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Liu Guoming

Liu Guoming has 2 papers in our library, in 2025, most of them on Efficient models & compression; often with Baoyuan Qi, Lefei Zhang, Shi Luohe.

2 papers in our library · 2025

OpenAlex · ORCID · h-index 26 · 127 works · 2,218 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 14 times in our library · ⭐ Paper Stars ranking: #9464

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

🎓Shandong University of Science and TechnologyQingdao · China2016–2026 · 68 paperscurrent🎓Fuzhou UniversityFuzhou · China2004–2025 · 8 papers🏢State Grid Shandong Electric Power CompanyJinan · China2019–2022 · 4 papers🎓Xi'an Jiaotong UniversityXi'an · China2008–2010 · 4 papers🎓Shenyang University of TechnologyShenyang · China2010 · 3 papers🏢Hebei Provincial Communications Planning, Design and Research Institute Co., Ltd.Shijiazhuang2017–2020 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 8 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.

📚 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 1Research subjects and languages the work is about.Efficient models & compression · 1 work

 Works by year

2020s

20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. SpindleKV: A Novel KV Cache Reduction Method Balancing Both Shallow and Deep Layers (2025 · 9 citations)
    Long-context inference with LLMs is limited by the GPU memory consumed by the KV cache. Earlier token eviction, merging and quantization methods compress deep layers well, but shallow layers resist them. In a preliminary analysis of LLaMA2-7B-chat on 2WikiMQA, the authors found two patterns. Deep layers concentrate…
  2. KV-Latent: Dimensional-level KV Cache Reduction with Frequency-aware Rotary Positional Embedding (2025 · 6 citations)
    Decoder-only LLMs store keys and values for every generated token. This KV Cache grows with sequence length and becomes a major limit on memory and bandwidth during inference. KV-Latent tackles this at the level of individual head dimensions, a direction earlier head-, layer- and token-level methods have left largely…

 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 14 times (of the 149,439 papers whose bibliographies we read); 4 citing papers with a page here →.

Updated

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