The Signal Was in the Model All Along: A Self-Correction, Theoretical Analysis, and Revised Direction for Noise-Based Seed Pre-Screening in Diffusion Models

In a previously published technical report (Ticudean, 2026c), I claimed that spectral features of the initial noise tensor zT could predict the structural fidelity of AI-generated visualizations with AUC = 0.932, significantly outperforming seed-bit baselines. Upon careful re-examination, I discovered two methodological errors that invalidate this central claim. First, the spectral models were inadvertently trained with output-type one-hot features while the seed-bit baselines were not - a confounded comparison. Second, a data-alignment bug caused seed-bit models to train and evaluate on mismatched labels, producing spuriously sub-random AUCs. After correcting both errors, spectral-only AUC falls to 0.454, indistinguishable from chance. The AUC 0.932 is entirely attributable to the output-type encoding, a finding already established in Paper 2 (Ticudean, 2026b). I disclose these errors here, explain why they occurred, report the corrected experimental results in full, and provide a theoretical argument for why aggregate spectral statistics of a white-noise tensor must fail: with 16,384 i.i.d. N(0,1) samples, all summary statistics concentrate tightly around their expectations, leaving spatial configuration, accessible only through the model, as the sole carrier of image-determining information. This analysis confirms the direction taken by the leading methods in the field (InitNO, NPNet, NoiseQuery) and reframes the open research problem: the correct screening signal must be extracted through the denoising model, not before it. I conclude by identifying the unique asset this research program has contributed, an automated structural fidelity scorer with no precedent in the model-informed noise literature, and outline three concrete experiments that exploit it.