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Qineng Wang

Qineng Wang has 2 papers in our library, in 2024–2025, most of them on Reasoning; often with Hanghang Tong, Kangrui Wang, Kefan Yu.

2 papers in our library · 2024–2025

OpenAlex · ORCID · h-index 6 · 16 works · 106 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 131 times in our library · ⭐ Paper Stars ranking: #6717

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

🔬Innovation Academy for Microsatellites of Chinese Academy of SciencesShanghai2026 · 2 paperscurrent🏛️Chinese Academy of SciencesBeijing · China2026 · 1 papercurrent🎓Xi'an Jiaotong UniversityXi'an · China2020–2024 · 8 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 5The 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 work

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1 work

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key? (2024 · 107 citations)
    Earlier work suggests that LLM agents discussing a problem together reason better than a single model. This paper tests that claim more rigorously. The authors define a general pipeline for designing discussion frameworks with four parts: kick-start prompt, agents, algorithm and result. They also introduce CMD…
  2. RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning (2025 · 71 citations)
    The paper asks how LLM agents can be trained with reinforcement learning to improve through their own experience in interactive settings.

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

Updated

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