Stability and Diversity of Networked Self-Consuming Generative Ecosystems

arXiv:2610.09409v1 Announce Type: cross Abstract: The widespread deployment of generative AI has made it increasingly difficult to distinguish synthetic content from real data. Consequently, synthetic data is inevitably incorporated into the training pipelines of future model generations, forming a self-consuming training loop. Prior work has studied the effects of such recursive self-consuming training, but analyses have largely been limited to isolated models, where a model consumes only its own synthetic data, or to simplified interactions between two models. This paper takes a first step toward understanding networked self-consuming generative models, in which multiple models consume synthetic data generated by one another through complex interaction pathways. We introduce a theoretical framework representing models as nodes in a directed, weighted graph, with edge weights governing the flow of synthetic data among models. Using this framework, we analyze the long-term behavior of networked models under retraining dynamics, establishing conditions for convergence and characterizing the resulting fixed points. We further investigate how the system's long-term stability and diversity are shaped by each model's access to real data, cross-model data consumption, and the structure of the interaction graph.

Theoretical framework for networked models

This is theoretical groundwork on model collapse risk in interconnected AI training ecosystems, not a report of observed societal harm. It implies a real structural risk to future model quality and diversity as synthetic data proliferates, but offers no measured real-world impact.

The paper identifies a critical problem where generative AI's output, indistinguishable from real data, will inevitably contaminate future training sets. This 'self-consuming' process, especially in networked systems, poses a significant risk of reducing data diversity and system stability over time. The research focuses on analyzing conditions for convergence and characterizing the resulting fixed points, which implies a potential for undesirable equilibria.

The paper presents a theoretical framework for analyzing the behavior of networked generative AI models. It identifies factors that influence stability and diversity, which are crucial for the long-term development of AI. The focus on understanding and improving AI systems suggests a positive implication for human flourishing.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   eedeafaf-0ba0-4afc-8eb1-9315b041b1de
           https://arxiv.org/abs/2610.09409
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EVIDENCE   2 extracted excerpts
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MODEL RUN  4 runs, methodology 1.0.1
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SCORE      -12  (Mixed or uncertain)
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CONFIDENCE 70%