most citedKlotski: Efficient Mixture-of-Expert Inference via Expert-Aware Multi-Batch Pipeline

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cs.AI2026

Words & Weights: Streamlining Multi-Turn Interactions via Co-Adaptation

Chenxing Wei, Hong Wang, Ying He +4

Test-time policy adaptation for multi-turn interactions (T2PAM) is essential for aligning Large Language Models (LLMs) with dynamic user needs during inference time. However, exist…

cs.AI2025

Fate: Fast Edge Inference of Mixture-of-Experts Models via Cross-Layer Gate

Zhiyuan Fang, Zicong Hong, Yuegui Huang +5

Large Language Models (LLMs) have demonstrated impressive performance across various tasks, and their application in edge scenarios has attracted significant attention. However, sp…