Decoded but Unused: Instruction Tuning Routes Moral Framing into the Judgment Readout

Large language models change their moral verdicts when the same event is reframed, but the literature treats this as a behavioural fact about chat models without locating where in the network the change happens. We show that moral framing is already linearly decodable in the pretrained network yet has no causal effect on its judgment, while in the instruction-tuned checkpoint that same representation becomes aligned with and causally usable by the evaluative readout, with the within-model framing-judgment alignment 8.4× larger than in the matched pretrained checkpoint at the same layer. Instruction tuning changes how the representation is read out, not whether it exists.