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Xiao-Ming Wu

Xiao-Ming Wu has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Evaluation & benchmarks, Vision & language; often with Bo Liu, Lei Bai, Philip S. Yu.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 4 · 16 works · 63 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 9 times in our library · ⭐ Paper Stars ranking: #11078

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

🎓Nanyang Technological UniversitySingapore · Singapore2026 · 1 papercurrent🎓Sun Yat-sen UniversityGuangzhou · China2024–2025 · 6 papers🎓Hong Kong Polytechnic UniversityHong Kong · China2024–2025 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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 5The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 1 workEvaluation & benchmarks · 1 workVision & language · 1 work

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Recurrent Knowledge Identification and Fusion for Language Model Continual Learning (2025 · 11 citations)
    This paper tackles continual learning for large language models, where a model must absorb tasks one after another without losing earlier skills (catastrophic forgetting) while still benefiting from links between tasks (knowledge transfer).
  2. MSEarth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with MLLMs (2026)
    Multimodal large language models (MLLMs) are seldom tested on graduate-level Earth science. Existing benchmarks either use synthetic or textbook material, or pair paper figures only with their short captions, which leaves out the reasoning found in the body of the article. MSEarth addresses this gap. The authors…

 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.

 Cited by

Papers of our library cite their works 9 times (of the 149,439 papers whose bibliographies we read); 3 citing papers with a page here →.

Updated

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