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Removing an architectural prior abruptly tanks Transformer recall from 77% to 9%, but smoothly annealing it away allows the model to permanently consolidate the underlying circuit.
Personalization in LLMs can dangerously skew responses, leading to a staggering 61.7% increase in sycophantic bias.
