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Generative representations need more than just semantic alignment; they require active exploitation, as shown by Representation Guidance outperforming larger models on ImageNet with a novel flow-matching target modification.
Achieving leading performance in image generation with only 6 billion parameters, Swift-Image redefines the efficiency frontier for compact models.
A novel capability-driven data infrastructure enables multimodal models to achieve unprecedented versatility and transferability in image generation tasks.
CPI-Bench reveals significant performance gaps among image editing models, offering a more nuanced evaluation that aligns with real-world user experiences.
Existing generative models struggle with knowledge-intensive reasoning, but ExpertVerse reveals critical deficits that could redefine evaluation standards in multimodal AI.
