Ilia Shumailov

Machine-learning security researcher (Oxford, later Google DeepMind) and first author of "AI models collapse when trained on recursively generated data" (Nature, 2024; preprint title "The Curse of Recursion"), the paper that named and empirically demonstrated model collapse.

Matters to this vault as the methodological ancestor of two separate claim clusters already resting on his work without, until now, a page of his own: the general model-collapse literature (claim-model-collapse-recursive-training-erases-distribution-tails) and Wang et al.'s bias-amplification experiment, which explicitly builds its ten-generation iterated-fine-tuning design "following Shumailov et al. (2024)."

References

• claim-model-collapse-recursive-training-erases-distribution-tails
• claim-wang-2024-bias-amplification-persists-independent-of-model-collapse
• Capture: 10-inbox/raw/2026-09-14-has-any-study-run-a-genuine-multi-generation.md