[HN Gopher] Language Model Teams as Distrbuted Systems
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Language Model Teams as Distrbuted Systems
Author : jryio
Score : 57 points
Date : 2026-03-16 17:19 UTC (5 hours ago)
(HTM) web link (arxiv.org)
(TXT) w3m dump (arxiv.org)
| measurablefunc wrote:
| Next up, LLMs as actors & processes in p-calculus.
| timcobb wrote:
| Is it web scale?
| measurablefunc wrote:
| Abstractly? 100%. Realistically? Depends on how many
| trillions we can get from investors.
| robot-wrangler wrote:
| > Next up, LLMs as actors & processes in p-calculus.
|
| You jest, but agents are of course already useful and fairly
| formal primitives. Distinct from actors, agents can have things
| like goals/strategies. There's a whole body of research on
| multi-agent systems that already exists and is even implemented
| in some model-checkers. It's surprising how little interest
| that creates in most LLM / AI / ML enthusiasts, who don't seem
| that motivated to use the prior art to propose / study /
| implement topologies and interaction protocols for the new wave
| of "agentic".
| measurablefunc wrote:
| That's all nice & well but which protocol & topology will
| deliver the most dollars from investors?
| antonvs wrote:
| That's easy: the Torment Nexus.
| measurablefunc wrote:
| That's topologically the same as the pyramid of torment &
| seems to me it's already saturated w/ lots of VC dollars.
| andai wrote:
| Ten years ago at my old university we had a course called
| Multi-Agent Systems. The whole year built up to it: a course
| in Formal Logic with Prolog, Logic-Based AI (LBAI) with a
| robot in a block world, also with Prolog, and finally Multi-
| Agent Systems (MAS).
|
| In the MAS course, we used GOAL, which was a system built on
| top of Prolog. Agents had Goals, Perceptions, Beliefs, and
| Actions. The whole thing was deterministic. (Network lag
| aside ;)
|
| The actual project was that we programmed teams of bots for a
| Capture The Flag tournament in Unreal Tournament 3.
|
| So it was the most fun possible way to learn the coolest
| possible thing.
|
| The next year they threw out the whole curriculum and
| replaced it with Machine Learning.
|
| --
|
| The agentic stuff seems to be gradually reinventing a similar
| setup from first principles, especially as people want to
| actually use this stuff in serious ways, and we lean more in
| the direction of determinism.
|
| The main missing feature in LLM land is reliability. (Well,
| that and cost and speed. Of course, "just have it be code"
| gives you all three for free ;)
| robot-wrangler wrote:
| Regardless of whether it's framed as old-school MAS or new-
| school agentic AI, it seems like it's an area that's
| inherently multi-disciplinary where it's good to be humble.
| You do see some research that's interested in leveraging
| the strengths of both (e.g.
| https://www.nature.com/articles/s41467-025-63804-5.pdf) but
| even if news of that kind of cross pollination was more
| common, we should go further. Pleased to see TFA connecting
| agentic AI to amdahls law for example.. but we should be
| aggressively stealing formalisms from economics, game
| theory, etc and anywhere else we can get them. Somewhat
| related here is the camel AI mission and white papers:
| https://www.camel-ai.org/
| andai wrote:
| I have an example from 2023, when Auto-GPT (think OpenClaw
| but with GPT-3.5 and early GPT-4 -- yeah it wasn't great!)
| was blowing up.
|
| Most people were just using it for the same task. "Research
| this stuff and summarize it for me."
|
| I realized I could get the same result by just writing a
| script to do a Google search, scrape top 10 results and
| summarize them.
|
| Except it runs in 10 seconds instead of 10 minutes. And it
| actually runs deterministically instead of getting side
| tracked and going in infinite loops and burning 100x as
| much money.
|
| It was like 30 lines of Python. GPT wrote it for me.
|
| My takeaway here was, LLMs are missing executive function.
| The ability to consistently execute a plan. But code runs
| deterministically every time. And - get this - code can
| call LLMs!
|
| So if your LLM writes a program which does the task
| (possibly using LLMs), the task will complete the same way
| every time.
|
| And most of the tasks people use LLMs for are very
| predictable, and fit in this category.
|
| People are now repeating the exact same thing Auto-GPT
| thing with OpenClaw. They're using the slow, non-
| deterministic thing as the driver.
|
| It actually kinda works this time -- it usually doesn't get
| stuck anymore, if you use a good model -- but they're still
| burning a hundred times more money than necessary.
| charcircuit wrote:
| Could it just be that it is happening behind closed doors due
| to multi agents being part of the secret sauce of post
| training LLMs.
| keeganpoppen wrote:
| i cant wait for the world to catch up to process, session, et
| al. calculii. the closest i've seen is all this "choreo" stuff
| that is floating around nowadays, which is pretty neat in
| itself.
| 50lo wrote:
| Once you run more than one agent in a loop, you inevitably
| recreate distributed systems problems: message ordering, retries,
| partial failure, etc. Most agent frameworks pretend these don't
| exist. Some of them address those problems partially. None of the
| frameworks I've seen address all of them.
| woah wrote:
| The current fad for "agent swarms" or "model teams" seems
| misguided, although it definitely makes for great paper fodder
| (especially if you combine it with distributed systems!) and gets
| the VCs hot.
|
| An LLM running one query at a time can already generate a huge
| amount of text in a few hours, and drain your bank account too.
|
| A "different agent" is just different context supplied in the
| query to the LLM. There is nothing more than that. Maybe some of
| them use a different model, but again, this is just a setting in
| OpenRouter or whatever.
|
| Agent parallelism just doesn't seem necessary and makes
| everything harder. Not an expert though, tell me where I'm wrong.
| nateroling wrote:
| I tend to agree. After seeing http://chatjimmy.ai, I think
| multi-agent systems are mostly just solving for LLMs being slow
| currently.
| conception wrote:
| This is like saying "multi-core cpus are just solving cpus
| being slow". Which yes, exactly.
| woah wrote:
| Steelmanning the other side of this question:
|
| LLMs mostly do useful work by writing stories about AI
| assistants who issue various commands and reply to a user's
| prompts. These do work, but they are fundamentally like a
| screenplay that the LLM is continuing.
|
| An "agent" is a great abstraction since the LLM is used to
| continuing stories about characters going through narrative
| arcs. The type of work that would be assigned to a particular
| agent can also keep its context clean and distraction-free.
|
| So parallelism could be useful even if everything is completely
| sequential to study how these separate characters and narrative
| arcs intersect in ways that are similar to real characters
| acting independently and simultaneously, which is what LLMs are
| good at writing about.
|
| Seems like the important thing would be to avoid getting caught
| up on actual "wall time" parallelism
| bhewes wrote:
| This is how we design at HewesNguyen AI. We are both MIS so once
| LLMs came out we where like sweet whole teams that can be tasked
| for one thing done well. Thank you Unix Philosophy
| ElijahLynn wrote:
| Title has a typo: distrbuted
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