This is an incredible collaboration between @willsmithvision.bsky.social, @willrowan.bsky.social and myself. The whole YorkVGL team is now switching all our research code over to Neuralatex and seeing massive productivity gains because of it!

You can check out the source code (and paper!) right now on arxiv: arxiv.org/abs/2503.24187

There is so much work to do! We want this running on GPUs by summer, taping out custom Neuralatex silicon by autumn and starting the run of Neuralatex GPT by the end of the year!

We demonstrate how powerful our library is by training an MLP to 86% accuracy on the spiral dataset with only a moderate 48-hour document compile time!

And the Source code of the Method in the Source Code of the Paper (SCOMISCOP) metric is the proportion of the source code of a method that is contained within the source code of the paper.

We get state-of-the-art performance in both of our own brand new metrics!

The Written in Latex (WIL) metric is the proportion of the source code of a machine learning library written in LaTeX.

Now, as part of your latex document, you can specify the architecture of a neural network, generate or load training data and define hyperparameters and loss functions. When the document is compiled, the latex compiler will train the network, run experiments and create figures.

This is a fully functional scalar-based auto-grad library, similar to Andrej Karpathy's micrograd but in pure Latex!

Simply import our engine.tex and nn.tex files and start training models straight away!