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Xiaocheng Yang

Xiaocheng Yang has 2 papers in our library, in 2026, most of them on Dialogue systems; often with Dilek Hakkani-Tür, Gokhan Tur, Shikib Mehri.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 12 · 420 works · 1,044 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 122 times in our library · ⭐ Paper Stars ranking: #10778

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

🏛️Chinese Academy of SciencesBeijing · China2004–2026 · 88 paperscurrent🎓Tsinghua UniversityBeijing · China2005–2026 · 30 paperscurrent🎓University of Chinese Academy of SciencesBeijing · China2026 · 25 paperscurrent🎓University of Science and Technology of ChinaHefei · China2025–2026 · 20 paperscurrent🎓Beijing Institute of TechnologyBeijing · China2026 · 12 paperscurrent🎓Kunming University of Science and TechnologyKunming · China2026 · 11 paperscurrent🎓Henan University of Science and TechnologyLuoyang · China2021–2026 · 10 paperscurrent🎓Huazhong University of Science and TechnologyWuhan · China2026 · 7 paperscurrent🎓Shanghai Jiao Tong UniversityShanghai · China2026 · 7 paperscurrent🎓Xi'an Jiaotong UniversityXi'an · China2026 · 7 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 194 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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

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

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Goal Alignment in LLM-Based User Simulators for Conversational AI (2026 · 4 citations)
    LLM-based user simulators are used to build and evaluate conversational agents, but they often lose track of their assigned goals over long dialogues.
  2. GBC: Gradient-Based Connections for Optimizing Multi-Agent Systems (2026)
    LLM-based multi-agent systems often fail to beat strong single-agent setups. One key reason is that when the final answer is wrong, it is hard to tell which agent or which interaction step caused the error. Existing optimizers mostly use only overall task success as feedback. The authors propose Gradient-Based…

 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.

 Cited by

Papers of our library cite their works 122 times (of the 149,439 papers whose bibliographies we read); 7 citing papers with a page here →.

Updated

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