arXiv Machine Learning August 11, 2026 By Dongbin Jiao, Zisheng Chen, Xianyi Wang, Jintao Shi, Shengcai Liu, Shi Yan

OD-Gear: Online Decomposition and Group Sampling for Expert-Guided Adversarial Routing in Scalable Capacitated Vehicle Routing

Read the original on arXiv Machine Learning →

arXiv:2602. 00488v3 Announce Type: replace Abstract: Solving large-scale capacitated vehicle routing problems (CVRP) is hindered by the high complexity of classical heuristics and the limited generalization of neural solvers.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 2

Learning to Reduce Search Space for Generalizable Neural Routing Solver

arXiv:2503. 03137v3 Announce Type: replace Abstract: Constructive neural combinatorial optimization (NCO) offers a promising paradigm for solving vehicle routing problems (VRPs) by directly learning to construct approximate optimal solutions, thereby reducing reliance on expert knowledge for algorithm design.

By Changliang Zhou, Xi Lin, Zhenkun Wang, Qingfu Zhang

efficiency

arXiv AI

Aug 25

Memory-Enhanced Neural Solvers for Routing Problems

The paper introduces MEMENTO, a memory‑enhanced neural solver that improves routing problem solutions by using online data from repeated attempts to adjust action distributions during inference. It targets NP‑hard routing tasks such as the Traveling Salesman and Capacitated Vehicle Routing problems, outperforming existing tree‑search and policy‑gradient fine‑tuning methods. MEMENTO demonstrates strong scalability and data efficiency, achieving state‑of‑the‑art results on 11 of 12 evaluated tasks and enabling zero‑shot integration with diversity‑based solvers.

By Felix Chalumeau, Refiloe Shabe, Noah De Nicola, Arnu Pretorius, Thomas D. Barrett, Nathan Grinsztajn

reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Aug 12

URS: A Unified Neural Routing Solver for Cross-Problem Zero-Shot Generalization

arXiv:2509. 23413v3 Announce Type: replace 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.

By Changliang Zhou, Canhong Yu, Shunyu Yao, Xi Lin, Zhenkun Wang, Yu Zhou, Qingfu Zhang

rag fine-tuning safety

arXiv Machine Learning

Jul 7

Heterogeneous Graph Condensation via Role-Aware Clustering

arXiv:2607. 03097v1 Announce Type: new Abstract: Heterogeneous Graph Neural Networks (HGNNs) have exhibited remarkable efficacy in modeling complex systems with multiple types of nodes and relations, yet their training on large-scale heterogeneous graphs remains computationally prohibitive.

By Fuyan Ou, Yulin Hu, Ye Yuan

rag benchmarks

arXiv AI

Jun 18

Enhancing CVRP Solver through LLM-driven Automatic Heuristic Design

arXiv:2602. 23092v2 Announce Type: replace Abstract: The Capacitated Vehicle Routing Problem (CVRP), a fundamental combinatorial optimization challenge, focuses on optimizing fleet operations under vehicle capacity constraints.

By Zhuoliang Xie, Fei Liu, Zhenkun Wang, Qingfu Zhang

llms benchmarks

arXiv Machine Learning

Jul 27

Integrated Order Dispatching and Routing for Last-Mile Pickup via Deep Reinforcement Learning

arXiv:2607. 22356v1 Announce Type: new Abstract: In recent years, the growing complexity of last-mile pickup operations has increased the need for fast and accurate decision-making on logistics platforms.

By Yida Xu, Zhaofang Mao, Yuheng Miao, Jiaxin Zhang, Yiting Sun

reinforcement-learning benchmarks

One email a morning, machine-written

Related stories

Learning to Reduce Search Space for Generalizable Neural Routing Solver

Memory-Enhanced Neural Solvers for Routing Problems

URS: A Unified Neural Routing Solver for Cross-Problem Zero-Shot Generalization

Heterogeneous Graph Condensation via Role-Aware Clustering

Enhancing CVRP Solver through LLM-driven Automatic Heuristic Design

Integrated Order Dispatching and Routing for Last-Mile Pickup via Deep Reinforcement Learning