Programmatic Context Augmentation for LLM-based Symbolic Regression

📰 ArXiv cs.AI

Learn to improve symbolic regression using programmatic context augmentation with LLMs, enhancing scalability and expressivity in scientific discovery

advanced Published 6 May 2026

Action Steps

Apply programmatic context augmentation to LLM-based symbolic regression models to enhance expressivity
Use large language models (LLMs) as a basis for evolutionary search methods in symbolic regression
Configure LLM hyperparameters to optimize performance in symbolic regression tasks
Test the efficacy of programmatic context augmentation in improving model scalability and expressivity
Compare the results of LLM-based symbolic regression with traditional genetic algorithm-based approaches

Who Needs to Know This

Data scientists and machine learning engineers working on symbolic regression tasks can benefit from this approach to improve model performance and overcome traditional limitations

Key Insight

💡 Programmatic context augmentation can enhance the scalability and expressivity of LLM-based symbolic regression models

Key Takeaways

Learn to improve symbolic regression using programmatic context augmentation with LLMs, enhancing scalability and expressivity in scientific discovery

Full Article

Title: Programmatic Context Augmentation for LLM-based Symbolic Regression

Abstract:
arXiv:2605.03101v1 Announce Type: new Abstract: Symbolic regression (SR), the task of discovering mathematical expressions that best describe a given dataset, remains a fundamental challenge in scientific discovery. Traditional approaches, primarily based on genetic algorithms and related evolutionary methods, have proven useful but suffer from scalability and expressivity limitations. Recently, large language model (LLM)-based evolutionary search methods have been introduced into SR and show pr

Read full paper → ← Back to Reads