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Peisong Wang

Peisong Wang has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining; often with Bang Zhang, Jiaqi Chen, Ruotian Ma.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 22 · 78 works · 2,381 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 34 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #8398

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

🏛️Chinese Academy of SciencesBeijing · China2016–2026 · 44 paperscurrent🔬Institute of AutomationBeijing · China2016–2026 · 42 paperscurrent🎓University of Chinese Academy of SciencesBeijing · China2016–2025 · 14 papers🎓Shanghai University of Electric PowerYangpu · China2019–2021 · 5 papers🎓Southern University of Science and TechnologyShenzhen · China2020–2023 · 3 papers🎓Hangzhou Vocational and Technical CollegeHangzhou · China2024 · 2 papers🎓Center for Excellence in Brain Science and Intelligence TechnologyShanghai · China2019–2020 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 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).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksSafety Research · 1 workSocial Sciences · 1 work

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

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. S²R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning (2025 · 26 citations)
    S$^2$R targets a practical gap. Methods that give LLMs o1-style "deep thinking" usually need huge datasets, heavy compute, or distillation from much stronger models, and it is unclear how to help weaker base models reason better. The authors treat reasoning as a sequence of alternating "solve" and "verify" actions.…
  2. Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models (2026 · 6 citations)
    Standard LLM leaderboards reward textual skill and helpfulness, but they rarely check whether a model actually reads people well.

All works

4 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. ODE-Inspired Network Design for Single Image Super-Resolution ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetXiangyu He et al.2019 · 261 citations
  2. Accelerating Convolutional Neural Networks for Mobile Applications ↗ACM International Conference on Multimedia (ACM MM) · no summary yetPeisong Wang, Jian Cheng2016 · 79 citations
  3. S²R: Teaching LLMs to Self-verify and Self-correct via Reinforcement LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Ruotian Ma, Peisong Wang, Cheng Liu, et al.2025 · 26 citations
  4. Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language ModelsFindings of the Association for Computational LinguisticsBang Zhang, Ruotian Ma, Qingxuan Jiang, et al.2026 · 6 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 34 times (of the 149,439 papers whose bibliographies we read); 9 citing papers with a page here →.

Updated

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