Semantic Relativity Theory v3.0: A complete empirical validation of observer-dependent meaning in AI-mediated communication

Semantic Relativity Theory posits that meaning in AI-mediated communication exhibits properties analogous to physical relativity: observer-dependent curvature, gravitational effects, and measurement uncertainty. This paper presents TRS v3.0, integrating three prior theoretical advances—CHORDS++ multidimensional framework, Euler topological stability analysis, and Kullback-Leibler divergence quantification—into unified empirical validation.We analyzed 303 evaluations (101 texts × 3 LLMs: Grok, Gemini, GPT-4o) across complete measurement suite: dimensional profiles, topological stability (χ ∈ [2,6]), inter-model divergence, paraphrastic resistance (IRP), and gravitational field strength (G). Analysis reveals universal positive divergence (KL > 0 across 267 model-pair comparisons), definitively rejecting objective meaning hypothesis and confirming observer-dependent semantic relativity as fundamental property of AI-mediated interpretation. Results confirm all core predictions: (1) topological variance discriminates stable (χ=2, 55%) from collapsing architectures (χ=6, 23%); (2) systematic inter-model divergence averages KL=0.0018 with 26.2% exhibiting semantic collapse (G_im < 0.3); (3) IRP-χ coupling predicts stability (correlation 0.878); (4) gravitational field strength quantifies interpretative influence independent of surface metrics. TRS v3.0 closes theoretical circle: from conceptual framework through operational measurement to empirical validation, establishing relativistic semantics as predictive science for AI content evaluation, cross-platform consistency, and observer-dependent meaning dynamics.