Algorithmic Citability and the Collapse Mechanism in Hybrid Search-Generation Systems: Empirical Validation of Semantic Relativity Theory v2

The emergence of hybrid search-generation systems—exemplified by AI Overviews (AIOs)—has revealed a structural discontinuity between deterministic ranking signals and generative outputs. Empirical observations across multiple independent studies demonstrate that high-ranking documents frequently disappear in AIO responses while low-ranking sources gain disproportionate prominence. Formal analysis yields corr(R,C) ≈ 0, where R represents retrieval ranking and C represents generative collapse state, indicating that generative collapse does not propagate algorithmic relevance hierarchies. This phenomenon necessitates a theoretical framework capable of explaining why traditional SEO metrics fail to predict persistence in generative environments. Semantic Relativity Theory v2 (TRS v2) provides such a framework by modeling meaning as a relativistic field governed by semantic mass, gravity, and resonance rather than surface visibility. This paper introduces algorithmic citability as the capacity of a discourse to persist within generative systems' cognitive fields, demonstrating through empirical validation using State of Search Q3 2025 data (Datos/SparkToro) that all seven core principles of TRS v2 align with observable patterns in digital search behavior across 15 months and two major geographic markets. The analysis integrates: (1) formal demonstration of ranking-citability independence through corr(R,C) ≈ 0, (2) theoretical explanation via regime discontinuity between deterministic retrieval and relativistic generation, (3) empirical validation across market dynamics, AI tool adoption, zero-click behavior, and geographic convergence patterns, and (4) the theoretical foundations of algorithmic citability as emergent evidence of semantic gravity. Results confirm that meaning operates according to field-theoretic principles rather than linear cause-effect models, establishing algorithmic citability as a fundamental construct for understanding persistence in AI-mediated information ecosystems. All seven theoretical principles received empirical confirmation, with validation strength ranging from strong to very strong across independent observables.