OpenAI released BioBasicsBench, a 100-task benchmark designed to measure AI agent performance on routine wet-lab biology, specifically how well models interpret raw experimental data without hand-holding.
The benchmark targets the unglamorous core of biological research: reading gel images, interpreting spectrophotometer outputs, parsing flow cytometry files, tasks that consume researcher hours daily but rarely appear in AI capability evaluations. That gap is the reason this paper is worth reading in full, not just the leaderboard.
No single model aces it. The performance spread across tested agents reveals where reasoning breaks down when biology gets procedural and messy. If you work in AI safety, drug discovery, or automated lab systems, the failure modes documented here are more instructive than the top scores.
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