arXiv:2605. 02050v2 Announce Type: replace-cross Abstract: This work establishes a framework for standardizing AI evaluation RCTs (sometimes called human uplift studies).
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2606. 19899v1 Announce Type: cross Abstract: This paper addresses a rapidly emerging policy challenge: how to generate and interpret credible evidence about the biological capabilities and risks of AI scientists, or agentic AI systems capable of autonomously or collaboratively performing multi-step scientific tasks.
arXiv:2603. 11001v3 Announce Type: replace-cross Abstract: Human uplift studies, or studies that measure the effects of AI access on human performance via randomized controlled trials (RCT) or similar methodologies, increasingly inform frontier AI governance and deployment decisions.
arXiv:2601. 09753v2 Announce Type: replace-cross Abstract: AI science evaluation tools aim to assess research credibility.
arXiv:2511. 19735v2 Announce Type: replace-cross Abstract: Randomized controlled trials (RCTs)have been the cornerstone of clinical evidence; however, their cost, duration, and restrictive eligibility criteria limit power and external validity.
arXiv:2605. 08827v2 Announce Type: replace Abstract: The safety of mental health AI is often judged at the wrong temporal scale.
arXiv:2606. 02458v1 Announce Type: new Abstract: Organizations routinely run experiments for A/B testing, yet the data generated from one experiment is underutilized to inform subsequent intervention design.