This work presents Structural Differentiation Information (SDI) v1.6, a minimal and falsifiable framework in which information is defined not as passive storage, symbolic encoding, or abstract correlation, but as the stabilized outcome of accumulated structural differentiation. In SDI, information emerges when local structural differentiation becomes sufficiently persistent to produce measurable retention across artificial, biological, and physical systems. The framework is expressed through a compact logarithmic relation, explicitly visualized via representative numerical behavior, and connected to observable proxies for empirical testing. The paper is structured as follows: A minimal analytical definition of structural information Numerical behavior demonstrating parameter roles (saturation and rise) Observable proxies across AI, biology, and physics Conceptual integration within a broader structural framework SDI provides a direct bridge between local structural dynamics and observable persistence, enabling cross-domain comparison and empirical evaluation. This framework is minimal, falsifiable, and directly testable, offering a unified structural interpretation of information grounded in measurable persistence.
Koji Okino (Mon,) studied this question.