I think it can both true that 1) OpenAI is being inconsiderate/harmful/ with their math releases, and 2) there is now a treasure trove of mathematical results ready for the taking.
Yes, the situation sucks overall and mathematics as a whole is in a turbulent time now.
But it also sucks when mathematicians, who are considered experts on a particular problem, refuse to engage with breakthrough results about that problem. #2 above is still true regardless of where it came from or how hard it can be to absorb.
While reading "The Mathocalypse" post [0] by Scott Aaronson, Scott described his wife Dana's reaction to one of the newly solved results in her primary domain of expertise, on which she'd been working for decades.
After her initial shock, and annoyance with the format/style, she decided to start using Astra - for the first time - to help her understand the new result. And he reported in the comments that she had made a lot of progress understanding it in one day, and may be even excited to give a talk about it!
That seems like a much healthier attitude towards these new results.
Yes, everything else sucks about this messy period. But there are still diamonds (in the rough) in this drop that perhaps should be looked into. If the author is too busy, perhaps one of their students can take a look? Someone will, eventually.
Let us not forget that there's much more to this than just OpenAI being lazy and incompetent and willingly ignoring the high standards that researchers usually holds themselves to.
There's also the case of ethical violations, straight up scientific misconduct, as when OpenAI steals results of others (their customers) and present them as their own.
> The result is also contained in a paper [8] released by OpenAI on October 6, 2026, in which the proof strategy and specific choices of notation are identical to a preliminary version of the present paper that was uploaded to ChatGPT on September 8, 2026.
Of course it's hard to say what to make of that without knowing what exactly went into the machine, but it certainly looks bad. And there's obviously a non-zero probability that it is indeed another instance of plagiarism, given that that's how they operate.
In this case, the author is a grad student, so what we're looking at is a company willing to steal from a student, ignoring whatever impact that could have on their career prospects, for a tiny piece of marketing material.
So... If you use ChatGPT for anything of value, including abstract stuff like obscure maths problems, you should assume that at some point in the future OpenAI will include that in their training set and sell it onto other people.
But this is even worse, because there is no way that OpenAI "trained" on this data between September 8, 2026, the date Chenglong Ma uploaded the paper to ChatGPT; and October 6, 2026, the date that OpenAI released a paper with "identical proof strategy and specific choices of notation" (Ma). That's one month, that's not the timescale for model training.
So this implies _not_ that OpenAI is training on user input, in the conventional sense of adjusting weights; but rather that they are *straight-up channeling ideas from user input*, and with a very short lag. You would think there would be about a million controls to prevent this.
This is next-level alarming. I would be very interested in knowing whether Chenlong activated the privacy (do not train, etc) options in ChatGPT, and any other details of their setup (which plan, etc). Also, note that "do not train" might be, in a lawyerly sense, considered by OpenAI to be strictly about weights, and not covering "we hoover up your results and regurgitate them".
Impossible to know without either more information about the actual occurrence or a deep understanding of the person making the claims. Just as OpenAI could easily be acting improperly, researchers who (understandably) feel deeply attacked by their problem getting solved right from under them might not be the most unbiased, either.
At this point I would be surprised if internal sandboxes are not trivially by-passed and that openai's agents do not (at the very least) have complete read access to all user accounts, chat histories and uploaded documents. Orthonogally, openai could still be wholesale lying about not training on this user data, of course.
> But it also sucks when mathematicians, who are considered experts on a particular problem, refuse to engage with breakthrough results about that problem.
It might be a shock for you but they are very few in numbers. Most of researchers I know are always busy with something. They cannot just drop other responsibilities for something like this. They will take their own time getting through the proofs (if they want to).
> That seems like a much healthier attitude towards these new results.
Another thing to consider is not all mathematicians are from US or with good funding. The PI or graduate students cannot afford to pay 200/month.
I think the role of specific _human_ mathematicians at OpenAI should not be understated.
TFA was about a niche topic that OpenAI doesn't have in-house expertise in.
Otoh Aaronson is the co-author on Lijie Chen's (reasoning lead at OAI) top cited paper. OAI have deployed their resources more effectively against UGC that some of their staff are already familiar with
That's true. OpenAI has some of the best talents. In my experience, domain experts get the most benefits from the models. They can work much faster, catch false positives, and stir the model in right direction.
I wish they take a bit of more time to communicate the findings effectively.
There are good reasons not to delay publishing at all:
> They should release all their results immediately. (Imagine working on one of the problems they already solved.)
This is the most popular answer to a question regarding AI advisory group and immediate access on a popular website for professional mathematicians: https://mathoverflow.net/a/515442/473286
The whole debate regarding the behaviour of OpenAI is a red herring. Mathematics need to redefine their profession and how they work (like us software developers too). There are very good reasons to believe mathematics has an important role to play. If they could just stop talking about OpenAI and get back to work - they are very much needed, in particular now!
> If they could just stop talking about OpenAI and get back to work - they are very much needed, in particular now
This kind of phrasing sounds particularly empty. We are not in WWII researching the nuclear bomb. What are they so urgently needed for to drop everything and work on understanding openai's proof on partition principle and axiom of choice?
Where did I say they should "drop everything"? I hope to read more about how they envision their future, instead of all this regretting and whaling how one company (that I very much dislike too) published a large amount of proofs. They will get more of them, very soon - if they like it or not -, and I wish that would be the primary subject of the discussion. And yes, I'd also hope they engage with the published proofs. The more raw these proofs are, the better. If AI companies start selecting mathematicians to write nice expositions of their proofs, this is doomed to become a very elitist science.
Says who? The professional mathematician writing the article disagreed. Why should people start dancing the tune that openai wants to play for their own reasons and interests? And I do not see how taking the time and effort to write a proper exposition makes it "a very elitist science" when this exact effort and time is needed to actually get other experts understand and build on a result. Unless you equate spending time and effort learning math as "elitism", which is the ai-shilling moto some time now with everything time and effort related. I cannot see how spending time and effort to understand a field and then spend time and effort to make a proper exposition so that other people can also understand it as "elitist" vs throw everything out there "in raw form".
This was my opinion. But anyone arguing to publish "results immediately" is likely to imply something like it. I guess in chemistry we have the situation you envision - for different reasons: Laboratories holding back their data, until their scientists have published their papers or developed their products. There is a real danger AI companies will do something similar too.
Who do you expect they will select for the exposition?
On which basis do you want a (likely US based) AI company to decide who is to untangle a proof that their latest internal model has just spit out?
Do you expect this to fall to an aspiring, but still unknown mathematician at - say - the mathematics department of Nairobi university?! This is what I meant with my rather unclear "elite" reference: The first publication will always show the name of a mathematician already known to the field, more likely than not to come from the same country as the company ("Our message ... is, you’re a great American company, but you’ve got to hire great American workers"). Are you not worried at all? Don't you think it would be good, if anyone in mathematics had a chance to write that first paper on a new proof?
There are parts of mathematics where the result is the important part. That's not what we're seeing here. Knowing whether the partition principle implies the axiom of choice doesn't meaningfully shape downstream knowledge and decisions. For these more foundational problems, clever proof techniques and the exposition around them are literally the point. Without that, neither humans nor AI can take this slop and derive anything useful.
And if AI can do that then great. I don't care about being elitist or not, and I'm fine with AI taking over math. That's not what it's done though, at least not yet.
> Mathematics need to redefine their profession and how they work (like us software developers too). There are very good reasons to believe mathematics has an important role to play. If they could just stop talking about OpenAI and get back to work - they are very much needed, in particular now!
The root issue is OpenAI et al.'s thoughtlessness in their engagement with a field.
OpenAI has resources.
That they fail to allocate enough of those to cleaning up pre-print papers (that seem to be a corporate PR priority for them to release) so they can be consumed and engaged with by the field they're targeting is... acting like a jackass?
It's the same "Meta / Alphabet can't vs won't hire more human reviewers" problem.
OpenAI could, at an immaterial salary level to them, pay a ton of PhD students and mathematicians just to clean up their proofs and papers.
Not doing so is a leadership and financial choice.
I'd prefer AI companies don't decide who's "cleaning up pre-print papers". This should remain the job of mathematicians at universities, which I am happy to pay with my taxes. Ideally there was something like a Bermuda Principles declaration for mathematics (https://en.wikipedia.org/wiki/Bermuda_Principles). This gave mathematicians even at poor universities and beyond the chance to participate in mathematical progress.
What do you think is gained, if AI companies manage "cleaning up"? Tax money?
No one is "responsible". If the paper is read, depends on the interest of the individual scientist. Many mathematicians interested in a theorem proven/disproven in a new AI paper will be curious, even if it is utterly cumbersome to extract the relevant line of thought. But don't you agree that the job can only be done by a mathematician anyway - be she/he paid by the company or by a university?!
What you are arguing for is somewhat like the "proprietary period" in astronomy (e.g. see https://www.scientificamerican.com/article/nasas-plan-to-mak... ). But there is no mathematician who asked for the run of AI, i.e. there is no mathematician, who can be regarded as the owner of the result, even if ownership was temporary. Complex proofs might take years to explain in detail (think of the ternary Goldbach problem, https://en.wikipedia.org/wiki/Goldbach%27s_weak_conjecture ). It's just unfair, if AI companies held back with their data until a proof has been put into a nicely readable article, and at the same time mathematicians elsewhere are spending all their time trying to solve it.
I'm not. To the extent my taxes are used for maths research (which is minute as a proportion of them) I want them to be used for the development of human mathematical understanding, culture and education, not trawling through a mountain of slop mechanically generated by a Silicon Valley startup that's about to IPO for trillions in the hope there might be some nuggets of insight hiding in there. If the latter is an activity with some value the startup in question can pay for it.
Paraphrasing Hardy, 'exposition is for second-rate minds'.
I don't believe this myself. But I do believe that if you've formed your very ideas about what is good and desirable on the basis of a culture that has held certain values dear for hundreds of years, and have fought against every doubt and difficulty in life for decades to mold yourself into that image, that it does not 'suck' that you are unable to adapt to a new reality overnight.
Very few people that would love to be craftsmen would love to be factory foremen. It is far too insensitive to the human experience to expect people to just deal.
> Paraphrasing Hardy, 'exposition is for second-rate minds'.
Forgive me if I have no sympathy for current mathematicians who think this way. It's a pretty ugly kind of arrogance.
Some people told themselves they were the pinnacle, the first-rate mind, as opposed to all the second-rater. Well guess what, now your first-rate mind is a commodity and exposition is more valuable. They'd better learn to live with it.
> Forgive me if I have no sympathy for current mathematicians who think this way. It's a pretty ugly kind of arrogance.
Thankfully, very few mathematicians share Hardy's opinion, just as very few share his opinion that "mathematics is a young man's game" (and indeed we now have prizes like the Abel Prize with no age limit).
In fact, many of the greatest mathematicians throughout history have taken exposition very seriously, e.g. Euclid, Euler, Lagrange, Cauchy, Dirichlet, Kolmogorov etc. all wrote textbooks. Many mathematicians today carry on that tradition of taking exposition seriously and write books and freely share their lecture notes.
So we should not take Hardy's opinion as representing the opinion of all mathematicians or even most mathematicians. In fact, Hardy's statement is somewhat self-contradictory since he himself wrote several expository books (e.g. "A Course of Pure Mathematics").
His statement was self-deprecating (in a not so endearing way), as he was referencing his young self as one of those first-rate minds but his current old self doing exposition as second-rate.
I know that it was self-deprecating, but that doesn't redeem it much to me. I also still find it pretty contradictory/ironic because he wrote A Course of Pure Mathematics when he was in his early thirties.
A first rate mind communes with mathematics not matter terrestrial. An Uber-Erdos entering to lecture is indifferent to the auditorium as well as it's contents. He's at the board silently communicating new mathematics. Not for himself, not for you, but for mathematics.
It turns out your an expendable commodity, and my computer system is predicting that society will profit from your annihilation, really sorry about that and I hope there's no hard feelings.
To make an analogy: math is like OSS. You are free to fork and create your own feature, maintainer has no obligation to look at your PR and improve it to a state that’s worth merging.
Eg This happened to Zig where the Bun team maintained a fork for a while. And I’m sure there’re countless examples.
An instance of a maintainer finding a PR so interesting and keep digesting it and eventually even give a talk about it, is irrelevant. It is entirely their free will to do so.
I think people sometimes overestimate what proving something means. For example the 4 color problem was a computer assisted proof from a long time ago. I doubt that many people actually read the proof even though leafing through the book is kind of fun if you can find a copy. Another example is the Kepler conjecture on sphere packing. The peer reviewers said they couldn't vouche for its correctness. While reviewers are volunteers with busy schedules, a lack of interest surely was part of the issue. This motivated Hales to formaly check his own proof (Gonthier formalized the four color problem earlier). But you can be sure that no one had been waiting for the Kepler conjecture to be proved in order to pack their spheres. Otoh, at the opposite extreme, the proof of FLT greatly advanced the field because the modularity theorem behind Wiles' proof is at the heart of a great section of math theory.
> there are still diamonds (in the rough) in this drop that perhaps should be looked into.
Right, and for some reason that "trillion dollar" company with mathematicians on staff didn't "look into" their own results. Almost like they don't care about engaging with the actual community they're dumping on.
> Part of me would like a little longer in the world where the problem is still open and I am still looking for its solution. But reaching a summit, even by someone else’s route, comes with a view. From here I can see new mountains, and I look forward to climbing them with my students, collaborators and the machines.
> But it also sucks when mathematicians, who are considered experts on a particular problem, refuse to engage with breakthrough results about that problem.
For me the problem is that rigth now the structure of incentives that has been built (e.g. you publish more = you get a grant; good exposition < solving a conjecture) is now broken. So, for instance, you would be very irresponsible if you throw your student into one of those AI papers, it's too much the risk. This part is mathematician's responsability, they need to change this incentives structure.
In any case, OpenAI is being a dickhead here. They throw millions of dollars at these problems, but they can't afford basic literature reviews (the drafts barely cite previous work)? Or checking that Lean's formalizations really correspond to what they claim to prove (even for Navier-Stokes they made this mistake)? It's obvious that for them this is just a PR stunt.
Why are you two-siding this. There is only one misaligned agent here and that is the company OpenAI. What OpenAI is doing sucks, and people are calling OpenAI out for sucking. However mathematicians (the main victims of OpenAI’s lousy behavior) behave is not the issue here.
If some mathematicians complain about OpenAI sucking, that is fine actually, and if others are more “mature” about it, that that is fine too. Neither of these reactions should be at put as an equivalence to the blame OpenAI deserves for this stunt.
Are they fully “doing math” in an aligned way though? They are finding mathematical proofs, which is important, but contextualizing results in the prior literature and clearly communicating the approach and implications is just as much part of mathematical research.
You might argue that these aspects of math are less important in the new AI accelerated math world, because agents will inevitably be smarter than humans, but I think clear framing and communication is even more important than before because with this technology we can choose to augment our intelligence instead of defer it
> Are they fully “doing math” in an aligned way though?
No matter what you think "aligned" means here, publicly releasing advancements in a scientific field shouldn't be gatekept or be perceived as misaligned in any possible way.
If anything, the true misalignment comes from people trying to prevent these advancements from happening or being disclosed, or putting research behind BS paywalls.
I don‘t think this is gatekeeping. OpenAI is filthy rich, they can afford to pay mathematicians to go over these papers and produce results consistent with the scientific method. The fact they don‘t is evidence of their misalignment. Their motives are obviously ulterior and have nothing to do with advancing knowledge. If they were they would adhere to the scientific method, hire experts, and produce reproducible results.
> Putting research behind BS paywalls.
Yes, that is gatekeeping. But there are more then one way to be misaligned. And the practice of publishers is not being discussed here. No need for whataboutism.
Do other researchers or research institutions “pay mathematicians (other than those in their employ) to go over these papers” or do they simply publish their work for review as part of producing “reproducible results”?
AI may be taking over Mathematicians’ jobs, along with everyone else’s, and it’s OK to hate that, but the scientific method says nothing about that, or hiring experts, or ulterior motives, or “being filthy rich.”
OpenAI has not released the model for replication (not even to select few), they have not even shared the prompts. All we have is their results and their compute times (which may even be lies). This is the opposite of the scientific method. The scientific method asks for falsifiability and replication. If a hypothesis is not falsifiable, it is not scientific, if results cannot be replicated, they are not scientific. OpenAI fails on the second account here.
Not being scientific is excusable in some context. A high-school prodigy might stumble upon an unexpected result that they had a hunch about, but fail to make those results replicatable. Similarly a YouTuber with under a 1000 subscribers might propose an unfalsifiable hypothesis as entertainment. Being scientific takes skill and effort, and it is not free, so we can’t expect anybody to follow the standards. However OpenAI claims to be a trillion dollar company, and they do not have the same excuses as a small YouTuber or a high school student. OpenAI absolutely can afford to follow the standards, if they claim to be furthering the human understanding, then they should be scientific about it.
I know I am, though maybe not as visibly so as your parent. Us anti-AI Luddites are arguing against a massive propaganda machine with trillions of dollars on the line. It is not easy, and it is mentally draining. Especially since AI-companies are behaving in an obviously anti-human, anti-science, and anti-industry ways for their own short term profits.
And worse yet, we have seen this before (albeit on a lesser scale). GMO was supposed to solve global hunger, Carbon capture was supposed to solve climate change, etc. etc. These were obvious lies and propaganda back then as much as the lies where AI are supposed to help humanity today. It gets tiring, and at some point we simply crash out.
unfortunately technology will never fix humanity's tendency to ruin itself (see: war, climate change, etc; all of which we know the solutions for ie 'stop killing people' 'stop using oil' etc) but that is not a fault of the technology itself (gmo, carbon capture, crispr, llms, etc)
This is simply not true. Plenty of technology exists which has helped humanity unambiguously. But there is a difference between good and helpful technology and a grift. GMO could have been helpful but it was mostly just a grift, GMO solving world hunger was always an obvious marketing stunt, and it worked, even though many of us (particularly on the political left) saw it as such and called it out as such. Ditto Carbon capture.
AI is currently the grift of choice.
Also I fundamentally disagree that there is any tendency for humanity to ruin it self. There are perverse intensives, which rewards undesirable behavior, there are unfair, undemocratic, or otherwise hostile powerstructures which allows one class of peoples to exploit another class of peoples. And some technology sometimes plays a pivotal role in maintaining or furthering these hostile power structures/perverse intensives.
Dredging every inch of the lake to catch every single fish and dumping them all in one big pile the town square is just “solving fishing and releasing the fish to the public”, this anti-trawler hysteria is getting really funny.
I think eventually there will be sort of a centralized more or less automated repository for ingesting and sorting ai-generated lean proofs and making them searchable and re-usable. On some level it kind of doesn't matter if mathematicians can ingest the results, if coding agents can just search for them online and use them in their own proofs.
I actually think it would be very smart for the big AI labs to get together to fund an independent organization to manage such a thing, and hire mathematicians to run it.
What is happening now is that some aspects of mathematics are turning into essentially an exercise in software engineering. It is well known that proofs and computer programs have an isomorphism, and I think the eventual merger is more or less inevitable.
That's not to say that there isn't an infinite amount of work remaining for mathematicians to do. There are only so many problems that are going to be amenable to this approach.
Not by hand, no, the code was generated with claude code. The readme too, but with some extra efforts to avoid the awful ai generated readme.
It took multiple sessions to get to this result. At first I only generated annotated pgns and standalone html page inspired by lichess. The video generation was the cherry on top, it took few iterations too to fix issues and add markers and arrows. I only use consume the generated video these days, for the moment.
Big Clive (a Youtuber from Scotland) often reverse-engineers circuits, resulting in him drawong the schematic in purple sign marker, often while commenting on the virtues (or lack thereof) of the design.
I was just starting to think "doesn't Big Clive visit Edinburgh periodically?" and then OP's suggestion scrolled into view. :)
They slow down model releases, but don't slow down research and model development. They're not suddenly going to tell their army of researchers to go home. (In fact they can then focus/spend more on research, instead of the engineering needed to keep churning new models for production.)
This protects them from continuous model distillation by inferior models. So they only release a new model when a 3rd competitor catches up to their public production models' capabilities.
And the public gets a new model in 6 months or a year instead of every few months. But the new model would be a (1) gigantic leap in undisputed capabilities, (2) catastrophic in terms of job losses/replacements worldwide and they've had a year to prepare for the politics of it, and (3) the public would have access to the "tiny/medium/..." models while the big money/gov clients get the real super juice hidden behind the gates.
> they've had a year to prepare for the politics of it
There is little to no political will for preventative measures. Conversely, there is an overabundance of political will to react to disasters.
Thus, there are preventionists whose plan B strategy is to write legislation and wait for a disaster to leverage newfound will to get it passed. Better late than never.
All that said, there's no political will to prepare for the politics of AI. It doesn't exist. The best we can accomplish is to be ready with a plan for when disaster hits.
It's been around in slang for a few years with a few variants, e.g. "I haven't seen her in a hot minute." I think it's just filtered into wider cultural vernacular.
I had not heard it before (or if I did I did not interpret it correctly, eg if one told me "see you in a minute" I would interpret it as "see you in a bit".
But there is this podcast discussing it in 2021, and it comes from black community slang from 70s (which is where most slang I encounter comes from). And these terms take a while to catch up usually, but it seems it was already circulating more broadly since 2000s.
Is there a clear definition of what Alignment is in OpenAI's perspective, and what the model user can expect of it?
It's one thing if to them it means "it will do what you want following your intentions to the best of its abilities" vs "we will not let you do something dangerous with it unless you're one of us, and that's it".
and for Anthropic it's the Constitution, which they actually include in training to the point Claude can recite segments of it by heart: https://www.anthropic.com/constitution
> Many companies now routinely attach Prop 65 warning labels to any product of theirs that they think might possibly contain one of the 900 listed chemicals without testing to see whether the chemical is really present in their product and without reformulating their product, because it is cheaper to do so than to run the risk of being sued by Prop 65 enforcers.
Given the existence of this technology now and the incentives of the AI companies, both of which are not going away; what's a good future here?
A major part of the complaint is that there's no conceptual understanding and building of new ideas coming out of the AI proofs, thus defeating the purpose of the original pursuit.
If in 2027 the AI models start producing, with every mathematics or science breakthrough they make, well-written documents tailored for human understanding, with intermediate concepts, expositions of failed-but-once-promising paths, etc. Would that be good alignment with the mathematics community?
Yes, the situation sucks overall and mathematics as a whole is in a turbulent time now.
But it also sucks when mathematicians, who are considered experts on a particular problem, refuse to engage with breakthrough results about that problem. #2 above is still true regardless of where it came from or how hard it can be to absorb.
While reading "The Mathocalypse" post [0] by Scott Aaronson, Scott described his wife Dana's reaction to one of the newly solved results in her primary domain of expertise, on which she'd been working for decades.
After her initial shock, and annoyance with the format/style, she decided to start using Astra - for the first time - to help her understand the new result. And he reported in the comments that she had made a lot of progress understanding it in one day, and may be even excited to give a talk about it!
That seems like a much healthier attitude towards these new results.
Yes, everything else sucks about this messy period. But there are still diamonds (in the rough) in this drop that perhaps should be looked into. If the author is too busy, perhaps one of their students can take a look? Someone will, eventually.
[0] https://scottaaronson.blog/?p=10169
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