AI Scientist workshop result shows bounded automation with human selection

Engage principal investigators, research development leaders and computing services where experimental iteration consumes scarce time. Ask which computational steps are repeatable, how candidate ideas are selected and what evidence an investigator needs before trusting a manuscript. Offer a bounded pilot around a known public dataset and an established scientific question. The value hypothesis is faster exploration with accountable selection, to be tested against the existing workflow. Avoid equating workshop review with transformative discovery or promising autonomous publication. Budget discarded candidates, domain review and correction effort; the selected sample cannot establish campus-wide savings.

Implement separate modules for idea proposals, authorized data access, code execution and evidence-linked write-up. Use resource caps, reproducible containers and external experiment records. Treat generated citations and figures as testable claims. For a proof of value, compare a scientist-led baseline with the assisted workflow using the same data, compute budget and acceptance rubric; retain all candidates to expose selection effects. Verify numerical outputs independently and evaluate total reviewer time. External model services require approved handling of unpublished data, while local execution still requires sandboxing and dependency controls.

Name the principal investigator as scientific owner and research computing as execution support. Agree on the question, data permissions, baseline and stopping rules before adoption. Train researchers to document both human selection and automated steps. Proposed acceptance criteria include a replayable experiment, verified citations, disclosure of every excluded candidate and independent sign-off on each retained conclusion. Separate manuscript quality from scientific contribution in the final review. Record compute costs and correction time. Risks include selective reporting, confident unsupported claims and staff mistaking polished outputs for validated results; the observed workshop result does not remove these responsibilities.