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arXiv · 2603.01396

HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shifts

Wenxuan Huang·Mingyu Tsoi·Yanhao Huang·Xinjie Mao·Xue Xia·Hao Wu·Jiaqi Wei·Yuejin Yang·Lang Yu·Cheng Tan·Xiang Zhang·Zhangyang Gao·Siqi Sun
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Abstract

Single-cell perturbation studies face dual heterogeneity bottlenecks: (i) semantic heterogeneity--identical biological concepts encoded under incompatible metadata schemas across datasets; and (ii) statistical heterogeneity--distribution shifts from biological variation demanding dataset-specific inductive biases. We propose HarmonyCell, an end-to-end agent framework resolving each challenge through a dedicated mechanism: an LLM-driven Semantic Unifier autonomously maps disparate metadata into a canonical interface without manual intervention; and an adaptive Monte Carlo Tree Search engine operates over a hierarchical action space to synthesize architectures with optimal statistical inductive biases for distribution shifts. Evaluated across diverse perturbation tasks under both semantic and distribution shifts, HarmonyCell achieves a 95% valid execution rate on heterogeneous input datasets (versus 0% for general agents) while matching or even exceeding expert-designed baselines in rigorous out-of-distribution evaluations. This dual-track orchestration enables scalable automatic virtual cell modeling without dataset-specific engineering.

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Wenxuan Huang, Mingyu Tsoi, Yanhao Huang, Xinjie Mao, Xue Xia, Hao Wu, Jiaqi Wei, Yuejin Yang, Lang Yu, Cheng Tan, Xiang Zhang, Zhangyang Gao, Siqi Sun. 2026-03-08. HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shifts. https://arxiv.org/abs/2603.01396

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