4 papers
ORAgentBench: Can LLM Agents Solve Challenging Operations Research Tasks End to End?
Jiajun Li, Mingshu Cai, Yixuan Li +5
Large language models are increasingly deployed as autonomous agents for multi-step tasks in executable environments, yet their ability to perform realistic operations research (OR…
Constraint Matters: Multi-Modal Representation for Reducing Mixed-Integer Linear programming
Jiajun Li, Yixuan Li, Ran Hou +8
Model reduction, which aims to learn a simpler model of the original mixed integer linear programming (MILP), can solve large-scale MILP problems much faster. Most existing model r…
MicLog: Towards Accurate and Efficient LLM-based Log Parsing via Progressive Meta In-Context Learning
Jianbo Yu, Yixuan Li, Hai Xu +5
Log parsing converts semi-structured logs into structured templates, forming a critical foundation for downstream analysis. Traditional syntax and semantic-based parsers often stru…
Fast and Interpretable Mixed-Integer Linear Program Solving by Learning Model Reduction
Yixuan Li, Can Chen, Jiajun Li +6
By exploiting the correlation between the structure and the solution of Mixed-Integer Linear Programming (MILP), Machine Learning (ML) has become a promising method for solving lar…