PeopleofSuperintelligence

Researcher

Lecong Zhang

Lecong Zhang is a member of the DeepSeek-AI team in Hangzhou, China, and a coauthor of the DeepSeek language-model reports published between 2024 and 2026. Their name appeared on DeepSeek LLM in January 2024, on the Mixture-of-Experts model DeepSeek-V2 in May 2024 and on DeepSeek-V3, a model with 671B total parameters, in December 2024. In January 2025, the DeepSeek-R1 report showed that pure reinforcement learning can draw out reasoning in large language models, and Nature published the work in September 2025. Later reports by the team, each crediting Zhang, covered DeepSeek-V3.2 in December 2025, the million-token DeepSeek-V4 series in April 2026 and the KV-cache-saving DeepSeek-V4.1-Flash in September 2026.

Now at
DeepSeek

Timeline

  1. January 2024

    The DeepSeek LLM report

    In January 2024, they are among the DeepSeek-AI authors of DeepSeek LLM, a project to scale open-source language models in 7B and 67B configurations. The team builds a pre-training dataset of 2 trillion tokens, and DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks.

  2. May 2024

    Mixture of experts in DeepSeek-V2

    Zhang coauthors DeepSeek-V2, posted in May 2024, a Mixture-of-Experts language model with 236B total parameters, of which 21B are active for each token. The model uses Multi-head Latent Attention and DeepSeekMoE, and reduces the KV cache by 93.3% compared with DeepSeek 67B.

  3. December 2024

    The DeepSeek-V3 technical report

    The DeepSeek-V3 technical report of December 2024 lists Zhang among its authors. The model has 671B total parameters with 37B activated for each token, is pre-trained on 14.8 trillion tokens, and needs 2.788M H800 GPU hours for its full training.

  4. January 2025

    The DeepSeek-R1 report

    They coauthor DeepSeek-R1, posted in January 2025, which shows that the reasoning abilities of large language models can be incentivized through pure reinforcement learning. The trained model performs strongly on mathematics, coding competitions and STEM tasks.

  5. September 2025

    DeepSeek-R1 in Nature

    Nature publishes the DeepSeek-R1 work in September 2025, listing Zhang among the authors from the DeepSeek-AI Team in Hangzhou, China.

  6. December 2025

    Sparse attention in DeepSeek-V3.2

    In December 2025, Zhang is an author of DeepSeek-V3.2, which introduces DeepSeek Sparse Attention to cut computing costs in long-context scenarios. Its high-compute variant, DeepSeek-V3.2-Speciale, reaches gold-medal performance at the 2025 International Mathematical Olympiad and the International Olympiad in Informatics.

  7. April 2026

    Million-token DeepSeek-V4 models

    The DeepSeek-V4 preview report of April 2026 lists them among its authors. It presents DeepSeek-V4-Pro with 1.6T parameters and DeepSeek-V4-Flash with 284B parameters, both supporting a context length of one million tokens.

  8. September 2026

    KV cache compression in V4.1-Flash

    In September 2026, they appear among the DeepSeek-AI authors of DeepSeek-V4.1-Flash, a multimodal Mixture-of-Experts model with 552B backbone parameters and contexts of up to one million tokens. Its design shrinks the global KV cache to 890 bytes per token.

  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.

Sources and credits

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  1. [2401.02954] DeepSeek LLM: Scaling Open-Source Language Models with Longtermism arxiv.org. Checked 20 October 2026.
  2. [2405.04434] DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model arxiv.org. Checked 20 October 2026.
  3. [2412.19437] DeepSeek-V3 Technical Report arxiv.org. Checked 20 October 2026.
  4. DeepSeek-V3 Technical Report | Cool Papers - Immersive Paper Discovery papers.cool. Checked 20 October 2026.
  5. [2501.12948] DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning arxiv.org. Checked 20 October 2026.
  6. DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning | Nature nature.com. Checked 20 October 2026.
  7. [2512.02556] DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models arxiv.org. Checked 20 October 2026.
  8. [2606.19348] DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence arxiv.org. Checked 20 October 2026.
  9. [2609.19969] DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression arxiv.org. Checked 20 October 2026.