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researcher

Lin Niu

7 papers hereh-index 4456 citations9 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author6

Across the 7 of 7 papers where every author was matched, so the position is known.

fields
  • cs.LG3
  • cs.CL2
  • cs.CV2
same name
  • Lin Niu — 6 papers, h 2
  • Lin Niu — 4 papers, h 4
  • Lin Niu — 3 papers, h 2
  • Lin Niu — 3 papers, h 2
  • Lin Niu — 3 papers, h 1
  • Lin Niu — 2 papers, h 12

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedHunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation

9 citations · 14 across the 7 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.CL2026

CoSA: Accelerating Long-Context Inference via Proxy-Kernel Co-Designed Sparse Attention

Yufei Xue, Lin Niu, Hong Liu +6

The quadratic cost of self-attention makes long-context inference prohibitively expensive, and proxy-based block-sparse attention has become a practical remedy. Existing methods ty…

cs.CL2026

PIVOT: Efficient Query-Group Indexing for Token-Level Sparse Attention

Hong Liu, Yuan Cheng, Lin Niu +5

Token-level sparse attention, as implemented by DeepSeek Sparse Attention (DSA) in production systems, makes the downstream attention efficient but shifts the bottleneck to the ind…

cs.LG2026

AngelSlim: A more accessible, comprehensive, and efficient toolkit for large model compression

Rui Cen, QiangQiang Hu, Hong Huang +10

This technical report introduces AngelSlim, a comprehensive and versatile toolkit for large model compression developed by the Tencent Hunyuan team. By consolidating cutting-edge a…

cs.LG2026

Stem: Rethinking Causal Information Flow in Sparse Attention

Lin Niu, Xin Luo, Linchuan Xie +4

The quadratic computational complexity of self-attention remains a fundamental bottleneck for scaling Large Language Models (LLMs) to long contexts, particularly during the pre-fil…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.