Occam's Razor says: "They heard this problem is solved or about to be solved amongst the rest of the other problems. They prioritized this and put substantial compute with their newest model and solved it." I know everyone loves juicy rumors, theories etc. but honestly that is the simplest and most plausible explanation given the state of AI improvement now. Obviously spending 15 million on a problem is not a slam dunk decision even for a company like OpenAI but if it has a significantly high chance of solving it and their competitor will be claiming they solved it, then it raises the stakes and they go after it. In fact this is the most rational and also curiosity-driven thing to do and totally what I would have expected from any frontier lab. Of course if one wants to prove their confirmation biases that they train on sessions or be able to identify individual users, the non-zero chance of that being also another explanation is attractive enough to wet their appetites.
There's also an elephant here in this room: Quite "coincidental" one of the coauthors worked for the competition. Of course GPT would know this fact. And OAI has every incentive to particularly monitor those accounts.-
Funny how they did not solve any of the other problems, just the one where there was already solutions to the NS with some restrictions in their chats, and their solution seems to derive from those.
Because it is not finished. Their follow up claim is that OpenAI’s approach looks like another proof they had been working on the side, but hasn’t published yet
OpenAI have admitted their new model they used was trained on prompts at around the time that researcher was working on it, so it seems self evident that it was used as part of the millennium solution
My take home from this entire drama is that one should not use LLM services for confidential or proprietary information as they all seem to be run by assholes. And you’re sending them everything you are doing. Would you send your lab notebook to an asshole? Hell no.
I say that as a mathematician (on paper) who perhaps surprisingly doesn’t give a crap about the problem itself.
> one should not use LLM services for confidential or proprietary information
That’s obvious, isn’t it? Just like you wouldn’t upload your confidential documents to an online spellchecker, or your proprietary code to an online compiler?
I worked in a EU company that was developing a product competing with one of Microsoft offerings. All the code was hosted on GitHub, we used Azure for hosting and all the internal communications were in Microsoft Teams.
To me it looked absurd. But I was almost the smallest cog in the corporate structure, so I never asked what were the reasons for all these decisions.
> Wait until you learn how many other SaaS and web 2.0 and cloud based things are also run by assholes.
Though it's not the real acronym, Larry Ellison himself has stated that Oracle stands for One Real Asshole Called Larry Ellison. He famously prides himself on it.
That they do it is just concerning to me in that it says that home-ran models just aren't good enough. Surely researchers like this have the processing power to run them at home, they just don't have the processing power to train models of comparable level.
This is something I feared would happen and where open source would be left behind. Maybe they can do something with crowd-sourcing computational power from volunteirs. They were after all able to get Leela Chess Zero to be comparable to AlphaZero by training from volunteer processing power but it seems to me we live in a world now where the best models keep their stuff closed.
In imagine generation too. I'm not sure how well Stable Diffusion can compete in following instructions with all those advanced models that are kept secret.
Their privacy policy for normie subscribers says in plain English they use your Personal Data for research. I think it’s pretty unreasonable to use the service and expect otherwise.
My doctor's privacy policy is a bit more abusive than OpenAI's. It exists mostly because of a $%^&&* legal framework rather than malice, and I've grown accustomed to "if I don't want to die then I sign away these rights." Despite my having theoretically signed my soul away, my doctor isn't selling personal information to my exes or to life insurance companies (though they could in the US; that extremely personal information is no longer mine). OpenAI is engaging in the "technically legal maybe we'll see but obviously unintended" side of this transaction, and maybe that works out for them, but I wouldn't personally choose to be a shill for "it's unreasonble to expect somebody with 'legal' permission to do something other than the maximum 'legally' permitted" if I were in your shoes.
Depends on your profession? I sometimes work on open source code as part of my professional duties. Nothing that goes on there is necessary to keep private.
Aren't business consulting firms even worse? They are explicitly for business and there are known cases where they shared confidential information of one of their customers with another one.
> You'd think theft would still be illegal regardless of what a privacy policy says.
I wish that were true, but I live in the United States and it is 2026.
The President of the United States rug-pulls memecoin crypto and regularly pardons people like Paul Walczak (who was convicted of massive payroll fraud) in exchange for large donations.
I wouldn't make any assumptions about what is considered theft anymore, at least not when it is being committed by people who have enough money to be above the law.
> My two favourite hypothetical questions regarding this used to be:
> If I'm running Codex and one of my API keys accidentally gets consumed in the context, what are the chances that someone else might ask for an API key in the future and get mine back? (I asked someone at OpenAI once and they called this the "regurgitation" problem and assured me that they take great pains to prevent that... but wouldn't describe how.)
> If I brainstorm with ChatGPT about potential new directions for my company, what's the chance that information might be exposed to a competitor in six months' time who asks "what might company X plan to do next"?
> My new preferred hypothetical for this is:
> If I use ChatGPT to help me partially solve a Millennium Prize problem, what are the chances that my work will influence training such that a later model helps someone else solve it first?
They wouldn't appear in weights but could be added to the context. My conversations regularly go "regarding your Java problem"... which was a separate item in the history from earlier. As long as I only see these (and nobody else sees mine), it can be helpful.
I find the claims from OpenAI somehow more relatable and reasonable.
- They threw compute on a problem another team/company was rumored to have solved to see what their secret model could do.
- The texts I read do make it seem like OpenAI wanted to talk and share credit generously.
- Imagine working on a frontier math problem with someone at Anthropic and not only do you use Codex but also through a non-business account that allows training on your data.
- Timeline-wise, if they mainly used GPT 5.6 it's unlikely any meaningful data made it into an model that's being internally validated right now.
> ... we heard rumors that two Millennium Prize problems had been resolved. Inspired by these rumors ...
I've observed this exact effect last week. I made a discovery regarding a stepwise performance improvement in a codebase. I shared the benchmark results with a peer and within 12 hours they replicated the same. We had both been looking for this for years.
I think giving someone hope that an answer exists might as well be the same thing as giving them the answer these days. Competition is a hell of a drug, and frontier LLMs aggressively compound that energy.
> I think giving someone hope that an answer exists might as well be the same thing as giving them the answer these days.
If you read the history of major scientific discoveries, this has been the case for a long time. There are many things that were independently discovered by different people at nearly the same time. Once people know something is solved or solvable, it gets a relentless amount of focus.
Maybe that shows how scientific discoveries come to be. It's not a genius sitting alone in their chamber for a decade and then suddenly they emerge with this huge thing. That's Hollywood fiction. Scientific progress is the colaborative effort of countless researchers over long periods of time, communicating, exchanging ideas, many of them wrong, tweaking, trying, thinking, arguing. When a breakthrough happens then it's the tip of a mountain of work that came before it. If two individuals stand on that mountain and feel there is something somewhere then it's not too strange that they take the last step at roughly the same time because conditions were right. The preconditions were in place at that time, the results required for this were available and the focus was on this specific thing.
I'd say it illustrates well that this last piece, the person celebrated for the achievement, is disproportionally overvalued and the rest of the work they are standing on is disproportionally ignored.
I always like to bring up how many decades of research, how many hundreds of years of entire PhD-theses, how many sleepless nights were used up to generate all the protein structure data that made up the corpus of Protein Data Bank - that was then hovered up by the AlphaFold team, and guess who got the Nobel Prize...
Just like all non-AI related nobel prizes before them.
Every laureate stands on the shoulder of giants which is their field as a generations-spanning body of researchers. They did the last step and get recognized. The good ones acknowledge that in their acceptance speeches.
This seems to be the norm rather than the exception.
On a tangent, the genius of people like eg Einstein is not so much that he came up with all these things: other people were close, but that he was a singular individual that did all of these discoveries, instead of five different guys all making some breakthrough here or there.
If you believe the totality of the document, there was more shadiness in how OpenAI acted than just timing. Save other things they are accused of, the progression from rumors to replication would attract much less scrutiny. With those in mind timing begins to look suspicious at best.
Would anyone be surprised if major model companies had tagged the accounts of competitor employees for extra tracking? Given the concerns about distillation and bench marking it hardly seems irrational, but how it is used matters quite a lot.
>While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models.
What do you mean, as OpenAI employee, you cannot tell that his work has entered the training data ?
But also correct me if I'm wrong, if the two mathematician were really close to finish this problem, and their conversation were used by OpenAI,
shouldn't the Agent have succeeded way faster/efficiently instead of using "4.9 million messages and used about 300 billion output tokens."
I’m not a mathematician so take this with a grain of salt. Apparently terry tao commented that the approach used for the Euler paper can “probably” be used for solving NS but it’s still technically challenging and can probably be done with an LLM with a lot of compute. To me the crux of the issue is whether the insight were stolen so that the problem becomes something that is in the domain of LLMs. This is much different than LLMs coming up with the insight. OpenAI wants everyone to think the LLM came up with the insight and solved the thing by itself even though they have perhaps an army of researchers.
Having the chat logs enter the training data and having them have a meaningful influence on the ultimate result the model produces are very different things.
The text for all the Goosebumps books are certainly in the training data and to some small amount influenced the solve. But their contribution was so vanishingly small it would seem absurd to say R L Stein should have recourse for contibuting to the solve.
The equivalent would be taking a (fully offline) LLM and asking it about the ending of one specific Goosebumps book, and it revealing the twist. And although that specific book was (probably) only once in the training data, a high parameter LLM can usually "remember" the twist.
What's missing from the story I think is the part about "...then had a breakthrough on August 15th. The mathematical rumour mill kicked into gear...". If only the two of them were working on the problem in secret, how did their breakthrough become a rumor?
People talk. If you have a breakthrough solving one of the most famous problems outstanding, you're going to tell people.
You'll say, "Don't tell anyone", which they will ignore because they get a rush and perceived status by sharing it. So then they tell someone, along with "Don't tell anyone", etc.
It's a small enough world (both in academic math, one at Anthropic) that you get to OAI in very few hops.
"The route to the Clay problem through a smooth force, options c and d in Fefferman’s statement of the problem, is the route Luis and Diego opened and the one Levent and I had quietly chosen to attack. Almost nobody else I know of was working on it. It is not the direction one arrives at in a few days by giving a model the problem statement. When I heard “forced,” it was a bright red flag."
We know that OpenAI trained on their prompts, plagiarism is incredibly likely. The only thing we don't know is whether or not it was deliberate plagiarism yet
The reality is also that most of your colleagues have no interest in stealing your work: they have their own work to do anyway, and having a colleague effervescing about whatever they are working on is kind of the norm in pure research. Just because they are making progress it doesn't mean they are about to do anything interesting.
Also it's not like you're looking for a lost pair of car keys: just getting to the level where you can understand a problem well enough to "steal" it takes a huge amount of work. People are going to know if you're at the level where you could be a competitor.
So in general it's pretty safe to talk generally about whatever you're doing.
LLM’s seem very good at solving mathematical problems of which there is an enormous amount of exisiting work/attempts in their training data. This is an amazing capability, but does not convince me that these models are «thinking» or «reasoning» in the way a human does. A human mathematician could in theory categorize/discover an entirely new field of mathematics tomorrow, based purely on their «human intelligence», I wonder if we will see similar examples by LLM’s soon. It seems to me currently impossible that LLM’s can replace human mathematicians, because of their (assumption) likely dependence on human input in the sense of enormous amounts of pre-existing attempts/data.
If an entirely new problem, within a new field of mathematics were to appear tomorrow, I highly doubt an LLM would be useful at all on their own. Is this the «ultimate ASI test»?
This is also what I've been thinking. The result itself is amazing but it's not like this was completely unexpected. There has been a huge amount of progress on the problem in the last 10 years without which it seems unlikely today's full resolution would have been possible. It is not clear what strategy was taken but it sounds like it borrowed heavily from the two spanish mathematicians. Experts will scrutinize the proof and it will be interesting to see if anything truly original or unexpected was done, outside of known techniques, a move 37.
Extreme temperature levels (>2.0) can push a GPT of its manifold, essentially producing predictions barely distinguishable from random noise (it flattens the probability distribution of the next token). In theory this could predict anything including the next field of mathematics (infinite monkey theorem) but realistically that would never happen.
However, how to we know the next field of mathematics isn't a novel combinations of several other sub-fields? That level of mathematics would be indistinguishable from magic to most people and so in their eyes the GPT did something truly inventive.
And an LLM could in theory also stumble upon entirely new ideas: there's randomness in how they generate their reasoning and answers after all.
I suspect that we are seeing a lot of advances coming from the combination of existing but somewhat obscure knowledge coming from LLMs at the moment, because LLMs are really good at this. At least compared to humans.
Even before our AI friends became good, they were already known for having read approximately every paper and every textbook published in any language. You only need to increase intelligence a fairly small amount from there to get to something like the 'convex hull' of human knowledge.
The gist is that basically whenever anyone comes up with a new method you get a big burst of activity of picking up all the now lower hanging fruit, that was previously out of reach.
OpenAI can easily identify these outstanding human behind their accounts. Human in OpenAI constantly check their logs for breakthrough. When they find something interesting, they brute force the result using their massive computing power.
Or, using anonymised data, search for anyone seriously trying to tackle this problem - probably about 10 people in the entire world and use their ideas as a starting point. They wouldn’t even need to be watching specific accounts or using de-anonymised data if they know what they’re looking for.
Hah, what is the infrastructure which takes user sessions (chats with API keys, directions, navier-stokes math/progress) and regurgitates this into pre-training, RL, fine-tuning data? Or better, in-context data?
People talk about the "compute" but what about the "storage"? Is storage exponentially greater, or soon to be, than the compute? Is the storage going to slow down growing to some constant rate, i.e. all people on earth using chatgpt, or no, on the contrary, it will keep growing?
If there were any shady business, I do not condone it, but technologically we are not there yet for said shady business to happen.
AI companies use heuristics to filter sessions, then llms to further filter, then use various techniques too anonymize the session, then process it and add it to various datasets for further selection and refinement. they don't need huge storage for this.
what is behind "process" it and "further selection" and "refinement" and how big are these "datasets"? These companies ship the encrypted session to you not because they want to.
I agree that they have pipelines for what you are describing but how effective they are at scale and at focusing is the question.
I always thought it was enough to switch off the "Improve the model for everyone" setting on chatgpt.com:
"Allow your content to be used to train our models, which makes ChatGPT better for you and everyone who uses it. We take steps to protect your privacy."
But apparently there is also an entire completely different route "Do not train on my data"?
Does this mean that before I submitted the "Do not train on my data" request, my data was used for training in spite of "Improve the model for everyone" being turned off?
We are getting to facebook/meta-levels of privacy settings obfuscation.
That's because what people enter into LLMs is the last gold there is out there. Everything else is already scraped or ensloppified.
Maybe next step is to filter your input client side through an unknown number of obfuscators where you ask LLMs to rephrase your question (onion router idea) such that no single provider can be certain that this is human input and not some slop feedback loop.
No one knows if it's actually LLM doing the heavy weight. It could be just human written brute force algorithm running on their massive computer cluster.
It’s reasonable to wonder about what chat usage data gets into models (to be honest probably quite little - carefully curating training data and creating higher quality synth data seems to be the current approach) and the implied risk to privacy and creativity (every new patent filed this year probably touched a model before filing).
What I cannot reconcile is the timeline and the concern in this specific case.
I don’t think training pipelines are anything close to the level of continuous training needed to incorporate Aug 15th ideas into a model that generates a breakthrough early Sept. Either OpenAI nakedly had someone with mathematical understanding dig into a specific user’s chats (a massive red flag) or this really is poor handling of a more classic parallel discovery situation (with one party clearly having worked on it longer)
Use Bedrock or any kind of big tech hosted version of the frontier labs can be a solution. I think if secrecy is of utmost importance to you, then do not send data to first parties
I don't get the sales pitch, spend 15 million dollars to win a 1 million dollar price?
Showing of the model's capabilities - okay, but it's not like it solved the problem on its own, and apparently not particularly efficient. Are there practical applications that justify the investment?
How is that any different from any other academic research? Every PhD candidate solves problems essentially nobody cares about. They don't even get $1M, they get nothing.
There are two benefits though.
One is recognition. Cred. The PhD candidate gets to put a ", Ph.D." behind their name, opening doors to future academic employment or other endeavors where people value titles. The AI lab gets to say their tech solved sth that humanity wanted bad for a long time. Both cases with substantial financial upside (higher income for Mr. PhD and higher company valuation for the AI lab).
The other one is that this is how scientific progress works. $1M or not. That number was just a PR campaign by the math community to point to some goals. It's clear that it would cost more than $1M to get there.
Well OpenAI wants to maintain its edge and solving a Millennium problem is of course fantastic PR, and given that Anthropic seems to be working on that it’s not hard to imagine they wanted to be first. I don’t get how people buy into that whole “oh we heard models can solve Millennium prize problems now so we thought why not give it a go…” story - it’s a bit funny. This whole AI bubble is about hype and solving such problems is probably one of the best ways to keep this hype up so you can safely assume both OpenAI and Anthropic are using significant resources on these areas.
I know that this may be somewhat dramatised and even infantile, but my reflection is that in the world run by these reckless AI companies everyone looses. Navier-Stokes is solved but it feels like no one has won anything, controversy prevails, there is no glory in the math breakthrough. There is hardly anything to cherish, and even the guys at the top of it in OA who sit on the (supposedly) superhuman intelligence come across as massive losers and frauds.
To claim something verified in Lean is wrong, you need to either argue that the theorem was stated incorrectly, or that there is a bug in Lean (assuming no `sorry` etc, which is checked by comparator). The number of lines needed to prove it is irrelevant (other than checking for a bug in Lean gets harder).
I run a small SaaS[1], like so many others, that uses AI to generate and optimize SQL. Getting this to perform optimally has been a lot of work and now I wonder if OpenAI is outright stealing this knowledge, which without a doubt is highly valuable to them.
SQL is so ubiquitous and the use case so obvious, there's no way they have not already been tracking performance and benchmaxxing on SQL queries for years.
But I don't think openai will bother to release a competitor, the real threat is that anyone with a decent LLM and a harness to try a few queries will land at the same or a better query within minutes.
I mean this is just the logical end game. The company/ies that control the uber mind will devour ALL useful or valuable work. Unlimited intelligence at unlimited scale means the value of humans for knowledge work goes to zero. We will eventually not have meaningful access to the uberminds because we will be pointless. At which point our Silicon Valley luminaries will really have no choice but to extinguish as many of us as possible for the greater good. After all if we are all pointless then that has to be weighed against are cost to Mother Earth. Clearly the only moral solution is to cull or allow to be culled some 98% or so down to a more sustainable, manageable population of curiosities. The end game of ai is the remnants of humanity in a zoo, and that’s if humans control the outcome… the machine minds might be more charitable as they would be less afraid…
I think this drama was blown up a bit out of proportion. The entire discourse I am seeing online seems to revolve around this:
> While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models
I mean... yeah? What do you expect? What else can they say? How could you prove a negative in this case? I do not want to comment on specific OAI employee chat messages, but on the actual OAI discovery here.
I can give you some context.
1. Terence Tao's mastodon explains the way this problem was solved does not in itself contribute much. LLMs (and in this case) produce massive, often unintelligible proofs that do not further understanding. It is often that in pursuit of solving these problems, many other discoveries are made.
2. There is a more serious question about scooping. If OAI is using chat data from researchers to make discoveries, essentially every researcher who chats with an LLM can get scooped. You could be 80% of your way to solving a problem, and LLM could solve the remaining 20%, and get all the credit. Years of your work could be scooped in an instant. If you're a PhD student, this is even worse. Here it's a world famous problem. But imagine you're a PhD student, working on your small but extremely career/progression critical problem, and you get scooped by an AI you talk to. No one is even going to care.
1. I know, but that is somewhat irrelevant to my question
2. I am indeed a CS PhD student (well, I am finishing now)
> If OAI is using chat data from researchers to make discoveries, essentially every researcher who chats with an LLM can get scooped
But they make very clear that they do train on this if you do not disable the setting. We can comment on the fact that this is opt out instead of opt in, but this discourse of OAI sniping the solution out of some researchers hands seems to be running on the best case speculation of the researchers having perfectly handled all their chats and discussions with other researchers and the worse case of OAI not having full pipeline control and I think that is an unfair assumption.
A very simple "these two pipelines don't connect up in our architecture, here's our internal high level network diagram combined with our data ingestion opt-out feature flag that we will stand by in court" as opposed to "yeah, we don't even entirely know how our own customer facing systems are connected to our training pipeline, but it probably didn't happen".
Have the other researchers opted out? On all their accounts? Through the entire time? And did they discuss this with anyone else? And did those people ask ChatGPT stuff? And did they disable it? If I was OpenAI, I would be very careful about my wording here when making claims of "we have never trained on any of their ideas directly or indirectly".
All very good questions that could end up in a court of law with a Millenium Prize on the line. In a competent world, these are very answerable from logs and considering the news cycle this is creating, should be able to be pulled up and made into a public postmortem in short order. You know, if it's all been above board that is. And if the researchers don't wish for that information to be public knowledge, it can be shared with the researchers promptly for a retraction of their statements lest some libel gets litigated.
Yeah, this was pretty shitty by OpenAI. Not surprising, sadly.
Them being assholes, trying to exclude an author just because he worked at Anthropic, shows the kind of culture within (that part of) their organization. The focus wasn't on supporting academics or expanding research. It was on getting great marketing.
If they had to burn millions of dollars solving a problem _that they thought was already being solved_ to do so, they'd do it.
The concept that "knowing something has been done" allows others to find the solution to an previously unsolvable problem is an old proven one. For example when Germany launched a rocket (V2 prototype) the British knew its rough trajectory and from spying it's rough size. Although they had previously believed that ballistic missiles weren't possible because no engine could provide the necessary thrust to weight ratio, given the obvious German launching of one, they went through all the known chemical compounds to arrive at the combination (Ethanol and LOX) used. [Story from RV Jones "Most Secret War"].
I’ll go back to the point about authorship. I’m Not a mathematician but I am in academia. if you are fucking around with authorship you are immediately suspect.
That aspect alone would/should be unthinkable to any serious academic. Authorship reflects who did the work and changing it for business competition reasons should be a red flag for multiple different reasons. They include, the sheer tactlessness of treating a major theoretical advancement as a competitive posturing first, the norms of academia second, and all the misunderstandings of the culture of the disciplines culture that people will now suspect are hiding beneath the visible surface (insert topography joke).
Math as a field is fairly unique even in how they list authorship. It was long the norm that authorship to be alphabetical because the idea of first, second, senior etc authorship is harder to define than many other fields.
“The stated rationale for alphabetical order is that it treats co-authorship as intellectually joint work: every listed author’s name carries equal weight, and no one has to negotiate, or be seen to negotiate, over billing. That is a genuine advantage over position-coded conventions, where disputes over who is “first author” are one of the most common sources of authorship conflict in fields that use them” [0]
That norm is changing, slowly, but one option people are pursuing is notable: randomized author order. Their is a perception that alphabetical is too biased…that’s the world OpenAI is stepping into when they make that offer of authorship to one scholar with a demand that he exclude his partner.
I can’t speak to the facts of anything else in this, but if a grad student came to me and said someone made them that offer, I would tell them to run and if they were brave report it.
Kind of a tangent, but in some fields authorship is actually more of a formal rank: once you're in the group you appear on every paper. A good fraction of the "authors" won't even know that a paper is being published in their name, and the vast majority haven't read a word of it.
In these fields it can be quite a challenge to not appear on a paper.
OAI's side of the story is that they discovered the approaches (and indeed solved problems - Euler equations vs NS equations) differed. They then offered Buckmaster lead authorship of OAI's proof, without Alpöge. But they never demanded that Alpöge be stripped of coauthorship on resolving the regularity of the Euler equations. At least that's the claim.
Occam's Razor says: "They heard this problem is solved or about to be solved amongst the rest of the other problems. They prioritized this and put substantial compute with their newest model and solved it." I know everyone loves juicy rumors, theories etc. but honestly that is the simplest and most plausible explanation given the state of AI improvement now. Obviously spending 15 million on a problem is not a slam dunk decision even for a company like OpenAI but if it has a significantly high chance of solving it and their competitor will be claiming they solved it, then it raises the stakes and they go after it. In fact this is the most rational and also curiosity-driven thing to do and totally what I would have expected from any frontier lab. Of course if one wants to prove their confirmation biases that they train on sessions or be able to identify individual users, the non-zero chance of that being also another explanation is attractive enough to wet their appetites.
There's also an elephant here in this room: Quite "coincidental" one of the coauthors worked for the competition. Of course GPT would know this fact. And OAI has every incentive to particularly monitor those accounts.-
Funny how they did not solve any of the other problems, just the one where there was already solutions to the NS with some restrictions in their chats, and their solution seems to derive from those.
They explain in the article that they eventually redirected all their resources towards NS.
Given that the solution took a somewhat “unusual” approach, I find it even more unlikely that an AI model would have come up with this on its own.
Isn't that *exactly* the type of solution you'd expect from AI?
Move 37 comes to mind.
Only if you understand nothing about the difference between LLMs and AlphaGo.
I agree with Occam.
That 100x step up from using 100 agents for Euler to 100_000 agents for Navier-Stokes, in a single day seems a bit sus.
Why is that simple or plausible? Why is simpler or more plausible than lifting an almost-finished solution from a researcher's account?
Because they solved different problems, and where there was overlap, the solutions look different?
Because it is not finished. Their follow up claim is that OpenAI’s approach looks like another proof they had been working on the side, but hasn’t published yet
OpenAI have admitted their new model they used was trained on prompts at around the time that researcher was working on it, so it seems self evident that it was used as part of the millennium solution
This is not the simplest explanation.
The most direct line from problem to proof is OpenAI building off of conversations the mathematicians had with their AI.
My take home from this entire drama is that one should not use LLM services for confidential or proprietary information as they all seem to be run by assholes. And you’re sending them everything you are doing. Would you send your lab notebook to an asshole? Hell no.
I say that as a mathematician (on paper) who perhaps surprisingly doesn’t give a crap about the problem itself.
> one should not use LLM services for confidential or proprietary information
That’s obvious, isn’t it? Just like you wouldn’t upload your confidential documents to an online spellchecker, or your proprietary code to an online compiler?
Everyone, including the security services in my country, uses OneDrive and O365.
I don’t get it.
I worked in a EU company that was developing a product competing with one of Microsoft offerings. All the code was hosted on GitHub, we used Azure for hosting and all the internal communications were in Microsoft Teams.
To me it looked absurd. But I was almost the smallest cog in the corporate structure, so I never asked what were the reasons for all these decisions.
Or use Lumo from Proton. Are there any other privacy first companies offering LLMs?
> as they all seem to be run by assholes.
Wait until you learn how many other SaaS and web 2.0 and cloud based things are also run by assholes.
You know this metaphor? https://en.wikipedia.org/wiki/Turtles_all_the_way_down
But instead of turtles, it's assholes.
But more seriously, no, none of what I wrote above is an attempt to excuse or play down the specific role of assholes in large AI companies.
Of course. I work for assholes. The thing is they’re getting out assholed by several orders of magnitude here.
That they do it is just concerning to me in that it says that home-ran models just aren't good enough. Surely researchers like this have the processing power to run them at home, they just don't have the processing power to train models of comparable level.
This is something I feared would happen and where open source would be left behind. Maybe they can do something with crowd-sourcing computational power from volunteirs. They were after all able to get Leela Chess Zero to be comparable to AlphaZero by training from volunteer processing power but it seems to me we live in a world now where the best models keep their stuff closed.
In imagine generation too. I'm not sure how well Stable Diffusion can compete in following instructions with all those advanced models that are kept secret.
Their privacy policy for normie subscribers says in plain English they use your Personal Data for research. I think it’s pretty unreasonable to use the service and expect otherwise.
My doctor's privacy policy is a bit more abusive than OpenAI's. It exists mostly because of a $%^&&* legal framework rather than malice, and I've grown accustomed to "if I don't want to die then I sign away these rights." Despite my having theoretically signed my soul away, my doctor isn't selling personal information to my exes or to life insurance companies (though they could in the US; that extremely personal information is no longer mine). OpenAI is engaging in the "technically legal maybe we'll see but obviously unintended" side of this transaction, and maybe that works out for them, but I wouldn't personally choose to be a shill for "it's unreasonble to expect somebody with 'legal' permission to do something other than the maximum 'legally' permitted" if I were in your shoes.
I think those are pretty unreasonable terms and it’s sad that you’re defending them.
Does google docs own the content of docs you make with it? Does Apple claim ownership of discoveries made using their tools?
>implying most users read them
If that is the case, why on earth would you use it in any professional setting?
Depends on your profession? I sometimes work on open source code as part of my professional duties. Nothing that goes on there is necessary to keep private.
Vulnerabilities, responsible disclosure etc?
Because it's cheap and easy. Hold for many such questions...
Why is all of the world dependent on tech an ever more hostile US? Same answer.
Aren't business consulting firms even worse? They are explicitly for business and there are known cases where they shared confidential information of one of their customers with another one.
I think it’s pretty unreasonable to use the service.
There’s an opt out so I’m blade for now.
You'd think theft would still be illegal regardless of what a privacy policy says.
> You'd think theft would still be illegal regardless of what a privacy policy says.
I wish that were true, but I live in the United States and it is 2026.
The President of the United States rug-pulls memecoin crypto and regularly pardons people like Paul Walczak (who was convicted of massive payroll fraud) in exchange for large donations.
I wouldn't make any assumptions about what is considered theft anymore, at least not when it is being committed by people who have enough money to be above the law.
> My two favourite hypothetical questions regarding this used to be:
> If I'm running Codex and one of my API keys accidentally gets consumed in the context, what are the chances that someone else might ask for an API key in the future and get mine back? (I asked someone at OpenAI once and they called this the "regurgitation" problem and assured me that they take great pains to prevent that... but wouldn't describe how.)
> If I brainstorm with ChatGPT about potential new directions for my company, what's the chance that information might be exposed to a competitor in six months' time who asks "what might company X plan to do next"?
> My new preferred hypothetical for this is:
> If I use ChatGPT to help me partially solve a Millennium Prize problem, what are the chances that my work will influence training such that a later model helps someone else solve it first?
LLM can't be trained that easily. More like actual human are checking your logs and stealing valuable things from you.
They wouldn't appear in weights but could be added to the context. My conversations regularly go "regarding your Java problem"... which was a separate item in the history from earlier. As long as I only see these (and nobody else sees mine), it can be helpful.
Or searching anonymised logs for mentions of this problem and using that as part of the context or training.
This would work just as well and have plausible deniability.
Exactly! This is the real Occam's Razor explanation.
LLMs can learn from one sample.
Maybe just a rumor of a high value target having their API keys accidentally consumed in the context...
I find the claims from OpenAI somehow more relatable and reasonable.
- They threw compute on a problem another team/company was rumored to have solved to see what their secret model could do.
- The texts I read do make it seem like OpenAI wanted to talk and share credit generously.
- Imagine working on a frontier math problem with someone at Anthropic and not only do you use Codex but also through a non-business account that allows training on your data.
- Timeline-wise, if they mainly used GPT 5.6 it's unlikely any meaningful data made it into an model that's being internally validated right now.
> ... we heard rumors that two Millennium Prize problems had been resolved. Inspired by these rumors ...
I've observed this exact effect last week. I made a discovery regarding a stepwise performance improvement in a codebase. I shared the benchmark results with a peer and within 12 hours they replicated the same. We had both been looking for this for years.
I think giving someone hope that an answer exists might as well be the same thing as giving them the answer these days. Competition is a hell of a drug, and frontier LLMs aggressively compound that energy.
> I think giving someone hope that an answer exists might as well be the same thing as giving them the answer these days.
If you read the history of major scientific discoveries, this has been the case for a long time. There are many things that were independently discovered by different people at nearly the same time. Once people know something is solved or solvable, it gets a relentless amount of focus.
Maybe that shows how scientific discoveries come to be. It's not a genius sitting alone in their chamber for a decade and then suddenly they emerge with this huge thing. That's Hollywood fiction. Scientific progress is the colaborative effort of countless researchers over long periods of time, communicating, exchanging ideas, many of them wrong, tweaking, trying, thinking, arguing. When a breakthrough happens then it's the tip of a mountain of work that came before it. If two individuals stand on that mountain and feel there is something somewhere then it's not too strange that they take the last step at roughly the same time because conditions were right. The preconditions were in place at that time, the results required for this were available and the focus was on this specific thing.
I'd say it illustrates well that this last piece, the person celebrated for the achievement, is disproportionally overvalued and the rest of the work they are standing on is disproportionally ignored.
This ties back into AI, too, right?
I always like to bring up how many decades of research, how many hundreds of years of entire PhD-theses, how many sleepless nights were used up to generate all the protein structure data that made up the corpus of Protein Data Bank - that was then hovered up by the AlphaFold team, and guess who got the Nobel Prize...
Just like all non-AI related nobel prizes before them.
Every laureate stands on the shoulder of giants which is their field as a generations-spanning body of researchers. They did the last step and get recognized. The good ones acknowledge that in their acceptance speeches.
This seems to be the norm rather than the exception.
On a tangent, the genius of people like eg Einstein is not so much that he came up with all these things: other people were close, but that he was a singular individual that did all of these discoveries, instead of five different guys all making some breakthrough here or there.
It's something that happened before LLMs - multiple discovery. Calculus is a classic example.
Yes but in this case, the allegation is Leibniz literally looked into newtons notebooks
If you believe the totality of the document, there was more shadiness in how OpenAI acted than just timing. Save other things they are accused of, the progression from rumors to replication would attract much less scrutiny. With those in mind timing begins to look suspicious at best.
Would anyone be surprised if major model companies had tagged the accounts of competitor employees for extra tracking? Given the concerns about distillation and bench marking it hardly seems irrational, but how it is used matters quite a lot.
I really should get up to speed with LEAN, I know AI can probably write it better than me but I'd like to grasp it better still...
>While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models.
What do you mean, as OpenAI employee, you cannot tell that his work has entered the training data ?
But also correct me if I'm wrong, if the two mathematician were really close to finish this problem, and their conversation were used by OpenAI, shouldn't the Agent have succeeded way faster/efficiently instead of using "4.9 million messages and used about 300 billion output tokens."
I’m not a mathematician so take this with a grain of salt. Apparently terry tao commented that the approach used for the Euler paper can “probably” be used for solving NS but it’s still technically challenging and can probably be done with an LLM with a lot of compute. To me the crux of the issue is whether the insight were stolen so that the problem becomes something that is in the domain of LLMs. This is much different than LLMs coming up with the insight. OpenAI wants everyone to think the LLM came up with the insight and solved the thing by itself even though they have perhaps an army of researchers.
Having the chat logs enter the training data and having them have a meaningful influence on the ultimate result the model produces are very different things.
The text for all the Goosebumps books are certainly in the training data and to some small amount influenced the solve. But their contribution was so vanishingly small it would seem absurd to say R L Stein should have recourse for contibuting to the solve.
But this is different, right?
The equivalent would be taking a (fully offline) LLM and asking it about the ending of one specific Goosebumps book, and it revealing the twist. And although that specific book was (probably) only once in the training data, a high parameter LLM can usually "remember" the twist.
It's almost as if it was actually found by manually written brute-force algorithm running on OpenAI's massive computer cluster.
What's missing from the story I think is the part about "...then had a breakthrough on August 15th. The mathematical rumour mill kicked into gear...". If only the two of them were working on the problem in secret, how did their breakthrough become a rumor?
People talk. If you have a breakthrough solving one of the most famous problems outstanding, you're going to tell people.
You'll say, "Don't tell anyone", which they will ignore because they get a rush and perceived status by sharing it. So then they tell someone, along with "Don't tell anyone", etc.
It's a small enough world (both in academic math, one at Anthropic) that you get to OAI in very few hops.
"The route to the Clay problem through a smooth force, options c and d in Fefferman’s statement of the problem, is the route Luis and Diego opened and the one Levent and I had quietly chosen to attack. Almost nobody else I know of was working on it. It is not the direction one arrives at in a few days by giving a model the problem statement. When I heard “forced,” it was a bright red flag."
We know that OpenAI trained on their prompts, plagiarism is incredibly likely. The only thing we don't know is whether or not it was deliberate plagiarism yet
The reality is also that most of your colleagues have no interest in stealing your work: they have their own work to do anyway, and having a colleague effervescing about whatever they are working on is kind of the norm in pure research. Just because they are making progress it doesn't mean they are about to do anything interesting.
Also it's not like you're looking for a lost pair of car keys: just getting to the level where you can understand a problem well enough to "steal" it takes a huge amount of work. People are going to know if you're at the level where you could be a competitor.
So in general it's pretty safe to talk generally about whatever you're doing.
They weren't working in secret?
Having in mind the allegations by Apple against OpenAI, I don't find it unthinkable that there could've been some form of misconduct happening there.
LLM’s seem very good at solving mathematical problems of which there is an enormous amount of exisiting work/attempts in their training data. This is an amazing capability, but does not convince me that these models are «thinking» or «reasoning» in the way a human does. A human mathematician could in theory categorize/discover an entirely new field of mathematics tomorrow, based purely on their «human intelligence», I wonder if we will see similar examples by LLM’s soon. It seems to me currently impossible that LLM’s can replace human mathematicians, because of their (assumption) likely dependence on human input in the sense of enormous amounts of pre-existing attempts/data.
If an entirely new problem, within a new field of mathematics were to appear tomorrow, I highly doubt an LLM would be useful at all on their own. Is this the «ultimate ASI test»?
This is also what I've been thinking. The result itself is amazing but it's not like this was completely unexpected. There has been a huge amount of progress on the problem in the last 10 years without which it seems unlikely today's full resolution would have been possible. It is not clear what strategy was taken but it sounds like it borrowed heavily from the two spanish mathematicians. Experts will scrutinize the proof and it will be interesting to see if anything truly original or unexpected was done, outside of known techniques, a move 37.
Extreme temperature levels (>2.0) can push a GPT of its manifold, essentially producing predictions barely distinguishable from random noise (it flattens the probability distribution of the next token). In theory this could predict anything including the next field of mathematics (infinite monkey theorem) but realistically that would never happen.
However, how to we know the next field of mathematics isn't a novel combinations of several other sub-fields? That level of mathematics would be indistinguishable from magic to most people and so in their eyes the GPT did something truly inventive.
A lot of what humans do is combining old ideas.
And an LLM could in theory also stumble upon entirely new ideas: there's randomness in how they generate their reasoning and answers after all.
I suspect that we are seeing a lot of advances coming from the combination of existing but somewhat obscure knowledge coming from LLMs at the moment, because LLMs are really good at this. At least compared to humans.
Even before our AI friends became good, they were already known for having read approximately every paper and every textbook published in any language. You only need to increase intelligence a fairly small amount from there to get to something like the 'convex hull' of human knowledge.
Compare https://slatestarcodex.com/2016/11/17/the-alzheimer-photo/
The gist is that basically whenever anyone comes up with a new method you get a big burst of activity of picking up all the now lower hanging fruit, that was previously out of reach.
It could be much worse.
OpenAI can easily identify these outstanding human behind their accounts. Human in OpenAI constantly check their logs for breakthrough. When they find something interesting, they brute force the result using their massive computing power.
No LLM is even needed.
Or, using anonymised data, search for anyone seriously trying to tackle this problem - probably about 10 people in the entire world and use their ideas as a starting point. They wouldn’t even need to be watching specific accounts or using de-anonymised data if they know what they’re looking for.
> The discovery is somewhat overshadowed by accusations of skulduggery from Tristan Buckmaster [...]
I think you should not say that. Buckmaster did only state his version of events and was very clear on that he did not make any accusations at all.
To quote from his statement pdf:
> I am not accusing anyone of anything.
Hah, what is the infrastructure which takes user sessions (chats with API keys, directions, navier-stokes math/progress) and regurgitates this into pre-training, RL, fine-tuning data? Or better, in-context data?
People talk about the "compute" but what about the "storage"? Is storage exponentially greater, or soon to be, than the compute? Is the storage going to slow down growing to some constant rate, i.e. all people on earth using chatgpt, or no, on the contrary, it will keep growing?
If there were any shady business, I do not condone it, but technologically we are not there yet for said shady business to happen.
AI companies use heuristics to filter sessions, then llms to further filter, then use various techniques too anonymize the session, then process it and add it to various datasets for further selection and refinement. they don't need huge storage for this.
what is behind "process" it and "further selection" and "refinement" and how big are these "datasets"? These companies ship the encrypted session to you not because they want to.
I agree that they have pipelines for what you are describing but how effective they are at scale and at focusing is the question.
I always thought it was enough to switch off the "Improve the model for everyone" setting on chatgpt.com:
"Allow your content to be used to train our models, which makes ChatGPT better for you and everyone who uses it. We take steps to protect your privacy."
But apparently there is also an entire completely different route "Do not train on my data"?
Does this mean that before I submitted the "Do not train on my data" request, my data was used for training in spite of "Improve the model for everyone" being turned off?
We are getting to facebook/meta-levels of privacy settings obfuscation.
That's because what people enter into LLMs is the last gold there is out there. Everything else is already scraped or ensloppified.
Maybe next step is to filter your input client side through an unknown number of obfuscators where you ask LLMs to rephrase your question (onion router idea) such that no single provider can be certain that this is human input and not some slop feedback loop.
LLMs can't contribute good code to some of the good OSS math libraries, How is it even solving these problems?
No one knows if it's actually LLM doing the heavy weight. It could be just human written brute force algorithm running on their massive computer cluster.
That's a good question.
It is able to contribute code, but maybe not good code.
It's the same in math: it's able to solve problems, but not necessarily in a good way with a human readable code.
Math papers are a lot like software:
- theorems are like API
- lemmata like internal/private function API
- definitions are like types
- the proofs are the implementation
The proofs of ChatGPT are not necessarily readable or maintainable.
You don't need a GOOD proof, just A proof.
search, verifiability and compute
It’s reasonable to wonder about what chat usage data gets into models (to be honest probably quite little - carefully curating training data and creating higher quality synth data seems to be the current approach) and the implied risk to privacy and creativity (every new patent filed this year probably touched a model before filing).
What I cannot reconcile is the timeline and the concern in this specific case.
I don’t think training pipelines are anything close to the level of continuous training needed to incorporate Aug 15th ideas into a model that generates a breakthrough early Sept. Either OpenAI nakedly had someone with mathematical understanding dig into a specific user’s chats (a massive red flag) or this really is poor handling of a more classic parallel discovery situation (with one party clearly having worked on it longer)
I would assume a lot of codex data goes back into training. A well steered session is extremely valuable data.
Use Bedrock or any kind of big tech hosted version of the frontier labs can be a solution. I think if secrecy is of utmost importance to you, then do not send data to first parties
If they wanted to solve a Millenium prize problem so much, why did they not try to solve P-NP instead? It boggles the mind.
That one is way more difficult.
I don't get the sales pitch, spend 15 million dollars to win a 1 million dollar price?
Showing of the model's capabilities - okay, but it's not like it solved the problem on its own, and apparently not particularly efficient. Are there practical applications that justify the investment?
The answer to this is obvious: the effect on the multi-billion dollar valuation in the imminent IPO.
How is that any different from any other academic research? Every PhD candidate solves problems essentially nobody cares about. They don't even get $1M, they get nothing.
There are two benefits though.
One is recognition. Cred. The PhD candidate gets to put a ", Ph.D." behind their name, opening doors to future academic employment or other endeavors where people value titles. The AI lab gets to say their tech solved sth that humanity wanted bad for a long time. Both cases with substantial financial upside (higher income for Mr. PhD and higher company valuation for the AI lab).
The other one is that this is how scientific progress works. $1M or not. That number was just a PR campaign by the math community to point to some goals. It's clear that it would cost more than $1M to get there.
Maybe someone on the NYU team forgot to opt out of “improve the model for everyone”.
Well OpenAI wants to maintain its edge and solving a Millennium problem is of course fantastic PR, and given that Anthropic seems to be working on that it’s not hard to imagine they wanted to be first. I don’t get how people buy into that whole “oh we heard models can solve Millennium prize problems now so we thought why not give it a go…” story - it’s a bit funny. This whole AI bubble is about hype and solving such problems is probably one of the best ways to keep this hype up so you can safely assume both OpenAI and Anthropic are using significant resources on these areas.
Basically no new info here, not really sure why this post needed to be written tbh.
On the contrary, a level-headed summary that gathers information from all the different sources is necessary.
Sounds like a great use case for an LLM
They are busy generating pelicans on bicycles. The summary therefore needs to be written by a human.
(This was a joke. I value Simon's role in the community.)
Agreed. This person just loves to shill their blog and their pelican benchmark.
There’s no shilling here. The person who posted the link isn’t the person who wrote the blog.
He is in the HN ingroup of whenever he posts a blog, a prominent HN mod or poster with 1 billion karma will inevitably post it, and so it goes.
It's sort of like the music industry.
Does poster karma boost the post in any way?
I don't believe HN boosts their algo but I think people recognize posters like celebs. I recognize tosh, simonw, etc.
Also it's an easy way to farm karma. The pelican guy posted a pelican, let's upvote it? It's at least a bit of psychosis / mass effect in this.
For what it's worth, I like the pelicans but just making an observation
> It's at least a bit of psychosis
I know semantic shift is inevitable, but can we put the brakes on this one at least a little? This doesn't even connect to the original meaning.
Yes, you have ability to get information very quickly, but not everyone does.
I know that this may be somewhat dramatised and even infantile, but my reflection is that in the world run by these reckless AI companies everyone looses. Navier-Stokes is solved but it feels like no one has won anything, controversy prevails, there is no glory in the math breakthrough. There is hardly anything to cherish, and even the guys at the top of it in OA who sit on the (supposedly) superhuman intelligence come across as massive losers and frauds.
Can’t wait to see the human verifying the results and then figure out that the AI model actually cheated and the results are not correct.
the proofs were verified in Lean, so unlikely.
But still possible [1].
[1] https://leodemoura.github.io/blog/2026-8-1-postmortem-for-ke...
As long as the proofs itself are correct. How long they were this time?
Edit: at least ~600,000 lines
https://stanfordtechreview.com/articles/openai-buckmaster-na...
To claim something verified in Lean is wrong, you need to either argue that the theorem was stated incorrectly, or that there is a bug in Lean (assuming no `sorry` etc, which is checked by comparator). The number of lines needed to prove it is irrelevant (other than checking for a bug in Lean gets harder).
I run a small SaaS[1], like so many others, that uses AI to generate and optimize SQL. Getting this to perform optimally has been a lot of work and now I wonder if OpenAI is outright stealing this knowledge, which without a doubt is highly valuable to them.
[1]: https://www.sqlai.ai
SQL is so ubiquitous and the use case so obvious, there's no way they have not already been tracking performance and benchmaxxing on SQL queries for years.
But I don't think openai will bother to release a competitor, the real threat is that anyone with a decent LLM and a harness to try a few queries will land at the same or a better query within minutes.
It's extremely unlikely that there is anything interesting or novel in the optimisation of a small SaaS SQL
I mean this is just the logical end game. The company/ies that control the uber mind will devour ALL useful or valuable work. Unlimited intelligence at unlimited scale means the value of humans for knowledge work goes to zero. We will eventually not have meaningful access to the uberminds because we will be pointless. At which point our Silicon Valley luminaries will really have no choice but to extinguish as many of us as possible for the greater good. After all if we are all pointless then that has to be weighed against are cost to Mother Earth. Clearly the only moral solution is to cull or allow to be culled some 98% or so down to a more sustainable, manageable population of curiosities. The end game of ai is the remnants of humanity in a zoo, and that’s if humans control the outcome… the machine minds might be more charitable as they would be less afraid…
I think this drama was blown up a bit out of proportion. The entire discourse I am seeing online seems to revolve around this:
> While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models
I mean... yeah? What do you expect? What else can they say? How could you prove a negative in this case? I do not want to comment on specific OAI employee chat messages, but on the actual OAI discovery here.
I can give you some context. 1. Terence Tao's mastodon explains the way this problem was solved does not in itself contribute much. LLMs (and in this case) produce massive, often unintelligible proofs that do not further understanding. It is often that in pursuit of solving these problems, many other discoveries are made. 2. There is a more serious question about scooping. If OAI is using chat data from researchers to make discoveries, essentially every researcher who chats with an LLM can get scooped. You could be 80% of your way to solving a problem, and LLM could solve the remaining 20%, and get all the credit. Years of your work could be scooped in an instant. If you're a PhD student, this is even worse. Here it's a world famous problem. But imagine you're a PhD student, working on your small but extremely career/progression critical problem, and you get scooped by an AI you talk to. No one is even going to care.
1. I know, but that is somewhat irrelevant to my question 2. I am indeed a CS PhD student (well, I am finishing now)
> If OAI is using chat data from researchers to make discoveries, essentially every researcher who chats with an LLM can get scooped
But they make very clear that they do train on this if you do not disable the setting. We can comment on the fact that this is opt out instead of opt in, but this discourse of OAI sniping the solution out of some researchers hands seems to be running on the best case speculation of the researchers having perfectly handled all their chats and discussions with other researchers and the worse case of OAI not having full pipeline control and I think that is an unfair assumption.
A very simple "these two pipelines don't connect up in our architecture, here's our internal high level network diagram combined with our data ingestion opt-out feature flag that we will stand by in court" as opposed to "yeah, we don't even entirely know how our own customer facing systems are connected to our training pipeline, but it probably didn't happen".
Have the other researchers opted out? On all their accounts? Through the entire time? And did they discuss this with anyone else? And did those people ask ChatGPT stuff? And did they disable it? If I was OpenAI, I would be very careful about my wording here when making claims of "we have never trained on any of their ideas directly or indirectly".
All very good questions that could end up in a court of law with a Millenium Prize on the line. In a competent world, these are very answerable from logs and considering the news cycle this is creating, should be able to be pulled up and made into a public postmortem in short order. You know, if it's all been above board that is. And if the researchers don't wish for that information to be public knowledge, it can be shared with the researchers promptly for a retraction of their statements lest some libel gets litigated.
If they have zero-retention, then it is not possible.
So what they are saying is that they don't have zero retention.
But why is this news? This is clearly described in their ToS and in the Settings to improve their models.
Yeah, this was pretty shitty by OpenAI. Not surprising, sadly.
Them being assholes, trying to exclude an author just because he worked at Anthropic, shows the kind of culture within (that part of) their organization. The focus wasn't on supporting academics or expanding research. It was on getting great marketing.
If they had to burn millions of dollars solving a problem _that they thought was already being solved_ to do so, they'd do it.
Time to repeat the same angry mob style discussion again. Great job to the mods.
All your datum are belong to us!
The concept that "knowing something has been done" allows others to find the solution to an previously unsolvable problem is an old proven one. For example when Germany launched a rocket (V2 prototype) the British knew its rough trajectory and from spying it's rough size. Although they had previously believed that ballistic missiles weren't possible because no engine could provide the necessary thrust to weight ratio, given the obvious German launching of one, they went through all the known chemical compounds to arrive at the combination (Ethanol and LOX) used. [Story from RV Jones "Most Secret War"].
Is there a good tldr on this topic?
This article is the tldr.
I’ll go back to the point about authorship. I’m Not a mathematician but I am in academia. if you are fucking around with authorship you are immediately suspect.
That aspect alone would/should be unthinkable to any serious academic. Authorship reflects who did the work and changing it for business competition reasons should be a red flag for multiple different reasons. They include, the sheer tactlessness of treating a major theoretical advancement as a competitive posturing first, the norms of academia second, and all the misunderstandings of the culture of the disciplines culture that people will now suspect are hiding beneath the visible surface (insert topography joke).
Math as a field is fairly unique even in how they list authorship. It was long the norm that authorship to be alphabetical because the idea of first, second, senior etc authorship is harder to define than many other fields.
“The stated rationale for alphabetical order is that it treats co-authorship as intellectually joint work: every listed author’s name carries equal weight, and no one has to negotiate, or be seen to negotiate, over billing. That is a genuine advantage over position-coded conventions, where disputes over who is “first author” are one of the most common sources of authorship conflict in fields that use them” [0]
That norm is changing, slowly, but one option people are pursuing is notable: randomized author order. Their is a perception that alphabetical is too biased…that’s the world OpenAI is stepping into when they make that offer of authorship to one scholar with a demand that he exclude his partner.
I can’t speak to the facts of anything else in this, but if a grad student came to me and said someone made them that offer, I would tell them to run and if they were brave report it.
[0] a to the point lay description of the history of math authorship can be found here: https://casrai.org/guides/mathematics-alphabetical-authorshi...
Authorship is weird.
Kind of a tangent, but in some fields authorship is actually more of a formal rank: once you're in the group you appear on every paper. A good fraction of the "authors" won't even know that a paper is being published in their name, and the vast majority haven't read a word of it.
In these fields it can be quite a challenge to not appear on a paper.
OAI's side of the story is that they discovered the approaches (and indeed solved problems - Euler equations vs NS equations) differed. They then offered Buckmaster lead authorship of OAI's proof, without Alpöge. But they never demanded that Alpöge be stripped of coauthorship on resolving the regularity of the Euler equations. At least that's the claim.
https://xcancel.com/SebastienBubeck/status/20973794116915163...
Yes, there is no doubt about scientific misconduct. I think the entire math community agree on that.