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Aug 2026
Diffusion vision-language models (dVLMs) iteratively denoise masked responses while conditioning each denoising step on visual evidence, making visual conditioning a substantial recurring inference cost. Unlike autoregressive decoding, diffusion generation repeatedly revisits the entire response as uncertainty evolves....
Yongkang Zhou, Xiang-Wen Xia, Cheng Yan et al.
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Preprint
Aug 2026
REFLEX is proposed, a training-free method that keeps the default router unchanged while reorganizing expert computation around the evolving refinement process, and introduces a coarse-to-fine hierarchy for expert-budget allocation that aligns computation with block-relative refinement roles while using the Frontier-Pr...
Xiang-Wen Xia, Chen Yan, Yiming Zhang et al.
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Preprint
Aug 2026
Mixture-of-experts vision-language models (MoE-VLMs) increase model capacity with sparse expert activation, yet deployment requires storing the full expert pool. Training-free expert merging reduces this burden, and many routing-based methods aggregate routing statistics across all tokens to determine merge compatibili...
Hong-Yu Zhang, Cheng Yan, Xiang-Wen Xia et al.
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