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Current embedding models misinterpret mathematical equivalence, grouping statements by terminology rather than content, but a new contrastive learning approach can bridge this gap.
Uncovers how GNNs internally implement and reuse algorithmic steps, paving the way for understanding and controlling neural algorithmic reasoning.