Published April 15, 2026
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Monolith: Differentiable EML Trees for Symbolic Regression via Gradient Descent on a Universal Operator
Authors/Creators
Description
Differentiable implementation of EML trees (arXiv 2603.21852) as trainable PyTorch modules for symbolic regression. Demonstrates recovery of 7/7 elementary functions with ≤24 parameters via gradient descent on a single universal operator. Includes hierarchical training for depth 4+, symbolic decompilation, and honest baselines against PySR and MLP.
Files
Monolith.pdf
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Additional details
Related works
- Is supplement to
- Publication: arXiv:2603.21852 (arXiv)
Software
- Repository URL
- https://github.com/seetrex-ai/monolith
- Programming language
- Python
- Development Status
- Active
References
- Odrzywołek, A. (2026). All elementary functions from a single binary operator. arXiv:2603.21852