Banger paper from Meta on AI research agents.

(bookmark it)

If you build AI-scientist systems, this one is worth your time.

The result:

A 27B open model beats the strongest open autoresearch baseline by 14.0%, mostly on novelty, and beats Claude Code SDK and Codex SDK setups by up to 5.9%.

How it works:

IdeaScientist splits ideation into three roles trained separately with RL. A gap finder reads related work for limitations, an innovator retrieves mechanisms that solved similar problems in other fields, and a writer turns the result into a full proposal.

Retrieval runs over a corpus of 2.77M decomposed research ideas.

The evaluation only allows literature published before a cutoff date and scores proposals against directions explored later in 15K human-written papers.

Paper: https://arxiv.org/abs/2610.04074

Chat with Paper: https://academy.dair.ai/papers/ideascientist-orchestrating-agents-for-grounded-scientific-ideation-2610.04074