MIT's HardFlow hits 100% on hard constraints

MIT researchers released HardFlow, a deployment-time method that achieved 100% constraint satisfaction across experiments in robotic manipulation, maze navigation, and text-guided image editing without retraining the underlying generative model. The MIT research summary says the method preserves output quality while enforcing final hard constraints, and it complements Google’s text-diffusion model work by moving from how models generate to how deployments constrain them. Teams putting generative models into physical or safety-critical workflows should file HardFlow as a control technique to test—not a substitute for system-level validation; its evidence is still laboratory evaluation.