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Junyu Luo

Junyu Luo has 2 papers in our library, in 2025, most of them on GPU & parallel computing, Efficient models & compression; often with Chong Ruan, Damai Dai, Huazuo Gao.

2 papers in our library · 2025

OpenAlex · ORCID · h-index 17 · 56 works · 1,090 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 201 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #4685

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

🎓Peking UniversityBeijing · China2024–2026 · 23 paperscurrent🎓Pennsylvania State UniversityState College · United States2020–2023 · 11 papers🎓Beihang UniversityBeijing · China2020–2022 · 5 papers🎓Sichuan UniversityChengdu · China2017–2019 · 5 papers🎓University of California, Los AngelesLos Angeles · United States2024–2025 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 7 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksArts and Humanities · 1 workComputer Vision and Pattern Recognition · 1 workLanguage and Linguistics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Efficient models & compression · 1 workGPU & parallel computing · 1 work

 Works by year

2020s

20252 papersJunyu Luo's papers of 2025

 Papers

The most cited first (OpenAlex and our library).

  1. Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention (2025 · 135 citations)
    Standard softmax attention becomes the main cost in long-context language models. The authors estimate it takes 70–80% of decoding latency at 64k tokens. Earlier sparse attention methods often save compute on paper but not in wall-clock time. Many speed up only prefilling or only decoding, clash with GQA/MQA memory…
  2. Large Language Model Agent: A Survey on Methodology, Applications and Challenges (2025 · 59 citations)
    Work on LLM-based agents has grown quickly but remains scattered. Existing reviews tend to cover one slice, such as games, deployment settings, multimodality or security, or they give broad overviews without a method-level breakdown. Luo et al. address this with a survey built around how agents are engineered. Its…

All works

4 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. Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse AttentionarXiv:2502.11089Jingyang Yuan, Huazuo Gao, Damai Dai, et al.2025 · 135 citations
  2. Large Language Model Agent: A Survey on Methodology, Applications and ChallengesarXiv:2503.21460Junyu Luo, Weizhi Zhang, Ye Yuan, et al.2025 · 59 citations
  3. FinMME: Benchmark Dataset for Financial Multi-Modal Reasoning Evaluation ↗Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetJunyu Luo et al.2025 · 19 citations
  4. A Survey on Efficient Large Language Model Training: From Data-centric Perspectives ↗Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetJunyu Luo et al.2025 · 14 citations

 Co-authors here

8 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 201 times (of the 141,840 papers whose bibliographies we read); 2 citing papers with a page here →.

Updated

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