> Certainly interesting for very low latency applications which need < 10k tokens context.

I’m really curious if context will really matter if using methods like Recursive Language Models[0]. That method is suited to break down a huge amount of context into smaller subagents recursively, each working on a symbolic subset of the prompt.

The challenge with RLM seemed like it burned through a ton of tokens to trade for more accuracy. If tokens are cheap, RLM seems like it could be beneficial here to provide much more accuracy over large contexts despite what the underlying model can handle

0. https://arxiv.org/abs/2512.24601