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Yuxiang Huang

Yuxiang Huang has 3 papers in our library, in 2024–2025, most of them on Language models & pretraining; often with Weilin Zhao, Xu Han, Ao Sun.

3 papers in our library · 2024–2025

OpenAlex · ORCID · Wikidata · h-index 15 · 65 works · 3,330 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 286 times in our library · ⭐ Paper Stars ranking: #4899

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

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 13 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.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies (2024 · 269 citations)
    Training very large language models is expensive, which makes experiments hard to afford, and the resulting models are difficult to run on phones or laptops.
  2. MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe (2025 · 24 citations)
    MiniCPM-V 4.5 is an 8B-parameter multimodal LLM from OpenBMB. It targets three costly parts of MLLM development: long visual token sequences, brittle document-parsing pipelines and verbose reasoning. To shorten visual input, the authors extend the 2D-Resampler of earlier MiniCPM-V models into a unified 3D-Resampler.…
  3. 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…

 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 286 times (of the 149,439 papers whose bibliographies we read); 70 citing papers with a page here →.

Updated

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