everything I'm going to show in here is is interactive. Okay. What are recursive language models? So, I like to say that in an RLM, the context itself is the object of computation. Um and this is essentially a marriage of tool calling and reasoning. We're going to talk a lot more more about that in the next slide. But, the idea is that the full prompt is not a simple user query. The full prompt is a variable. The full prompt could be a file or many files. Um and we have this read-evaluate-print loop, REPL, um that the agent is interacting with. In the original paper, that's Python. And the RLM is instructed to operate symbolically on that prompt. So, don't just read the whole thing into your context window. Um explore it symbolically. And uh even more, you don't even directly explore symbolically, or maybe you do a little bit of poking around, but have uh other LLMs, uh and I guess other RLMs, if you allow the recursion depth to be to be greater than one. Uh have these uh other recursive um sub agents and and again we'll get uh a little bit into the the weeds of the the lingo um sub RLMs sub LLMs uh do this symbolic manipulation to pick apart the answer and then work our way back up to a final answer. So, it looks something like like this in this in this tree below. So, my take here is that RLMs are the new reasoning models and I see this as the next paradigm of test time compute, inference time compute, whatever you want to call it. And why does it seem obvious or maybe like hey, why is this even a thing? Um I think it's very elegant because it's a very elegant marriage of two things, reasoning and code execution. So, the code execution is reasoning. Um and so instead of we we had long um we had chain of thought as a prompting strategy that evolved into reasoning models that explicitly expressed the chain of thought as the reasoning tokens. We already had function calling, tool calling, parallel tool calling, um and RLMs really puts that together in a way that gets amazing results. So,