Uncovering Understanding-Generation Synergy in Native Unified Multimodal Models: From Representation, Task to System

TL;DR

This study explores how visual understanding and image generation tasks interact within unified multimodal models without relying on pretrained vision features. It finds that while both tasks benefit from shared knowledge, their joint optimization can lead to imbalance unless the architecture decouples conflicting visual processing paths. End-to-end unified models outperform modular pipelines on complex tasks requiring both understanding and generation.

Why It Matters

The findings guide the design of more effective multimodal systems by showing how architectural choices and task alignment can unlock synergistic performance, moving beyond mere functional unification to true task interdependence. This is critical for building intelligent systems that reason and create coherently in real-world applications.

Key Findings

• Visual understanding improves generation quality by strengthening vision-language alignment, while generation enhances visual feature learning for understanding.
• Forcing both tasks through a shared computation path causes one to dominate, but decoupling visual processing paths while maintaining semantic interaction prevents asymmetric degradation.
• End-to-end unified models surpass pipeline-based planners and executors on complex tasks requiring both image understanding and generation, demonstrating system-level synergy.