5 ms·
Recursive Language Models
- zed31726 9mo agoT̶u̶r̶t̶l̶e̶s̶ LLMs all the way down
- downboots 9mo agoattention is all you need but over and over and over and over... Precision is what we should ask for.
- bob1029 9mo ago> The key insight is that long prompts should not be fed into the neural network (e.g., Transformer) directly but should instead be treated as part of the environment that the LLM can symbolically interact with. How is this fundamentally different from RAG? Looking at Figure 4, it seems like the key innovation here is that the LLM is responsible for implementing the retrieval mechanism as opposed to a human doing it.
- NitpickLawyer 9mo agoTwo differences that I see: 1. RAG (as commonly used) is more of a workflow, this thing is more "agentic" 2. The recursive nature of it First, the way I see workflow vs. agentic: the difference is where the "agency" is. In a workflow, the coder decides (i.e. question -> embed -> retrieve -> (optional) llm_call("rerank these parts with the question {q} in mind") -> select chunks -> llm_call("given question {q} and context {c}, answer the question to the best of your knowledge") ) The "agentic" stuff has the agent decide what to search for, how many calls to make and so on, and it then decides when to answer (i.e. if you've seen claude code / codex work on a codebase, you've seen them read files, ripgrep a repo, etc). The second thing, about recurrence has been tried before (babyagi was one of the first that I remember, ~ '23) but the models weren't up to it. So there was a lot of glue around them to make them kinda sorta work. Now they do.
- alansaber 9mo agoThe terminology we use is rather imprecise, the interpretation of RAG inflates year on year
- mccoyb 9mo agoMy wishlist for 2026: Anthropic / OpenAI expose “how compaction is executed” to plugin authors for their CLI tools. This technique should be something you could swap in for whatever Claude Code bakes in — but I don’t think the correct hooks or functionality is exposed.
- rockwotj 9mo agoIsn’t codex open source and you can just go read what they do? I have read the gemini source and it’s a pretty simple prompt to summarize everything when the context window is full
- MillionOClock 9mo agoIt should be noted that OpenAI now has a specific compaction API which returns opaque encrypted items. This is AFAICT different from deciding when to compact, and many open source tools should indeed be inspectable to that regard.
- omneity 9mo agoIt's likely to either be an approach like this [0] or something even less involved. 0: https://github.com/apple/ml-clara https://github.com/apple/ml-clara
- cubefox 9mo agoSeems similar to this paper: https://arxiv.org/abs/2510.14826 https://arxiv.org/abs/2510.14826
- yawnxyz 9mo agohere's a more readable version: https://alexzhang13.github.io/blog/2025/rlm/ https://alexzhang13.github.io/blog/2025/rlm/
- Legend2440 9mo agoIsn't this just subagents? You call another LLM to go read a file and extract some piece of information or whatever, so that you don't clutter up the main context with the whole file. Neat idea, but not a new idea.
- lelanthran 9mo agoUnless that subagebt you call can call subagents itself which can call subagents themselves, ad infinitum, it's not recursive.
- songodongo 9mo agoThe paper says they used a recursive depth of 1. Does that mean subagents or sub-subagents?
- johnnyfived 9mo agoA recursive depth of 1? So it's just subagents..? How exactly can this be described as recursive then?
- seeknotfind 9mo agoYeah, from the title, it sounds like perhaps the entire operation is differentiable and therefore trainable as a whole model and that such training is done. However, upon close inspection, I can't find any evidence that more is done than calling the model repeatedly.
- AlexCoventry 9mo agoNo, there's no training going on, here, as far as I can tell. E.g., they use GPT-5 as their base model. Also, AFAICT from a quick skim/search there's no mention of loss functions or derivatives, FWIW.
- alextheparrot 9mo ago
- deleted 9mo ago[deleted]