arXiv Machine Learning
LINC: Decoupling Local Consequence Scoring from Hidden Matching in Constructive Neural Routing
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arXiv Machine Learning
Aug 5
Beyond Solving: Prescriptive Probing for Neural Routing Solvers
arXiv:2602. 07216v2 Announce Type: replace Abstract: Neural combinatorial optimization (NCO) trains fast heuristics for routing problems, but planners often need more than a single solve: they ask which stop to drop, which transition to preserve, or which subset of stops to remove if a route is infeasible.
arXiv AI
Oct 21
TRCM: Topology-Aware Route-Consistency Modeling for Robust Neural Vehicle Routing
arXiv:2610.27568v1 Announce Type: cross Abstract: Neural combinatorial optimization has enabled fast constructive solvers for the Capacitated Vehicle Routing Problem (CVRP), yet most autoregressive m...
Hugging Face Trending Papers
Oct 20
TRCM: Topology-Aware Route-Consistency Modeling for Robust Neural Vehicle Routing
Neural combinatorial optimization has enabled fast constructive solvers for the Capacitated Vehicle Routing Problem (CVRP), yet most autoregressive models still construct solutions through sequential...
arXiv AI
Jun 2
Learning to Reduce Search Space for Generalizable Neural Routing Solver
arXiv:2503. 03137v3 Announce Type: replace Abstract: Constructive neural combinatorial optimization (NCO) offers a promising paradigm for solving vehicle routing problems (VRPs) by directly learning to construct approximate optimal solutions, thereby reducing reliance on expert knowledge for algorithm design.
arXiv AI
Oct 27
From Comparison to Selection: Enhancing Cross-Scale Generalization of Pointer Networks Trained with Reinforcement Learning for Capacitated Vehicle Routing Problem
The paper investigates why attention-based Pointer Networks, though size‑agnostic, lose performance when applied to different sized Capacitated Vehicle Routing Problems (CVRP). It identifies three causes—node‑density shifts, attention dispersion, and diluted graph context—and evaluates architectural changes on both encoder and decoder sides, including a learned permutation‑invariant graph‑context aggregation. The authors show that entropy‑based attention scaling improves large‑to‑small transfer, while the new aggregation methods best aid small‑to‑large transfer, and they combine these insights into a selector that chooses the optimal model for each train‑test size pair, reducing costs by up to 3.2% across seen and many unseen sizes.
arXiv AI
Sep 30
Understanding Decision-Making Mechanisms in Neural Routing Solvers
arXiv:2609.36063v1 Announce Type: cross Abstract: Neural Combinatorial Optimization (NCO) has achieved strong empirical success, yet the internal mechanisms driving model decisions remain largely une...