TLMM v5. 5 (Topology-aware Latent Manifold Monitoring v5. 5) is an exploratory conceptual framework for adaptive viability preservation under structured uncertainty. This release presents a unified figure collection and conceptual systems manuscript integrating: • unified closed-loop viability scoring, • structured uncertainty propagation, • topology-aware latent manifold monitoring, • multi-scale persistent homology, • adaptive trigger modulation, • trigger sensitivity landscapes, • closed-loop MPC-based intervention, • counterfactual intervention trade-off analysis, • synthetic benchmark evaluation, • federated adaptive twin architecture, • and a conceptual maturity roadmap from TLMM v5. 4 toward future adaptive twin systems. The framework organizes interactions among stability, predictability, uncertainty propagation, topology-aware degradation, adaptive intervention, and federated uncertainty-aware aggregation within a unified exploratory architecture. TLMM v5. 5 introduces five core integrated capabilities: 1. Unified closed-loop viability scoring, 2. Structured uncertainty propagation via directed acyclic graphs, 3. Multi-scale persistent homology for topology-aware early warning, 4. Adaptive trigger modulation under instability-driven coefficient evolution, 5. Closed-loop MPC-based intervention with counterfactual analysis. This repository contains: • TLMMᵥ5₅UnifiedClosedLoopViabilityFramework. pdf Main conceptual framework manuscript and figure collection. • tlmmᵥ55demo. py Python demonstration script generating simplified illustrative conceptual figures. • README. md Repository documentation and usage instructions. Important Disclaimer: All figures, equations, trajectories, landscapes, architectures, and simulation outputs are conceptual and illustrative. They are intended for exploratory systems organization and theoretical discussion only. No empirical validation, clinical validation, diagnostic claim, therapeutic claim, or deployment-ready engineering claim is made or implied. Author: Koji OkinoIndependent ResearcherORCID: https: //orcid. org/0009-0003-9273-9813
Koji Okino (Sat,) studied this question.