TLMM v6.3: Geometric Steering and Hierarchical Stability in Anticipatory Meta-Viability Systems

TLMM v6.3 presents an exploratory geometry-aware framework for anticipatory meta-viability systems integrating topology-aware monitoring, Bayesian calibration, causal repair, hierarchical coordination, collective intelligence optimization, and federated topological learning. The framework introduces: Meta-Viability Index (MVI) Viability Risk Index (VRI) Causal Topological Repair Automaton (TRA) Cross-scale resonance boundary conditions Collective Intelligence Gain (CIG) Predictive Viability Horizon (PVH) Geometric steering of collective meta-viability Federated topology-aware learning An illustrative ADNI-oriented reproducibility pipeline is included to demonstrate how topology-aware viability modeling may connect to longitudinal neuroimaging workflows. This repository contains: Full PDF manuscript Supplementary material Python demo script generating illustrative synthetic figures README documentation All figures, workflows, and numerical outputs are illustrative, synthetic, exploratory, and hypothesis-generating unless explicitly stated otherwise.No clinical validation or deployment-ready claims are made.