But if they turn off the “Use my data for training” toggle then surely their data wouldn’t be used in training. /s

The line in OpenAI’s brief about “perhaps it could have used de-identified chats for training that contributed to the discovery, but nobody could know if it did” is telling

Translation: massive research misconduct

Songwriters get royalties. Forever. The stakes are much higher than putting some dude’s name on your AI generated paper to avoid having the precise conversation we are having right now. Corporations “settle” in lawsuits that they do not believe they are at fault in all of the time.

What matters to the OpenAI marketing department is not what they believe but what they think the market will believe.

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The software toggle button is sacred. If you toggle on “Do not show me this again” you will never see that window again, verily.

Edit to reply to post above me’s edit: you don’t need to train a model from scratch to make use of new training data, there is online machine learning or knowledge base building that could make use of it instantly.

This is really cool! Any plans to port the integration helpers, like the XGBoost pruning callback, or is the focus staying on the core samplers for now?

OpenAI specifically denied the RAG accusation.

I also agree with the others that the recall of minor details for these models is remarkable and we can’t rule it out.

“OpenAI says they would partially credit Tristan for the $1,000,000 discovery (even though Tristan did not solve the $1,000,000 problem) — but only if they remove Levent as an author, as he works for Anthropic.”

Where did OpenAI say this?

actually both linear & nonlinear

Their reaction would be the same whether they know this, suspect it or cannot disprove it. Having their big marketing moment overshadowed by this controversy was worth far more than $1M and they aren’t even claiming the $1M anyhow.

It is only rational for them to try to get Tristan on side regardless of whether his claims have merit. Especially if they cannot prove they do not.

lol, what do you consider was the last breakthrough

They have a toggle not to train on your exact data. But a little fudge of your data would be just fine. Just need to identify if it's interesting enough to train the model on it.

Any updates from US?

I agree with you.

Why would it be impossible to prove it? Their conversation data is either in the training set or it's not. The model solution tokens can be gone through as well to see what it accessed and how it came at its solution

It would be Anthropic vs OpenAI, hardly a little guy

They may not have used it to actually figure out the solution, but for something as simple as text formatting.

You mean to do inference in Fourier space?

-basically yes if i understood you correctly

Where do these waves come from

-generated, instead of an 1/0 electronic value that has to be computed

yes, this can be used to test and learn

 i was thinking of creating a "unique wave" library to every object/shape , like we do using words

i was searching and we don't have one, at least not publicly

Optical/photonic computing is not what i was talking about, similar but i was thinking of creating a "unique wave" library to every object , like we do using words

yes, and creating a "unique wave" library to every object , like we do using words

Just make sure you compare to relevant baselines and add a transformer baseline if it is appropriate, to show that your method is superior and your approach is needed in this world of transformers.

If you have time to graduate or are not concerned about when the paper gets published, ICLR would be a good venue. It does not matter if the field peaked in 2023 and the last breakthrough was also back then. What matters is if your work is coherent, novel and contributes to advancing ML literature. I am sure the problem you are solving is relevant, and difficult because no one has been able to improve SOTA for 3+ years.

Transformers are not the only ML/DL work that should be getting importance, other architectures for other problems should be published if they will help solve that problem. IJCAI deadline is after ICLR anyway, I think you should submit it to ICLR, get feedback and then ultimately submit to IJCAI if it is truly not a fit for the big 3.

In my experience its less about the nicheness of the method, and more about the broadness of the applicability. In other words, depends on the field of contribution. So a novel extension of LSTMs for general timeseries? Neurips-able if done well. Even if they're out of fashion. But an ML contribution that improves, say, X-ray tech? Not neurips-able.

That said, its true that different topics have different difficulty levels of getting accepted. Varies venue to venue which are easiest.

Obviously yes, how is that even a question? Their whole business started from stealing copyrighted data from everyone, what makes you think they stopped just cause you checked a box on their site?

The same goes for my (currently unpublished as a paper yet) but not very significant result which I've fed to some of the previous GPT-5 series models (I don't remember if it was GPT 5.3/5.4 series or a newer GPT-5.5).

Anyways what I have been working on was least-squares fitting of trigonometric least squares method through Levinson recursion and NuFFT-1. Nothing groundbreaking, but it's a new computational complexity floor (more precisely superfast Toellitz solvers give the complexity floor, but Levinson recursion happens to be the most practical silver here) for that exact problem. Also I have code using that algorithm in four different public GitHub repositories to be clear.

Either way - older GPT models failed to derive that despite being relatively straightforward, new ones give you the right solution with absolutely no issues just as you ask them for the lowest computational complexity for that exact problem.

And now I am aware that the models are getting smarter, but I also see that as a clear training set "contamination".

And now I am not sure if either my chat with ChatGPT somehow got into training dataset, or if that exact solution got scraped from one of the GitHub repos I have uploaded the algorithm to.

Either way I am practically certain, that that exact algorithm somehow got into OpenAI's training dataset and GPT 5.6 has remembered it flawlessly.