arxiv
PublishedJune 5, 2026 at 4:00 AM
Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics
Publisher summary· verbatim
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 c
Stay posted· Newsletter
A 5-min weekly brief — top movers, price watch, story of the week.
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivSFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning19harxivOptical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning19harxivDynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models19harxivTemporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents19hThe Bubble Brief
WEEKLYRead AI insights every Tuesday — top movers, new releases, story of the week.
Originally published on arxiv ↗