mlsanityData to 14 Oct 2026

Transforming Calabi-Yau Constructions: Generating New Calabi-Yau Manifolds with Transformers

arXiv 2507.03732 ↗First posted 4 Jul 2025hep-th cs.LG math.AGcited by 5

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Abstract (arXiv)

Fine, regular, and star triangulations (FRSTs) of four-dimensional reflexive polytopes give rise to toric varieties, within which generic anticanonical hypersurfaces yield smooth Calabi-Yau threefolds. We introduce CYTransformer, a deep learning model based on the transformer architecture, to automate the generation of FRSTs. We demonstrate that CYTransformer efficiently and unbiasedly samples FRSTs for polytopes across a range of sizes, and can self-improve through retraining on its own output. These results lay the foundation for AICY: a community-driven platform designed to combine self-improving machine learning models with a continuously expanding database to explore and catalog the Calabi-Yau landscape.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 16 of its 49 references that are in the map. First posted on arXiv 4 Jul 2025.

Builds on

16 of 49 references.

References · 16

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4 more
  1. 2024Transforming the Bootstrap: Using Transformers to Compute Scattering Amplitudes in Planar N = 4 Super Yang-Mills Theorycited by 18
  2. 2019Searching the Landscape of Flux Vacua with Genetic Algorithmscited by 10
  3. 2021Breeding realistic D-brane modelscited by 4
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Can Neural Networks Learn Small Algebraic Worlds? An Investigation Into the Group-theoretic Structures Learned By Narrow Models Trained To Predict Group Operationscited by 3
  2. 2026Reconstructing conformal field theoretical compositions with Transformerscited by 0
  3. 2026Sampling Triangulations and Calabi-Yau Threefolds with Autoregressive GNNscited by 0
  4. 2026TriSearch: Learning to Optimize Triangulations via Bistellar Flipscited by 0
  5. 2026Generating Special Triangulations with Transformerscited by 0