Title: Compiling Deterministic Structure into SLM Harnesses

Source: http://arxiv.org/abs/2604.17450v1

Summary:

This paper introduces Semantic Gradient Descent (SGDe), a novel PAC-learning-based framework that compiles complex agentic workflows into optimized, deterministic execution plans for small language models. It establishes a new architectural primitive for agent design by leveraging frontier teacher models to iteratively refine student workflows via discrete semantic gradients.