TLMM v6.0: Self-Organizing Meta-Viability Systems — Anticipatory Structural Reconfiguration in Neural Digital Twin Networks

TLMM v6.0 introduces a conceptual and simulation-oriented framework for anticipatory meta-viability systems in neural digital twin networks. Building on the population-scale viability modeling architecture developed in TLMM v5.x, this work extends the framework toward topology-aware, self-organizing, and self-evolving collective resilience systems. The framework integrates: • Predictive Viability Horizon (PVH)• Dynamic Regime Boundary under Uncertainty• Topology Repair Automaton (TRA)• Adaptive Structural Reconfiguration• Persistent Homology of Meta-Viability Fields• Adaptive Memory and Meta-Learning• Inter-Population Coupling (IPC)• Emergent Collective Intelligence• Fully Autonomous Meta-Viability Ecosystems A central contribution of TLMM v6.0 is the transition from threshold-reactive intervention toward trajectory-anticipatory viability stewardship through topology-aware collective adaptation. Persistent homology is incorporated as an anticipatory topological early-warning layer, while cooperative multi-population learning enables emergent collective intelligence across scales. All figures, trajectories, simulations, and numerical examples are conceptual and illustrative unless otherwise stated. This work is intended as a conceptual systems-science framework and exploratory simulation architecture for future anticipatory resilience research. No clinical, operational, or policy deployment claims are made.