task was solvable at the time that you are being asked it. Um and in terms of doing this, a very useful thing is scaling judges. And so, a lot of times we will have a model that does something and it will make mistakes along the way and it's easier to tell what went wrong in hindsight. And so, the fact that you've already seen the chain of events after and you can look backwards and say, "Okay, the model made a mistake here. This thing doesn't feel quite right." Or we asked seven different models and they all kind of agree this thing is wrong. This is a very useful way of kind of spending compute to do search to then extract rubrics. These rubric questions are are are very effective at like kind of distilling down the the search into something that we can then use to more cheaply audit and kind of also ground, once we have these rubrics, as a a way of saying, "Okay, we need tasks that target these kinds of failure modes as well." Um all of this is under the umbrella of scaling search with