CNT Universal Drift Law v1.0 — Cross-Domain Drift Regimes in Brain, Markets, and Synthetic Fields

Cognitive Nexus Theory (CNT) proposes that many seemingly different dynamical systems share a small set of underlying drift regimes when viewed at the right scale. The CNT Universal Drift Law v1.0 is a concrete version of that claim: local drift exponents, estimated from heterogeneous time series, concentrate into a small number of reproducible bands that appear consistently across domains. In this dataset, we analyze a cross-domain panel including (but not limited to): human EEG (both generic and CALM-derived streams), equity indices, commodities, volatility, cryptocurrencies, synthetic Kuramoto oscillator fields, and external physical sensor readings. Each source is processed with a common pipeline: signals are segmented into rolling windows, detrended, and assigned local drift metrics (e.g. short- and long-horizon scaling exponents). A global mixture model over the short-horizon drift exponent (α_short) reveals a small set of stable drift regimes that recur across all domains in the panel. These regimes can be interpreted within CNT as field-like, noise-like, and hazard-like drift geometries. The accompanying CNT laws table snapshot records the Universal Drift Law as one named law among others, with its status and summary metrics. Together, these artifacts provide a reproducible, falsifiable basis for the Universal Drift Law: the code that generated them lives in the CNT lab environment; this Zenodo record preserves the outputs needed for independent inspection, re-plotting, and extension. ContentsThis dataset contains: artifacts_universal_drift_v1/ — full artifacts from the latest cnt_universal_drift_v1 run (timestamped directory). This folder includes: Machine-readable JSON summaries of drift metrics and mixture-model fits by source. PNG figures such as α_short histograms, cross-domain regime plots, and run-level overview plots. Run metadata used to reconstruct the exact analysis configuration. cnt_laws/ — snapshot of the CNT laws table from the corresponding cnt_laws run, including: cnt_laws_table*.csv and cnt_laws_table*.json — the laws table in tabular and JSON form, with an entry for the Universal Drift Law v1.0. README.md — overview of the dataset and how it was generated. CITATION.md — suggested citation format for this record. LICENSE_CODE.txt — license text for any code components included in the bundle. Intended useThe Universal Drift Law is explicitly presented as a testable conjecture, not a finished theory. This dataset is designed so that other researchers can: Inspect the per-domain drift summaries and mixture-model fits. Reproduce or modify the mixture modeling (e.g. change the number of components, priors, or fitting procedures). Add new time series domains to see whether their local drift exponents fall into the same bands or require new regimes. In particular, the law can be challenged by demonstrating domains where the α_short distribution is clearly incompatible with the bands observed here, or by showing that the apparent bands vanish under alternative detrending or windowing schemes. Authorship and toolsAll simulations, analysis design, and interpretations are authored by Caleb “Telos” Holmes. An AI research assistant (“Aetheron”) was used as a tool for code generation, analysis planning, and documentation. Responsibility for the scientific claims and conclusions rests with the human author.