{"ID":2864803,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.23413","arxiv_id":"2509.23413","title":"URS: A Unified Neural Routing Solver for Cross-Problem Zero-Shot Generalization","abstract":"Multi-task neural routing solvers have emerged as a promising paradigm for their ability to solve multiple vehicle routing problems (VRPs) using a single model. However, existing neural solvers typically rely on predefined problem constraints or require per-problem fine-tuning, which substantially limits their zero-shot generalization ability to unseen VRP variants. To address this critical bottleneck, we propose URS, a unified neural routing solver that achieves zero-shot generalization across a wide range of unseen VRPs with a single model. We propose a unified data representation (UDR) that replaces problem enumeration with data unification, thereby broadening the problem coverage and reducing reliance on domain expertise. In addition, we introduce a Mixed Bias Module (MBM) during encoding to improve node embeddings, which efficiently captures multiple priors inherent to various problems. On top of the UDR, we develop a problem-conditioned parameter generator to further improve zero-shot generalization. Extensive experiments show that URS consistently produces high-quality solutions for 110 VRP variants (including 99 unseen variants) while demonstrating impressive scalability to large-scale instances with up to 7000 nodes. To the best of our knowledge, URS is the first neural solver to handle over 100 VRP variants with a single model. Our code is available at https://github.com/CIAM-Group/URS.","short_abstract":"Multi-task neural routing solvers have emerged as a promising paradigm for their ability to solve multiple vehicle routing problems (VRPs) using a single model. However, existing neural solvers typically rely on predefined problem constraints or require per-problem fine-tuning, which substantially limits their zero-sho...","url_abs":"https://arxiv.org/abs/2509.23413","url_pdf":"https://arxiv.org/pdf/2509.23413v2","authors":"[\"Changliang Zhou\",\"Canhong Yu\",\"Shunyu Yao\",\"Xi Lin\",\"Zhenkun Wang\",\"Yu Zhou\",\"Qingfu Zhang\"]","published":"2025-09-27T17:11:09Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":609201,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2864803,"paper_url":"https://arxiv.org/abs/2509.23413","paper_title":"URS: A Unified Neural Routing Solver for Cross-Problem Zero-Shot Generalization","repo_url":"https://github.com/CIAM-Group/URS","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}