From my testing of open weights LLMs with audio support, they basically are only trained to recognize audio as an alternative to text input, they treat audio as basically equivalent to a transcript, and can't recognize or distinguish things like music, accents, background sounds, etc.

So they're only really good for transcribing or summarizing or using audio input in place of text input for prompts, but not anything that requires distinguishing any information about the audio that would not be present in a transcript.

It can be tempting to try to use an LLM for a variety of tasks; kind of the whole thing about an LLM is that you don't have to do a separate complex training run for every task, but can just provide instructions in natural language. But it only works as far as what the training data covers, if the training basically always treated audio and a text transcript as equivalent, the model has nothing causing it to learn other relevant features of the audio. If there's enough bird call identification in the training data of an LLM, it might be able to do that, but I think multimodal training data tends to be much more limited than the text training corpus

You can also do something like a release of a GPT-OSS v2, where you actually release training data and checkpoints, and do an experiment where you have some held out math problem dataset, then demonstrate how much training it takes on solutions (or partial solutions) to that dataset before the model saturates that test. While of course that would be a test on a much smaller model, it would cost a tiny fraction of the training on your big model, and it could be used to demonstrate just how much effect data contaminaiton like this could have, especially if you did the same experiment on a few different sized of model to show the scaling laws involved.

Even better would be more research and tools to help determine the impact of particular training data on models. Right now, proprietary LLM providers get to hide a lot behind "we just train it, we don't know what inputs affect the outputs," and that can be a problem, both because of lack of traceability of factual informaiton as well as lack of traceability of things like this, where the model itself may have had unpublished work in its training set.

According to the statement by Tristan Buckmaster, he was in communication by email and calls several times over the past week with you (OpenAI that is, not you personally), asked about whether his chats were trained on, and was declined an answer (https://cims.nyu.edu/~tristanb/statement.pdf).

However, it seems like there was great pressure to hurry the release to compete with Anthropic's recent release, so he was unable to get an answer in time.

The mealy mouthed statement in the release "We (the researchers and the agents) did not see any of their work through any means until they released it publicly — in particular, no specific user data was accessed in order to solve this problem. While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models ." is realy not much. If OpenAI had wanted to be transparent about this, you could have worked with him to identify if his data was used in the training of your new model, and actually made a somewhat more certain statement on that basis. But you have chosen not to; it was more important to scoop Anthropic on this than it was to be transparent about your training data.

> (2) We'd have to prove those chats changed model behavior. How would we do this? We'd need to retrain many models with those specific chats removed, and ask those models to solve the Navier-Stokes problem many times, and keep doing this until reaching the desired level of statistical significance.

Just the information from step (1) would improve transparency. Yes, you still can't prove one way or another how much the effect of the training is. But if it's included in the training data, it provided some effect.

This kind of question is exactly what a company named _Open_AI and founded as a nonprofit is supposed to be doing; open research on AI that helps inform, rather than obscure.

Anyhow, you do have the data available about the documents in the user's accounts, what they opted into (or were forced into via non-negotiable ToS), and whether they pressed a "thumbs up" button. You can answer whether the data entered the training pipeline or not. Yes, how much influence it had is an open question, and one that would be good to have research on and better tools for exploring, but I'll accept that it can't currently be answered precisely.

But whether the data entered the trianing pipeline can be answered. And how to provide better tools for quantifying and tracing this kind of thing is exactly what should be studied.

You're right; if the data was used in training, then it gets much trickier; it would be very difficult to show whether some particular data had a significant effect on the outcome.

This is one of the big problems with giant models like these; it becomes nearly impossible to discern what is and isn't plagiarism, or copyright violation.

It would in theory be possible to have things like n-gram databases or rolling hashes of training data, somewhat similar to OLMoTrace (https://arxiv.org/abs/2504.07096), which would allow for detecting whether particular documents ended up in the training data or not (you'd have to keep this for every model used in the whole training chain, as synthetic data generated by earlier models could be influenced by training data that wasn't included in later models). I'm sure there are practical issues with providing such a tool, but I think that it's necessary if you want to be able to categorically say "no, this document has never been present in the training data of this model."

Or look at it the other way: if your model wasn't influenced by things in your training data, why include them in the first place? Clearly, you train on all of these documents because they influence the model. Yes, it's hard to trace the exact influence of each one. But if they're not affecting the output, then why not just stop training on them? You could just not train on any private documents; only train on public, traceable data.

But instead, you choose to train on these private documents, so you have to admit, your model and its outputs are influenced by them.

But that doesn't preclude being able to index and track what the sources of data are. For your data sets, I would hope you are including source information for where the data came frome. And at OpenAI's scale, I would presume they are doing some amount of rolling hashing or similar to weed out duplication, training on too much duplicate data can cause problems.

AllenAI have at least attempted to add some amount of traceability to their models with OLMoTrace (https://arxiv.org/abs/2504.07096), by letting you find n-gram matches from the outputs in their training data. It's not the most useful, there's a reason that LLMs use full fledged attention mechanisms and not just n-grams, a lot of times the n-gram matches it finds aren't all that related to the given output, it might be better to supplement this index with a vector search or other ways of keeping track of what training data would have most influenced particular parts of the output.

But anyhow, this is something that is an important question, and the big labs should be working on to make their products more trustworthy. Instead, they are hiding information about how they train, hiding their reasoning traces, and just producing output with no information on what might have influenced the training.

This is one of the major problems with these enormous closed models, and even most open-weights models, which don't disclose their training process or training data. You can never be sure what went into its training. Did it come up with an idea originally, or is it just plagiarising its training data? Are there malicious inputs being used to train in particular behaviors when given certain trigger phrases? What are the characteristics of the RLHF data and what kind of biases are those embedding in the models?

With proprietary closed models, or even open weights models that don't have open training datasets, you just can't answer these questions.

It's the training code that is not up yet, but this group has a history of publishing code so I would expect it, though of course you can never count on it until posted.

* https://github.com/ifm-ai/xllm * https://github.com/ifm-ai/horizon-post-train

Their previous model, K2 Think V2, was release with fully open training data and recipe, so I would imagine that they are committed to that, but yeah, the repos for this new model are still just placeholders.

* https://mbzuai.ac.ae/news/k2-think-v2-a-fully-sovereign-reas... * https://github.com/LLM360/Reasoning360

edit: just checked the code, and they cover both layouts by using the physical key code rather than the character. And that was committed before I tried it. So it must have been the fact that it was a bit slow to respond that got me.

https://huggingface.co/spaces/pollen-robotics/microduck-simu...

It is possible. But it's quite uncommon in this industry in the US.

And given the current administration, it is hard to trust you'd get fair enforcement of labor laws if the company did illegal things to block unionization.

A union doesn't give you unilateral power; it just gives you a better seat at the bargaining table. Capital pools its resources to negotiate better as a single bloc; why shouldn't labor as well?

For example: https://sites.suffolk.edu/jhtl/2025/10/30/game-over-for-unau...

Software developers, unfortunately, have been convinced that they don't need to unionize, so have no collective bargaining power for dealing with situations like this.

So I'm curious, what makes you think that what he calls for isn't ethnic cleansing?

From Wikipedia (and please, fill me in if you have a better definition):

"Ethnic cleansing is the systematic forced removal of ethnic, racial, or religious groups from a given area, with the intent of making the society ethnically homogeneous."

From DHH:

Calling for the deportation (forced removal) of a specific ethnic group (also note the violent analogy to wolves): "When wolves get out of control, you shoot them. When gypsies take over public spaces, you deport them."

Calling for the forced removal of millions of people of various ethnic groups he considers undesirable from Europe: "Because the sacred cow here is not just that mass migration must stop going forward. It's also that millions who are already in Europe must go. Remigration has gone from a fringe concept to the mainstream discourse in record time on account of that realization. The Overton window is swinging wide open, but the cow is still there."

Talking about how much he wanted to move to London but that he no longer has that desire due to its ethnic diversity: "As soon as I was old enough to travel on my own, London was where I wanted to go.... That was then. Now, I wouldn't dream of it. London is no longer the city I was infatuated with in the late '90s and early 2000s. Chiefly because it's no longer full of native Brits. In 2000, more than sixty percent of the city were native Brits. By 2024, that had dropped to about a third. A statistic as evident as day when you walk the streets of London now.... Recently, a projection that Danes would be a minority in their own country by 2096 caused an enormous stir in Denmark. Politicians across the spectrum decried what a catastrophe that would be for this world's oldest continuous monarchy. But a demographic nightmare worse than that has already enveloped London!"

As you can see, DHH is explicitly calling for the force removal (deportation and "remigration") of ethnic groups, because he is afraid of the results of the population becoming less ethnically homogeneous. I don't know how you could argue that what he's calling for is anything other than ethnic cleansing.

Also, note that none of this is due to any actualy on the ground experience; he lives in LA, he's lived in the US for over 20 years. This is purely an exercise in ideology. In fact, LA has a smaller percentage of the population that is white Anglo-American than London does of white British people.

Anyhow: I have laid out in detail the arugment for calling what he calls for "ethnic cleansing". Do you have any argument that it isn't ethnic cleansing that he's calling for? It seems pretty clear to me, so I'm confused why you have kept on insisting "if this is what you think he's arguing for, you either need to re-read the blog post", because I've read all three of these posts of his and more, and it all points towards him advocating for ethnic cleansing.

They may want to at least add a preference for keyboard layout, pretty sure that QWERTY and QWERTZ are much more common worldwide than AZERTY.