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arXiv · 2604.19606

AblateCell: A Reproduce-then-Ablate Agent for Virtual Cell Repositories

Xue Xia·Chengkai Yao·Mingyu Tsoi·Xinjie Mao·Wenxuan Huang·Jiaqi Wei·Hao Wu·Cheng Tan·Lang Yu·Yuejin Yang·Mengdi Liu·Siqi Sun·Zhangyang Gao
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Abstract

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.

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Xue Xia, Chengkai Yao, Mingyu Tsoi, Xinjie Mao, Wenxuan Huang, Jiaqi Wei, Hao Wu, Cheng Tan, Lang Yu, Yuejin Yang, Mengdi Liu, Siqi Sun, Zhangyang Gao. 2026-04-21. AblateCell: A Reproduce-then-Ablate Agent for Virtual Cell Repositories. https://arxiv.org/abs/2604.19606

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