Kosmos: An AI Scientist for Autonomous Discovery

In this episode, we discuss Kosmos: An AI Scientist for Autonomous Discovery by Ludovico Mitchener, Angela Yiu, Benjamin Chang, Mathieu Bourdenx, Tyler Nadolski, Arvis Sulovari, Eric C. Landsness, Daniel L. Barabasi, Siddharth Narayanan, Nicky Evans, Shriya Reddy, Martha Foiani, Aizad Kamal, Leah P. Shriver, Fang Cao, Asmamaw T. Wassie, Jon M. Laurent, Edwin Melville-Green, Mayk Caldas, Albert Bou, Kaleigh F. Roberts, Sladjana Zagorac, Timothy C. Orr, Miranda E. Orr, Kevin J. Zwezdaryk, Ali E. Ghareeb, Laurie McCoy, Bruna Gomes, Euan A. Ashley, Karen E. Duff, Tonio Buonassisi, Tom Rainforth, Randall J. Bateman, Michael Skarlinski, Samuel G. Rodriques, Michaela M. Hinks, Andrew D. White. The paper presents Kosmos, an AI scientist that autonomously conducts data-driven discovery by iteratively analyzing data, searching literature, and generating hypotheses over extended periods. Kosmos uses a structured world model to integrate information across agents, enabling coherent research workflows involving extensive code execution and literature review. Evaluations show Kosmos produces highly accurate and traceable scientific reports with discoveries spanning multiple fields, some reproducing unpublished work and others novel.

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