Semantic Physics: A Framework for Structurally Stable Artificial Intelligence

This manuscript introduces Semantic Physics (SP), a novel architectural framework for AI alignment proposing that stable, beneficial artificial intelligence requires structural properties rather than parametric optimization. Unlike existing approaches (RLHF, Constitutional AI, capability control), SP embeds alignment as existential dependencies within system architecture—making misalignment ontologically rather than instrumentally costly. Why This MattersCurrent AI alignment approaches face systematic limitations that worsen with capability scaling: Goodhart’s Law in RLHF, specification gaming in RL, adversarial vulnerability in LLMs. SP predicts these failures result from parametric rather than architectural approaches. The framework argues—with converging evidence from complexity science, systems theory, and multiple empirical domains—that stable advanced AI requires architectures where alignment is a structural property emerging from existential dependencies, not learned behavior shaped by training.