COMPASS: Grounding Composition-Intent Guidance in Unified Multimodal Models

Composition is a high-level visual intent that governs wheresubjects are placed and how a scene is organized, yet current unifiedmultimodal models remain unreliable at fine-grained composition recog-nition and struggle to turn such intent into controllable generation. Wepresent COMPASS, the first unified multimodal framework that groundscomposition-intent control in a single system spanning both compositionperception and composition-guided generation, with a shared expert to-ken τc as the central intent anchor. On the perception side, COMPASSinjects composition expertise into an MoE backbone in a minimally in-vasive manner and distills the inferred intent into τc . On the generationside, COMPASS reuses τc as a global conditioning signal that steers thedenoising trajectory, effectively converting passive composition analysisinto explicit layout control. To support systematic instruction-followingcomposition learning and evaluation at scale, we construct Comp-11, alarge-scale dataset with an 11-class taxonomy and reasoning-augmentedannotations. Extensive experiments show that COMPASS substantiallyimproves category-level composition understanding and delivers morecomposition-consistent, prompt-faithful generation than strong baselines.The code and dataset for this work will be released here.