ABF v2.2 advances the Appearance–Behavior Framework from validation-grounded inference toward adaptive personalized monitoring through the introduction of Sequential Bayesian Updating and a Digital Twin architecture. Building upon the predictive forecasting capabilities of ABF v2.0 and the validation and uncertainty quantification framework of ABF v2.1, this release introduces five tightly integrated contributions: Sequential Bayesian Updating Architecture for incremental posterior updating without full re-estimation (Fig. 28) ABF Digital Twin Dashboard for individualized behavioral monitoring (Fig. 29) Batch vs. Sequential Inference comparison using a synthetic ADNI-inspired pilot (Fig. 30) Personalized Behavioral Forecasts across heterogeneous risk profiles (Fig. 31) Adaptive Weight Evolution under Sequential Bayesian Updating (Fig. 32) The framework enables continuous updating of: Behavioral Transformation Risk Score (BTRS) Estimated critical transition time (τ̂crit) Forecast trajectories Uncertainty decomposition through a closed-loop Digital Twin architecture. ABF v2.2 represents the adaptation phase of the framework roadmap: v1.x (Theory) → v2.0 (Prediction) → v2.1 (Validation) → v2.2 (Adaptation) → v2.3 (Intervention) The repository contains: Full manuscript Figure collection (Figs. 1–32) Demonstration Python implementation README documentation All results are simulation-derived and intended for methodological illustration only. ABF v2.2 is a risk assessment support framework and not a diagnostic or prognostic instrument. Empirical validation on real-world longitudinal datasets remains the primary future direction.