AblateCell: A Reproduce-then-Ablate Agent for Virtual Cell Repositories
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915970041249792 |
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| author | Xia, Xue Yao, Chengkai Tsoi, Mingyu Mao, Xinjie Huang, Wenxuan Wei, Jiaqi Wu, Hao Tan, Cheng Yu, Lang Yang, Yuejin Liu, Mengdi Sun, Siqi Gao, Zhangyang |
| author_facet | Xia, Xue Yao, Chengkai Tsoi, Mingyu Mao, Xinjie Huang, Wenxuan Wei, Jiaqi Wu, Hao Tan, Cheng Yu, Lang Yang, Yuejin Liu, Mengdi Sun, Siqi Gao, Zhangyang |
| contents | Systematic ablations are essential to attribute performance gains in AI Virtual Cells, yet they are rarely performed because biological repositories are under-standardized and tightly coupled to domain-specific data and formats. While recent coding agents can translate ideas into implementations, they typically stop at producing code and lack a verifier that can reproduce strong baselines and rigorously test which components truly matter. We introduce AblateCell, a reproduce-then-ablate agent for virtual cell repositories that closes this verification gap. AblateCell first reproduces reported baselines end-to-end by auto-configuring environments, resolving dependency and data issues, and rerunning official evaluations while emitting verifiable artifacts. It then conducts closed-loop ablation by generating a graph of isolated repository mutations and adaptively selecting experiments under a reward that trades off performance impact and execution cost. Evaluated on three single-cell perturbation prediction repositories (CPA, GEARS, BioLORD), AblateCell achieves 88.9% (+29.9% to human expert) end-to-end workflow success and 93.3% (+53.3% to heuristic) accuracy in recovering ground-truth critical components. These results enable scalable, repository-grounded verification and attribution directly on biological codebases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_19606 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AblateCell: A Reproduce-then-Ablate Agent for Virtual Cell Repositories Xia, Xue Yao, Chengkai Tsoi, Mingyu Mao, Xinjie Huang, Wenxuan Wei, Jiaqi Wu, Hao Tan, Cheng Yu, Lang Yang, Yuejin Liu, Mengdi Sun, Siqi Gao, Zhangyang Artificial Intelligence Multiagent Systems Systematic ablations are essential to attribute performance gains in AI Virtual Cells, yet they are rarely performed because biological repositories are under-standardized and tightly coupled to domain-specific data and formats. While recent coding agents can translate ideas into implementations, they typically stop at producing code and lack a verifier that can reproduce strong baselines and rigorously test which components truly matter. We introduce AblateCell, a reproduce-then-ablate agent for virtual cell repositories that closes this verification gap. AblateCell first reproduces reported baselines end-to-end by auto-configuring environments, resolving dependency and data issues, and rerunning official evaluations while emitting verifiable artifacts. It then conducts closed-loop ablation by generating a graph of isolated repository mutations and adaptively selecting experiments under a reward that trades off performance impact and execution cost. Evaluated on three single-cell perturbation prediction repositories (CPA, GEARS, BioLORD), AblateCell achieves 88.9% (+29.9% to human expert) end-to-end workflow success and 93.3% (+53.3% to heuristic) accuracy in recovering ground-truth critical components. These results enable scalable, repository-grounded verification and attribution directly on biological codebases. |
| title | AblateCell: A Reproduce-then-Ablate Agent for Virtual Cell Repositories |
| topic | Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2604.19606 |