TLMM v6.5: Bayesian-Calibrated Viability Risk Modeling and Risk-Driven Topological Repair

This release presents TLMM v6.5 (Two-Layer Modulation Model), a Bayesian-calibrated adaptive framework for Viability Risk Index (VRI) modeling and risk-driven topological repair in neurodegenerative disease contexts.

Building on TLMM v6.4, this version introduces five major advances:

1. Sequential Bayesian VRI updating with external cohort transfer validation (ADNI → AIBL/OASIS)
2. Epistemic–aleatoric uncertainty decomposition with domain-wise analysis
3. Predictive Viability Horizon (PVH/CPVH) as a time-to-event framework
4. Connectome-Inspired Topology Repair Automation (TRA) with uncertainty-aware repair efficacy evaluation
5. Cross-scale resonance stability boundaries with exploratory partial geometric steering

The framework integrates Bayesian inference, uncertainty quantification, predictive viability estimation, topology-aware repair modeling, and exploratory steering concepts within a unified adaptive architecture.

The release includes:

• Full TLMM v6.5 manuscript
• Figures 1–10
• Reproducible demo script (tlmm_v65_demo.py)
• Documentation and validation roadmap

All quantitative results are illustrative and simulation-derived unless otherwise noted. Real-data validation using longitudinal cohorts remains ongoing and will be reported in future versions.

TLMM v6.5 is intended as a conceptual and methodological framework for uncertainty-aware risk estimation, adaptive intervention planning, and future digital twin research. It is not a clinical diagnostic or treatment system.