mlsanityData to 14 Oct 2026

Transforming the Bootstrap: Using Transformers to Compute Scattering Amplitudes in Planar N = 4 Super Yang-Mills Theory

arXiv 2405.06107 ↗First posted 9 May 2024cs.LG cs.SC hep-phcited by 18

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Abstract (arXiv)

We pursue the use of deep learning methods to improve state-of-the-art computations in theoretical high-energy physics. Planar N = 4 Super Yang-Mills theory is a close cousin to the theory that describes Higgs boson production at the Large Hadron Collider; its scattering amplitudes are large mathematical expressions containing integer coefficients. In this paper, we apply Transformers to predict these coefficients. The problem can be formulated in a language-like representation amenable to standard cross-entropy training objectives. We design two related experiments and show that the model achieves high accuracy (> 98%) on both tasks. Our work shows that Transformers can be applied successfully to problems in theoretical physics that require exact solutions.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 24 of its 61 references that are in the map. First posted on arXiv 9 May 2024.

Builds on

24 of 61 references.

References · 24

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
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  4. 2020Language Models are Few-Shot Learnerscited by 32,419
  5. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  6. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  7. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
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12 more
  1. 2018Self-Attention with Relative Position Representationscited by 1,120
  2. 2023The Impact of Positional Encoding on Length Generalization in Transformerscited by 327
  3. 2019Deep Learning for Symbolic Mathematicscited by 270
  4. 2021Investigating the Limitations of Transformers with Simple Arithmetic Taskscited by 139
  5. 2022A Length-Extrapolatable Transformercited by 127
  6. 2023Teaching Arithmetic to Small Transformerscited by 123
  7. 2021Linear algebra with transformerscited by 63
  8. 2022Deep Symbolic Regression for Recurrent Sequencescited by 54
  9. 2023Learning the greatest common divisor: explaining transformer predictionscited by 33
  10. 2022SALSA: Attacking Lattice Cryptography with Transformerscited by 28
  11. 2022Simplifying Polylogarithms with Machine Learningcited by 15
  12. 2022SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learningcited by 13

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 7; 2025: 4; 2026: 7.

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  1. 2024A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discoverycited by 96
  2. 2024Formal Mathematical Reasoning: A New Frontier in AIcited by 81
  3. 2025Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Modelscited by 14
  4. 2024Meta-Designing Quantum Experiments with Language Modelscited by 12
  5. 2024Can Transformers Do Enumerative Geometry?cited by 8
  6. 2024Transformer Normalisation Layers and the Independence of Semantic Subspacescited by 6
  7. 2024Learning the Simplicity of Scattering Amplitudescited by 6
  8. 2025Refining Integration-by-Parts Reduction of Feynman Integrals with Machine Learningcited by 6
  9. 2024Machine-learning emergent spacetime from linear response in future tabletop quantum gravity experimentscited by 5
  10. 2025Transforming Calabi-Yau Constructions: Generating New Calabi-Yau Manifolds with Transformerscited by 5
  11. 2025Generating particle physics Lagrangians with transformerscited by 1
  12. 2026Can AI Follow In Einstein's Footsteps?cited by 1
6 more
  1. 2026Machine learning modularitycited by 0
  2. 2026Reconstructing conformal field theoretical compositions with Transformerscited by 0
  3. 2026Learning First Integrals via Backward-Generated Data and Guided Reinforcement Learningcited by 0
  4. 2026Efficient AI-Inspired Reduction of Feynman Integrals via Tube Seedingcited by 0
  5. 2026Generating Special Triangulations with Transformerscited by 0
  6. 2026Graph Neural Networks for the Graphical Bootstrapcited by 0