> To improve the stability of the resulting designs, we employ an efficient validity check and physics-aware rollback during autoregressive inference, which prunes infeasible token predictions using physics laws and assembly constraints.<p>I'm far from an AI expert, but I've long felt that this is one of the most interesting ways to use AI: to generate and optimize possibilities <i>within</i> a set of domain-specific constraints that are programmed manually.<p>For example, imagine an AI that is designed to optimize traffic light patterns. You want a hard constraint that no intersection gives a combination of green lights that could cause collisions. But within that set of constraints, which you could manually specify, the AI could go wild trying whatever ideas it can come up with.<p>At that point, the interesting work is deciding how to design the problem space and the set of constraints. In this case it's a set of lego bricks and how they can be built (and be stable).