Resampling Reality: When Your AI Needs to See the Same Thing Twice
Twice. That is usually not a word deployment teams enjoy hearing. Running the same model twice sounds like paying twice for the same answer, which is not the sort of efficiency story anyone proudly puts in a cloud-cost review. But the paper behind today’s article makes a more interesting claim: sometimes the second inference is not the same inference. It is the same underlying reality shown to the model through a different, mathematically equivalent view. If those views preserve the structure of the problem but make the model’s mistakes partly decorrelate, then combining the answers can reduce inference error without retraining, enlarging the network, or begging the infrastructure budget for mercy. ...