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

תקציר מקורי באנגליתarXiv:2606.00435v3 Announce Type: replace-cross Abstract: 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, recently described as mirage (Asadi et al., 2026), is especially concerning in medical and document VQA, where visually ungrounded answers may be mistaken for image-based evidence. We study pre-release mirage detection: given an image-question pair, determine whether a VLM's answer should be released or the system should abstain before the answer reaches the user. We propose Text-Conditioned Layer-wise Internal Alignment (TC-LIA), a model-agnostic method that probes patch-token representations across the layers of a CLIP ViT-H/14 vision encoder. The key idea is