TLMM v5.2: Longitudinal Adaptive Resilience Twins with External Validation, Dynamic Predictability Horizons, Personalized Risk Indexing, and Exploratory Real-Time Feasibility

TLMM v5.2 (Topological Landscape Manifold Model) is an exploratory framework for adaptive resilience modeling in EEG dynamics integrating: - site-level external validation,- dynamic predictability horizon estimation,- Personalized Risk Index (PRI),- time-varying coupling dynamics,- uncertainty decomposition,- baseline forecasting comparison,- and exploratory real-time feasibility assessment. The framework combines adaptive resilience twins, online predictability estimation, uncertainty-aware monitoring, and conceptual closed-loop adaptation strategies. External validation on an independent holdout site (TUH EEG Corpus) demonstrates improved robustness, calibration, and predictability-horizon estimation relative to standard forecasting baselines including AR(p), LSTM, linear state-space, and moving-average models. The repository includes:- full manuscript PDF,- figure set,- and a lightweight synthetic demonstration script (tlmm_v52_demo.py). All analyses are exploratory and hypothesis-generating only.No diagnostic, therapeutic, or clinical decision-making claims are made.