Our model balances thinking and non-thinking performance – on average showing better accuracy in the default “mixed-reasoning” behavior than when forcing thinking vs. non-thinking. Only in a few cases does forcing a specific mode improve performance (MathVerse and MMU_val for thinking and ScreenSpot_v2 for non-thinking). Compared to recent popular, open-weight models, our model provides a desirable trade-off between accuracy and cost (as a function of inference time compute and output tokens), as discussed previously.
In recent weeks, Citigroup and Morgan Stanley have made payroll cuts, though they didn’t cite AI. Outside the finance sector, Salesforce slashed 4,000 customer-support roles last year due to AI, and Pinterest laid off nearly 15% of its workforce to focus on AI-related roles.
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assert is_sublist([1], [1,2,3]) == [True, False, False]