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Reconstructing conformal field theoretical compositions with Transformers

arXiv 2605.01072 ↗First posted 1 May 2026hep-th cs.LGcited by 0

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Abstract (arXiv)

We study the use of transformers to reconstruct the compositions of tensor products of two-dimensional rational conformal field theories (RCFTs) based on their low-energy spectra. The task is challenging due to its combinatorial nature. The constituent theories are characterized by their central charges and affine Lie algebra labels. We achieve 98% accuracy in recovering the constituents of tensor products theories constructed from Wess-Zumino-Witten models. We further demonstrate that our method generalizes to CFTs with larger central charge and unseen classes of RCFTs by adding a small number of out-of-domain examples. Our results show that transformers are effective at this task and point towards a new tool for bulk reconstruction in AdS/CFT.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 55 references that are in the map. First posted on arXiv 1 May 2026.

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References · 16

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  4. 2020Language Models are Few-Shot Learnerscited by 32,199
  5. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  6. 2014How transferable are features in deep neural networks?cited by 1,216
  7. 2019Recurrent Neural Networks (RNNs): A gentle Introduction and Overviewcited by 68
  8. 2024PatternBoost: Constructions in Mathematics with a Little Help from AIcited by 32
  9. 2024Global Lyapunov functions: a long-standing open problem in mathematics, with symbolic transformerscited by 29
  10. 2024Transforming the Bootstrap: Using Transformers to Compute Scattering Amplitudes in Planar N = 4 Super Yang-Mills Theorycited by 17
  11. 2022Simplifying Polylogarithms with Machine Learningcited by 14
  12. 2024Learning the Simplicity of Scattering Amplitudescited by 5
4 more
  1. 2024Machine-learning emergent spacetime from linear response in future tabletop quantum gravity experimentscited by 5
  2. 2025Transforming Calabi-Yau Constructions: Generating New Calabi-Yau Manifolds with Transformerscited by 5
  3. 2024Learning BPS Spectra and the Gap Conjecturecited by 3
  4. 2026Descending into the Modular Bootstrapcited by 2

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