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This paper introduces SPACE, the first source-free unlearning framework for multimodal large language models (MLLMs), which uses text-guided proxy anchor selection and dual-constraint semantic isolation to erase target concepts without requiring access to original training data, achieving performance comparable to data-dependent methods.