CellMaster: Collaborative Cell Type Annotation in Single-Cell Analysis

Cell-type annotation remains difficult for rare, transitional, and context-dependent populations because fixed references have incomplete tissue and state coverage. We present CellMaster, an expression-grounded LLM agent that iteratively generates hypotheses, proposes marker panels, inspects cluster-level expression and detection patterns, and assigns labels with auditable rationales. Given a preprocessed scRNA-seq object and short study context, CellMaster-Core annotates without querying an external annotated atlas, fixed marker database, or specialist tool at inference time. Rather than forcing a label, it preserves unresolved clusters for another iteration, targeted sub-clustering, or selective escalation to a specialist or human expert. We evaluate this design with a nine-dataset breadth benchmark and a deeper comparison on three biologically distinct datasets against contemporary agents, zero-shot scGPT, and reference-based specialists. Core outperforms Biomni on each depth dataset, with up to a 17.0% relative increase in Cell Ontology score, while optional CellTypist closure provides smaller additional gains. Under fixed closure, removing marker memory causes the largest degradation among the tested components. The collaborative interface exposes the same expression evidence used by the agent and records interventions, making uncertainty and escalation auditable rather than hiding them behind a single final label.