Author

Shengyuan Ye

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#edge computing Book Nov 2026

MOSAIC: Efficient Collaborative Inference for Multi-Modal Large Language Models with Distributed Edge Computing

MOSAIC is presented, which adopts a component-level decomposition and distributed deployment paradigm for efficient MLLM inference at the edge, realizing three core ideas: parallel encoding, token pruning, and device-server collaboration.

Kongyange Zhao, Zhi-Xuan Liao, Sheng-Yuan Ye et al. · 0 citations

#computer vision Preprint Aug 2026

PACE: A Unified Condense-and-Extract Paradigm for Fast VLM Inference

PACE (Pixel-Adaptive Condense and Extract), a training-free inference framework that accelerates both the vision encoder and the Large Language Model (LLM) via a unified Condense-and-Extract paradigm, is proposed.

Jun-Jie Liu, Shengyuan Ye, Xu Chen · 0 citations