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In this work, we propose a Dynamically Adaptive Density Control Strategy based on the degree of reconstruction of the background of the scene, which adaptive the spatial sample point generation strategy dynamically according to the training results and prevents the generation of redundant data in the model.

Abstract: Multimodal Retrieval-Augmented Generation (RAG) systems have become essential in knowledge-intensive and open-domain tasks. As retrieval complexity increases, ensuring the robustness of these systems is critical. However, current RAG models are highly sensitive to the order in which evidence is presented, often resulting in unstable performance and biased reasoning, particularly as the number of retrieved items or modality diversity grows.