6/24/2026 at 2:02:04 AM
Very interesting. Haven't heard of RLMs before.https://github.com/alexzhang13/rlm
> We propose Recursive Language Models (RLMs), a general inference paradigm that treats long prompts as part of an external environment and allows the LLM to programmatically examine, decompose, and recursively call itself over snippets of the prompt.
> We find that RLMs can successfully process inputs up to two orders of magnitude beyond model context windows and, even for shorter prompts, dramatically outperform the quality of vanilla frontier LLMs and common long-context and coding scaffolds [...] across four diverse long-context tasks while having comparable cost.
https://arxiv.org/abs/2512.24601
I had a similar thought the other day. When doing a research task, you don't want to crap up the context with all the web scrapes. But you want to ask follow up questions on the full context, not the anemic subagent summaries. So what you actually want is an "extended context" you can grep.
by andai
6/24/2026 at 8:43:11 AM
I started building my agent loops based on the RLM paper and I am finding the recursive part serves two major purposes. First, it pushes down token consumption as you describe. The other thing it does is prevent the agent from returning too soon, since most of the real work happens at depth. Especially if you forbid tool use in the root.I am starting to wonder if maybe I could just focus on these aspects more directly as opposed to treating them as side effects of symbolic recursion. I do have to agree with the paper in that recursive depth beyond 1 doesn't seem to matter. At least not with the current frontier of models. If we can't recurse more than once and extract much uplift, then I question us labeling this a recursive scheme.
by bob1029