ANSWERS ARE NOT ROUTES: WHAT LOCAL AUDITS ESTABLISH UNDER VISUAL SUPPORT CONSTRAINTS

Vision-language models (VLMs) can answer correctly and pass local tests while still depending on visual context unrelated to the task. When admissible inputs must satisfy constraints, those tests can omit the comparisons that would expose this dependence. We address this problem with a support-auditing procedure built on the Intervenable Causal Route Bottleneck (I-CRB), an interface for controlling object inputs. For each locally tested task pair, the audit completes its legal contexts and checks whether predictions agree when task inputs are unchanged. It returns counterexamples, completed support checks or unresolved comparisons, with finite-domain guarantees and bounds for partial queries. In a constrained four-object task, we show that even a one-object shortcut can evade every legal one-object context change. Evaluating a task-adapted VLM on 256 new images, our audit uncovers previously undetected context dependence in ten of the 234 images that pass all local tests. Donor-free corrections raise image-equal full-domain accuracy from 97.04% to 97.82–98.34%, with net repair in all twelve runs. Re-auditing identifies remaining support violations; matched comparisons do not establish a mechanism-specific repair advantage. Our approach makes hidden context dependence testable and tracks both accuracy and visual support after model updates.