SOURCE-LINKED INTELLIGENCE
Certifying Model Upgrades with Slice-Wise Non-Regression and Incumbent Fallback
An updated model can improve an aggregate metric while degrading a slice that matters to a downstream user. We study checkpoint selection subject to non-regression tolerances relative to a retained incumbent. The central distinction is between failing to detect harm and certifying non-inferiority: the former can release harmful updates with high probability when evaluation is noisy. We give a reproducible release procedure that separates candidate search from independent, paired evaluation and returns the exact incumbent when certification fails. Applying established intersection-union and Lea
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-12T05:00:50.000Z
First collected: 2026-09-20T16:41:15.630Z. This is not the publication date.