Author

Xing Hu

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#artificial intelligence Preprint Oct 2026

LoopQuant: Accurate and Efficient Post-Training Quantization for Looped Transformers

Looped Language Models (LoopLMs) improve parameter efficiency by recursively reusing shared Transformer modules, yet their repeated computation over evolving recurrent states poses new challenges for low-bit deployment. Existing post-training quantization (PTQ) methods, largely designed for feed-forward Transformers, f...

Zhi-Xiong Zhao, Zu-Kang Xu, Xing Hu et al. · 0 citations

#artificial intelligence Preprint Sep 2026

ACE: Adaptive Calibration-Free Expert Skipping for MoE-based LLMs

ACE is proposed, a training-free, calibration-free, and checkpoint-preserving framework for token-adaptive expert skipping in MoE-based LLMs that consistently outperforms existing static and dynamic baselines.

Zu-Kang Xu, Zhi-Xiong Zhao, Xing Hu et al. · 0 citations