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The paper introduces KeplerAgent, an LLM-based agent designed for symbolic equation discovery that mimics the scientific reasoning process of inferring physical properties before guessing equations. KeplerAgent coordinates physics-based tools to extract intermediate structure from data and uses this information to configure symbolic regression engines like PySINDy and PySR. Experiments on physical equation benchmarks demonstrate that KeplerAgent achieves significantly higher symbolic accuracy and robustness to noisy data compared to existing LLM and traditional baselines.
LLMs can discover equations with significantly higher accuracy and robustness by explicitly modeling the multi-step reasoning process scientists use, rather than guessing directly from data.
Explaining observed phenomena through symbolic, interpretable formulas is a fundamental goal of science. Recently, large language models (LLMs) have emerged as promising tools for symbolic equation discovery, owing to their broad domain knowledge and strong reasoning capabilities. However, most existing LLM-based systems try to guess equations directly from data, without modeling the multi-step reasoning process that scientists often follow: first inferring physical properties such as symmetries, then using these as priors to restrict the space of candidate equations. We introduce KeplerAgent, an agentic framework that explicitly follows this scientific reasoning process. The agent coordinates physics-based tools to extract intermediate structure and uses these results to configure symbolic regression engines such as PySINDy and PySR, including their function libraries and structural constraints. Across a suite of physical equation benchmarks, KeplerAgent achieves substantially higher symbolic accuracy and greater robustness to noisy data than both LLM and traditional baselines.
