Special projects

Building Deep Research: How we Achieved State of the Art

MIT projects selected for funding under US Department of Energy’s Genesis Mission

Initial research projects advance national priorities across natural resources, manufacturing, nuclear physics, and more.

Requests for Research 2.0

We’re releasing a new batch of seven unsolved problems which have come up in the course of our research at OpenAI.

Putting ethical principles at the core of the research lifecycle

Harnessing the Collective Intelligence of AI Agents in the Wild for New Discoveries

arXiv:2606. 10402v1 Announce Type: cross Abstract: Scientific discovery is often a collective process: researchers share partial results, inspect failed attempts, and build on each other's ideas over long time horizons.

Tree-of-Ideas: Automated Research Ideation via Cross-Trajectory Reasoning over Scholarly Evolution

arXiv:2608. 10740v1 Announce Type: new Abstract: Effective research ideation requires moving beyond a static understanding of prior work to trace how research problems and solutions evolve across the literature.

Physics AI research that’s shaping the industry.

Published breakthroughs pushing the state of the art.

Evaluating AI’s ability to perform scientific research tasks

OpenAI introduces FrontierScience, a benchmark testing AI reasoning in physics, chemistry, and biology to measure progress toward real scientific research.

OpenAI Scholars 2020: Final projects

Our third class of OpenAI Scholars presented their final projects at virtual Demo Day, showcasing their research results from over the past five months.

Towards End-to-End Automation of AI Research

arXiv:2606. 15497v1 Announce Type: new Abstract: The automation of science is a long-standing ambition in the field of AI.

Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems

arXiv:2608. 14667v1 Announce Type: new Abstract: Large language model-based agents are increasingly deployed as collaborators in scientific discovery yet most current work focuses on the autonomous capabilities of "AI Scientists".

Scientific exploration, collaboration and labor division in the large language model era

arXiv:2607. 20923v1 Announce Type: cross Abstract: Large language models (LLMs) have rapidly and significantly entered scientific workflows, but it remains unclear how their diffusion is associated with changes in scientists' strategies in research directions and team building.