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arXiv Machine Learning By Xiukun Wei, Yang Zhang, Xueru Zhang

Stability and Diversity of Networked Self-Consuming Generative Ecosystems

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The paper examines how multiple generative models can form a networked self‑consuming loop, where each model consumes synthetic data produced by others. It introduces a graph‑based theoretical framework that models these interactions as directed, weighted edges and uses it to analyze long‑term retraining dynamics, identifying conditions for convergence and the nature of fixed points. The study also explores how real‑data access, cross‑model consumption, and graph structure influence the system’s stability and diversity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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