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Yuxuan Li

Yuxuan Li has 4 papers in our library, in 2025–2026, most of them on Language models & pretraining; often with Xu Han, MiniCPM Team, Ao Sun.

4 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 2 · 15 works · 28 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 37 times in our library · ⭐ Paper Stars ranking: #9331

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

🎓Space Engineering UniversityBeijing · China2024–2026 · 11 paperscurrent🎓Harbin Institute of TechnologyHarbin · China2023–2024 · 2 papers

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 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 worksHardware and Architecture · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3 works

 Works by year

2020s

20262 papers20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. MiniCPM4: Ultra-Efficient LLMs on End Devices (2025 · 22 citations)
    Running capable LLMs locally on phones, embedded boards and consumer GPUs is limited by compute, memory and the cost of attention over long contexts.
  2. FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling (2025 · 15 citations)
    Speculative sampling speeds up LLM decoding. A small draft model proposes tokens, and the full model checks them in parallel. The authors find that this benefit shrinks for models with big vocabularies, such as Llama-3-8B (128k tokens). They re-implemented EAGLE-2 in native C and CUDA to strip away Python and…
  3. MiniCPM-SALA: Hybridizing Sparse and Linear Attention for Efficient Long-Context Modeling (2026 · 2 citations)
    Full attention in Transformers is costly for very long inputs. Compute grows quadratically with length, and the KV-Cache can reach tens or hundreds of gigabytes at million-token scale. Sparse attention cuts computation but still keeps the whole cache. Linear attention is cheap but compresses context lossily and loses…
  4. ArcLight: A Lightweight LLM Inference Architecture for Many-Core CPUs (2026)
    CPU frameworks for LLM inference, such as llama.cpp, scale poorly on many-core server CPUs. These machines split cores and memory into several NUMA nodes, and reading memory attached to another node is much slower than reading local memory. Existing tools largely ignore this cost. The authors present ArcLight, a…

 Co-authors here

12 of 23 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 37 times (of the 149,439 papers whose bibliographies we read); 15 citing papers with a page here →.

Updated

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