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HYDRA-X presents a unified multimodal model that integrates image and video tokenization within a single Vision Transformer, achieving strong performance across understanding and generation tasks.
This paper introduces UniKE, the first benchmark for cross-modal knowledge editing in unified multimodal models (UMMs), revealing a significant modality gap where text edits achieve 92% efficacy but only 18.5% transfer to image generation. It proposes Reasoning-augmented Parameter Editing to improve cross-modal transfer, with gains up to 18.6 percentage points.
LatentUMM introduces dual latent alignment to improve cross-modal consistency in unified multimodal models by aligning transformations and stabilizing latent dynamics.
UniPath proposes a framework for adaptive coordination of understanding and generation in unified multimodal models, leveraging coordination-path diversity to improve performance over fixed strategies.