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

• URL: http://arxiv.org/abs/2604.25693v1
• Date: Tue, 28 Apr 2026 14:21:03 GMT
• Title: RADD: Retrieval-Augmented Discrete Diffusion for Multi-Modal Knowledge Graph Completion
• Abstract summary: We propose a Retrieval-Augmented Discrete Diffusion framework to decouple retrieve and reranking for MMKGC.<n>A relation-aware multimodal KGE retriever serves as both global retriever and distillation teacher, while a conditional discrete denoiser performs shortlist-level entity-identity generation for reranking.
• Score: 14.144464555343385
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 MMKGC. A relation-aware multimodal 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 recall is a strict prerequisite for precision. Experiments on three MMKGC benchmarks show that RADD achieves the best performance and consistent gains over strong unimodal, multimodal, and LLM-based baselines, while ablations further verify the contribution of each component.

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