Two papers question internal mechanisms of sheaf graph neural networks

Yi Liu argues learned restriction maps in sheaf GNNs do not prove useful edge geometry, as parameter movement and ablation can conceal missing task-level value. Grover and Bourgerie introduce a measure-intervene-control study to test if sheaf neural networks actually use holonomy.