TLMM v4.4: Continual Adaptive Risk Mapping under Non-Stationary Dynamics Fokker–Planck Closure, Exploratory Quantitative Fitting, Predictive Control, and Translational Scope

TLMM v4. 4 presents an exploratory theoretical framework for continual adaptive risk mapping under non-stationary dynamics. The framework extends earlier nonlinear Threshold-Limited Mode Modulation (TLMM) formulations from deterministic mode-level suppression dynamics to a stochastic landscape-level formalism based on: • Fokker–Planck closure• Time-varying effective potential landscapes V (E, t) • Operational-window narrowing• Analytical narrowing exponent β• Adiabatic reduction validity analysis• Exploratory EEG-based quantitative fitting (CHB–MIT dataset) • Inter-subject parameter variability• Continual adaptive risk mapping• Predictive vs reactive control simulation• Future-facing adaptive digital-twin architecture• Scope and limitation mapping The report emphasizes staged translational framing, uncertainty awareness, and explicit limitation management. Key contributions include: • Analytical interpretation of the narrowing exponent β in terms of local landscape curvatures• Quantification of adiabatic reduction breakdown near λτc ~ 1• Exploratory Bayesian parameter inference and variability analysis• Continual adaptive risk tracking under parameter drift• Monte Carlo comparisons between predictive and reactive control policies• Explicit scope/limitation mapping for exploratory translational systems This work is theoretical and exploratory. CHB–MIT EEG analyses are used as proof-of-contact examples only and do not constitute clinical validation. No diagnostic, therapeutic, medical-device, or clinical decision-making claims are made. Repository contents include: • Full manuscript PDF• Publication-ready infographic figures (Figs. 1–10) • Simplified conceptual Python demo scripts• README documentation Author: Koji Okino (2026)