With the rapid development of biotechnology, how to efficiently and accurately analyze complex biological data has become a major challenge for researchers. To help AI models demonstrate stronger analytical capabilities in this field, OpenAI has recently launched a new benchmark test called GeneBench-Pro. This benchmark focuses on evaluating the practical research capabilities of AI in areas such as genomics and proteomics, especially their ability to make judgments and decisions when facing messy and incomplete data.

GeneBench-Pro is significantly different from traditional benchmark tests. Traditional tests often focus on a model's memory capacity and fixed task completion processes, while GeneBench-Pro emphasizes the model's practicality in real research environments. The test tasks are designed to consider a "fuzzy, incomplete, and noisy" data environment, allowing the model to explore and analyze data under these conditions, thereby more realistically reflecting its judgment abilities.

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This benchmark test covers a wide range of biological fields, including genomics, quantitative biology, and translational medicine, with a total of 129 questions covering subfields such as statistical genetics, population genetics, functional genomics, and proteomics. Each question provides a dataset close to a real research environment for the model, and requires the model to independently select analysis methods and adjust strategies based on brief experimental backgrounds and related questions, ultimately drawing conclusions.

To avoid scoring bias commonly seen in traditional long-process tests, OpenAI used synthetic data when designing GeneBench-Pro. This approach allows OpenAI to better control the data generation process, ensuring that the model's performance better reflects its true understanding ability, rather than just obtaining correct answers through guessing or shortcuts.

Currently, OpenAI has open-sourced 10 representative GeneBench-Pro sample questions on the Hugging Face platform, allowing external researchers to experience them through an interactive interface. In the future, OpenAI plans to assign 50 of these questions to Artificial Analysis for independent evaluation, to verify the actual performance of different models on this benchmark test.