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Why is that less exciting? A machine competing in an unconstrained natural language difficult math contest and coming out on top by any means is breath taking

Why is that less exciting? A machine competing in an unconstrained natural language difficult math contest and coming out on top by any means is breath taking science fiction a few years ago - now it’s not exciting? Regardless of the tools for verification or even solvers - why is the goal post moving so fast? There is no bonus for “purity of essence” and using only neural networks. We live in an era where it’s hard to tell if machines are thinking or not, which for since the first computing machines was seen as the ultimate achievement. Now we Pooh Pooh the results of each iteration - which unfold month over month not decade over decade now. You don’t have to be hyped to be amazed. You can retain the ability to dream while not buying into the snake oil. This is amazing no matter what ensemble of techniques used. In fact - you should be excited if we’ve started to break out of the limitations of forcing NN to be load bearing in literally everything. That’s a sign of maturing technology not of limitations.

parasubvert 1y ago
I think the main hesitancy is due to rampant anthropomorphism. These models cannot reason, they pattern match language tokens and generate emergent behaviour as a result. Certainly the emergent behaviour is exciting but we tend to jump to conclusions as to what it implies. This means we are far more trusting with software that lacks formal guarantees than we should be. We are used to software being sound by default but otherwise a moron that requires very precise inputs and parameters and testing to act correctly. System 2 thinking. Now with NN it's inverted: it's a brilliant know-it-all but it bullshits a lot, and falls apart in ways we may gloss over, even with enormous resources spent on training. It's effectively incredible progress on System 1 thinking with questionable but evolving System 2 skills where we don't know the limits. If you're not familiar with System 1 / System 2, it's googlable .
- logicchains 1y ago
  >I think the main hesitancy is due to rampant anthropomorphism. These models cannot reason, they pattern match language tokens and generate emergent behaviour as a result This is rampant human chauvinism. There's absolutely no empirical basis for the statement that these models "cannot reason", it's just pseudoscientific woo thrown around by people who want to feel that humans are somehow special. By pretty much every empirical measure of "reasoning" or intelligence we have, SOTA LLMs are better at it than the average human.
  - cguess 1y ago
    > This is rampant human chauvinism What in the accelerationist hell?
    - logicchains 1y ago
      There's nothing accelerationist about recognising that making unfalsifiable statements about LLMs lacking intelligence or reasoning ability serves zero purpose except stroking the speaker's ego. Such people are never willing to give a clear criteria for what would constitute proof of machine reasoning for them, which shows their belief isn't based on science or reason.
  - bamboozled 1y ago
    I’ve used these AI tools for multiple hours a day for months. Not seeing the reasoning party honestly. I see the heuristics part.
    - logicchains 1y ago
      I guess your work doesn't involve any maths then, because then you'd see they're capable of solving maths problems that require a non-trivial amount of reasoning steps.
      - cycomanic 1y ago
        Just the other day I needed to code some interlocked indices. It wasn't particularly hard but I didn't want to context switch and think so instead I asked gpt 4o. After a back and worth for 4 or 5 times, where it gave wrong answers I finally decided to just take a pen and paper and do it by hand. I have a hard time believing that these models are reasoning, because if they are they are very poor at it.
        simonw 1y ago
        GPT-4o isn't classified as a "reasoning" model (by common 2025 terminology at least) - I suggest trying again with o3 or Claude 4 or Gemini 2.5.
- lordnacho 1y ago
  > These models cannot reason Not trying to be a smarty pants here, but what do we mean by "reason"? Just to make the point, I'm using Claude to help me code right now. In between prompts, I read HN. It does things for me such as coding up new features, looking at the compile and runtime responses, and then correcting the code. All while I sit here and write with you on HN. It gives me feedback like "lock free message passing is going to work better here" and then replaces the locks with the exact kind of thing I actually want. If it runs into a problem, it does what I did a few weeks ago, it will see that some flag is set wrong, or that some architectural decision needs to be changed, and then implements the changes. What is not reasoning about this? Last year at this time, if I looked at my code with a two hour delta, and someone had pushed edits that were able to compile, with real improvements, I would not have any doubt that there was a reasoning, intelligent person who had spent years learning how this worked. It is pattern matching? Of course. But why is that not reasoning? Is there some sort of emergent behavior? Also yes. But what is not reasoning about that? I'm having actual coding conversations that I used to only have with senior devs, right now, while browsing HN, and code that does what I asked is being produced.
  - facefactsdamnit 1y ago
    You raise a far point. These criticisms based on "it's merely X" or "it's not really Y" don't hold water when X and Y are poorly defined. The only thing that should matter is the results they get. And I have a hard time understanding why the thing that is supposed to behave in an intelligent way but often just spew nonsense gets 10x budget increases over and over again. This is bad software. It does not do the thing it promises to do. Software that sometimes works and very often produces wrong or nonsensical output is garbage software. Sink 10x, 100x, 1000x more resources into it is irrational. Nothing else matters. Maybe it reasons, maybe it's intelligent. If it produces garbled nonsense often, giving the teams behind it 10x the compute is insane.
    - falcor84 1y ago
      "Software that sometimes works and very often produces wrong or nonsensical output" can be extremely valuable when coupled with a way to test whether the result is correct.
YeGoblynQueenne 1y ago
>> Why is that less exciting? A machine competing in an unconstrained natural language difficult math contest and coming out on top by any means is breath taking science fiction a few years ago - now it’s not exciting? Half the internet is convinced that LLMs are a big data cheating machine and if they're right then, yes, boldly cheating where nobody has cheated before is not that exciting.
- falcor84 1y ago
  I don't get it, how do you "big data cheat" an AI into solving previously unencountered problems? Wouldn't that just be engineering?
  - YeGoblynQueenne 1y ago
    It depends on what you mean by "engineering". For example, "engineering" can mean that you train and fine-tune a machine learning system to beat a particular benchmark. That's fun times but not really interesting or informative.
  - QuadmasterXLII 1y ago
    I don’t know how you’d cheat at it either, but if you could, it would manifest as the model getting gold on the test and then in six months when its released to the public, exhibiting wild hallucinations and basic algebra errors. I don’t kbow if that’s how it’ll play out this time but I know how it played out the last ten.
  - emp17344 1y ago
    I mean, solutions for the 2025 IMO problems are already available on the internet. How can we be sure these are “unencountered” problems?
    - hyghjiyhu 1y ago
      They probably have an archived data set from before then that they trained on.
bwfan123 1y ago
Without sharing their methodology, how can we trust the claim ? questions like: 1) did humans formalize the input 2) did humans prompt the llm towards the solution etc.. I am excited to hear about it, but I remain skeptical.
Dilettante_ 1y ago
>Why is that less exciting? Because if I have to throw 10000 rocks to get one in the bucket, I am not as good/useful of a rock-into-bucket-thrower as someone who gets it in one shot. People would probably not be as excited about the prospect of employing me to throw rocks for them.
- beering 1y ago
  It’s exciting because nearly all humans have 0% chance of throwing the rock into the bucket, and most people believed a rock-into-bucket-thrower machine is impossible. So even an inefficient rock-into-bucket-thrower is impressive. But the bar has been getting raised very rapidly. What was impressive six months ago is awful and unexciting today.
  - Dilettante_ 1y ago
    You're putting words in my mouth. It's not "awful and unexciting", it is certainly an important step, but the hype being invited with the headline is the immensely greater one of an accurate rock-thrower. And if they have the inefficient one and trying to pretend to have the real deal, that's flim-flam-man levels of overstatement.
- machiaweliczny 1y ago
  If you don't have automatic way to verify solution then picking correct answer from 10 000 is more impressive than coming with some answer in the first place. If AI tech will be able to effectively prune tree search without eval that would be super big leap but I doubt they achieved this.
lanza 1y ago
Because the usefulness of an AI model is reliably solving a problem, not being able to solve a problem given 10,000 tries. Claude Code is still only a mildly useful tool because it's horrific beyond a certain breadth of scope. If I asked it to solve the same problem 10,000 times I'm sure I'd get a great answer to significantly more difficult problems, but that doesn't help me as I'm not capable of scaling myself to checking 10,000 answers.

parasubvert 1y ago

I think the main hesitancy is due to rampant anthropomorphism. These models cannot reason, they pattern match language tokens and generate emergent behaviour as a result. Certainly the emergent behaviour is exciting but we tend to jump to conclusions as to what it implies. This means we are far more trusting with software that lacks formal guarantees than we should be. We are used to software being sound by default but otherwise a moron that requires very precise inputs and parameters and testing to act correctly. System 2 thinking. Now with NN it's inverted: it's a brilliant know-it-all but it bullshits a lot, and falls apart in ways we may gloss over, even with enormous resources spent on training. It's effectively incredible progress on System 1 thinking with questionable but evolving System 2 skills where we don't know the limits. If you're not familiar with System 1 / System 2, it's googlable .

logicchains 1y ago
>I think the main hesitancy is due to rampant anthropomorphism. These models cannot reason, they pattern match language tokens and generate emergent behaviour as a result This is rampant human chauvinism. There's absolutely no empirical basis for the statement that these models "cannot reason", it's just pseudoscientific woo thrown around by people who want to feel that humans are somehow special. By pretty much every empirical measure of "reasoning" or intelligence we have, SOTA LLMs are better at it than the average human.
- cguess 1y ago
  > This is rampant human chauvinism What in the accelerationist hell?
  - logicchains 1y ago
    There's nothing accelerationist about recognising that making unfalsifiable statements about LLMs lacking intelligence or reasoning ability serves zero purpose except stroking the speaker's ego. Such people are never willing to give a clear criteria for what would constitute proof of machine reasoning for them, which shows their belief isn't based on science or reason.
- bamboozled 1y ago
  I’ve used these AI tools for multiple hours a day for months. Not seeing the reasoning party honestly. I see the heuristics part.
  - logicchains 1y ago
    I guess your work doesn't involve any maths then, because then you'd see they're capable of solving maths problems that require a non-trivial amount of reasoning steps.
    - cycomanic 1y ago
      Just the other day I needed to code some interlocked indices. It wasn't particularly hard but I didn't want to context switch and think so instead I asked gpt 4o. After a back and worth for 4 or 5 times, where it gave wrong answers I finally decided to just take a pen and paper and do it by hand. I have a hard time believing that these models are reasoning, because if they are they are very poor at it.
      - simonw 1y ago
        GPT-4o isn't classified as a "reasoning" model (by common 2025 terminology at least) - I suggest trying again with o3 or Claude 4 or Gemini 2.5.
lordnacho 1y ago
> These models cannot reason Not trying to be a smarty pants here, but what do we mean by "reason"? Just to make the point, I'm using Claude to help me code right now. In between prompts, I read HN. It does things for me such as coding up new features, looking at the compile and runtime responses, and then correcting the code. All while I sit here and write with you on HN. It gives me feedback like "lock free message passing is going to work better here" and then replaces the locks with the exact kind of thing I actually want. If it runs into a problem, it does what I did a few weeks ago, it will see that some flag is set wrong, or that some architectural decision needs to be changed, and then implements the changes. What is not reasoning about this? Last year at this time, if I looked at my code with a two hour delta, and someone had pushed edits that were able to compile, with real improvements, I would not have any doubt that there was a reasoning, intelligent person who had spent years learning how this worked. It is pattern matching? Of course. But why is that not reasoning? Is there some sort of emergent behavior? Also yes. But what is not reasoning about that? I'm having actual coding conversations that I used to only have with senior devs, right now, while browsing HN, and code that does what I asked is being produced.
- facefactsdamnit 1y ago
  You raise a far point. These criticisms based on "it's merely X" or "it's not really Y" don't hold water when X and Y are poorly defined. The only thing that should matter is the results they get. And I have a hard time understanding why the thing that is supposed to behave in an intelligent way but often just spew nonsense gets 10x budget increases over and over again. This is bad software. It does not do the thing it promises to do. Software that sometimes works and very often produces wrong or nonsensical output is garbage software. Sink 10x, 100x, 1000x more resources into it is irrational. Nothing else matters. Maybe it reasons, maybe it's intelligent. If it produces garbled nonsense often, giving the teams behind it 10x the compute is insane.
  - falcor84 1y ago
    "Software that sometimes works and very often produces wrong or nonsensical output" can be extremely valuable when coupled with a way to test whether the result is correct.

YeGoblynQueenne 1y ago

>> Why is that less exciting? A machine competing in an unconstrained natural language difficult math contest and coming out on top by any means is breath taking science fiction a few years ago - now it’s not exciting? Half the internet is convinced that LLMs are a big data cheating machine and if they're right then, yes, boldly cheating where nobody has cheated before is not that exciting.

falcor84 1y ago
I don't get it, how do you "big data cheat" an AI into solving previously unencountered problems? Wouldn't that just be engineering?
- YeGoblynQueenne 1y ago
  It depends on what you mean by "engineering". For example, "engineering" can mean that you train and fine-tune a machine learning system to beat a particular benchmark. That's fun times but not really interesting or informative.
- QuadmasterXLII 1y ago
  I don’t know how you’d cheat at it either, but if you could, it would manifest as the model getting gold on the test and then in six months when its released to the public, exhibiting wild hallucinations and basic algebra errors. I don’t kbow if that’s how it’ll play out this time but I know how it played out the last ten.
- emp17344 1y ago
  I mean, solutions for the 2025 IMO problems are already available on the internet. How can we be sure these are “unencountered” problems?
  - hyghjiyhu 1y ago
    They probably have an archived data set from before then that they trained on.

bwfan123 1y ago

Without sharing their methodology, how can we trust the claim ? questions like: 1) did humans formalize the input 2) did humans prompt the llm towards the solution etc.. I am excited to hear about it, but I remain skeptical.

Dilettante_ 1y ago

>Why is that less exciting? Because if I have to throw 10000 rocks to get one in the bucket, I am not as good/useful of a rock-into-bucket-thrower as someone who gets it in one shot. People would probably not be as excited about the prospect of employing me to throw rocks for them.

beering 1y ago
It’s exciting because nearly all humans have 0% chance of throwing the rock into the bucket, and most people believed a rock-into-bucket-thrower machine is impossible. So even an inefficient rock-into-bucket-thrower is impressive. But the bar has been getting raised very rapidly. What was impressive six months ago is awful and unexciting today.
- Dilettante_ 1y ago
  You're putting words in my mouth. It's not "awful and unexciting", it is certainly an important step, but the hype being invited with the headline is the immensely greater one of an accurate rock-thrower. And if they have the inefficient one and trying to pretend to have the real deal, that's flim-flam-man levels of overstatement.
machiaweliczny 1y ago
If you don't have automatic way to verify solution then picking correct answer from 10 000 is more impressive than coming with some answer in the first place. If AI tech will be able to effectively prune tree search without eval that would be super big leap but I doubt they achieved this.

lanza 1y ago

Because the usefulness of an AI model is reliably solving a problem, not being able to solve a problem given 10,000 tries. Claude Code is still only a mildly useful tool because it's horrific beyond a certain breadth of scope. If I asked it to solve the same problem 10,000 times I'm sure I'd get a great answer to significantly more difficult problems, but that doesn't help me as I'm not capable of scaling myself to checking 10,000 answers.