Perturbation screen identifying genes that render T cells resistant to inhibitory signals.
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31 evaluations · 51 results. Different protocols are not a single leaderboard.
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: BDA, llama-3-1-8b backbone (Gupta et al. 2025) | Protocol: Independent replication of 1-gene perturbation design: cumulative hits after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 32.4 true-positive-count count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceBDA (Llama-3.1-8B backbone) on Carnevale (Gupta et al. 2025) tgtval-20261009-protocol-gupta2025-cumulative-hits-round5 Aggregation: Not reported LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? · Table 1, 'Llama-3.1-8B backbone' block, row 'BDA', column 'Carnevale' |
| Configuration: BDA, qwen-2-7b backbone (Gupta et al. 2025) | Protocol: Independent replication of 1-gene perturbation design: cumulative hits after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 27.2 true-positive-count count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceBDA (Qwen-2-7B backbone) on Carnevale (Gupta et al. 2025) tgtval-20261009-protocol-gupta2025-cumulative-hits-round5 Aggregation: Not reported LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? · Table 1, 'Qwen-2-7B backbone' block, row 'BDA', column 'Carnevale' |
| Configuration: BDA-Rand, claude-3-5-sonnet backbone (Gupta et al. 2025) | Protocol: Independent replication of 1-gene perturbation design: cumulative hits after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 42 true-positive-count count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceBDA-Rand (Claude 3.5 Sonnet backbone) on Carnevale (Gupta et al. 2025) tgtval-20261009-protocol-gupta2025-cumulative-hits-round5 Aggregation: Not reported LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? · Table 1, 'Claude 3.5 Sonnet backbone' block, row 'BDA-Rand', column 'Carnevale' |
| Configuration: BDA-Rand, llama-3-1-8b backbone (Gupta et al. 2025) | Protocol: Independent replication of 1-gene perturbation design: cumulative hits after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 31.6 true-positive-count count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceBDA-Rand (Llama-3.1-8B backbone) on Carnevale (Gupta et al. 2025) tgtval-20261009-protocol-gupta2025-cumulative-hits-round5 Aggregation: Not reported LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? · Table 1, 'Llama-3.1-8B backbone' block, row 'BDA-Rand', column 'Carnevale' |
| Configuration: BDA-Rand, qwen-2-7b backbone (Gupta et al. 2025) | Protocol: Independent replication of 1-gene perturbation design: cumulative hits after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 29 true-positive-count count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceBDA-Rand (Qwen-2-7B backbone) on Carnevale (Gupta et al. 2025) tgtval-20261009-protocol-gupta2025-cumulative-hits-round5 Aggregation: Not reported LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? · Table 1, 'Qwen-2-7B backbone' block, row 'BDA-Rand', column 'Carnevale' |
| Configuration: BDA (Replicated), claude-3-5-sonnet backbone (Gupta et al. 2025) | Protocol: Independent replication of 1-gene perturbation design: cumulative hits after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 43.8 true-positive-count count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceBDA (Replicated) (Claude 3.5 Sonnet backbone) on Carnevale (Gupta et al. 2025) tgtval-20261009-protocol-gupta2025-cumulative-hits-round5 Aggregation: Not reported LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? · Table 1, 'Claude 3.5 Sonnet backbone' block, row 'BDA (Replicated)', column 'Carnevale' |
| Configuration: BDA (Reported Numbers), claude-3-5-sonnet backbone (Gupta et al. 2025) | Protocol: Independent replication of 1-gene perturbation design: cumulative hits after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 39.6 true-positive-count count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceBDA (Reported Numbers) (Claude 3.5 Sonnet backbone) on Carnevale (Gupta et al. 2025) tgtval-20261009-protocol-gupta2025-cumulative-hits-round5 Aggregation: Not reported LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? · Table 1, 'Claude 3.5 Sonnet backbone' block, row 'BDA (Reported Numbers)', column 'Carnevale' |
| Configuration: GP over llama-3-1-8b embeddings (Gupta et al. 2025) | Protocol: Independent replication of 1-gene perturbation design: cumulative hits after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 22.2 true-positive-count count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceGP (Llama-3.1-8B backbone) on Carnevale (Gupta et al. 2025) tgtval-20261009-protocol-gupta2025-cumulative-hits-round5 Aggregation: Not reported LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? · Table 2, 'Llama-3.1-8B backbone' block, row 'GP', column 'Carnevale' |
| Configuration: GP over qwen-2-7b embeddings (Gupta et al. 2025) | Protocol: Independent replication of 1-gene perturbation design: cumulative hits after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 22.2 true-positive-count count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceGP (Qwen-2-7B backbone) on Carnevale (Gupta et al. 2025) tgtval-20261009-protocol-gupta2025-cumulative-hits-round5 Aggregation: Not reported LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? · Table 2, 'Qwen-2-7B backbone' block, row 'GP', column 'Carnevale' |
| Configuration: Linear UCB over llama-3-1-8b embeddings (Gupta et al. 2025) | Protocol: Independent replication of 1-gene perturbation design: cumulative hits after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 38 true-positive-count count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceLinear UCB (Llama-3.1-8B backbone) on Carnevale (Gupta et al. 2025) tgtval-20261009-protocol-gupta2025-cumulative-hits-round5 Aggregation: Not reported LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? · Table 2, 'Llama-3.1-8B backbone' block, row 'Linear UCB', column 'Carnevale' |
| Configuration: Linear UCB over qwen-2-7b embeddings (Gupta et al. 2025) | Protocol: Independent replication of 1-gene perturbation design: cumulative hits after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 31 true-positive-count count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceLinear UCB (Qwen-2-7B backbone) on Carnevale (Gupta et al. 2025) tgtval-20261009-protocol-gupta2025-cumulative-hits-round5 Aggregation: Not reported LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? · Table 2, 'Qwen-2-7B backbone' block, row 'Linear UCB', column 'Carnevale' |
| Configuration: Badge acquisition function on an MLP surrogate (Roohani et al. 2025) | Protocol: BioDiscoveryAgent 1-gene perturbation design: hit ratio after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 0.044 recall fraction · higher Uncertainty: Not yet extracted: Appendix Table 7 prints one standard deviation over 10 runs for these values; not extracted in this pass. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceBadge on Carnev. (Roohani et al. 2025) tgtval-20261009-protocol-roohani2025-hitratio-round5 Aggregation: Not reported BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments · Table 1, row 'Badge', column 'All' under 'Carnev.' |
| Configuration: Badge acquisition function on an MLP surrogate (Roohani et al. 2025) | Protocol: BioDiscoveryAgent 1-gene perturbation design: hit ratio after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 0.036 recall fraction · higher Uncertainty: Not yet extracted: Appendix Table 7 prints one standard deviation over 10 runs for these values; not extracted in this pass. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceBadge on Carnev. (Roohani et al. 2025) tgtval-20261009-protocol-roohani2025-hitratio-round5 Aggregation: Not reported BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments · Table 1, row 'Badge', column 'N/E' under 'Carnev.' |
| Configuration: BioDiscoveryAgent (No-Tools), Claude 3.5 Sonnet (Roohani et al. 2025) | Protocol: BioDiscoveryAgent 1-gene perturbation design: hit ratio after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 0.042 recall fraction · higher Uncertainty: Not yet extracted: Appendix Table 7 prints one standard deviation over 10 runs for these values; not extracted in this pass. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceClaude 3.5 Sonnet on Carnev. (Roohani et al. 2025) tgtval-20261009-protocol-roohani2025-hitratio-round5 Aggregation: Not reported BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments · Table 1, row 'Claude 3.5 Sonnet', column 'All' under 'Carnev.' |
| Configuration: BioDiscoveryAgent (No-Tools), Claude 3.5 Sonnet (Roohani et al. 2025) | Protocol: BioDiscoveryAgent 1-gene perturbation design: hit ratio after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 0.044 recall fraction · higher Uncertainty: Not yet extracted: Appendix Table 7 prints one standard deviation over 10 runs for these values; not extracted in this pass. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceClaude 3.5 Sonnet on Carnev. (Roohani et al. 2025) tgtval-20261009-protocol-roohani2025-hitratio-round5 Aggregation: Not reported BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments · Table 1, row 'Claude 3.5 Sonnet', column 'N/E' under 'Carnev.' |
| Configuration: BioDiscoveryAgent (No-Tools), Claude 3 Haiku (Roohani et al. 2025) | Protocol: BioDiscoveryAgent 1-gene perturbation design: hit ratio after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 0.032 recall fraction · higher Uncertainty: Not yet extracted: Appendix Table 7 prints one standard deviation over 10 runs for these values; not extracted in this pass. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceClaude 3 Haiku on Carnev. (Roohani et al. 2025) tgtval-20261009-protocol-roohani2025-hitratio-round5 Aggregation: Not reported BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments · Table 1, row 'Claude 3 Haiku', column 'All' under 'Carnev.' |
| Configuration: BioDiscoveryAgent (No-Tools), Claude 3 Haiku (Roohani et al. 2025) | Protocol: BioDiscoveryAgent 1-gene perturbation design: hit ratio after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 0.034 recall fraction · higher Uncertainty: Not yet extracted: Appendix Table 7 prints one standard deviation over 10 runs for these values; not extracted in this pass. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceClaude 3 Haiku on Carnev. (Roohani et al. 2025) tgtval-20261009-protocol-roohani2025-hitratio-round5 Aggregation: Not reported BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments · Table 1, row 'Claude 3 Haiku', column 'N/E' under 'Carnev.' |
| Configuration: BioDiscoveryAgent (No-Tools), Claude 3 Opus (Roohani et al. 2025) | Protocol: BioDiscoveryAgent 1-gene perturbation design: hit ratio after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 0.043 recall fraction · higher Uncertainty: Not yet extracted: Appendix Table 7 prints one standard deviation over 10 runs for these values; not extracted in this pass. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceClaude 3 Opus on Carnev. (Roohani et al. 2025) tgtval-20261009-protocol-roohani2025-hitratio-round5 Aggregation: Not reported BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments · Table 1, row 'Claude 3 Opus', column 'All' under 'Carnev.' |
| Configuration: BioDiscoveryAgent (No-Tools), Claude 3 Opus (Roohani et al. 2025) | Protocol: BioDiscoveryAgent 1-gene perturbation design: hit ratio after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 0.043 recall fraction · higher Uncertainty: Not yet extracted: Appendix Table 7 prints one standard deviation over 10 runs for these values; not extracted in this pass. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceClaude 3 Opus on Carnev. (Roohani et al. 2025) tgtval-20261009-protocol-roohani2025-hitratio-round5 Aggregation: Not reported BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments · Table 1, row 'Claude 3 Opus', column 'N/E' under 'Carnev.' |
| Configuration: BioDiscoveryAgent (No-Tools), Claude 3 Sonnet (Roohani et al. 2025) | Protocol: BioDiscoveryAgent 1-gene perturbation design: hit ratio after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 0.041 recall fraction · higher Uncertainty: Not yet extracted: Appendix Table 7 prints one standard deviation over 10 runs for these values; not extracted in this pass. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceClaude 3 Sonnet on Carnev. (Roohani et al. 2025) tgtval-20261009-protocol-roohani2025-hitratio-round5 Aggregation: Not reported BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments · Table 1, row 'Claude 3 Sonnet', column 'All' under 'Carnev.' |
| Configuration: BioDiscoveryAgent (No-Tools), Claude 3 Sonnet (Roohani et al. 2025) | Protocol: BioDiscoveryAgent 1-gene perturbation design: hit ratio after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 0.042 recall fraction · higher Uncertainty: Not yet extracted: Appendix Table 7 prints one standard deviation over 10 runs for these values; not extracted in this pass. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceClaude 3 Sonnet on Carnev. (Roohani et al. 2025) tgtval-20261009-protocol-roohani2025-hitratio-round5 Aggregation: Not reported BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments · Table 1, row 'Claude 3 Sonnet', column 'N/E' under 'Carnev.' |
| Configuration: BioDiscoveryAgent (No-Tools), Claude v1 (Roohani et al. 2025) | Protocol: BioDiscoveryAgent 1-gene perturbation design: hit ratio after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 0.038 recall fraction · higher Uncertainty: Not yet extracted: Appendix Table 7 prints one standard deviation over 10 runs for these values; not extracted in this pass. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceClaude v1 on Carnev. (Roohani et al. 2025) tgtval-20261009-protocol-roohani2025-hitratio-round5 Aggregation: Not reported BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments · Table 1, row 'Claude v1', column 'All' under 'Carnev.' |
| Configuration: BioDiscoveryAgent (No-Tools), Claude v1 (Roohani et al. 2025) | Protocol: BioDiscoveryAgent 1-gene perturbation design: hit ratio after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 0.045 recall fraction · higher Uncertainty: Not yet extracted: Appendix Table 7 prints one standard deviation over 10 runs for these values; not extracted in this pass. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceClaude v1 on Carnev. (Roohani et al. 2025) tgtval-20261009-protocol-roohani2025-hitratio-round5 Aggregation: Not reported BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments · Table 1, row 'Claude v1', column 'N/E' under 'Carnev.' |
| Configuration: Coreset acquisition function on an MLP surrogate (Roohani et al. 2025) | Protocol: BioDiscoveryAgent 1-gene perturbation design: hit ratio after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 0.047 recall fraction · higher Uncertainty: Not yet extracted: Appendix Table 7 prints one standard deviation over 10 runs for these values; not extracted in this pass. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCoreset on Carnev. (Roohani et al. 2025) tgtval-20261009-protocol-roohani2025-hitratio-round5 Aggregation: Not reported BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments · Table 1, row 'Coreset', column 'All' under 'Carnev.' |
| Configuration: Coreset acquisition function on an MLP surrogate (Roohani et al. 2025) | Protocol: BioDiscoveryAgent 1-gene perturbation design: hit ratio after 5 rounds of 128 genes Dataset: Carnevale et al. 2022 screen, T-cell resistance to tumour-microenvironment inhibitory signals | 0.038 recall fraction · higher Uncertainty: Not yet extracted: Appendix Table 7 prints one standard deviation over 10 runs for these values; not extracted in this pass. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCoreset on Carnev. (Roohani et al. 2025) tgtval-20261009-protocol-roohani2025-hitratio-round5 Aggregation: Not reported BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments · Table 1, row 'Coreset', column 'N/E' under 'Carnev.' |
Source checking is not independent reproduction. Release 2026-10-10-7fcc3e48a123.
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Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.
One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.
14 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| attributes.assay Pooled CRISPR perturbation screen with a phenotypic readout Context-only references | LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? Roohani et al. 2025 section 4.1; Gupta et al. 2025 appendix B.1.1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Findings of the Association for Computational Linguistics: EMNLP 2025, pages 15482-15510 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py |
| attributes.assay Pooled CRISPR perturbation screen with a phenotypic readout Context-only references | BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments Roohani et al. 2025 section 4.1; Gupta et al. 2025 appendix B.1.1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: arXiv:2405.17631 version 3, updated 2025-03-09; published as a conference paper at ICLR 2025 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py |
| attributes.population Over 18,000 genes (Roohani et al. 2025 section 4.1) Context-only references | LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? Roohani et al. 2025 section 4.1; Gupta et al. 2025 appendix B.1.1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Findings of the Association for Computational Linguistics: EMNLP 2025, pages 15482-15510 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py |
| attributes.population Over 18,000 genes (Roohani et al. 2025 section 4.1) Context-only references | BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments Roohani et al. 2025 section 4.1; Gupta et al. 2025 appendix B.1.1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: arXiv:2405.17631 version 3, updated 2025-03-09; published as a conference paper at ICLR 2025 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py |
| attributes.scope_note Replayed offline: the benchmark looks up the measured phenotypic response of each selected gene instead of running a new experiment. Genes the screen did not assay are absent from the candidate pool, so they are neither hits nor non-hits. Context-only references | LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? Roohani et al. 2025 section 4.1; Gupta et al. 2025 appendix B.1.1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Findings of the Association for Computational Linguistics: EMNLP 2025, pages 15482-15510 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py |
| attributes.scope_note Replayed offline: the benchmark looks up the measured phenotypic response of each selected gene instead of running a new experiment. Genes the screen did not assay are absent from the candidate pool, so they are neither hits nor non-hits. Context-only references | BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments Roohani et al. 2025 section 4.1; Gupta et al. 2025 appendix B.1.1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: arXiv:2405.17631 version 3, updated 2025-03-09; published as a conference paper at ICLR 2025 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py |
| attributes.source_locator Roohani et al. 2025 section 4.1; Gupta et al. 2025 appendix B.1.1 Context-only references | LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? Roohani et al. 2025 section 4.1; Gupta et al. 2025 appendix B.1.1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Findings of the Association for Computational Linguistics: EMNLP 2025, pages 15482-15510 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py |
| attributes.source_locator Roohani et al. 2025 section 4.1; Gupta et al. 2025 appendix B.1.1 Context-only references | BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments Roohani et al. 2025 section 4.1; Gupta et al. 2025 appendix B.1.1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: arXiv:2405.17631 version 3, updated 2025-03-09; published as a conference paper at ICLR 2025 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py |
| attributes.version As replayed by the cited benchmark from the original screen Context-only references | LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? Roohani et al. 2025 section 4.1; Gupta et al. 2025 appendix B.1.1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Findings of the Association for Computational Linguistics: EMNLP 2025, pages 15482-15510 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py |
| attributes.version As replayed by the cited benchmark from the original screen Context-only references | BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments Roohani et al. 2025 section 4.1; Gupta et al. 2025 appendix B.1.1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: arXiv:2405.17631 version 3, updated 2025-03-09; published as a conference paper at ICLR 2025 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py |
Release 2026-10-10-7fcc3e48a123 · Record review: source checked
Stable ID: tgtval-20261009-data-carnevale2022-tcell