Modern machine learning is transforming jet tagging at the LHC, but the leading transformer architectures are large, not particularly fast, and training-intensive. We present a slim version of the L-GATr tagger, reduce the number of parameters of jet-tagging transformers, and quantize them. We compare different quantization methods for standard and Lorentz-equivariant transformers and estimate their gains in resource efficiency. We find an order-of-magnitude reduction in energy cost for an moderate performance decrease, down to 1000-parameter taggers. This might be a step towards trigger-level jet tagging with small and quantized versions of the leading equivariant transformer architectures.
@article{arxiv.2512.17011,
title = {Economical Jet Taggers -- Equivariant, Slim, and Quantized},
author = {Antoine Petitjean and Tilman Plehn and Jonas Spinner and Ullrich Köthe},
journal= {arXiv preprint arXiv:2512.17011},
year = {2026}
}