arXiv cs.AI
· Papers
Can Computational Reducibility Lead to Transferable Models for Graph Combinatorial Optimization?
arXiv:2603.02462v2 Announce Type: replace-cross Abstract: A key challenge in developing unified neural solvers for combinatorial optimization (CO) is the efficient generalization of models from a given set of tasks to new tasks unseen during initial training. To address this, we first establish a new GNN encoder, which