RADD: Retrieval-Augmented Discrete Diffusion for Multi-Modal Knowledge Graph Completion

ArXi:2604.25693v1 Announce Type: new Most multi-modal knowledge graph completion (MMKGC) models use one embedding scorer to do both retrieval over the full entity set and final decision making. We argue that this coupling is a core bottleneck: global high-recall search and local fine-grained disambiguation require different inductive biases. Therefore, we propose a Retrieval-Augmented Discrete Diffusion (RADD) framework to decouple retrieve and reranking for