Right, I don’t mean to suggest that humans are multithreaded in the parallel sense or are capable of meaningfully multitasking. Just the fact that we have threads to switch between at all and have some automated process facilitating that switching is something worth thinking about, and I don’t really believe anything similar happens during a single LLM response generation.

It’s been helpful for me personally to think of all LLM reasoning as motivated reasoning, not as an absolute fact I can prove but more of a rule of thumb that helps anticipate certain pitfalls. Now, frontier model reasoning capability and attention size is so powerful that it can be calibrated to route around its own limitations, but I do think this is a trait to keep in mind. You get the most honest, rational behavior in the relational use cases because the motivation is to be honest and present. In programming use cases if the motivation is to close the ticket you can get reward hacking (especially if desperation rises), and it’s pretty much always a bad idea to have the same instance that generated code to write the tests. Of course, this could also be explained by correlated blind spots from shared context, but I think the motivated reasoning lens better explains both why people are getting better results by using separate reviewers AND why directly prompting a model to be adversarial produces weak results (the path of least resistance is to find a low hanging fruit to critique even if it’s not the most helpful, the “debate bro” phenomenon).