Measuring how AI agents handle everyday wet-lab science

Our mission is to ensure that artificial general intelligence benefits all of humanity. As part of that mission, we want to help scientists accelerate biological research—from understanding disease to developing new treatments. For AI agents to contribute, they need to reliably interpret the experimental evidence scientists work with every day in wet labs, where they run experiments with biological samples. Everyday wet-lab work involves failed experiments, ambiguous images and conflicting observations. Existing benchmarks are built around charts and images selected for scientific papers may miss this ambiguity and the troubleshooting it requires. That’s why we’re introducing BioBasicsBench, a new benchmark developed with Ligo, a biotech company focused on enzyme design, to measure how well agents handle routine wet-lab analysis.

The benchmark’s 100 tasks test whether agents can interpret raw experimental data, identify errors, and recognize when the evidence cannot support a conclusion. Getting these tasks right matters because misreading an image or overlooking a problem with the controls used to check an experiment’s results can lead scientists to draw the wrong conclusion and spend time on experiments that won’t help answer their question.

At the ‘xhigh’ setting, which allows more reasoning per task, GPT‑6 Astra earned an average of 66.7% of the available credit, compared with 43.8% for GPT‑5.6 Sol. The results show progress on routine lab analysis, with room for further improvements in interpreting images, checking controls and applying experimental context.

Inside BioBasicsBench

BioBasicsBench uses raw experimental evidence, from gel images and colony plates to microscopy and instrument readouts.

What the agent is given

Number of tasks that include each kind of data. Categories overlap; reference sheets are supporting material.