Same-Model Self-Critique

Problem

Because producer and critic come from the same weights and read overlapping prompts, the critic shares the producer's blind spots; whatever the model is confidently wrong about, it is also confidently wrong about when wearing the critic hat. Wrong answers come back from the loop endorsed and slightly polished, and the team reports higher confidence on what is, statistically, the same error rate. Replication studies through 2025 have repeatedly confirmed that single-model self-critique catches surface mistakes but does not act as independent verification.

Solution

Don't pretend it is independent. Either accept that self-critique catches surface errors only, or use a different model family for the critic. See reflection, evaluator-optimizer, llm-as-judge.

When to use

• Never use this; the critic shares the producer's blind spots and reinforces wrong answers.
• If self-critique is the only option, treat it as catching surface errors only.
• Use a different model family for the critic (see llm-as-judge or evaluator-optimizer).