CALT: A Library for Computer Algebra with Transformer
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Abstract (arXiv)
Recent advances in artificial intelligence have demonstrated the learnability of symbolic computation through end-to-end deep learning. Given a sufficient number of examples of symbolic expressions before and after the target computation, Transformer models - highly effective learners of sequence-to-sequence functions - can be trained to emulate the computation. This development opens up several intriguing challenges and new research directions, which require active contributions from the symbolic computation community. In this work, we introduce Computer Algebra with Transformer (CALT), a user-friendly Python library designed to help non-experts in deep learning train models for symbolic computation tasks.
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References · 10
- 2017Attention Is All You Needcited by 60,361
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
- 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,546
- 2019Deep Learning for Symbolic Mathematicscited by 270
- 2021Linear algebra with transformerscited by 63
- 2024Transformers Provably Solve Parity Efficiently with Chain of Thoughtcited by 50
- 2024Global Lyapunov functions: a long-standing open problem in mathematics, with symbolic transformerscited by 29
- 2022SALSA: Attacking Lattice Cryptography with Transformerscited by 28
- 2025Computational Algebra with Attention: Transformer Oracles for Border Basis Algorithmscited by 7
- 2025Geometric Generality of Transformer-Based Gr"obner Basis Computationcited by 3
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