7 ms·

The answer is reasoning. It is obvious now that whatever quality LLM have, they don't think and reason, they are just statistical machine outputing whatever the

The answer is reasoning. It is obvious now that whatever quality LLM have, they don't think and reason, they are just statistical machine outputing whatever they training set as most probable. They are useful, and they can mimic thinking to a certain level, mainly because they have been trained on a inhumane amount of data that no person could learn in one life. But they do not think, and the current algorithms are clearly a dead end for thinking machines.

cbdevidal 10mo ago
Reasoning keeps improving, but they still have a ways to go https://arcprize.org/leaderboard https://arcprize.org/leaderboard
- vaylian 10mo ago
  What we need is reasoning as in "drawing logical conclusions based on logic". LLMs do reasoning by recursively adding more words to the context window. That's not logical reasoning.
  - tim333 10mo ago
    It's debatable that humans do "drawing logical conclusions based on logic". Look at politics and what people vote for. They seem to do something more like pattern matching.
    - coldtea 10mo ago
      Maybe we say that when we don't like those conclusions? After all I can guarantee the other side (whatever it is) will say the same thing for your "logical" conclusions. It is logic, we just don't share the same predicates or world model...
    - isodev 10mo ago
      Humans are far from logical. We make decisions within the context of our existence. This includes emotions, friends, family, goals, dreams, fears, feelings, mood, etc. it’s one of the challenges when LLMs are being anthropomorphised, reasoning/logic for bots is not the same as that for humans.
      - R_D_Olivaw 10mo ago
        And yet, when we make bad calls or do illogical things, because of hormones, emotions, energy levels, etc we still calling it reasoning. But, to LLMs we don't afford the same leniency. If they flip some bits and the logic doesn't add up we're quick to point that "it's not reasoning at all". Funny throne we've built for ourselves.
        habinero 10mo ago
        Yes, because different things are different.
    - virgil_disgr4ce 10mo ago
      Just because all humans don't use reason all the time doesn't mean reasoning isn't a good and desirable strategy.
    - vaylian 10mo ago
      I agree that some people use intuition or pattern matching to make decisions. But humans are also able to use logical reasoning to come to conclusions.
  - elzbardico 10mo ago
    I don't know why you were downvoted. It is a bit more complicated, but that's the gist of it. LLMs don't actually reason.
    - dagss 10mo ago
      Whether LLM is reasoning or not is an independent question to whether it works by generating text. By the standard in the parent post, humans certainly do not "reason". But that is then just choosing a very high bar for "reasoning" that neither humans nor AI meets...what is the point then? It is a bit like saying: "Humans don't reason, they just let neurons fire off one another, and think the next thought that enters their mind" Yes, LLMs need to spew out text to move their state forward. As a human I actually sometimes need to do that too: Talk to myself in my head to make progress. And when things get just a tiny bit complicated I need to offload my brain using pen and paper. Most arguments used to show that LLMs do not "reason" can be used to show that humans do not reason either. To show that LLMs do not reason you have to point to something else than how it works.
      - irishcoffee 10mo ago
        I’ll take a stab. If LLMs were actually able to think/reason and you acknowledge that they’ve been trained on as much data as everyone could get their hands on such that they’ve been “taught” an infinite amount more than any ten humans could learn in a lifetime, I would ask: Why can’t they solve novel, unsolved problems?
        dagss 10mo ago
        When coding they are solving "novel, unsolved problems" related to coding problems set up. So I will assume you mean within maths, science etc? Basically things they can't solve today. Well 99.9% of humans cannot solve novel, unsolved problems in those fields. LLMs cannot learn, there is just the initial weight estimation process. And that process currently does not make them good enough on novel math/theoretical physics problems. That does not mean they do not "reason" in the same way that those 99.9% of humans still "reason". But they definitely do not learn, the way humans do. (Anyway, if LLMs could somehow get 1000x as large context window and get to converse with themselves for a full year, it does not seem out of the question they could come out with novel research?)
joenot443 10mo ago
> But they do not think I see this argument made a lot. I'm not sure if the distinction really holds weight once we start to unravel though. What's a topic you're able to think about that an LLM is not able to think about?
- mopsi 10mo ago
  Any topic with little coverage in the training data. LLMs will keep circling around the small bits in the training data, unable synthesize new connections. This is very obvious when trying to use LLMs to modify scripts in vendor-specific languages that have not been widely documented and don't have many examples available. A seasoned programmer will easily recognize common patterns like if-else blocks and loops, but LLMs will get stuck and output gibberish.
- cgriswald 10mo ago
  I can solve a mystery novel based on the evidence alone. Assuming an LLM doesn’t already have the answer it will offer solutions based on meta-information like how similar mysteries conclude or are structured. While this can be effective, it’s not really solving the mystery and will fail with anything truly novel.
  - Tteriffic 10mo ago
    People more often do the same thing and act like llm’s.
- ted_bunny 10mo ago
  I asked GPT for rules on 101-level French grammar. That should be well documented for someone learning from English, no? The answers were so consistently wrong that it seemed intentional. Absolutely nothing novel asked of it. It could have quoted verbatim if it wanted to be lazy. I can't think of an easier question to give an LLM. If it's possible to "prompt wrong" a simple task that my six-year old nephew could easily do, the burden of proof is not on the people denying LLM intelligence, it's on the boosters.
  - 10mo ago
griffzhowl 10mo ago
> the current algorithms are clearly a dead end for thinking machines. These discussions often get derailed into debates about what "thinking" means. If we define thinking as the capacity to produce and evaluate arguments, as the cognitive scientists Sperber and Mercier do, then we can see LLMs are certainly producing arguments, but they're weak at the evaluation. In some cases, arguments can be formalised, and then evaluating them is a solved problem, as in the examples of using the Lean proofchecker in combination with LLMs to write mathematical proofs. That suggests a way forward will come from formalising natural language arguments. So LLMs by themselves might be a dead end but in combination with formalisation they could be very powerful. That might not be "thinking" in the sense of the full suite of human abilities that we group with that word but it seems an important component of it.
- kjellsbells 10mo ago
  > suggests a way forward will come from formalising natural language arguments If by this you mean "reliably convert expressions made in human natural language to unambiguous, formally parseable expressions that a machine can evaluate the same way every time"... isn't that essentially an unreachable holy grail? I mean, everyone from Plato to Russell and Wittgenstein struggled with the meaning of human statements. And the best solution we have today is to ask the human to restrict the set of statement primitives and combinations that they can use to a small subset of words like "const", "let foo = bar", and so on.
  - griffzhowl 10mo ago
    Whether the Holy Grail is unreachable or not is the question. Of course, the problem in full generality is hard, but that doesn't mean it can't be approached in various partial ways, either by restricting the inputs as you suggest or by coming up with some kind of evaluation procedures that are less strict than formal verifiability. I don't have any detailed proposals tbh
- benrutter 10mo ago
  > That suggests a way forward will come from formalising natural language arguments. Hot take (and continue with the derailment), but I'd argue that analytic philosophy from the last 100 years suggests this is a dead end. The idea that belief systems could be formalized was huge in the early 20th century (movements like Logical Positivism, or Russell's principia mathematica being good examples of this). Those approaches haven't really yielded many results, and by far the more fruitful form of analysis has been to conceptually "reframe" different problems (folks like Hillary Putnam, Wittgenstein, Quine being good examples). We've stacked up a lot of evidence that human language is much too loose and mushy to be formalised in a meaningful way.
lerp-io 10mo ago
they can think just not in the same abstract platonic way that a human mind can
- deleted 10mo ago
  [deleted]
- HPsquared 10mo ago
  I rather approach it from a Cartesian perspective. A context window is just that, it's not "existence". And because they do not exist in the world the same way as a human does, they do not think in the same way a human does (reversal of "I think therefore I am")
  - bonesss 10mo ago
    I have a context matrix, therefore I transform?
    - deleted 10mo ago
      [deleted]
    - HPsquared 10mo ago
      An LLM doesn't have a valid concept of "I" because the data and algorithm it comprises could be run deterministically on any hardware, even multiple times, held in storage, rewound, edited and so on. There is no unique subject, there is no "I". The "life" or "soul" or "session ID" when that sentence is generated, is not known by itself to be unique.
      - fc417fc802 10mo ago
        > because the data and algorithm it comprises could be run deterministically on any hardware, even multiple times, held in storage, rewound, edited and so on. Quite the assumption you're making that the same thing does not apply to humans. Not having the technology is not the same thing as not being possible.
      - 9mo ago
trash_cat 10mo ago
What constitutes real "thinking" or "reasoning" is beside the point. What matters is what results we getting. And the challenge is rethinking how we do work, connecting all the data sources for agents to run and perform work over the various sources that we perform work. That will take ages. Not to mention having the controls in place to make that the "thinking" was correct in the end.
- neutronicus 10mo ago
  > connecting all the data sources for agents to run Copilot can't jump to definition in Visual Studio. Anthropic got a lot of mileage out of teaching Claude to grep, but LLM agents are a complete dead-end for my code-base until they can use the semantic search tools that actually work on our code-base and hook into the docs for our expensive proprietary dependencies.
- virgil_disgr4ce 10mo ago
  Thinking is not besides the point, it is the entire point. You seem to be defining "thinking" as an interchangeable black box, and as long as something fits that slot and "gets results", it's fine. But it's the code-writing that's the interchangeable black box, not the thinking. The actual work of software development is not writing code, it's solving problems. With a problem-space-navigation model, I'd agree that there are different strategies that can find a path from A to B, and what we call cognition is one way (more like a collection of techniques) to find a path. I mean, you can in principle brute-force this until you get the desired result. But that's not the only thing that thinking does. Thinking responds to changing constraints, unexpected effects, new information, and shifting requirements. Thinking observes its own outputs and its own actions. Thinking uses underlying models to reason from first principles. These strategies are domain-independent, too. And that's not even addressing all the other work involved in reality: deciding what the product should do when the design is underspecified. Asking the client/manager/etc what they want it to do in cases X, Y and Z. Offering suggestions and proposals and explaining tradeoffs. Now I imagine there could be some other processes we haven't conceived of that can do these things but do them differently than human brains do. But if there were we'd probably just still call it 'thinking.'
Balgair 10mo ago
I know a lot of people with access to Claude Code and the like will say that 'No, it sure seems to reason to me!' Great. But most (?) of the business out there aren't paying for the big boy models. I know of a F100 that got snookered into a deal with GPT 4 for 5 years, max of 40 responses per session, max of 10 sessions of memory, no backend integration. Those folks rightly think that AI is a bad idea.
notepad0x90 10mo ago
Do you think reasoning models don't count? there is a lot of work around those and things like RAGs.
- vrighter 10mo ago
  "Reasoning" in this context is just a marketing gimmick that means "run the llm in a loop". But people who don't know how they work (the people buying them for others) just take that word at face value and assume it means what it usually means in totally different contexts.
  - notepad0x90 10mo ago
    That's fair, thanks.
NuclearPM 10mo ago
LLMs
klooney 10mo ago
> It is obvious now that whatever quality LLM have, they don't think and reason, they are just statistical machine outputing whatever they training set as most probable I have kids, and you could say the same about toddlers. Terrific mimics, they don't understand the whys.
- gls2ro 10mo ago
  IMHO when toddlers say mama they really understand that to a much much bigger degree than any LLM. They might not be able to articulate it but the deep understanding is there. So I think younger kids have purpose and associate meaning to a lot of things and they do try to get to a specific path toward an outcome. Of course (depending on the age) their "reasoning" is in a different system than hours where the survival instincts are much more powerful than any custom defined outcome so most of the time that is the driving force of the meaning. Why I talk about meaning? Because, of course, the kids cannot talk about the why, as that is very abstract. But meaning is a big part of the Why and it continues to be so in adult life it is just that the relation is reversed: we start talking about the why to get to a meaning. I also think that kids starts to have more complex thoughts than the language very early. If you got through the "Why?" phase you might have noticed that when they ask "Why?" they could mean very different questions. But they don't know the words to describe it. Sometimes "Why?" means "Where?" sometimes means "How?" sometimes means "How long?" .... That series of questioning is, for me, a kind of proof that a lot of things are happening in kids brain much more than they can verbalise.
md2020 10mo ago
> they are just statistical machine outputing whatever they training set as most probable. How is this sentiment still the top comment on an article about AI on HN in 2026? It's not true with today's models. They undergo vast amounts of reinforcement learning optimizing an objective that is NOT just predict the most likely next token given the training corpus. I would say even without the RL the "predict the next token" objective doesn't preclude thinking and reasoning, but that's a separate discussion. Generative sequence modeling learns to (approximately) model the process that produced the sequence. When you consider that text sequences are produced by human minds, which most would consider to be thinking and reasoning, well...

cbdevidal 10mo ago

Reasoning keeps improving, but they still have a ways to go https://arcprize.org/leaderboard https://arcprize.org/leaderboard

vaylian 10mo ago
What we need is reasoning as in "drawing logical conclusions based on logic". LLMs do reasoning by recursively adding more words to the context window. That's not logical reasoning.
- tim333 10mo ago
  It's debatable that humans do "drawing logical conclusions based on logic". Look at politics and what people vote for. They seem to do something more like pattern matching.
  - coldtea 10mo ago
    Maybe we say that when we don't like those conclusions? After all I can guarantee the other side (whatever it is) will say the same thing for your "logical" conclusions. It is logic, we just don't share the same predicates or world model...
  - isodev 10mo ago
    Humans are far from logical. We make decisions within the context of our existence. This includes emotions, friends, family, goals, dreams, fears, feelings, mood, etc. it’s one of the challenges when LLMs are being anthropomorphised, reasoning/logic for bots is not the same as that for humans.
    - R_D_Olivaw 10mo ago
      And yet, when we make bad calls or do illogical things, because of hormones, emotions, energy levels, etc we still calling it reasoning. But, to LLMs we don't afford the same leniency. If they flip some bits and the logic doesn't add up we're quick to point that "it's not reasoning at all". Funny throne we've built for ourselves.
      - habinero 10mo ago
        Yes, because different things are different.
  - virgil_disgr4ce 10mo ago
    Just because all humans don't use reason all the time doesn't mean reasoning isn't a good and desirable strategy.
  - vaylian 10mo ago
    I agree that some people use intuition or pattern matching to make decisions. But humans are also able to use logical reasoning to come to conclusions.
- elzbardico 10mo ago
  I don't know why you were downvoted. It is a bit more complicated, but that's the gist of it. LLMs don't actually reason.
  - dagss 10mo ago
    Whether LLM is reasoning or not is an independent question to whether it works by generating text. By the standard in the parent post, humans certainly do not "reason". But that is then just choosing a very high bar for "reasoning" that neither humans nor AI meets...what is the point then? It is a bit like saying: "Humans don't reason, they just let neurons fire off one another, and think the next thought that enters their mind" Yes, LLMs need to spew out text to move their state forward. As a human I actually sometimes need to do that too: Talk to myself in my head to make progress. And when things get just a tiny bit complicated I need to offload my brain using pen and paper. Most arguments used to show that LLMs do not "reason" can be used to show that humans do not reason either. To show that LLMs do not reason you have to point to something else than how it works.
    - irishcoffee 10mo ago
      I’ll take a stab. If LLMs were actually able to think/reason and you acknowledge that they’ve been trained on as much data as everyone could get their hands on such that they’ve been “taught” an infinite amount more than any ten humans could learn in a lifetime, I would ask: Why can’t they solve novel, unsolved problems?
      - dagss 10mo ago
        When coding they are solving "novel, unsolved problems" related to coding problems set up. So I will assume you mean within maths, science etc? Basically things they can't solve today. Well 99.9% of humans cannot solve novel, unsolved problems in those fields. LLMs cannot learn, there is just the initial weight estimation process. And that process currently does not make them good enough on novel math/theoretical physics problems. That does not mean they do not "reason" in the same way that those 99.9% of humans still "reason". But they definitely do not learn, the way humans do. (Anyway, if LLMs could somehow get 1000x as large context window and get to converse with themselves for a full year, it does not seem out of the question they could come out with novel research?)

joenot443 10mo ago

> But they do not think I see this argument made a lot. I'm not sure if the distinction really holds weight once we start to unravel though. What's a topic you're able to think about that an LLM is not able to think about?

mopsi 10mo ago
Any topic with little coverage in the training data. LLMs will keep circling around the small bits in the training data, unable synthesize new connections. This is very obvious when trying to use LLMs to modify scripts in vendor-specific languages that have not been widely documented and don't have many examples available. A seasoned programmer will easily recognize common patterns like if-else blocks and loops, but LLMs will get stuck and output gibberish.
cgriswald 10mo ago
I can solve a mystery novel based on the evidence alone. Assuming an LLM doesn’t already have the answer it will offer solutions based on meta-information like how similar mysteries conclude or are structured. While this can be effective, it’s not really solving the mystery and will fail with anything truly novel.
- Tteriffic 10mo ago
  People more often do the same thing and act like llm’s.
ted_bunny 10mo ago
I asked GPT for rules on 101-level French grammar. That should be well documented for someone learning from English, no? The answers were so consistently wrong that it seemed intentional. Absolutely nothing novel asked of it. It could have quoted verbatim if it wanted to be lazy. I can't think of an easier question to give an LLM. If it's possible to "prompt wrong" a simple task that my six-year old nephew could easily do, the burden of proof is not on the people denying LLM intelligence, it's on the boosters.
- 10mo ago

griffzhowl 10mo ago

> the current algorithms are clearly a dead end for thinking machines. These discussions often get derailed into debates about what "thinking" means. If we define thinking as the capacity to produce and evaluate arguments, as the cognitive scientists Sperber and Mercier do, then we can see LLMs are certainly producing arguments, but they're weak at the evaluation. In some cases, arguments can be formalised, and then evaluating them is a solved problem, as in the examples of using the Lean proofchecker in combination with LLMs to write mathematical proofs. That suggests a way forward will come from formalising natural language arguments. So LLMs by themselves might be a dead end but in combination with formalisation they could be very powerful. That might not be "thinking" in the sense of the full suite of human abilities that we group with that word but it seems an important component of it.

kjellsbells 10mo ago
> suggests a way forward will come from formalising natural language arguments If by this you mean "reliably convert expressions made in human natural language to unambiguous, formally parseable expressions that a machine can evaluate the same way every time"... isn't that essentially an unreachable holy grail? I mean, everyone from Plato to Russell and Wittgenstein struggled with the meaning of human statements. And the best solution we have today is to ask the human to restrict the set of statement primitives and combinations that they can use to a small subset of words like "const", "let foo = bar", and so on.
- griffzhowl 10mo ago
  Whether the Holy Grail is unreachable or not is the question. Of course, the problem in full generality is hard, but that doesn't mean it can't be approached in various partial ways, either by restricting the inputs as you suggest or by coming up with some kind of evaluation procedures that are less strict than formal verifiability. I don't have any detailed proposals tbh
benrutter 10mo ago
> That suggests a way forward will come from formalising natural language arguments. Hot take (and continue with the derailment), but I'd argue that analytic philosophy from the last 100 years suggests this is a dead end. The idea that belief systems could be formalized was huge in the early 20th century (movements like Logical Positivism, or Russell's principia mathematica being good examples of this). Those approaches haven't really yielded many results, and by far the more fruitful form of analysis has been to conceptually "reframe" different problems (folks like Hillary Putnam, Wittgenstein, Quine being good examples). We've stacked up a lot of evidence that human language is much too loose and mushy to be formalised in a meaningful way.

lerp-io 10mo ago

they can think just not in the same abstract platonic way that a human mind can

deleted 10mo ago
[deleted]
HPsquared 10mo ago
I rather approach it from a Cartesian perspective. A context window is just that, it's not "existence". And because they do not exist in the world the same way as a human does, they do not think in the same way a human does (reversal of "I think therefore I am")
- bonesss 10mo ago
  I have a context matrix, therefore I transform?
  - deleted 10mo ago
    [deleted]
  - HPsquared 10mo ago
    An LLM doesn't have a valid concept of "I" because the data and algorithm it comprises could be run deterministically on any hardware, even multiple times, held in storage, rewound, edited and so on. There is no unique subject, there is no "I". The "life" or "soul" or "session ID" when that sentence is generated, is not known by itself to be unique.
    - fc417fc802 10mo ago
      > because the data and algorithm it comprises could be run deterministically on any hardware, even multiple times, held in storage, rewound, edited and so on. Quite the assumption you're making that the same thing does not apply to humans. Not having the technology is not the same thing as not being possible.
    - 9mo ago

trash_cat 10mo ago

What constitutes real "thinking" or "reasoning" is beside the point. What matters is what results we getting. And the challenge is rethinking how we do work, connecting all the data sources for agents to run and perform work over the various sources that we perform work. That will take ages. Not to mention having the controls in place to make that the "thinking" was correct in the end.

neutronicus 10mo ago
> connecting all the data sources for agents to run Copilot can't jump to definition in Visual Studio. Anthropic got a lot of mileage out of teaching Claude to grep, but LLM agents are a complete dead-end for my code-base until they can use the semantic search tools that actually work on our code-base and hook into the docs for our expensive proprietary dependencies.
virgil_disgr4ce 10mo ago
Thinking is not besides the point, it is the entire point. You seem to be defining "thinking" as an interchangeable black box, and as long as something fits that slot and "gets results", it's fine. But it's the code-writing that's the interchangeable black box, not the thinking. The actual work of software development is not writing code, it's solving problems. With a problem-space-navigation model, I'd agree that there are different strategies that can find a path from A to B, and what we call cognition is one way (more like a collection of techniques) to find a path. I mean, you can in principle brute-force this until you get the desired result. But that's not the only thing that thinking does. Thinking responds to changing constraints, unexpected effects, new information, and shifting requirements. Thinking observes its own outputs and its own actions. Thinking uses underlying models to reason from first principles. These strategies are domain-independent, too. And that's not even addressing all the other work involved in reality: deciding what the product should do when the design is underspecified. Asking the client/manager/etc what they want it to do in cases X, Y and Z. Offering suggestions and proposals and explaining tradeoffs. Now I imagine there could be some other processes we haven't conceived of that can do these things but do them differently than human brains do. But if there were we'd probably just still call it 'thinking.'

Balgair 10mo ago

I know a lot of people with access to Claude Code and the like will say that 'No, it sure seems to reason to me!' Great. But most (?) of the business out there aren't paying for the big boy models. I know of a F100 that got snookered into a deal with GPT 4 for 5 years, max of 40 responses per session, max of 10 sessions of memory, no backend integration. Those folks rightly think that AI is a bad idea.

notepad0x90 10mo ago

Do you think reasoning models don't count? there is a lot of work around those and things like RAGs.

vrighter 10mo ago
"Reasoning" in this context is just a marketing gimmick that means "run the llm in a loop". But people who don't know how they work (the people buying them for others) just take that word at face value and assume it means what it usually means in totally different contexts.
- notepad0x90 10mo ago
  That's fair, thanks.

NuclearPM 10mo ago

LLMs

klooney 10mo ago

> It is obvious now that whatever quality LLM have, they don't think and reason, they are just statistical machine outputing whatever they training set as most probable I have kids, and you could say the same about toddlers. Terrific mimics, they don't understand the whys.

gls2ro 10mo ago
IMHO when toddlers say mama they really understand that to a much much bigger degree than any LLM. They might not be able to articulate it but the deep understanding is there. So I think younger kids have purpose and associate meaning to a lot of things and they do try to get to a specific path toward an outcome. Of course (depending on the age) their "reasoning" is in a different system than hours where the survival instincts are much more powerful than any custom defined outcome so most of the time that is the driving force of the meaning. Why I talk about meaning? Because, of course, the kids cannot talk about the why, as that is very abstract. But meaning is a big part of the Why and it continues to be so in adult life it is just that the relation is reversed: we start talking about the why to get to a meaning. I also think that kids starts to have more complex thoughts than the language very early. If you got through the "Why?" phase you might have noticed that when they ask "Why?" they could mean very different questions. But they don't know the words to describe it. Sometimes "Why?" means "Where?" sometimes means "How?" sometimes means "How long?" .... That series of questioning is, for me, a kind of proof that a lot of things are happening in kids brain much more than they can verbalise.

md2020 10mo ago

> they are just statistical machine outputing whatever they training set as most probable. How is this sentiment still the top comment on an article about AI on HN in 2026? It's not true with today's models. They undergo vast amounts of reinforcement learning optimizing an objective that is NOT just predict the most likely next token given the training corpus. I would say even without the RL the "predict the next token" objective doesn't preclude thinking and reasoning, but that's a separate discussion. Generative sequence modeling learns to (approximately) model the process that produced the sequence. When you consider that text sequences are produced by human minds, which most would consider to be thinking and reasoning, well...