Multimodal Generation

While multimodal generation has achieved remarkable gains in visual fidelity and benchmark performance, bridging the gap between aesthetic output and controllable, reliable synthesis remains a critical frontier. Our research investigates the core mechanisms of multimodal generation and editing, exploring how deep semantic understanding translates into precise control. We focus on developing models that accurately handle complex compositionality, spatial relationships, and cross-modal reasoning to enable truly intentional and trustworthy visual creation.