Knowledge Externalization: Reversible Unlearning and Modular Retrieval in Multimodal Large Language Models

Multimodal Large Language Models (MLLMs) achieve remarkable cross-modal understanding by training on vast web-scale datasets, but inadvertently internalize sensitive personal and proprietary information. Existing machine unlearning methods address this by irreversibly altering model parameters to permanently erase knowledge. This destructive paradigm conflicts with modern privacy regulations that mandate auditable, reversible, and user-controllable data management. To address these challenges, we propose Knowledge Externalization, a novel framework for reversible and modular knowledge management in MLLMs. We first propose Dual-Stream Memory Tuning, a method that transfers targeted knowledge from a model's internal parameters into external memory tokens. To mitigate gradient interference when externalizing multiple concepts, we further introduce Soft Orthogonal Weighting, a technique that preserves the independence of each token. Our resulting framework demonstrates three key capabilities: (i) It achieves effective forgetting of target concepts within the base model, while enabling high-fidelity knowledge restoration using the corresponding memory token. (ii) It supports continuous knowledge editing, allowing the information stored within an external token to be dynamically updated post-externalization. (iii) It displays a remarkable emergent ability for compositionality, where multiple memory tokens (including edited ones) can be freely combined to simultaneously recover knowledge corresponding to each concept. Our source code will be released in the near future.