Think Before You Link: On-Policy Distillation Enhanced Graph Reasoning for Multimodal Knowledge Graph Completion

Multimodal knowledge graph completion (MKGC) predicts missing links from graph structure, entity images, and textual descriptions. Existing methods first construct static multimodal entity representations and then predict links by matching these representations. This representation–prediction procedure does not create a query-specific reasoning process before prediction. We propose MKGC-CoT, which reformulates MKGC as reasoning followed by entity prediction. MKGC-CoT uses Multimodal Fusion to prepare the query context, MKG Chain-of-Thought (CoT) Reasoning to assemble relation-specific evidence, and Reasoning-Based Entity Prediction to rank all entities from the hidden state after the CoT. We construct Answer-Free MKG CoT Data, in which the student receives neither the gold edge nor the gold answer and an answer-aware teacher provides structured traces only during training. Two-Stage Training first uses SFT to learn the reasoning format, then uses Answer-Aware On-Policy Distillation (OPD) to strengthen reasoning on the student's own sampled CoT prefixes. At inference, the student receives only the answer-free query. On the DB15K and MKG-Y datasets, MKGC-CoT achieves the best results: it reaches 50.44 MRR on DB15K, a 26.8% relative MRR improvement over the strongest prior multimodal method, and leads on MRR, Hits@1, and Hits@10 on MKG-Y.