This paper introduces a novel neural network framework that combines a discrete noise graph diffusion model with an autoregressive solver to enhance solutions for Vehicle Routing Problems (VRPs). By learning and integrating problem constraints through a generated constraint matrix, the approach improves robustness and achieves state-of-the-art performance on various benchmarks.