Detect Before You Leap: Mirage Detection in Vision-Language Models

ArXi:2606.00435v1 Announce Type: cross Vision-language models (VLMs) can produce confident visual answers even when the required visual evidence is missing, blank, or unrelated to the question. This failure mode, known as mirage (Asadi 2026), is especially concerning in medical and document visual question answering, where plausible but visually ungrounded responses may be mistaken for image-based evidence. We study pre-release mirage detection: given an image-question pair, the goal is to determine whether a VLM should answer or abstain before producing a response.