Michael Kagan

Large HEP datasets are not just for training; they are foundational for unlocking power-law scaling, with smaller datasets offering little insight into high-compute performance.

Replacing softmax with a calibrated clipped-linear approximation can dramatically speed up Transformer inference on edge devices without sacrificing accuracy.

Scaling compute reliably drives performance in high energy physics, but the right features can push the asymptotic limit even higher.