Elamur Research⌕
☰
home / authors / Yuling Liu

Yuling Liu

Yuling Liu has 2 papers in our library, in 2026, most of them on Reasoning, Language models & pretraining, Efficient models & compression; often with Yunpeng Li, Wei Dong, Wei Zhang.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 2 · 23 works · 5 citations OpenAlex, 2026-10-14

📄 2 works in our library · ⭐ Paper Stars ranking: #14091

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

🔬Institute of Information EngineeringBeijing · China2022–2026 · 24 paperscurrent🏛️Chinese Academy of SciencesBeijing · China2022–2026 · 23 paperscurrent🎓University of Chinese Academy of SciencesBeijing · China2022–2026 · 10 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 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).Artificial Intelligence · 2 worksComputer Science · 2 worksArts and Humanities · 1 workComputer Vision and Pattern Recognition · 1 workHardware and Architecture · 1 workLanguage and Linguistics · 1 work

🧭 Topics 3Research subjects and languages the work is about.Reasoning · 2 worksEfficient models & compression · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. MirageBackdoor: A Stealthy Attack that Induces Think-Well-Answer-Wrong Reasoning (2026)
    The paper presents MirageBackdoor (MirageBD), a backdoor attack on Chain-of-Thought reasoning in LLMs. Earlier CoT backdoors such as BadChain, ShadowCoT and DecepChain produce wrong answers by corrupting the reasoning itself, which process-monitoring defenses can catch. MirageBD instead leaves the visible reasoning…
  2. ReasMark: A Robust Watermark for Attributing LLM Reasoning Under Knowledge Distillation Attacks (2026)
    Reasoning-capable LLMs are costly to build. A competitor can still copy their step-by-step behavior by querying a deployed model and training a student on its outputs, including the reasoning traces. Conventional text watermarks (token-level logit biases or sampling rules) tend to disappear in this process, because…

 Co-authors here

5 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

This page in Markdown → https://md.research.elamur.ai/free/en/authors/yuling-liu/