[HarHarVeryFunny]

> Do submarines swim?

It doesn't matter HOW LLMs "swim" as long as they can, but the point being raised is whether they actually can.

It's as if LLMs can swim in the ocean, in rough surf, but fail to swim in rivers or swimming pools, because they don't have a generalized ability to swim - they've just been RL-trained on the solution steps to swimming in surf, but since those exact conditions don't exist in a river (which might seem like a less challenging environment), they fail there.

So, the question that might be asked is when LLMs are trained to perform well in these vertical domains like math and programming, where it's easy to verify results and provide outcome- or process-based RL rewards, are they really learning to reason, or are they just learning to pattern match to steer generation in the direction of problem-specific reasoning steps that they had been trained on?

Does the LLM have the capability to reason/swim, or is it really just an expert system that has been given the rules to reason/swim in certain cases, but would need to be similarly hand fed the reasoning steps to be successful in other cases?

I think the answer is pretty obvious given that LLM's can't learn at runtime - can't try out some reasoning generalization they may have arrived at, find that it doesn't work in a specific case, then explore the problem and figure it out for next time.

Given that it's Demis Hassabis who it pointing out this deficiency of LLMs (and has a 5-10 year plan/timeline to fix it - AGI), not some ill-informed LLM critic, it seems silly to deny it.

[schrectacular]

>> Do submarines swim?

>It doesn't matter HOW LLMs "swim" as long as they can, but the point being raised is whether they actually can.

>It's as if LLMs can swim in the ocean, in rough surf, but fail to swim in rivers or swimming pools

Just like submarines!

[MobiusHorizons]

What? Submarines can definitely “swim” in rivers, although shallow water is certainly more challenging for a submerged vessel. Most submarines are a bit big for most swimming pools, but small ones like ROVs are frequently tested in pools.

[gwd]

> I think the answer is pretty obvious given that LLM's can't learn at runtime - can't try out some reasoning generalization they may have arrived at, find that it doesn't work in a specific case, then explore the problem and figure it out for next time.

This is just a problem of memory. Supposing that an LLM did generate a genuinely novel insight, it could in theory they could write a note for itself so that next time they come online, they can read through a summary of the things they learned. And it could also write synthetic training data for itself so that the next time they're trained, that gets incorporated into its general knowledge.

OpenAI allows you to fine-tune GPT models, I believe. You could imagine a GPT system working for 8 hours in a day, then spending a bunch of time looking over all its conversation looking for patterns or insights or things to learn, and then modifying its own fine-tuning data (adding, removing, or modifying as appropriate), which it then used to train itself overnight, waking up the next morning having synthesized the previous day's experience.

[HarHarVeryFunny]

> This is just a problem of memory

How does memory (maybe later incorporated via fine tuning) help if you can't figure out how to do something in the first place ?

That would be a way to incorporate new declarative data at "runtime" - feedback to the AI intern as to what it is doing wrong. However, in order to do something effectively by yourself generally requires more than just new knowledge - it requires personal practice/experimentation etc, since you need to learn how to act based on the contents of your own mind, not that of the instructor.

Even when you've had enough practice to become proficient at a taught skill, you may not be able to verbalize exactly what you are doing (which is part of the teacher-student gap), so attempting to describe then capture that as textual/context "sensory input" is not always going to work.

[naasking]

> are they really learning to reason, or are they just learning to pattern match to steer generation in the direction of problem-specific reasoning steps that they had been trained on?

Are you sure there's a real difference? Do you have a definition of "reasoning" that excludes this?

[gwd]

So I do think there are two distinct types of activities involved in knowledge work:

1. Taking established techniques or concepts and appropriately applying them to novel situations.

2. Inventing or synthesizing new, never-before-seen techniques or concepts

The vast majority of the time, humans do #1. LLMs certainly do this in some contexts as well, as demonstrated by my example above. This to me counts as "understanding" and "thinking". Some people define "understanding" such that it's something only humans can do; to which I respond, I don't care what you call it, it's useful.

Can LLMs do #2? I don't know. They've got such extensive experience that how would you know if they'd invented a technique vs had seen it somewhere?

But I'd venture to argue that most humans never or rarely do #2.

[HarHarVeryFunny]

> But I'd venture to argue that most humans never or rarely do #2.

That seems fair, although the distinction between synthesizing something new and combining existing techniques is a bit blurry.

What's missing from LLMs though is really part of 1). If techniques A, B, C & D are all the tools you need to solve a novel problem, then a human has the capability of learning WHEN to use each of these tools, and in what order/combination, to solve that problem - a process of trial and error, generalization and exception, etc. It's not just the techniques (bag of tools) you need, but also the rules (acquired knowledge) of how they can be used to solve different problems.

LLMs aren't able to learn at runtime from their own experience, so the only way they can learn these rules of when to apply given tools (aka reasoning steps) - is by RL training on how they have been successfully used to solve a range of problems in the training data. So, the LLM may have learnt that in specific context it should first apply tool A (generate that reasoning step), etc, etc, but that doesn't help it to solve a novel problem where the same solution step selection process doesn't apply, even if the tools A-D are all it needs (if only it could learn how to apply them to this novel problem).

[mjr00]

It's trivial to demonstrate that LLMs are pattern matching rather than reasoning. A good way is to provide modified riddles-that-aren't. As an example:

> Prompt: A man working at some white collar job gets an interview scheduled with an MBA candidate. The man says "I can't interview this candidate, he's my son." How is this possible?

> ChatGPT: Because the interviewer is the candidate’s mother. (The riddle plays on the assumption that the interviewer must be a man.)

This is clearly pattern matching and overfitting to the "doctor riddle" and a good demonstration of how there's no actual reasoning going on. A human would read the prompt and initially demonstrate confusion, which LLMs don't demonstrate because they don't actually reason.

[Workaccount2]

Over fitting isn't evidence of non-reasoning, but that aside, what's interesting is that ChatGPT (free) trips on this, as did older models. But GPT-5 thinking, Opus 4, and Gemini 2.5 Pro all pointed out that there is no trick and it's likely the man just views it as a conflict of interest to interview his son.

It's hard to say whether this has been trained out (it's an old example) or if it's just another hurdle that general model progression has overcome.

[2ap]

OK. But, in Claude Sonnet 4:

'This is possible because the man is the candidate's father. When he says "he's my son," he's simply stating their family relationship. The scenario doesn't present any logical contradiction - a father could very well be in a position where he's supposed to interview his own son for a job. This would create a conflict of interest, which is why he's saying he can't conduct the interview. It would be inappropriate and unfair for a parent to interview their own child for a position, so he would need to recuse himself and have someone else handle the interview. The phrasing might initially seem like it's setting up a riddle, but it's actually a straightforward situation about professional ethics and avoiding conflicts of interest in hiring.'

EDIT - this is described better by other posters.

[naasking]

> It's trivial to demonstrate that LLMs are pattern matching rather than reasoning.

Again, this is just asserting the premise that reasoning cannot include pattern matching, but this has never been justified. What is your definition for "reasoning"?

> This is clearly pattern matching and overfitting to the "doctor riddle" and a good demonstration of how there's no actual reasoning going on.

Not really, no. "Bad reasoning" does not entail "no reasoning". Your conclusion is simply too strong for the evidence available, which is why I'm asking for a rigourous definition of reasoning that doesn't leave room for disagreement about whether pattern matching counts.

[mjr00]

If your assertion is that you can't prove reasoning isn't just pattern matching, then I counter by saying you can't prove reasoning isn't just chaining a large number of IF/THEN/ELSE logic statements and therefore computers have been generally intelligent since ~1960.

[naasking]

The difference between ML models and computers since the 1960s is that the ML models weren't programmed with predicates, they "learned" them from analyzing data, and can continue to learn in various ways from further data. That's a meaningful difference, and why the former may qualify as intelligent and the latter cannot.

But I agree in principle that LLMs can be distilled into large IF/THEN/ELSE trees, that's the lesson of BitNet 1-bit LLMs. The predicate tree being learned from data is the important qualifier for intelligence though.

Edit: in case I wasn't clear, I agree that a specific chain of IF/THEN/ELSE statements in a loop can be generally intelligent. How could it not, specific kinds of these chains are Turing complete after all, so unless you think the brain has some kind of magic, it too is reducible to such a program, in principle. We just haven't yet discovered what kind of chain this is, just like we didn't understand what kind of chain could produce distributed consensus before PAXOS.

[DenisM]

We kinda move from the situation “LLM can only do what it seen before” to “LLM can do something by composing several things it has seen before”. We didn’t get to the situation “LLM can do things it has not seen before”.

The practicality of the situation is that a lot of problems fall into the second bucket. We all like to think we deal with novel problems, but most of what we can think of was already considered by another human and captured by llm. You had to invent something deliberately unique, and that’s telling. Most startup ideas are invented more than once, for example.

The key shortcoming of the llm is that it is not aware of its own limits. If it ever becomes aware it can outsource such rare things to mechanical Turk.

[adastra22]

I routinely use LLMs to do things that have never been done before. It requires carefully structured prompting and context management, but it is quite doable.

[adastra22]

People make the same sort of mistakes.

[mjr00]

Please explain how this is relevant to the topic at hand. Thanks!

[adastra22]

You claim that AI is patterned matching instead of reasoning, but the psychological literature is clear that people reason by pattern matching. As evidenced by the fact that people tend to make the same sorts of mistakes when reasoning quickly.

Ask someone who has made such a mistake to think a little more on it, and they’ll notice their error. Ask a reasoning model to do literally the same thing, to “think” on it, and it will also notice its error.

If you’re still insist that AI are not reasoning here, then neither are people.

[HarHarVeryFunny]

I define intelligence as prediction (degree of ability to use past experience to correctly predict future action outcomes), and reasoning/planning as multi-step what-if prediction.

Certainly if a human (or some AI) has learned to predict/reason over some domain, then what they will be doing is pattern matching to determine the generalizations and exceptions that apply in a given context (including a hypothetical context in a what-if reasoning chain), in order to be able to select a next step that worked before.

However, I think what we're really talking about here isn't the mechanics of applying learnt reasoning (context pattern matching), but rather the ability to reason in the general case, which requires the ability to LEARN to solve novel problems, which is what is missing from LLMs.

A system that has a fixed set of (reasoning/prediction) rules, but can't learn new ones for itself, seems better regarded as an expert system. We need to make the distinction between a system that can only apply rules, and one that can actually figure out the rules in the first place.

In terms of my definitions of intelligence and reasoning, based around ability to use past experience to learn to predict, then any system that can't learn from fresh experience doesn't meet that definition.

Of course in humans and other intelligent animals the distinction between past and ongoing experience doesn't apply since they can learn continually and incrementally (something that is lacking from LLMs), so for AI we need to use a different vocabulary, and "expert system" seems the obvious label for something that can use rules, but not discover them for itself.

[naasking]

> but rather the ability to reason in the general case, which requires the ability to LEARN to solve novel problems, which is what is missing from LLMs.

I don't think it's missing, zero shot prompting is quite successful in many cases. Maybe you find the extent that LLMs can do this to be too limited, but I'm not sure that means they don't reason at all.

> A system that has a fixed set of (reasoning/prediction) rules, but can't learn new ones for itself, seems better regarded as an expert system.

I think expert systems are a lot more limited than LLMs, so I don't agree with that classification. LLMs can generate output that's out of distribution, for instance, which is not something that's classic expert systems can do (even if you think LLM OOD is still limited compared to humans).

I've elaborated in another comment [1] what I think part of the real issue is, and why people keep getting tripped up by saying that pattern matching is not reasoning. I think it's perfectly fine to say that pattern matching is reasoning, but pattern matching has levels of expressive power. First-order pattern matching is limited (and so reasoning is limited), and clearly humans are capable of higher order pattern matching which is Turing complete. Transformers are also Turing complete, and neural networks can learn any function, so it's not a matter of expressive power, in principle.

Aside from issues stemming from tokenization, I think many of these LLM failures are because they aren't trained in higher order pattern matching. Thinking models and the generalization seen from grokking are the first steps on this path, but it's not quite there yet.

[1] https://news.ycombinator.com/item?id=45277098

[HarHarVeryFunny]

Powerful pattern matching is still just pattern matching.

How is an LLM going to solve a novel problem with just pattern matching?

Novel means it has never seen it before, maybe doesn't even have the knowledge needed to solve it, so it's not going to be matching any pattern, and even if it did, that would not help if it required a solution different to whatever the pattern match had come from.

Human level reasoning includes ability to learn, so that people can solve novel problems, overcome failures by trial and error, exploration, etc.

So, whatever you are calling "reasoning" isn't human level reasoning, and it's therefore not even clear what you are trying to say? Maybe just that you feel LLMs have room for improvement by better pattern matching?

[naasking]

> Powerful pattern matching is still just pattern matching.

Higher order pattern matching is Turing complete. Transformers are Turing complete. Memory augmented LLMs are Turing complete. Neural networks can learn to reproduce any function. These have all been proven.

So if computers can be intelligent and can solve novel problems in principle, then LLMs can too if given the right training. If you don't think computers can be intelligent, you have a much higher burden to meet.

> Human level reasoning includes ability to learn, so that people can solve novel problems, overcome failures by trial and error, exploration, etc.

You keep bringing this up as if it's lacking, but basically all existing LLM interfaces provide facilities for memory to store state. Storing progress just isn't an issue if the LLM has the right training. HN has some recent articles about Claude code just being given the task to port some GitHub repos to other programming languages, and they woke up the next morning and it did it autonomously, using issue tracking, progress reports, PRs the hole nine yards. This is frankly not the hard part IMO.

[HarHarVeryFunny]

Being Turing machine complete means that the system in question can emulate a Turing machine, which you could then program to do anything since it's a universal computer. So sure, if you know how to code up an AGI to run on a Turing machine you would be good to go on any Turing machine!

I'm not sure why you want to run a Turing machine emulator on an LLM, when you could just write a massively faster one to run on the computer your LLM is running on, cutting out the middle man, but whatever floats your boat I suppose.

Heck, if you really like emulation and super slow speed then how about implementing Conway's game of Life to run on your LLM Turing machine emulator, and since Life is also Turing complete you could run another Turing machine emulator on that (it's been done), and finally run your AGI on top of that! Woo hoo!

I do think you'll have a challenge prompting your LLM to emulate a Turing machine (they are really not very good at that sort of thing), especially since the prompt/context will also have to do double duty as the Turing machines (infinite length) tape, but no doubt you'll figure it out.

Keep us posted.

I'll be excited to see your AGI program when you write that bit.

[freejazz]

It seems readily apparent there is a difference given their inability to do tasks we would otherwise reasonably describe as achievable via basic reasoning on the same facts.

[naasking]

I agree LLMs have many differences in abilities relative to humans. I'm not sure what this implies for their ability to reason though. I'm not even sure what examples about their bad reasoning can prove about the presence or absence of any kind of "reasoning", which is why I keep asking for definitions to remove the ambiguity. If examples of bad reasoning sufficed, then this would prove that humans can't reason either, which is silly.

A rigourous definition of "reasoning" is challenging though, which is why people consistently can't provide a general one that's satisfactory when I ask, and this is why I'm skeptical that pattern matching isn't a big part of it. Arguments that LLMs are "just pattern matching" are thus not persuasive arguments that they are not "reasoning" at some cruder level.

Maybe humans are just higher order pattern matchers and LLMs are only first or second-order pattern matchers. Maybe first-order pattern matching shouldn't count as "reasoning", but should second-order? Third-order? Is there evidence or some proof that LLMs couldn't be trained to be higher order pattern matchers, even in principle?

None of the arguments or evidence I've seen about LLMs and reasoning is rigourous or persuasive on these questions.

[freejazz]

Nothing about the uncertainty of the definition for 'reasoning' requires that pattern matching be part of the definition.

[naasking]

Did someone in this thread claim that?