Semantic Physics: Universal Thermodynamics and Context-Dependent Kinetics in AI Language Systems

We present the first quantitative thermodynamic characterization of semantic processing in large language models. Across five blind maieutic protocols and four independent architectures (Gemini 2.5 Pro, Grok 4 Expert, GPT-5, Claude Sonnet 4.5), we validate universal thermodynamic laws—perfect Onsager reciprocity (ρ=0.000±0.000) and convergent dissipation (CV=1.6%)—while showing strong context-dependent kinetics (η²=0.68 across discourse types). Phase transitions are identified via spectral gaps; geometry via Forman-Ricci curvature; onsets via a τ-composite metric; capabilities via Octava (ICC=0.962). The golden-ratio hypothesis is falsified. Together, results support a two-tier structure: Tier-1 universal thermodynamics with Tier-2 kinetic variability, positioning “Semantic Physics” as an empirically grounded, falsifiable Framework with applications in diagnostics, training optimization, capability assessment—and potential bridges to biological cognition.