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    Kimi Linear: The AI Architecture Revolution

    7 分钟
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    2025年11月15日
    Technology

    Discover how Kimi Linear's breakthrough architecture processes million-token contexts 6.3x faster while using 75% less memory, potentially ending the era of traditional Transformers through intelligent forgetting and hybrid attention mechanisms.

    Kimi Linear: The AI Architecture Revolution

    Kimi Linear: The AI Architecture Revolution最佳语录

    “

    Kimi Linear shows that you can have Transformer-level quality with linear scaling. When you can process million-token contexts in real-time while using 75% less memory, that's not just an improvement, that's a paradigm shift.

    ”
    J

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    知识来源
    Kimi-Linear : An Expressive, Efficient Attention Architecture
    link
    https://vizuara.substack.com/p/kimi-linear-an-expressive-efficient
    AI Just Broke the Million-Token Barrier: How Kimi Linear's 6.3
    link
    https://levelup.gitconnected.com/ai-just-broke-the-million-token-barrier-how-kimi-linears-6-3-767cf9a80d25
    Kimi Linear: An Expressive, Efficient Attention Architecture | alphaXiv
    link
    https://www.alphaxiv.org/overview/2510.26692v2
    Kimi-Linear : Bye Bye Transformers | by Mehul Gupta - Medium
    link
    https://medium.com/data-science-in-your-pocket/kimi-linear-bye-bye-transformers-c79f843f208c
    [PDF] Kimi Linear: An Expressive, Efficient Attention Architecture - arXiv
    link
    https://arxiv.org/pdf/2510.26692
    Kimi linear attention architecture outperforms full attention - Facebook
    link
    https://www.facebook.com/groups/chiefai/posts/2909207429272540/

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    该学习计划的一部分

    Transformers
    学习计划

    Transformers

    5 h 54 m•5 集数

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    1

    Opening & Setting the Stage

    0:00
    0:10
    0:25
    2

    The Fundamental Problem That's Been Haunting AI

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    0:52
    1:06
    1:22
    3

    The Kimi Linear Revolution

    1:34
    1:52
    2:02
    2:19
    4

    The Hybrid Architecture Breakthrough

    2:32
    2:42
    2:48
    3:05
    1:06
    5

    Revolutionary Performance Gains

    3:28
    3:39
    4:00
    4:12
    6

    The Death of Positional Encoding

    4:27
    4:37
    4:44
    5:02
    7

    Real-World Applications and Future Impact

    5:16
    2:48
    5:44
    5:54
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