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Xiangfeng Wang

Xiangfeng Wang has 2 papers in our library, in 2026, most of them on Evaluation & benchmarks, Reasoning; often with Chengyuan Yao, Hangyu Guo, Liang Zhao.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 26 · 126 works · 2,471 citations OpenAlex, 2026-10-14

📄 2 works in our library · ⭐ Paper Stars ranking: #14002

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

🎓East China Normal UniversityShanghai · China2014–2026 · 69 paperscurrent🏢State Grid Corporation of ChinaBeijing · China2026 · 1 papercurrent🔬Shanghai Key Laboratory of Trustworthy ComputingShanghai · China2014–2020 · 17 papers🎓Nanjing UniversityNanjing · China2010–2014 · 9 papers🎓Liaoning UniversityShenyang · China2004–2011 · 8 papers🎓Yanshan UniversityQinhuangdao · China2024–2025 · 5 papers🎓Ningbo University of TechnologyNingbo · China2005–2012 · 3 papers🎓University of Science and Technology of ChinaHefei · China2024–2025 · 2 papers🎓Tongji UniversityShanghai · China2019–2020 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 11 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 5The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksComputer Science · 2 worksArts and Humanities · 1 workComputational Theory and Mathematics · 1 workLanguage and Linguistics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Evaluation & benchmarks · 1 workReasoning · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Agentic Episodic Control (2026)
    Episodic reinforcement learning agents store past experiences in an external memory to learn faster. The authors argue that such agents have two weaknesses. First, small trainable encoders produce narrow state embeddings, which they call a representation bottleneck. Second, the agent queries memory with KNN at every…
  2. PRIME: A Process-Outcome Alignment Benchmark for Verifiable Reasoning in Mathematics and Engineering (2026)
    RLVR (reinforcement learning with verifiable rewards) relies on verifiers that usually check only whether a model's final answer matches the reference.

 Co-authors here

7 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

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