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Ziniu Li

Ziniu Li has 2 papers in our library, in 2025, most of them on Language models & pretraining, Reasoning; often with Boyi Wei, Chenyang Wang, Fan Yin.

2 papers in our library · 2025

OpenAlex · ORCID · h-index 6 · 19 works · 115 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 39 times in our library · ⭐ Paper Stars ranking: #9778

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

🎓Chinese University of Hong Kong, ShenzhenShenzhen · China2020–2026 · 18 paperscurrent🎓Shenzhen Research Institute of Big DataShenzhen · China2021–2026 · 10 paperscurrent🎓Chinese University of Hong KongHong Kong · China2023–2025 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution 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).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 worksComputational Theory and Mathematics · 1 work

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

 Works by year

2020s

20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. A Survey on Large Language Models for Mathematical Reasoning (2025 · 26 citations)
    This survey, by Peng-Yuan Wang et al. (mainly Nanjing University), maps how large language models became capable of solving mathematical problems. It contrasts this with older rule-based and semantic-parsing systems that relied heavily on hand engineering. The authors split the capability into two parts:
  2. Scaling Latent Reasoning via Looped Language Models (2025 · 22 citations)
    Most large language models learn to reason through explicit chain-of-thought added after pre-training. This lengthens outputs and makes little use of pre-training data. The authors propose Ouro, a family of Looped Language Models (LoopLM) in which a stack of weight-tied layers is applied several times, so hidden…

 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 39 times (of the 149,439 papers whose bibliographies we read); 8 citing papers with a page here →.

Updated

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