Watch any modern agent framework long enough and you'll see it: the model produces output, then "reflects" on the output, then "corrects" itself, then ships a final answer. It looks like metacognition. It isn't. It's the same model, with the same weights, sampled twice, with the second sample conditioned on the first. There is no separate critic. There is no privileged vantage point. The reviewer and the reviewed are the same network — and the reviewer cannot see anything the original didn't already encode. This is reflection theatre. What actually happens When you prompt "now critique your previous answer," the model does not consult a deeper layer of itself. It re-decodes from the same distribution, with a critique-shaped prefix. The output looks like self-correction because the prompt biases it toward correction-shaped tokens. If the original answer was wrong because the model lacked the relevant fact, the reflection step also lacks the fact.…