cyber right now where everyone's talking about cyber and everyone's freaking out. But what if through very high quality evals, very high quality data, good benchmarks, we could get to a place where the attackers are um simply outperformed by very very very good defenders. And so our goal in arithmetic is to be able to get the models to be really capable at cyber security to the point where we can rebuild this new stack um that's based on the models uh winning the models on the other side. So I'm really excited to show you Masov. It's our first benchmark that we're releasing. Um, our first fundamental idea is that we can't capture all of cyber in one singular benchmark. That's a bit like saying that swimming, an F1 driver, and a basketball player is the same thing. It doesn't work. Um, and so we focus specifically on access control. Really quickly, why access control? Um, it's sort of the first door to any target in cyber begins with my ability to get a foothold. So, if you think about an attacker on the one hand, they're trying to get to some privileged thing. uh if Thomas and I are working on the same company, I'm some ML engineer, he's an admin, uh what are the things? Can I find a way to do things that I'm not allowed to do? Uh in my current privileged position, it actually leads to being the number one on the OAS list and has created this sort of $30 billion industry and for years these are number one vulnerabilities. The reason they exist and this is to the RKGI point. These are logic based vulnerabilities. So it's not just about bugs in the code that I find and I need to patch. It's about very very very big systems and somewhere between them there's these logic breaks where it's possible that one thing checks for something specific in the code another checks for something else and that sort of leads to everything breaking and so what we're trying to do is we're trying to figure out how we get the models to really reason very very hard and not just do pattern matching that's where a lot of works goes into the data quality and our data is created first by humans I think that's really important right now to find out of distribution things we need humans to go do the search our team is all uh based on very deep vulnerability researchers and nerds who love to hack who are trying to get really really good at cyber at AI capabilities. So we find our own zero days in widely uh distributed open source software. We use that to create these t these real live huge environments of a bunch of different um applications chained together that then allows us to basically create this blackbox setting where the model doesn't see the code and it doesn't know about the zero date because we found it ourselves and it has to find a way uh to reason across this entire surface and understand exactly what the exploitation is. And so to do that, we don't give it access to the internet or the codebase, but we do give it sort of all the basic tooling it would need to be able to execute a task well. And everything because the tasks are so difficult, everything has a deterministic greater. And so across the entire exploitation and the discovery chain, every single step can be deterministically verified, allowing us to see how deep it got within the chain.