This paper reframes oversmoothing in neural sheaf diffusion as a representation-degeneracy problem and brings quiver/sheaf theory to bear on the dynamics. It is mathematically rich, but the practical payoff for GenAI builders is indirect.
arXiv:2605.11178v1 Announce Type: new Abstract: Neural Sheaf Diffusion (NSD) generalizes diffusion-based Graph Neural Networks by replacing scalar graph Laplacians with sheaf Laplacians whose learned restriction maps define a task-adapted geometry. While the diffusion limit of NSD is known to be the space of global sections, the representation-theoretic structure of this harmonic space remains largely implicit. We develop a quiver-theoretic interpretation of NSD by identifying cellular sheaves…