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Na Zou

Na Zou has 2 papers in our library, in 2025, most of them on Language models & pretraining, Reasoning, Information retrieval & search; often with Guanchu Wang, Andrew Wen, Chia-Yuan Chang.

2 papers in our library · 2025

OpenAlex · ORCID · Wikidata · h-index 19 · 74 works · 1,562 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 180 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5498

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

🎓University of HoustonHouston · United States2023–2025 · 12 paperscurrent🎓Texas A&M UniversityCollege Station · United States2016–2024 · 48 papers🎓Arizona State UniversityTempe · United States2015 · 3 papers🎓University of VirginiaCharlottesville · United States2019–2023 · 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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work

🧭 Topics 3Research subjects and languages the work is about.Information retrieval & search · 1 workLanguage models & pretraining · 1 workReasoning · 1 work

 Works by year

2020s

20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models (2025 · 157 citations)
    Reasoning-focused LLMs such as OpenAI o1 and DeepSeek-R1 get better at math and coding by writing long Chain-of-Thought traces.
  2. MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation (2025 · 65 citations)
    Retrieval-augmented generation can be undermined by retrieved passages that are off-topic or misleading. Such passages lower answer quality and add inference cost. MAIN-RAG is a post-retrieval filtering framework that needs no training or fine-tuning and runs on top of a standard retriever. It uses three LLM agents:

All works

3 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. Non-Local U-Nets for Biomedical Image Segmentation ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetZhengyang Wang et al.2020 · 177 citations
  2. Stop Overthinking: A Survey on Efficient Reasoning for Large Language ModelsarXiv:2503.16419Yang Sui, Yu-Neng Chuang, Guanchu Wang, et al.2025 · 157 citations
  3. MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented GenerationAnnual Meeting of the Association for Computational Linguistics (ACL)Chia-Yuan Chang, Zhimeng Jiang, Vineeth Rakesh, et al.2025 · 65 citations

 Co-authors here

12 of 14 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 180 times (of the 149,439 papers whose bibliographies we read); 55 citing papers with a page here →.

Updated

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