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Most "long-range" GNN benchmarks fail to actually test long-range interactions, but a new framework provides provably long-ranged tasks and a per-range error oracle.
Graph Transformer attention doesn't degrade into unstructured noise at scale: learned interaction matrices converge to continuous, predictable graphons that transfer directly to larger graphs.
Graph models can now generalize to entirely new datasets with different input features, thanks to a simple projection into a shared random space.
