mlsanityData to 14 Oct 2026

Int2Int: a framework for mathematics with transformers

arXiv 2502.17513 ↗First posted 22 Feb 2025cs.LG cs.AI cs.MScited by 1

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Abstract (arXiv)

This paper documents Int2Int, an open source code base for using transformers on problems of mathematical research, with a focus on number theory and other problems involving integers. Int2Int is a complete PyTorch implementation of a transformer architecture, together with training and evaluation loops, and classes and functions to represent, generate and decode common mathematical objects. Ancillary code for data preparation, and Jupyter Notebooks for visualizing experimental results are also provided. This document presents the main features of Int2Int, serves as its user manual, and provides guidelines on how to extend it. Int2Int is released under the MIT licence, at https://github.com/f-charton/Int2Int.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 18 of its 24 references that are in the map. First posted on arXiv 22 Feb 2025.

Builds on

18 of 24 references.

References · 18

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  4. 2016Gaussian Error Linear Units (GELUs)cited by 3,502
  5. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
  6. 2016Adaptive Computation Time for Recurrent Neural Networkscited by 506
  7. 2019Deep Learning for Symbolic Mathematicscited by 270
  8. 2021Neural Symbolic Regression that Scalescited by 192
  9. 2021Investigating the Limitations of Transformers with Simple Arithmetic Taskscited by 139
  10. 2023Teaching Arithmetic to Small Transformerscited by 123
  11. 2021Linear algebra with transformerscited by 63
  12. 2021The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalizationcited by 62
6 more
  1. 2022Deep Symbolic Regression for Recurrent Sequencescited by 54
  2. 2024PatternBoost: Constructions in Mathematics with a Little Help from AIcited by 34
  3. 2023Learning the greatest common divisor: explaining transformer predictionscited by 33
  4. 2024Global Lyapunov functions: a long-standing open problem in mathematics, with symbolic transformerscited by 29
  5. 2024Emergent properties with repeated examplescited by 11
  6. 2025Learning Euler Factors of Elliptic Curvescited by 4

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Discovering Learning-Friendly Generation Orders for Sequential Computationcited by 1