CellBench-LS: Benchmark Evaluation of Single-cell Foundation Models for Low-supervision Scenarios

While single-cell foundation models (SCFMs) have shown promise across various downstream tasks, their generalization performance in label-scarce settings remains a critical bottleneck. The absence of systematic benchmarks for these low-resource scenarios hinders their translation to realworld biomedical research. To bridge this gap, we present CellBench-LS, a comprehensive framework designed to rigorously evaluate SCFMs generalization under low-supervision conditions. This framework employ a stratified evaluation protocol to systematically compare traditional methods and foundation models. We evaluate their zero-shot representational abilities on cell clustering and batch correction tasks, and apply lightweight fine-tuning of task-specific heads for predictive tasks, such as celltype annotation, expression reconstruction, and perturbation prediction. Experimental results demonstrate a biologically stratified landscape, with foundation models showing distinct advantages in tasks critically reliant on celltype recognition, while traditional methods remain competitive in those requiring precise quantification of gene expression patterns. CellBench-LS provides critical guidance for developing more biologically grounded and generalizable computational approaches in single-cell analysis.