From the 3 of 14 linked papers with an AI index.
14 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…
AutoPref: Automatic Discovery of Task-Specific Preference Objectives for Neural Combinatorial Optimization
Shengda Gu, Kai Li, Xinyi Ke +3
AutoPref uses a large language model to automatically discover and compose pairwise loss and weighting programs that define preference objectives for neural combinatorial optimizat…
EvoPINN: Agentic Discovery of Executable Algorithms for Physics-Informed Neural Networks
Peng Yin, Kai Li, Yifan Zhang +1
EvoPINN is an agentic framework that uses a large language model to automatically generate and verify executable algorithms for physics-informed neural networks, improving the accu…
RefineEvo: Planning-Guided Heuristic Evolution with Bidirectional Experience
Yang Wu, Junran Pan, Yifan Zhang +3
RefineEvo is an evolutionary framework that uses a planner to choose operators and a reflector to store positive and negative experiences, turning automatic heuristic design into a…
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…
Mean Flow Policy Optimization
Xiaoyi Dong, Xi Sheryl Zhang, Jian Cheng
Diffusion models have recently emerged as expressive policy representations for online reinforcement learning (RL). However, their iterative generative processes introduce substant…