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Model Collapse

Date of Addition

17 October 2025

A degenerative phenomenon where AI models trained on recursively generated synthetic data progressively lose diversity, accuracy, and quality over successive training iterations, ultimately producing increasingly homogeneous and corrupted outputs. This feedback loop occurs when models consume their own generated content or outputs from similar models as training data, causing statistical distributions to narrow and tail events to disappear from learned representations. Model collapse poses existential risks to the long-term viability of AI systems as synthetic content proliferates across the internet, contaminating datasets used for future model training.

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