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Accelerating Unified Multimodal Models with Core-Expansion Routing and Unified Computation Scheduling

arXiv · AI, language, vision and robotics · article · Aug 29, 2026 · UTC

Unified multimodal models jointly support understanding and generation, but incur substantial redundant computation across tokens, layers, and generation timesteps. Through token-importance probing, we identify an asymmetric core-expansion structure: understanding exhibits a stable importance component, while generation largely shares this component but requires progress-dependent corrections. We therefore propose CE-Router, which uses a task-shared core scorer and progress-conditioned generation expansions, optimized through generation decomposition and cross-task core alignment. At inference

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