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Jiaxing Liu

Jiaxing Liu has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Low-resource languages; often with Bo Peng, Cong Liu, Haifeng Sun.

2 papers in our library · 2025–2026

OpenAlex · h-index 0 · 1 works · 0 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 23 times in our library · ⭐ Paper Stars ranking: #8880

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

🎓Beijing University of Posts and TelecommunicationsBeijing · China2026 · 1 papercurrent🔬State Key Laboratory of Networking and Switching TechnologyBeijing · China2026 · 1 papercurrent

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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1 workLow-resource languages · 1 work

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. RWKV-7 "Goose" with Expressive Dynamic State Evolution (2025 · 23 citations)
    Transformers become expensive on long inputs because their key-value cache grows with sequence length. Recurrent alternatives keep a fixed-size state but have struggled to match Transformer quality and expressiveness. This paper introduces RWKV-7 "Goose", an RNN that uses constant memory and constant per-token…
  2. ModularMoE: Fast LLM Customization with Parameter-Sharing Mixture-of-Experts for Low-Resource Settings (2026)
    Running large language models on modest hardware is costly. Current customization routes either compress and fine-tune separately for every target task, which is expensive, or tune once on general data, which tends to hurt task-level quality through interference and forgetting. ModularMoE addresses this by turning an…

 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 23 times (of the 149,439 papers whose bibliographies we read); 6 citing papers with a page here →.

Updated

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