Multi-modal knowledge graph completion aims to infer missing links by integrating structural, visual, and textual information. However, global retrieval can leave fine-grained entity ambiguity unresolved, while shortlist-only prediction excludes answers missed by candidate selection. We propose Retrieval-Anchored Discrete Diffusion (RADD), a unified framework that turns retrieval uncertainty into a probabilistic reference for multimodal refinement. By anchoring discrete diffusion to the query-specific retrieval distribution, RADD learns how complementary multimodal evidence should revise entity preferences, coupling global retrieval with fine-grained disambiguation. Bidirectional learning unifies head and tail refinement, while coverage-aware inference adapts ranking corrections to candidate coverage without restricting predictions to the retrieved set. Experiments on three MMKGC benchmarks demonstrate RADD's effectiveness and consistent improvements over strong baselines and its matched retriever.

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