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Arstechnica
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New Apple study challenges whether AI models truly “reason” through problems
- Apple researchers published a study suggesting that Simulated Reasoning models produce outputs consistent with pattern-matching, rather than true reasoning, when faced with novel problems.
- The study, titled 'The Illusion of Thinking,' evaluated large reasoning models against classic puzzles of varying difficulties.
- Results showed that models struggled on tasks requiring extended systematic reasoning, achieving low scores on novel mathematical proofs.
- Critics like Gary Marcus found the results 'devastating' to Large Language Models (LLMs) and questioned their logical and intelligent processes.
- The study revealed that SR models behave differently from standard models depending on task difficulty, sometimes 'overthinking' and failing on complex puzzles.
- An identified scaling limit showed that reasoning models reduce their effort beyond a certain complexity threshold.
- Not all researchers agree with the study's interpretation, suggesting that limitations may reflect deliberate training constraints rather than inherent inabilities.
- Some critics argue that the study's findings may be measuring engineered constraints rather than fundamental reasoning limits.
- The Apple researchers caution against over-extrapolating the study's results, noting that puzzle environments may not capture the diversity of real-world reasoning problems.
- While the study challenges claims about AI reasoning models, it does not render these models useless, indicating potential uses for tasks like coding and writing.
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