3 papers
cs.CL2026
CAT-Q: Cost-efficient and Accurate Ternary Quantization for LLMs
Shigeng Wang, Chao Li, Yangyuxuan Kang +2
In this paper, we present CAT-Q, Cost-efficient and Accurate Ternary Quantization, for compressing and accelerating LLMs. Unlike existing state-of-the-art ternary quantization meth…
cs.CV2026
Chain-of-Models Pre-Training: Rethinking Training Acceleration of Vision Foundation Models
Jiawei Fan, Shigeng Wang, Chao Li +2
In this paper, we present Chain-of-Models Pre-Training (CoM-PT), a novel performance-lossless training acceleration method for vision foundation models (VFMs). This approach fundam…
cs.AI2026
SliderQuant: Accurate Post-Training Quantization for LLMs
Shigeng Wang, Chao Li, Yangyuxuan Kang +3
In this paper, we address post-training quantization (PTQ) for large language models (LLMs) from an overlooked perspective: given a pre-trained high-precision LLM, the predominant…