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Kosmos: An AI Scientist for Autonomous Discovery

Paper · 2025 · 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; and Andrew D. White

Summary

LLM-based research agents tend to lose track of their goal after a limited number of steps, which caps how deep their analyses can go. This paper introduces Kosmos, an autonomous "AI scientist" from Edison Scientific. A researcher gives it a dataset and a broad research goal. Kosmos then runs repeated cycles, for up to 12 hours, alternating parallel data-analysis and literature-search agents. A structured world model sits at the center of the design. It collects summaries of each task's output and is queried to plan the next round of work, keeping the many agents coordinated over long runs. The final output is a set of scientific reports in which every claim links back to either a Jupyter notebook or a cited paper. Expert evaluators judged most report statements correct, and collaborators estimated that a full run equals months of human work. Kosmos produced seven discoveries across several fields. This suggests that long-horizon, traceable, data-driven discovery by AI agents is becoming practical, though its synthesis claims remain the least reliable part.

Key points

  • Scale per run: about 200 agent rollouts (roughly 166 data-analysis and 36 literature-review), around 42,000 lines of code, and about 1,500 full papers read. That is 9.8x the code output of the earlier Robin system. Each cycle handles up to ten tasks, and each run ends in three or four reports.
  • Accuracy: experts checked 102 statements from three reports and found 79.4% accurate overall. By statement type, accuracy was 85.5% for data analysis, 82.1% for literature-based claims, and only 57.9% for interpretive synthesis.
  • Expert-equivalent effort: the authors' own task-count estimate is about 4.1 expert-months per run (n=6). Collaborating groups estimated about 6.14 months for a 20-cycle run (n=7).
  • Scaling with runtime: collaborators reported that the number of valuable findings grows roughly linearly with cycle count, tested up to 20 cycles.
  • Discoveries: seven findings covering four fields: statistical genetics, neuroscience, materials science, and metabolomics.
    • Of these, three match results from preprints or unpublished work that Kosmos had no access to, reproducing them independently.
    • Four are new, including a neuronal-aging mechanism and genetic evidence that higher circulating SOD2 may reduce myocardial fibrosis.
    • Analysis code is available at github.com/EdisonScientific/kosmos-figures.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Stanford UniversityStanford · United States567 works · 335,190 citations🎓Massachusetts Institute of TechnologyCambridge · United States274 works · 82,013 citations🎓University of OxfordOxford · United Kingdom138 works · 161,448 citations🎓University College LondonLondon · United Kingdom119 works · 23,545 citations🎓Washington University in St. LouisSt Louis · United States17 works · 1,159 citations🤝Future House, Inc.San Francisco · United States6 works · 595 citations🔬Broad InstituteCambridge · United States5 works · 39,829 citations🏥Stanford MedicineStanford · United States6 works · 1,507 citations

Also on the byline: Swiss Center for Electronics and Microtechnology · Spanish National Cancer Research Centre · St. Louis VA Medical Center · Institute for Scientific Analysis · UK Dementia Research Institute · Edison Scientific Inc. · UCL Queen Square Institute of Neurology.

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 19,503 worksArtificial Intelligence · 18,805 worksDecision Sciences · 306 worksInformation Systems and Management · 96 worksMaterials Science · 58 worksMaterials Chemistry · 58 works

Cite

Cite as 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; and Andrew D. White. 2025. Kosmos: An AI Scientist for Autonomous Discovery. arXiv:2511.02824.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2511.02824 · arXiv 2511.02824

@misc{hinks2025kosmos,
  title = {{Kosmos: An AI Scientist for Autonomous Discovery}},
  author = {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; and Andrew D. White},
  year = {2025},
  doi = {10.48550/arxiv.2511.02824},
  eprint = {2511.02824},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2511.02824}
}

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