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Internal representations can serve as powerful lie detectors, revealing discrepancies in LLM forecasts that CoT reasoning may obscure.
Sparse autoencoders, despite their popularity for extracting interpretable features, often fail to capture the underlying manifold structure of concepts, instead fragmenting them across multiple, diluted features.
