Wu's Method can Boost Symbolic AI to Rival Silver Medalists and AlphaGeometry to Outperform Gold Medalists at IMO Geometry
Shiven Sinha, Ameya Prabhu, Ponnurangam Kumaraguru, Siddharth Bhat,, Matthias Bethge

TL;DR
This paper demonstrates that Wu's method, when combined with classical geometric techniques, significantly enhances automated theorem proving for IMO problems, rivaling and surpassing previous neuro-symbolic models like AlphaGeometry.
Contribution
It reveals Wu's method's strength in solving IMO geometry problems and establishes a new symbolic baseline that outperforms AlphaGeometry and even IMO gold medalists.
Findings
Wu's method solves 15 IMO problems independently.
Combining Wu's method with classical techniques solves 21 problems.
The combined approach solves 27 out of 30 problems, surpassing AlphaGeometry.
Abstract
Proving geometric theorems constitutes a hallmark of visual reasoning combining both intuitive and logical skills. Therefore, automated theorem proving of Olympiad-level geometry problems is considered a notable milestone in human-level automated reasoning. The introduction of AlphaGeometry, a neuro-symbolic model trained with 100 million synthetic samples, marked a major breakthrough. It solved 25 of 30 International Mathematical Olympiad (IMO) problems whereas the reported baseline based on Wu's method solved only ten. In this note, we revisit the IMO-AG-30 Challenge introduced with AlphaGeometry, and find that Wu's method is surprisingly strong. Wu's method alone can solve 15 problems, and some of them are not solved by any of the other methods. This leads to two key findings: (i) Combining Wu's method with the classic synthetic methods of deductive databases and angle, ratio, and…
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Taxonomy
TopicsArtificial Intelligence in Healthcare and Education · Explainable Artificial Intelligence (XAI) · Medical Imaging and Analysis
MethodsSparse Evolutionary Training
