Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics
arXiv:2606.05168v1 Announce Type: new Abstract: Training on synthetic data causes model collapse, but existing analyses treat this as single-chain degradation. In reality, the AI ecosystem involves cross-contamination: models ingest synthetic data from other models, produce new synthetic text, and contaminate shared corpora. We propose a bilayer coupled SIR/SIRS framework -- a phenomenological mean-field model treating data corpora and AI models as two interacting populations, each with susceptible, infected, and recovered compartments linked by cross-layer transmission. The SIRS variant (our
The proliferation of synthetic data and the increasing reliance on it for AI training highlights the urgency of understanding its long-term impact on model integrity and collective AI capabilities.
This research provides a foundational framework for understanding 'model collapse' not as isolated incidents but as an epidemiological phenomenon with cross-model contamination, critical for sustained AI development and trust.
The understanding of AI model degradation shifts from single-model issues to a systemic ecosystem challenge, necessitating new approaches to data provenance, model evaluation, and collective AI health.
- · AI ethics researchers
- · Data provenance solutions
- · High-quality data providers
- · AI model developers with robust validation
- · Generative AI companies relying heavily on synthetic data recycling
- · AI models trained exclusively on contaminated data
- · Platforms without data lineage tracking
- · Developers ignoring systemic contamination
Increased investment in data hygiene, synthetic data detection, and novel training methodologies.
Development of industry standards and regulatory frameworks for synthetic data usage and AI model certification based on data purity.
A 'data quality arms race' where access to and verification of high-quality, non-contaminated data becomes a critical strategic asset, potentially leading to a bifurcation of the AI landscape.
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