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Guanchu Wang

Guanchu Wang has 4 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning, Information retrieval & search; often with Shaochen Zhong, Xia Hu, Yu-Neng Chuang.

4 papers in our library · 2025–2026

OpenAlex · h-index 2 · 3 works · 62 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 194 times in our library · ⭐ Paper Stars ranking: #5376

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

🎓Rice UniversityHouston · United States2022–2025 · 3 paperscurrent

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 · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 2 worksInformation Systems · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 3 worksInformation retrieval & search · 1 workReasoning · 2 works

 Works by year

2020s

20262 papers20252 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:
  3. FaithLM: Towards Faithful Explanations for Large Language Models (2026 · 9 citations)
    LLMs often explain their answers in natural language, but these explanations frequently do not reflect what actually drove the prediction. Classic fidelity tests, which delete important tokens, also fit poorly with free-text rationales whose content is semantic rather than token-level.
  4. AutoL2S: Auto Long-Short Reasoning for Efficient Large Language Models (2026 · 7 citations)
    Distilling reasoning from strong teacher LLMs into non-reasoning students often causes "overthinking": the students produce very long chain-of-thought (CoT) traces even for easy questions, which raises decoding time and cost.

 Co-authors here

12 of 19 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 194 times (of the 149,439 papers whose bibliographies we read); 57 citing papers with a page here →.

Updated

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