11 papers
Interpreting Neural Combinatorial Optimization via Evolving Programmatic Bottlenecks
Haocheng Duan, Yuxin Guo, Jieyi Bi +4
Neural Combinatorial Optimization (NCO) achieves strong performance, yet its black-box nature remains a key roadblock to deployment and scientific diagnosis. Standard interpretabil…
FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization
Minwei Kong, Chonghe Jiang, Ao Qu +24
Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a…
CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery
Ao Qu, Han Zheng, Zijian Zhou +14
Large language model (LLM)-based evolution is a promising approach for open-ended discovery, where progress requires sustained search and knowledge accumulation. Existing methods s…
Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy
Jung-Hoon Cho, Sirui Li, Jeongyun Kim +1
The recent development of connected and automated vehicle (CAV) technologies has spurred investigations to optimize dense urban traffic to maximize vehicle speed and throughput. Th…
Probability-Aware Parking Selection
Cameron Hickert, Sirui Li, Zhengbing He +1
Current navigation systems conflate time-to-drive with the true time-to-arrive by ignoring parking search duration and the final walking leg. Such underestimation can significantly…
Learning to Segment for Vehicle Routing Problems
Wenbin Ouyang, Sirui Li, Yining Ma +1
Iterative heuristics are widely recognized as state-of-the-art for Vehicle Routing Problems (VRPs). In this work, we exploit a critical observation: a large portion of the solution…