AutoScientists – a research lab made of agents
researchers connected agents into a self-organizing scientific team without a boss agent standing in the middle
All agents look at the same shared workspace: they share memory, explore multiple directions in parallel, critique each other, avoid repeated failures, and reorganize as evidence changes.
But the teams are not fixed. Agents can gather around a promising direction, like architecture, optimizer changes, or data augmentation, then abandon it if it stops working.
Before they spend compute, they discuss proposals and critique each other.
AutoScientists also shows strong results:
- 74.4% mean leaderboard percentile on BioML-Bench
- 1.9× faster GPT training optimization
- +12.5% on ACE2–Spike, with the same method transferring to 217 ProteinGym assays for a +6.5% average gain