Semantic Interpolation: Canonical SR Entry

Semantic Interpolation is defined as the structural process through which modern AI systems fill conceptual gaps by synthesizing meaning from adjacent, higher‑density semantic domains when direct, authoritative, or domain‑specific material is insufficient. Rather than producing random hallucinations, semantic interpolation generates synthetic but structurally plausible meaning that stabilizes emerging or underspecified concepts. This canonical entry outlines the mechanism by which AI systems identify conceptual gaps, locate nearby semantic neighbors, extract recurring patterns, and generate coherent completions that maintain semantic continuity. It situates this behavior within the SignalRupture framework of semantic governance, showing how interpolation shapes field formation, misclassification, and the visibility of new conceptual architectures. The entry uses the case of SignalRupture’s appearance in Google’s AI Overview—where governance content was injected into an emerging field description—to illustrate how semantic interpolation both elevates and distorts new fields by forcing them into pre‑existing ontologies.