1 citations · 1 across the 3 of their papers we have counts for
4 papers
MOT-SR: Multi-Objective Tool-Augmented Scientific Equation Discovery with Large Language Models
Boxiao Wang, Runxiang Wang, Kai Li +4
Symbolic Regression (SR) aims to discover analytical equations from observational data and plays a central role in scientific modeling. While recent Large Language Model (LLM) base…
When Good Equations Get Bad Scores: Improving Symbolic Regression Through Better Parameter Optimization
Boxiao Wang, Kai Li, Zhiwei Chen +5
Symbolic Regression (SR) plays a central role in scientific knowledge discovery by distilling mathematical equations from observational data. Most existing SR methods function with…
LLM-Based Scientific Equation Discovery via Physics-Informed Token-Regularized Policy Optimization
Boxiao Wang, Kai Li, Tianyi Liu +4
Symbolic regression aims to distill mathematical equations from observational data. Recent approaches have successfully leveraged Large Language Models (LLMs) to generate equation…
DrSR: LLM based Scientific Equation Discovery with Dual Reasoning from Data and Experience
Runxiang Wang, Boxiao Wang, Kai Li +2
Symbolic regression is a fundamental tool for discovering interpretable mathematical expressions from data, with broad applications across scientific and engineering domains. Recen…