SciΠvot: Benchmarking Principal-Investigator Judgment at Pivotal Moments of Scientific AutoResearch Agents
Large language model (LLM) agents can keep scientific experiments running for months with little human supervision, but reliable autonomous research also requires scientific judgment. Evaluating scientific judgment is difficult because research decisions are open-ended and depend on project context. We construct SciΠvot, a 248-case benchmark of scientific judgment based on huma
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Large language model (LLM) agents can keep scientific experiments running for months with little human supervision, but reliable autonomous research also requires scientific judgment. Evaluating scientific judgment is difficult because research decisions are open-ended and depend on project context. We construct SciΠvot, a 248-case benchmark of scientific judgment based on huma
This work introduces a crucial benchmark for evaluating the scientific judgment of AI agents, moving beyond simple task completion to assess complex decision-making in autonomous research. For ML engineers, understanding how to design and evaluate agents for scientific discovery is becoming increasingly important as AI takes on more complex, open-ended tasks.