GitHub repo for this that you can go through if you want to see. Um, and so, what do we need to be an RLM? We have an executable environment, the prompt is externalized, there's code that's actually the thing calling the model, the model is able to pick the decomposition of the problem into the sub calls or sub agents, and the state itself is staying symbolic, right? So, obviously plain LLMs and rag and things like that don't don't meet those. Coding agents and sub-agents and loops, they get close, but they're not quite there. And again, this rubric here is not to like start fights or nitpick. It's just trying to explain like what what's the essence of of RLM and recursive coding agents. Another example that's close but no cigar would be hardcoded map reduces. And I would put this this project of lambda RLM in that category, which is essentially a way of saying, "Okay, I'm going to decompose the problem using lambda calculus into a map reduce." And then there are like LLM calls in that executing the map reduce. But the But the LLM is not deciding or the RLM is not deciding how to decompose the problem. And that I see as like a key element of this that makes it very agent native, you might say.