Agentic Discovery: Closing the Loop with Cooperative Agents
This work tackles the problem of accelerating scientific discovery for researchers by introducing a new paradigm, though it is conceptual without concrete results.
The paper addresses the bottleneck of human decision-making in scientific discovery by proposing cooperative agents to augment humans and enable autonomous discovery, requiring progress in AI and infrastructure.
As data-driven methods, artificial intelligence (AI), and automated workflows accelerate scientific tasks, we see the rate of discovery increasingly limited by human decision-making tasks such as setting objectives, generating hypotheses, and designing experiments. We postulate that cooperative agents are needed to augment the role of humans and enable autonomous discovery. Realizing such agents will require progress in both AI and infrastructure.