MIT researchers have developed a new technique that helps generative artificial intelligence models find solutions to high-stakes problems.In these settings, a plausible answer is not enough: The output often must also satisfy nonnegotiable safety, physical, or task-specific requirements, known as hard constraints.The researchers developed a method that helps generative models meet these strict requirements without sacrificing the quality of their outputs. The key to their technique is to give the model more freedom during the generation process and enforce hard constraints on the final output, rather than at every intermediate step. In experiments spanning robotics, control of physical proc...