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Introduces Semantic Generative Tuning (SGT), a paradigm that uses image segmentation as a generative proxy to align visual understanding and generation in unified multimodal models, improving both comprehension and fidelity.
This paper introduces SenseNova-U1, a unified multimodal architecture that integrates understanding and generation tasks, releasing two variants (8B and 30B) that perform competitively in both perception and image synthesis.
The paper introduces JoyAI-Image, a unified multimodal foundation model that integrates a spatially enhanced MLLM with MMDiT to achieve state-of-the-art performance in visual understanding, text-to-image generation, and instruction-guided editing.