ATHOS-SHIELD: Epistemic Robustness Framework for Low-Intensity Tapering in Declared Dependencies via SHADOW/ANCOVA

ATHOS-SHIELD operationalizes user-initiated, non-clinical harm reduction for self-declared substance (alcohol, cannabis, stimulants) and behavioral (gaming, social media, gambling) dependencies within the ATHOS governance ecosystem. Eschewing diagnosis or prediction, it delivers proportional tapering schedules anchored to sustainable real-world activities (social connection, hobbies, temporal structure). **Theoretical core**: SHADOW (Lévy-stable variance sampling of neurobehavioral discontinuities) stress-tests against \(10^{308}\)-scale interactions (IEEE 754 limit); ANCOVA (covariate-adjusted residual modeling) ensures longitudinal drift stability. Kernel 10 provides cryptographic reversibility/traceability, satisfying EU AI Act Articles 12–15 (limited-risk classification). **Contributions**: (1) First epistemic framework for infinite-scale behavioral robustness; (2) Open pseudocode/prototypes; (3) Ethical blueprint for scalable harm reduction. Validation invited on synthetic/anonymized datasets.