TLMM v6.1: Anticipatory Meta-Viability Systems — Toward Predictive Structural Reconfiguration and Collective Intelligence

TLMM v6.1 introduces an anticipatory meta-viability framework integrating predictive viability horizons, topology-aware risk quantification, adaptive structural reconfiguration, collective intelligence emergence, recursive self-observation, and meta-learning into a unified anticipatory systems architecture. Core components include: • Predictive Viability Horizon (PVH) with adaptive uncertainty decomposition• Viability Risk Index (VRI) integrating structural stability, topology, and uncertainty• Persistent homology-based topology-aware early-warning signals• Topology Repair Automaton (TRA) for predictive structural reconfiguration• Anticipatory trajectory steering via geometric viability boundaries• Collective Intelligence Gain (CIG) through topology-aware information integration• Wasserstein geometry-based Inter-Population Coupling (IPC)• Recursive self-observation and adaptive meta-learning• Minimal synthetic population simulation framework The framework is designed to explore anticipatory viability preservation across multi-scale coupled systems, ranging from individuals to large-scale interacting populations. This release contains:• Full conceptual manuscript (PDF)• Figure set integrated into the manuscript• README documentation• Minimal synthetic simulation script (Python) Important note:The included simulations are synthetic and illustrative. This work presents a conceptual and architectural framework and does not report empirical or clinical validation. Author:Koji Okino (Scathed Runner)SD Lab LLCORCID: 0009-0003-9273-9813