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

MOT-SR is a tool-augmented symbolic regression framework that uses external analysis tools to infer structural priors and two collaborating LLM modules to refine search strategies and candidate equations. Instead of optimizing fitting error alone, it maintains a dynamic Pareto front over accuracy, structural complexity, and generalization. The authors report improvements over existing symbolic regression methods across 40 standard tasks. They also evaluate the framework on extreme mass-ratio inspiral orbital modeling, where its interpretable correction reportedly achieves the lowest trajectory-level integration error on held-out configurations. The supplied abstract does not include numerical results, baselines, model identities, or compute costs.