Sci$Π$vot: 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.
ProofPaper ↗
Key points
- Evaluating scientific judgment is difficult because research decisions are open-ended and depend on project context.
- We construct Sci$Π$vot, a 248-case benchmark of scientific judgment based on human corrections.
- We compare LLM and human performance and examine the effects of reasoning effort, access to the project record, and reconsideration.
- Even with a human-corrected alternative available, frontier models struggle with the selected research decisions, while Sci$Π$vot supports systematic evaluation of scientific judgment for LLM agents.
Sources (2)
- [1]Sci$Π$vot: Benchmarking Principal-Investigator Judgment at Pivotal Moments of Scientific AutoResearch AgentsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 01:34 AM
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.
- [2]SciΠvot: Benchmarking Principal-Investigator Judgment at Pivotal Moments of Scientific AutoResearch AgentsHugging Face Daily Papers · Oct 15, 12:00 AM · same content
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