This OP claims the publicly available models all failed to get Bronze.
OpenAI tweet claims there is an unreleased model that can get Gold.
sigmoid10 · 3h ago
I'd also be highly wary of the method they used because of statements like this:
>we note that the vast majority of its answers simply stated the final answer without additional justification
While the reasoning steps are obviously important for judging human participant answers, none of the current big-game providers disclose their actual reasoning tokens. So unless they got direct internal access to these models from the big companies (which seems highly unlikely), this might be yet another failed study designed to (of which we have seen several in recent months, even by serious parties).
bgwalter · 2h ago
The model did not fit in the margin.
We'll never know how many GPUs and other assistance (like custom code paths) this model got.
dmitrygr · 3h ago
My (unreleased) cat did even better than the OpenAI model. No you cannot see. Yes you have to trust me. Now gimme more money.
klabb3 · 3h ago
Wow, that’s incredible. Cats are progressing so fast, especially unreleased cats seem to be doing much better. My two orange kitties aren’t doing well on math problems but obviously that’s because I’m not prompting the right way – any day now. If I ever get it to work, I’ll be sure to share the achievements on X, while carefully avoiding explaining how I did it or provide any data that can corroborate the claims.
raincole · 3h ago
I don't know the details (of course, it's unreleased), but note that MathArena evaluated "average of 4 attempts", and limited token usages to 64k.
OpenAI likely had unlimited tokens, and evaluated "best of N attempts."
amelius · 3h ago
That's a claim that is far less plausible. OpenAI could have thrown more resources at the problem and I would be surprised if that didn't improve the results.
untitled2 · 3h ago
Exactly. Whom to believe?
JohnKemeny · 3h ago
The last time someone claimed a medal in an olympiad like this, turned out they manually translated the problem into Lean and then ran a brute force search algorithm to find a proof. For 60 hours. On a supercomputer.
Meanwhile high schoolers get a piece of paper and 4.5 hours.
throwawaymaths · 3h ago
kinda wild that an llm cant translate to lean?
wslh · 3h ago
Even though chess is now effectively solved against human players, I still remember Kasparov's suspicion that one of Deep Blue's moves had a human touch. It was never proven or disproven, but I trust Kasparov's deep intuition amplified by Kasparov requesting access to Deep Blue’s logs, and IBM refusing to share them in full. For more discussions see [1][2][3].
Thanks. I parsed that wrong. In either case not the same thing Math Arena used.
idiotsecant · 3h ago
Actually, I did it a year ago but I just don't want to release my model.
senkora · 3h ago
Where should I address the billion dollar check?
emp17344 · 3h ago
My buddy did it 5 years ago. You wouldn’t know him, he lives in Canada.
souldeux · 3h ago
my model goes to a different school
esafak · 2h ago
The dog ate mine. And the solution didn't fit in the margin, anyway.
magicalhippo · 3h ago
One interesting takeaway for me, a non-practitioner, was that the models appears to be fairy decent at judging their own output.
They used best-of-32 and used the same model to judge a "tournament" to find the best answer. Seems like something that could be boltet on reasonably easy, eg in say WebUI.
edit: forgot to add that I'm curious if this translates to smaller models as well, or if it requires these huge models.
esjeon · 3h ago
> For Problem 5, models often identified the correct strategies but failed to prove them, which is, ironically, the easier part for an IMO participant. This contrast ... suggests that models could improve significantly in the near future if these relatively minor logical issues are addressed.
Interesting but I'm not sure if this is really due to "minor logical issues". This sounds like a failure due to the lack of the actual understanding (the world model problem). Perhaps the actual answers from AIs might have some hints, but I can't find them.
(EDIT: ooops, found the output on the main page of their website. Didn't expect that.)
> Best-of-n is Important ... the models are surprisingly effective at identifying the relative quality of their own outputs during the best-of-n selection process and are able to look past coherence to check for accuracy.
Yes, it's always easier to be a backseat driver.
wiremine · 3h ago
How quickly we shift our expectations. If you told me 5 years ago we'd have technology that can do this, I wouldn't believe you.
This isn't to say we shouldn't think critically about the use and performance of models, but "Not Even Bronze..." turned me off to this critique.
raincole · 3h ago
In 2024 AlphaProof got Silver level, so people righteously expect a lot now.
(It's specifically trained on formalized math problems, unlike most LLM, so it's not an apple to apple comparison.)
wat10000 · 3h ago
LLMs are really good with words and kind of crap at “thinking.” Humans are wired to see these two things as tightly connected. A machine that thinks poorly and talks great is inherently confusing. A lot of discussion and disputes around LLMs comes down to this.
It wasn’t that long ago that the Turing Test was seen as the gold standard of whether a machine was actually intelligent. LLMs blew past that benchmark a year or two ago and people barely noticed. This might be moving the goalposts, but I see it as a realization that thought and language are less inherently connected than we thought.
So yeah, the fact that they even do this well is pretty amazing, but they sound like they should be doing so much better.
thaumasiotes · 2h ago
> LLMs are really good with words and kind of crap at “thinking.” Humans are wired to see these two things as tightly connected. A machine that thinks poorly and talks great is inherently confusing. A lot of discussion and disputes around LLMs comes down to this.
It's not an unfamiliar phenomenon in humans. Look at Malcolm Gladwell.
daedrdev · 3h ago
Here are the IMO problems if you want to give them a try:
> Each model was run with the recommended hyperparameters and a maximum token limit of 64,000. No models needs more than this number of tokens
I'm a little confused by this. My assumptions (possibly incorrect!): 64k tokens per prompt, they are claiming the model wouldn't need more tokens even for reasoning
Is that right? Would be helpful to see how many tokens the models actually used.
throwawaymaths · 3h ago
they didn't even do a (non-ml) agentic descent? like have a quicky api that requeries itself generating new context?
"ok here is my strategy here are the five steps", then requery with a strategy or proof of step 1, 2, 3...
in a dfs
gcanyon · 3h ago
99.99+% of all problems humans face do not require particularly original solutions. Determining whether LLMs can solve truly original (or at least obscure) problems is interesting, and a problem worth solving, but ignores the vast majority of the (near-term at least) impact they will have.
wavemode · 1h ago
To be frank, I take precisely the opposite view. Most people solve novel problems every day, mostly without thinking much about it. Our inability to perceive the immense complexity of the things we do every day is merely due to familiarity. In other words we're blind to the details because our brain handles them automatically, not because they don't exist.
Software engineers understand this better than most - describing a task in general terms, and doing it yourself, can be incredibly easy, even while writing the code to automate the task is difficult or impossible, because of all the devilish details we don't often think about.
lottin · 3h ago
15 years ago they were predicting that AI would turn everything upside down in 15 years time. It hasn't.
HEmanZ · 2h ago
People who say this don’t understand the breakthrough we had in the last couple of years. 15 years ago I was laughing at people predicting AI would turn everything upside down soon. I’m not laughing anymore. I’ve been around long enough to see some AI hype cycles and this time it is different.
15 years ago I, working on AI systems at a FAANG, would have told you “real” AI probably wasn’t coming in my lifetime. 15 years ago the only engineers I knew who thought AI was coming soon were dreamers and Silicon Valley koolaiders. The rest of us saw we needed a step-function break through that may not even exist. But it did, and we got there, a couple of years ago.
Now I’m telling people it’s here. We’ve hit a completely different kind of technology, and it’s so clear to people working in the field. The earthquake has happened and the tsunami is coming.
csa · 2h ago
Thank you for sharing your experience. It makes the impact of the recent advances palpable.
Barrin92 · 3h ago
the value of human beings isn't in their capacity to do routine tasks but to respond with some common sense to all the critical issues in the 2% at the tail.
This is why original problems are important, it's a measure of how sensible something is in an open-ended environment, and here they're completely useless, not just because they fail but how they fail. The fact that these LLMS according to the article "invent non-existent math theorems", i.e. gibberish instead of even being able to know what they don't know, is an indication of how limited this still is.
wat10000 · 3h ago
I really doubt a contest for high schoolers contains any truly original problems.
chvid · 3h ago
In a few months (weeks, days - maybe it has already happened) models will have much better performance on this test.
Not because of actual increased “intelligence” but because the test would be included in model’s training data - either directly or indirectly where model developers “tune” their model to give better performance on this particular attention driving test.
sorokod · 3h ago
From the post:
"Evaluation began immediately after the 2025 IMO problems were released to prevent contamination."
Doe this address your concern?
os2warpman · 3h ago
What they mean is that in a couple of weeks there are going to be stories titled "LLMS NOW BETTER THAN HUMANS AT 2025 INTERNATIONAL MATH OLYMPIAD" (stories published as thinly-veiled investment solicitations) but in reality they're still shitty-- they've just had the answers fed in to be spit back out.
sorokod · 3h ago
Companies would game metrics whenever they have the opportunity. What else is new?
esafak · 2h ago
I suppose what's new is that the models aren't as smart as their companies claimed.
chvid · 3h ago
Not really.
yunwal · 3h ago
Luckily there’s a new set of problems every year
chvid · 3h ago
You can really only do a fair reproducible test if the models are static and not sitting behind an api where you have no idea on how they are updated or continuously tweaked.
chvid · 3h ago
This particular test is heralded as some sort of breakthrough and the companies in this field are raising billions of dollars from investors and paying their star employees tens of millions.
The economic incentives to tweak, tune, or cheat are through the roof.
ipsin · 3h ago
I was hoping to see the questions (which I can probably find online), but also the answers from models and the judge's scores! Am I missing a link? Without that I can't tell whether I should be impressed or not.
The whole competition is unfair anyway. An "AI" has access to millions of similar problems stolen and encoded in the model. Humans would at least need access to a similar database; think open database exam, a nuclear version of open book exam.
AndrewKemendo · 3h ago
Can someone tell me where your average every day human that’s walking around and has a regular job and kids and a mortgage would land on this leaderboard? That’s who we should be comparing against.
The fact that the only formal comparisons for AI systems that are ever done are explicitly based on the highest performing narrowly focused humans, tells me how unprepared society is for what’s happening.
Appreciate that: at the point in which there is unambiguous demonstration of superhuman level performance across all human tasks by a machine, (and make no mistake, that *is the bar that this blog post and every other post about AI sets*) it’s completely over for the human race; unless someone figures out an entirely new economic system.
zdragnar · 3h ago
The average person is bad at literally almost everything.
If I want something done, I'll seek out someone with a skill set that matches the problem.
I don't want AI to be as good as an average person. I want AI to be better than the person I would go to for help. A person can talk with me, understand where I've misunderstood my own problem, can point out faulty assumptions, and may even tell me that the problem isn't even a problem that needs solving. A person can suggest a variety of options and let me decide what trade-offs I want to make.
If I don't trust the AI to do that, then I'm not sure why I'd use it for anything other than things that don't need to be done at all, unless I can justify the chance that maybe it'll be done right, and I can afford the time lost getting it done right without the AI afterwards.
SirFatty · 2h ago
"The average person is bad at literally almost everything."
Wow... that's quite a generalization. And not my experience at all.
Retric · 2h ago
The average person can’t play 99% of all musical instruments, speak 99% of all languages, do 99% of surgeries, recite 99% of all poems from memory etc.
We don’t ask the average person to do most things, either finding a specialist or providing training beforehand.
krapp · 2h ago
One cannot be bad at the things one doesn't even do. None of this demonstrates that humans are bad at "literally almost everything."
gundmc · 2h ago
You and the parent poster seem to be conflating the ideas of:
- Does not have the requisite skills and experiences to do X successfully
- Inherently does not have the capacity to do X
I think the former is a reasonable standard to apply in this context. I'd definitely say I would be bad if I tried to play the guitar, but I'm not inherently incapable of doing it. It's just not very useful to say "I could be good at it if I put 1000 hours of practice in."
zdragnar · 2h ago
That's why there's the qualifier of "average person". If one learns to play the guitar well, they are no longer the average person in the context of guitar playing.
Retric · 2h ago
> One cannot be bad at the things one doesn't even do.
??? If you don’t know how to do something you’re really bad at it. I’m not sure what that sentence is even trying to convey.
krapp · 2h ago
> Obviously you could train someone to recite the The Raven from memory, but they can’t do it now.
That doesn't make them bad at reciting The Raven from memory. Being trained to recite The Raven from memory and still being unable to do so would be a proper application of the term. There is an obvious difference between the two states of being and conflating them is specious.
If you want to take seriously the premise that humans are bad at almost everything because most humans haven't been trained at doing almost everything humans can do, then you must apply the same rubric to LLMs, which are only capable of expressions within their specific dataset (and thus not the entire corpus of data on which they haven't been trained) and even then which tend to confabulate far more frequently than human beings at even simple tasks.
edit: never mind, I guess you aren't willing to take this conversation on good faith.
mysterydip · 1h ago
Didn't this start with "Can someone tell me where your average every day human that’s walking around and has a regular job and kids and a mortgage would land on this leaderboard? That’s who we should be comparing against."
And the average person would do poorly. Not because they couldn't be trained to do it, but because they haven't.
krapp · 1h ago
It's obvious that the average person would do bad at the International Math Olympiad. Although I don't know why the qualifiers of "regular job and kids and a mortgage" are necessary, except as a weird classist signifier. I strongly suspect most people on HN, who consider themselves set apart from the average, with some also having a regular job, kids and a mortgage, would also not do well at the International Math Olympiad.
But that isn't the claim I'm objecting to. The claim I'm objecting to is "The average person is bad at literally almost everything," which is not an equivalent claim to "people who aren't trained at math would be bad at math at a competitive level," because it implicitly includes everything that a person is trained it and is expected to be qualified to do.
It was just bad, cynical hyperbole. And it's weird that people are defending it so aggressively.
rahimnathwani · 2h ago
It's obvious that 'bad at' in this context means 'incapable of doing well'.
Nitpicking language doesn't help to move the conversation. One thing most humans are good at is understanding meaning even when the speaker wasn't absolutely precise.
rahimnathwani · 2h ago
More than 50% of people cannot write a 'hello world' program in any programming language.
More than 50% of people employed as software engineers cannot read an academic paper in a field like education, and explain whether the conclusions are sound, based on the experiment description and included data.
More than 50% of people cannot interpret an X-ray.
csa · 2h ago
> More than 50% of people employed as software engineers cannot read an academic paper in a field like education, and explain whether the conclusions are sound, based on the experiment description and included data.
I know this was meant as a dig, but I’m actually guessing that software engineers score higher on this task than non-engineers who hold M.Ed. degrees.
rahimnathwani · 2h ago
Agreed! Probably 3% of software could do it, vs 1% for M.Ed holders.
The only reason I chose software engineers is because I was trying to show that people who can write 'hello world' programs (first example) are not good at all intellectual tasks.
bgwalter · 2h ago
Average humans, no. Mathematicians with enough time and a well indexed database of millions of similar problems, probably.
We don't allow chess players to access a Syzygy tablebase in a tournament.
pragmatic · 1h ago
That’s not how modern societies/economies work.
We have specialists everywhere.
AndrewKemendo · 55m ago
My literal last sentence addresses this
baobabKoodaa · 3h ago
Average human would score exactly 0 at IMO.
raincole · 3h ago
> average every day human
Average math major can't get Brozne.
pphysch · 3h ago
Machines have always had superhuman capabilities in narrow domains. The LLM domain is quite broad but it's still just a LLM, beholden to its training.
The average everyday human does not have the time to read all available math texts. LLMs do, but they still can't get bronze. What does that say about them?
WD-42 · 3h ago
> Gemini 2.5 Pro achieved the highest score with an average of 31% (13 points). While this may seem low, especially considering the $400 spent on generating just 24 answers
What? That’s some serious cash for mostly wrong answers.
john-h-k · 3h ago
The time investment a human has to make to get 31% on the IMO is worth far more than $400
WD-42 · 2h ago
The human still has to put in that time. How would you know what 31% is correct?
ysofunny · 3h ago
this makes me really wonder about what is the underlying practical mathematical skill?
intuition????
samat · 2h ago
plus a little of skills
akomtu · 3h ago
Easy benchmark that's hard to fake: data compression. Intelligence is largely about creating compact predictive models and so is data compression. The output should be a program generating the sequence or the dataset, based on entry id or nearby data points. Typical LLM bullshit won't work here because the output isn't English prose that can fool a human.
strangescript · 3h ago
"You know that really hard test thing that most humans on the planet can't do, or even understand, yeah, LLMs kind of suck at it too"
Meanwhile Noam "well aschtually..."
I love how people are still betting against AI, its hilarious. Please write more 2000-esk "The internet is a fad" articles
boringg · 3h ago
Its quite reasonable. We have yet to meet anything more intelligent than humans so why do we think we can create something more intelligent than us when we don't fully understand the complexities how we work?
AI still has a long way to go, though it has proven to be a useful tool at this point.
strangescript · 3h ago
who said anything about creating something more intelligent than us, these articles have the air of "why are we wasting our time on this stuff", people like gary marcus link them, meanwhile they get better week over week
This OP claims the publicly available models all failed to get Bronze.
OpenAI tweet claims there is an unreleased model that can get Gold.
>we note that the vast majority of its answers simply stated the final answer without additional justification
While the reasoning steps are obviously important for judging human participant answers, none of the current big-game providers disclose their actual reasoning tokens. So unless they got direct internal access to these models from the big companies (which seems highly unlikely), this might be yet another failed study designed to (of which we have seen several in recent months, even by serious parties).
We'll never know how many GPUs and other assistance (like custom code paths) this model got.
OpenAI likely had unlimited tokens, and evaluated "best of N attempts."
Meanwhile high schoolers get a piece of paper and 4.5 hours.
[1] https://chess.stackexchange.com/questions/9959/did-deep-blue...
[2] https://nautil.us/why-the-chess-computer-deep-blue-played-li...
[3] https://en.chessbase.com/post/deep-blue-s-cheating-move
They used best-of-32 and used the same model to judge a "tournament" to find the best answer. Seems like something that could be boltet on reasonably easy, eg in say WebUI.
edit: forgot to add that I'm curious if this translates to smaller models as well, or if it requires these huge models.
Interesting but I'm not sure if this is really due to "minor logical issues". This sounds like a failure due to the lack of the actual understanding (the world model problem). Perhaps the actual answers from AIs might have some hints, but I can't find them.
(EDIT: ooops, found the output on the main page of their website. Didn't expect that.)
> Best-of-n is Important ... the models are surprisingly effective at identifying the relative quality of their own outputs during the best-of-n selection process and are able to look past coherence to check for accuracy.
Yes, it's always easier to be a backseat driver.
This isn't to say we shouldn't think critically about the use and performance of models, but "Not Even Bronze..." turned me off to this critique.
(It's specifically trained on formalized math problems, unlike most LLM, so it's not an apple to apple comparison.)
It wasn’t that long ago that the Turing Test was seen as the gold standard of whether a machine was actually intelligent. LLMs blew past that benchmark a year or two ago and people barely noticed. This might be moving the goalposts, but I see it as a realization that thought and language are less inherently connected than we thought.
So yeah, the fact that they even do this well is pretty amazing, but they sound like they should be doing so much better.
It's not an unfamiliar phenomenon in humans. Look at Malcolm Gladwell.
https://www.imo-official.org/year_info.aspx?year=2025 (download page)
They are very difficult.
I'm a little confused by this. My assumptions (possibly incorrect!): 64k tokens per prompt, they are claiming the model wouldn't need more tokens even for reasoning
Is that right? Would be helpful to see how many tokens the models actually used.
"ok here is my strategy here are the five steps", then requery with a strategy or proof of step 1, 2, 3...
in a dfs
Software engineers understand this better than most - describing a task in general terms, and doing it yourself, can be incredibly easy, even while writing the code to automate the task is difficult or impossible, because of all the devilish details we don't often think about.
15 years ago I, working on AI systems at a FAANG, would have told you “real” AI probably wasn’t coming in my lifetime. 15 years ago the only engineers I knew who thought AI was coming soon were dreamers and Silicon Valley koolaiders. The rest of us saw we needed a step-function break through that may not even exist. But it did, and we got there, a couple of years ago.
Now I’m telling people it’s here. We’ve hit a completely different kind of technology, and it’s so clear to people working in the field. The earthquake has happened and the tsunami is coming.
This is why original problems are important, it's a measure of how sensible something is in an open-ended environment, and here they're completely useless, not just because they fail but how they fail. The fact that these LLMS according to the article "invent non-existent math theorems", i.e. gibberish instead of even being able to know what they don't know, is an indication of how limited this still is.
Not because of actual increased “intelligence” but because the test would be included in model’s training data - either directly or indirectly where model developers “tune” their model to give better performance on this particular attention driving test.
Doe this address your concern?
The economic incentives to tweak, tune, or cheat are through the roof.
On their website you can see the full answers LLM gave ("click cells to see...")
The whole competition is unfair anyway. An "AI" has access to millions of similar problems stolen and encoded in the model. Humans would at least need access to a similar database; think open database exam, a nuclear version of open book exam.
The fact that the only formal comparisons for AI systems that are ever done are explicitly based on the highest performing narrowly focused humans, tells me how unprepared society is for what’s happening.
Appreciate that: at the point in which there is unambiguous demonstration of superhuman level performance across all human tasks by a machine, (and make no mistake, that *is the bar that this blog post and every other post about AI sets*) it’s completely over for the human race; unless someone figures out an entirely new economic system.
If I want something done, I'll seek out someone with a skill set that matches the problem.
I don't want AI to be as good as an average person. I want AI to be better than the person I would go to for help. A person can talk with me, understand where I've misunderstood my own problem, can point out faulty assumptions, and may even tell me that the problem isn't even a problem that needs solving. A person can suggest a variety of options and let me decide what trade-offs I want to make.
If I don't trust the AI to do that, then I'm not sure why I'd use it for anything other than things that don't need to be done at all, unless I can justify the chance that maybe it'll be done right, and I can afford the time lost getting it done right without the AI afterwards.
Wow... that's quite a generalization. And not my experience at all.
We don’t ask the average person to do most things, either finding a specialist or providing training beforehand.
- Does not have the requisite skills and experiences to do X successfully
- Inherently does not have the capacity to do X
I think the former is a reasonable standard to apply in this context. I'd definitely say I would be bad if I tried to play the guitar, but I'm not inherently incapable of doing it. It's just not very useful to say "I could be good at it if I put 1000 hours of practice in."
??? If you don’t know how to do something you’re really bad at it. I’m not sure what that sentence is even trying to convey.
That doesn't make them bad at reciting The Raven from memory. Being trained to recite The Raven from memory and still being unable to do so would be a proper application of the term. There is an obvious difference between the two states of being and conflating them is specious.
If you want to take seriously the premise that humans are bad at almost everything because most humans haven't been trained at doing almost everything humans can do, then you must apply the same rubric to LLMs, which are only capable of expressions within their specific dataset (and thus not the entire corpus of data on which they haven't been trained) and even then which tend to confabulate far more frequently than human beings at even simple tasks.
edit: never mind, I guess you aren't willing to take this conversation on good faith.
And the average person would do poorly. Not because they couldn't be trained to do it, but because they haven't.
But that isn't the claim I'm objecting to. The claim I'm objecting to is "The average person is bad at literally almost everything," which is not an equivalent claim to "people who aren't trained at math would be bad at math at a competitive level," because it implicitly includes everything that a person is trained it and is expected to be qualified to do.
It was just bad, cynical hyperbole. And it's weird that people are defending it so aggressively.
Nitpicking language doesn't help to move the conversation. One thing most humans are good at is understanding meaning even when the speaker wasn't absolutely precise.
More than 50% of people employed as software engineers cannot read an academic paper in a field like education, and explain whether the conclusions are sound, based on the experiment description and included data.
More than 50% of people cannot interpret an X-ray.
I know this was meant as a dig, but I’m actually guessing that software engineers score higher on this task than non-engineers who hold M.Ed. degrees.
The only reason I chose software engineers is because I was trying to show that people who can write 'hello world' programs (first example) are not good at all intellectual tasks.
We don't allow chess players to access a Syzygy tablebase in a tournament.
We have specialists everywhere.
Average math major can't get Brozne.
The average everyday human does not have the time to read all available math texts. LLMs do, but they still can't get bronze. What does that say about them?
What? That’s some serious cash for mostly wrong answers.
intuition????
Meanwhile Noam "well aschtually..."
I love how people are still betting against AI, its hilarious. Please write more 2000-esk "The internet is a fad" articles
AI still has a long way to go, though it has proven to be a useful tool at this point.