Solving Open Research Problems Together: Six Papers from Meta AI and Mathematicians

Summary

Solving Open Research Problems Together Mathematicians and Muse Spark collaborate on six research papers Earlier this year, our models achieved gold-medal-level performance across five high-school Olympiad competitions in mathematics, physics, and chemistry. Those results inspired us to see whether AI could also help scientists solve open research problems. Competition problems can be incredibly difficult, but those problems already have a solution. Open research is different. There is no answer key, no guarantee that an approach will work. Making progress means trying and retrying ideas, making new mistakes and resolving them, and sometimes going back to the beginning. Over the past several months, we've partnered with mathematicians to explore problems across several areas of mathematics. They used both Muse Spark 1.1 and 1.2 in Thinking Mode through the regular meta.ai chat interface, with no custom research scaffold. Open research takes time. Our goal here wasn't to mass-produce papers, but to empower researchers and help them develop mathematical insights that others can understand and build on. We’re proud to partner with the scientific community to help them advance research. These collaborations were done under the following principles: - A team of mathematicians guided the research and worked with Muse Spark to explore ideas and develop the arguments. - A second group of mathematicians then reviewed their work. - Each paper clearly marks which passages were primarily drafted by researchers and which were drafted by AI. - Each paper gives credit to the earlier research and mathematical ideas it builds on. Today, we're sharing six such papers from that collaboration. Five present answers to previously open research questions. After completing our work, we learned that other teams outside Meta had independently announced solutions to some of the same problems using different approaches. We recognize and appreciate their contributions and clearly acknowledge their work and how it relates to ours in the papers. Probability: The Strict Threshold for Gaussian Ellipsoid Fitting Aykut Arslan with Muse Spark via meta.ai. Review by Babak Modami, Alexander Roitershtein, Mark Sepanski, and Grigory Sokolov Paper: The Strict Threshold for Gaussian Ellipsoid Fitting. The paper answers a long-standing question about fitting random Gaussian points in high dimensions…