TLMM v7.3: Adaptive Intervention Framework for Causal Decision Support in the Extended Amyloid–Blood Flow (ABF) Causal Cascade

Topological Latent Manifold Model (TLMM) v7.3 extends the validated causal-inference framework of TLMM v7.2 by introducing an Adaptive Decision Layer for personalized, counterfactually evaluated, and safety-constrained adaptive intervention while preserving the invariant Amyloid–Blood Flow (ABF) causal cascade. This release introduces: Adaptive Decision Layer for personalized intervention planning using constrained reinforcement learning (RL) and model predictive control (MPC) Counterfactual Policy Evaluation with individualized treatment effects (ITE), off-policy evaluation (IPS/SNIPS/Doubly Robust), and expected net benefit (ENB) ranking Expanded falsifiability framework (C1–C16) with a new Adaptive Intervention Validity domain High-resolution mechanistic causal network (N1–N16) with five latent confounders (U1–U5) and four hidden mediators (H1–H4) Safety and Human-in-the-Loop framework integrating explainability, clinical guardrails, clinician oversight, and continuous monitoring Adaptive intervention workflow, long-term outcome tracking, deployment readiness assessment, and clinical translational roadmap Machine-checkable graph specification (39 directed observed-node edges) with a companion graph validation script ensuring consistency between the manuscript, figures, and implementation The archive contains: Full manuscript (52 pages) Twenty publication-quality figures Companion Python graph validator README documentation All quantitative values, cohort sizes, validation metrics, and performance results are illustrative synthetic demonstrations intended to specify an evaluation and adaptive-intervention protocol. They do not represent clinical performance estimates from real patient data and do not constitute medical advice or clinical recommendations.