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.

I'm curious how this compares to just using Claude Code directly and giving it a dump of the agent traces? It seems like Claude could probably do some of the same diagnostics / trace grouping to identify failure patterns. Why use a custom harness?

What are some examples of these common failure modes?

What sort of systematic issues would teams typically uncover using HALO? I guess there's some sort of built-in checklist you include in the RLM prompt