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

• URL: http://arxiv.org/abs/2603.01396v2
• Date: Sun, 08 Mar 2026 03:21:33 GMT
• Title: HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shifts
• Abstract summary: HarmonyCell is an end-to-end agent framework for single-cell perturbation studies.<n>It maps disparate metadata into a canonical interface without manual intervention.<n>An adaptive Monte Carlo Tree Search engine operates over a hierarchical action space to synthesize with optimal statistical inductive biases for distribution shifts.
• Score: 34.62096175943109
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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