[2603.01396] HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shifts

Computer Science > Artificial Intelligence arXiv:2603.01396 (cs) [Submitted on 2 Mar 2026] Title:HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shifts Authors: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 View a PDF of the paper titled HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shifts, by Wenxuan Huang and 12 other authors View PDF HTML (experimental) 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) whil...