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

[Submitted on 28 Apr 2026 (v1), last revised 20 Jul 2026 (this version, v2)]

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Abstract: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. 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 recall is a strict requirement 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 each component’s contribution.

Submission history

From: Guanglin Niu [view email]
[v1] Tue, 28 Apr 2026 14:21:03 UTC (352 KB)
[v2] Mon, 20 Jul 2026 12:20:22 UTC (353 KB)