RoME: Domain-Robust Mixture-of-Experts for MILP Solution Prediction across Domains

Chao Chen (National University of Defense Technology) · Jie Wang (Southeast University) · Tianle Pu (National University of Defense Technology) · Zijie Geng (University of Science and Technology of China) · Haoyang Liu (University of Science and Technology of China) · Shixuan Liu (National University of Defense Technology) · Li Zeng (Yuyao biotechnology) · Changjun Fan (National University of Defense Technology)

cross-domain trainingdistributionally robust optimizationdomain-robust mixture-of-expertsgeneralization capabilityinter-domainintra-domainintrinsic combinatorial patternslearning-based methodslocal robustnessmiplibmixed-integer linear programmingoptimization problemsproblem instancestask embeddingszero-shot setting

Mixed-Integer Linear Programming (MILP) is a fundamental and powerful framework for modeling complex optimization problems across diverse domains. Recently, learning-based methods have shown great promise in accelerating MILP solvers by predicting high-quality solutions. However, most existing approaches are developed and evaluated in single-domain settings, limiting their ability to generalize to unseen problem distributions. This limitation poses a major obstacle to building scalable and general-purpose learning-augmented solvers. To address this challenge, we introduce RoME, a domain-Robust Mixture-of-Experts (MoE) framework for predicting MILP solutions across domains. RoME dynamically routes problem instances to specialized experts based on learned task embeddings. The model is trained using a two-level distributionally robust optimization strategy: inter-domain to mitigate global shifts across domains, and intra-domain to enhance local robustness by introducing perturbations on task embeddings. We reveal that cross-domain training not only enhances the model's generalization capability to unseen domains but also improves performancewithin each individual domain by encouraging the model to capture more general intrinsic combinatorial patterns. Specifically, a single RoME model trained on three domains achieves an average improvement of $67.7\%$ then evaluated on five diverse domains. We further test the pretrained model on MIPLIB in a zero-shot setting, demonstrating its ability to deliver measurable performance gains on challenging real-world instances where existing learning-based approaches often struggle to generalize.