CellUMM: A Unified Multimodal Model for Cellular Semantic Understanding and Quantitative Generation

Single-cell measurements describe cell state as high-dimensional quantitative expression, whereas biological knowledge is organized as semantics such as gene function, cell type, and experimental condition. Prior work optimizes these two views separately within a specific modality and task space, so semantic understanding, quantitative generation, and state transition have not been placed in one framework. We present CellUMM, a unified cell–language multimodal model that casts cellular modeling as four conditional mappings between a semantic space and a quantitative cell-state space. CellUMM connects biological semantics with cellular expression through a shared semantic–cellular representation space and continuous latent cell programs, and it performs conditional generation and perturbation state transition by latent flow matching in the continuous cell-state space. Joint training on gene function annotation, cell type annotation, conditional cell generation, and perturbation response prediction makes the learned semantic representation both describe cell states and drive executable cell-distribution generation and perturbation response prediction. On the benchmarks, CellUMM outperforms task-specific baselines, leading on the majority of metrics in four tasks.