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

Key points

The assumption that multi-agent LLM systems inherently broaden idea diversity on complex creative tasks has been only weakly validated.
When the underlying LLMs are homogeneous, different roles in a MAS often amplify shared pretraining biases rather than introducing true novelty, leading to computational inefficiency and early convergence.
A lack of idea diversity in exploratory domains can trap users in narrow solution spaces, create overconfidence in suboptimal answers, and suppress unconventional hypotheses.
We conducted a systematic empirical study using scientific research proposal generation as the task and evaluated diversity at three levels: model intelligence, agent cognition, and system dynamics.
We developed a general three-stage multi-agent ideation pipeline consisting of role instantiation, iterative deliberation using a specified topology, and proposal synthesis by an editor agent.
We quantified diversity with four complementary metrics, such as Vendi Score and Semantic Dispersion, and explored solutions by varying agent personas, group size, and communication topologies such as Nominal Group Technique (NGT) and Subgroups.

External research summaries. These are not HDATF publications or measured product results.