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Ao Sun

Ao Sun has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining; often with Hao Zhou, Jianyong Wang, Jie Zhou.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 5 · 38 works · 89 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 15 times in our library · ⭐ Paper Stars ranking: #9350

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 University of Posts and TelecommunicationsBeijing · China2024–2026 · 2 paperscurrent🎓Hong Kong University of Science and TechnologyHong Kong · China2023–2025 · 4 papers🎓Hefei University of TechnologyHefei · China2024–2025 · 2 papers🏛️Chinese People's Liberation ArmyBeijing · China2006–2020 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 14 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 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksSafety Research · 1 workSocial Sciences · 1 work

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

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling (2025 · 15 citations)
    Speculative sampling speeds up LLM decoding. A small draft model proposes tokens, and the full model checks them in parallel. The authors find that this benefit shrinks for models with big vocabularies, such as Llama-3-8B (128k tokens). They re-implemented EAGLE-2 in native C and CUDA to strip away Python and…
  2. CuMA: Aligning LLMs with Sparse Cultural Values via Demographic-Aware Mixture of Adapters (2026 · 1 citation)
    Standard LLM alignment fits one shared set of weights to pooled human feedback. That works when a single correct answer exists, but it breaks down on culturally contested questions. The authors argue that the value distributions of different demographic groups form well-separated clusters, which they call Cultural…

 Co-authors here

10 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 15 times (of the 149,439 papers whose bibliographies we read); 4 citing papers with a page here →.

Updated

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