Author

Yunpu Ma

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#artificial intelligence Preprint Oct 2026

MWOP: Modality-aware Width-wise Operation Pruning for Efficient MLLMs

Multimodal large language models (MLLMs) incur substantial inference costs when processing long visual-textual sequences. While existing operation compression methods exploit modality-level redundancy, they largely treat computation within attention heads and shared feed-forward network (FFN) channels as unified units,...

Xu-Dong Wang, Hao Wu, Hao-Zhe Hu et al. · 0 citations

Jul 2026

WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning

WIDE is presented, the first end-to-end differentiable token-level dynamic width pruning framework designed for both prefill and decode scenarios, and a pruning--kernel co-design framework that decomposes dynamic sparsity acceleration into mask reordering, hardware-agnostic block-level skipping, and hardware-dependent...

Haozhe Hu, Hao Wu, Pei-Ran Yin et al. · arXiv.org · 1 citation