Dynamic Mixture of Adaptive Experts for Lifelong Multimodal Knowledge Editing

Updating Multimodal Large Language Models (MLLMs) without catastrophic forgetting remains a fundamental challenge. Existing lifelong knowledge editing methods face two critical limitations: interference from previously learned knowledge, and knowledge capacity dilemma arising from the difficulty of predefining appropriate parameters. We propose Dynamic Mixture of Adaptive Experts (MoAE), a novel framework that addresses these challenges through dynamic expert expansion and adaptive knowledge capacity. Each expert comprises a shared LoRA and multiple specific LoRAs that expand progressively based on expert utilization frequency. A hierarchical routing mechanism is proposed to select relevant experts and their corresponding specific LoRAs for each input. Extensive experiments on ComprehendEdit and VLKEB benchmarks across MiniGPT-4, LLaVA-1.5, and Qwen2.5-VL demonstrate that MoAE achieves superior overall performance on reliability, generality, and locality in lifelong editing scenarios.