NEPF decomposes routing policies into node permutation and edge selection stages for scalable solving of multigraph VRPs, achieving competitive quality with faster training and inference.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
WeCon introduces gated residual fusion in the encoder, residual fusion in the decoder, and efficient preference optimization to match state-of-the-art hypervolume on MOCOPs while cutting inference time by 40%.
citing papers explorer
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Two-Stage Learned Decomposition for Scalable Routing on Multigraphs
NEPF decomposes routing policies into node permutation and edge selection stages for scalable solving of multigraph VRPs, achieving competitive quality with faster training and inference.
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WeCon: An Efficient Weight-Conditioned Neural Solver for Multi-Objective Combinatorial Optimization Problems
WeCon introduces gated residual fusion in the encoder, residual fusion in the decoder, and efficient preference optimization to match state-of-the-art hypervolume on MOCOPs while cutting inference time by 40%.