MOT-SR: Multi-Objective Tool-Augmented Scientific Equation Discovery with Large Language Models

Discovering scientific equations from data is central to modeling physical systems, but existing LLM-based symbolic regression methods often ignore variable relationships and optimize only for fitting accuracy, causing premature convergence. MOT-SR solves this with a multi-objective framework balancing accuracy, complexity, and generalization, using collaborative LLM modules that select analytical tools and generate candidate equations along a Pareto front. Validated on 40 tasks and applied to gravitational-wave orbital modeling, it produces interpretable corrections with the lowest long-term trajectory errors. This has strong applications in physics, astronomy, and any scientific field requiring interpretable, generalizable equation discovery. Authors: Boxiao Wang, Runxiang Wang, Kai Li, Chongming Li, Zhiwei Chen, Paper: https://arxiv.org/abs/2607.29561v1