Apple Researchers Reveal Structural Failures in Large Reasoning Models Using Puzzle-Based Evaluation

Recent advancements in artificial intelligence have led to the development of Large Reasoning Models (LRMs), which aim to emulate human-like thinking by generating intermediate reasoning steps before reaching conclusions, shifting the focus from merely producing accurate outputs to understanding the reasoning process itself. This paradigm shift highlights the importance of evaluating models based on their internal reasoning capabilities rather than final answer accuracy, which can be misleading due to training data contamination and pattern memorization. A notable study by Apple researchers revealed structural weaknesses in LRMs through puzzle-based evaluations, emphasizing the need for more controlled testing environments that can accurately assess a models reasoning depth and