Multi-agent systems (MAS) are increasingly used for open-ended idea generation, driven by the expectation that collective interaction will broaden the exploration diversity. However, when and why such collaboration truly expands the solution space remains unclear. We present a systematic empirical study of diversity in MAS-based ideation across three bottom-up levels: model intelligence, agent cognition, and system dynamics. At the model level, we identify a compute efficiency paradox, where stronger, highly aligned models yield diminishing marginal diversity despite higher per-sample quality. At the cognition level, authority-driven dynamics suppress semantic diversity compared to junior-dominated groups. At the system level, group-size scaling yields diminishing returns and dense communication topologies accelerate premature convergence. We characterize these outcomes as collective failures emerging from structural coupling, a process where interaction inadvertently contracts agent exploration and triggers diversity collapse. Our analysis shows that this collapse arises primarily from the interaction structure rather than inherent model insufficiency, highlighting the importance of preserving independence and disagreement when designing MAS for creative tasks. Our code is available at https://github.com/Xtra-Computing/MAS_Diversity.

翻译：多智能体系统（MAS）正越来越多地被用于开放式创意生成，其背后的期望是集体互动能够拓宽探索的多样性。然而，这种协作何时以及为何真正拓展了解决方案空间，目前尚不清楚。我们针对基于MAS的创意构思中的多样性，在三个自下而上的层面开展了一项系统的实证研究：模型智能、智能体认知和系统动力学。在模型层面，我们识别出一个计算效率悖论：更强、对齐度更高的模型在提供更高单样本质量的同时，其边际多样性却递减。在认知层面，与初级主导的群体相比，权威驱动的动态机制会抑制语义多样性。在系统层面，群体规模扩大会带来收益递减，而密集的通信拓扑则加速了过早收敛。我们将这些结果定性为由结构耦合引发的集体失效——一种互动过程，其中交互无意中压缩了智能体的探索范围，触发了多样性崩溃。我们的分析表明，这种崩溃主要源于交互结构，而非模型固有的能力不足，这凸显了在设计用于创造性任务的MAS时，保留独立性与分歧的重要性。我们的代码可在 https://github.com/Xtra-Computing/MAS_Diversity 获取。