LLMs assigned different personas for multi-agent systems tend to collapse into stereotyped behaviors rather than maintaining genuine diversity, even when individually accurate—a critical issue for applications requiring population heterogeneity.
When LLMs are assigned different personas for multi-agent simulations, they often converge into similar behaviors instead of staying diverse—a problem called Persona Collapse. Researchers created metrics to measure this (Coverage, Uniformity, Complexity) and found that 10 LLMs fail to maintain distinct personalities, instead falling back on coarse stereotypes.