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Jiancan Wu

Jiancan Wu has 2 papers in our library, in 2024–2026, most of them on Language models & pretraining, Reasoning; often with Xiang Wang, Chang Zhou, Chengpeng Li.

2 papers in our library · 2024–2026

OpenAlex · ORCID · Wikidata · h-index 17 · 65 works · 1,026 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 38 times in our library · ⭐ Paper Stars ranking: #9897

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

🎓University of Science and Technology of ChinaHefei · China2022–2026 · 56 paperscurrent

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 7The 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 workComputer Vision and Pattern Recognition · 1 workSignal Processing · 1 work

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

 Works by year

2020s

20261 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning (2024 · 43 citations)
    Open-source LLMs still trail proprietary systems at math word problems. A common remedy is supervised fine-tuning on synthetic data. This paper asks which augmentation strategies matter, how accuracy grows with the amount of synthetic data, and whether gains carry over to other math domains. The authors used…
  2. SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs (2026 · 1 citation)
    Modern LLMs advertise very long context windows, yet they handle long sequences of numbers poorly. Tasks such as counting, filtering, or spotting trends need exact access to every value. The authors trace this failure to attention dispersion: as inputs grow, Softmax spreads attention over many similar numeric tokens…

 Co-authors here

12 of 15 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 38 times (of the 149,439 papers whose bibliographies we read); 20 citing papers with a page here →.

Updated

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