A Dynamic Phase Transition Model in the Subatomic Reduction of Thought ― Construction of an Empirical Research Program via Rational Ratio Metrics, Latent Variable Modeling, and Non-Linear Model Comparisons ―

Abstract

Abstract This paper formulates the intellectual production process under a human–artificial intelligence (AI) co-creation environment as the "subatomic reduction of thought" and a "phase transition model," presenting it as an empirically testable and falsifiable research program. In contrast to conventional equilibrium representations based on subtraction—which reduce opposing forces to a "static null"—this model introduces the "state ratio S(t)" expressed through ratios of multiplication and division. Rather than equating S(t) = 0.5 (a state of equal opposing forces) directly with homeostasis, we rigorously redefine dynamic homeostasis through time-series stationarity (\frac{dS}{dt} \approx 0) and resilience to external disturbances. Furthermore, the thought resolution model D = O \times A \times U is positioned not as an a priori deterministic axiom, but as a hypothesis model composed of latent variables representing observation, alignment, and structural understanding. Furthermore, critical thresholds such as \mathrm{p}K_a and "12-hour sessions" are designated not as physical-chemical entities, but as mathematical analogies for critical transitions and observational case examples. By treating the AI amplification effect (null hypothesis H_0: k=1) and non-linear phase transitions (e.g., logistic models) as testable hypotheses, we construct a rigorous protocol allowing third parties to objectively verify and replicate the model against conventional linear models using statistical model evaluation metrics (AIC, BIC, cross-validation).