CASSIA: Multi-agent LLMs for trustworthy single-cell annotation Single-cell RNA-seq studies live or die by good cell type annotation. Today, that often means stitching together multiple tools, curated marker lists, and a lot of expert time—only to end up with inconsistent labels that are hard to interpret and even harder to audit. LLM-based methods like GPTCelltype showed that large models can help, but they also inherit classic LLM failure modes: hallucinations, overconfidence, and opaque reasoning. Elliot Xie and coauthors present CASSIA, a multi-agent LLM framework designed specifically for automated and interpretable cell annotation. Instead of a single prompt, CASSIA orchestrates a team of agents: one for annotation, one for validation, one for formatting, one for quality scoring, and one for reporting. Optional agents handle harder cases—subclustering, uncertainty quantification, “annotation boosting” for low-confidence clusters, and a RAG module that pulls in cell marker databases and ontologies when local knowledge is thin. The result is not just a label, but a full reasoning trace plus a 0–100 quality score that tells you how much to trust it. On benchmarks spanning 970 cell types from GTEx, Tabula Sapiens, Human Cell Landscape, Mouse Cell Atlas, and Azimuth, CASSIA increases fully correct annotations by 12–41% and boosts overall accuracy by more than 20% compared with state-of-the-art methods, including GPT-based approaches. It holds up in harder settings too: cancer vs. non-cancer cells, metastatic signatures, fine-grained T-cell states, mixed cell populations, and even cross-species datasets (from shark to tiger). The quality scores and consensus metrics reliably flag low-quality or heterogeneous clusters and can even uncover errors in “gold-standard” reference annotations. Beyond single-cell analysis, CASSIA is a compelling blueprint for how multi-agent LLM systems can move from one-shot prompting to adaptive, expert-like workflows with built-in self-critique and uncertainty estimates. For single-cell biologists, it promises faster, more accurate, and auditable annotations. For AI-for-science more broadly, it points toward LLMs that behave less like black boxes and more like transparent collaborators in complex data analysis. Paper: nature.com/articles/s41467-0…