TLMM v5.7: Topology-aware Long-horizon Viability Modeling under Structured Uncertainty

TLMM v5. 7 is a conceptual framework for topology-aware long-horizon viability modeling under structured uncertainty. The framework integrates: • latent twin representation learning • structured uncertainty propagation • Predictive Persistent Homology (PPH) • coupled viability dynamics • long-horizon adaptive Model Predictive Control (MPC) • Pareto-guided intervention design • topology-aware probabilistic forecasting • adaptive resonance manifold dynamics • federated twin coordination • conceptual population-scale viability extensions This release contains: • a full conceptual manuscript • 13 high-resolution figure panels • illustrative demonstration code (`tlmmᵥ57demo. py`) • README documentation Core concepts include: • topology-aware early warning of viability degradation • structured uncertainty cascades and regime-shift detection • long-horizon viability preservation via adaptive MPC • topology-aware probabilistic forecasting • Pareto-optimal intervention strategies • federated topology-sharing digital twins • conceptual evolution toward TLMM v6. x adaptive twin ecosystems The repository also introduces a proposed v5. 8 extension toward population-scale viability manifolds and Global Viability Index (GVI) aggregation. All figures, metrics, equations, benchmark values, and translational examples are illustrative and intended for conceptual and methodological communication only. No clinical, deployment, operational, or medical claims are made.