1 citations · 1 across the 4 of their papers we have counts for
5 papers
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…
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…
Answer-Centric or Reasoning-Driven? Uncovering the Latent Memory Anchor in LLMs
Yang Wu, Yifan Zhang, Yiwei Wang +5
While Large Language Models (LLMs) demonstrate impressive reasoning capabilities, growing evidence suggests much of their success stems from memorized answer-reasoning patterns rat…
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…