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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.
This paper introduces GEM, a concept erasure framework for Rectified Flow models that combines trajectory-based unlearning with teacher-guided flow matching, achieving 5× faster and safer content suppression while preserving benign generation.
This paper introduces Orthogonal Concept Erasure (OCE), a method for precisely removing target concepts from diffusion models using multiplicative orthogonal parameter updates, enabling efficient single- and multi-concept erasure up to 100 concepts in seconds.