AI Scientists as Engines of Discovery: A Case for Development within Reformed Institutions
It addresses the challenge of integrating AI as an epistemic actor in scientific discovery, but remains a conceptual proposal without empirical results.
The paper argues that multi-agent AI systems can evolve into 'AI scientists' that qualitatively expand hypothesis generation and verification in science, and proposes institutional redesign for verification, accountability, and safety, illustrated by the Denario framework.
Agentic artificial intelligence (AI) systems are beginning to assist, accelerate, and partially automate scientific discovery, performing tasks that span literature synthesis, code generation, data analysis, hypothesis proposal, and model criticism. We argue that this transition is qualitative rather than incremental, and that suitably designed multi-agent systems may evolve from passive computational tools into ``AI scientists'' that can expand the hypothesis-generating and verification capacity of science. Such systems must be developed and deployed within a scientific ecosystem fit for purpose: institutions must be redesigned for verification, accountability, interpretability, and dual-use safety. We sketch how multi-agent architectures, illustrated by the prototype framework \textit{Denario}, accelerate the discovery cycle and traverse model spaces beyond human reach; examine what this implies for authorship, peer review, and the enduring role of human scientists; and close with recommendations for governing AI as an epistemic actor rather than a mere instrument.