针对多模态大模型的持续隐私删除难题，提出新基准ICU-Bench。

Privacy deletion requests often arrive sequentially, creating a continual unlearning challenge for deployed multimodal large language models (MLLMs). However, existing benchmarks mainly focus on static or short-sequence settings, offering limited support for evaluating continual privacy deletion on privacy-critical documents. To bridge this gap, we introduce ICU-Bench, an Identity-centric Continual Unlearning benchmark (pronounced ``I see you'') for privacy-critical multimodal documents. ICU-Bench contains 1,000 synthetic privacy-sensitive profiles from medical reports and labor contracts, comprising 9,500 document images, 16,000 question-answer pairs, and 100 sequential forget tasks. We further introduce history-aware evaluation protocols and sequence-aware metrics to assess current forgetting, historical forgetting preservation, retained utility, and stability throughout the unlearning sequence. Experiments with representative unlearning methods on two MLLMs reveal that methods effective on current targets often fail to preserve earlier forgetting or retained capabilities over long sequences. Some methods further obtain low forget accuracy through severe model degradation. These results expose long-horizon failure modes overlooked by conventional evaluations and highlight the need for multimodal unlearning methods explicitly designed for continual privacy deletion. Our code and benchmark are publicly available at: https://github.com/AstorYH/ICU-Bench.