Neural solvers for Vehicle Routing Problems (VRPs) have shown great advantages in solving various kinds of problem types. However, they also face critical challenges in generalizing from small-scale training to large-scale problems and in identifying the most salient topological information for decision-making. To mitigate these gaps, we introduce ScaleNet, a novel hierarchical framework that integrates a U-Net architecture into a unified, multi-task VRP solver. Scale-Net explicitly captures multi-scale structural patterns by processing a nested hierarchy of input graph instances. This enriched, coarse-to-fine representation is extracted by the encoder and fed directly into the decoder, empowering decoder module with superior topological awareness for routing decisions while simultaneously reducing computational overhead in the encoder. We conducted extensive experiments on 16 VRP variants with instances ranging from 50 to 5,000 nodes. The experimental results show that Scale-Net demonstrates significant performance gains over state-of-the-art baselines across in-distribution, zero-shot, and real-world settings.

Graph Domain Adaptation (GDA) facilitates knowledge transfer from labeled source graphs to unlabeled target graphs by learning domain-invariant representations, which is essential in applications such as molecular property prediction and social network analysis. However, most existing GDA methods rely on the assumption of clean source labels, which rarely holds in real-world scenarios where annotation noise is pervasive. This label noise severely impairs feature alignment and degrades adaptation performance under domain shifts. To address this challenge, we propose Nested Graph Pseudo-Label Refinement (NeGPR), a novel framework tailored for graph-level domain adaptation with noisy labels. NeGPR first pretrains dual branches, i.e., semantic and topology branches, by enforcing neighborhood consistency in the feature space, thereby reducing the influence of noisy supervision. To bridge domain gaps, NeGPR employs a nested refinement mechanism in which one branch selects high-confidence target samples to guide the adaptation of the other, enabling progressive cross-domain learning. Furthermore, since pseudo-labels may still contain noise and the pre-trained branches are already overfitted to the noisy labels in the source domain, NeGPR incorporates a noise-aware regularization strategy. This regularization is theoretically proven to mitigate the adverse effects of pseudo-label noise, even under the presence of source overfitting, thus enhancing the robustness of the adaptation process. Extensive experiments on benchmark datasets demonstrate that NeGPR consistently outperforms state-of-the-art methods under severe label noise.

Solving various types of vehicle routing problems (VRPs) using a unified neural solver has garnered significant attentions in recent years. Despite their effectiveness, existing neural multi-task solvers often fail to account for the geometric structures inherent in different tasks, which may result in suboptimal performance. To address this limitation, we propose a curvature-aware pre-training framework. Specifically, we leverage mixed-curvature spaces during the feature fusion stage, encouraging the model to capture the underlying geometric properties of each instance. Through extensive experiments, we evaluate the proposed pre-training strategy on existing neural multi-task solvers across a variety of testing scenarios. The results demonstrate that the curvature-aware pre-training approach not only enhances the generalization capabilities of existing neural VRP solvers on synthetic datasets but also improves solution quality on real-world benchmarks.

Multimodal dialogue systems attract much attention recently, but they are far from skills like: 1) automatically generate context- specific responses instead of safe but general responses; 2) naturally coordinate between the different information modalities (e.g. text and image) in responses; 3) intuitively explain the reasons for generated responses and improve a specific response without re-training the whole model. To approach these goals, we propose a different angle for the task - Reflecting Experiences for Response Generation (RERG). This is supported by the fact that generating a response from scratch can be hard, but much easier if we can access other similar dialogue contexts and the corresponding responses. In particular, RERG first uses a multimodal contrastive learning enhanced retrieval model for soliciting similar dialogue instances. It then employs a cross copy based reuse model to explore the current dialogue context (vertical) and similar dialogue instances' responses (horizontal) for response generation simultaneously. Experimental results demonstrate that our model outperforms other state-of-the-art models on both automatic metrics and human evaluation. Moreover, RERG naturally provides supporting dialogue instances for better explainability. It also has a strong capability in adapting to unseen dialogue settings by simply adding related samples to the retrieval datastore without re-training the whole model.