BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML
Bibliographic record
Abstract
Abstract Large language model (LLM) agents hold promise for accelerating biomedical research and development (R&D). Several biomedical agents have recently been proposed, but their evaluation has largely been restricted to question answering (e.g., LAB-Bench) or narrow bioinformatics tasks. Presently, there remains a lack of benchmarks evaluating agent capability in multi-step data analysis workflows or in solving the machine learning (ML) challenges central to AI-driven therapeutics development, such as perturbation response modeling or drug toxicity prediction. We introduce BioML-bench , the first benchmarking suite for evaluating AI agents on end-to-end biomedical ML tasks. BioML-bench spans four domains (protein engineering, single-cell omics, biomedical imaging, and drug discovery) with tasks that require agents to parse a task description, build a pipeline, implement models, and submit predictions graded by established metrics (e.g., AUROC, Spearman). We evaluate four open-source agents: two biomedical specialists (STELLA, Biomni) and two generalists (AIDE, MLAgentBench). On average, agents underperform relative to human baselines, and biomedical specialization does not confer a consistent advantage. We also found that agents which employed more diverse ML strategies more often tended to score highest, suggesting that architecture and scaffolding may be stronger determinants of performance. These findings underscore both the potential and current limits of agentic systems for biomedical ML, and highlight the need for systematic, reproducible evaluations. BioML-bench is provided open-source at github.com/science-machine/biomlbench .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".