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Yulong Ao

Yulong Ao has 3 papers in our library, in 2024–2026, most of them on Efficient models & compression, Vision & language; often with Hao Zhang, Lyu Mengsi, Yonghua Lin.

3 papers in our library · 2024–2026

OpenAlex · h-index 8 · 22 works · 358 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 25 times in our library · ⭐ Paper Stars ranking: #10826

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

🔬Beijing Academy of Artificial IntelligenceBeijing · China2024–2026 · 3 paperscurrent🎓Peking UniversityBeijing · China2018–2024 · 7 papers🏛️Chinese Academy of SciencesBeijing · China2015–2018 · 6 papers🎓University of Chinese Academy of SciencesBeijing · China2015–2017 · 6 papers🔬Institute of SoftwareBeijing · China2015–2017 · 3 papers🔬Peng Cheng LaboratoryShenzhen · China2019 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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).Computer Science · 3 worksComputer Vision and Pattern Recognition · 3 worksArts and Humanities · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 2Research subjects and languages the work is about.Efficient models & compression · 2 worksVision & language · 1 work

 Works by year

2020s

20262 papers20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. PDTrim: Targeted Pruning for Prefill-Decode Disaggregation in Inference (2026 · 6 citations)
    Production LLM serving often splits inference into two parts: a prefill stage that processes the prompt and builds the KV cache, and a decode stage that generates tokens.
  2. TrimTokenator: Towards Adaptive Visual Token Pruning for Large Multimodal Models (2026 · 6 citations)
    Large multimodal models turn an image into thousands of visual tokens. Because self-attention cost grows quadratically with sequence length, these tokens make inference slow and memory-hungry. The authors first compare the two modalities through attention patterns, semantic distribution and repeated information. They…
  3. Aquila2 Technical Report (2024 · 1 citation)
    Most LLMs are pretrained on a fixed dataset over a long run, so it is hard to change the data mix or add new sources partway through. BAAI's Aquila2 is a family of Chinese–English bilingual models at 7B, 34B and an experimental 70B size. They were trained with HeuriMentor (HM), a framework with three parts:

 Co-authors here

9 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 25 times (of the 149,439 papers whose bibliographies we read); 10 citing papers with a page here →.

Updated

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