Original post · AI translation
OpenAI 开发了 BioBasicsBench，这项测试向 AI 智能体提供真实实验室工作中杂乱的原始数据，例如凝胶照片和显微镜视频，并根据它们是否正确解读这些数据评分。
在一项新的湿实验室基准测试中，GPT-6 Astra 获得了可得分数的 66.7%，而 GPT-5.6 Sol 为 43.8%。
BioBasicsBench 由 OpenAI 与酶设计公司 Ligo 合作开发，从科学家的原始凝胶图像、菌落培养皿、显微镜图像、测序读数和实验台视频中提取了 100 项任务。
其评分标准允许给部分分，包括在发现错误或指出证据不足以支持结论时给分。
在 xhigh 设置下，Astra 每项任务使用 4,704 个输出 token，约为较旧模型的一半，因此性能提升并非由更长的推理带来。
Original
OpenAI built BioBasicsBench, a test that gives AI agents the messy raw data of real lab work, like gel photos and microscope videos, and grades whether they read it correctly. GPT-6 Astra earned 66.7% of available credit on a new wet-lab benchmark, versus 43.8% for GPT-5.6 Sol. BioBasicsBench, built with enzyme-design company Ligo, draws 100 tasks from scientists’ raw gels, colony plates, microscopy, sequencing reads and bench videos. Its rubrics give partial credit, including for spotting errors or saying evidence can’t support conclusions. At the xhigh setting, Astra used 4,704 output tokens per task, about half the older model’s, so longer reasoning didn’t drive the gain.
一种衡量 AI 智能体如何处理湿实验室科学常规分析工作的新方法——BioBasicsBench。
这项基准测试与专注于酶设计的生物技术公司 Ligo 合作开发，采用来自真实实验的 100 项任务，测试智能体能否解读原始实验室数据、识别错误，以及意识到证据何时不足以支持结论。
加速生物学研究始于把日常工作做好。BioBasicsBench 揭示了模型需要改进的地方，并帮助我们追踪这些进步对科学进展、生物安全和生物安保意味着什么。
https://t.co/wN2xLk0YeW
Original
A new way to measure how AI agents handle the routine analyses of wet-lab science—BioBasicsBench. Developed with Ligo, a biotech company focused on enzyme design, the benchmark draws on 100 tasks from real experiments to test whether agents can interpret raw lab data, identify errors, and recognize when the evidence doesn’t support a conclusion. Accelerating biological research starts with getting the everyday work right. BioBasicsBench shows where models need to improve, and helps us track what those gains mean for scientific progress, biosafety, and biosecurity. https://t.co/wN2xLk0YeW