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Yuting Ning

Yuting Ning has 2 papers in our library, in 2024–2025; often with Zeyi Liao, Bo Liu, Boshi Wang.

2 papers in our library · 2024–2025

OpenAlex · ORCID · h-index 8 · 29 works · 363 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 99 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #8282

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

🎓Beijing University of Chemical TechnologyBeijing · China2022–2026 · 15 paperscurrent🎓University of Science and Technology BeijingBeijing · China2026 · 1 papercurrent🎓University of Science and Technology of ChinaHefei · China2021–2024 · 8 papers🎓The Ohio State UniversityColumbus · United States2023–2025 · 4 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 worksArtificial Intelligence · 2 worksDecision Sciences · 1 workInformation Systems · 1 workInformation Systems and Management · 1 workArts and Humanities · 1 workLanguage and Linguistics · 1 work

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery (2024 · 81 citations)
    Claims that LLM-based agents can automate whole research pipelines are hard to verify. The authors argue that agents should first be tested on the individual steps of a scientific workflow. For this purpose they introduce ScienceAgentBench, which collects 102 tasks from 44 peer-reviewed papers spanning four fields…
  2. Agent Learning via Early Experience (2025 · 24 citations)
    Reinforcement learning is hard to apply to language agents when environments give no verifiable reward, as on websites, or demand long, costly multi-turn rollouts.

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific DiscoveryarXiv:2410.05080Ziru Chen, Shijie Chen, Yuting Ning, et al.2024 · 81 citations
  2. Agent Learning via Early ExperiencearXiv:2510.08558Kai Zhang, Xiangchao Chen, Bo Liu, et al.2025 · 24 citations
  3. When Benign Inputs Lead to Severe Harms: Eliciting Unsafe Unintended Behaviors of Computer-Use Agents ↗arXiv (Cornell University) · no summary yetJaylen Jones et al.2026
  4. SkillHarm: Lifecycle-Aware Skill-Based Attacks via Automated Construction ↗arXiv (Cornell University) · no summary yetYuting Ning et al.2026

 Co-authors here

12 of 13 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 99 times (of the 149,439 papers whose bibliographies we read); 45 citing papers with a page here →.

Updated

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