Kullback-Leibler divergence as evidence for semantic relativity: Inter-model meaning variance in AI-generated content

Semantic Relativity Theory posits that meaning exhibits observer-dependent properties in AI-mediated communication, analogous to physical relativity. This paper tests a core prediction: different AI models should produce systematically divergent semantic interpretations of identical texts, quantifiable through information-theoretic metrics. We analyzed 89 texts evaluated by three large language models (Grok, Gemini, GPT-4o) using the CHORDS++ multidimensional framework, calculating Kullback-Leibler divergence between dimensional probability distributions. Results confirm systematic inter-model divergence: KL(Grok||Gemini)=0.001, KL(Grok||GPT)=0.002, KL(Gemini||GPT)=0.002. All pairwise divergences exceeded zero, rejecting the hypothesis of universal meaning. Furthermore, 20-32% of texts exhibited semantic collapse (inter-model gravity G