You mentioned in your article that the bottleneck is not the intelligence of the model, but rather problem specification and context. I think this is a bit too optimistic. In fact, when we provide problem specifications and context to humans, they are often neither precise nor complete, yet people can still use their intelligence to infer the missing but necessary information. Or, when their output does not meet our needs, we can give feedback, and they revise their answers. After repeating this process a few times, we usually arrive at a better result. In this respect, models are not at a greater disadvantage than humans. But the current situation is that when a model’s output is unsatisfactory, even if we provide multiple rounds of feedback and additional information, it still fails to reach the level that a human would achieve in the same situation. Many times, humans can solve problems that models cannot, which clearly shows that the model’s intelligence is still insufficient.