HiLL: LLM-based Recursive Semantic Refinement for Reference-Free Cell Type Annotation

Single-cell RNA sequencing and spatial transcriptomics provide high-resolution profiles of tissues, but translating these profiles into biological insights critically depends on accurate cell type annotation. Manual annotation is labor-intensive, difficult to scale, and dependent on expert knowledge, motivating automatic annotation methods. However, automatic cell type annotation often relies on external annotated atlases, which may be unavailable, mismatched to the target data, or insufficiently resolved to capture fine-grained cell subtypes. Reference-free annotation addresses these limitations by inferring cell identities directly from the data. Existing reference-free LLM-based methods usually operate on a fixed clustering partition and rely on conventional marker extraction, limiting adaptive subtype refinement and accurate annotation. To address this limitation, we propose HiLL, a hierarchical LLM-based framework for reference-free cell type annotation. HiLL formulates reference-free annotation as a hierarchical refinement process, using LLM-based split validation to determine when further refinement is warranted and comparative evidence to support accurate annotation. Across human and mouse scRNA-seq and single-cell spatial transcriptomics datasets, HiLL consistently outperforms reference-free LLM baselines, with especially strong gains on fine-grained subtype distinctions. It also remains competitive with reference-based methods and zero-shot foundation models, supporting recursive local refinement as an effective paradigm for reference-free annotation.