When Rejected Properties Return: Measuring Veto Persistence in Conversational Image Editing

Iterative AI image editing gives creators increasingly fine-grained control over an evolving visual work, yet current evaluation does not test whether a decision that has been explicitly rejected remains effective as editing continues. This work formalizes \emph{rejected-property reversion}: a visual property is visibly present, explicitly rejected, successfully removed, and later reappears during an edit that neither requests nor entails its return. We introduce \textbf{Counter-Revision}, a paired diagnostic and an eligibility-aware Rejected-Property Reversion Rate (RPRR) that distinguish this longitudinal failure from immediate rejection noncompliance. Counter-Revision comprises 12 controlled trajectories across three property families, two image editors, four session representations, and two seeds, yielding 192 verified runs in which all session representations fork from the same byte-identical activated image. Under strict dual-VLM scoring, rejected properties reappeared in 14 of 112 judgeable eligible later observations (12.5\%), with reversion observed at every tested continuation distance and in both property families for which the complete eligibility chain was estimable. A proposition-matched comparison further found no supported persistence advantage for typed Intent Ledger serialization over Constraint Replay ($\Delta\mathrm{RPRR}=+3.8$ percentage points, 95\% trajectory-bootstrap CI $[0.0,18.8]$). These results identify \emph{veto persistence} as a distinct dimension of conversational image editing: successfully honoring a creator's rejection at one turn does not guarantee that the decision will continue to constrain later revisions.