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 question formats and clean reference answers. While valuable, they often fail to truly assess whether a model can contribute across the broader span of research-level work.

We designed LifeSciBench to help close this gap. Every task is grounded in the judgment of practicing life scientists with Ph.D.-level training and direct experience advancing drug discovery programs in biotech and pharmaceutical settings.

LifeSciBench includes 750 expert-authored tasks spanning seven workflows and seven biological domains.

LifeSciBench measures whether AI systems can support realistic life science research tasks, not just answer biology questions. To define the benchmark taxonomy, we surveyed practicing life scientists about the workflows they use most often in applied research settings. Then, we grouped their responses into seven recurring categories: evidence handling, analysis, design and optimization, scientific reasoning, validation and operations, translation, and scientific communication.

Each task is structured like a request a scientist might give to a knowledgeable collaborator: scientific prompt, any relevant context or artifacts, and a free-response answer.

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