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Xinyu Dai

Xinyu Dai has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining; often with Zhen Wu, Hongbo Zhang, Meishan Zhang.

3 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 3 · 16 works · 53 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 68 times in our library · ⭐ Paper Stars ranking: #8103

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

🎓Nanjing UniversityNanjing · China2024–2026 · 10 paperscurrent

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 9The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksSocial Sciences · 1 workArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksGeneral Social Sciences · 1 workSociology and Political Science · 1 workComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work

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

 Works by year

2020s

20262 papers20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. AutoSurvey: Large Language Models Can Automatically Write Surveys (2024 · 91 citations)
    The volume of AI research, especially on large language models, now grows faster than human authors can write literature reviews, which leaves many subfields without an overview.
  2. CogGen: A Cognitively Inspired Recursive Framework for Deep Research Report Generation (2026)
    Agent pipelines that write long research reports with LLMs usually run in one direction: they draft an outline, then fill it in order.
  3. Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning (2026)
    Rerankers in RAG pipelines are usually trained on fixed human relevance labels and judged by IR metrics such as NDCG, independent of the LLM that consumes the documents.

 Co-authors here

12 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 68 times (of the 149,439 papers whose bibliographies we read); 25 citing papers with a page here →.

Updated

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