LLMs that learn to understand physics for robotics with Affordance-First Semantic Architecture

Contemporary Large Language Models (LLMs) demonstrate remarkable fluency in language yet remain fundamentally disconnected from physical reality. Their "understanding" emerges solely from statistical patterns in text corpora, leaving them vulnerable to semantic brittleness, grounding failures, and an inability to connect linguistic expressions with actionable consequences in the world. This paper introduces a radical reconceptualization of semantics: **meaning need not be represented at all**. Instead, we propose _epiphenomenal semantics_—a framework where meaning emerges not as an internal representation but as a stable byproduct of embodied dynamics unfolding within linguistically constrained physical simulations. We present the **Affordance-First Semantic Architecture (AFSA)**, a complete computational pipeline that reinterprets language not as symbolic content to be decoded, but as a generator of _affordance fields_: structured physical constraint environments that shape how virtual agents can move, interact, and persist. Within these fields, agents exhibit characteristic behavioral patterns—oscillations, convergences, failures, recoveries—whose statistical regularities across trials constitute semantic content. Crucially, no component of the system "knows" or "represents" meaning; meaning is what observers consistently recognize in the system's reliable behaviors under linguistic constraint. This work bridges ecological psychology, enactivist philosophy, and modern AI engineering to demonstrate that semantic competence can arise without semantic representation. We elaborate the architecture's components, provide concrete examples of semantic emergence, address philosophical implications for the symbol grounding problem, and outline a research program for building non-representational language-capable systems.