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arXiv · 2604.18673

Neural Networks Reveal a Universal Bias in Conformal Correlators

Kausik Ghosh·Sidhaarth Kumar·Vasilis Niarchos·Andreas Stergiou
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Abstract

We propose that simple neural networks (NNs) trained on crossing symmetry can reconstruct conformal correlators restricted to a line to remarkable accuracy. The input is minimal: an external scaling dimension, a spectral gap, and the value of the correlator at a single point. We present evidence across a wide range of conformal theories and dimensions, for both four-point and thermal two-point functions. We attribute these observations to the spectral bias of gradient-based NN training, which appears to align with an intrinsic smoothness property of conformal field theory. This suggests a novel variational principle for conformal correlators and opens a path towards a powerful new computational framework for non-perturbative quantum field theory.

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Kausik Ghosh, Sidhaarth Kumar, Vasilis Niarchos, Andreas Stergiou. 2026-04-20. Neural Networks Reveal a Universal Bias in Conformal Correlators. https://arxiv.org/abs/2604.18673

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