PeopleofSuperintelligence

Researcher

Hanwei Xu

Hanwei Xu is an AI researcher whose work spans open language models, multimodal understanding, code intelligence and model reasoning. He coauthored DeepSeek LLM, DeepSeek-VL, DeepSeek-V2, DeepSeek-Coder-V2 and DeepSeek-V3 in 2024. DeepSeek-R1 and DeepSeek-V3.2 explored reinforcement-learning methods for reasoning in 2025. As of October 2026, he is affiliated with DeepSeek-AI and coauthored DeepSeek Elastic Compute, a production sandbox platform for agentic training.

Timeline

  1. January 2024–March 2024

    DeepSeek LLM and DeepSeek-VL

    In January 2024, he coauthors DeepSeek LLM, a project for open language models whose pre-training dataset contains 2 trillion tokens. In March 2024, he coauthors DeepSeek-VL, an open-source vision-language model that uses a hybrid encoder for 1024 x 1024 images.

  2. May 2024

    The DeepSeek-V2 model

    In May 2024, he coauthors DeepSeek-V2, a mixture-of-experts language model with 236 billion total parameters and 21 billion active for each token.

  3. June 2024

    DeepSeek-Coder-V2 for code

    In June 2024, he coauthors DeepSeek-Coder-V2, an open-source mixture-of-experts model for code that achieves performance comparable to GPT4-Turbo in code-specific tasks.

  4. December 2024

    DeepSeek-V3 technical report

    The December 2024 DeepSeek-V3 technical report lists him as a coauthor and describes a 671-billion-parameter mixture-of-experts model with 37 billion parameters active per token.

  5. September 2025

    DeepSeek-R1 and reasoning

    In September 2025, he coauthors DeepSeek-R1, a study showing that pure reinforcement learning can elicit reasoning in large language models.

  6. December 2025

    DeepSeek-V3.2 reasoning model

    In December 2025, he coauthors DeepSeek-V3.2, a model designed to balance computational efficiency with reasoning and agent performance.

  7. As of October 2026

    DeepSeek Elastic Compute

    In September 2026, he appears among the authors of DeepSeek Elastic Compute (DSec), which lists DeepSeek-AI as an affiliation. The report presents a production sandbox platform for agentic training at scale.

  1. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI from 1961 to 2025, with a year-by-year chart.
  2. The United States employs an estimated 53% of people in our data with a known employment countryOur estimates of where AI careers are concentrated now and how countries’ shares of dated positions changed over time.
  3. 46% of living people in our data with known birth and employment countries work outside their birth countryOur estimate of cross-border careers in AI, from country of birth and the country of each current organization.
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionOur estimates of where the people shaping AI studied and where their careers led, from education records in cited biographies.

Sources and credits

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  1. DeepSeek LLM: Scaling Open-Source Language Models with Longtermism arxiv.org. Checked 19 October 2026.
  2. DeepSeek-VL: Towards Real-World Vision-Language Understanding arxiv.org. Checked 19 October 2026.
  3. Haoyu Lu (卢浩宇)'s Homepage haoyulu1998.github.io. Checked 19 October 2026.
  4. DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model arxiv.org. Checked 19 October 2026.
  5. DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence arxiv.org. Checked 19 October 2026.
  6. DeepSeek-V3 Technical Report arxiv.org. Checked 19 October 2026.
  7. DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning nature.com. Checked 19 October 2026.
  8. DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models arxiv.org. Checked 19 October 2026.
  9. DeepSeek Elastic Compute (DSec): A Sandbox Infrastructure for Effective Agentic Training at Scale arxiv.org. Checked 19 October 2026.
  10. DeepSeek Elastic Compute (DSec): A Sandbox Infrastructure for Effective Agentic Training at Scale arxiv.org. Checked 19 October 2026.