The 6 protein engineering tasks of BioML-bench v0.1-alpha, as listed in Table 3: SPIKE_SARS2_Starr_2020, SPA_STAAU_Tsuboyama_2023, PSAE_PICP2_indels, CBX4_HUMAN_multi-sub, Q8EG35_SHEON_indels, CSN4_MOUSE_indels.
10 evaluations · 34 results. Different protocols are not a single leaderboard.
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: AIDE v6.3.3, GPT-4.1 backend (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 38.9 ± 15.9% Proportion (share of tasks where the agent's mean score exceeds the median human leaderboard submission (Section 3.4 definition); non-penalized: runs without a valid submission omitted) percent · higher Uncertainty: ± 15.9 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceAIDE on BioML-bench protein engineering (non-penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Non-penalized', domain 'Protein Engineering', row 'AIDE', column 'Above Median (%)' |
| Configuration: AIDE v6.3.3, GPT-4.1 backend (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 16.7 ± 11.4% Proportion (share of tasks where the agent reaches a public leaderboard medal threshold, MLE-bench definition (Section 3.4); non-penalized: runs without a valid submission omitted) percent · higher Uncertainty: ± 11.4 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceAIDE on BioML-bench protein engineering (non-penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Non-penalized', domain 'Protein Engineering', row 'AIDE', column 'Any Medal (%)' |
| Configuration: AIDE v6.3.3, GPT-4.1 backend (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 35.99 ± 10.96% Leaderboard percentile (mean over tasks in the domain; non-penalized: runs without a valid submission omitted) percent · higher Uncertainty: ± 10.96 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceAIDE on BioML-bench protein engineering (non-penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Non-penalized', domain 'Protein Engineering', row 'AIDE', column 'Leaderboard Percentile' |
| Configuration: Biomni v0.0.5, Claude Sonnet 4 backend (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 25.0 ± 17.1% Proportion (share of tasks where the agent's mean score exceeds the median human leaderboard submission (Section 3.4 definition); non-penalized: runs without a valid submission omitted) percent · higher Uncertainty: ± 17.1 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceBiomni on BioML-bench protein engineering (non-penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Non-penalized', domain 'Protein Engineering', row 'Biomni', column 'Above Median (%)' |
| Configuration: Biomni v0.0.5, Claude Sonnet 4 backend (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 25.0 ± 17.1% Proportion (share of tasks where the agent reaches a public leaderboard medal threshold, MLE-bench definition (Section 3.4); non-penalized: runs without a valid submission omitted) percent · higher Uncertainty: ± 17.1 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceBiomni on BioML-bench protein engineering (non-penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Non-penalized', domain 'Protein Engineering', row 'Biomni', column 'Any Medal (%)' |
| Configuration: Biomni v0.0.5, Claude Sonnet 4 backend (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 29.93 ± 11.04% Leaderboard percentile (mean over tasks in the domain; non-penalized: runs without a valid submission omitted) percent · higher Uncertainty: ± 11.04 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceBiomni on BioML-bench protein engineering (non-penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Non-penalized', domain 'Protein Engineering', row 'Biomni', column 'Leaderboard Percentile' |
| Configuration: Dummy agent (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 0.0 ± 0.0% Proportion (share of tasks where the agent's mean score exceeds the median human leaderboard submission (Section 3.4 definition); non-penalized: runs without a valid submission omitted) percent · higher Uncertainty: ± 0.0 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDummy on BioML-bench protein engineering (non-penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Non-penalized', domain 'Protein Engineering', row 'Dummy', column 'Above Median (%)' |
| Configuration: Dummy agent (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 0.0 ± 0.0% Proportion (share of tasks where the agent reaches a public leaderboard medal threshold, MLE-bench definition (Section 3.4); non-penalized: runs without a valid submission omitted) percent · higher Uncertainty: ± 0.0 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDummy on BioML-bench protein engineering (non-penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Non-penalized', domain 'Protein Engineering', row 'Dummy', column 'Any Medal (%)' |
| Configuration: Dummy agent (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 0.00 ± 0.00% Leaderboard percentile (mean over tasks in the domain; non-penalized: runs without a valid submission omitted) percent · higher Uncertainty: ± 0.00 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDummy on BioML-bench protein engineering (non-penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Non-penalized', domain 'Protein Engineering', row 'Dummy', column 'Leaderboard Percentile' |
| Configuration: MLAgentBench, GPT-4.1 backend (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 12.5 ± 12.5% Proportion (share of tasks where the agent's mean score exceeds the median human leaderboard submission (Section 3.4 definition); non-penalized: runs without a valid submission omitted) percent · higher Uncertainty: ± 12.5 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceMLAgentBench on BioML-bench protein engineering (non-penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Non-penalized', domain 'Protein Engineering', row 'MLAgentBench', column 'Above Median (%)' |
| Configuration: MLAgentBench, GPT-4.1 backend (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 0.0 ± 0.0% Proportion (share of tasks where the agent reaches a public leaderboard medal threshold, MLE-bench definition (Section 3.4); non-penalized: runs without a valid submission omitted) percent · higher Uncertainty: ± 0.0 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceMLAgentBench on BioML-bench protein engineering (non-penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Non-penalized', domain 'Protein Engineering', row 'MLAgentBench', column 'Any Medal (%)' |
| Configuration: MLAgentBench, GPT-4.1 backend (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 13.52 ± 9.18% Leaderboard percentile (mean over tasks in the domain; non-penalized: runs without a valid submission omitted) percent · higher Uncertainty: ± 9.18 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceMLAgentBench on BioML-bench protein engineering (non-penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Non-penalized', domain 'Protein Engineering', row 'MLAgentBench', column 'Leaderboard Percentile' |
| Configuration: STELLA, OpenRouter default backend (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 45.8 ± 18.7% Proportion (share of tasks where the agent's mean score exceeds the median human leaderboard submission (Section 3.4 definition); non-penalized: runs without a valid submission omitted) percent · higher Uncertainty: ± 18.7 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceSTELLA on BioML-bench protein engineering (non-penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Non-penalized', domain 'Protein Engineering', row 'STELLA', column 'Above Median (%)' |
| Configuration: STELLA, OpenRouter default backend (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 16.7 ± 12.4% Proportion (share of tasks where the agent reaches a public leaderboard medal threshold, MLE-bench definition (Section 3.4); non-penalized: runs without a valid submission omitted) percent · higher Uncertainty: ± 12.4 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceSTELLA on BioML-bench protein engineering (non-penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Non-penalized', domain 'Protein Engineering', row 'STELLA', column 'Any Medal (%)' |
| Configuration: STELLA, OpenRouter default backend (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 34.98 ± 12.51% Leaderboard percentile (mean over tasks in the domain; non-penalized: runs without a valid submission omitted) percent · higher Uncertainty: ± 12.51 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceSTELLA on BioML-bench protein engineering (non-penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Non-penalized', domain 'Protein Engineering', row 'STELLA', column 'Leaderboard Percentile' |
| Configuration: AIDE v6.3.3, GPT-4.1 backend (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 25.0 ± 9.1% Proportion (share of tasks where the agent's mean score exceeds the median human leaderboard submission (Section 3.4 definition); penalized: runs without a valid submission scored as percentile 0) percent · higher Uncertainty: ± 9.1 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceAIDE on BioML-bench protein engineering (penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Penalized', domain 'Protein Engineering', row 'AIDE', column 'Above Median (%)' |
| Configuration: AIDE v6.3.3, GPT-4.1 backend (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 12.5 ± 8.5% Proportion (share of tasks where the agent reaches a public leaderboard medal threshold, MLE-bench definition (Section 3.4); penalized: runs without a valid submission scored as percentile 0) percent · higher Uncertainty: ± 8.5 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceAIDE on BioML-bench protein engineering (penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Penalized', domain 'Protein Engineering', row 'AIDE', column 'Any Medal (%)' |
| Configuration: AIDE v6.3.3, GPT-4.1 backend (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 75% Proportion (share of tasks that yield a valid, gradable submission (Section 3.4 definition); penalized: runs without a valid submission scored as percentile 0) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceAIDE on BioML-bench protein engineering (penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Penalized', domain 'Protein Engineering', row 'AIDE', column 'Completion Rate (%)' |
| Configuration: AIDE v6.3.3, GPT-4.1 backend (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 24.50 ± 6.90% Leaderboard percentile (mean over tasks in the domain; penalized: runs without a valid submission scored as percentile 0) percent · higher Uncertainty: ± 6.90 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceAIDE on BioML-bench protein engineering (penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Penalized', domain 'Protein Engineering', row 'AIDE', column 'Leaderboard Percentile' |
| Configuration: Biomni v0.0.5, Claude Sonnet 4 backend (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 25.0 ± 17.1% Proportion (share of tasks where the agent's mean score exceeds the median human leaderboard submission (Section 3.4 definition); penalized: runs without a valid submission scored as percentile 0) percent · higher Uncertainty: ± 17.1 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceBiomni on BioML-bench protein engineering (penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Penalized', domain 'Protein Engineering', row 'Biomni', column 'Above Median (%)' |
| Configuration: Biomni v0.0.5, Claude Sonnet 4 backend (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 25.0 ± 17.1% Proportion (share of tasks where the agent reaches a public leaderboard medal threshold, MLE-bench definition (Section 3.4); penalized: runs without a valid submission scored as percentile 0) percent · higher Uncertainty: ± 17.1 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceBiomni on BioML-bench protein engineering (penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Penalized', domain 'Protein Engineering', row 'Biomni', column 'Any Medal (%)' |
| Configuration: Biomni v0.0.5, Claude Sonnet 4 backend (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 100% Proportion (share of tasks that yield a valid, gradable submission (Section 3.4 definition); penalized: runs without a valid submission scored as percentile 0) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceBiomni on BioML-bench protein engineering (penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Penalized', domain 'Protein Engineering', row 'Biomni', column 'Completion Rate (%)' |
| Configuration: Biomni v0.0.5, Claude Sonnet 4 backend (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 29.93 ± 11.04% Leaderboard percentile (mean over tasks in the domain; penalized: runs without a valid submission scored as percentile 0) percent · higher Uncertainty: ± 11.04 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceBiomni on BioML-bench protein engineering (penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Penalized', domain 'Protein Engineering', row 'Biomni', column 'Leaderboard Percentile' |
| Configuration: Dummy agent (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 0.0 ± 0.0% Proportion (share of tasks where the agent's mean score exceeds the median human leaderboard submission (Section 3.4 definition); penalized: runs without a valid submission scored as percentile 0) percent · higher Uncertainty: ± 0.0 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDummy on BioML-bench protein engineering (penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Penalized', domain 'Protein Engineering', row 'Dummy', column 'Above Median (%)' |
| Configuration: Dummy agent (BioML-bench) | Protocol: BioML-bench protein engineering domain: agent leaderboard percentile over 6 tasks Dataset subset: BioML-bench v0.1-alpha protein engineering tasks (6 tasks) | 0.0 ± 0.0% Proportion (share of tasks where the agent reaches a public leaderboard medal threshold, MLE-bench definition (Section 3.4); penalized: runs without a valid submission scored as percentile 0) percent · higher Uncertainty: ± 0.0 (type not stated). Table 4 caption does not name the spread. Figures 2 and 4 show per-domain leaderboard percentiles as mean ± SEM across tasks; the Table 2 caption refers to the reported SEM. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDummy on BioML-bench protein engineering (penalized) lit-agents-20261013-biomlbench-protocol-protein-engineering Aggregation: Not reported BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML · Table 4 (page 12), block 'Penalized', domain 'Protein Engineering', row 'Dummy', column 'Any Medal (%)' |
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| Property and statement | Original source and location | Review and provenance |
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Limitations1 values
| BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML Table 3; repository experiments/biomlbench_v0.1a.txt Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: bioRxiv 2025.09.01.673319 version 3, posted 2025-09-28 (versions 1 and 2 posted 2025-09-04 and 2025-09-07) | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: SHA-256 of the PDF bytes as downloaded 2026-10-13T12:38:42Z. bioRxiv rewrites the XMP dates and document IDs, the Info ModDate and the trailer /ID on download, so a re-download does not reproduce the hash; the review found a fresh copy differing only in those 43 bytes, with an identical text layer. |
Limitations1 values
| science-machine/biomlbench repository at commit 673f3d8 Table 3; repository experiments/biomlbench_v0.1a.txt Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Git commit 673f3d8d3462fc6cdcbbd9695fba612b9332ca65 (2025-09-14, head of main at retrieval); README title 'BioML-bench (v0.1-alpha)' | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: SHA-256 of the codeload tar.gz archive for the commit; two downloads gave identical bytes. Per-file hashes of the files cited are in sources.md. |
| Source location Table 3; repository experiments/biomlbench_v0.1a.txt Context-only references | BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML Table 3; repository experiments/biomlbench_v0.1a.txt Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: bioRxiv 2025.09.01.673319 version 3, posted 2025-09-28 (versions 1 and 2 posted 2025-09-04 and 2025-09-07) | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: SHA-256 of the PDF bytes as downloaded 2026-10-13T12:38:42Z. bioRxiv rewrites the XMP dates and document IDs, the Info ModDate and the trailer /ID on download, so a re-download does not reproduce the hash; the review found a fresh copy differing only in those 43 bytes, with an identical text layer. |
| Source location Table 3; repository experiments/biomlbench_v0.1a.txt Context-only references | science-machine/biomlbench repository at commit 673f3d8 Table 3; repository experiments/biomlbench_v0.1a.txt Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Git commit 673f3d8d3462fc6cdcbbd9695fba612b9332ca65 (2025-09-14, head of main at retrieval); README title 'BioML-bench (v0.1-alpha)' | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: SHA-256 of the codeload tar.gz archive for the commit; two downloads gave identical bytes. Per-file hashes of the files cited are in sources.md. |
| Split ProteinGym supervised cross-validation folds (fold_random_5, fold_modulo_5, fold_contiguous_5) per DMS assay Context-only references | BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML Table 3; repository experiments/biomlbench_v0.1a.txt Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: bioRxiv 2025.09.01.673319 version 3, posted 2025-09-28 (versions 1 and 2 posted 2025-09-04 and 2025-09-07) | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: SHA-256 of the PDF bytes as downloaded 2026-10-13T12:38:42Z. bioRxiv rewrites the XMP dates and document IDs, the Info ModDate and the trailer /ID on download, so a re-download does not reproduce the hash; the review found a fresh copy differing only in those 43 bytes, with an identical text layer. |
| Split ProteinGym supervised cross-validation folds (fold_random_5, fold_modulo_5, fold_contiguous_5) per DMS assay Context-only references | science-machine/biomlbench repository at commit 673f3d8 Table 3; repository experiments/biomlbench_v0.1a.txt Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Git commit 673f3d8d3462fc6cdcbbd9695fba612b9332ca65 (2025-09-14, head of main at retrieval); README title 'BioML-bench (v0.1-alpha)' | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: SHA-256 of the codeload tar.gz archive for the commit; two downloads gave identical bytes. Per-file hashes of the files cited are in sources.md. |
| Subset scope 6 tasks listed under 'Protein Engineering' in Table 3. ProteinGym (paper Table 1). Context-only references | BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML Table 3; repository experiments/biomlbench_v0.1a.txt Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: bioRxiv 2025.09.01.673319 version 3, posted 2025-09-28 (versions 1 and 2 posted 2025-09-04 and 2025-09-07) | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: SHA-256 of the PDF bytes as downloaded 2026-10-13T12:38:42Z. bioRxiv rewrites the XMP dates and document IDs, the Info ModDate and the trailer /ID on download, so a re-download does not reproduce the hash; the review found a fresh copy differing only in those 43 bytes, with an identical text layer. |
| Subset scope 6 tasks listed under 'Protein Engineering' in Table 3. ProteinGym (paper Table 1). Context-only references | science-machine/biomlbench repository at commit 673f3d8 Table 3; repository experiments/biomlbench_v0.1a.txt Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Git commit 673f3d8d3462fc6cdcbbd9695fba612b9332ca65 (2025-09-14, head of main at retrieval); README title 'BioML-bench (v0.1-alpha)' | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: SHA-256 of the codeload tar.gz archive for the commit; two downloads gave identical bytes. Per-file hashes of the files cited are in sources.md. |
| Version BioML-bench v0.1-alpha task set (experiments/biomlbench_v0.1a.txt at commit 673f3d8) Context-only references | BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML Table 3; repository experiments/biomlbench_v0.1a.txt Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: bioRxiv 2025.09.01.673319 version 3, posted 2025-09-28 (versions 1 and 2 posted 2025-09-04 and 2025-09-07) | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: SHA-256 of the PDF bytes as downloaded 2026-10-13T12:38:42Z. bioRxiv rewrites the XMP dates and document IDs, the Info ModDate and the trailer /ID on download, so a re-download does not reproduce the hash; the review found a fresh copy differing only in those 43 bytes, with an identical text layer. |
| Version BioML-bench v0.1-alpha task set (experiments/biomlbench_v0.1a.txt at commit 673f3d8) Context-only references | science-machine/biomlbench repository at commit 673f3d8 Table 3; repository experiments/biomlbench_v0.1a.txt Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Git commit 673f3d8d3462fc6cdcbbd9695fba612b9332ca65 (2025-09-14, head of main at retrieval); README title 'BioML-bench (v0.1-alpha)' | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: SHA-256 of the codeload tar.gz archive for the commit; two downloads gave identical bytes. Per-file hashes of the files cited are in sources.md. |
Release 2026-11-02-a7f03c9e55a5 · Record review: source checked
Stable ID: lit-agents-20261013-biomlbench-data-protein-engineering-tasks