揭示原生统一多模态模型中的理解-生成协同：从表征、任务到系统

摘要

虽然统一多模态模型（UMMs）在单一模型内联合执行视觉理解与生成任务，但功能上的统一并不能保证学习上的协同效应：这两个目标可能相互促进、竞争模型容量，或仅仅是共存而已。我们在一个受控的、结构上原生、且无预训练视觉先验的设置中，从表征、任务和系统三个层面考察了它们之间的关系。在表征层面，我们发现每个目标都为另一个目标提供了有用的信号：生成为理解任务丰富了所学到的视觉特征，而理解则增强了生成任务所需的视觉-语言对齐。然而，当两个目标被迫经由同一条计算路径时，往往会出现一方主导的情况。一种任务解耦的架构，在保持语义交互的同时对相互冲突的视觉计算进行专门化处理，能够避免这种不对称的性能退化。在任务层面，通过三项案例研究，我们发现当理解任务和生成任务依赖共享知识时，会产生正向的双向迁移。在系统层面，我们表明，在明确需要同时具备图像理解与生成能力的复杂任务上，端到端的UMM要优于与之匹配的规划器-执行器流水线。综合来看，这些结果表明UMM的价值不仅限于统一接口：恰当的特化、共享的任务知识以及端到端优化，能够将共存转变为协同。

English

While unified multimodal models (UMMs) jointly perform visual understanding and generation within a single model, functional unification does not guarantee learning synergy: the two objectives may reinforce each other, compete for capacity, or merely coexist. We investigate their relationship at the representation, task, and system levels in a controlled, structurally native setting without pretrained vision priors. At the representation level, we find that each objective provides useful signal to the other: generation enriches the visual features learned for understanding, while understanding strengthens vision--language alignment for generation. However, when both objectives are forced through the same computation path, one tends to dominate. A task-decoupled architecture that specializes conflicting visual computation while preserving semantic interaction avoids this asymmetric degradation. At the task level, through three case studies, we find positive bidirectional transfer when understanding and generation tasks rely on shared knowledge. At the system level, we show that an end-to-end UMM outperforms a matched planner--executor pipeline on complex tasks that explicitly require both image understanding and generation. Together, these results show that the value of UMMs extends beyond a unified interface: appropriate specialization, shared task knowledge, and end-to-end optimization can turn coexistence into synergy.