WWDC前夕，苹果论文“炮轰”AI推理模型“假思考”，测试方法遭质疑

Core Viewpoint - The paper published by Apple's Machine Learning Research Center argues that existing reasoning models create an illusion of "thinking" without a stable and understandable thought process, suggesting that their reasoning capabilities are fundamentally flawed [1][4][6] Group 1: Paper Findings - The paper critiques the reasoning models developed by companies like OpenAI, Anthropic, Google, and DeepMind, claiming that these models do not possess a reliable reasoning process [4][6] - Apple's team designed four types of puzzle environments to test reasoning models, including Tower of Hanoi, checkers exchange, river crossing, and block world, to evaluate their reasoning capabilities under controlled difficulty [4][6] - Experimental results indicate that non-reasoning models outperform reasoning models in low-complexity tasks, while reasoning models show advantages in moderately complex tasks [6][7] Group 2: Limitations of Reasoning Models - Both reasoning and non-reasoning models experience a significant drop in performance when task complexity exceeds a certain threshold, with accuracy dropping to zero [7][9] - As problem complexity increases, reasoning models initially invest more thinking tokens, but their reasoning ability collapses when faced with overly difficult problems, leading to reduced effort in thinking [9][10] - In simpler problems, models often find correct solutions early but engage in unnecessary thinking later, while in high-complexity problems, reasoning becomes chaotic and incoherent [10][11] Group 3: Controversy and Reactions - The paper has sparked controversy, with some researchers arguing that the failure of models in tests is due to output token limitations rather than a lack of reasoning ability [12] - Critics suggest that Apple's focus on the limitations of current methods may reflect frustration over its own AI advancements, especially with the upcoming WWDC event expected to yield limited AI updates [13][14] - Internal challenges at Apple, including leadership styles and privacy policies, have reportedly hindered progress in AI development, contributing to the perception of stagnation in their AI initiatives [14][15]