SR-Scientist: Scientific Equation Discovery With Agentic AI
Authors: name: Shijie Xia, name: Yuhan Sun, name: Pengfei Liu

Recently, Large Language Models (LLMs) have been applied to scientific
equation discovery, leveraging their embedded scientific knowledge for
hypothesis generation. However, current methods typically confine LLMs to the
role of an equation proposer within search algorithms like genetic programming.
In this paper, we present SR-Scientist, a framework that elevates the LLM from
a simple equation proposer to an autonomous AI scientist that writes code to
analyze data, implements the equation as code, submits it for evaluation, and
optimizes the equation based on experimental feedback. Specifically, we wrap
the code interpreter into a set of tools for data analysis and equation
evaluation. The agent is instructed to optimize the equation by utilizing these
tools over a long horizon with minimal human-defined pipelines. Empirical
results show that SR-Scientist outperforms baseline methods by an absolute
margin of 6% to 35% on datasets covering four science disciplines.
Additionally, we demonstrate our method's robustness to noise, the
generalization of the discovered equations to out-of-domain data, and their
symbolic accuracy. Furthermore, we develop an end-to-end reinforcement learning
framework to enhance the agent's capabilities.