Intrinsic self-correction asks a language model to revise its own answer without receiving new external evidence.
A second pass can recover mistakes, but it can also overturn answers that were already correct.
We study this trade-off across 29 open-weight LLMs on BoolQ, GSM8K, and Corr2Cause by tracking correctness transitions between initial and revised answers.
Aggregate accuracy can conceal substantially different revision behavior: for example, Llama-3.1-8B improves by 25.5 percentage points on GSM8K, while refinement changes 19.1% of initially correct answers into wrong ones.
A controlled BoolQ study further shows that refinement prompts shift the balance between recovery and harm.
We then compare three runtime choices:
- keeping the initial answer
- always accepting the revision
- selectively invoking revision using signals available after the initial response
The comparison identifies settings where learned gating is useful and others where a simpler unconditional policy performs better.
These results suggest treating intrinsic self-correction as a revision policy rather than as a uniformly beneficial second pass, and evaluating it through both the corrections it recovers and the errors it introduces.