1 citations · 1 across the 16 of their papers we have counts for
9 papers · 1 filter
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
Combinatorial optimization problems (COPs) underpin many real-world decisions, but their exponentially large search spaces make high-quality solutions costly to obtain. Neural comb…
Reflex: Reinforcement Learning with Reflection Symmetry Exploitation in State-Based Continuous Control
Shuai Zhen, Yifan Zhang, Yuling Wang +1
Reinforcement learning has long struggled with poor sample efficiency. One promising approach to mitigate this problem is leveraging group-invariant Markov Decision Processes (-…
OScaR: The Occam's Razor for Extreme KV Cache Quantization in LLMs and Beyond
Zunhai Su, Rui Yang, Chao Zhang +12
The rapid advancement toward long-context reasoning and multi-modal intelligence has made the memory footprint of the Key-Value (KV) cache a dominant memory bottleneck for efficien…
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…
HGCN2SP: Hierarchical Graph Convolutional Network for Two-Stage Stochastic Programming
Yang Wu, Yifan Zhang, Zhenxing Liang +1
Two-stage Stochastic Programming (2SP) is a standard framework for modeling decision-making problems under uncertainty. While numerous methods exist, solving such problems with man…
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…