Semantic Flip proposes a framework to synthesize out-of-distribution samples by transforming queries and video memory to create unanswerable pairs. These pairs train a lightweight rejection module that attaches to existing vision-language models without retraining, improving refusal performance in embodied question answering and spatial localization. On the new SpaceReject benchmark, it achieves an F1 score of 0.9559.