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Existing "faithfulness" metrics for chain-of-thought reasoning are essentially useless, performing near chance at identifying whether a CoT actually reflects the model's computation.
Unlocking interpretability just got easier: ROTATE disentangles MLP neurons without data, revealing sparse, concept-aligned vocabulary channels directly from model weights.
