💡 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.

💡 The paper introduces SenseNova-U1, a unified multimodal model that integrates understanding and generation into a single process, overcoming the traditional divide between these two tasks. Current large vision-language models treat understanding and generation as separate problems, leading to fragmented architectures and misaligned representation spaces. The authors argue that this divide hinders the emergence of native multimodal intelligence and propose a new paradigm, NEO-unify, which views understanding and generation as synergistic aspects of a single process.

The authors present two variants of SenseNova-U1, built on dense and mixture-of-experts understanding baselines, and demonstrate their performance across various tasks, including text understanding, vision-language perception, knowledge reasoning, agentic decision-making, and spatial intelligence. The models also excel in image synthesis, infographic generation, and interleaved vision-language generation, showing strong semantic consistency and visual fidelity.

The paper provides detailed information on model design, data preprocessing, pre- and post-training, and inference strategies, supporting community research. The results show that SenseNova-U1 models perform strongly in vision-language-action and world model scenarios, indicating a broader roadmap where models can think and act across modalities in a native manner. The authors conclude that multimodal AI should focus on building a unified system, rather than connecting separate systems, allowing necessary capabilities to emerge from within. Overall, the paper contributes to the development of unified multimodal models that can integrate understanding and generation, paving the way for more advanced and native multimodal intelligence.