Beyond Human Language: Neural Manifolds, Vector Geometry, and the Emergence of Post-Cognitive AI

This paper develops a mathematically rigorous theory of semantic geometry, formalizing language as a differentiable manifold embedded in high-dimensional vector space. By integrating structural linguistics, information theory, and transformer engineering, the author proves a dimensional divergence theorem establishing that AI-native semantic systems exceed human interpretability thresholds.