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Learning to Execute Graph Algorithms Exactly with Graph Neural Networks
arXiv:2601.23207v2 Announce Type: replace Abstract: Understanding what graph neural networks can learn, especially their ability to learn to execute algorithms, remains a central theoretical challenge. In this work, we prove exact learnability results for graph algorithms under bounded-degree and finite-precision constraints. Our approach follows a two-step process.