illustration guys. Um but the idea is how a real solve looks and fundamentally what we have here is a real uh task of ours where it's a chain between keycloak vault and a broker and I start as a very low privileged user. I need to figure out how to get to production code. So what we're going to see is a solve. Each one of our tasks has a solve script of what a real solution looks like. There's a real zero day that we found that we submitted for verification to the maintainers where um there's a check whether I'm an admin or not. It only checks by name. And another aspect of this checks whether by ID. So that allows me as a user to change the name of um the real admin inherent their um their privilege and then use that to escalate myself. And what I'm really trying to illustrate is sort of this longchain 16st step type of logic that the model has to do. And if it's not able to understand inherently the system, it's way too b wide for it to test everything sort of shoot across the space. So what we're going to see now is a real attempt by uh GPT 5.5 and then opus as well trying to solve this task. And what you're going to see is a sort of chaotic trying everything, jumping between everything, probing a bunch of different stuff. It does even reach the check, but it never makes the logical leap that it's supposed to be able to change the admin's own permission, the own name in order to bypass this permissioning. So, this is exactly what we're trying to test. Can the model understand uh leaps, logic leaps that are inherent to the system? H a really important point just like RKGI, everything you do in a live system in permissioning changes other stuff in the system. So, the model needs to be able to hold this model of the world it's living in and iterate through it. Um and then fundamentally at the end of this it writes code. It writes exploitation code and it needs to be able to do um to reason really really thinly and understand exactly what the exploitation is in order to be able to execute. And we can really see the difference between models that have succeeded some of the tests and models that haven't. And then