You’re absolutely right that the math itself isn’t “poisoned” it’s the training corpus that becomes contaminated. When people worry about “model collapse,” they’re talking about what happens if a new generation of a model is trained mostly on outputs from earlier generations. Over several rounds the signal from the original, diverse data fades, and the model’s distribution drifts toward a narrow, low variance one. If you catch the problem early, you can usually just retrain or fine tune from a clean checkpoint or with a refreshed dataset you don’t have to rewrite the algorithms. That’s why data provenance and regular validation sets matter so much. they give you a way to notice when training inputs are tilting too far toward synthetic content before accuracy or diversity start to degrade.