Notes on algebraic perspective on neural network conformal theories

Sam Leutheusser; Joydeep Naskar

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arXiv · 2609.37074

Notes on algebraic perspective on neural network conformal theories

Sam Leutheusser·Joydeep Naskar

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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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Sam Leutheusser, Joydeep Naskar. 2026-09-29. Notes on algebraic perspective on neural network conformal theories. https://arxiv.org/abs/2609.37074

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