💡 The paper presents JoyAI-Image, a unified multimodal foundation model that integrates visual understanding, text-to-image generation, and instruction-guided image editing. The model combines a spatially enhanced Multimodal Large Language Model with a Multimodal Diffusion Transformer, allowing for a shared multimodal interface between perception and generation. The authors propose a scalable training recipe that incorporates unified instruction tuning, long-text rendering supervision, spatially grounded data, and general and spatial editing signals. This design enables the model to achieve broad multimodal capabilities while strengthening geometry-aware reasoning and controllable visual synthesis. The experiments demonstrate that JoyAI-Image achieves state-of-the-art or highly competitive performance across various benchmarks, including understanding, generation, long-text rendering, and editing tasks. The model's bidirectional loop between enhanced understanding, controllable spatial editing, and novel-view-assisted reasoning enables it to move beyond general visual competence toward stronger spatial intelligence. The results suggest a promising path for unified visual models in downstream applications such as vision-language-action systems and world models. Overall, the paper contributes to the development of a unified multimodal model that can effectively understand and generate visual content with enhanced spatial intelligence.