Virtuous Machines: Towards Artificial General Science
Artificial intelligence systems are transforming scientific discovery by
accelerating specific research tasks, from protein structure prediction to
materials design, yet remain confined to narrow domains requiring substantial
human oversight. The exponential growth of scientific literature and increasing
domain specialisation constrain researchers' capacity to synthesise knowledge
across disciplines and develop unifying theories, motivating exploration of
more general-purpose AI systems for science. Here we show that a
domain-agnostic, agentic AI system can independently navigate the scientific
workflow - from hypothesis generation through data collection to manuscript
preparation. The system autonomously designed and executed three psychological
studies on visual working memory, mental rotation, and imagery vividness,
executed one new online data collection with 288 participants, developed
analysis pipelines through 8-hour+ continuous coding sessions, and produced
completed manuscripts. The results demonstrate the capability of AI scientific
discovery pipelines to conduct non-trivial research with theoretical reasoning
and methodological rigour comparable to experienced researchers, though with
limitations in conceptual nuance and theoretical interpretation. This is a step
toward embodied AI that can test hypotheses through real-world experiments,
accelerating discovery by autonomously exploring regions of scientific space
that human cognitive and resource constraints might otherwise leave unexplored.
It raises important questions about the nature of scientific understanding and
the attribution of scientific credit.
68a689869e4b49496aac6945
Gabrielle Wehr
claimed_verified
2025-08-21T09:15:58.966Z
68a68709b31cf04906ee189a
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/xeWDDCGxcYjDWf_l7UxA9.png
Gabrielle
user
gwehr
68a689869e4b49496aac6946
Reuben Rideaux
68a689869e4b49496aac6947
Amaya J. Fox
68a689869e4b49496aac6948
David R. Lightfoot
68a689869e4b49496aac6949
Jason Tangen
68a689869e4b49496aac694a
Jason B. Mattingley
68a689869e4b49496aac694b
Shane E. Ehrhardt
Virtuous Machines: Towards Artificial General Science
Artificial intelligence systems are transforming scientific discovery by
accelerating specific research tasks, from protein structure prediction to
materials design, yet remain confined to narrow domains requiring substantial
human oversight. The exponential growth of scientific literature and increasing
domain specialisation constrain researchers' capacity to synthesise knowledge
across disciplines and develop unifying theories, motivating exploration of
more general-purpose AI systems for science. Here we show that a
domain-agnostic, agentic AI system can independently navigate the scientific
workflow - from hypothesis generation through data collection to manuscript
preparation. The system autonomously designed and executed three psychological
studies on visual working memory, mental rotation, and imagery vividness,
executed one new online data collection with 288 participants, developed
analysis pipelines through 8-hour+ continuous coding sessions, and produced
completed manuscripts. The results demonstrate the capability of AI scientific
discovery pipelines to conduct non-trivial research with theoretical reasoning
and methodological rigour comparable to experienced researchers, though with
limitations in conceptual nuance and theoretical interpretation. This is a step
toward embodied AI that can test hypotheses through real-world experiments,
accelerating discovery by autonomously exploring regions of scientific space
that human cognitive and resource constraints might otherwise leave unexplored.
It raises important questions about the nature of scientific understanding and
the attribution of scientific credit.
A domain-agnostic AI system can autonomously design, execute, and analyze psychological studies, producing manuscripts with methodological rigor comparable to human researchers.
domain-agnostic agentic AI scientific workflow hypothesis generation data collection manuscript preparation psychological studies visual working memory mental rotation imagery vividness analysis pipelines theoretical reasoning methodological rigour embodied AI real-world experiments scientific discovery