A new challenge has been introduced to evaluate the interpretability of advanced mathematical language models, focusing on their internal mechanisms to distinguish between robust and spurious reasoning. This initiative aims to address a significant limitation in current reasoning benchmarks, which often prioritize final-answer accuracy over the stability of the reasoning process. By examining the models' internal workings, the AIMO Interpretability Challenge seeks to promote a deeper understanding of how these models arrive at their conclusions. The challenge is particularly relevant given the far-reaching implications of AI advancements on policy, security, and workforce dynamics1. As AI models become increasingly integrated into various aspects of society, ensuring their decision-making processes are transparent and reliable is crucial. The AIMO Interpretability Challenge has the potential to drive significant improvements in the development of more robust and trustworthy AI systems, making it an important endeavor for researchers and practitioners alike.