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Shi-Xiong Zhang

Shi-Xiong Zhang has 2 papers in our library, in 2026, most of them on Language models & pretraining, Tokenization & segmentation; often with Elias Stengel-Eskin, Genta Indra Winata, Justin Chen.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 20 · 77 works · 1,442 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1 time in our library · ⭐ Paper Stars ranking: #11682

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

🏢Capital OneTysons Corner · United States2026 · 2 paperscurrent🎓City University of Hong KongHong Kong · China2018–2023 · 16 papers🎓Xidian UniversityXi'an · China2021–2025 · 7 papers🎓Nanjing University of Chinese MedicineNanjing · China2022–2025 · 6 papers🏢TencentShenzhen · China2020–2025 · 4 papers🎓Zhejiang University of Finance and EconomicsHangzhou · China2016–2018 · 4 papers🔬State Key Laboratory for Innovation and Transformation of Luobing TheoryShijiazhuang2024–2025 · 2 papers🎓Macau University of Science and TechnologyMacao · China2022–2024 · 2 papers🎓Sun Yat-sen UniversityGuangzhou · China2022–2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksSignal Processing · 1 workInformation Systems · 1 work

🧭 Topics 2Research subjects and languages the work is about.Tokenization & segmentation · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Temporal Tokenization Strategies for Event Sequence Modeling with Large Language Models (2026 · 1 citation)
    LLMs process discrete tokens, but events in real sequences happen in continuous time. How to turn timestamps or inter-event gaps into tokens ("temporal tokenization") has received little systematic study. This paper compares five families of encodings: plain numeric strings, float32 byte tokens (as in Language-TPP)…
  2. Routing with Generated Data: Annotation-Free LLM Skill Estimation and Expert Selection (2026)
    LLM routers choose which model in a pool should handle a given query. Most existing routers learn from human-labelled, in-domain data, which is often unavailable when real user traffic is varied and unpredictable. The authors propose Routing with Generated Data (RGD). In this setting, a generator LLM writes queries…

 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.

 Cited by

Papers of our library cite their works 1 time (of the 149,439 papers whose bibliographies we read).

Updated

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