COMPASS: Grounding Composition-Intent Guidance in Unified Multimodal Models
COMPASS is the first unified multimodal framework that grounds composition-intent control in a single system, using a shared expert token τ_c for both perception and generation. It injects composition expertise into an MoE backbone, distills intent into τ_c, and reuses it as a conditioning signal for layout control. The companion Comp-11 dataset features an 11-class taxonomy and reasoning-augmented annotations. Experiments show significant improvements in composition understanding and generation consistency.
[2606.28696] COMPASS: Grounding Composition-Intent Guidance in Unified Multimodal Models
[Submitted on 27 Jun 2026]
Title:COMPASS: Grounding Composition-Intent Guidance in Unified Multimodal Models
View a PDF of the paper titled COMPASS: Grounding Composition-Intent Guidance in Unified Multimodal Models, by Ziqi Zhou and 8 other authors
View PDF HTML (experimental)
Abstract:Composition is a high-level visual intent that governs where subjects are placed and how a scene is organized, yet current unified multimodal models remain unreliable at fine-grained composition recognition and struggle to turn such intent into controllable generation. We present COMPASS, the first unified multimodal framework that grounds composition-intent control in a single system spanning both composition perception and composition-guided generation, with a shared expert token $\tau_c$ as the central intent anchor. On the perception side, COMPASS injects composition expertise into an MoE backbone in a minimally invasive manner and distills the inferred intent into $\tau_c$. On the generation side, COMPASS reuses $\tau_c$ as a global conditioning signal that steers the denoising trajectory, effectively converting passive composition analysis into explicit layout control. To support systematic instruction-following composition learning and evaluation at scale, we construct Comp-11, a large-scale dataset with an 11-class taxonomy and reasoning-augmented annotations. Extensive experiments show that COMPASS substantially improves category-level composition understanding and delivers more composition-consistent, prompt-faithful generation than strong baselines.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.28696 [cs.AI]
(or arXiv:2606.28696v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.28696
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ziqi Zhou [view email] [v1] Sat, 27 Jun 2026 02:43:13 UTC (20,107 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled COMPASS: Grounding Composition-Intent Guidance in Unified Multimodal Models, by Ziqi Zhou and 8 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.AI
new | recent | 2026-06
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)