A Random Matrix Theory Perspective on the Consistency of Diffusion Models

Investigates why diffusion models trained on separate data splits can generate similar outputs from the same noise. Random-matrix analysis isolates shared Gaussian statistics and finite-data variability as a baseline for nonlinear models.

Investigates why diffusion models trained on separate data splits can generate similar outputs from the same noise. Random-matrix analysis isolates shared Gaussian statistics and finite-data variability as a baseline for nonlinear models.

When reading the original paper, identify its problem, inputs, outputs, and methodological assumptions.

Investigates why diffusion models trained on separate data splits can generate similar outputs from the same noise. Random-matrix analysis isolates shared Gaussian statistics and finite-data variability as a baseline for nonlinear models.

Check the baselines, datasets, evaluation protocol, and ablations in the original paper. This directory overview does not provide an independent evaluation or experimental results table.