Research
Introducing LifeSciBench
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Agentic AI systems are becoming increasingly capable of performing scientific tasks. However, their usefulness to life science researchers depends on how well they handle the complexity of real research. That work rarely looks like a single fact-recall question or a clean prediction problem. Researchers interpret incomplete evidence, reconcile conflicting results, design difficult experiments, troubleshoot assays, evaluate translational risk, and decide what to do next under uncertainty. Current benchmarks do not fully capture these capabilities. Many life science evaluations focus on narrow domains or isolated skills, resulting in questions with structured…
OpenAI · 17 Jun 2026 · 02:00 CESTRead story →Join free ↗
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