Maintaining reliable relationships between original conditions, representations, transformations, and outcomes

This research formalizes a cross-domain source-fidelity framework within SICOHERENCE™ (Structural Intelligence Coherence), investigating the preservation of referential integrity, representational accuracy, transformation invariance, provenance continuity, and outcome consistency across interconnected information-processing systems. The proposed architecture distinguishes internal logical consistency from source-relative coherence by examining how distortions originate within representational encodings, propagate through hierarchical transformation and transmission pathways, and produce consequential deviations at the observable output layer. The framework integrates domain-specific distortion metrics, provenance-aware verification, structural invariant preservation, uncertainty quantification, and evidence-based correction protocols. Using the Sefirotic functional hierarchy, Hod corresponds to representation and classification, Yesod to integration and information transmission, and Malkuth to observable manifestation and operational consequences. Applications span human cognitive classification and identity attribution, artificial intelligence inference pipelines, automated decision architectures, and physical measurement systems. A comparative experimental methodology is proposed to evaluate upstream error detection, distortion propagation, corrective intervention efficacy, and measurable source-fidelity restoration. The central proposition is that structural coherence requires more than the absence of internal contradiction; it requires preservation of reliable, traceable relationships between original conditions, their representations, subsequent transformations, and manifested outcomes.