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Internal representations in LLMs can serve as powerful lie detectors, revealing hidden shifts in forecasts that chain-of-thought reasoning fails to capture.
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.
