The three-year initiative aims to establish methods and standards to determine when AI models targeting antimicrobial resistance are accurate, reliable, and ready for deployment.
A Jev-as-a-Judge paper reports that accepting TypeSafe AI’s confident JEV verdicts and escalating the rest to GPT-6 Astra kept 99% of GPT-6 accuracy on 510 held-out preference pairs, at about 57% of GPT-6’s fee. The authors say the escalation threshold did not transfer to every fallback model and should be set on one’s own data.
Tenure-track faculty at U.S. universities can submit Fall 2026 proposals on the scientific foundations of AI evaluation. Each project may receive up to $50,000, and the deadline is October 30, 2026.
