Li, J.; Ho, A.

We introduce GeneBench, a benchmark for AI agents on realistic multi-stage scientific data analysis in genetics and quantitative biology. Existing biology benchmarks mostly measure knowledge retrieval, execution of routine pipelines, or a single analysis step. Yet they do not capture the broader scope of work that occupies much of computational scientists' time: cleaning and normalizing assay, phenotype, or clinical data; exploratory data analysis; statistical model selection and diagnostic iteration; and producing a conclusion that informs a downstream scientific or translational decision. GeneBench addresses this gap with 103 evaluations targeting quantities of direct practical relevance across 10 domains, with a genomics-centered core and adjacent coverage in other 'omics and quantitative biology settings. Each problem comprises an encapsulated multi-step analysis with staged data, prompts that define a quantity of interest while otherwise providing minimal guidance, and verifiable answers. Solving each problem requires identifying and addressing realistic obstacles such as measurement error, selection bias, confounding, QC failures, and choosing among competing model classes. Through extensive ablation studies, we verify that each problem admits a single defensible answer. Each problem involves multiple dependent decision points; i.e., substantive inferential forks where a plausible wrong choice changes the downstream analysis, such that errors propagate through the inferential chain and into the final graded target. In initial evaluations, the mainline GPT family reaches an eval-level pass rate of 25.0% with GPT-5.5 at the xhigh reasoning setting. In separately reported GPT Pro runs, GPT-5.5 Pro reaches 33.2%, GPT-5.4 Pro reaches 25.6%, and GPT-5.2 Pro reaches 10.8%. Even for the two strongest reported Pro-harness settings, 60.2% and 62.1% of problems, respectively, remain below 20% pass rate over repeated runs. The strongest external baseline, Gemini 3.1 Pro, achieves 11.2%. Models often complete substantial portions of the workflow but exhibit a consistent gap between noticing and acting: they identify local diagnostic signals but fail to propagate the implication to the corresponding analysis decision, and as a result select wrong estimators or persist on initially plausible but incorrect analysis paths. GeneBench therefore measures an emerging capability that remains as yet unreliable.