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Junxian Guo

Junxian Guo has 2 papers in our library, in 2024–2025, most of them on Efficient models & compression, Language models & pretraining, Information retrieval & search; often with Guangxuan Xiao, Song Han, Haotian Tang.

2 papers in our library · 2024–2025

OpenAlex · ORCID · h-index 9 · 55 works · 326 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 170 times in our library · ⭐ Paper Stars ranking: #5944

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

🎓Xinjiang Agricultural UniversityÜrümqi · China2008–2026 · 16 paperscurrent🔬Collaborative Innovation Center of Chemistry for Energy MaterialsShanghai · China2026 · 1 papercurrent🎓Fudan UniversityShanghai · China2026 · 1 papercurrent🎓Jinan UniversityGuangzhou · China2023–2025 · 7 papers🏢Commercial Aircraft Corporation of ChinaShanghai · China2024–2025 · 2 papers🎓Shanghai Jiao Tong UniversityShanghai · China2024–2025 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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 3Research subjects and languages the work is about.Efficient models & compression · 2 worksInformation retrieval & search · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads (2024 · 114 citations)
    Serving LLMs over very long inputs is costly because the KV cache grows linearly with context length, and attention compute grows with it.
  2. XAttention: Block Sparse Attention with Antidiagonal Scoring (2025 · 59 citations)
    Long-context Transformers are costly because attention grows quadratically with input length, which makes the pre-filling stage a bottleneck.

 Co-authors here

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

 Cited by

Papers of our library cite their works 170 times (of the 149,439 papers whose bibliographies we read); 16 citing papers with a page here →.

Updated

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