5 papers
When to Screen, When to Bypass: LLM-Judges in Resource-Scarce AI-Human Workflow
Ruihan Lin, Jiheng Zhang
AI systems can generate outputs at scale, but most outputs require human approval before release. This creates a bottleneck: humans cannot keep pace with AI-generated volume. A nat…
OR-R1: Automating Modeling and Solving of Operations Research Optimization Problem via Test-Time Reinforcement Learning
Zezhen Ding, Zhen Tan, Jiheng Zhang +1
Optimization modeling and solving are fundamental to the application of Operations Research (OR) in real-world decision making, yet the process of translating natural language prob…
Adaptive Inertial Method
Han Long, Bingsheng He, Yinyu Ye +1
In this paper, we introduce the Adaptive Inertial Method (AIM), a novel framework for accelerated first-order methods through a customizable inertial term. We provide a rigorous co…
Make Optimization Once and for All with Fine-grained Guidance
Mingjia Shi, Ruihan Lin, Xuxi Chen +8
Learning to Optimize (L2O) enhances optimization efficiency with integrated neural networks. L2O paradigms achieve great outcomes, e.g., refitting optimizer, generating unseen solu…
FlowTS: Time Series Generation via Rectified Flow
Yang Hu, Xiao Wang, Zezhen Ding +7
Diffusion-based models have significant achievements in time series generation but suffer from inefficient computation: solving high-dimensional ODEs/SDEs via iterative numerical s…