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Chenrui Cao

Chenrui Cao has 2 papers in our library, in 2025, most of them on Reasoning, Language models & pretraining; often with Di Huang, Fan Yang, Hui Xue.

2 papers in our library · 2025

OpenAlex · ORCID · h-index 12 · 33 works · 351 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 14 times in our library · ⭐ Paper Stars ranking: #11605

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

🏥Nanjing Drum Tower HospitalNanjing · China2020–2026 · 14 paperscurrent🎓Nanjing UniversityNanjing · China2020–2026 · 13 paperscurrent🔬Institute of Computing TechnologyBeijing · China2026 · 2 paperscurrent🎓University of Science and Technology of ChinaHefei · China2025–2026 · 2 paperscurrent🏛️Chinese Academy of SciencesBeijing · China2026 · 1 papercurrent🎓Beijing Institute of TechnologyBeijing · China2023–2025 · 4 papers🎓Taiyuan University of TechnologyTaiyuan · China2024 · 2 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).Computer Science · 2 worksArtificial Intelligence · 2 worksComputational Theory and Mathematics · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 2 worksLanguage models & pretraining · 1 work

 Works by year

2020s

20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Reviving DSP for Advanced Theorem Proving in the Era of Reasoning Models (2025 · 7 citations)
    The paper asks whether formal theorem proving in Lean 4 really requires costly RL-trained prover models. The authors return to the older Draft, Sketch and Prove (DSP) pipeline and rebuild it as DSP+, which needs no training or fine-tuning. Each stage combines neural and rule-based components:
  2. StepFun-Formalizer: Unlocking the Autoformalization Potential of LLMs through Knowledge-Reasoning Fusion (2025 · 7 citations)
    Autoformalization means converting natural-language math problems into formal statements, here in Lean 4. Current LLM approaches are still inaccurate. General-purpose models often misuse Lean details, such as calling functions that do not exist. Specialized translators trained on narrow annotated data tend to misread…

 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 14 times (of the 149,439 papers whose bibliographies we read); 5 citing papers with a page here →.

Updated

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