PreprintCC BY 4.0arXiv
Notes on algebraic perspective on neural network conformal theories
Abstract
Neural network conformal theories were first introduced in \cite{Halverson:2024axc} and exact two-, three- and four-point correlators were computed from an ensemble of infinite-width neural networks. In particular, the four-point correlator of any scalar operator satisfied crossing-symmetry and conformal block decomposition across different channels were in agreement without any constraint on moments. An infinite tower of degenerate operators with the same scaling dimension were found. In this note, we take a closer look at the structure of correlators and relation between operators. We also layout a recipe to construct the algebra of the theory.
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BibTeX
@misc{leutheusser2026notes,
title = {{Notes on algebraic perspective on neural network conformal theories}},
author = {Sam Leutheusser and Joydeep Naskar},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.37074},
eprint = {2609.37074},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.37074},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Notes on algebraic perspective on neural network conformal theories
AU - Sam Leutheusser
AU - Joydeep Naskar
PY - 2026
DA - 2026/09/29
PB - arXiv
DO - 10.48550/arxiv.2609.37074
UR - https://arxiv.org/abs/2609.37074
AB - Neural network conformal theories were first introduced in \cite{Halverson:2024axc} and exact two-, three- and four-point correlators were computed from an ensemble of infinite-width neural networks. In particular, the four-point correlator of any scalar operator satisfied crossing-symmetry and conformal block decomposition across different channels were in agreement without any constraint on moments. An infinite tower of degenerate operators with the same scaling dimension were found. In this note, we take a closer look at the structure of correlators and relation between operators. We also layout a recipe to construct the algebra of the theory.
ER -