【How to Approach This Series】 This paper is constructed upon a dynamic cosmological perspective, featuring a multi-layered and internally consistent structure. Consequently, readers may find the content recursive or densely interconnected. To maximize understanding, we recommend the following approach: 1.Load multiple papers (3–5) from this series into an AI system (such as Claude or ChatGPT). 2.Request a summary and a structural analysis of the interconnected concepts. 3.Once you have grasped the holistic picture, proceed to read the specific sections of interest.This friction is not a flaw, but rather a natural characteristic of engaging with a theory that possesses a dynamically updating universe. [ Recommended Prompt for AI Analysis ] You can use the following prompt to analyze the series efficiently: "I have uploaded multiple papers from this series.Please analyze them as a unified theoretical framework,rather than as independent or static papers. 1.Identify the core 'dynamic structure' and explain how the concepts are internally consistent and recursive. 2.Synthesize the definitions of key terms that appear across the papers. 3.Provide a high-level summary of the overall cosmological view presented. 4.Outline the logical flow connecting these specific papers."

We propose the Variance-First Principle: in dynamically updated physical systems, environmental or control perturbations manifest first in the variance (or failure rate) of observables, while the mean value responds only secondarily. This principle is shown to unify experimental, engineering, and theoretical observations across electrochemical systems, electromagnetic environments, and microfluidic reactors. We demonstrate that variance is not a secondary statistical artifact but a primary physical observable encoding update stability, coherence, and robustness. Monte Carlo simulations verify that variance responds 2.7× faster than mean in electrochemical systems, with |ΔVar/ΔMean| ≈ 100 in EM environments. The principle naturally extends the V63/V64 framework and provides experimentally testable predictions that do not rely on mean-value shifts.