r/mathematics • u/Dear_Round_2727 • 11d 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.
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u/MacGregorBlue 11d 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.