MUSE: Adaptive Input-Conditioned System-Prompt Embeddings for Multimodal Large Language Model Unlearning

Multimodal large language models (MLLMs) may retain sensitive or undesirable knowledge, motivating effective multimodal unlearning. Existing parameter-level methods often incur substantial training and storage overhead. We propose MUSE, a lightweight and adaptive framework that reformulates multimodal unlearning as system-prompt embedding intervention over a frozen MLLM. MUSE trains a Multimodal Embedding Adapter (MEA) to generate system-prompt embeddings conditioned on each image–question pair, enabling selective knowledge suppression or preservation according to the multimodal context. To explicitly control the forget–retain boundary, MUSE further introduces an asymmetric dual-space objective that jointly constrains prompt embeddings and internal representations. Experiments on MLLMU-Bench demonstrate strong forgetting, favorable forget–retain trade-offs, and robustness to prompt attacks. Moreover, MUSE achieves up to 4.9$\times$ faster training and 74.2% lower GPU memory usage than the strongest baseline, demonstrating effective and efficient multimodal unlearning.