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Xin Men

Xin Men has 3 papers in our library, in 2024–2025, most of them on Language models & pretraining, Efficient models & compression; often with Bingning Wang, Hongyu Lin, Mingyu Xu.

3 papers in our library · 2024–2025

OpenAlex · h-index 2 · 5 works · 62 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 181 times in our library · ⭐ Paper Stars ranking: #5414

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

🏢BaichuanBeijing · China2024–2025 · 2 paperscurrent

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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksComputer Vision and Pattern Recognition · 3 worksLanguage and Linguistics · 3 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1 workEfficient models & compression · 1 work

 Works by year

2020s

20251 paper20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. ShortGPT: Layers in Large Language Models are More Redundant Than You Expect (2024 · 170 citations)
    Modern LLMs are expensive to deploy, and many existing pruning techniques are complicated, for example those that require gradients.
  2. Kimi Linear: An Expressive, Efficient Attention Architecture (2025 · 51 citations)
    Softmax attention becomes a bottleneck for agentic and RL-heavy LLM workloads. Its cost grows quadratically with sequence length, and its KV cache keeps expanding during long decoding. Earlier linear-attention and hybrid designs saved compute but usually lost quality or were only tested at small scale. The Kimi Team…
  3. Base of RoPE Bounds Context Length (2024 · 12 citations)
    RoPE is the standard position encoding in many LLMs, and context windows are usually extended by changing its single hyperparameter, the base.

 Co-authors here

12 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 181 times (of the 149,439 papers whose bibliographies we read); 28 citing papers with a page here →.

Updated

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