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Wenxiang Zheng

Wenxiang Zheng has 2 papers in our library, in 2026, most of them on Language models & pretraining, Reasoning, Knowledge graphs & ontologies; often with Guo Tang, Ming Liu, Bing Qin.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 16 · 88 works · 941 citations OpenAlex, 2026-10-14

📄 2 works in our library · ⭐ Paper Stars ranking: #13990

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

🎓Inner Mongolia University of Science and TechnologyBaotou · China2014–2026 · 23 paperscurrent🎓Harbin Institute of TechnologyHarbin · China2024–2026 · 3 paperscurrent🎓Tsinghua UniversityBeijing · China1993–2012 · 28 papers🎓Beijing Institute of TechnologyBeijing · China2021–2024 · 17 papers🎓Taiyuan University of TechnologyTaiyuan · China2014 · 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 5The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 4Research subjects and languages the work is about.Information retrieval & search · 1 workKnowledge graphs & ontologies · 1 workLanguage models & pretraining · 1 workReasoning · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. From Implicit Graph Encoding to Explicit Evidence: A Training-Free LLM Framework for Temporal Knowledge Graph Reasoning (2026)
    Forecasting future facts in Temporal Knowledge Graphs (TKGs) is difficult for parametric models. They degrade under distribution shift and cannot handle unseen entities or relations without retraining. Existing LLM-based alternatives have their own problems: some flatten graphs into long text and lose information…
  2. LCR-RAG: Enhancing Logical Consistency in Retrieval-Augmented Generation via Neuro-symbolic Reinforcement Learning (2026)
    Retrieval-augmented generation (RAG) helps large language models stay factually grounded. In multi-step reasoning, however, answers can still contradict themselves or skip inference steps. Iterative and self-reflective systems such as IRCoT and Self-RAG try to fix this, but they depend on the model judging its own…

 Co-authors here

6 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/wenxiang-zheng/