Introducing LifeSciBench

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 evaluates scientific reasoning alongside the less well-defined, practical skills necessary for real-world scientific use. Its tasks ask models to work through realistic research problems: interpreting evidence, making domain-grounded judgments, and communicating conclusions that would be useful to expert reviewers. Many tasks also require models to handle uncertainty and reason over supporting data files rather than relying on prompt text alone.

Reviewers scored whether each task reflected the qualities needed for a strong benchmark question: alignment with real-world research work, appropriate testing of scientific reasoning and domain expertise, grounding in evidence or expert consensus, and overall usefulness for assessing model performance. Agreement exceeded 96% in every category.

Model performance varies substantially by task type, workflow, and response format.

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.

The answer format also matters. Tasks requiring exact sequence, structure, or construct-level outputs show lower pass rates: GPT‑Rosalind reaches only 14.8% on numeric tasks and 24.0% on sequence or structure outputs. Construct-generation tasks are also brittle, with GPT‑Rosalind at 27.3% and showing little improvement over GPT‑5.5. Some of this gap may reflect a stricter grading surface for exact-answer tasks, where small differences in calculation or formatting can cause a response to fall under pass threshold. Still, these failures are scientifically meaningful because many life science workflows require outputs that are exact enough to be used directly, such as in CRISPR/HDR donor design or siRNA design.

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

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. Expert-written rubrics evaluate whether a model can produce the right answer for a specific problem, with the right level of detail, justification, caveats, and formatting a scientist would expect.

The benchmark is designed to reflect the complexity of life science work. Overall, 79% of tasks require multiple reasoning or decision-making steps, with an average of four steps per task. LifeSciBench includes 1,062 attached artifacts spanning figures, PDFs, tables, sequence files, structure or chemical files, and web references. More than half of tasks (53%) require models to interpret or synthesize information from at least one artifact.