OpenAI has introduced LifeSciBench, a new benchmark designed to assess how well AI systems can support complex life science research tasks. The benchmark includes 750 expert-authored tasks spanning seven biological domains and workflows, created by scientists with Ph.D.-level training and industry experience. It aims to reflect the complexity of real-world scientific work by incorporating tasks that require multiple reasoning steps and the interpretation of various scientific artifacts.
LifeSciBench measures whether AI systems can perform tasks such as interpreting evidence, making domain-grounded judgments, and communicating conclusions useful to expert reviewers. The benchmark includes 1,062 attached artifacts like figures, tables, and experimental records, with more than half of tasks requiring models to interpret or synthesize information from at least one artifact. Tasks were developed through a rigorous review process, with each undergoing multiple revision cycles and at least two rounds of expert reviews, ensuring scientific accuracy and clarity.
The benchmark is structured to reflect how scientific work is evaluated in practice, with detailed rubrics that assess both scientific correctness and the usefulness of responses for research decisions. Each task is graded using a rubric with 19,020 criteria, an average of 25 per task, to evaluate not only final-answer accuracy but also the validity and operational utility of the reasoning process. This design ensures that models are assessed on their ability to contribute meaningfully to real-world research, rather than just answer isolated biology questions.
Source: openai
