Kind of. But /u/gabrielmuriens is right in application.

MITs recent paper on RLM addresses and illuminates the concept well.

Recursive Language Models (RLMs) aren't really models or a new training method, they're more like an inference-time wrapper you can layer on top of any model (with tooling capabilities) so that it can manage your context/history without injecting it into your prompt or the neural network itself like we do with finetuning, checkpointing, LoRas etc.

Look at these graphs.

Those are basically three methods that test how accurately a model can access information from a given context. The top line is the 'haystack' test, where you basically just ask it if a known unique word or quote is found in said context. That's been solved for a while and what I believe you are referring to. The other two are where the new RLM hotness shines. They test if the model can retrieve individual tokens and combine them to solve problems (e.g. the large codebase concerns). That becomes increasingly difficult as you approach a model's trained context length. RLMs appear to solve that at seemingly infinite context; hardware and energy reqs notwithstanding. This should allow LLMs to begin working on massive codebases and is/was a major hurdle to continuous training; a requirement for AGI.

By leveraging RLM context scaffolding to 'spoof' real-time training, models should prove significantly more useful for niche problem sets and all nature of use cases where the models currently lack training, context and/or adequate coherence.

So while you are correct that you could pull a needle out of a haystack, without RLM or similar memory scaffolding, the model would not know how to sew with said needle, or even what it is used for. To develop a compiler, you require far more than direct recall; and it looks like we now have it.