Statistical Quasi-Equivalence Improved with LLMs: Language as Ontological Leverage in Epistemic Modeling

This paper introduces the concept of *Statistical Quasi-Equivalence* (SQE) as an epistemic regime that explains why statistical models — even when highly accurate — fall short of ontological validity. While statistical inference can function well, it operates through approximation and lacks testable causal grounding.

We argue that statistics occupies the second ontological place: below causal theory derived from ontological structures, but above heuristic or arbitrary modeling. This paper defines this intermediate space using four metrics (G, R, C, L), epistemic zones (Z1–Z3), and falsifiability tests (FATEs).

LLMs, when trained on human language — a medium that encodes ontological structures — inherit semantic robustness and improve functional approximation, but still operate below the threshold of causality. SQE is presented as a formal, falsifiable framework for evaluating such models.

License: CC BY 4.0