Language models annotate mass cytometry clusters within the range where human experts disagree

[bioRxiv] Hunewald, Grant, Boehm and others have posted a preprint study proposing a pipeline that requires no gating hierarchy, manually set marker thresholds or task-specific training data: median marker intensities from mass cytometry clustering (such as FlowSOM and GigaSOM) are scaled by marker and merged into dozens of metaclusters, and then the continuous marker profiles are handed to a large language model in a single pass, which returns cell type labels and confidence scores. The paper says that across four human and mouse datasets covering blood, bone marrow and intestine, a single selected model achieved 85-98% agreement with the reference-annotated metaclusters at the major lineage level (Claude Sonnet 4.6: 85-98%; the best local model: 83-96%), and Cohen's kappa of 0.67-0.83 after correcting for chance agreement. The authors say local open-weight models were comparable to cloud models on an independent benchmark, but reliability varied by model, and Claude Opus 5 was not more reliable than Sonnet 4.6. Two experts who blindly annotated the same heatmaps agreed at about 99% at the major lineage level but only about 73% on T cell maturation status, and the best model fell within the range of expert disagreement.