by deep-symbolic-mathematics · Agent Tool · ★ 214
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: Scientific Equation Discovery and Symbolic Regression via Programming with LLMs Official Implementation of paper LLM-SR: Scientific Equation Discovery via Programming with Large Language Models (ICLR 2025 Oral). Updates Our recent more comprehensive benchmark LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models (to appear at ICML 2025 as Oral) is released following this work to effectively test LLM-based scientific equation discovery methods beyond memorization. Check out the benchmark data on huggingface and evaluation codes here. Overview In this paper, we introduce LLM-SR, a novel approach for scientific equation discovery and symbolic regression that leverages strengths of Large Language Models (LLMs). LLM-SR combines LLMs' scientific knowledge and code generation capabilities with evolutionary search to discover accurate and interpretable equations from d
| Stars | 214 |
| Forks | 44 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 37.25/100 |
| Open Issues | 7 |
| Last Updated | 2025-07-31 |
| Created | 2024-04-19 |
| Platforms | python |
| Est. Tokens | ~598k |
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LLM-SR is [ICLR 2025 Oral] This is the official repo for the paper "LLM-SR" on Scientific Equation Discovery and Symbolic Regression with Large Language Models. It is categorized as a Agent Tool with 214 GitHub stars.
LLM-SR is primarily written in Python. It covers topics such as ai4code, ai4math, ai4science.
You can find installation instructions and usage details in the LLM-SR GitHub repository at github.com/deep-symbolic-mathematics/LLM-SR. The project has 214 stars and 44 forks, indicating an active community.
LLM-SR is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to LLM-SR on Agent Skills Hub include PairCoder, bigcodebench, promptdesk. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.