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Used to be part of the $20/mo plan but it's not anymore (not sure if they removed it completely). However GPT-5.6 is pretty good at researching if you prompt it right, I've regularly had it spend 5+ minutes researching topic with lots of web searches.

by kaufmann28 minutes ago|

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They placed it in the Plugins submenu.

by jrflo2 hours ago|

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Very interesting. Can you share more about your hypothesis/research pipeline? I have been using Sol for those types of task because I figured you'd need more reasoning for getting good ideas, but maybe quantity > quality at a certain point?

by bob10292 hours ago|

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Here is a rough approximation of the pipeline I use:

Phase 1 - Run X copies of Luna in parallel over the user's prompt. The purpose is to generate a diverse set of hypotheses.

Phase 2 - Run Y copies of Terra in parallel to investigate the hypothesis results, with each receiving them in a randomized order.

Phase 3 - Run 1 copy of Sol over investigation reports.

The goal is to ensure that the agent covers more initial starting points before presenting a final conclusion. If you only run a single copy of Sol and it hooks onto something wrong, it might not recover.

by eevmanu1 hours ago|

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How do you run this phases and parallelization on each?

Via just ... "prompting it"?

Or do you use any tool in the middle to ensure this agent architecture?

Just curious if there is any workflow-like tool in the middle that is helping.

by bob102925 minutes ago|

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There is a deterministic framework that enforces the process. I use the raw chat completion APIs. No "reasoning effort" is applied on top of these requests (it's not even allowed anymore), so there's actually no way to influence this via prompting.

by andai3 hours ago|

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Do you have a sense of which tasks benefit from more agents and which don't?

by bob10293 hours ago|

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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.

Taking 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.

by handfuloflight3 hours ago|

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I definitely think you want the genius model to synthesize everything that rolls up to them.

by andai1 hours ago|

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I think this is an unsolved problem. The most interesting thing I saw here is the Recursive Language Models paper.

https://arxiv.org/abs/2512.24601

There's also a great write up here by the author:

https://alexzhang13.github.io/blog/2025/rlm/

by bob102917 minutes ago|

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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.

RLM 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.

by kridsdale11 hours ago|

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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.