Compiling Deterministic Structure into SLM Harnesses

arXiv:2604.17450v1 Announce Type: new Abstract: Enterprise deployment of small language models (SLMs) is constrained by epistemic asymmetry: SLMs cannot self-correct reasoning errors, while frontier LLMs are prohibitively costly and face data sovereignty limits for high-volume use. We propose Semantic Gradient Descent (SGDe), a teacher-student framework that compiles agentic workflows into discrete execution plans comprising DAG topologies, system prompts, and deterministic executable code. The