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Ying Wen

Ying Wen has 4 papers in our library, in 2024–2026, most of them on Reasoning; often with Jun Wang, Muning Wen, Cheng Deng.

4 papers in our library · 2024–2026

OpenAlex · ORCID · Wikidata · h-index 11 · 36 works · 548 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 102 times in our library · ⭐ Paper Stars ranking: #7963

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

🎓Shanghai Jiao Tong UniversityShanghai · China2019–2026 · 92 paperscurrent🎓Shanghai Innovation InstituteShanghai · China2026 · 8 paperscurrent🎓University College LondonLondon · United Kingdom2016–2020 · 10 papers🎓University of ManchesterManchester · United Kingdom2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 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 6The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksArtificial Intelligence · 4 worksInformation Systems · 2 worksArts and Humanities · 1 workComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 1 work

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

 Works by year

2020s

20261 paper20251 paper20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning (2024 · 50 citations)
    Agents built on large language models often fail at automated machine learning because they propose poorly grounded experiment plans and hallucinate, even with GPT-4.
  2. OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models (2024 · 50 citations)
    OpenAI's o1 suggested that LLMs reason better when they use extra compute at inference and learn through reinforcement learning.
  3. A Survey of AI Agent Protocols (2025 · 20 citations)
    As LLM-based agents spread into areas such as customer support, analytics and healthcare, they still lack a common way to talk to external tools, data sources and one another.
  4. Model-Based Imaginative Planning for Embodied Agents (2026)
    Large language models plan well in symbolic domains such as math and code, where transition rules are already absorbed during pretraining.

 Co-authors here

12 of 20 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 102 times (of the 149,439 papers whose bibliographies we read); 44 citing papers with a page here →.

Updated

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