GeoRouteNet: A Geometry-Aware Non-Autoregressive Neural Solver for the Euclidean Traveling Salesman Problem
Non-autoregressive neural solvers amortize computation across traveling salesman problem (TSP) instances, but models trained on random Euclidean instances can degrade when the number or spatial distribution of nodes changes. We study whether explicit geometric features and a richer within-instance training signal improve transfer across graph sizes and spatial distributions. We introduce GeoRouteNet, which augments a non-autoregressive TSP solver with centered node offsets and radii, learnable radial distance bases, distance-aware graph attention, explicit edge messages, and cross-layer representation mixing. We also introduce multi-candidate self-comparison reinforcement learning (MCS-RL...
| Authors | Xiang Li |
|---|---|
| Primary category | cs.LG |
| Published | 22 June 2026 |
| Last updated at source | 17 July 2026 |
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