Evidence, Pressure, or Neither: What Actually Moves Costly Decisions in Large Language Models

Language models are increasingly asked to commit to costly judgements and to revise them when challenged. Evaluations report a single revision rate against a control with the pushback removed, conflating revision that tracks the argument with revision that tracks the arguer and assuming that the control does not move. We propose Matched-Presence Decomposition (MPD), which separates revision into a content, a social and a re-ask component by replacing each manipulated element of the prompt with the same element from another item, together with six audits that decide whether each component is identified. On a prediction market, a bare second ask in the same conversation changed the answer in $65.6$ to $86.6\%$ of episodes in four open-weight families, and neither the framing sentence, greedy decoding nor dropping the word “updated” removed most of it. Across seven families, argument content moved every one and direction-free reputation none. The audits also mark where a reading fails: a peer-review placebo carries direction, and on political items part of what a published sycophancy benchmark scores is agreement with the persona's last assertion. A prompt that makes the model state whether the material is new removed $19.4$ to $46.8$ points of drift while preserving the response to evidence; the largest reduction, which a reduction-only evaluation would select, came from an instruction that removed up to $57\%$ of that response.