David Edward Scherer Formula (DES) V3.6: Meta-Stable Quant Intelligence Layer — A Controlled Adaptive Learning Framework for Financial Systems Under Uncertainty

DES V3.6 presents a meta-stable quantitative intelligence framework designed to regulate adaptive learning in financial systems operating under uncertainty. The methodology introduces a conditional learning paradigm in which model updates are governed by a Meta-Stability Gate that evaluates market stability, signal quality, and regime confidence before permitting adaptation. The framework incorporates a Noise Separation Layer to distinguish true signal from regime noise, microstructure effects, and event-driven distortions, ensuring that only validated information contributes to model updates. A Confidence-Weighted Learning Rate Controller dynamically adjusts adaptation intensity based on system certainty and environmental stability. A Regime-Aware Policy Switchboard translates market regime classification into direct control over factor weighting, risk exposure, and portfolio behavior. Structural risks are managed through a Structural Break Detector that identifies discontinuities such as correlation breakdowns, volatility regime shifts, and factor inversions, triggering protective responses including learning suspension and parameter reset. At the highest level, a Meta-Learning Controller evaluates the effectiveness of prior updates and refines learning behavior across regimes, enabling continuous improvement in adaptation strategy without assuming that all new data is informative. The system architecture integrates signal validation, controlled learning permissions, regime-driven policy control, and recursive performance evaluation into a unified framework. DES V3.6 is designed to reduce overfitting, prevent noise-driven adaptation, and maintain robustness across stable, transitional, and disrupted market environments. This work is intended for research and educational purposes in quantitative finance, adaptive systems, and algorithmic decision-making under uncertainty.