ADAPT: An Agent-Driven Autonomous Paradigm for Trans-Species Single-Cell Annotation

Cross-species knowledge transfer provides a powerful strategy for single-cell RNA sequencing analysis in species with limited reference annotations, while offering insights into cellular diversity and evolutionary biology. However, cross-species transfer remains challenging due to species-specific expression programs, cellular differences, and complex gene homology. Existing methods often rely on predefined adaptation strategies that require manual trial-and-error for new transfer scenarios and lack closed-loop feedback to refine these strategies based on experimental outcomes. To address this challenge, we introduce ADAPT, a feedback-driven agentic framework for cross-species single-cell annotation that automatically searches and selects candidate adaptation approaches for transferring cell-type knowledge from reference to target species. Starting from frozen scGPT embeddings, ADAPT performs iterative hypothesis-driven exploration: an outer agent proposes scientific hypotheses and experimental plans, while an inner agent implements candidate adaptation models under controlled experimental protocols. The validation results are fed back to guide subsequent search rounds. We evaluate ADAPT on two cross-species transfer settings involving knowledge transfer from Tabula Sapiens to Tabula Muris and Macaca fascicularis. ADAPT achieves the highest average shared-class Macro-F1 among the compared methods, reaching 0.81 for human-to-mouse transfer and 0.73 for human-to-macaque transfer. These results suggest the potential of feedback-driven agentic search for cross-species adaptation.