> 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).

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.

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.

> 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.

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.

People make the same sort of mistakes.

> 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

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.