🧑‍🔬 Xianzhi Yu

🏛️ Affiliations

🎓University of Chinese Academy of SciencesBeijing · China2024–2026 · 3 paperscurrent🔬Institute of High Energy PhysicsBeijing · China2024–2026 · 2 paperscurrent🎓Lanzhou UniversityLanzhou · China2025–2026 · 2 paperscurrent🎓Zhengzhou UniversityZhengzhou · China2025–2026 · 2 paperscurrent🎓Tsinghua UniversityBeijing · China2018–2025 · 8 papers🎓Shanxi UniversityTaiyuan · China2009–2014 · 4 papers🔬State Key Laboratory of Quantum Optics and Quantum Optics DevicesTaiyuan2009–2014 · 4 papers🎓National University of Defense TechnologyChangsha · China2018–2025 · 3 papers

🏛️Institute of High Energy Physics, Chinese Academy of SciencesBeijing · ChinaEmployment

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 10 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity (2025 · 4 citations)
   Sparse Mixture-of-Experts LLMs are cheap per token. However, when experts are spread across accelerators, uneven routing overloads some devices, and the slowest device limits overall speed. The Pangu Team at Huawei proposes Mixture of Grouped Experts (MoGE). Experts are split into equal, non-overlapping groups…
2. Analytical FFN-to-MoE Restructuring via Activation Pattern Analysis (2026 · 1 citation)
   Most inference compute in large language models goes to the feed-forward (FFN) blocks. Converting a dense model into a Mixture-of-Experts (MoE) can cut that cost, but earlier conversion methods needed continual training on billions of tokens. Pei et al. instead restructure FFNs after training, in a single analytical…
3. Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats (2026 · 1 citation)
   The paper asks whether Huawei's HiFloat number formats, built for Ascend NPUs, work well for post-training quantization (PTQ) of large language models.
4. Benchmarking Post-Training Quantization of Large Language Models under Microscaling Floating Point Formats (2026)
   Most post-training quantization (PTQ) methods for LLMs were built for integer formats. It is unclear how they behave under Microscaling Floating-Point (MXFP), a block-scaled format with growing hardware support. The authors benchmark PTQ under MXFP8 and MXFP4, simulated with Microsoft's microxcaling library. They…

🤝 Co-authors here

12 of 19 co-authors with a page, the most papers together first.

• Hui-Ling Zhen ×3
• Mingxuan Yuan ×2
• Zhenhua Dong ×2
• Bei Yu
• Fangcheng Liu
• Fei Mi
• Hang Zhou
• Haoli Bai
• Jinpeng Li
• Kai Han
• Pengxiang Zhao
• Sinno Jialin Pan

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Annual Meeting of the Association for Computational Linguistics (ACL) ×3: ACL 2026 ×3

🔗 Cited by

Papers of our library cite their works 12 times (of the 149,439 papers whose bibliographies we read); 3 citing papers with a page here →.

Updated 2 November 2026

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