Stability and Diversity of Networked Self-Consuming Generative Ecosystems
The authors created a graph-based framework to analyze how multiple generative models interact when they continuously train using synthetic data generated by themselves and their peers. They established mathematical conditions for stability, showing that network topology and synthetic data consumption can cause diversity to collapse over time. This provides a tool to predict risks like instability and homogenization in multi-model systems.