Mitigating Routing Mismatch in Multimodal Knowledge Editing

Multimodal knowledge editing aims to update specific knowledge in multimodal large language models (MLLMs) without full retraining. We identify that in methods requiring editing parameter selection, the gap between teacher-forcing training and autoregressive generation is mainly caused by routing mismatch: optimal parameters selected in training are not reliably activated during generation. To bridge this gap, we propose a generation-aligned optimization framework that formulates knowledge editing as a sequential decision process. We simulate generation scenarios to allow the model to perform parameter routing and optimization on prefixes it may encounter during generation. To prevent gradient corruption caused by erroneous prefixes, we further propose a Generation-Aligned Prefix (GAP) loss that selectively propagates gradients only along valid tokens. Experiments on VLKEB and ComprehendEdit demonstrate that our method significantly improves generation accuracy under both single-edit and lifelong-edit settings.