A focus on smaller and faster vision–language models
The key idea: pad shorter answers, then penalise via the correction factor. A model that nails 90% of the digits but drops the last one still gets substantial credit — but less than one that gets every digit. This turned out to be crucial for discriminating between configurations that were close in intuitive math ability.
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Also: I'm not swapping my Apple Watch for the AirPods Pro 3's heart rate tracking - here's why
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