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Enzhe Lu

Enzhe Lu has 2 papers in our library, in 2025, most of them on Language models & pretraining, Efficient models & compression; often with Jiaxi Hu, Kimi Team, Xin Men.

2 papers in our library · 2025

OpenAlex · h-index 4 · 7 works · 180 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 688 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6428

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏛️Guangzhou Center for Disease Control and PreventionGuangzhou · China2006–2020 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1 workEfficient models & compression · 1 work

 Works by year

2020s

20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. MoBA: Mixture of Block Attention for Long-Context LLMs (2025 · 80 citations)
    Standard softmax attention grows quadratically with sequence length, which makes long-context LLMs expensive to train and run.
  2. Kimi Linear: An Expressive, Efficient Attention Architecture (2025 · 51 citations)
    Softmax attention becomes a bottleneck for agentic and RL-heavy LLM workloads. Its cost grows quadratically with sequence length, and its KV cache keeps expanding during long decoding. Earlier linear-attention and hybrid designs saved compute but usually lost quality or were only tested at small scale. The Kimi Team…

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Muon is Scalable for LLM Training ↗arXiv:2502.16982 · no summary yetJingyuan Liu et al.2025 · 83 citations
  2. MoBA: Mixture of Block Attention for Long-Context LLMsConference on Neural Information Processing Systems (NeurIPS)Enzhe Lu, Zhejun Jiang, Jingyuan Liu, et al.2025 · 80 citations
  3. Kimi Linear: An Expressive, Efficient Attention ArchitecturearXiv:2510.26692Kimi Team, Yu Zhang, Zongyu Lin, et al.2025 · 51 citations

 Co-authors here

6 co-authors with a page, the most papers together first.

Venues

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

 Cited by

Papers of our library cite their works 688 times (of the 149,439 papers whose bibliographies we read); 174 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/enzhe-lu/