COMPASS: Grounding Composition-Intent Guidance in Unified Multimodal Models

arXiv:2606.28696 · cs.AI · Submitted 2026-06-27 · Read on arXiv

cs.AI

Submitted: 2026-06-27

Updated: 2026-08-28

Terminology

• Rule of Thirds — 22× in this paper
• Benchmark — 12× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Computer Vision and Pattern Recognition — 6× in this paper
• Grounding — 6× in this paper · explained in 3 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• 11-Class Taxonomy — 4× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• Cosine Similarity — 3× in this paper · explained in 3 episodes
• Decoupling — 3× in this paper · explained in 2 episodes
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Domain Shift — 3× in this paper · explained in 4 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• alpha — 3× in this paper · explained in 1 episode
• Average Precision — 2× in this paper
• CVF International Conference on Computer Vision — 2× in this paper
• Composition-specific Mixture-of-Experts — 2× in this paper
• Entanglement — 2× in this paper · explained in 3 episodes
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Cognitive Load — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• International Conference on Learning Representations — 1× in this paper
• Interpretability — 1× in this paper · explained in 15 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes

Sources

• Qwen3-VL Technical Report
• UniPercept: Towards Unified Perceptual-Level Image Understanding across Aesthetics, Quality, Structure, and Texture
• Chameleon: Mixed-Modal Early-Fusion Foundation Models
• Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling
• Emerging Properties in Unified Multimodal Pretraining
• SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation
• Ming-Lite-Uni: Advancements in Unified Architecture for Natural Multimodal Interaction
• AesBench: An Expert Benchmark for Multimodal Large Language Models on Image Aesthetics Perception
• Step1X-Edit: A Practical Framework for General Image Editing
• JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation
• Qwen2.5-VL Technical Report
• InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation
• Emu3: Next-Token Prediction is All You Need
• VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation
• IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
• UNIAA: A Unified Multi-modal Image Aesthetic Assessment Baseline and Benchmark
• InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

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