TLMM v5.4: Exploratory Adaptive Viability Maintenance Under Uncertainty

TLMM v5.4 presents an exploratory conceptual framework for adaptive viability maintenance under uncertainty, integrating uncertainty-aware monitoring, geometry-informed latent dynamics, adaptive trigger modulation, viability-constrained predictive control, federated adaptive learning, and exploratory edge deployment strategies. The framework introduces three primary conceptual directions: uncertainty decomposition using input, parameter, structural, and domain uncertainty components, geometry- and topology-inspired latent manifold degradation indicators, viability-constrained adaptive predictive control under evolving uncertainty conditions. The repository includes: a 13-figure conceptual framework collection, exploratory simulation-derived visualizations, an accompanying Python demo script, methodological notes and scope limitations. All figures, metrics, trajectories, intervention outputs, uncertainty estimates, and deployment benchmarks are illustrative and simulation-derived. This work is intended exclusively for exploratory methodological research and conceptual systems analysis. No clinical deployment, therapeutic efficacy, regulatory approval, or production-grade validation is claimed. Author: Koji OkinoAffiliation: SD Lab LLCORCID: 0009-0003-9273-9813