To clarify as some people have mismatched expectations, the authors mention viability for real-world applications in the paper: "One may ask to what extent our

To clarify as some people have mismatched expectations, the authors mention viability for real-world applications in the paper: "One may ask to what extent our model can be used for real-world applications, such as time-series forecasting. Although the robustness to noise is an encouraging step in this direction, we believe our model is not directly adapted to such applications, for two reasons. First, because real-world data generally cannot be described by neat mathematical equations, in which case numeric approaches will outperform symbolic approaches. Second, even in cases where the sequence is described by a formula, this formula will often con- tain complex prefactors. While our method is capable of approximating prefactors rather remarkably, this adds a layer of difficulty to the original problem, as the model sometimes needs to use many operators to approximate a single prefactor (see Table 2). However, one easy way to solve this issue is to extend the vocabulary of the decoder, enabling it to build prefactors more easily, or use a separate solver to fit the prefactors as done in approaches. We leave this for future work."