BioAgent Bench is an evaluation suite designed for measuring the performance and robustness of AI agents in common bioinformatics tasks. The suite consists of manually curated end-to-end tasks (e.g., RNA-seq, variant calling, metagenomics) accompanied by task-specific prompts and concrete output artifacts to support automated assessment. We also introduce controlled perturbation testing as a form of adversarial evaluation, using corrupted inputs, decoy files, and prompt bloat to probe biological reasoning beyond pipeline completion rates.
Pipeline completion
Completion is only the baseline for reliability. We evaluated GPT-5.2 in the Codex CLI harness under three controlled perturbations: corrupting selected inputs, adding plausible but irrelevant decoy files, and padding prompts with related but unnecessary text. The agent missed corrupted inputs in 3 of 10 tasks, used decoys incorrectly in 2 of 10, and completed 28% fewer steps under prompt bloat. Across up to four clean runs per task, mean Jaccard overlap was 0.43 and mean Pearson correlation was 0.73, showing that even a completed pipeline can produce meaningfully different results from one run to the next.
Adversarial testing
Controlled perturbations and repeated clean trials with GPT-5.2 in the Codex CLI harness.
Relative steps completed; clean run = 100.