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Zixuan Ke

Zixuan Ke has 2 papers in our library, in 2024–2025, most of them on Language models & pretraining, Reasoning, Information retrieval & search; often with Austin Xu, Caiming Xiong, Cheng Li.

2 papers in our library · 2024–2025

OpenAlex · h-index 10 · 43 works · 361 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 68 times in our library · ⭐ Paper Stars ranking: #8759

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

🏢SalesforceSan Francisco · United States2024–2026 · 13 paperscurrent🎓University of Illinois ChicagoChicago · United States2021–2023 · 16 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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 worksInformation Systems · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksInformation retrieval & search · 1 workReasoning · 1 work

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Bridging the Preference Gap between Retrievers and LLMs (2024 · 46 citations)
    Retrieval-augmented generation (RAG) systems usually pair a retriever built around human reading habits, where rank order matters most, with an LLM that may want its context assembled differently.
  2. A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems (2025 · 39 citations)
    Research on LLM reasoning has grown rapidly since chain-of-thought prompting appeared in 2022. Earlier surveys mostly covered test-time techniques for a single model. This survey, by authors from Salesforce AI Research and universities in Singapore and Hong Kong, organizes the field along two independent axes.

 Co-authors here

12 of 15 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 68 times (of the 149,439 papers whose bibliographies we read); 18 citing papers with a page here →.

Updated

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