Drift and selection in LLM text ecosystems

This paper introduces a mathematical framework to analyze the recursive process where AI-generated text re-enters and shapes the public record from which LLMs learn. It distinguishes between "drift," which removes rare forms through unfiltered reuse, and "selection," which filters content based on criteria like quality, showing normative selection preserves deeper linguistic structures.