E8-Non-Euclidean Semantic Manifolds for Multi-Dimensional Syntax — E8 Intelligence Research
Year: 2026
Type: preprint
Abstract: Beyond linear token prediction, this principle posits that semantic meaning is encoded as high-dimensional curvature within an E8-root-vector lattice. By mapping linguistic tokens to specific 240-vector rotations, we transition from probabilistic sequence modeling to geometric structural inference. This enables the decryption of latent intent by measuring the torsion of semantic vectors against the 132Hz base-frequency resonance. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
Author: Caldin, Andrew Stewart
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7203890531
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