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IntReal introduces IntReal-100K, a real-world interleaved multimodal dataset, and a seven-dimension evaluation suite for diagnosing unified multimodal models, showing that modality-specific training can improve interleaved text-image generation.
论文提出 MT Forcing，通过单轮分布匹配蒸馏与教师强制引导，提升基于自回归扩散的统一多模态模型在多轮图像编辑中的视觉质量稳定性与跨轮内容一致性。
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.