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Apple Research Questions AI Reasoning Models Just Days Before WWDC
A newly published Apple Machine Learning Research study has challenged the prevailing narrative around AI "reasoning" large-language models...
For the study, rather than using standard math benchmarks that are prone to data contamination, Apple researchers designed controllable puzzle environments including Tower of Hanoi and River Crossing. All tested reasoning models – including o3-mini, DeepSeek-R1, and Claude 3.7 Sonnet – experienced complete accuracy collapse beyond certain complexity thresholds, and dropped to zero success rates despite having adequate computational resources. The researchers' analysis of reasoning traces showed inefficient "overthinking" patterns, where models found correct solutions early but wasted computational budget exploring incorrect alternatives.
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