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.
@article{arxiv.2506.08600,
title = {CALT: A Library for Computer Algebra with Transformer},
author = {Hiroshi Kera and Shun Arakawa and Yuta Sato},
journal= {arXiv preprint arXiv:2506.08600},
year = {2025}
}