LatentUMM：面向统一多模态模型的双重潜在对齐

摘要

统一多模态模型（UMMs）通过学习共享潜在空间，在理解与生成任务上均展现出强大性能，然而这两种能力之间常存在功能不一致性。我们观察到，这一问题并非源于共享表征的缺失，而是由于映射进出潜在空间的变换之间缺乏显式对齐。其结果是，生成与重编码过程可能遵循不一致的轨迹，导致模态转换下的语义漂移。为此，我们提出LatentUMM框架，通过构建增强型共享潜在空间来显式对齐这些变换，从而提升跨模态一致性。LatentUMM包含两个阶段：首先，双潜在对齐在模态和容量两个层面强化一致性——跨模态对齐利用更强的嵌入模型施加结构化的跨模态语义，而双容量对齐则在生成与重编码过程中强制执行双向一致性；其次，潜在动态稳定化通过随机潜在展开和偏好优化提升鲁棒性，优先选择更有利于保持语义一致性的轨迹。实验表明，LatentUMM能够一致地提升不同架构下的多模态一致性。代码开源地址：https://github.com/AIFrontierLab/TorchUMM/tree/main/src/umm/post_training/LatentUMM。

English

Unified multimodal models (UMMs) achieve strong performance in both understanding and generation by learning a shared latent space, yet they often exhibit functional inconsistency between these two capabilities. We observe that this issue does not stem from a lack of shared representations, but from the absence of explicit alignment between the transformations that map into and out of the latent space. As a result, generation and re-encoding can follow inconsistent trajectories, leading to semantic drift under modality transitions. In this work, we propose LatentUMM, a framework that constructs an enhanced shared latent space to explicitly align these transformations and improve cross-modal consistency. LatentUMM consists of two stages. First, dual latent alignment enforces consistency at both the modality and capacity levels: cross-modal alignment uses a stronger embedding model to impose structured cross-modal semantics, while dual capacity alignment enforces bidirectional consistency under generation and re-encoding. Second, latent dynamics stabilization improves robustness via stochastic latent rollouts and preference optimization, favoring trajectories that better preserve semantic consistency. Experiments show that LatentUMM consistently improves multimodal consistency across diverse architectures. Code is available at: https://github.com/AIFrontierLab/TorchUMM/tree/main/src/umm/post_training/LatentUMM.