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This research establishes a rigorous mathematical bridge between neural network ensembles and conformal field theory, demonstrating how specific neuron architectures can recover known physical models like the 4d Maxwell CFT in high-dimensional limits. By constructing spinning fields directly from networks using embedding formalism, the study advances our understanding of the fundamental statistical mechanics underlying deep learning structures beyond simple scalar approximations.
Spinning Conformal Correlators from Neural Networks
arXiv:2608.15001v1 Announce Type: cross Abstract: We construct spinning conformal fields from neural networks and the embedding formalism, computing their two-, three- and four-point functions in examples, building on scalar conformal field techniques introduced in \cite{Halverson:2024axc}. For a particular ensemble of i.i.d. neurons we recover the 4d Maxwell CFT in the infinite-width limit.
Abstract:We construct spinning conformal fields from neural networks and the embedding formalism, computing their two-, three- and four-point functions in examples, building on scalar conformal field techniques introduced in \cite{Halverson:2024axc}. For a particular ensemble of i.i.d. neurons we recover the 4d Maxwell CFT in the infinite-width limit.
High Energy Physics - Theory (hep-th); Machine Learning (cs.LG)
arXiv:2608.15001 [hep-th]
(or arXiv:2608.15001v1 [hep-th] for this version)
https://doi.org/10.48550/arXiv.2608.15001 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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