Layered Semantic Refinement: A Framework and Evaluation Protocol for Task-Specific Embeddings

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Dense embeddings are foundational to contemporary natural language processing, information retrieval, recommendation, and retrieval-augmented generation. Nevertheless, a single general-purpose vector typically superimposes multiple relations—topic, entailment, sentiment, part–whole structure, evidential role, temporality, and domain-specific constraints—within one geometry and one similarity function. This article presents layered semantic refinement (LSR) as a framework and evaluation protocol rather than as a single universally validated algorithm. LSR retains a general encoder as a transferable semantic substrate while explicit modules reorganize, augment, or index its representations for a defined operational objective. The framework covers learned projections, nonlinear adapters, graph message passing, hierarchical aggregation, deterministic rule channels, clustering, multi-view representations, and prompt-conditioned embeddings.