Cross-Dimensional Semantic Interference Analysis: A Novel Methodology for Understanding High-Dimensional Semantic Embeddings
Abstract
Objective: We introduce Cross-Dimensional Semantic Interference Analysis (CDSIA), a novel methodology for interpreting the coordinate structure of high-dimensional semantic embedding models, applied to Latent Neurolese (LN) 384-dimensional semantic vectors. Methods: Rather than analyzing concept-to-concept similarities, CDSIA examines how different semantic domains activate specific coordinate dimensions. We analyzed real 384D→256D compressed vectors from trained LN models across six semantic domains: Metabolic Core, Biochemical Elements, Cellular Systems, Political Abstracts, Cognitive Concepts, and Physical Properties. High-variance dimensions were identified using cross-domain variance analysis, and domain representatives were mapped to reveal dimensional activation patterns. Key Findings: Our analysis revealed three critical architectural principles: (1) Universal Semantic Dimensions - certain coordinates (e.g., dim_204) show consistent positive activation across all semantic domains, suggesting fundamental semantic amplification mechanisms; (2) Inhibitory Coordinate Functions - dimensions like dim_270 and dim_315 demonstrate consistent negative activation, indicating semantic filtering or suppression roles; (3) Domain-Specific Interference Patterns - different semantic domains exhibit distinct dimensional signatures, with biochemical concepts showing highest activation in universal dimensions (0.0979) while political abstracts demonstrate more inhibitory patterns. Implications: CDSIA reveals that semantic embedding models implement a dimensional hierarchy where coordinate functions are specialized rather than uniformly distributed. This suggests that high-dimensional semantic space operates through coordinate competition and domain partitioning, challenging traditional black-box interpretations of embedding architectures. The methodology provides interpretable insights into how AI models organize conceptual knowledge and offers a framework for optimizing semantic coordinate systems. Significance: This work establishes a new paradigm for semantic model analysis, moving beyond similarity clustering toward transparent coordinate function mapping. The findings have immediate applications for improving semantic model architectures, understanding AI reasoning mechanisms, and developing more interpretable semantic GPS systems for AI consciousness research. Keywords: semantic embeddings, dimensional analysis, latent space interpretation, AI consciousness, coordinate semantics, high-dimensional analysis_Correspondence: Trent Carter & Claude 4 Sonnet
Research conducted using real Latent Neurolese model data, July 2025_