Semantic Routing Language Model (SRLM): A Blueprint for Sequence-Level Semantic Routing in Encoder–Decoder Language Models

The Semantic Routing Language Model (SRLM) is a conceptual architectural framework that separates contextual encoding, semantic aggregation, semantic routing, specialized computation, latent representation, and autoregressive generation into distinct computational stages. Rather than introducing new neural components, SRLM formalizes a sequence-level routing paradigm in which routing decisions are performed on a global semantic representation constructed after contextual encoding. The paper presents the architectural motivation, design philosophy, and mathematical formulation of the framework using established neural network building blocks. The objective is to establish a formal architectural blueprint that can serve as a foundation for future implementations and empirical evaluation rather than to claim empirical superiority over existing language models