A New Benchmark for Scientific Equation Discovery with Large Language Models

The discovery of scientific equations is a fundamental component of scientific progress. It enables the derivation of laws that describe natural phenomena. In recent years, large language models (LLMs) have garnered increasing interest for this task due to their potential to leverage embedded scientific knowledge for hypothesis generation. However, evaluating the actual discovery capabilities of these methods remains a challenge. Existing benchmarks often rely on known equations that can be memorized by LLMs. This leads to inflated performance metrics that do not reflect the true discovery performance.

To address this challenge, a new benchmark called LLM-SRBench has been developed. This comprehensive benchmark encompasses 239 challenging problems from four scientific domains and is specifically designed to evaluate LLM-based methods for discovering scientific equations while preventing trivial memorization. The benchmark consists of two main categories:

LSR-Transform: This category transforms known physical models into less common mathematical representations. This tests the ability of LLMs to think beyond memorized forms and understand complex relationships.

LSR-Synth: This category comprises synthetic, discovery-oriented problems that require data-driven reasoning. Here, the LLMs must derive new equations from given data, which requires a significantly higher level of understanding and abstraction.

Comprehensive evaluations of various state-of-the-art methods, using both open and closed LLMs, have shown that the best-performing system so far only achieves a symbolic accuracy of 31.5%. These results highlight the challenges in scientific equation discovery and position LLM-SRBench as a valuable resource for future research.

The Importance of LLM-SRBench for AI Research

LLM-SRBench provides a standardized environment for evaluating and comparing different LLM-based approaches to equation discovery. By avoiding problems that can be solved by memorization, the benchmark enables a more realistic assessment of the capabilities of LLMs in this area. The results of the benchmark tests provide valuable insights into the strengths and weaknesses of current methods and help guide the development of future, more powerful algorithms.

The development of AI systems capable of making scientific discoveries is a long-term goal of AI research. LLM-SRBench represents an important step in this direction by providing a robust and reliable platform for evaluating progress in this field.

For Mindverse, a German company specializing in the development of AI solutions, these developments are of particular interest. The insights from benchmarks like LLM-SRBench can help further improve their own AI models and tools and develop customized solutions for clients in the scientific field. From chatbots and voicebots to AI search engines and knowledge systems, the ability to understand and model complex scientific relationships is a crucial factor in developing innovative AI applications.

Bibliography: Shojaee, P., Nguyen, N.-H., Meidani, K., Farimani, A. B., Doan, K. D., & Reddy, C. K. (2025). LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models. arXiv preprint arXiv:2504.10415. Creddy, C.K. et al. (2025). [Title of Publication]. In *Proceedings of the International Conference on Learning Representations (ICLR 2025)*. Sharlin, S. et al. (2025). [Title of Publication].