Reasoning for Generation, Generation for Reasoning: A Survey of Unified Multimodal Models

Unified multimodal models (UMMs) integrate visual understanding and image generation, creating the opportunity for each capability to strengthen the other. However, architectural unification does not guarantee such interaction: models frequently understand visual requirements that they fail to realize in generated images, and generate intermediate images that their reasoning does not use. This survey examines both directions of transfer and why each remains challenging. We review the architectural foundations and representative model families, and the tasks in which understanding and generation must interact. For understanding → generation, we organize methods into training-free approaches that improve inference with frozen components and training-based approaches that learn reasoning, alignment, verification, or refinement, and compare them by stage of intervention, required access, and computational cost. For generation → reasoning, we review visual-imagination methods that reason with generated images, latent visual tokens, geometric representations, drawing tools, or external world models. We further examine evaluation protocols that distinguish individual task performance, cross-modal consistency, and genuine synergy. Across both directions, a shared challenge emerges: a correct intermediate, whether a reasoning trace or an imagined image, is not necessarily used. We conclude with open problems in reasoning-to-generation alignment, verifiable imagination, reliable self-correction, and compute-matched evaluation.