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Multi-Dimensional Conjunctive Preference Optimization (M-DPO) is developed, which enforces simultaneous correctness across all axes and adaptively routes gradients to the most deficient dimension in post-training, bringing open scientific diagram generation close to proprietary-level structural fidelity.
Mage-VL is presented, an efficient codec-native streaming foundation model for real-time multimodal understanding and interaction and establishes AI4AI data pipelines encompassing prompt-code joint optimization for multimodal captioning and AI-driven performance diagnosis to guide training recipes.
The results show that careful tokenizer--backbone--system co-design can deliver strong high-resolution generation and editing within an efficient 4B model family.
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