this post was submitted on 08 Jun 2025
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[–] Nanook@lemm.ee 230 points 3 days ago (51 children)

lol is this news? I mean we call it AI, but it’s just LLM and variants it doesn’t think.

[–] MNByChoice@midwest.social 77 points 3 days ago (1 children)

The "Apple" part. CEOs only care what companies say.

[–] kadup@lemmy.world 51 points 3 days ago (5 children)

Apple is significantly behind and arrived late to the whole AI hype, so of course it's in their absolute best interest to keep showing how LLMs aren't special or amazingly revolutionary.

They're not wrong, but the motivation is also pretty clear.

[–] homesweethomeMrL@lemmy.world 29 points 3 days ago

“Late to the hype” is actually a good thing. Gen AI is a scam wrapped in idiocy wrapped in a joke. That Apple is slow to ape the idiocy of microsoft is just fine.

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[–] Clent@lemmy.dbzer0.com 19 points 2 days ago (3 children)

Proving it matters. Science is constantly proving any other thing that people believe is obvious because people have an uncanning ability to believe things that are false. Some people will believe things long after science has proven them false.

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[–] minoscopede@lemmy.world 66 points 2 days ago* (last edited 2 days ago) (13 children)

I see a lot of misunderstandings in the comments 🫤

This is a pretty important finding for researchers, and it's not obvious by any means. This finding is not showing a problem with LLMs' abilities in general. The issue they discovered is specifically for so-called "reasoning models" that iterate on their answer before replying. It might indicate that the training process is not sufficient for true reasoning.

Most reasoning models are not incentivized to think correctly, and are only rewarded based on their final answer. This research might indicate that's a flaw that needs to be corrected before models can actually reason.

[–] Knock_Knock_Lemmy_In@lemmy.world 16 points 2 days ago (5 children)

When given explicit instructions to follow models failed because they had not seen similar instructions before.

This paper shows that there is no reasoning in LLMs at all, just extended pattern matching.

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[–] theherk@lemmy.world 15 points 2 days ago

Yeah these comments have the three hallmarks of Lemmy:

  • AI is just autocomplete mantras.
  • Apple is always synonymous with bad and dumb.
  • Rare pockets of really thoughtful comments.

Thanks for being at least the latter.

[–] REDACTED@infosec.pub 12 points 2 days ago* (last edited 2 days ago) (4 children)

What confuses me is that we seemingly keep pushing away what counts as reasoning. Not too long ago, some smart alghoritms or a bunch of instructions for software (if/then) was officially, by definition, software/computer reasoning. Logically, CPUs do it all the time. Suddenly, when AI is doing that with pattern recognition, memory and even more advanced alghoritms, it's no longer reasoning? I feel like at this point a more relevant question is "What exactly is reasoning?". Before you answer, understand that most humans seemingly live by pattern recognition, not reasoning.

https://en.wikipedia.org/wiki/Reasoning_system

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[–] mavu@discuss.tchncs.de 58 points 3 days ago

No way!

Statistical Language models don't reason?

But OpenAI, robots taking over!

[–] billwashere@lemmy.world 49 points 2 days ago (13 children)

When are people going to realize, in its current state , an LLM is not intelligent. It doesn’t reason. It does not have intuition. It’s a word predictor.

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[–] sev@nullterra.org 49 points 3 days ago (35 children)

Just fancy Markov chains with the ability to link bigger and bigger token sets. It can only ever kick off processing as a response and can never initiate any line of reasoning. This, along with the fact that its working set of data can never be updated moment-to-moment, means that it would be a physical impossibility for any LLM to achieve any real "reasoning" processes.

[–] kescusay@lemmy.world 18 points 3 days ago (3 children)

I can envision a system where an LLM becomes one part of a reasoning AI, acting as a kind of fuzzy "dataset" that a proper neural network incorporates and reasons with, and the LLM could be kept real-time updated (sort of) with MCP servers that incorporate anything new it learns.

But I don't think we're anywhere near there yet.

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[–] Jhex@lemmy.world 48 points 3 days ago (1 children)

this is so Apple, claiming to invent or discover something "first" 3 years later than the rest of the market

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[–] brsrklf@jlai.lu 47 points 3 days ago (2 children)

You know, despite not really believing LLM "intelligence" works anywhere like real intelligence, I kind of thought maybe being good at recognizing patterns was a way to emulate it to a point...

But that study seems to prove they're still not even good at that. At first I was wondering how hard the puzzles must have been, and then there's a bit about LLM finishing 100 move towers of Hanoï (on which they were trained) and failing 4 move river crossings. Logically, those problems are very similar... Also, failing to apply a step-by-step solution they were given.

[–] auraithx@lemmy.dbzer0.com 39 points 3 days ago

This paper doesn’t prove that LLMs aren’t good at pattern recognition, it demonstrates the limits of what pattern recognition alone can achieve, especially for compositional, symbolic reasoning.

[–] technocrit@lemmy.dbzer0.com 16 points 3 days ago* (last edited 3 days ago)

Computers are awesome at "recognizing patterns" as long as the pattern is a statistical average of some possibly worthless data set. And it really helps if the computer is setup to ahead of time to recognize pre-determined patterns.

[–] Mniot@programming.dev 42 points 2 days ago

I don't think the article summarizes the research paper well. The researchers gave the AI models simple-but-large (which they confusingly called "complex") puzzles. Like Towers of Hanoi but with 25 discs.

The solution to these puzzles is nothing but patterns. You can write code that will solve the Tower puzzle for any size n and the whole program is less than a screen.

The problem the researchers see is that on these long, pattern-based solutions, the models follow a bad path and then just give up long before they hit their limit on tokens. The researchers don't have an answer for why this is, but they suspect that the reasoning doesn't scale.

[–] reksas@sopuli.xyz 37 points 3 days ago (4 children)

does ANY model reason at all?

[–] 4am@lemm.ee 34 points 3 days ago (3 children)

No, and to make that work using the current structures we use for creating AI models we’d probably need all the collective computing power on earth at once.

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[–] bjoern_tantau@swg-empire.de 36 points 3 days ago* (last edited 1 day ago)
[–] technocrit@lemmy.dbzer0.com 29 points 2 days ago* (last edited 2 days ago) (3 children)

Peak pseudo-science. The burden of evidence is on the grifters who claim "reason". But neither side has any objective definition of what "reason" means. It's pseudo-science against pseudo-science in a fierce battle.

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[–] skisnow@lemmy.ca 26 points 2 days ago (1 children)

What's hilarious/sad is the response to this article over on reddit's "singularity" sub, in which all the top comments are people who've obviously never got all the way through a research paper in their lives all trashing Apple and claiming their researchers don't understand AI or "reasoning". It's a weird cult.

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[–] vala@lemmy.world 25 points 3 days ago
[–] SplashJackson@lemmy.ca 24 points 3 days ago (1 children)
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[–] technocrit@lemmy.dbzer0.com 23 points 3 days ago* (last edited 3 days ago) (5 children)

Why would they "prove" something that's completely obvious?

The burden of proof is on the grifters who have overwhelmingly been making false claims and distorting language for decades.

[–] TheRealKuni@midwest.social 33 points 3 days ago (2 children)

Why would they "prove" something that's completely obvious?

I don’t want to be critical, but I think if you step back a bit and look and what you’re saying, you’re asking why we would bother to experiment and prove what we think we know.

That’s a perfectly normal and reasonable scientific pursuit. Yes, in a rational society the burden of proof would be on the grifters, but that’s never how it actually works. It’s always the doctors disproving the cure-all, not the snake oil salesmen failing to prove their own prove their own product.

There is value in this research, even if it fits what you already believe on the subject. I would think you would be thrilled to have your hypothesis confirmed.

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[–] yeahiknow3@lemmings.world 23 points 3 days ago* (last edited 3 days ago) (1 children)

They’re just using the terminology that’s widespread in the field. In a sense, the paper’s purpose is to prove that this terminology is unsuitable.

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[–] tauonite@lemmy.world 16 points 3 days ago

That's called science

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[–] GaMEChld@lemmy.world 20 points 2 days ago (8 children)

Most humans don't reason. They just parrot shit too. The design is very human.

[–] elbarto777@lemmy.world 26 points 2 days ago (3 children)

LLMs deal with tokens. Essentially, predicting a series of bytes.

Humans do much, much, much, much, much, much, much more than that.

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[–] FreakinSteve@lemmy.world 20 points 2 days ago (4 children)

NOOOOOOOOO

SHIIIIIIIIIITT

SHEEERRRLOOOOOOCK

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[–] flandish@lemmy.world 18 points 3 days ago

stochastic parrots. all of them. just upgraded “soundex” models.

this should be no surprise, of course!

[–] RampantParanoia2365@lemmy.world 18 points 2 days ago* (last edited 2 days ago) (2 children)

Fucking obviously. Until Data's positronic brains becomes reality, AI is not actual intelligence.

AI is not A I. I should make that a tshirt.

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[–] sp3ctr4l@lemmy.dbzer0.com 17 points 3 days ago* (last edited 3 days ago) (2 children)

This has been known for years, this is the default assumption of how these models work.

You would have to prove that some kind of actual reasoning capacity has arisen as... some kind of emergent complexity phenomenon.... not the other way around.

Corpos have just marketed/gaslit us/themselves so hard that they apparently forgot this.

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[–] Auli@lemmy.ca 15 points 2 days ago

No shit. This isn't new.

[–] atlien51@lemm.ee 14 points 3 days ago (2 children)

Employers who are foaming at the mouth at the thought of replacing their workers with cheap AI:

🫢

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[–] mfed1122@discuss.tchncs.de 13 points 3 days ago* (last edited 3 days ago) (14 children)

This sort of thing has been published a lot for awhile now, but why is it assumed that this isn't what human reasoning consists of? Isn't all our reasoning ultimately a form of pattern memorization? I sure feel like it is. So to me all these studies that prove they're "just" memorizing patterns don't prove anything other than that, unless coupled with research on the human brain to prove we do something different.

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