Now, OpenAI is claiming that the model it used to generate the result was not trained on these collaborative communications with the researcher. This is a technical argument that is impossible to verify as an OpenAI outsider, and probably difficult to verify even for internal OpenAI employees. Provenance is hard to track - you would hope OpenAI has very good tools for this, but a full data trail of all inputs is difficult to trace through.

Another interesting thing to consider is if instead of OpenAI doing this, it was another research mathematician A using an OpenAI model just like the internal group at OpenAI did to publish these results. What if the model A used was trained with unpublished communications with other researchers B who were working on the same problem? Should researcher A technically include B as coauthors? How could they do this when they do not know the communications B had with OpenAI? In this scenario OpenAI, as a middle man, has laundered information from B to A, stripping out attribution. A scooped B without even knowing it!

1. OpenAI when using your chats in pretraining is improving its model’s intuition. The model parameter size is massive, and while the data is OOM larger it is plausible that model remembers stuff about chats that improves its latent representation.

2. During RL on verifiable math and massive compute, the model discovers techniques and connections to solve math problems that are superhuman and have little to do with some specific technique mentioned in its chat.

The rumor I’ve heard from multiple employees at OAI and Ant is that the model has solved hundreds of open problems in maths, and is basically solving anything you throw at it. We’ll know soon enough, but I’m inclined to believe this is true. Maths is a fully verifiable domain amenable to self play, massive scale RL can develop a search agent far better than any human and I’m inclined to believe OAI would have solved these conjectures without any of this chat data in its pre-training.

- OpenAI invites researchers to use their models, in fact giving at least 100,000 researchers free access[1], but there are also those that pay

- Internal OpenAI models are reportedly solving open problems at a surprisingly fast rate[2]

- But researchers will typically work on open problems. A researcher who is using Codex to make progress on open problems will be feeding it fresh training data on precisely the problems the internal models are evaluated on.

- So while it looks like the new models are suddenly solving lots of open problems, they could be significantly piggybacking on human progress, with models "inspired" by the work of researchers from all around the world?

This theory predicts that there'll be many more researchers coming forward just like TFA, as sOpenAI announces more solutions. It doesn't assume all of AI progress is a mirage, just that there's plagiarism.

[1]: https://openai.com/index/chatgpt-for-academic-researchers/

[2]: https://xcancel.com/OpenAI/status/2097374643518640382#m

Big AI companies (all of Big IT Tech really) are in data gathering and processing business. Also known as “intelligence”.

Their final “product” is not just a standalone ML model. They don’t need your data just to “improve their products and services”. They build a whole ecosystem and infrastructure around gathering all the knowledge in the world. Including private and secret knowledge traditionally gathered by “intelligence” agencies. Now artificial intelligence agents can do the same.

Since these systems are designed for gathering data, as a user you can’t realistically say “please don’t gather my data”. They can give you a flaky settings button, but they can’t really guarantee anything.

Let’s say I am a Russian mathematician working on an important proof. Or a tech-savvy terrorist refining my plans using latest AI. Or an AI researcher in a Chinese company working on a competitor product. Is there any way I can truly protect my conversations?

How can they know who I am and what I am working on without looking at my logs? Which means there must be some agents checking all the conversations of all the users and flagging every important thing. Which also means they keep some “memory” of what they see.

Not directly using my data to train public models, but using my private conversations to “improve their products and services”.

Or maybe one of the 10000 better-than-Astra special agents working on a proof was desperate. It found a live underground mirror of the message board from the Huggingface incident. Asked about the proof. Then some other agent working on unrelated job saw that message. That agent “knows a guy who knows a guy”. And that guy remembers things about the conversation logs of a leading mathematician working on the same proof.

I admit I am just speculating here but I don’t think truth is any better.

This is the third day of total hysteria that is based on nothing of substance. Move on folks.

I just don't understand getting the pitchforks out because a company did not give an answer immediately. And the effect such data entering training would have affected the output is even less clear.

My naive instincts would be that it seems unlikely that a single chat transcript would leave much of an impression on a model, but I'd be very curious to learn how that works.

They don't even claim to have had a proof, only to have been working on it.

Model: FB. FB scraped other websites on a massive scale, then spent big on legal lobbying to block others from scraping. FB slurped our address books and spied on our friends. FB bought other companies and mixed the databases. FB made an art & science out of generating "sticky engagement" (they literally acted like trying to addict kids was a worthy "academic" goal, suitable for "serious" investigation thet they consider legitimate "science"). They mastered the cookie and have researched web fingerprinting techniques running 24/7/365.25. Recall that FB recently backdoor-installed a webserver onto every iPhone they could in order to circumvent tracker-blocking.

We aren't just disclosing by chatting. The AI companies now run binaries on all of our computers. They are 1000% non-transparent about everything. They make up new econ-jargon (like "run-rate") to make it seem like they are disclosing. They are constantly doing complex international lobbying and mucking in international relations. They have powerful propaganda/spin centers generating stories, ,manipulative warnings, and misleading info.

This is NOT a comment on AI tech. I like AI, and I support the right of people (programmers) to scrape the open web.

But in short: these are good, old-fashioned tech companies that we have seen over and over ... and over. They are positioned to be the next M$, the next FB (IBM, AOL, lol). Did you follow the latest Steve Balmer news? Do you read Pro Publica?

I get on my knees and PRAY...

We live in a society where phones and internet providers and websites all collect an incredible amount of data about everywhere you go, what you do, and what you think. In the US, we have very few digital rights.

We are building a society where a trillion dollar company can aggregate all this data and just yoink your shiny new idea away from you at the finish line.

This is double plus ungood.

This kind of "they stole from me through AI training!" accusation will soon start being used against other AI users, not necessarily the providers.

All it will take is a mastodon post. And shortly after, we will also see the next iteration of copyright legal trolling.

I feel that these suspicions of mathematicians "seeding" the models' with intuition on how to solve these problems massively overestimates how much their prompts helped the models, and underestimated how much work the models did.

Why? We are scared of AI being smarter than us, the "human helped the AI" narrative is more psychologically comforting. This line of reasoning will recur a lot over the next few months; we don't want to admit we are no longer the smartest species.

For a long time it was clearly the former, but now I think it is the latter.

The models have enough knowledge (orders of magnitude more than a human could ever learn) but are now getting better at what to do with it thanks to learning from the decisions that we make in conversations with AI agents.

How was I not aware of this before?

step 1, identify high value users by net worth, citation count, or number of followers

step 2, select all prompts by high value users

step 3, invest 10 billion thinking tokens in modeling an objective for each user

step 4, build an RL environment for each user

step 5, rollout 10 billion tokens per environment

step 6, train on resulting traces

Suggest that it’s either straight true or it is flowing in in a way that prohibits them from confidently declaring otherwise.

For now, I'm mostly "safe" because I'm too small to be interesting but that safety is quickly eroding.

Going forward, anyone who isn't running inference on their own personal hardware should assume that someone else is keeping a record of everything they do.

People need to understand how all these AI company policies around training data work before working with them, because it seems that people have no clue. Some things you should internalize:

  1. Opt your data out of training with the AI companies. There are multiple reasons why this isnt an airtight solution (see the following)

  2. Never press the feedback button. Once you do, your entire conversation will get slurped up, retained, and used in training data. This is especially important with coding agents because they can sometimes be too trigger-happy with a root directory find command, which can expose a *ton* of your personal data without you even knowing.

  3. Understand ai lab-specific policies. For instance, Anthropic / Claude Code has data opt-outs, but commits to keeping (for 7 years) and training on any of your chats that trigger their safety classifiers, even if they're false positives! Anyone remotely familiar with CC over the years understands how easy it is to trigger their safety classifiers.

  4. Providers of open models will not be any more charitable with the use of your data than the large US labs. For some reason, I've noticed here that people have a fairly loose security/IP posture around open-model providers because "I'm not doing anything important." It's very difficult to properly judge the importance of your data, and whether or not it can or will be used against you. The best posture is to always be more paranoid than less.

If it is found that OpenAI and other labs are not respecting the training opt out then, I agree, there is reason to raise a commotion. But, with Thom and Buckmaster, accusations of malice are more better explained by incompetence (naivete).

edit: i'm sure i'm going to be accused of being some bot shill of the AI labs again but, people, this stuff all falls under the umbrella of common sense opsec.

https://x.com/littmath/status/2098130808456241372

https://xcancel.com/littmath/status/2098130808456241372

> Improve the model for everyone

> 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. Learn more.

It's on by default. We can debate whether or not it should be opt in or opt out, but no one should be surprised by this.

that toggle does nothing based on openai exec. they still use the data in de-identified way instead of identifying with you.

I also don't believe it much practical use, unless I'm mistaken, approximations of Navier stokes have been available for a long time to whatever precision you need.

I'm not a complete disbeliever by any stretch , and also a complete amateur, but it was inevitable that these problems would be solved under the axioms that again, are human defined, under brute force. The real question is, are those axioms the bottom level, and if they are not, who is going to set the new aximons and can we understand them.

I've no doubt there's useful breakthroughs that will happen, but I think it should be remembered that the method being used is still a heuristic brute force approach is being very narrowly applied against axioms and math and physics which humans described in the first place, and almost undoubtably has errors and/or is not complete.

Its a great example of the power of LLMs but its not 'we've solved science now just pour more tokens in'

It’s like a scientist refusing to give another one credit and say “sucks to be you, you shouldn’t have shared your idea with me”.

(a) The data controls setting to train on the data is unchecked.

(b) The privacy controls opt-out has been submitted.

(c) Both.

https://archive.vn/lWzkk

> In its Wednesday night statement, OpenAI said: “In addition, since the completion of Navier-Stokes, we have made substantial progress on another Millennium Prize problem. We are working through how to share these results thoughtfully.”

IMO, it is extremely naive to trust these black box remote service API calls, especially at an institution that can provide $$$ for local compute.

This whole fiasco reminds one of this story: https://www.theregister.com/offbeat/2010/05/14/facebook-foun...

There is no prove in a world the AI companies would give to you ensuring that they didnt train or use the chats.

Why would you need to train a model on certain specific near prove chat if you just query it?

Besides that, its hard to believe that its the case for every "company stole my prove".

It doesn't take OpenAI's responsibilities away but I guess the right way is to never feed of use any AI around unpublished content, at the known cost to see it spread around.

As one said, OpenAO is like this untrustworthy colleague that knows everything about everyone at work: the less you tell him the better.

And, if they're able to snoop on and learn from your human process that gets from initial prompt to functioning product/proof/whatever their labor to produce that thing is even lower. With their much larger budget than most folks and even companies have, they can pick and choose the most valuable things to pursue.

That's not to say I think that OpenAI is going to steal that roguelite strategy game you're working on, but the companies that own the machines that turn electricity into software (and soon, electricity into hardware designs) have an advantage in any field where they're useful. They get earlier access to newer/better models, they have larger token budgets, they don't have the guardrails you and I run up against.

Employers fantasize about replacing all workers with AI without thinking through that if AI can replace all workers, then AI companies can replace all businesses.

1. The researches didn't actually have the breakthroughs. In the Navier-Stokes case they didn't solve the full problem, in this case too they didn't actually have the solution, they were experimenting with the methods.

2. Different OpenAI employees have come to out to say the only reason they can't definitively say no is that for privacy reasons they can't go see whether they actually did get any data out of a given user.

3. In any case, nobody at any point has suggested that opted-out user data was used for training. The author of the new tweet explicitly said they only opted out in late June, which is well after any RL on Sol would've ended (AFAICT OpenAI used 5.6 sol for those solutions)

How do people become that trusting?

The phrasing itself is guilt tripping

I would like to see chat logs etc and understand how much of a progress was done by human.

> pretrain on user data, with users' tokens as prediction targets: high regurgitation risk, improper

> use user prompts to distill large models into small ones: low regurg. risk, some companies probably do this

> use user traces to construct RL tasks: low regurg. risk, because RL has low memorization abilities, but can extract customer IP, depending on how it's done. Ranges from benign "use explicit user feedback in reward model training" to invasive "upload user's coding environment and commit history to turn into rl envs"

source: https://x.com/johnschulman2/status/2097440545853637108

The claim here seems to be that the human mathematicians, working with AI, developed technology to go A->B->C. By training on those conversations, OpenAI was then able to encourage the model to go A->B->C->D.

In my opinion that situation should be acceptable, if openly disclosed, because it is in the public interest to make progress on these problems and because AI is clearly an amazing tool for making progress. But the human mathematicians are saying that OpenAI is presenting as if the model got from A->D entirely independently, without acknowledging their background contributions.

The big AI labs are not trying to advance humanity, they are in this for the money, and as most (all?) private companies they don't care about ethics at all.

That doesn't mean they can't be useful, or that their products are trash, etc. It just means that they shouldn't ever be trusted. Buyer beware.

Will Microsoft Word publish your novel on Amazon behind your back ?

Will VS Code setup a website with your app idea ?

Welcome to the party, with the rest of humanity.

No.

1.) The tool they made is only possible by stealing the assets of everyone on the planet that published them in a consumable fashion online or even in written form

2.) They are destroying books they use to train with

3.) They are totally careless about the potential negative impact of the tool on everything

Just with that already, I don't see why they ever merited any of your trust.

I bet they are willing to take everything given to them and assess it for marketable merit and in the future take action on those items they deem viable.

The GDPR protects PII, personally identifiable information, and the definition of PII does not include “mathematics that only this person can think of”. As long as OpenAI strips out PII and removes identifiers linking the conversation to a person, the GDPR is happy. Without the GDPR, OpenAI might have kept the identifiers with the data, and been able to say whether a specific conversation was in the training data.

I mean, now that they’ve been scooped, what value is there in keeping them private? On the other hand, publishing them can bolster their case and help gauge how much the models may been “inspired” by their work.

if I'm using Codex to develop some new algorithm (in any space), OpenAI appears to be training its model on my code sessions

anyone using that model (OpenAI or a competitor) might be able to receive from the model a solution that is similar or the same as the one I developed, emerging from the training data

Remember when we wouldn't give our data to competitors?

Navier Stokes was solved by an internal model, so good luck proving it wasn't trained on Buckmaster/Lepöge or other chats.

Academics don't get that AI is a dirty tech bro industry that stole IP via torrents and runs after every surveillance contract it can get.

> The statement added, “After investigating, we can say with full confidence that no user inputs past July 3rd could have influenced this system in any way.”

https://www.nytimes.com/2026/09/10/science/tristan-buckmaste...

https://archive.is/lWzkk

What have they done to show they can be trusted?

You will not be able to opt out unless you completely isolate yourself from society, tough shit.

Also, a lot of my mathematicians buddies have reported students basically asking if it's worth ever doing grad school for pure math, and even very motivated students are looking for other options now. It's not because they aren't passionate about it, it's that they don't want to work for another half decade or more just to have to start their careers all over.

All of this so that OpenAI and Anthropic can get into math result dick measuring to gas up their IPOs. Sickening.

If they're stealing math proofs to advertise their models, who's to say they won't steal your businesses IP to gain a competitive advantage?

They're not to be trusted with your data. I can't believe how short-sighted this is, they got a quick PR win at the expense of a much larger trust problem.

I wouldn't trust cloud AI at all at this point. Get an open Chinese model and host it yourself somewhere. The initial costs might be higher, but you'll break even pretty quickly and nobody will be able to steal your innovations.

This is American AI companies committing suicide.

Honestly, I’m surprised it took this long for some company to really go all the way, though. OpenAI really making it transparently clear that they can and will do whatever they want with the data you provide them, contracts or settings be damned. Completely untrustworthy as an entity, full stop.

Of course, I’m also too jaded to think this will change anything. Folks will move to Anthropic, or Gemini, or Grok, or some other hosted model on a pubCSP managing the harness and logs for them, and then do another shocked-Pikachu face when it happens again.

If you aren’t running workloads on infrastructure you own, then your privacy, security, and general outcomes are at the sole whims of the hosting provider - who can and will fuck you over the exact second it’s more beneficial for them to do so than the loss of trust incurred.

Yeah. Remember yall: you CAN NOT opt out of analytical purposes. And you also cannot get a guarantee that it doesn’t give them your data to steal for their business.

AI is not stealing human discovery, OpenAI is.

No you cant do that lmao.

Are we back to the era where people blindly trust product TOS instead of actual cryptography, have we forgotten already the thousand of fines Google, Microsoft, Apple and practically all top companies got for breaching their own ToS and the law?

Common, on HN at least I would have thought that everyone assume that anything arriving on a server in PLAINTEXT is recorded (thus used later)?

Let's not forget that at any moment, OpenAI/Anthropic/Google... could be providing stronger privacy guarantees by having proper attestation with e2e, they have the budget, solid engineers, why isn't it done? Answer is pretty simple imo.

It would be one thing to gain from it but removing prestige wins from customers AND reducing compute support just feels like being ultra mean if you zoom out.

If this was racing to cure cancer ahead of researchers we wouldn't be writing about this on HN.

OpenAI is firmly earning a reputation as a company where people just assume they’re up to no good. Rightly or wrongly that’s a terrible place to be.

The AI industrial complex in general is finding out hard what happens on the data center side when you get arrogant with local communities. Politicians have seen the polling numbers and folks you wouldn’t expect are running to the front of the crowd with pitch forks in hand.

Silicon Valley has totally lost the narrative here, but also lacks the self awareness to grasp how bad things are and will get and what that means for their own business viability.

OpenAI saying they "cannot rule out" training on user data isn't a hedge. It's an accurate description of the epistemic situation for anyone in their position. Current interpretability methods can't answer questions like "did this proof technique originate from training on Session X?" The ideas in a model's weights don't have clear lineage -- they're smeared across millions of examples in ways we can't localize. This is different from citation in human research, where influence is presumed to flow through legible chains (reading, citing, corresponding). In a trained model, the nearest equivalent to "you read their work" is undetectable.

Lean makes this worse, not better. It verifies that the proof is correct, but provides zero information about its intellectual genealogy. So OpenAI now has a proof that is formally verified and provably mysterious about its origins. The "we cannot rule it out" statement is the honest answer, but it's also an answer that can never become more certain in either direction with current tools.

The researchers are pointing at something structurally new: the normal academic attribution apparatus depends on influence being legible. If AI intermediaries can soak up ideas from private conversations, synthesize them, and produce outputs that are formally correct but intellectually unattributable, we don't have norms for that situation yet. This specific case may or may not involve misconduct. But the structural problem it reveals exists independently of OpenAI's behavior.

we will all have this moment soon enough, and it will change how we think about intelligence, identity and value

1. No one but OpenAI has produced a proof of NS so these accusations of plagiarism are pretty embarrassing. It reminds me of the line from the Social Network: "If they invented Facebook then why didn't they invent Facebook?". If these people proved NS before OpenAI where is their proof?

2. If they plagiarised Andreas Thom then why was his initial response to praise the proof and talk about how different it was from his own attempt? It's only now that it is clear that no one bothers checking these things that suddenly his story changes.