Kosmos
Autonomous AI scientist for long research campaigns that analyzes data and literature to produce fully cited scientific reports.
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MAESTRO 7-layer threat model + OWASP AIVSS risk score for Kosmos, derived from its capabilities.
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Overview
Kosmos is an autonomous AI scientist that automates data-driven discovery across domains like neuroscience, metabolomics, materials science, and genetics. Given an open-ended research objective and one or more datasets, Kosmos runs long research campaigns of up to ~12 hours, iterating between data analysis, literature search, and hypothesis generation. It uses a structured world model to coordinate agents and maintain coherence over hundreds of rollouts, typically executing tens of thousands of lines of code and reading around 1,500 papers per run before synthesizing a fully cited scientific report whose statements are traceable to code cells or primary literature. Independent scientists have estimated that a 20-cycle Kosmos run can compress roughly six months of PhD-level work into a single day, with most statements judged accurate and reproducible. Researchers can access Kosmos via the Edison Scientific platform with a credit-based pricing model, while an open-source implementation on GitHub allows teams to self-host and extend the system using multiple model providers.
Key features
- autonomous discovery
- scientific research automation
- literature review
- hypothesis generation
- data analysis pipelines
- world model memory
- multi-agent orchestration
- omics workflows
- materials science
- drug discovery support
Use cases
- Running end-to-end research campaigns that combine data analysis, literature review, and hypothesis generation.
- Screening complex biological, chemical, or materials datasets to uncover mechanisms, biomarkers, and targets.
- Rapidly validating or stress-testing internal results before committing wet-lab, clinical, or engineering resources.
- Generating fully cited discovery reports where every claim links back to code execution or primary papers.
- Exploring multiple alternative hypotheses in parallel by running independent trajectories on the same datasets.
- Building custom AI research workflows by extending the open-source implementation with domain-specific tools.