Topological Latent Manifold Model (TLMM) v7.4 is a mechanistically grounded methodological framework for closed-loop sequential adaptive intervention and large-scale outcome evaluation, developed in the context of Alzheimer's disease. This release introduces an explicit separation between mechanistic falsifiable claims (M1–M20) and validation checks (V1–V20), providing complete traceability between causal hypotheses and their corresponding validation procedures. The framework is built upon an extended Appearance–Behavior Framework (ABF) causal network (N1–N16) and integrates causal inference, uncertainty quantification, sequential policy optimization, counterfactual evaluation, continuous learning, deployment readiness assessment, and precision structural medicine. The repository contains: TLMM v7.4 complete preprint (37 pages) Twenty-four publication-quality figures Supplementary Material (Figure–Claim Mapping, Validation Catalogue, Mechanistic Catalogue, Numeric QC) Reproducible Python demonstration script README and documentation The included demonstration code reproduces the internal organizational structure of TLMM v7.4, including: Mechanistic claim catalogue (M1–M20) Validation check catalogue (V1–V20) Figure–Claim Mapping Unified numeric quality-control registry Illustrative sequential adaptive intervention workflow Illustrative quantitative values are included solely to demonstrate the intended analytical workflow and should not be interpreted as completed clinical evidence. Prospective multi-center validation is planned for future work. This work is intended as a transparent, falsifiable, and extensible methodological foundation for future research in mechanistic machine learning, causal inference, sequential adaptive intervention, and precision structural medicine. License: Creative Commons Attribution 4.0 International (CC BY 4.0).
Koji Okino (Thu,) studied this question.