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Zhenkai Wu

2 accepted papers

2026

Expert Merging: Model Merging with Unsupervised Expert Alignment and Importance-Guided Layer Chunking

ICLR 2026poster

Model merging, which combines multiple domain-specialized experts into a single model, offers a practical path to endow Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) with broad capabilities without the cost of joint training or serving many models. However, training-free…

Cited by 0SourcecodeScholar
2026

VLM-Pruner: Buffering for Spatial Sparsity in an Efficient VLM Centrifugal Token Pruning Paradigm

CVPR 2026

Vision-language models (VLMs) excel at image understanding tasks, but the large number of visual tokens imposes significant computational costs, hindering deployment on mobile devices. Many pruning methods rely solely on token importance and thus overlook inter-token redundancy, retaining numerous d

Cited by 0SourcecodeScholar