HICELLS: HIERARCHICAL CELL-TYPE ANNOTATION WITH SELF-EVOLVING SKILL AGENTS

Accurate cell-type annotation is fundamental to scRNA-seq analysis, yet rare and fine-grained cell types remain difficult to distinguish. In common workflows, experts iteratively determine clustering granularity, interpret marker evidence, and refine cell identities from broad lineages to fine-grained subtypes. LLM agents offer a new opportunity to incorporate biological knowledge into iterative annotation. However, current LLM-based annotators follow fixed prompting workflows: their annotation actions are rarely adapted to observed outcomes, and effective strategies are not explicitly summarized and reused across datasets. We present HiCellS, which casts hierarchical annotation as a learnable agent task under the guidance of a self-evolving skill bank. At each step, the agent observes the current annotation state (clustering outputs, marker evidence, biological knowledge) and decides the next actions to adaptively refine cell identities. Supervised fine-tuning provides the model with initial annotation capability, while reinforcement learning optimizes the model's annotation actions using final accuracy as feedback. Meanwhile, rollout trajectories are distilled into new skills that better guide future annotations, allowing the model and skill bank to co-evolve toward more accurate annotation. We demonstrate that HiCellS achieves strong performance across diverse scRNA-seq annotation benchmarks. Specifically, powered by a 14B-parameter backbone, HiCellS achieves an average Cell Ontology (CL)-informed matching score of 0.75, outperforming the strongest baseline CellTypist by up to 0.21. Beyond annotation accuracy, HiCellS demonstrates robust performance under varying levels of marker information.