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.
Yes, however I do suspect that at some point comprehension trumps context. Meaning it will be evedent giving the ability to increase comprehension and retaining whatever context you have goes a long way. I dont have the ability to play such large models. Within the means of my hardware I have already playd around with LLM enough to know where balances will start to cause confliction. Priotizing effeciency within the accuracy. This can mean compromising some other aspect of the AI, or this can be viewed as a positive interoperability.
Consider context as hyper-dimensional coordinate vectors gesturing at the starting concept cluster of a synthesis chain or thread to unspool.
If the model's comprehensive training activated by your context locates the right thread to pull, this could be considered comprehension? That it "got" it?
Speaking of which, does anyone know a resource that lists these "tells"? I used to notice them all the time, but now that they've permeated 2/3rds of what I see and hear, I'm starting to go a bit numb.
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.
No, I don't think OP's comment is useless. It's not just a blanket statement, "Wow, LLM's quick", but also critically reflective, like a brief limitations section. This opens the discussion, by indirectly posing questions like, "Is it really this fast or "cheating"? How could we measure this experimentally? Etc.
What to do about it? A reinassance of knowledge curators to swim against the current? A revolution slop salmon men? I doubt the incentives are aligned.
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[ 4.4 ms ] story [ 33.1 ms ] threadWe'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.
The results were promising: https://zzbbyy.substack.com/p/reinventing-daydreaming-machin...
Consider context as hyper-dimensional coordinate vectors gesturing at the starting concept cluster of a synthesis chain or thread to unspool.
If the model's comprehensive training activated by your context locates the right thread to pull, this could be considered comprehension? That it "got" it?
Speaking of which, does anyone know a resource that lists these "tells"? I used to notice them all the time, but now that they've permeated 2/3rds of what I see and hear, I'm starting to go a bit numb.
https://tropes.fyi/tropes-md