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Song Han

5 papers hereh-index 333 citations6 works total

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

author position
  • middle author3
  • last author1

Across the 4 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CL2
  • cs.AR1
  • cs.CV1
  • cs.LG1
same name
  • Song Han — 29 papers, h 15
  • Song Han — 14 papers, h 13
  • Song Han — 12 papers, h 8
  • Song Han — 7 papers, h 10
  • Song Han — 6 papers, h 4
  • Song Han — 4 papers, h 1

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

collaborators

5 papers

cs.LG2026

Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence

NVIDIA, :, Amala Sanjay Deshmukh +204

We introduce Nemotron 3 Nano Omni, the latest model in the Nemotron multimodal series and the first to natively support audio inputs alongside text, images, and video. Nemotron 3 N…

cs.CL2026

TriAttention: Efficient Long Reasoning with Trigonometric KV Compression

Weian Mao, Xi Lin, Wei Huang +5

Extended reasoning in large language models (LLMs) creates severe KV cache memory bottlenecks. Leading KV cache compression methods estimate KV importance using attention scores fr…

cs.CL2026

Adaptive Block-Scaled Data Types

Jack Cook, Hyemin S. Lee, Kathryn Le +4

NVFP4 has grown increasingly popular as a 4-bit format for quantizing large language models due to its hardware support and its ability to retain useful information with relatively…

cs.CV2025

SparseVILA: Decoupling Visual Sparsity for Efficient VLM Inference

Samir Khaki, Junxian Guo, Jiaming Tang +6

Vision Language Models (VLMs) have rapidly advanced in integrating visual and textual reasoning, powering applications across high-resolution image understanding, long-video analys…

cs.AR2025

Transitive Array: An Efficient GEMM Accelerator with Result Reuse

Cong Guo, Chiyue Wei, Jiaming Tang +4

Deep Neural Networks (DNNs) and Large Language Models (LLMs) have revolutionized artificial intelligence, yet their deployment faces significant memory and computational challenges…

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