SEMQ - Symbolic Embedding Multi-Quantization; A symbolic representation layer for AI embeddings

This document presents an initial research insight introducing SEMQ™ (Symbolic Embedding Multi-Quantization), a discrete symbolic representation layer derived from continuous embeddings. SEMQ replaces raw vector representations with fixed-dimensional symbolic structures designed to preserve relational properties such as relative similarity ordering and neighborhood structure, while decoupling semantic representation from specific similarity metrics, indexing schemes, and execution semantics.

The manuscript focuses exclusively on representation-level semantics and formal properties, outlining the relationship between continuous embedding spaces and their symbolic counterparts. It is released as a technical preprint and has not undergone peer review. Evaluation protocols, empirical benchmarks, and downstream applications are intentionally deferred to subsequent work.