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Wen Yao

Wen Yao has 2 papers in our library, in 2026, most of them on Reasoning; often with Bo Zhang, Guotong Geng, Jiawei Zhang.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 37 · 337 works · 6,613 citations OpenAlex, 2026-10-14

📄 2 works in our library · ⭐ Paper Stars ranking: #13984

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

🔬PLA Academy of Military ScienceBeijing · China2018–2026 · 104 paperscurrent🎓Zhejiang UniversityHangzhou · China2023 · 1 papercurrent🎓National University of Defense TechnologyChangsha · China2009–2024 · 51 papers🎓Delft University of TechnologyDelft · Netherlands2010–2016 · 7 papers🏛️Chinese People's Liberation ArmyBeijing · China2022–2026 · 6 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).Artificial Intelligence · 2 worksComputer Science · 2 worksArts and Humanities · 1 workComputer Vision and Pattern Recognition · 1 workLanguage and Linguistics · 1 work

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

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. DisCal: Distribution-Aware Calibration for Mathematical Reasoning Under Character-Level Noisy Inputs (2026)
    Large reasoning models solve math word problems well on clean text. When the input contains typos or similar character-level corruption, their accuracy falls, but their confidence stays high. The authors document this mismatch and introduce DisCal, a calibration method built specifically for noisy inputs.
  2. Model-Based Imaginative Planning for Embodied Agents (2026)
    Large language models plan well in symbolic domains such as math and code, where transition rules are already absorbed during pretraining.

 Co-authors here

9 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.

Updated

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