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Bottleneck of Recursive Self-Improving AI: Evaluation Signals Determine Success or Failure
A review of 1250 papers reveals that the core bottleneck of AI self-improvement lies in the quality of evaluation signals. Experiments show that models stop improving after 10 rounds of self-criticism without external checks, and only resume after adding a grounding step. Self-improvement is sustainable only when signals are reliable (e.g., proof checkers or test passes); otherwise, the loop reinforces errors.
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