13 papers
Formalizing Task-Space Complexity for Zero-Shot Generalization
Jung-Hoon Cho, Heling Zhang, Siqi Du +2
Policies must operate across diverse conditions, yet a single policy is often conservative while fully adaptive schemes can be complex. We study zero-shot generalization in context…
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…
Dynamic Gradient-Based Calibration for Robust and Accurate Traffic Macrosimulation
Shreyaa Raghavan, Cameron Hickert, Monica Chan +1
Robust and accurate calibration of macroscopic traffic flow models such as METANET is critical for reliable prediction and effective control. While gradient-based methods are desir…
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…
Open-Source METANET Calibration for Reproducible Freeway Traffic Macroscopic Simulation
Monica Chan, Shreyaa Raghavan, Cathy Wu
METANET is a widely used second-order macroscopic traffic flow model for freeway networks, supporting applications across traffic simulation, ramp metering, and variable speed limi…
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…