Scale-Aware Adversarial Analysis: A Diagnostic for Generative AI in Multiscale Complex Systems

• URL: http://arxiv.org/abs/2605.00510v1
• Date: Fri, 01 May 2026 08:36:52 GMT
• Title: Scale-Aware Adversarial Analysis: A Diagnostic for Generative AI in Multiscale Complex Systems
• Abstract summary: We introduce a diagnostic framework driven by Constrained Diffusion Decomposition (CDD)<n>We execute deterministic interventions directly within the continuous, CDD-based scale space.<n>We demonstrate that under moderate physical perturbations, the unconstrained generative model exhibits localized structural freezing and non-linear instability.
• Score: 5.173587482427292
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Complex physical systems, from supersonic turbulence to the macroscopic structure of the universe, are governed by continuous multiscale dynamics. While modern machine learning architectures excel at mapping the high-dimensional observables of these systems, it remains unclear whether they internalize the governing physical laws or merely interpolate discrete statistical correlations. Standard Explainable AI (XAI) architectures, particularly perturbation-based and gradient-saliency methods, rely on pixel-wise perturbations, which generate unphysical artifacts and push inputs off the valid empirical distribution. To resolve this, we introduce a diagnostic framework driven by Constrained Diffusion Decomposition (CDD), a diffusion-based multiscale data decomposition algorithm that enables physically constrained data generation and model evaluation via scale-aware modifications. Applying this framework to a Denoising Diffusion Probabilistic Model (DDPM), we execute deterministic interventions directly within the continuous, CDD-based scale space. We demonstrate that under moderate physical perturbations, the unconstrained generative model exhibits localized structural freezing and non-linear instability rather than continuous PDE-like responses. The network fails to maintain cross-scale continuity, causing the generative trajectory to diverge when pushed into unseen physical states. By synthesizing a continuum of physically coherent states, this scale-informed methodology establishes a controlled test ground to evaluate algorithmic vulnerabilities, providing the rigorous physical constraints necessary for future architectures to respect the multiscale causality of the natural universe.

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