[Published today by DeepMind]

Our AI system surpasses the state-of-the-art approach for geometry problems, advancing AI reasoning in mathematics

Reflecting the Olympic spirit of ancient Greece, the International Mathematical Olympiad is a modern-day arena for the world's brightest high-school mathematicians. The competition not only showcases young talent, but has emerged as a testing ground for advanced AI systems in math and reasoning.

In a paper published today in Nature, we introduce AlphaGeometry, an AI system that solves complex geometry problems at a level approaching a human Olympiad gold-medalist - a breakthrough in AI performance. In a benchmarking test of 30 Olympiad geometry problems, AlphaGeometry solved 25 within the standard Olympiad time limit. For comparison, the previous state-of-the-art system solved 10 of these geometry problems, and the average human gold medalist solved 25.9 problems.

Links to the paper appear broken, but here is a link:

https://www.nature.com/articles/s41586-023-06747-5

Interesting that the transformer used is tiny. From the paper:

We use the Meliad library for transformer training with its base settings. The transformer has 12 layers, embedding dimension of 1,024, eight heads of attention and an inter-attention dense layer of dimension 4,096 with ReLU activation. Overall, the transformer has 151 million parameters, excluding embedding layers at its input and output heads. Our customized tokenizer is trained with ‘word’ mode using SentencePiece and has a vocabulary size of 757. We limit the maximum context length to 1,024 tokens and use T5-style relative position embedding. Sequence packing is also used because more than 90% of our sequences are under 200 in length.