TLMM v4.7: A Feasibility-Oriented Adaptive Computational Framework for Resilience-Aware Inference, Streaming Adaptation, and Exploratory Digital Twin Co-Evolution

This repository contains the manuscript, figures, and demonstration code for TLMM v4.7 (Two-Layer Modulation Model v4.7), an exploratory and simulation-based adaptive computational framework for resilience-aware inference under nonstationary conditions. TLMM v4.7 integrates: • adaptive landscape dynamics• resilience-aware risk mapping• uncertainty-aware inference• sensitivity landscape analysis• LOSO-CV baseline comparison• streaming adaptive inference• exploratory public EEG proof-of-contact analysis• structured limitation mapping• conceptual digital twin co-evolution architecture The framework is intended as a feasibility-oriented computational methodology and does not constitute a clinical system. Key additions relative to earlier versions include:• parameter sensitivity landscapes across forgetting, diffusion, and adaptation regimes• robustness-oriented LOSO-CV benchmarking against AR and HMM baselines• streaming adaptive inference under nonstationary dynamics• exploratory qualitative comparison with public EEG datasets• structured roadmap for limitation-aware feasibility progression• conceptual adaptive digital twin co-evolution framework All results are:• simulation-based• exploratory• illustrative• feasibility-oriented No clinical inference, diagnosis, prognosis, biomarker claim, therapeutic claim, medical-device claim, or regulatory claim is intended or implied. Repository contents:• manuscript PDF• figure set (Fig.1–Fig.10)• demonstration Python script• README documentation Author:Koji OkinoIndependent Researcher / SD Lab2026