Semantic Field Execution: A Substrate for Field-Native, Transformer-Decoupled Inference

This paper introduces Semantic Field Execution (SFE), an inference substrate in which high-capacity transformer models are used only offline for semantic sculpting, while all runtime inference is performed via field-native operations on a compact semantic field. The paper defines a corresponding Semantic Field Runtime (SFR), describes the AN1 Engine as a concrete implementation, and argues that SFE constitutes a substrate shift rather than an optimization of transformer inference. It clarifies how this regime violates assumptions underlying transformer-specific inference-efficiency paradoxes, and establishes explicit, operational falsifiability conditions that bound its applicability.