Video language models can give right answers for wrong reasons; this work forces models to provide verifiable visual evidence (tracked object masks + time segments) alongside answers, revealing and fixing a critical gap between QA accuracy and actual visual understanding.
This paper introduces Evidence-Backed Video Question Answering (E-VQA), which requires video AI models to not just answer questions but also show their work by providing temporal segments and pixel-level object tracking masks as visual proof.