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Author

Mohammed Ahnouch

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#artificial intelligence Preprint Sep 2026

Whitening Inverts the Hierarchy: What the Norm of a Whitened Embedding Measures

Whitening a foundation-model embedding and using its squared norm as a training-free likelihood surrogate is motivated by the observation that whitened coordinates often appear approximately standard normal. We show that this observation follows from the projection central limit theorem and therefore does not imply a G...

Mohammed Ahnouch, Lotfi Elaachak · 0 citations
Preprint Aug 2026

Spectral Rank Certification for Foundation Model Adapters

Nominal LoRA rank is a design parameter; calibrated spectral evidence is a separate inferential quantity. This article develops a finite-sample framework for inferring effective rank structure in public foundation-model adapters. The theoretical core is an exact chi-square divergence for the fixed-dimensional Gaussian...

Mohammed Ahnouch, Lotfi Elaachak · 0 citations
#artificial intelligence Preprint Sep 2026

Interpolation Is Not Invariance: Pair Count Is Not Coverage in Transformation Audits

Four complementary quantities are distinguished---edge count $m$ effective contrast rank $s$, effective contrast rank $s$, population support rank $r$, and graph spectral gap $\eta$---and characterize their roles in audit coverage and deployment reliability.

Mohammed Ahnouch, Lotfi Elaachak · 0 citations
#artificial intelligence Preprint Sep 2026

Semantic Fibers and Cross-Gram Interference: A Calculus of Safety Drift in Overcomplete Representations

An intrinsic calibrated exposure measure is introduced, governed by the leverage duality $\chi^2=1/\ell-1$, which separates observed drift into three diagnostically distinct regimes: a reader fault removable by recalibration, an exact correction that is too ill-conditioned to be reliable, and a representation-level col...

Mohammed Ahnouch, Lotfi Elaachack · 0 citations

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