OpenAI researcher announced GPT-5 math breakthrough that never happened

https://the-decoder.com/leading-openai-researcher-announced-a-gpt-5-math-breakthrough-that-never-happened/

by Topfi • 9 months ago

430 234 9 months ago

amelius• 9 months ago

Summary (from the article)

* OpenAI researchers claimed or suggested that GPT-5 had solved unsolved math problems, but in reality, the model only found known results that were unfamiliar to the operator of erdosproblems.com.

* Mathematician Thomas Bloom and Deepmind CEO Demis Hassabis criticized the announcement as misleading, leading the researchers to retract or amend their original claims.

* According to mathematician Terence Tao, AI models like GPT-5 are currently most helpful for speeding up basic research tasks such as literature review, rather than independently solving complex mathematical problems.

HarHarVeryFunny4• 9 months ago

GPT-5 had only surfaced existing research that Bloom had missed

So GPT-5 didn't derive anything itself - it was just an effective search engine for prior research, which is useful, but not any sort of breakthough whatsoever.

jgalt212• 9 months ago

After the circular financing schemes involving hundreds of billions of dollars were uncovered, nothing I read about the AI business and its artificial hype machine surprises me anymore.

bbor2• 9 months ago

This is just tit-for-tat clickbait. The researcher’s wording was a bit unclear for sure, but far from incorrect.

resoluteteeth• 9 months ago

I disagree. There is no way to interpret "GPT-5 just found solutions to 10 (!) previously unsolved Erdos problems" as saying something other than GPT-5 having solved them.

If it just found existing solutions then they obviously weren't "previously unsolved" so the tweet is wrong.

He clearly misunderstood the situation and jumped to the conclusion that GPT-5 had actually solved the problems because that's what he wanted to believe.

That said, the misunderstanding is understandable because the tweet he was responding to said they had been listed as "open", but solving unsolved erdos problems by itself would be such a big deal that he probably should have double checked it.

bbor2• 9 months ago

But… it found them… and they were previously marked unsolved…

Note that “solved” does not equal “found”

andrewstuart• 9 months ago

Humans hallucinating about AI.

MattGaiser3• 9 months ago

Humans "hallucinate" in the AI way constantly, which is why I don't see them as a barrier to LLMs replacing humans in many contexts. It really isn't unusual for a human to make stuff up or be unaware of stuff.

pas• 9 months ago

it's the same thing with self-driving, if you can make it safer than a good human driver it's enough. but the bar is pretty low with driving (as evidenced by the hundreds of thousands of collisions and deaths and permanent disabilities each year). and rather high in scientific publishing.

zeknife• 9 months ago

A human being informed of a mistake will usually be able to resolve it and learn something in the process, whereas an LLM is more likely to spiral into nonsense

zeroonetwothree2• 9 months ago

Humans are a bit better at knowing which things are important and doing more research. Also better at being honest when directly pressed. And infinitely better at learning from errors.

(Yes, not everyone, but we do have some mechanisms to judge or encourage)

lapcat• 9 months ago

Humans "hallucinate" in the AI way constantly

This claim is ambiguous. The use of the word "Humans" here obscures rather than clarifies the issue. Individual humans typically do not "hallucinate" constantly, especially not on the job. Any individual human who is as bad at their job as an LLM should indeed be replaced, by a more competent individual human, not by an equally incompetent LLM. This was true long before LLMs were invented.

In the movie "Bill and Ted's Excellent Adventure," the titular characters attempt to write a history report by asking questions of random strangers in a convenience store parking lot. This of course is ridiculous and more a reflection of the extreme laziness of Bill and Ted than anything else. Today, the lazy Bill and Ted would ask ChatGPT instead. It's equally ridiculous to defend the wild inaccuracy and hallucinations of LLMs by comparing them to average humans. It's not the job of humans to answer random questions on any subject.

Human subject matter experts are not perfect, but they’re much better than average and don’t hallucinate on their subjects. They also have accountability and paper trails, can be individually discounted for gross misconduct, unlike LLMs.

skeeter20203• 9 months ago

Do you think the OpenAI human, when informed of their "oopsie" replied "You're right, there is existing evidence that this problem has already been solved. Blah Blah Blah ... and that's why our new model has made a huge breakthrough against previously unsolved math problems!"

topaz02• 9 months ago

Humans "hallucinate" in the AI way constantly

This is more and more clearly false. Humans get things wrong certainly, but the manner in which they get things wrong is just not comparable to how the LLMs get things wrong, beyond the most superficial comparison.

random97498323• 9 months ago

Best case: Hallucination

Worst case (more probable): Lying

MPSimmons• 9 months ago

Hanlon's Razor

JKCalhoun2• 9 months ago

"OpenAI Researcher Hallucinates GPT-5 Math Breakthrough" could be a headline from The Onion.

antegamisou• 9 months ago

Seriously those headlines are getting DailyMail sensationalism levels of ridiculous.

nicce• 9 months ago

I the old world we would just use the word bullshit.

reaperducer2• 9 months ago

"OpenAI Researcher Hallucinates GPT-5 Math Breakthrough" could be a headline from The Onion.

Off topic, but I saw The Onion on sale in the magazine rack of Barnes and Noble last month.

For those who miss when it was a free rag in sidewalk newsstands, and don't want to pony up for a full subscription, this is an option.

pera2• 9 months ago

Heh stockholders are not hallucinating: They know very well what they are doing.

skeeter20203• 9 months ago

retail investors? no way. The fever-dream may continue for a while but eventually it will end. Meanwhile we don't even know our full exposure to AI. It's going to be ugly and beyond burying gold in my backyard I can't even figure out how to hedge against this monster.

rixed• 9 months ago

These days AI just obsequiously praise whatever stupid ideas the human throw at them, which encourage humans into hallucinating breakthroughs.

But it's only a matter of time before AI gets better at prompt engineering.

/s?

Palmik2• 9 months ago

More like humans hallucinating about humans hallucinating about AI, see here: https://news.ycombinator.com/item?id=45634120

alkyon• 9 months ago

They started believing the very lies they invented.

moffkalast• 9 months ago

"The truth is usually just an excuse for a lack of imagination."

Kiboneu• 9 months ago

No no, openai is actually secretly run by AI.

mentalgear• 9 months ago

Another instance of openAI manipulating results to prolong their unsustainable circular hype bubble.

The inevitable collapse could be even more devastating than the 2008 financial crisis.

All while so vast resources are being wasted on non-verifiable gen AI slob, while real approaches (neuro-symbolic like DeepMind's AlphaFold) are mostly ignored financially because they don't generate the quick stock market increases that hype does.

the_duke4• 9 months ago

People keep spouting this, but I don't see how the AI bubble bursting would be all that devastating.

2008 was a systemic breakdown rippling through the foundations of the financial system.

It would lead to a market crash (80% of gains this year were big tech/AI) and likely a full recession in the US, but nothing nearly as dramatic as a global systemic crisis.

In contrast to the dot com bubble, the huge AI spending is also concentrated on relatively few companies, many with deep pockets from other revenue sources (Google, Meta, Microsoft, Oracle), and the others are mostly private companies that won't have massive impact on the stock market.

A sudden stop in AI craze would be hard for hardware companies and a few big AI only startups , but the financial fallout would be much more contained than either dot com or 2008.

Topfi5• 9 months ago

Isn't the dot com bubble a far better proxy? Notably, todays spending is both higher and more concentrated in a few companies that a large part of the population has exposure to (most dot com companies weren't publicly traded and far smaller vs MSFT, Alphabet, Meta, Oracle, NVDA making up most investment today) by way of pension funds, ETFs, etc.?

MattGaiser3• 9 months ago

People keep spouting this, but I don't see how the AI bubble bursting would be all that devastating.

Well, an enormous amount of debt is being raised and issued for AI and US economic growth is nearly entirely AI. Crypto bros showed the other day that they were leveraged to the hilt on coins and it wouldn't surprise me if people are the same way on AI. It is pretty heavily tied to the financial system at this point.

techblueberry• 9 months ago

I think my theory into contagion would be that There’s been lots of talk about these companies starting to rack up debt, and I think AI is so tied into the US GDP that things like -

If the stock market crashes, there’s lots of talk about how wealth and debt are interlinked. Could the crash be general enough to start calls on debt backed by stocks.

My recollection in 2008 was that we didn’t learn about how bad it was until after. The tech companies have been so desperate for a win, I wonder if some of them are over their skis in some way, and if there are banks that are risking it all on AI. (We know for some tech bros think the bet on AI is a longtermist like bet; closer to religion than reason and that it’s worth risking everything because the payback could be in the hundreds of trillions)

Combine this with the fact that AI is like what - 30% of the US economy? Magnificent 7 are 60%?

What happens if sustainable PE ratios in tech collapse. Does it take out Tesla?

Maybe the contagion is just the impact on the US economy which, classically anyways has been intermingled with everything.

I would bet almost everything that there is some lie at the center of this thing that we aren’t really aware of yet.

Theodores• 9 months ago

When America sneezes, the rest of the world catches a cold. This was said after the OG 1929 crash and I can remember it said in the 80s. Nobody says it anymore.

Due to exorbitant privilege, with the dollar as the only currency that matters, every country that trades with America is swapping goods and services for 'bits of green paper'. Unless buying oil from Russia, these bits of green paper are needed to buy oil. National currencies and the Euro might as well be casino chips, mere proxies for dollars.

Just last week the IMF issued a warning regarding AI stocks and the risk they pose to the global economy if promises are not delivered.

With every hiccup, whether that be the dot com boom, 2008 or the pandemic, the way out is to print more money, with this money going in at the top, for the banks, not the masses. This amounts to devaluation.

When the Ukraine crisis started, the Russian President stopped politely going along with Western capitalism and called the West out for printing too much money during the pandemic. Cut off from SWIFT and with many sanctions, Russia started trading in other currencies with BRICS partners. We are now at a stage of the game where the BRICS countries, of which there are many, already have a backup plan for when the next US financial catastrophe happens. They just won't use the dollar anymore. Note that currently, China doesn't want any dollars making it back to its own economy, since that would cause inflation. So they invest their dollars in Belt and Road initiatives, keeping those green bits of paper safely away from China. They don't even need exports to the USA or Europe since they have a vast home market to develop.

Note that Russia's reserve of dollars and euros was confiscated. They have nothing to lose so they aren't going to come back into the Western financial system.

Hence, you are right. A market crash won't be a global systematic crisis, it just means that Shanghai becomes the financial capital of the world, with no money printing unless it is backed up by mineral, energy or other resources that have tangible value. This won't be great for the collective West, but pretty good for the rest of the world.

jcranmer• 9 months ago

There's a few variables which can make it much worse.

The first is how much of the capital expenditures are being fueled by debt that won't be repaid, and how much that unpaid debt harms lending institutions. This is fundamentally how a few bad debts in 2008 broke the entire financial system: bad loans felled Lehman Brothers, which caused one money market fund to break the buck, which spurred a massive exodus from the money markets rather literally overnight.

The second issue is the psychological impact of 40% of market value just evaporating. A lot of people have indirect exposure to the stock market and these stocks in particular (via 401(k)s or pensions), and seeing that much of their wealth evaporate will definitely have some repercussions on consumer confidence.

kif• 9 months ago

This honestly doesn’t surprise me. We have reached a point where it’s becoming clearer and clearer that AGI is nowhere to be seen, whereas advances in LLM ability to ‘reason’ have slowed down to (almost?) a halt.

dawnerd• 9 months ago

But if you ask an AI hype person they’ll say we’re almost there we just need a bit more gigawatts of compute!

rhetocj233• 9 months ago

I hate to say this but I think the LLM story is going to go the same way as Teslas stock - everyone knows its completely detached from fundamentals and driven by momentum and hype but nobody wants to do the right thing.

vbezhenar2• 9 months ago

In my book, chat-based AGI has been reached years ago, when I couldn't reliably distinguish computer from human.

Solving problems that humanity couldn't solve is super-AGI or something like that. It's not there indeed.

38362936482• 9 months ago

Beating the Turing Test is not AGI, but it is beating the Turing Test and that was impressive enough when it happened

jdiff2• 9 months ago

We're not even solving problems that humanity can solve. There's been several times where I've posed to models a geometry problem that was novel but possible for me to solve on my own, but LLMs have fallen flat on executing them every time. I'm no mathematician, these are not complex problems, but they're well beyond any AI, even when guided. Instead, they're left to me, my trusty whiteboard, and a non-negligible amount of manual brute force shuffling of terms until it comes out right.

They're good at the Turing test. But that only marks them as indistinguishable from humans in casual conversation. They are fantastic at that. And a few other things, to be clear. Quick comprehension of an entire codebase for fast queries is horribly useful. But they are a long way from human-level general intelligence.

gitaarik• 9 months ago

What do you mean reliably distinguish a computer from a human? I haven't been surprised one time yet. I always find out eventually when I'm talking to an AI. It's easy usually, they get into loops, forget about conversation context, don't make connections between obvious things and do make connections between less obvious things. Etc.

Of course they can sound very human like, but you know you shouldn't be that naive these days.

Also you should of course not judge based on a few words.

steveBK123• 9 months ago

Hence the pivot into ads, shop-in-chat and umm.. adult content.

Analemma_• 9 months ago

“AGI achieved internally”

Another case of culture flowing from the top I guess.

strangescript3• 9 months ago

This entire thing has been pretty disingenuous on both sides of the fence. All the anti-AI (or anti OpenAI) people are doing victory laps, but what GPT-5 Pro did is still very valuable.

1) What good is your open problem set if really its a trivial "google search" away from being solved. Why are they not catching any blame here?

2) These answers still weren't perfectly laid out for the most part. GPT-5 was still doing some cognitive lifting to piece it together.

If a human would have done this by hand it would have made news and instead the narrative would have been inverted to ask serious questions about the validity of some these style problem sets and/or ask the question how many other solutions are out there that just need pieced together from pre-existing research.

But, you know, AI Bad.

nurettin• 9 months ago

AI great, but AI not creative, yet.

puttycat• 9 months ago

This is a strawman argument. No anti-AI sentiment was involved here. Simply the fact that finding and matching text on the Internet is several orders of magnitude easier than finding novel solutions to hard math problems.

strangescript3• 9 months ago

You didn't read the X replies if you believe that

matsemann• 9 months ago

You're moving the goal post.

Topfi5• 9 months ago

What good is your open problem set if really its a trivial "google search" away from being solved. Why are they not catching any blame here?

They are a community run database, not the sole arbiter and source of this information. We learned the most basic research back in highschool, I'd hope researchers from top institutions now working for one of the biggest frontier labs can do the same prior to making a claim, but microblogging has and continues to be a blight on any accurate information so nothing new there.

GPT-5 was still doing some cognitive lifting to piece it together.

Cognitive lifting? It's a model, not a person, but besides that fact, this was already published literature. Handy that a LLM can be a slightly better search, but calling claims of "solving maths problems" out as irresponsible and inaccurate is the only right choice in this case.

If a human would have done this by hand it would have made news [...]

"Researcher does basic literature review" isn't news in this or any other scenario. If we did a press release every journal club, there wouldn't be enough time to print a single page advert.

[...] how many other solutions are out there that just need pieced together from pre-existing research [...]

I am not certain you actually looked into the model output or why this was such an embarrassment.

But, you know, AI Bad.

AI hype very bad. AI anthropomorphism even worse.

andrepd2• 9 months ago

1) What good is your open problem set if really its a trivial "google search" away from being solved. Why are they not catching any blame here?

Please explain how this is in any way related to the matter at hand. What is the relation between the incompleteness of an math problem database, and AI hypesters lying about the capabilities of GPT5? I fail to see the relevance.

If a human would have done this by hand it would have made news

If someone updated information on an obscure math problem aggregator database this would be news?? Again, I fail to see your point here.

lukev3• 9 months ago

Framing this question as "AI good" OR "AI bad" is culture-war thinking.

The real problem here is that there's clearly a strong incentive for the big labs to deceive the public (and/or themselves) about the actual scientific and technical capabilities of LLMs. As Karpathy pointed out on the recent Dwarkesh podcast, LLMs are quite terrible at novel problems, but this has become sort of an "Emperor's new clothes" situation where nobody with a financial stake will actually admit that, even though it's common knowledge if you actually work with these things.

And this directly leads to the misallocation of billions of dollars and potentially trillions in economic damage as companies align their 5-year strategies towards capabilities that are (right now) still science fiction.

The truth is at stake.

strangescript3• 9 months ago

Except they weren't intentionally trying to deceive anyone. They made the faulty assumption that these problems were non-trivial to solve and didn't think it was simply GPT-5 aggregating solutions in the wild.

random97498323• 9 months ago

You are telling me a language model trained on Reddit can't solve novel problems? Shocking.

Edit: we are in peak damage control phase of the hype cycle.

creativeCak3• 9 months ago

Can't wait for the Buble to burst so we can get back to solving real problems (like the fact that we have very little competition in the CPU market right now, AMD is getting way too comfortable...). I do think though that when this bubble bursts it will hurt the machine learning field (which does have people doing practically useful stuff like protein folding) and investors might pull all funding because this generative nonsense(which has no real use beyond generating porn) will taint the entire field, even the parts of it that are actually useful.

phplovesong2• 9 months ago

How fing obvious was it that AI slop did not do anything other than scarpe some websites.

Jweb_Guru2• 9 months ago

I felt like I was going crazy when people uncritically accepted the original claim from OpenAI. Have people actually used these models?

phplovesong2• 9 months ago

From what i have seen, people using AI somehow get the mindset that the AI generated result is "godlike" and the "ultimate truth". Its really, really scary and im not that hopeful for what we will see int he next decade.

Once i told a coworker that a piece if his code looked rather funky (without doing a more deep CR), and he told me its "proven correct by AI". I was stunned, and asked him if he knows how LLMs generate their responses? He was genuinely in the belief that it was in fact "artificial intelligence" and was some sort of "all knowing entity".

amirhirsch3• 9 months ago

The sad truth about this incident is that it reveals that OpenAI does not have a serious effort to actually work on unsolved math problems.

rowanG077• 9 months ago

How so? I wouldn't put much stock into a roque employee announcing something wrong.

mrbungie3• 9 months ago

That's not any employee, its their VP of Science.

coldtea• 9 months ago

"rogue employee"

jebarker• 9 months ago

That’s a non sequitur. They’re a fairly large organization, I’d be amazed if they don’t have multiple research sub-teams pursuing all sorts of different directions.

amirhirsch3• 9 months ago

It is not about whether some team somewhere is dabbling in math, it’s about institutional emphasis. When serious math work exists, it tends to be checked before being broadcast. The fact that this didn’t happen suggests that genuine mathematical rigor isn’t a central focus.

grafmax3• 9 months ago

I realized they jumped the shark when they announced the pivots to ads and porn. Markets haven’t caught on yet.

zeroonetwothree2• 9 months ago

They know where the money is.

HarHarVeryFunny4• 9 months ago

The porn / sex-chat one is really disappointing. It seems they've given up even pretending that they are trying to do something beneficial for society. This is just a pure society-be-damned money grab.

goalieca5• 9 months ago

The porn pivot makes perfect sense. Porn is already quite fake and unconvincing and none of that matters.

make3• 9 months ago

they're hiring people for that right now if you look at their LinkedIn posts

JKCalhoun2• 9 months ago

I try not to lose sight of the first time that I heard (some years back) that people were using this new LLM thing for DM'ing ("dungeon mastering", leading) a game of Dungeons and Dragons. I thought, this must be bullshit or some kind of witchcraft.

Definitely not anti-AI here. I think I have been disappointed though, since then, to slowly learn that they're (still) little beyond that.

Still amazing though. And better than a Google search (IMHO).

llm_nerd• 9 months ago

Yann LeCun's "Hoisted by their own GPTards" is fantastic.

frays• 9 months ago

I might be missing context here, but I'm surprised to see Yann using language that plays on 'retard.'

That seems out of character for him - more like something I'd expect from Elon Musk. What's the context I'm missing?

nova22033• 9 months ago

It's a play on the word petard

grey-area• 9 months ago

Hoist (thrown in the air) by your own petard (bomb) is a common phrase.

znkr• 9 months ago

I don’t think it’s a wordplay with the r-word, but rather a reference to the famous Shakespeare quote: “Hoist with his own petard”. It’s become an English proverb. (A petard is a smallish bomb)

blitzar• 9 months ago

You have been Hoisted with your own retard

NitpickLawyer6• 9 months ago

While Yann is clearly brilliant, and has a deeper understanding of the roots of the filed than many of us mortals, I think he's been on a debbie downer trend lately, and more importantly, some of his public stances have been proven wrong in mere months / years after he made them.

I remember a public talk, where he was on the stage with some young researcher from MS. (I think it was one of the authors of the "sparks of brilliance in gpt4" paper, but not sure).

Anyway, throughout that talk he kept talking above the guy, and didn't seem to listen, even though he obviously didn't try the "raw", "unaligned" model that the folks at MS were talking about.

And he made 2 big claims:

1) LLMs can't do math. He went on to "argue" that LLMs trick you with poetry that sounds good, but is highly subjective, and when tested on hard verifiable problems like math, they fail.

2) LLMs can't plan.

Well, merely one year later, here we are. AIME is saturated (with tool use), gold at IMO, and current agentic uses clearly can plan (and follow up with the plan, re-write parts, finish tasks, etc etc).

So, yeah, I'd take everything any one singular person says with a huge grain of salt. No matter how brilliant said individual is.

Edit: oh, and I forgot another important argument that Yann made at that time:

3) because of the nature of LLMs, errors compound. So the longer you go in a session, the more errors accumulate so they devolve in nonsense.

Again, mere months later the o series of models came out, and basically proved this point moot. Turns out RL + long context mitigate this fairly well. And a year later, we have all SotA models being able to "solve" problems 100k+ tokens deep.

mrbungie3• 9 months ago

Pretty sure you can fill a room with serious researchers that at the very least will doubt about 2) being solved with LLMs, especially when talking about formal planning with pure LLMs and without a planning framwork.

PS: So just we're clear: formal planning in AI </> making a coding plan in Cursor.

Topfi5• 9 months ago

AIME is saturated (with tool use) [...]

But isn't tool use kinda the crux here?

Correct me if I'm mistaken, but wasn't the argument back then on whether LLMs could solve maths problems without e.g. writing python to solve? Cause when "Sparks of AGI" came out in March, prompting gpt-3.5-turbo to code solutions to assist solving maths problems over just solving them directly was already established and seemed like the path forward. Heck, it is still the way to go, despite major advancements.

Given that, was he truly mistaken on his assertions regarding LLMs solving maths? Same for "planning".

goalieca5• 9 months ago

LLMs can't do math. He went on to "argue" that LLMs trick you with poetry that sounds good, but is highly subjective, and when tested on hard verifiable problems like math, they fail.

They really can’t. Token prediction based on context does not reason. You can scramble to submit PRs to ChatGPT to keep up with the “how many Rs in blueberry” kind of problems but it’s clear they can’t even keep up with shitposters on reddit.

And your 2nd and third point about planning and compounding errors remain challenges.. probably unsolvable with LLM approaches.

Palmik2• 9 months ago

The original tweet was clearly misunderstood...

https://x.com/SebastienBubeck/status/1977181716457701775:

gpt5-pro is superhuman at literature search:
it just solved Erdos Problem #339 (listed as open in the official database https://erdosproblems.com/forum/thread/339) by realizing that it had actually been solved 20 years ago

https://x.com/MarkSellke/status/1979226538059931886:

Update: Mehtaab and I pushed further on this. Using thousands of GPT5 queries, we found solutions to 10 Erdős problems that were listed as open: 223, 339, 494, 515, 621, 822, 883 (part 2/2), 903, 1043, 1079.

It's clearly talking about finding existing solutions to "open" problems.

The main mistake is by Kevin Weil, OpenAI CTO, who misunderstood the tweet:

https://x.com/kevinweil/status/1979270343941591525:

you are totally right—I actually misunderstood @MarkSellke's original post, embarrassingly enough. Still very cool, but not the right words. Will delete this since I can't edit it any longer I think.

Obviously embarassing, but completely overblown reaction. Just another way for people to dunk on OpenAI :)

Topfi5• 9 months ago

If holding the CTO of OpenAI accountable for his wildly inaccurate statement constitutes "dunking on OpenAI", then I'd say dunk away.

He, more than anyone else, should be able to for one parse the original statements correctly and for another maybe realize that if one of their models had accomplished what he seemed to think GPT-5 had, that may require some more scrutiny and research before posting it. That would have, after all, been a clear and incredibly massive development for the space, something the CTO of OpenAI should recognize instantly.

The amount of people that told me this is clear and indisputable proof that AGI/ASI/whatever is either around the corner or already here is far more than zero and arguing against their misunderstanding was made all the more challenging because "the CTO of OpenAI knows more than you" is quite a solid appeal to authority.

I'd recommend maybe a waiting period of 48h before any authority in any field can send a tweet, that might resolve some of the inaccuracies and the incredibly annoying need to just jump on wild bandwagons...

zozbot234• 9 months ago

"you are totally right—I actually misunderstood" ...like, seriously? Did an AI come up with this retraction, or are humans actually talking like robots now?

Topfi5• 9 months ago

Guess even the CTO of OpenAI relies on Anthropic models in a pinch...

827a• 9 months ago

This happening the same week as DeepMind’s seemingly legitimate AI-assisted cancer treatment breakthrough is a startlingly bad look for OpenAI.

My boss always used to say “our only policy is, don’t be the reason we need to create a new policy”. I suspect OpenAI is going to have some new public communication policies going forward.

gpjt• 9 months ago

To be fair to the OpenAI team, if read in context the situation is at worst ambiguous.

The deleted tweet that the article is about said "GPT-5 just found solutions to 10 (!) previously unsolved Erdös problems, and made progress on 11 others. These have all been open for decades." If it had been posted stand-alone then I would certainly agree that it was misleading, but it was not.

It was a quote-tweet of this: https://x.com/MarkSellke/status/1979226538059931886?t=OigN6t..., where the author is saying he's "pushing further on this".

The "this" in question is what this second tweet is in turn quote-tweeting: https://x.com/SebastienBubeck/status/1977181716457701775?t=T... -- where the author says "gpt5-pro is superhuman at literature search: [...] it just solved Erdos Problem #339 (listed as open in the official database erdosproblems.com/forum/thread/3…) by realizing that it had actually been solved 20 years ago"

So, reading the thread in order, you get

  * SebastienBubeck: "GPT-5 is really good at literature search, it 'solved' an apparently-open problem by finding an existing solution"
  * MarkSellke: "Now it's done ten more"
  * kevinweil: "Look at this cool stuff we've done!"

I think the problem here is the way quote-tweets work -- you only see the quoted post and not anything that it in turn is quoting. Kevin Weil had the two previous quotes in his context when he did his post and didn't consider the fact that readers would only see the first level, so wouldn't have Sebastien Bubek's post in mind when they read his.

That seems like an easy mistake to entirely honestly make, and I think the pile-on is a little unfair.

card_zero3• 9 months ago

So the first guy said "solved [...] by realizing that it had actually been solved 20 years ago", and the second guy said "found solutions to 10 (!) previously unsolved Erdös problems".

Previously unsolved. The context doesn't make that true, does it?

glenstein5• 9 months ago

Right, and I would even go a step further and say the context from SebastienBubeck is stretching "solved" past its breaking point by equating literature research with self-bootsrapped problem solving. When it's later characterized as "previously unsolved" it's doubling down on the same equivocation.

Don't get me wrong, effectively surfacing unappreciated research is great and extremely valuable. So there's a real thing here but with the wrong headline attached to it.

moefh• 9 months ago

Kevin Weil had the two previous quotes in his context when he did his post and didn't consider the fact that readers would only see the first level, so wouldn't have Sebastien Bubek's post in mind when they read his.

No, Weil said he himself misunderstood Sellke's post[1].

Note Weil's wording (10 previously unsolved Erdos problems) vs. Sellke's wording (10 Erdos problems that were listed as open).

[1] https://x.com/kevinweil/status/1979270343941591525

GodelNumbering• 9 months ago

Also, previous comment omitted the part that now-deleted tweet from Bubeck begins with "Science revolution via AI has officially begun...".

Frieren• 9 months ago

"GPT-5 is really good at literature search, it 'solved' an apparently-open problem by finding an existing solution"

Survivor bias.

I can assure you that GPT-5 fucks up even relatively easy searches. I need to have a very good idea how the results looks like and the ability to test it to be able to use any result from GPT-5.

If I throw the dice 1000 times and post about it each time that I got a double six. Am I the best dice thrower that there is?

zacmps• 9 months ago

For literature search that might be ok. It doesn't need to replace any other tools, and if 1/10 it surfaces something you wouldn't have found otherwise it could be worth the time on the dud attempts.

wasabi9910113• 9 months ago

I'm not really sure what you mean. Literature search is about casting a wide net to make a reading list that is relevant to your research.

It is pretty hard to fuck that up, since you aren't expected to find everything anyway. The idea of "testing" and "using any result from GPT" is just, like, reading the papers and seeing if they are tangentially related.

If I may speak to my own experience, literature search has been the most productive application I've personally used, more than coding, and I've found many interesting papers and research directions with it.

saghm• 9 months ago

One time when I was a kid my dad and I were playing Yahtzee, and he rolled five 5s on his first roll of the turn. He was absolutely stunned, and at the time I was young enough that I didn't understand just how unlikely it was. If I only I knew that I was playing against the best dice thrower!

OtherShrezzing• 9 months ago

Am I correct in thinking this is the 2nd such fumble by a major lab? DeepMind released their “matrix multiplication better than SOTA” paper a few months back, which suggested Gemini had uncovered a new way to optimally multiply two matrices in fewer steps than previously known. Then immediately after their announcement, mathematicians pointed out that their newly discovered SOTA had been in the literature for 30-40 years, and was almost certainly in Gemini’s training set.

glenstein5• 9 months ago

It's an interesting type of fumble too, because it's easy to (mistakenly!) read it as "LLM tries and fails to solve problem but thinks it solved it" when really it's being credited with originality for discovering or reiterating solutions already out there in the literature.

It sounds like the content of the solutions themselves are perfectly fine, so it's unfortunate that the headline will leave the impression that these are just more hallucinations. They're not hallucinations, they're not wrong, they're just wrongly assigned credit for existing work. Which, you know, where have we heard that one before? It's like the stylistic "borrowing" from artists, but in research form.

card_zero3• 9 months ago

Well, it is important that we have some technology to prevent us from going round in circles by reinventing things, such as search.

ummonk2• 9 months ago

We also had the GPT-5 presentation which featured both incorrect bar charts (likely AI generated) and an incorrect explanation of lift.

ogogmad• 9 months ago

No, your claim about matrix multiplication is false. Google's new algorithm can be applied recursively to 4x4 block matrices (over the field of complex numbers). This results in an asymptotically faster algorithm for nxn matrix multiplication than Strassen's. Earlier results on 4x4 matrices by Winograd and others did not extend to block matrices..

Google's result has more recently been generalised: https://arxiv.org/abs/2506.13242

jsnell2• 9 months ago

That doesn't match my recollection of the AlphaEvolve release.

Some people just read the "48 multiplications for a 4x4 matrix multiplications" part, and thought they found prior art at that performance or better. But they missed that the supposed prior art had tighter requirements on the contents of the matrix, which meant those algorithms were not usable for implementing a recursive divide and conquer algorithm for much larger matrix multiplications.

Here is a HN poster claiming to be one of the authors rebutting the claim of prior art: https://news.ycombinator.com/item?id=43997136

whimsicalism• 9 months ago

no, you are incorrect

camillomiller• 9 months ago

I have some more mirrors for you to try and climb, if you need them.

jibal• 9 months ago

That's being disingenuous, not fair.

ripped_britches• 9 months ago

I make mistakes all the time. This seems like a genuine mistake, not malice.

Imagine if you were talking about your own work online, you make an honest mistake, then the whole industry roasts you for it.

I’m so tired of hearing everyone take stabs at people at OpenAI just because they don’t personally like sama or something.

strongbond• 9 months ago

Maybe Open AI shouldn't be so stabable?

manquer• 9 months ago

Mistakes are human yes we all do make them all time.

However when representing an reputable organization, people are expected to be cautious or otherwise required to have their comments reviewed and most organizations would enforce this to protect their brand or reputation.

As Carl Sagan said it best, extraordinary claims require extraordinary evidence. This was a pretty extraordinary claim and multiple senior staff at the org endorsed the comment without even a cursory check first. I would think serious observers are more concerned by the process and controls in OpenAI or lack thereof here, rather than a specific single mistake.

Like them or hate them, OpenAI is the leader in the industry, and everyone lookup up to them and their employees in a public forum for credible information so will hold them to higher standard than a lesser known lab.

The burden of checking for quality comes with having a reputable brand . The burden is being monetized / compensated with the valuation or size of fund raise a org is able to command.

Timsky7• 9 months ago

GPT-5 is proving useful as a literature review assistant

No, it does not. It only produces a highly convincing counterfeit. I am honestly happy for people who are satisfied with its output: life is way easier for them than for me. Obviously, the machine discriminates me personally. When I spend hours in the library looking for some engineering-related math made in the 70s-80s, as a last resort measure, I can try to play this gambling with chat, hoping for any tiny clue to answer my question. And then for the following hours, I am trying to understand what is wrong with the chat output. Most often, I experience the "it simply can't be" feeling, and I know I am not the only one having it.

glenstein5• 9 months ago

Struggling to understand this one. Is it that (1) it's lopsided toward reference materials found on the modern internet and not as useful for reviewing literature from the Before Times or (2) it's offering specific solutions but you're skeptical of them?

crazygringo3• 9 months ago

In my experience doing literature super-deep-dives, it hallucinates sources about 50% of the time. (For higher-level literature surveys, it's maybe 5%.)

Of the other 50% that are real, it's often ~evenly split into sources I'm familiar with and sources I'm not.

So it's hugely useful in surfacing papers that I may very well never have found otherwise using e.g. Google Scholar. It's particularly useful in finding relevant work in parallel subfields -- e.g. if you work in physics but it turns out their are math results, or you work in political science and it turns out there are relevant findings from anthropology. And also just obscure stuff -- a random thesis that never got published or cited but the PDF is online and turns out to be relevant.

It doesn't matter if 75% of the results are not useful to me or hallucinated. Those only waste me minutes. The other 25% more than make up for it -- they're things I simply might never find otherwise.

macrolime• 9 months ago

What is "it". Gpt-5 auto? Gpt-5 pro? Deep research? These have wildly different hallucination rates.

andrewflnr2• 9 months ago

So, the exact stuff Google used to be good at.

scosman2• 9 months ago

Saying it isn't useful is a bit of an overstatement. It can search, churn through 500k words in a few minutes, and come back with summaries, answers, and sources for each point.

Should you blindly trust the summary? No. Should you verify key claims by clicking through to the source? Yes. Is it still incredibly useful as a search tool and productivity booster? Absolutely.

scruple• 9 months ago

I gave it a PDF recently and asked it to help me generate some tables based on the information there in. I thought I'd be saving myself time. I spent easily twice as long as I would have if it I had done it myself. It kept making trivial mistakes, misunderstanding what was in the PDF, hallucinating, etc.

Timsky7• 9 months ago

It is excellent when just finding something is enough. Most often in my practice, I am dealing with questions that have no written-down answers, meaning the probability of finding a book/article that provides one is negligible. Instead, I am looking for indirect answers or proofs before I make a final engineering decision. Yet another problem is that the language itself changes over time. For instance, at the beginning of the 20th century, the integers were called integral numbers. IMHO, LLMs poorly handle such cases when considered as a substitute for search engines. For full-text vector search, I am using https://www.recoll.org/ a real time saver for me, especially for desktop search.

bootsmann• 9 months ago

If I knew what’s in the paper I don’t need the summary but if I don’t know what’s in the paper I cannot possibly judge the accuracy of its summary.

kianN2• 9 months ago

If you’re interested in a literature review tool, I built a public one for some friends in grad school that uses hierarchical mixture models to organize bulk searches and citation networks.

Example: https://platform.sturdystatistics.com/deepdive?search_type=e...

Timsky7• 9 months ago

Thank you for sharing! I like your dendrogram-like circular graphs! They are way more intuitive. That could be a nice companion for a bibliometrix/biblioshiny library for bibliometric analysis https://www.bibliometrix.org/. I tried "Deep Dive" with my own request, and ... it unfortunately stops at the end of "Organizing results". Maybe I should try again later.

andai• 9 months ago

There's this principle, I forget the name, but how everyone when reading the newspaper, when they read on a subject they're familiar with, will instantly spot all the holes, all the errors. And they will ask themselves, how was this even published in the first place?

But then they flip to the next page and they read a story on a subject they're not an expert on and they just accept all of it without question.

I think people might have a similar relationship with ChatGPT.

btrettel• 9 months ago

https://en.wikipedia.org/wiki/Gell-Mann_amnesia_effect

lukev3• 9 months ago

The Gell-Mann Amnesia effect. And you're absolutely right, it's extremely pronounced in LLM users.

happy_dog1• 9 months ago

I wonder whether for a lot of the search & literature review-type use-cases where people are trying to use GPT-5 and similar we'd honestly be much better off with a really powerful semantic search engine? Any time you ask a chatbot to summarize the literature for you or answer your question, there's a risk it will hallucinate and give you an unreliable answer. Using LLM-generated embeddings for documents to retrieve the nearest match, by contrast, doesn't run any risk of hallucination and might be a powerful way to retrieve things that Google / Bing etc. wouldn't be able to find using their current algorithms.

I don't know if something like this already exists and I'm just not aware of it to be fair.

Timsky7• 9 months ago

I think you have a very good point here: a semantic search would be the best option for such a search. The items would have unique identifiers so the language variations can be avoided. But unfortunately, I am not aware of any of these kinds of publicly available projects, except DBpedia and some biology-oriented ontologies that would massively analyze scientific reports.

Currently, I am applying RDF/OWL to describe some factual information and contradictions in the scientific literature. On an amateur level. Thus I do it mostly manually. The GPT-discourse somehow brings up not only the human-related perception problems, such as cognitive biases, but also truly philosophical questions of epistemology that should be resolved beforehand. LLM developers cannot solve this because it is not under their control. They can only choose what to learn from. For instance, when we consider a scientific text, it is not an absolute truth but rather a carefully verified and reviewed opinion that is based on the previous authorized opinions and subject to change in the future. So the same author may have various opinions over time. More recent opinions are not necessarily more "truthful" ones. Now imagine a corresponding RDF triple (subject-predicate-object tuple) that describes that. Pretty heavy thing, and no NLTK can decide for us what the truth is and what is not.

opdahl• 9 months ago

Since you specifically were wondering if something like this exist, I feel okay with mentioning my own tool https://keenious.com since I think it might fit your needs.

Basically we are trying to combine the benefits of chat with normal academic search results using semantic search and keyword search. That way you get the benefit of LLMs but you’re actually engaging with sources like a normal search.

Hope it was what you were looking for!

astrange4• 9 months ago

If your issue is "incorrect answers" (hallucination) then an embedding search will naturally also produce "incorrect answers" because an embedding is a lossy compression of the source.

malshe• 9 months ago

I think its scope is narrower than a lit review assistant. I use it mainly for finding papers that I or my RAs might have missed in our lit review.

I have a recent example where it helped me locate a highly relevant paper for my research. It was from an obscure journal and wouldn't show up in the first few pages of Google Scholar search. The paper was real and recently published.

However, using LLMs for doing lit review has been fraught with peril. LLMs often misinterpret the research findings or extrapolate them to make incorrect inferences.

cj• 9 months ago

I was reminded how terribly ChatGPT hallucinates this morning when I used it to look up the 7 point measurement locations for skin fold calipers to estimate body fat.

It correctly described the locations in text, then it offered to provide a diagram.

I said “sure”, and it generated an image saying the chest location is on the neck, and a bunch of other clearly incorrect locations for the other measurement sites.

It’s gotten better. But it’s still bad.

khuston• 9 months ago

LLM-powered deep research tools are very useful at finding whether someone has already published on a research question. You could spend days performing keyword searches and traversing citation graphs but still miss relevant pockets of the literature. Semantic search is a genuine improvement here.

As to not trusting the generated text, you’re totally right. That’s why I use it as a search tool but mostly ignore the content of what the LLM has to say and go to the source.

cedws• 9 months ago

Making such a claim should at the very least require proof that the information wasn’t in the training data.

d_tr• 9 months ago

Whatever happened to "don't get high on your own supply"?

DonHopkins• 9 months ago

Man, that's soap, man!!!

https://www.youtube.com/watch?v=RvGE-xhroy0

[drinks pee twice]

YesBox• 9 months ago

Wouldn't be surprised if OpenAI employees are being asked to phrase (market) things this way. This is not the first time they claimed GPT-5 "solved" something [1]

[1] https://x.com/SebastienBubeck/status/1970875019803910478

edit: full text

It's becoming increasingly clear that gpt5 can solve MINOR open math problems, those that would require a day/few days of a good PhD student. Ofc it's not a 100% guarantee, eg below gpt5 solves 3/5 optimization conjectures. Imo full impact of this has yet to be internalized...

flkiwi• 9 months ago

Thanks for calling that out. You're right to be upset.

sbaidon94• 9 months ago

You would think Open AI employees have a pretty good grasp of their model capabilities, but even if you don’t, you probably always want to be on the cautious side for every claim you see on the internet.

This just seems to be the Open AI culture, which for better or worse has helped foster the AI hype environment we are currently in.

bwfan123• 9 months ago

"It is difficult to get a man to understand something, when his salary depends upon his not understanding it,"

kenjackson2• 9 months ago

“Mathematician Thomas Bloom, who runs erdosproblems.com, pushed back right away. He called the statements "a dramatic misinterpretation," clarifying that "open" on his site just means he personally doesn't know the solution - not that the problem is actually unsolved.”

What mathematician uses this as the definition for “open”? I don’t go around saying that most problems in this textbook are open questions, just because I don’t know how to do them.

ModernMech• 9 months ago

He's pointing out that erdosproblems.com is an eventually consistent list of solved problems, so there may be a lag between when a problem is solved and when it's marked as such on his site.

imtringued• 9 months ago

If a book lists an open problem and you solve it, how would the book know?

kenjackson2• 9 months ago

Do people here really not know what an open problem in math is?

ares6232• 9 months ago

Imagine having your retirement fund manager investing in a couple million more based on the original tweet. Very cool.

classified• 9 months ago

Was that "researcher" just a hallucinating LLM?

OpenAI researcher announced GPT-5 math breakthrough that never happened

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