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Xianzhen Luo

Xianzhen Luo has 4 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning, Code & programming languages; often with Qingfu Zhu, Binhua Li, Chi Chen.

4 papers in our library · 2025–2026

OpenAlex · ORCID · Wikidata · h-index 19 · 64 works · 1,885 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 110 times in our library · ⭐ Paper Stars ranking: #7822

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

🔬South China Botanical GardenGuangzhou · China2019–2026 · 40 paperscurrent🏛️Chinese Academy of SciencesBeijing · China2019–2026 · 38 paperscurrent🎓Harbin Institute of TechnologyHarbin · China2022–2026 · 14 paperscurrent🎓University of Chinese Academy of SciencesBeijing · China2019–2026 · 7 paperscurrent🔬Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)Guangzhou · China2020–2022 · 8 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 3 worksInformation Systems · 2 works

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 3 worksCode & programming languages · 1 workVision & language · 1 workReasoning · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Scaling laws · 1 work

 Works by year

2020s

20262 papers20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation (2025 · 24 citations)
    Open-source multimodal LLMs read charts reasonably well, but they struggle to turn a chart image into plotting code. Their output often fails to run, picks the wrong chart type, or loses details such as axis sizes. The authors argue that code is a lossless description of a chart, unlike textual summaries.
  2. Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling (2025 · 22 citations)
    LLM inference is slow because autoregressive decoding emits one token per step, and each step is dominated by memory transfer of billions of weights.
  3. Scaling Laws for Code: A More Data-Hungry Regime (2026 · 3 citations)
    Scaling laws, which predict validation loss from model size (N), training tokens (D) and compute (C), have mostly been derived on natural language (NL).
  4. Format-Adapter: Improving Reasoning Capability of LLMs by Adapting Suitable Format (2026)
    Parallel test-time scaling (TTS) usually samples many answers using a single reasoning format chosen by a human.

 Co-authors here

12 of 20 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 110 times (of the 149,439 papers whose bibliographies we read); 38 citing papers with a page here →.

Updated

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