Anything related to reading and interpreting the environment seems to always benefit from the addition of more agents to the search party, assuming you have some rational way to synthesize their results.
replyTaking actions that mutate the environment is a different story. I think this is where you run into diminishing returns very quickly. You generally want one strong agent to act given the results of all the searching that was done. If the plan is clear, you don't need a genius model to execute it.
I definitely think you want the genius model to synthesize everything that rolls up to them.
replyI think this is an unsolved problem. The most interesting thing I saw here is the Recursive Language Models paper.
replyhttps://arxiv.org/abs/2512.24601
There's also a great write up here by the author:
I used this architecture for a while. The problem I have with it is that starting from one agent and fanning out keeps things mostly aligned with that single reasoning trajectory, even as you get a few layers into the stack. Every recursive invocation is a product of the caller's current state. Diversity doesn't really occur on its own unless the environment (tool calling) is complex/chaotic.
replyRLM might be more useful on the execution side than on the research side. In fact, these somehow feel like they might be exact inverses of each other in terms of what the ideal architecture looks like. At some point you definitely do need something in the middle that has it all sorted out.
This is why in your brain you have trillion threads processing and summarizing sensor data (immutable functions), but a SINGLE thread of “execution” which we call the conscious soul.
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