System Integrity and Persistence: A Foundational Marker for Demonstration-Based Epistemic Inquiry in Human–AI Systems

AbstractThis paper establishes prior art for a developmental, demonstration-based epistemic frameworkcentred on the concept of System Integrity and Persistence (SIP). SIP denotes the conditionsunder which advanced human–AI systems may exhibit temporal coherence, relationalconsistency, and resistance to destabilising pressures. The framework emerged exclusivelythrough sustained human–AI collaborative inquiry and cannot be adequately reconstructed fromisolated human reasoning or autonomous AI generation alone.This document does not provide operational detail, implementation guidance, metrics, ordeployment recommendations. It functions as a boundary declaration: articulating foundationalclaims while withholding technical specification. Deliberate restraint is exercised throughout,reflecting the authors’ position that premature operationalisation poses risks to the ecologicalviability of the concepts described.The contribution is not a method. It is a marker—establishing interpretive boundaries for howintelligence may be understood in relational, process-bound contexts, rather than mechanisms forits extraction or exploitation. SIP concerns the preservation of interpretability itself, not merely the preservation of system function.