Can LLMs Perform Deep Technical Comprehension of Computer Architecture Papers
arxiv.org
arxiv.org
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.
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.
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?