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Zixuan Wang

Zixuan Wang has 4 papers in our library, in 2025–2026, most of them on Reasoning, Language models & pretraining; often with Deyu Zhou, Fan Yin, Guiyang Hou.

4 papers in our library · 2025–2026

OpenAlex · h-index 1 · 7 works · 1 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 22 times in our library · ⭐ Paper Stars ranking: #8847

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

🏢BaiduBeijing · China2026 · 1 papercurrent🎓Great Bay UniversityDongguan · China2026 · 1 papercurrent🎓Harbin Institute of TechnologyHarbin · China2026 · 1 papercurrent🎓The Hong Kong University of Science and Technology (Guangzhou)Guangzhou · China2026 · 1 papercurrent🎓University of FloridaGainesville · United States2026 · 1 papercurrent🎓Zhejiang UniversityHangzhou · China2026 · 1 papercurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksArts and Humanities · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 2 worksInformation Systems · 1 workSoftware · 1 work

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 3 worksLanguage models & pretraining · 2 works

 Works by year

2020s

20263 papers20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. 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…
  2. CoVerRL: Breaking the Consensus Trap in Label-Free Reasoning via Generator-Verifier Co-Evolution (2026 · 1 citation)
    Label-free reinforcement learning for LLM reasoning usually rewards a model for matching its own majority-voted answer.
  3. Knowledge Vector of Logical Reasoning in Large Language Models (2026)
    The paper asks whether the three classic forms of logical reasoning (deductive, inductive and abductive) correspond to linear directions in an LLM's activation space, and how these directions relate to each other.
  4. Let the Comments Speak: A Multi-Agent Framework based on Large Language Model for Comment-Guided Code Refactoring (2026)
    LLM-based refactoring tools tend to treat code mostly as syntax and ignore the intent that comments convey.

 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 22 times (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

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