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

2026-04-20 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A systematic empirical study investigates diversity collapse in multi-agent LLM systems designed for open-ended idea generation. The research identifies a "compute efficiency paradox" at the model level, where more powerful, highly aligned models produce less marginal diversity despite higher individual sample quality. At the agent cognition level, authority-driven group dynamics are shown to suppress semantic diversity compared to groups with junior-dominated structures. Furthermore, the study finds that increasing group size yields diminishing returns and dense communication topologies accelerate premature convergence at the system level. These issues are characterized as collective failures stemming from structural coupling, where agent interaction inadvertently contracts exploration and triggers diversity collapse, primarily due to interaction structure rather than model limitations.

Key takeaway

For AI Engineers designing multi-agent LLM systems for creative tasks, you should prioritize interaction structures that preserve agent independence and foster disagreement. Avoid over-aligning models or creating dense communication networks, as these can inadvertently lead to diversity collapse and diminish the collective's creative output. Focus on enabling diverse perspectives rather than simply increasing model power or group size.

Key insights

Multi-agent LLM systems for ideation suffer diversity collapse due to interaction structures, not model insufficiency.

Principles

• Stronger models yield diminishing diversity returns.
• Authority-driven groups suppress semantic diversity.
• Dense communication accelerates convergence.

Method

The study empirically analyzed diversity in multi-agent LLM ideation across model intelligence, agent cognition, and system dynamics to identify factors leading to diversity collapse.

In practice

• Prioritize agent independence in creative MAS.
• Design for disagreement in multi-agent interactions.
• Avoid overly dense communication topologies.

Topics

• Multi-Agent Systems
• LLM Ideation
• Diversity Collapse
• Structural Coupling
• Compute Efficiency Paradox

Code references

• Xtra-Computing/MAS_Diversity

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.