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Research questionHow can unified multimodal models reduce redundant inference computation across understanding and generation without sacrificing quality?Unified models serve understanding and generation, but the computational contribution of tokens, layers, and timesteps varies across tasks. Removing this redundant work can degrade quality when the two tasks require different computation patterns.
Diffusion Models
Inference Optimization
Multimodal Models
Latest papersRecent research connected to this question, newest first.Accelerating Unified Multimodal Models with Core-Expansion Routing and Unified Computation SchedulingThe source studies CE-Router with Unified Computation Scheduling, combining task-shared token scoring and progress-conditioned generation routing with layer skipping, FFN pruning, diffusion-head cache reuse, and denoising-step early exit. Experiments on two representative unified multimodal architectures retain 98.03% of dense understanding performance with a reported 1.93× end-to-end inference speedup.research paper · Sep 2, 2026
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How can multimodal models integrate evidence across deeply interleaved text and images?How can multimodal models rely on images or audio rather than language shortcuts?How can inference prune activated MoE experts without confounding compute savings with output rescaling?How can multimodal chain-of-thought be compressed without losing image-dependent reasoning or inducing hallucinations?
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