Towards Benign Memory Forgetting for Selective Multimodal Large Language Model Unlearning

Multimodal Large Language Models (MLLMs) achieve remarkable capabilities but can inadvertently memorize privacy-sensitive information. Although existing unlearning methods can remove such knowledge, they fail to achieve benign forgetting because they often degrade the model's general image understanding performance. T o address this, we propose the Sculpted Memory F orget-ting Adapter (SMF A), which confines forgetting to targeted memory regions while preserving overall capabilities. SMF A first fine-tunes the model to replace sensitive responses with refusals, yielding a memory forgetting adapter, and then applies a retaining anchor-guided masking mechanism to prevent interference with unrelated knowledge and understanding ability. T o systematically evaluate selective MLLM unlearning, we introduce S-MLLMUn Bench, the first benchmark designed to jointly assess the removal of sensitive knowledge and retention of general visual understanding. Extensive experiments show that, unlike prior methods, SMF A achieves precise and controllable unlearning while maintaining the model's foundational image understanding.