[bob1029]

> you have a massive pile of text, and you need to split it into smaller pieces that fit into embedding models or context windows.

I think the recently posted Recursive Language Models paper approaches this in a far more compelling way. They put the long context into the environment and make the LLM write and iterate python code to query against it in a recursive loop. Fig. 2 & 4 are most relevant here.

https://news.ycombinator.com/item?id=46475395

https://arxiv.org/abs/2512.24601

I really like this because it is in The Bitter Lesson genre of solutions. Make the model learn the best way to retrieve info from a massive prompt on disk given the domain and any human feedback (explicit and otherwise).

The bigger the prompt.txt, the less relevant the LLM's raw context capabilities are. Context scaling is quadratic in cost. It's a very expensive rabbit to chase. Recursively invoking the same agent with decomposed problem bits is more of a logarithmic scaling thing. You could hypothetically manage a 1 gigabyte prompt with a relatively minuscule context window under a recursive scheme using nothing other than a shell/python interpreter.