End-to-end transformers can match or beat graph neural networks on complex physics tasks while being dramatically faster and more memory-efficient—showing that careful architecture design beats multi-stage pipelines.
HEPTv2 is an end-to-end transformer model that reconstructs particle tracks from detector measurements at the Large Hadron Collider. It uses locality-sensitive hashing and sectorized decoding to achieve 98.6% accuracy while running 7-50x faster than competing approaches, making it practical for real-time physics experiments.