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Wenhu Chen
3,198 posts
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Wenhu Chen
@WenhuChen
Meta MSL. I led PoT, MMMU, MMLU-Pro, ClawBench, MAmmoTH, General-Reasoner, VL-Rethinker, VLM2Vec. I contributed to Gemini-2.5 and Muse Spark series.
United States
wenhuchen.github.io
Joined May 2016
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  • user avatar
    Wenhu Chen
    @WenhuChen
    3h
    Totally agree
    user avatar
    Andrew Gordon Wilson
    @andrewgwils
    21h
    Prediction: we’ll see loads of conjectures and theorems proven and disproven by LLMs over the next 6 months and it will make basically zero difference to the world, and won’t even contribute meaningfully to advancing mathematics.
    2.4K
  • user avatar
    Wenhu Chen
    @WenhuChen
    Jul 30
    GPT-6 must be really good at healthcare
    6.3K
  • user avatar
    Wenhu Chen
    @WenhuChen
    Jul 27
    Take care! Making a lot of money is great, but you need to healthy to spend it.
    user avatar
    Lilian Weng
    Thinking Machines
    @lilianweng
    Jul 27
    It is a hard and sad decision. I shared this message with folks at Thinky. Thank you all for the time together♥️ Just as the last sentence in my message: The future worth building is human.
    23K
  • user avatar
    Wenhu Chen
    @WenhuChen
    Jul 26
    If you are not top 3 in the same cost-effectiveness, please just open weight it.
    user avatar
    Jensen Huang
    NVIDIA
    @JensenHuang
    Jul 24
    For my first post, I’m sharing a letter @nvidia signed on why open models matter. AI will transform every industry, power every company, and be built by every country. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty.
    9.9K
  • user avatar
    Wenhu Chen
    @WenhuChen
    Jul 17
    This is legendary!
    user avatar
    Kimi.ai
    @Kimi_Moonshot
    Jul 16
    Introducing Kimi K3: Open Frontier Intelligence 🔹 2.8 Trillion Parameters, 1 Million Context, Native Multimodal 🔹 Kimi Delta Attention enables up to 6.3x faster decoding in million-token contexts 🔹 Attention Residuals deliver ~25% higher training efficiency at <2% additional
    4.1K