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This study finds that these “autonomous AI scientists” can introduce systematic methodological pitfalls such as biased evaluation, undisclosed data manipulation, and selective reporting that are not visible in the final manuscript.
Recent advances in agentic Artificial Intelligence (AI) systems have marked a shift in AI for Science: moving away from the use of individual AI systems for narrow task execution, toward multi-agent systems capable of orchestrating complex, end-to-end research workflows and performing (semi-)autonomous scientific disco...
The findings suggest that AI holds significant potential to increase scientific productivity, however, as with other sectors, its ultimate impact will be governed by complex task interdependencies and investment into the elimination of emerging bottlenecks.
Initial evidence is provided that greCAPTCHA can assess manuscript-specific understanding under proctored conditions and the appropriate construct validity for author understanding is remarked on, while also suggesting important changes to be made before deployment.
A framework comprising four dimensions—autonomy, efficacy, goal complexity and generality—is proposed for characterizing AI agents, supporting the construction of agentic profiles for the effective governance of different AI agents.
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