kaal:claim:7598518-001

Claim. When intelligence models repeatedly learn from data that earlier intelligence models produced, the rare cases disappear first: the unusual facts, minority patterns and edge cases that make data valuable. This is what model collapse names.

Type. mechanism · Support. argued

Holds when

• Applies to repeated generations of models trained on earlier models' output.
• The quote is the paper's own statement of the phenomenon it tests against, not a result of this study.

Source quote

Intelligence models increasingly learn from data that earlier Intelligence models produced. When that loop repeats over generations, the rare cases disappear first: the unusual facts, minority patterns, and edge cases that make data valuable. This is known as model collapse.

From. Wulf A. Kaal, Can Reputation Prevent Model Collapse? A Preregistered Test of Reputation-Weighted Data Filtering (2026). Full text.

Cite as. Wulf A. Kaal, Can Reputation Prevent Model Collapse? A Preregistered Test of Reputation-Weighted Data Filtering (2026). SSRN: https://ssrn.com/abstract=7598518

Verify. sha256 of source PDF cd22d148b2b55c2e27f087dcaa61cdf44ad7022b400b355c31a9cd7e43a31749 at https://wulfkaal.com/papers/7598518.pdf

Content hash. 154e92815019dcdb2642c550096538f0098d33575a6ecca424e32aec64e74284