1 citations · 1 across the 7 of their papers we have counts for
7 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…
EvoPINN: Agentic Discovery of Executable Algorithms for Physics-Informed Neural Networks
Peng Yin, Kai Li, Yifan Zhang +1
Physics-informed neural networks (PINNs) have emerged as a powerful paradigm for solving partial differential equations (PDEs), yet their performance heavily relies on the manual,…
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…
Game-Theoretic Co-Evolution for LLM-Based Heuristic Discovery
Xinyi Ke, Kai Li, Junliang Xing +2
Large language models (LLMs) have enabled rapid progress in automatic heuristic discovery (AHD), yet most existing methods are predominantly limited by static evaluation against fi…
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…
Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization
Shengda Gu, Kai Li, Junliang Xing +2
Combinatorial optimization problems are notoriously challenging due to their discrete structure and exponentially large solution space. Recent advances in deep reinforcement learni…
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…