Core Distinguishability Relativity (CDR) is a pre-registered framework designed to detect deviations from structural sufficiency in observed dynamics while avoiding common inferential failures such as p-hacking, circular reasoning, and over-flexible model interpretation. In this work, we present the first consolidated empirical validation of CDR across a controlled toy model and eight empirical/experimental configurations: energy infrastructure, neural dynamics from fMRI, human mobility, ecological population dynamics, protein molecular dynamics, EEG-only experimental neural validation, RNG-only stochastic baseline validation, and a joint EEG + RNG experimental configuration. Across these domains, CDR consistently distinguished systems whose dynamics were adequately explained by the empirical reference kernel from domains exhibiting low but non-zero residual structure. Strongly structured macroscopic and physical systems clustered near ε ≈ 0, while neural domains showed low but detectable residual structure. The RNG-only baseline remained near ε ≈ 0 and collapsed cleanly under controls, confirming that the framework does not generate spurious structure in a stochastic null domain. An emergent domain property, injectability, was also identified as a systematic predictor of injection recovery behavior across high- and low-injectability domain classes. The joint EEG + RNG implementation remained stable and falsifiable after state-space redesign, but the observed joint epsilon stayed weak, providing no strong evidence of robust cross-domain coupling under the present observational setup. These results support CDR as a domain-sensitive detector of deviation from structural sufficiency and motivate a Phase III.1 redesign incorporating multi-subject EEG, informational latent variables, and quantum-aware proxy structure. This empirical validation paper builds on the original Core Distinguishability Relativity theoretical framework.
Thiago Siqueira da Luz e Silva (Mon,) studied this question.