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LLM judges may recognize important reasoning steps, but they still miss critical nuances, highlighting a gap between legibility and true interpretability in AI reasoning.
Fixed counterfactual explanations can lead LMs to generate more accurate introspections about their behaviors, even as those behaviors change over time.
