Can LLMs Perform Deep Technical Comprehension of Computer Architecture Papers

by Jimmc414 • 7 days ago

83 26 7 days ago

bob1029• 7 days ago

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.

zby• 7 days ago

I have made an experiment with 'idea generation' last year (with much worse llms): https://github.com/zby/DayDreamingDayDreaming

The results were promising: https://zzbbyy.substack.com/p/reinventing-daydreaming-machin...

mahirsaid2• 7 days ago

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.

zeusk• 7 days ago

Isn't the whole point of attention, some context comprehension in the stochastic parrot machine.

mahirsaid2• 7 days ago

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.

aetherspawn• 7 days ago

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

stingraycharles• 7 days ago

Can’t believe the abstract has an em dash, a “not X but Y” and a “rule of three” in the first sentence. This is ridiculous.

andai• 7 days ago

The reference implementation of slop :)

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.

tiahura• 6 days ago

Every time I see people complaining about clear writing with signposts, I feel like I'm in Idiocracy.

txdv2• 7 days ago

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.

thejokeisonme2• 7 days ago

Well if you can't judge, doesn't it make your comment completely useless?

nairboon• 7 days ago

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.

N_Lens• 7 days ago

In my experience: Absolutely Yes!

thejokeisonme2• 7 days ago

Slop about slop. Already tired of this new chapter of humanity...

suprjami2• 7 days ago

You're not just tired (em dash) you're exhausted!

thx672• 6 days ago

The callout is real, and I am glad you pushed back.

amelius• 7 days ago

Why not, most of computer architecture is just plumbing guided by quantitative experimentation (simulation).

Chris2048• 6 days ago

Whenever I put a whitepaper in notebooklm and ask it to create a presentation etc explaining the main points, it illustrates the obvious, easy to understand things in the brief, and misses (or oversimplifies) most of the hard technical parts..

elindiosolari• 6 days ago

You can try https://claude.com/ for this

Can LLMs Perform Deep Technical Comprehension of Computer Architecture Papers

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