A study by Apple researchers reveals that simulated reasoning (SR) models fail to consistently solve complex problems via logical reasoning, instead relying heavily on pattern-matching from their training data. This finding is echoed in earlier research demonstrating low performance of SR models on novel mathematical proofs. The study critiques existing evaluations that prioritize endpoint accuracy over the reasoning process itself. By challenging AI models with classic puzzles of varying complexity, the researchers highlight significant gaps in the reasoning capabilities of current large reasoning models (LRMs), raising concerns about their efficacy in true systematic thinking.
"Current evaluations primarily focus on established mathematical and coding benchmarks, emphasizing final answer accuracy...they don't examine whether the model actually reasoned its way to that answer."
"The new study suggests that SR models produce outputs consistent with pattern-matching...when faced with novel problems requiring systematic thinking."
Collection
[
|
...
]