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2026

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Articles 9061 - 9064 of 9064

Full-Text Articles in Physical Sciences and Mathematics

Scale-Net: A Hierarchical U-Net Framework For Cross-Scale Generalization In Multi-Task Vehicle Routing, Suyu Liu, Zhiguang Cao, Nan Yin, Yew-Soon Ong Jan 2026

Scale-Net: A Hierarchical U-Net Framework For Cross-Scale Generalization In Multi-Task Vehicle Routing, Suyu Liu, Zhiguang Cao, Nan Yin, Yew-Soon Ong

Research Collection School Of Computing and Information Systems

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 …

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Instance Generation For Meta-Black-Box Optimization Through Latent Space Reverse Engineering, Chen Wang, Yue-Jiao Gong, Zhiguang Cao, Zeyuan Ma Jan 2026

Instance Generation For Meta-Black-Box Optimization Through Latent Space Reverse Engineering, Chen Wang, Yue-Jiao Gong, Zhiguang Cao, Zeyuan Ma

Research Collection School Of Computing and Information Systems

To relieve intensive human-expertise required to design optimization algorithms, recent Meta-Black-Box Optimization (MetaBBO) researches leverage generalization strength of meta-learning to train neural network-based algorithm design policies over a predefined training problem set, which automates the adaptability of the low-level optimizers on unseen problem instances. Currently, a common training problem set choice in existing MetaBBOs is well-known benchmark suites CoCo-BBOB. Although such choice facilitates the MetaBBO's development, problem instances in CoCo-BBOB are more or less limited in diversity, raising the risk of overfitting of MetaBBOs, which might further results in poor generalization. In this paper, we propose an instance generation approach, …

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Bridging Synthetic And Real Routing Problems Via Llm-Guided Instance Generation And Progressive Adaptation, Jianghan Zhu, Yaoxin Wu, Zhuoyi Lin, Zhengyuan Zhang, Haiyan Yin, Zhiguang Cao, Senthilnath Jayavelu, Xiaoli Li Jan 2026

Bridging Synthetic And Real Routing Problems Via Llm-Guided Instance Generation And Progressive Adaptation, Jianghan Zhu, Yaoxin Wu, Zhuoyi Lin, Zhengyuan Zhang, Haiyan Yin, Zhiguang Cao, Senthilnath Jayavelu, Xiaoli Li

Research Collection School Of Computing and Information Systems

Recent advances in Neural Combinatorial Optimization (NCO) methods have significantly improved the capability of neural solvers to handle synthetic routing instances. Nonetheless, existing neural solvers typically struggle to generalize effectively from synthetic, uniformly-distributed training data to real-world VRP scenarios, including widely recognized benchmark instances from TSPLib and CVRPLib. To bridge this generalization gap, we present Evolutionary Realistic Instance Synthesis (EvoReal), which leverages an evolutionary module guided by large language models (LLMs) to generate synthetic instances characterized by diverse and realistic structural patterns. Specifically, the evolutionary module produces synthetic instances whose structural attributes statistically mimics those observed in authentic real-world instances. …

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Knowing The User: Representation, Contextualization, And Predictive Internalization In Personalized Models, Zhongyu Ouyang Jan 2026

Knowing The User: Representation, Contextualization, And Predictive Internalization In Personalized Models, Zhongyu Ouyang

Dartmouth College Ph.D Dissertations

Intelligent systems increasingly anticipate which items a person will prefer, adapt generated text to a familiar style, and carry relevant information across interactions. These capabilities invite the impression that a model knows its user. In the technical sense studied here, a model knows a user when it carries information specific to that user, uses that information in the relevant context, and generalizes beyond the observations used to build the user state. Knowing is thus a measurable relation among user evidence, model state, context, and held-out user-specific behavior. The research community generally studies this family of systems under the term personalization, …

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