PeopleofSuperintelligence

Researcher

Yuan Ou

Yuan Ou is an AI researcher and coauthor on DeepSeek-AI’s language-model reports. He coauthored DeepSeek-V3 in December 2024, a 671-billion-parameter mixture-of-experts model with 37 billion active parameters per token, and DeepSeek-R1 in January 2025, which uses pure reinforcement learning to encourage reasoning without human-labeled trajectories. He was affiliated with the DeepSeek-AI Team in Hangzhou in September 2025 and coauthored DeepSeek-V3.2 in December 2025, which introduces sparse attention and a reinforcement-learning framework for reasoning and agent performance. In April 2026, he coauthored DeepSeek-V4, a preview with two mixture-of-experts models for one-million-token contexts. As of October 2026, he is a coauthor of DeepSeek-AI’s DeepSeek-V4.1-Flash report, a multimodal model that uses cross-layer KV-cache reuse and FP4 caching to reduce key-value storage.

Timeline

  1. December 2024

    DeepSeek-V3 technical report

    In December 2024, he coauthors the DeepSeek-V3 technical report, which describes a 671-billion-parameter mixture-of-experts model with 37 billion active parameters per token.

  2. January 2025

    DeepSeek-R1 reasoning report

    In January 2025, he coauthors DeepSeek-R1. The report describes pure reinforcement learning as a way to encourage reasoning without human-labeled reasoning trajectories.

  3. September 2025

    DeepSeek-AI Team affiliation

    In September 2025, he is affiliated with the DeepSeek-AI Team in Hangzhou.

  4. December 2025

    DeepSeek-V3.2 model report

    In December 2025, he coauthors DeepSeek-V3.2, which introduces DeepSeek Sparse Attention and a scalable reinforcement-learning framework for reasoning and agent performance.

  5. April 2026

    DeepSeek-V4 preview models

    In April 2026, he coauthors the DeepSeek-V4 preview, which presents two mixture-of-experts models that support one-million-token contexts.

  6. As of October 2026

    DeepSeek-V4.1-Flash report

    As of October 2026, he is a coauthor of DeepSeek-AI’s DeepSeek-V4.1-Flash report, submitted in September 2026. It describes a multimodal model that uses cross-layer KV-cache reuse and FP4 caching to reduce key-value storage.

  1. The median h-index is 48 at universities and 14 at frontier AI labsIncludes DeepSeek. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  2. Companies supply the most prior-work hires at 7 of the 9 frontier AI labsIncludes DeepSeek. Our estimates of the organizations where 550 people worked directly before joining 9 frontier AI labs across 620 career transitions, comparing the main feeder organizations per lab.
  3. 5,000 frontier users would be 38.2% of the core labs' 13,100 reported staffIncludes DeepSeek. 5,000 frontier users would be 38.2% of the core labs' 13,100 reported staff. We test the estimate against 3,913 named model contributors and dated all-staff records across Google DeepMind, OpenAI and Anthropic, plus six additional frontier-grade labs.
  4. Google alumni have founded or led 133 other organizations, more than any other AI labIncludes DeepSeek. We estimate that alumni of 17 major AI labs went on to found or lead 350 other organizations, including 226 they founded, and we trace 472 career moves directly between 23 labs since 2010.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

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  1. DeepSeek-V3 Technical Report arxiv.org. Checked 21 October 2026.
  2. DeepSeek-V3 Technical Report | ArxivLens arxivlens.com. Checked 21 October 2026.
  3. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning arxiv.org. Checked 21 October 2026.
  4. DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning nature.com. Checked 21 October 2026.
  5. DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models arxiv.org. Checked 21 October 2026.
  6. DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence arxiv.org. Checked 21 October 2026.
  7. DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression arxiv.org. Checked 21 October 2026.