Epistemic diversity across language models mitigates knowledge collapse

ArXi:2512.15011v2 Announce Type: replace-cross As artificial intelligence (AI) becomes widely used, concerns are growing that model collapse could lead to knowledge collapse, i.e. a degradation to a narrow and inaccurate set of ideas. Prior work has nstrated single-model collapse, defined as performance decay in an AI model trained on its own outputs. Inspired by ecology, we ask whether increasing AI ecosystem diversity (i.e., the number of distinct models) can mitigate such collapse. To study the effect of diversity on model performance, we extend the single-model approach by segmenting the.