@TheTuringPost: AutoScientists – a research lab made of agents @Harvard researchers connected agents into a self-organizing scientific …
Summary
Harvard researchers present AutoScientists, a multi-agent system that forms self-organizing scientific teams without a central coordinator, achieving strong results on BioML-Bench and optimization tasks.
View Cached Full Text
Cached at: 06/09/26, 08:48 AM
AutoScientists – a research lab made of agents
@Harvard 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
Similar Articles
@Xudong07452910: This latest AutoScientists paper from Harvard is worth reading. It makes me think that AI doing research might not move toward "a single super AI scientist handling the entire process," but rather more like an AI lab that organizes itself. The core of this paper is: allowing multiple agents to share experimental status, organizing around...
Harvard University's AutoScientists proposes a decentralized multi-agent team approach, allowing multiple agents to share experimental status, automatically form teams, and review research plans, significantly outperforming existing methods on multiple benchmarks.
@EricTopol: Just published @ScienceMagazine Impressively broad range of life science research getting done autonomously with agenti…
A paper published in Science Magazine demonstrates a broad range of life science research being conducted autonomously using agentic AI to accelerate discovery.
@adaption_ai: Introducing AutoScientist. Most model training fails outside of frontier labs. AutoScientist automates the full researc…
Adaption AI introduces AutoScientist, a tool that automates the full research loop to make model training more accessible outside of frontier labs.
@dair_ai: https://x.com/dair_ai/status/2061104052818108476
A roundup of three notable AI papers: SkillOpt treats skill documents as trainable parameters to optimize frozen agents; a new method compiles agentic workflows into model weights for 100x cost reduction; and AutoScientists introduces a decentralized agent team for long-running science without a central planner.
auto-psych: Automating the science of mind using agent-driven theory discovery and experimentation
auto-psych is an agent-based system that automates theory discovery and experimentation in computational cognitive science, using LLM agents to generate hypotheses, design experiments, and analyze data from crowdsourced participants. It demonstrates faster and better theory generation compared to human-derived theories in a classic psychology paradigm.