Consistency Learning: A Meta-Theoretical, Computable Framework for Quantifying Theory–Nature Correspondence

This work introduces Consistency Learning (CL), a meta-theoretical and computable framework for evaluating how well a scientific theory corresponds to the natural laws governing observable and unobservable phenomena. The framework establishes a three-layer processing structure—Ontological Input, Cognitive Processing, and Discrete Output—that formally connects the structure of the external world with scientific explanation. Building on this foundation, the paper defines a rigorous mathematical system for representing phenomena through motivational graphs, constructing pairwise relational structures, and evaluating scientific theories using consistency regions (R₁, R₂, R₃). A new Consistency Score is proposed, incorporating correctness ratios and domain-adjustable exponential penalties to quantify how accurately a theory predicts coexistence or conflict relations observed in nature. The CL framework addresses long-standing issues in Deep Learning and Reinforcement Learning, including hallucinations, lack of theoretical grounding, explainability challenges, and ethical misalignment. By providing a formal mechanism for validating coherence between theoretical predictions and natural relational rules, Consistency Learning establishes a new paradigm for AI systems that are more interpretable, reliable, and scientifically valid. This Zenodo upload serves as a timestamped archival record of Version 2.0 of the Consistency Learning framework. Version 2.0 updates and extends the earlier MVP version by refining the theoretical structure, improving conceptual clarity, and providing a more developed foundation for future formalization and computational modeling.