We finally built a 'hard gate' for AI that makes it physically impossible for it to design something that breaks the laws of gravity or heat.
By embedding the second law of thermodynamics directly into the AI's architecture as a constitutional constraint, this framework rejects any physically impossible prediction. This ensures that AI-generated materials or systems are always thermodynamically sound, eliminating the risk of 'hallucinated' physics in engineering.
Towards Unified Material-State Tensors for Physics-Gated AI Thermodynamic Admissibility as Constitutional Constraint
SSRN · 6261038
AI systems for physical design lack formal safety guarantees as learned constraints fail under distribution shift; we ground safety in thermodynamics by rejecting (not penalizing) physics-violating predictions, extending constitutional AI (Bai et al., 2022) to continuous physics domains. We introduce the Unified Material-State Tensor (UMST), a structured encoding validated by physics engines enforcing the Clausius-Duhem inequality as a hard gate. Unlike prior soft-constraint approaches (PINNs, t