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Jianyuan Zhong

Jianyuan Zhong has 2 papers in our library, in 2025–2026, most of them on Reasoning; often with Haoxiong Liu, Jing Xiong, Roozbeh Yousefzadeh.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 8 · 11 works · 1,602 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 66 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #12037

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 University of Hong KongHong Kong · China2024–2025 · 3 paperscurrent🎓University of RochesterRochester · United States2019–2021 · 4 papers

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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputational Theory and Mathematics · 1 work

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

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Mathesis: Towards Formal Theorem Proving from Natural Languages (2025 · 9 citations)
    Current LLM theorem provers for Lean 4 assume their input is already a formal statement written by an expert. Real problems, however, are stated in natural language. Mathesis targets the full chain from informal problem to verified proof. Its pipeline has three stages:
  2. Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative Verifier (2026 · 1 citation)
    Step-by-step reasoning in LLMs creates a cost problem. Solvers keep generating text after they already have an answer, and checking their work is expensive. Process-based verifiers such as GenPRM re-read the context at every step. Cheap "NoThinking"-style checks are fast but flag many false positives.

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. UR-FUNNY: A Multimodal Language Dataset for Understanding Humor ↗Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · no summary yetMd Kamrul Hasan et al.2019 · 213 citations
  2. Mathesis: Towards Formal Theorem Proving from Natural LanguagesarXiv:2506.07047Yu Xuejun, Jianyuan Zhong, Zijin Feng, et al.2025 · 9 citations
  3. Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative VerifierAnnual Meeting of the Association for Computational Linguistics (ACL)Jianyuan Zhong, Zeju Li, Zhijian Xu, et al.2026 · 1 citation

 Co-authors here

6 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 66 times (of the 149,439 papers whose bibliographies we read); 14 citing papers with a page here →.

Updated

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