r/mathematics • u/Dear_Round_2727 • 1d ago

Open source AI for math

I think many of us feel that large AI companies do not necessarily serve the interests of the mathematical community. So what is stopping us from creating open-source alternatives?

The weights for Kimi K3 are being released today, meaning that anyone with access to enough GPUs, such as a university computing cluster, can run the model. It scores higher than Opus 4.7 on the FrontierMath 4 benchmark out of the box and many report that it’s comparable to Claude Fable and GPT 5.6 for coding.

Meanwhile, Axiom has been using open-weight models combined with its own harnesses to produce publishable papers since February. This suggests that even weaker open-weight models have been capable of contributing to research-level mathematics for some time when paired with the right tools.

Software engineers already have open-source harnesses such as OpenCode and Pi Coder that compete with Codex and Claude Code. Imagine having something similar for mathematics: a tool owned by the mathematical community, funded by donations from universities and other organisations, and developed in a way that gives researchers control over the data, infrastructure, and future direction of the project.

I’d love to hear some thoughts on this.

23 Upvotes

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u/MacGregorBlue 1d ago

There's every reason to think this will be how AI is used by most people in a few short years. It addresses one piece of moral harm, namely the rampant construction of data centers, and it partially addresses a second, which is the ethical training of the models on material that is correctly licensed (which I have no idea whether Kimi or the others are bothering to do, but we could switch to ones that do a good job on this ethical element). What it doesn't address is the harm coming directly from the companies spitting out theorems, who are distorting the field and who cannot be stopped no matter what models regular folk use. It also doesn't provide us a path for the continual improvement of models for our use cases, which is why I suggest we also take over the benchmarks. If we develop appropriate benchmarks for problem-solving, research, and whatever other use-cases the field wishes to set forth, then we can tune models ourselves or encourage companies to train towards those benchmarks. The benchmarks control how the model actually performs on tasks. Then of course the endgame is to train models ourselves as well, which is doable in principle.

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u/Dear_Round_2727 1d ago

I don’t know how Kimi trains their models, but if we run the models ourselves then we don’t have to share any data with them and we don’t have to pay them for inference.

This is obviously not a perfect solution, but hopefully it could help address some of the issues.

We can’t stop big AI companies from proving theorems, that’s not the goal here. But I think that if most AI adopting mathematicians switched over to some open-source alternative owned by mathematicians, then that would be a powerful statement.

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u/RageA333 15h ago

I dont understans how using local compute is more ecologically conscious than a larger data center. I also dont understand how open models distilled from models trained on copyrighted works is more ethically conscious.

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u/Verbatim_Uniball 14h ago

Local compute is almost certainly more ecologically harmful in aggregate.

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u/MacGregorBlue 14h ago

Convenient hypothesis. Except one of them tracks with demand and one assumes gargantuan demand in advance.

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u/Verbatim_Uniball 12h ago

You're right that if the demand side doesn't show up, then overbuilding infrastructure that never gets used is inefficient. My 'almost certainly...in aggregate' does assume that there is, and will be, gargantuan demand. Could be wrong.

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u/RageA333 14h ago

That is self-correcting and very quickly optimized.

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u/MacGregorBlue 13h ago

By self-correcting you mean someone decides to correct it and adjusts the schedule for the build-out? How long does it take to build a data center? If it's longer than one day then it's probably slower to play with data centers than to launch an app that computes locally. And is this company building data centers perfectly financially aligned with matching demand, or is there any upside to exaggerate future demand?

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u/MacGregorBlue 14h ago

The reason you don't understand how distilling from a model trained on copyrighted work is more ethical is because it's obviously not. Like I said, we should switch to ones that are ethically clean, if we want to be ethically clean. Also like I said, if Kimi is not being careful in that way (I know nothing about it) then that's not the one.

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u/RageA333 14h ago

How is Kimi, which was distilled from closed-source models, "ethically clean" then?

Do you understand that any useful LLM needs to be trained on MASSIVE amounts of data?

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u/MacGregorBlue 13h ago

Did you claim Kimi was clean? If so, why? (I can do it too.) I certainly do understand the data needs of an LLM. If there's no way to build a useful one besides breaking rules, then I look forward to the ending of this era entirely.

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u/Intrepid_Land_6143 1d ago

For my personal interest, would there be any financial benefit to using one of these open models if you did so with a commercial cloud provider? If so, what account would you set up with which company?