自动复现并系统性消融虚拟细胞模型，验证关键组件有效性。
AblateCell: A Reproduce-then-Ablate Agent for Virtual Cell Repositories
- 先自动复现基线，再闭环生成可隔离的代码变异实验
- 在3个数据集上实现88.9%的流程成功率与93.3%的组件识别准确率
- 适合生物信息学与AI可解释性研究者使用
系统性消融是归因人工智能虚拟细胞性能提升的关键，但因生物数据库标准化不足且与特定数据格式耦合，实际执行极少。尽管近期编码代理能将想法转化为代码，通常仅止于生成代码，缺乏可复现基线并严格测试组件重要性的验证机制。我们提出AblateCell，一种针对虚拟细胞库的‘复现-消融’代理，填补该验证空白。AblateCell首先通过自动配置环境、解决依赖与数据问题、重跑官方评估，端到端复现报告基线，并生成可验证的产物。随后，它基于奖励函数（权衡性能影响与执行成本），自适应选择实验，在隔离的仓库变更图中开展闭环消融。在三个单细胞扰动预测库（CPA、GEARS、BioLORD）上的评估显示，其端到端工作流成功率达88.9%（比人类专家高29.9%），在恢复真实关键组件方面准确率达93.3%（比启发式方法高53.3%）。该成果实现了基于生物代码库的可扩展、库级验证与归因。
原文摘要 · Abstract (English)
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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