Abstract:Physical Semantic AI (PSAI) introduces a new category of artificial intelligence grounded in physics-derived primitives rather than purely statistical pattern recognition. Cyber-physical systems—especially residential environments—are dynamic, noisy, and causally complex, making them poorly served by conventional AI architectures. PSAI formalizes a bottom-up semantic inference stack: (1) multi-modal sensor telemetry, (2) physics-grounded primitive extraction, (3) semantic event detection, (4) persistent state inference, and (5) a queryable FrostGraph capturing the evolving physical reality of a home. This paper defines the structure, theoretical foundation, and distinguishing characteristics of PSAI relative to Machine Learning, Symbolic AI, and neuro-symbolic hybrids. PSAI establishes a unifying semantic model for real-world physical environments and serves as the core intelligence framework of BlackFrost’s Infrastructure Intelligence Utility™.