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Prior audit-repair episodes can significantly lower false alarm rates in language model verifiers, challenging assumptions about accumulated-message effects.
Attention mechanisms can amplify the visibility of latent variables by up to 17x, depending on task demand, challenging our understanding of how language models manage internal representations.
LLMs can recover from planning failures with just one extra call, achieving near-perfect accuracy by replaying and repairing their "train of thought" from verified checkpoints.
