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Comment by mjr00 | Hacker News Reader

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.

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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.

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.

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.

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.

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.

People make the same sort of mistakes.

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

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.

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