Publications

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ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs

Published in ACL 2026, 2026

Augments activations with quantized residual channels to improve NVFP4 accuracy while retaining a unified format and efficient GEMM execution.

Recommended citation: Haoqian Meng, Yilun Luo, Yafei Zhao, Wenyuan Liu, Peng Zhang, and Xindian Ma. (2026). ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs. Proceedings of ACL 2026.
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SharQ: Bridging Activation Sparsity and FP4 Quantization for LLM Inference

Published in arXiv preprint, 2026

A training-free inference method that combines activation sparsity with FP4 quantization through an online sparse–dense decomposition.

Recommended citation: Haoqian Meng, Yilun Luo, Yafei Zhao, Wenyuan Liu, Huaqing Zheng, Xindian Ma, and Peng Zhang. (2026). SharQ: Bridging Activation Sparsity and FP4 Quantization for LLM Inference. arXiv:2606.26587.
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MicroMix: Efficient Mixed-Precision Quantization with Microscaling Formats for Large Language Models

Published in ICLR 2026, 2026

Co-designs mixed MXFP4, MXFP6, and MXFP8 quantization with matrix multiplication kernels for efficient LLM inference.

Recommended citation: Wenyuan Liu, Haoqian Meng, Yilun Luo, Peng Zhang, and Xindian Ma. (2026). MicroMix: Efficient Mixed-Precision Quantization with Microscaling Formats for Large Language Models. International Conference on Learning Representations (ICLR).
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Post-Training Quantization of OpenPangu Models for Efficient Deployment on Atlas A2

Published in arXiv preprint, 2025

Studies INT8 and W4A8 post-training quantization for deploying OpenPangu models efficiently on Huawei Atlas A2 NPUs.

Recommended citation: Yilun Luo, HuaQing Zheng, Haoqian Meng, Wenyuan Liu, and Peng Zhang. (2025). Post-Training Quantization of OpenPangu Models for Efficient Deployment on Atlas A2. arXiv:2512.23367.
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