AI RESEARCH
Rethinking Constraint Awareness for Efficient State Embedding of Neural Routing Solver
arXiv CS.AI
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ArXi:2605.10122v1 Announce Type: new Heavy-Encoder-Light-Decoder (HELD) neural routing solvers have emerged as a promising paradigm due to their broad applicability across multiple vehicle routing problems (VRPs). However, they typically struggle with VRP variants with complex constraints. To address this limitation, this paper systematically revisits existing neural solvers from the perspective of the generation mechanism for state embeddings (i.e., query vector prior to compatibility calculation) during decoding.