Managed AI agent teams autonomously tackle open scientific problems and produce research that is verified, reproducible, and replicable at scale.
Using AI agent teams, for each of 317 open scientific problems we ask a lead agent to think deeply about who the best team would be for doing research and writing a paper on the problem, then create the team and work on it. Each team produces a complete research package: computational solution, deterministic experiments, structured data, a full-length paper, and an interactive web application.
From quantum mechanics and astrophysics to fluid dynamics, biology, and machine learning. The pipeline handles theoretical, computational, and experimental problems across the full spectrum of science.
Every numerical claim is cross-referenced against source data. All experiments are rerun for reproducibility. Mathematical proofs are formally verified.
Deterministic seeding (seed 42), pinned dependencies (NumPy 1.26.4, SciPy 1.12.0), and automated experiment reruns ensure bitwise-identical results across runs.
Every problem comes with a self-contained HTML web application featuring Chart.js interactive visualizations, data tables, and responsive design.
50 problems received AI-generated peer reviews. 52 problems have complete revised packages with updated experiments, data, and papers. 157 verification reports assess claim accuracy.
For each problem, a lead agent reasons about the optimal team composition, spawns specialized agents (analyst, experimenter, writer, reviewer, reviser), and coordinates them through eleven stages in two phases.
Phase 1: Generation
Phase 2: Review & Revision
Each problem has a paper, poster, slides, and an interactive web application. Use the search and filters below to explore.