arXiv AI August 24, 2026 By Yantao Li, Huanlin Gao, Fang Zhao, Chao Tan, Qiang Hui, Shuting Liu, Fuyuan Shi, Ting Lu, Shaoan Zhao, Xueqiang Guo, Xinpei Su, Jianbing Zhang, Xinyu Dai, Kai Wang, Shiguo Lian

Is Multimodal Speculative Decoding Ready for Diffusion-Based Parallel Drafting? A Survey and Empirical Diagnosis

Read the original on arXiv AI →

The paper surveys multimodal speculative decoding, examining whether diffusion-based block‑parallel generative drafting—successful in text‑only LLMs—can be applied to Vision‑Language, Video‑Language, Audio, and Vision‑Language‑Action models. It introduces a taxonomy separating drafter‑side parallelism from other design choices, and presents an empirical comparison across benchmarks such as OCR, VQA, visual reasoning, and image captioning. The study highlights current limitations, outlines open challenges, and suggests future research directions for multimodal speculative decoding.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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MCIF: Multimodal Crosslingual Instruction-Following Benchmark from Scientific Talks

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