🧑‍🔬 Zhen Yang

🏛️ Affiliations

🎓Xi'an Jiaotong UniversityXi'an · China1991–2026 · 33 paperscurrent🎓Hubei University of TechnologyWuhan · China2022–2026 · 7 paperscurrent🎓Tsinghua UniversityBeijing · China2026 · 1 papercurrent🎓Jinan UniversityGuangzhou · China2012–2017 · 4 papers🎓Shandong UniversityJinan · China2007–2022 · 3 papers🏢Yunnan Power Grid Co., Ltd.Kunming · China2024 · 2 papers🔬Informatique, BioInformatique, Systèmes ComplexesÉvry-Courcouronnes · France2015 · 2 papers🎓Université d'Évry Val-d'EssonneÉvry-Courcouronnes · France2015 · 2 papers🎓Nanjing University of Posts and TelecommunicationsNanjing · China2013 · 2 papers🔬Centre National de la Recherche ScientifiqueParis · France2010–2012 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 10 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.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent (2024 · 25 citations)
   Most open-source LLMs use dense architectures, and the few public mixture-of-experts (MoE) models are relatively small.
2. HMoE: Heterogeneous Mixture of Experts for Language Modeling (2025 · 11 citations)
   Standard Mixture of Experts (MoE) language models use experts of identical size, even though some tokens are harder to process than others.
3. MathGLM-Vision: Solving Mathematical Problems with Multi-Modal Large Language Model (2024 · 10 citations)
   Most math-focused multi-modal LLMs (MLLMs) are trained almost entirely on geometry, using small public sets such as GeoQA and Geometry3K whose answers rarely show the reasoning.
4. Investigating Forgetting in Pre-Trained Representations Through Continual Learning (2023 · 10 citations)
   When a pre-trained language model is fine-tuned on a sequence of tasks, its contextualized representations shift.
5. Less is More: Improving LLM Reasoning with Minimal Test-Time Intervention (2026 · 2 citations)
   Test-time scaling techniques such as self-consistency or tree search make LLMs reason better, but they pay for it with heavy extra inference compute.
6. Thinking Beyond the Local: Multi-View Instructed Adaptive Reasoning in KG-Enhanced LLMs (2026 · 1 citation)
   Systems that pair knowledge graphs with LLMs for question answering usually walk the graph outward from the entities named in the query.
7. MathSight: A Benchmark Exploring Have Vision-Language Models Really Seen in University-Level Mathematical Reasoning? (2026)
   MathSight examines whether vision-language models (VLMs) actually use images when solving university-level math problems, or whether they mostly depend on the textual problem statement.

🤝 Co-authors here

12 of 21 co-authors with a page, the most papers together first.

• Jiaqi Zhu ×2
• Ruobing Xie ×2
• Shuaipeng Li ×2
• Xingwu Sun ×2
• Bin Xu
• Di Wang
• Fandong Meng
• Feng Chen
• Jie Chen
• Jie Tang
• Jie Zhou
• Kai Zhang

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Annual Meeting of the Association for Computational Linguistics (ACL) ×2: ACL 2026 ×2
• Conference on Empirical Methods in Natural Language Processing (EMNLP): EMNLP 2025
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026

🔗 Cited by

Papers of our library cite their works 49 times (of the 149,439 papers whose bibliographies we read); 13 citing papers with a page here →.

Updated 1 November 2026

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