Semantic Physics: Universal Thermodynamics and Context-Dependent Kinetics in AI Language Systems
Year: 2025
Type: article
Abstract: 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.
Author: Gebendorfer, Jonas Jakob
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7094944977
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