mlsanityData to 7 Oct 2026

BitHEP -- The Limits of Low-Precision ML in HEP

arXiv 2504.03387 ↗First posted 4 Apr 2025hep-ph cs.LG hep-excited by 1

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Abstract (arXiv)

The increasing complexity of modern neural network architectures demands fast and memory-efficient implementations to mitigate computational bottlenecks. In this work, we evaluate the recently proposed BitNet architecture in HEP applications, assessing its performance in classification, regression, and generative modeling tasks. Specifically, we investigate its suitability for quark-gluon discrimination, SMEFT parameter estimation, and detector simulation, comparing its efficiency and accuracy to state-of-the-art methods. Our results show that while BitNet consistently performs competitively in classification tasks, its performance in regression and generation varies with the size and type of the network, highlighting key limitations and potential areas for improvement.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 97 of its 215 references that are in the map. First posted on arXiv 4 Apr 2025.

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97 of 215 references.

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