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Uncertainty in LLM reasoning is largely a sampling artifact, allowing for more efficient analyses that cut costs without sacrificing accuracy.
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.
