arXiv:2511.20196v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) can inadvertently memorize privacy-sensitive information during training. While existing unlearning methods can remove such content, they often severely degrade the model's foundational capabilities, such as general image understanding. This critical shortfall motivates our investigation into benign memory forgetting, the precise removal of targeted, privacy-sensitive knowledge while rigorously preserving unrelated capabilities. To pioneer and evaluate progress toward this objective, we introduce S-MLL
The proliferation of advanced MLLMs and the increasing sensitivity around data privacy are driving the urgent need for robust unlearning mechanisms.
The ability to selectively unlearn sensitive data without degrading core model capabilities is crucial for regulatory compliance, ethical AI development, and broader enterprise adoption of MLLMs.
This research outlines a pathway to MLLMs that can more effectively manage and remove specific memorized content, improving their trustworthiness and deployability.
- · AI developers and researchers
- · Enterprises deploying MLLMs
- · Data privacy advocates
- · Customers using MLLM-powered services
- · Bad actors exploiting memorized data
- · AI models with poor unlearning capabilities
MLLMs can become more secure and privacy-preserving, leading to their wider integration into sensitive applications.
Improved unlearning techniques could lead to new regulatory frameworks and industry standards for AI model accountability and data governance.
The concept of 'benign forgetting' might extend to other complex AI systems, fundamentally altering how we design and manage AI memory.
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Read at arXiv cs.AI