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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 retrieval and reranking for MMKGC. A relation-aware multimodal knowledge graph embedding (KGE) retriever serves as both global retriever and distillation teacher, while a conditional discrete denoiser performs shortlist-level entity-identity generation for reranking. Training combines KGE supervision, denoising cross-entropy, and temperature-scaled distillation from the retriever to the denoiser. At inference, the designed Diff-Rerank first forms a top-K shortlist with the retriever and then reranks it with the denoiser, ensuring that re","title":"RADD: Retrieval-Augmented Discrete Diffusion for Multi-Modal Knowledge Graph Completion","url":"https://arxiv.org/abs/2604.25693","vendor":"arxiv_cs_ai"},"summary":"arXiv:2604.25693v2 Announce Type: replace \nAbstract: Most multi-modal knowledge graph completion (MMKGC) models use one embedding scorer to conduct both retrieval over the full entity set and final link prediction. 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 retrieval and reranking for MMKGC. 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