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[2602.08885] Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression
Summary
This article presents a novel approach to symbolic regression through the introduction of SimpliPy, a simplification engine that significantly enhances the efficiency of amortized neural symbolic regression methods.
Why It Matters
The research addresses a critical bottleneck in symbolic regression, which is essential for extracting interpretable models from data. By improving the speed and efficiency of simplification processes, this work has the potential to advance various scientific fields that rely on data interpretation and modeling.
Key Takeaways
- SimpliPy achieves a 100-fold speed-up in expression simplification compared to existing methods.
- The new framework, Flash-ANSR, demonstrates improved accuracy over traditional amortized symbolic regression baselines.
- The approach allows for better scalability to larger datasets and more efficient use of computational resources.
- Systematic training set decontamination enhances the quality of the regression results.
- The method recovers more concise expressions, which is beneficial for interpretability.
Computer Science > Machine Learning arXiv:2602.08885 (cs) [Submitted on 9 Feb 2026 (v1), last revised 21 Feb 2026 (this version, v4)] Title:Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression Authors:Paul Saegert, Ullrich Köthe View a PDF of the paper titled Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression, by Paul Saegert and 1 other authors View PDF HTML (experimental) Abstract:Symbolic regression (SR) aims to discover interpretable analytical expressions that accurately describe observed data. Amortized SR promises to be much more efficient than the predominant genetic programming SR methods, but currently struggles to scale to realistic scientific complexity. We find that a key obstacle is the lack of a fast reduction of equivalent expressions to a concise normalized form. Amortized SR has addressed this by general-purpose Computer Algebra Systems (CAS) like SymPy, but the high computational cost severely limits training and inference speed. We propose SimpliPy, a rule-based simplification engine achieving a 100-fold speed-up over SymPy at comparable quality. This enables substantial improvements in amortized SR, including scalability to much larger training sets, more efficient use of the per-expression token budget, and systematic training set decontamination with respect to equivalent test expressions. We demonstrate these advantages in our Flash-ANSR framework, which achieves much better accuracy than amortiz...
