8 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…
StarOR: Synergizing Tree Search and Test-Time Reinforcement Learning for Optimization Modeling
Jiajun Li, Yu Ding, Shisi Guan +2
Optimization modeling is inherently hierarchical, requiring a precise sequence of symbolic commitments. Traditional learning-based automated optimization modeling methods improve m…
Grasp-Then-Plan with Failure Attribution: A Closed Two-Stage Framework for Precise and Generalizable Robotic Manipulation
Jiahao Xu, Peiyuan Wang, Hanzhuo Zhang +8
In robotic manipulation, the tight coupling between grasping and motion planning often obscures the true source of failure, leading to inefficient trial-and-error. To enable effici…
MIND-Skill: Quality-Guaranteed Skill Generation via Multi-Agent Induction and Deduction
Yixuan Li, Mingshu Cai, Ziyang Xiao +3
Large language model (LLM) powered AI agents have emerged as a promising paradigm for autonomous problem-solving, yet they continue to struggle with complex, multi-step real-world…
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…
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…
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…
i-Rebalance: Personalized Vehicle Repositioning for Supply Demand Balance
Haoyang Chen, Peiyan Sun, Qiyuan Song +5
Ride-hailing platforms have been facing the challenge of balancing demand and supply. Existing vehicle reposition techniques often treat drivers as homogeneous agents and relocate…