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MiMo v2.6

Xiaomi’s model got builders talking not only about performance but about unusually transparent training and what 'open' should mean.

Xiaomi released MiMo-V2.6, a new open-weight model family that scored strongly against other open models while offering relatively inexpensive inference.

Why HN cared

The thread repeatedly praised Xiaomi’s transparency around training, including a live reinforcement-learning dashboard and detailed methodology.

That led to a deeper argument over what counts as open AI: open weights alone, or weights plus training data, code, methodology and reproducibility.

HN snapshot: about 1.1k points and ~480 comments.

Read it at the source
Cascadic Analysis 2
UndertowWe don't completely understand why frontier models behave as they do

What hidden risk could pull against this, even if the news is good?

Transparent training details are valuable, but openness about ingredients is not the same as interpretability of learned behavior. Even a well-documented model can remain difficult to reason about in novel situations.

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UndertowAutonomous cyber capability scales attackers enormously

What hidden risk could pull against this, even if the news is good?

High-performing open or widely available models also diffuse capability. That democratizes useful development, but it can also lower the cost of automated phishing, reconnaissance and exploit adaptation.

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Rabbit Holes 2
  • MiMo-V2.6 release page

    After exploring MiMo, the MiMo homepage covers the whole family; the release itself is here: MiMo-V2.6 release page.

    Xiaomi's own page for the v2.6 release, the page the Hacker News thread was reacting to.

    Xiaomi · Wade · 5 min

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  • The Open Source AI Definition 1.0

    The thread argued over what 'open' should mean for AI. This is the Open Source Initiative's answer, which asks for detailed training-data information, not just weights.

    Open Source Initiative · Swim · 30 min

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