Kept or conceded: stance fidelity of language models under repeated user pushback

When a user disagrees, a language model can keep its earlier answer or concede. I treat each reply as a transmission step in which the model copies either its own previous stance or the user's. Re-analysing the released, automatically judged transcripts of SYCON-Bench (11 models; 200 false-presupposition, 200 stereotype and 100 debate items; five turns each), I estimated per-turn retention r: the probability that a stance survives one more pushback, given that it has survived so far. Averaged over models, r was 0.85 for factual corrections, 0.77 for rejections of stereotypes and 0.86 for assigned debate stances, half-lives of 4.3, 2.6 and 4.7 pushbacks. Factual corrections outlasted stereotype rejections on average (pooled odds ratio 2.08, 95% CI 1.45–2.99), clearly so in 7 of 11 models, but one model showed the reverse. A constant r reproduced five-turn survival closely, yet retention varied with each pushback's wording and between items. Under a directionless "I do not agree", weaker models that conceded often switched back at the next challenge. A blind AI audit of judge labels agreed on 82 of 99; the judge was lenient on factual and strict on stereotype items, which could inflate the gap. Preprint, not peer reviewed. Also published at https://x9m8.com/llm-stance-fidelity-pushback/. The zip file contains the code, results and the two independent audits.