GeneVAR: Causal MeanFlow for Autoregressive Gene-to-WSI Tile Synthesis

Understanding how transcriptomic programs shape tissue morphology remains a central challenge in computational pathology. Gene-to-WSI tile synthesis offers a principled generative framework to translate molecular profiles into histological images. However, most existing methods compress RNA-Seq into a single global embedding injected once at initialization, an oversimplified design that weakens transcriptomic signals and induces spurious, non-biological associations. We present GeneVAR, an Autoregressive Gene-to-WSI model that reformulates synthesis as an iterative, coarse-to-fine generative process. At its core is a novel Causal MeanFlow module, which leverages counterfactual interventions to suppress non-biological variations (e.g., staining or contrast artifacts) and enforce an artifactinvariant generation process. Concurrently, it reinforces transcriptome-informed guidance across multiple stages, preserving biological fidelity throughout the generative trajectory. Combined with a β-VAE for compact gene embeddings and a multi-scale vector quantizer for discrete morphology representation, GeneVAR generates H&E-stained WSI tiles that are both visually realistic and transcriptomically faithful. Extensive experiments across five TCGA cancer benchmarks demonstrate consistent state-of-the-art performance, surpassing prior methods in both generative fidelity and downstream classification accuracy. All models and code are available at https://github.com/ JWZhao-uestc/GeneVAR.