Physics-First Natural Language Processing: Resolving Winograd Schemas via Universal Invariants

Large Language Models (LLMs) resolve linguistic ambiguities via statistical pattern matching, making them fragile to adversarial rephrasing and opaque in reasoning. We present Physics-First NLP, a framework that grounds natural language understanding in deterministic physical invariants. Our system decouples semantic parsing from logical resolution: (1) an LLM extracts structured roles (entities, properties, constraints), and (2) a physics kernel validates these roles against universal laws (Newtonian mechanics, structural integrity, spatial containment). We demonstrate 100% accuracy on spatial and structural Winograd schemas, maintaining performance under synonym substitution and adversarial rephrasing—scenarios where pure LLM approaches degrade. Our approach provides explainable, deterministic reasoning grounded in civilizational truth (physics) rather than statistical correlation.