Diversity Collapse in Multi-Agent LLM Systems: Structural Coupling and Collective Failure in Open-Ended Idea Generation

2026-04-20

AI Summary

This study addresses the persistent issue of diversity collapse in multi-agent large language models during open-ended creative generation, which severely constrains collective exploration yet remains mechanistically unclear. Adopting a structural coupling perspective, the work systematically investigates how interaction architectures precipitate diversity loss, demonstrating that the root cause lies in system design rather than inherent model limitations. Through multi-level controlled experiments, the authors examine the effects of model capability, role authority, group size, and communication topology, revealing that strong alignment yields diminishing diversity returns, authority dominance suppresses semantic variety, and dense communication accelerates premature convergence. The findings underscore the critical importance of preserving agent independence and constructive disagreement in creative tasks, highlighting the decisive role of structural design in sustaining diversity.

Technology Category

• Multiagent Systems: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

Abstract

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.

Problem

• Diversity Collapse
• Multi-Agent Systems
• Open-Ended Idea Generation
• Structural Coupling
• Collective Failure

Innovation

• diversity collapse
• structural coupling
• multi-agent systems
• open-ended ideation
• collective failure