Zhixiong Zhao

^† Equal Contribution, ^* Corresponding Author(s)

1. EMNLP-Main 2026

   All for 1-Bit: Towards Genuine 1-Bit Post-Training Quantization for LLMs

   Zhixiong Zhao^† , Zukang Xu^† , Guangyu Sun , Lifeng Liu^* , and Dawei Yang^*

   In Conference on Empirical Methods in Natural Language Processing, 2026 (Top Conf. in NLP) CCF-B

   Paper Code Cite

   @misc{zhao20261bitgenuine1bitposttraining,
       title={All for 1-Bit: Towards Genuine 1-Bit Post-Training Quantization for LLMs}, 
       author={Zhixiong Zhao and Zukang Xu and Guangyu Sun and Lifeng Liu and Dawei Yang},
       year={2026},
       eprint={2609.06161},
       archivePrefix={arXiv},
       primaryClass={cs.LG},
       url={https://arxiv.org/abs/2609.06161}, 
   }

2. ICML 2026

   TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization

   Zhixiong Zhao^† , Zukang Xu^† , Zhixuan Chen , Xing Hu , Zhe Jiang , and Dawei Yang^*

   In International Conference on Machine Learning, 2026 (Top Conf. in ML) CCF-A

   Paper Code Cite

   @misc{zhao2026twlaachievingternaryweights,
       title={TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization}, 
       author={Zhixiong Zhao and Zukang Xu and Zhixuan Chen and Xing Hu and Zhe Jiang and Dawei Yang},
       year={2026},
       eprint={2606.13054},
       archivePrefix={arXiv},
       primaryClass={cs.LG},
       url={https://arxiv.org/abs/2606.13054}, 
   }

3. ACL-Main 2026

   BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs

   Zhixiong Zhao^† , Zukang Xu^† , and Dawei Yang^*

   In Annual Meeting of the Association for Computational Linguistics, 2026 (Top Conf. in NLP) CCF-A

   Paper Code Cite

   @misc{zhao2026bwlabreakingbarrierw1ax,
       title={BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs}, 
       author={Zhixiong Zhao and Zukang Xu and Dawei Yang},
       year={2026},
       eprint={2605.00422},
       archivePrefix={arXiv},
       primaryClass={cs.LG},
       url={https://arxiv.org/abs/2605.00422}, 
   }

4. ICLR 2026

   KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models

   Zukang Xu^† , Zhixiong Zhao^† , Xing Hu , Zhixuan Chen , and Dawei Yang^*

   In International Conference on Learning Representations, 2026 (Top Conf. in ML) CCF-A

   Paper Code Cite

   @article{xu2026kbvq,
     title={KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models},
     author={Xu, Zukang and Zhao, Zhixiong and Hu, Xing and Chen, Zhixuan and Yang, Dawei},
     journal={arXiv preprint arXiv:2602.11184},
     year={2026}
   }

5. AAAI 2026

   SpecQuant: Spectral Decomposition and Adaptive Truncation for Ultra-Low-Bit LLMs Quantization

   Zhixiong Zhao^† , Fangxin Liu^† , Junjie Wang , Chenyang Guan , Zongwu Wang , Li Jiang , and Haibing Guan^*

   In Association for the Advancement of Artificial Intelligence, 2026 (Top Conf. in AI) CCF-A

   Paper Code Cite

   @inproceedings{zhao2026specquant,
     title={Specquant: Spectral decomposition and adaptive truncation for ultra-low-bit llms quantization},
     author={Zhao, Zhixiong and Liu, Fangxin and Wang, Junjie and Guan, Chenyang and Wang, Zongwu and Jiang, Li and Guan, Haibing},
     booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
     volume={40},
     number={34},
     pages={28786--28794},
     year={2026}
   }

6. ICCAD 2025

   QUARK: Quantization-Enabled Circuit Sharing for Transformer Acceleration by Exploiting Common Patterns in Nonlinear Operations

   Zhixiong Zhao^† , Haomin Li^† , Fangxin Liu , Yuncheng Lu , Zongwu Wang , Tao Yang , Li Jiang , and Haibing Guan^*

   In International Conference on Computer-Aided Design, 2025 (Top Conf. in EDA) CCF-B

   Paper Code Cite

   @inproceedings{zhao2025quark,
     title={QUARK: Quantization-Enabled Circuit Sharing for Transformer Acceleration by Exploiting Common Patterns in Nonlinear Operations},
     author={Zhao, Zhixiong and Li, Haomin and Liu, Fangxin and Lu, Yuncheng and Wang, Zongwu and Yang, Tao and Jiang, Li and Guan, Haibing},
     booktitle={2025 IEEE/ACM International Conference On Computer Aided Design (ICCAD)},
     pages={1--9},
     year={2025},
     organization={IEEE}
   }

^† Equal Contribution, ^* Corresponding Author(s)

1. arXiv 2026

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

   Zhixiong Zhao , Zukang Xu , Xing Hu , Lifeng Liu , Guangyu Sun , and Dawei Yang

   arXiv preprint, 2026

   Paper Code Cite

   @misc{zhao2026loopquantaccurateefficientposttraining,
       title={LoopQuant: Accurate and Efficient Post-Training Quantization for Looped Transformers},
       author={Zhixiong Zhao and Zukang Xu and Xing Hu and Lifeng Liu and Guangyu Sun and Dawei Yang},
       year={2026},
       eprint={2610.16368},
       archivePrefix={arXiv},
       primaryClass={cs.LG},
       url={https://arxiv.org/abs/2610.16368},
   }

2. arXiv 2026

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

   Zukang Xu^† , Zhixiong Zhao^† , Xing Hu , Jiangyong Yu , Houji Wen , Jun Li , Zhe Jiang , and Dawei Yang^*

   arXiv preprint, 2026

   Paper Code Cite

   @misc{xu2026aceadaptivecalibrationfreeexpert,
       title={ACE: Adaptive Calibration-Free Expert Skipping for MoE-based LLMs},
       author={Zukang Xu and Zhixiong Zhao and Xing Hu and Jiangyong Yu and Houji Wen and Jun Li and Zhe Jiang and Dawei Yang},
       year={2026},
       eprint={2609.05228},
       archivePrefix={arXiv},
       primaryClass={cs.AI},
       url={https://arxiv.org/abs/2609.05228},
   }