In fact, we should collectively refuse to abide to these fantasy license before weight copyrightability gets created out of thin air because it's been commonplace for long enough.
An mp3 file is also a machine-generated lossy compression of a cd-quality .wav file, but it's clearly copyrightable.
To that extent, the main difference between a neural network and an .mp3 is that the mp3 compression cannot be used to interpolate between two copyrighted works to output something in the middle. This is, on the other hand, perhaps the most common use case for genAI, and it's actually tricky to get it to not output something "in the middle" (but also not impossible).
I think the copyright argument could really go either way here.
Not the .mp3 itself, the creative piece of art that it encode.
You can't record Taylor Swift at a concert and claim copyright on that. Nor can you claim copyright on mp3 re-encoded old audio footage that belong to the public domain.
Whether LLMs are in the first category (copyright infringement of copyright holders of the training data) or in the second (public domain or fair use) is an open question that jurisprudence is slowly resolving depending on the jurisdiction, but that doesn't address the question of the weight themselves.
In this analogy, distributing the weights would be akin to distributing an mp3, and offering a genAI service, like charGPT inference or a stable diffusion API, would be akin to broadcasting.
That's beyond the definition of Open Source. Doing a bit of license research now, only the GPL has such a requirement - GPLv3:
> The "Corresponding Source" for a work in object code form means all the source code needed to generate, install, and (for an executable work) run the object code and to modify the work, including scripts to control those activities.
But all other Open Source compliant licenses I checked don't, and just refer to making whatever is in the repo available to others.
But when people are talking about open source, they generally mean "oh I can see the source code and build it my self." rather than freeware which is "I can run the binary and not have to pay"
For something to be open source it needs to have sources released. Sources are the things in the preferred format to be edited. So the code used for training is obviously source (people can edit the training code to change something about the released weights). Also the training data, under the same rationale: people can select which data is used for training to change the weights
I understand that these days, businesses and hobbyists just want to use free LLMs without paying subscriptions for economic motives, that is, either saving money or making money. They don't really care whether the source is truly available or not. They are just end users of a product, not open-source developers by any means.
if it's the latter, it's not the source. it's free as in beer. not freedom.
This is exactly the tradeoff that we had made in the industry a couple of decades ago. We could have pushed all-in on Stallman's vision and the FSF's definition of Free Software, but we (collectively) decided that it's more important to get the practical benefits of having all these repos up there on GitHub and us not suing each other over copyright infringement. It's absolutely legitimate to say that we made the wrong choice, and I might agree, but a choice was made, and Open Source != Free Software.
https://www.gnu.org/philosophy/open-source-misses-the-point....
This is incorrect, given the definitions in the license.
> (Apache 2.0) "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files.
(emphasis mine)
In LLMs, the weights are the preferred form of making modifications. Weights are not compiled from something else. You start with the weights (randomly initialised) and at every step of training you adjust the weights. That is not akin to compilation, for many reasons (both theoretical and practical).
In general licenses do not give you rights over the "know-how" or "processes" in which the licensed parts were created. What you get is the ability to inspect, modify, redistribute the work as you see fit. And most importantly, you modify the work just like the creators modify the work (hence the preferred form). Just not with the same data (i.e. you can modify the source of chrome all you want, just not with the "know-how and knowledge" of a google engineer - the license can not offer that).
This is also covered in the EU AI act btw.
> General-purpose AI models released under free and open-source licences should be considered to ensure high levels of transparency and openness if their parameters, including the weights, the information on the model architecture, and the information on model usage are made publicly available. The licence should be considered to be free and open-source also when it allows users to run, copy, distribute, study, change and improve software and data, including models under the condition that the original provider of the model is credited, the identical or comparable terms of distribution are respected.
No they aren't. We happen to be able to do things to modify the weights, sure, but why would any lab ever train something from scratch if editing weights was preferred?
I can edit the executable at the byte level if I so desire, and this is also what compilers do, but the developer would instead be modifying the source code to make changes to the program and then feed that through a compiler.
Similarly, I can edit the weights of a neural network myself (using any tool I want) but the developers of the network would be altering the training dataset and the training code to make changes instead.
define_architecture (what the operations are, and the order in which they're performed)
initialise_model(defined_arch) -> weights. Weights are "just" hardcoded values. Nothing more, nothing less.
The weights are the result of the arch, at "compile" time.
optimise_weights(weights, data) -> better_weights.
----
You can, should you wish, totally release a model after iitialisation. It would be a useless model, but, again, the license does not deal with that. You would have the rights to run, modify and release the model, even if it were a random model.
tl;dr; Licenses deal with what you can do with a model. You can run it, modify it, redistribute it. They do not deal with how you modify them (i.e. what data you use to arrive at the "optimal" hardcoded values). See also my other reply with a simplified code example.
Emphasis mine.
Weights are not open source.
You can define terms to mean whatever you want, but fundametally if you cannot modify the “output” the way the original creators could, its not in the spirit of open source.
Isnt that literally what you said?
How can you possibly claim both that a) you can modify it the creators did, b) thats all you need to be open source, but…
Also c) the categorically incorrect assertion that the weights allow you to do this?
Whatever, I guess, but your argument is logically wrong, and philosophically flawed.
If they are released under an open source license, they are.
I think you are confusing two concepts. One is the technical ability to modify weights. And that's what the license grants you. The right to modify. The second is the "know-how" on how to modify the weights. That is not something that a license has ever granted you.
Let me put it this way:
```python
THRESHOLD = 0.73214
if input() < THRESHOLD:
print ("low")
print ("high")If I release that piece of code under Apache 2.0, you have the right to study it, modify it and release it as you see fit. But you can not have the right (at least the license doesn't deal with that) to know how I reached that threshold value. And me not telling you does not in any way invalidate the license being Apache 2.0. That's simply not something that licenses do.
In LLMs the source is a collection of architecture (when and how to apply the "ifs"), inference code (how to optimise the computation of the "ifs") and hardcoded values (weights). You are being granted a license to run, study, modify and release those hardcoded values. You do not, never had, never will in the scope of a license, get the right to know how those hardcoded values were reached. The process by which those values were found can be anything from "dreamt up" to "found via ML". The fact that you don't know how those values were derived does not in any way preclude you from exercising the rights under the license.
To give you an example: I can release a binary (without sources) under the MIT - an open source license. That will give you the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of said binary. In doing so, I would have released the binary under an open source license. However, most people would agree that the software would not be open source under the conventional definition, as the sources would not be published. While people could modify it by disassembling it and modifying it, there is a general understanding that Open Source requires distributing the _sources_.
This is very similar to what is being done here. They're releasing the weights under an open source license - but the overall software is not open source.
All the problems they claim to have solved are on are the Internet and they explicitly say they crawled them. They do not mention doing any benchmark decontamination or excluding 2024/2025 competition problems from training.
IIRC correctly OpenAI/Google did not have access to the 2025 problems before testing their experimental math models.
OpenAI: https://xcancel.com/alexwei_/status/1946477756738629827#m "we are releasing GPT-5 soon, and we’re excited for you to try it. But just to be clear: the IMO gold LLM is an experimental research model. We don’t plan to release anything with this level of math capability for several months."
DeepMind: https://deepmind.google/blog/advanced-version-of-gemini-with... "we additionally trained this version of Gemini on novel reinforcement learning techniques that can leverage more multi-step reasoning, problem-solving and theorem-proving data. We also provided Gemini with access to a curated corpus of high-quality solutions to mathematics problems, and added some general hints and tips on how to approach IMO problems to its instructions."
>we achieved gold medal level performance on the 2025 IMO competition with a general-purpose reasoning system! to emphasize, this is an LLM doing math and not a specific formal math system; it is part of our main push towards general intelligence.
asterisks mine
> Typically for these AI results, like in Go/Dota/Poker/Diplomacy, researchers spend years making an AI that masters one narrow domain and does little else. But this isn’t an IMO-specific model. It’s a reasoning LLM that incorporates new experimental general-purpose techniques.
People use Instagram too.
I can't trust any of their output until they become honest enough to change their name to CloseAI.
So they are either earning money directly or on the API calls.
Now, competition can come and compete on that, but they will probably still be the first choice for foreseeable future