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Kang An

4 papers here

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

author position
  • middle author2

Across the 2 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV2
  • cs.CL1
  • cs.LG1
same name
  • Kang An — 2 papers
  • Kang An — 2 papers
  • Kang An — 2 papers
  • Kang An — 1 paper, h 8
  • Kang An — 1 paper
  • Kang An — 1 paper

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

most citedStep-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model

1 citations · 1 across the 3 of their papers we have counts for

collaborators

4 papers

cs.CL2025

Step-DeepResearch Technical Report

Chen Hu, Haikuo Du, Heng Wang +64

As LLMs shift toward autonomous agents, Deep Research has emerged as a pivotal metric. However, existing academic benchmarks like BrowseComp often fail to meet real-world demands f…

cs.LG2025

Step-3 is Large yet Affordable: Model-system Co-design for Cost-effective Decoding

StepFun, :, Bin Wang +195

Large language models (LLMs) face low hardware efficiency during decoding, especially for long-context reasoning tasks. This paper introduces Step-3, a 321B-parameter VLM with hard…

cs.CV2025

Step-Video-TI2V Technical Report: A State-of-the-Art Text-Driven Image-to-Video Generation Model

Haoyang Huang, Guoqing Ma, Nan Duan +51

We present Step-Video-TI2V, a state-of-the-art text-driven image-to-video generation model with 30B parameters, capable of generating videos up to 102 frames based on both text and…

cs.CV2025★ 1 cited

Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model

Guoqing Ma, Haoyang Huang, Kun Yan +112

We present Step-Video-T2V, a state-of-the-art text-to-video pre-trained model with 30B parameters and the ability to generate videos up to 204 frames in length. A deep compression…

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