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Ollivier-Ricci curvature reveals and rectifies spectral representation errors in GNN-to-MLP distillation, unlocking significant accuracy gains for graph-free student models.
This work introduces CoRA-NAS (COarse Ranking + Anchor-residual), a two-stage framework combining a static ranking prior with low-cost learning-curve refinement that combines cross-space ranking robustness with low-cost architecture selection.
Infinite language generation from positive examples succeeds if and only if finite witnesses preserve an infinite common core, unlocking an infinite complexity hierarchy verified entirely in Lean.
Stochastically deflecting the reverse diffusion trajectory breaks 98–100% of generative watermarks across nine leading schemes without requiring gradient optimization, model internals, or visual quality trade-offs.
Even when an input activates only $k$ of $s$ hidden units, sample complexity cannot escape the total parameter count, incurring an inescapable $\tilde{\Theta}(\sqrt{sk/m})$ rate while eliminating all explicit dependencies on input dimension.
