Unlocking Neural Network Logic: A New Path to Interpretability
![SymTorch facilitates symbolic distillation by wrapping neural network components, collecting input-output data during forward passes, and employing PySR to generate increasingly complex symbolic regressions-allowing for the replacement of these components with optimized equations and the creation of hybrid neural-symbolic models that balance expressiveness and computational efficiency, effectively distilling the function of a neural network into a mathematically provable form [latex] f(x) = \sum_{i=0}^{n} a_i x^i [/latex].](https://arxiv.org/html/2602.21307v1/images/figure1.png)
Researchers have developed a framework to automatically extract symbolic expressions from deep learning models, bridging the gap between black-box prediction and human-understandable reasoning.





