SAVE: A Generalizable Framework for Multi-Condition Single-Cell Generation with Gene Block Attention

arXiv:2604.16776v1 Announce Type: new Abstract: Modeling single-cell gene expression across diverse biological and technical conditions is crucial for characterizing cellular states and simulating unseen scenarios. Existing methods often treat genes as independent tokens, overlooking their high-level biological relationships and leading to poor performance. We introduce SAVE, a unified generative framework based on conditional Transformers for multi-condition single-cell modeling. SAVE leverages