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S1-Omni consolidates fragmented AI capabilities into a single model, outperforming leading benchmarks and domain-specific models in scientific reasoning tasks.
Outlier tokens in Diffusion Transformers aren't just extreme values; they corrupt local patch semantics, and can be tamed with Dual-Stage Registers to boost image generation quality.
Representation-Pivoted Autoencoders enable diffusion models to generate and edit images with higher fidelity by learning a compressed latent space that preserves the semantics of pre-trained visual representations.
