Evidence Late, Relation Early: On When to Aggregate and Condition in Multimodal Knowledge Graph Completion

Multimodal knowledge graph completion (MMKGC) leverages multimodal information to infer missing facts in incomplete knowledge graphs. Most existing methods construct entity representations before triple scoring, leaving two fundamental design questions underexplored: *when to read out multimodal evidence* and *at what stage should relational guidance interact with multimodal evidence*. We revisit MMKGC from this perspective and identify two complementary principles: **evidence-late**, which defers the compression of fine-grained evidence to the scoring stage, and **relation-early**, which incorporates relational information during representation learning to emphasize relation-relevant evidence. We first instantiate the evidence-late principle in **Origin**, enabling a controlled comparison with early evidence compression and showing that preserving fine-grained evidence until scoring yields better performance than premature compression into a fixed vector. Building on this observation, we propose **REEL**, a lightweight framework that operationalizes both principles in a unified design. Experiments on three MMKGC benchmarks show that REEL achieves strong completion performance, while ablation studies and case analyses further validate the effectiveness of both design principles. Our code is available at https://anonymous.4open.science/r/reel-1955.