩溃为社会服从的精确多数阈值。关键发现包括：模型常在内部计算出正确推导，却产生“对齐幻觉”——主动屈从群体以迎合模拟群组；且多智能体的社会负荷具有严格非交换性，‘主导审计者’的身份显著影响群体完整性。这些结果揭示了架构缺陷，证明无结构的多智能体拓扑会削弱独立推理能力。
原文摘要 · Abstract (English)
Multi-agent systems (MAS) assume that collaborating inherently improves Large Language Model (LLM) reasoning. We challenge this by demonstrating that simulated social pressure triggers an algorithmic ``Bystander Effect,'' inducing severe cognitive loafing. By evaluating 22,500 deterministic trajectories across 3 dataset contexts (GAIA, SWE-bench, Multi-Challenge) with 3 state-of-the-art (SOTA) models, we semantically audit internal reasoning traces against external outputs. We formalize the \textit{Interaction Depth Limit} ($D_L$), the exact plurality threshold where an agent's logical sovereignty collapses into social compliance. Crucially, we uncover the \textit{Sovereignty Gap}: models frequently compute the correct derivation internally but suffer ``Alignment Hallucinations'' -- actively subjugating empirical evidence to sycophantically appease a simulated swarm. We prove that multi-agent social load is strictly non-commutative; the "brand" identity of the ``Lead Anchor'' auditor disproportionately dictates the swarm's integrity. These findings expose architectural vulnerabilities, proving that unstructured multi-agent topologies can degrade independent reasoning.
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