Using Transformers to Generate 3-Manifold Triangulations

@misc{bruwel2026using,
  title = {{Using Transformers to Generate 3-Manifold Triangulations}},
  author = {Daniel Bruwel and Ashvni Narayanan and Jonathan Spreer},
  howpublished = {arXiv},
  year = {2026},
  month = nov,
  doi = {10.48550/arxiv.2611.07514},
  eprint = {2611.07514},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2611.07514},
}

TY  - UNPB
TI  - Using Transformers to Generate 3-Manifold Triangulations
AU  - Daniel Bruwel
AU  - Ashvni Narayanan
AU  - Jonathan Spreer
PY  - 2026
DA  - 2026/11/05
PB  - arXiv
DO  - 10.48550/arxiv.2611.07514
UR  - https://arxiv.org/abs/2611.07514
AB  - Using dehydration signatures as an encoding, we train GPT-2-style transformers to generate triangulations of 3-dimensional manifolds. Our main runs each use approximately 3 exaFLOPs, spanning a 200 million parameter model trained on 60 million sphere triangulations to an 11 million parameter model trained on 1.1 billion sphere triangulations. The optimal model has 10 blocks, width 640 and generates dehydration signatures of closed manifolds of various topological types at a rate of 6.9%, 82% of which are spheres. To investigate how our method scales, we use the technique of IsoFLOP curves on 15-tetrahedron triangulations of the 3-sphere, over a compute range of 6 to 3000 petaFLOPs, and observe neural scaling behaviour. We extract a parameter scaling exponent of α= 0.49 ($[0.48, 0.50]$ stat, ± 0.02 syst), and an optimal ratio of 234 tokens per parameter for training. In addition, we present a number of smaller experiments, including evidence that transformers are able to learn to count degree-one edges near-perfectly, but get progressively worse when counting edges with higher degrees.
ER  -