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Jun-Yu Ma

Jun-Yu Ma has 2 papers in our library, in 2024–2026, most of them on Language models & pretraining, Reasoning; often with Boyang Xue, Dong Yu, Haitao Mi.

2 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 4 · 13 works · 46 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 53 times in our library · ⭐ Paper Stars ranking: #7378

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 · 7 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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work

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

 Works by year

2020s

20261 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue (2024 · 67 citations)
    Model editing updates facts stored in large language models without full retraining. However, it is usually judged only on whether the edit sticks, generalizes to paraphrases and leaves unrelated facts intact. This EMNLP 2024 paper asks whether such edits quietly damage the models' broader capabilities. The authors…
  2. WebAggregator: Enhancing Compositional Reasoning Capabilities of Deep Research Agent Foundation Models (2026 · 1 citation)
    Deep Research agents need more than retrieval. They must combine scattered, heterogeneous evidence through multi-step reasoning. Existing training and evaluation resources mostly reward locating a single entity, so agents learn to call tools often instead of reasoning carefully.

 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 53 times (of the 149,439 papers whose bibliographies we read); 10 citing papers with a page here →.

Updated

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