Congrats: completely broken methodology, with a big conflict of interest. Giving specific bug hints, with an isolated function that is suspected to have bugs, is not the same task, NOR (crucially) is a task you can decompose the bigger task into. It is basically impossible to segment code in pieces, provide pieces to smaller models, and expect them to find all the bugs GPT 5.4 or other large models can find. Second:…
Idk, it seems reasonable to me > "Our tests gave models the vulnerable function directly, often with contextual hints. A real autonomous discovery pipeline starts from a full codebase with no hints. The models' performance here is an upper bound on what they'd achieve in a fully autonomous scan. That said, a well-designed scaffold naturally produces this kind of scoped context through its targeting and iterative prom…
What happens then is that, for example, the model looks through that particular file, identifies potential problems, and works upwards through the codebase to check whether those could actually be hit.
“Hum, here we assume that the input has been validated, is there any way that might not be the case?”
This is not unique to Mythos. You can already do this with publicly available models. Mythos does appear to be significantly more capable, so it would get better results.
The research discussed here provided models with just a known buggy function, missing the whole process required to find that bug in the first place.