--- title: "Yuchen Yan · Zhejiang University — author profile and papers" description: "Yuchen Yan, Zhejiang University: 6 papers on Elamur Research (2025–2026), h-index 1 — affiliations, topics, co-authors and who cites them…" canonical: "https://research.elamur.ai/free/en/authors/yuchen-yan/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/yuchen-yan/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Yuchen Yan # Yuchen Yan Yuchen Yan has 6 papers in our library, in 2025–2026, most of them on [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/), [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Efficient models & compression](https://md.research.elamur.ai/free/en/topics/efficient-models/); often with [Yongliang Shen](https://md.research.elamur.ai/free/en/authors/yongliang-shen/), [Hanghang Tong](https://md.research.elamur.ai/free/en/authors/hanghang-tong/), [Jin Jiang](https://md.research.elamur.ai/free/en/authors/jin-jiang/). 6 papers in our library · 2025–2026 [OpenAlex](https://openalex.org/A5139723513) · [**h-index 1**](https://openalex.org/authors/A5139723513) · [5 works](https://openalex.org/works?filter=author.id:A5139723513) · [2 citations](https://openalex.org/works?filter=author.id:A5139723513&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 6 works in our library](https://md.research.elamur.ai/free/en/authors/yuchen-yan/#papers) · [🔗 Cited 40 times in our library](https://md.research.elamur.ai/free/en/authors/yuchen-yan/cited-by/) · [⭐ Paper Stars ranking: #9237](https://md.research.elamur.ai/free/en/paper-stars/page/93/#star-9237) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Yuchen+Yan) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Yuchen+Yan) · [dblp](https://dblp.org/search?q=Yuchen+Yan) · [ACL Anthology](https://aclanthology.org/search/?q=Yuchen+Yan) ## Affiliations [🎓**Zhejiang University** Hangzhou · China 2025–2026 · 3 papers*current*](https://md.research.elamur.ai/free/en/universities/zhejiang-university/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 6 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 6 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 6 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Information Systems · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) 🧭 Topics 3 Research subjects and languages the work is about.[Reasoning · 4 works](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Language models & pretraining · 3 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Efficient models & compression · 1 work](https://md.research.elamur.ai/free/en/topics/efficient-models/) ## Works by year ### 2020s [**2026** 4 papers](https://md.research.elamur.ai/free/en/authors/yuchen-yan/year/2026/) · [**2025** 2 papers](https://md.research.elamur.ai/free/en/authors/yuchen-yan/year/2025/) ## Papers The most cited first (OpenAlex and our library). 1. **[InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/yan-2025-inftythink-breaking-length-limits-long/)** *(2025 · 29 citations)* Reasoning-oriented LLMs such as DeepSeek-R1 handle hard problems by writing very long chains of thought. This approach has three costs. Attention cost grows quadratically with output length. Generation can be cut off at the maximum context size before an answer appears. Quality also drops once text exceeds the short… 2. **[LogicPro: Improving Complex Logical Reasoning via Program-Guided Learning](https://md.research.elamur.ai/free/en/papers/jiang-2025-logicpro-improving-complex-logical-reasoning/)** *(2025 · 8 citations)* Large language models often fail at multi-step logical reasoning when a task is posed in plain text, even when the same task is easy to solve with code. 3. **[Harnessing Consistency for Robust Test-Time LLM Ensemble](https://md.research.elamur.ai/free/en/papers/zeng-2026-harnessing-consistency-robust-test-time/)** *(2026 · 2 citations)* Test-time ensembles of large language models aim to combine the complementary strengths of different LLMs. 4. **[Prune as You Generate: Online Rollout Pruning for Faster and Better RLVR](https://md.research.elamur.ai/free/en/papers/xu-2026-prune-as-you-generate-online/)** *(2026 · 2 citations)* RLVR methods such as GRPO and DAPO are expensive because they sample many rollouts per prompt. Their learning signal is also often weak: when a group's rewards are almost all correct or almost all wrong, group-normalized advantages approach zero. The authors propose ARRoL (Accelerating RLVR via online RoLlout… 5. **[CoVerRL: Breaking the Consensus Trap in Label-Free Reasoning via Generator-Verifier Co-Evolution](https://md.research.elamur.ai/free/en/papers/pan-2026-coverrl-breaking-consensus-trap-label/)** *(2026 · 1 citation)* Label-free reinforcement learning for LLM reasoning usually rewards a model for matching its own majority-voted answer. 6. **[Pause or Fabricate? Training Language Models for Grounded Reasoning](https://md.research.elamur.ai/free/en/papers/qiu-2026-pause-or-fabricate-training-language/)** *(2026)* The paper examines a failure mode the authors call ungrounded reasoning. When a question lacks a needed fact, a language model often notices the gap early but then invents a value and reasons confidently to a wrong answer. The authors argue that the cause is weak awareness of when valid inference is possible, not… ## Co-authors here 12 of 27 co-authors with a page, the most papers together first. - [Yongliang Shen](https://md.research.elamur.ai/free/en/authors/yongliang-shen/) ×3 - [Hanghang Tong](https://md.research.elamur.ai/free/en/authors/hanghang-tong/) ×2 - [Jin Jiang](https://md.research.elamur.ai/free/en/authors/jin-jiang/) ×2 - [Jingrui He](https://md.research.elamur.ai/free/en/authors/jingrui-he/) ×2 - [Jun Xiao](https://md.research.elamur.ai/free/en/authors/jun-xiao/) ×2 - [Mengdi Zhang](https://md.research.elamur.ai/free/en/authors/mengdi-zhang/) ×2 - [Ruizhong Qiu](https://md.research.elamur.ai/free/en/authors/ruizhong-qiu/) ×2 - [Weiming Lu](https://md.research.elamur.ai/free/en/authors/weiming-lu/) ×2 - [Wenqi Zhang](https://md.research.elamur.ai/free/en/authors/wenqi-zhang/) ×2 - [Yang Liu](https://md.research.elamur.ai/free/en/authors/yang-liu/) ×2 - [Chen Luo](https://md.research.elamur.ai/free/en/authors/chen-luo/) - [Daoxin Zhang](https://md.research.elamur.ai/free/en/authors/daoxin-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)](https://md.research.elamur.ai/free/en/venues/acl/) ×3: [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/) ×2, [ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/) - [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/) ×2: [Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/) ×2 ## Cited by Papers of our library cite their works 40 times (of the 149,439 papers whose bibliographies we read); [13 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/yuchen-yan/cited-by/). Updated 1 November 2026