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Yushun Dong

Yushun Dong has 2 papers in our library, in 2025–2026, most of them on Reasoning; often with Jundong Li, Song Wang, Xiyang Hu.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 17 · 59 works · 1,127 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 10 times in our library · ⭐ Paper Stars ranking: #9859

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

🎓Florida State UniversityTallahassee · United States2024–2026 · 17 paperscurrent🎓University of VirginiaCharlottesville · United States2021–2024 · 31 papers🎓Beijing University of Posts and TelecommunicationsBeijing · China2019–2023 · 3 papers🎓Northeastern UniversityBoston · United States2023–2024 · 2 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 7The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArts and Humanities · 1 workArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 1 workLanguage and Linguistics · 1 workSocial Sciences · 1 workSociology and Political Science · 1 work

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

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Learning from Diverse Reasoning Paths with Routing and Collaboration (2025 · 11 citations)
    Compact student models trained by black-box distillation usually see only one teacher rationale per question, which captures little of the teacher's reasoning repertoire.
  2. Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs (2026)
    LLM agents increasingly work in teams that exchange messages over a communication graph. This paper asks how the shape of that graph affects how easily private data stored in one agent's memory can be extracted by a malicious teammate. The authors build MAMA (Multi-Agent Memory Attack), a controlled benchmark.

 Co-authors here

8 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 10 times (of the 149,439 papers whose bibliographies we read); 4 citing papers with a page here →.

Updated

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