In my experience: Absolutely Yes!

I recently just referenced the selective applicative functors paper and let it write me an implementation in scala. There is one already available in github, so I can't judge if it really just read the paper and implemented it, but the result was so minimal quick and amazing.

The abstract is AI generated and pretty poorly written at that. A paper about grading AI output doesn’t even grade their own abstract.

I think the most effective part of this architecture is having multiple initial conditions to sample from.

We've been looking at using gpt-5.6-luna to do hypothesis generation at scale. Running many copies of something approximately as powerful as gpt5.4 just to get a sense of what options exist before we put a stick into the mud.

Single agent loop does not work very reliably for deep research. Especially in domains with complex tool calling and environments. You can get it to perform sometimes (often enough for a demo to work), but the team will rarely adopt it because they want it to work ~100% of the time, not ~40%. The anchoring you get with initial findings makes it really hard to get unstuck without user intervention later on. When all findings occur as part of a deterministic research pipeline (tool), things tend to work better at the edges.

I've been considering a three stage pipeline that does hypothesis generation => investigation => synthesis using luna => terra => sol. This is the first LLM family where I feel like we can actually use the full range.

Actually deep reasoning, can't reason without comprehension. This will make sense for technical uses as it's intended. I can see this being very useful for code error mitigation and fixes.