I think you misunderstand. Yet the model is doing something impressive still by writing code to fix model weights on problems. But that's a whole different ballgame than just reading weights and knowing what to change. That would mean that they have an internal representation of how the model weights map to information or a specific task, which if true, throws all the interpretability research out of the window

To me, the original article is more about what LLMs can *do* in weight space, I don't really see the part where it would tie to an internal representation.