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Jian Luan

Jian Luan has 3 papers in our library, in 2023–2026, most of them on Language models & pretraining, Reasoning, Efficient models & compression; often with Wei Liu, Bin Wang, Haohao Luo.

3 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 11 · 74 works · 477 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 173 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #7005

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

🏢XiaomiBeijing · China2021–2026 · 34 paperscurrent🎓Zhejiang UniversityHangzhou · China2026 · 1 papercurrent🏢MicrosoftRedmond · United States2020 · 4 papers🏢Microsoft Research AsiaBeijing · China2020 · 3 papers
🏛️University of TokyoTokyo · Japan2001–2003Education 2001–2003 · Master, Environment & Ocean Engineering
🎓Tsinghua UniversityBeijing · China1996–2000Education 1996–2000 · Bachelor, Mechanical Engineering

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 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksApplied Mathematics · 1 workMathematics · 1 workComputer Vision and Pattern Recognition · 2 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 3 worksEfficient models & compression · 1 workReasoning · 1 work

 Works by year

2020s

20261 paper20251 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. CMATH: Can Your Language Model Pass Chinese Elementary School Math Test? (2023 · 67 citations)
    Existing math benchmarks for large language models are mostly English, often pitched at college level, and produce scores that are hard for non-specialists to interpret.
  2. Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains (2025 · 19 citations)
    Chain-of-Thought prompting improves LLM accuracy on math problems, but it produces long token sequences that are slow and costly to generate.
  3. Attention Basin: Why Contextual Position Matters in Large Language Models (2026 · 2 citations)
    Large language models handle information unevenly depending on where it appears in the prompt. This paper asks why. Across 10 LLMs, the authors measured how much attention query tokens pay to each block in structured inputs, such as sets of retrieved documents or few-shot examples. Blocks at the start and end of the…

All works

7 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security ↗arXiv:2401.05459 · no summary yetYuanchun Li et al.2024 · 100 citations
  2. A Comprehensive Evaluation of Quantization Strategies for Large Language Models ↗Findings of the Association for Computational Linguistics: ACL 2024 · no summary yetRenren Jin et al.2024 · 81 citations
  3. CMATH: Can Your Language Model Pass Chinese Elementary School Math Test?arXiv:2306.16636Tianwen Wei, Jian Luan, Wei Liu, et al.2023 · 67 citations
  4. Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning ChainsConference on Neural Information Processing Systems (NeurIPS)Wenhui Tan, Jiaze Li, Jianzhong Ju, et al.2025 · 19 citations
  5. Attention Basin: Why Contextual Position Matters in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Zihao Yi, Zhenqing Ling, Delong Zeng, et al.2026 · 2 citations
  6. MobileBench-OL: A Comprehensive Chinese Benchmark for Evaluating Mobile GUI Agents in Real-World Environment ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetQinzhuo Wu et al.2026
  7. Doc-V*: Coarse-to-Fine Interactive Visual Reasoning for Multi-Page Document VQA ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetYuanlei Zheng et al.2026

 Co-authors here

9 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 173 times (of the 149,439 papers whose bibliographies we read); 52 citing papers with a page here →.

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