MIT's HardFlow makes AI meet every hard constraint in simulation
MIT creates method to force AI to comply with safety rules
MIT researchers developed HardFlow, a method that enforces strict constraints on flow-matching generative models by checking only the final output, not every intermediate step. In simulated tests, it satisfied constraints every time and produced better results than six rival methods, without retraining. However, no independent lab has reproduced the results, and it remains untested on real hardware or language models.
For constraint satisfaction, what ultimately matters is the model's final output, since the internal process is discarded. By not requiring every intermediate step to satisfy the constraints, we give the model more freedom to find high-quality solutions that are still feasible in the end.
- mixdup
This kind of seems like a no-brainer. Instead of just letting a model have unfettered "physical" ability to do things and hope you can cognitively control it, why not let the AI do whatever it wants, but its access to the tools go through a hard-coded set of rules that is not subject to fuzzy interpretation
Of course that depends on having controls that can't be circumvented which is a big if
- Mr_P
If you click through to the paper, it has approximately nothing to do with what this HN post title suggests.
- jcfrei
> In experiments spanning robotics, control of physical processes, and computer vision, the new method consistently satisfied the required constraints while identifying better solutions than existing techniques.
I guess this method works when you can precisely quantify the allowed output - like the degrees an arm can move or the path a robot can take, etc. But it doesn't appear to be applicable to an AI writing code - which is where our main concerns currently are.