The paper introduces the Compositional Geometry Routing Problem and proposes DiCon, a differential-attention plus double-level contrastive learning solver that reports strong performance and generalization on mixed-geometry routing instances.
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Contextual Plackett-Luce extends the classical Plackett-Luce model with context-dependent Ising parameterization to enable efficient parallel scoring followed by incremental autoregressive selection for ambiguous sequence tasks.
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Learning to Solve Compositional Geometry Routing Problems
The paper introduces the Compositional Geometry Routing Problem and proposes DiCon, a differential-attention plus double-level contrastive learning solver that reports strong performance and generalization on mixed-geometry routing instances.
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Contextual Plackett-Luce: An Efficient Neural Model for Probabilistic Sequence Selection under Ambiguity
Contextual Plackett-Luce extends the classical Plackett-Luce model with context-dependent Ising parameterization to enable efficient parallel scoring followed by incremental autoregressive selection for ambiguous sequence tasks.