The representation learning of knowledge graphs (KGs) is a longstanding research problem. While graph neural networks (GNNs) have driven recent progress, they still struggle with encoding textual features and subtle relationships of KGs, particularly in conveying key information to large language models (LLMs). The emergence of multi-modal LLMs (MLLMs), which combine linguistic and visual understanding, presents an intriguing opportunity: Could their vision capabilities inspire mental visualization, facilitating conceptual thinking and semantic reasoning akin to human cognition? To investigate this premise, we propose SeeKG, an innovative framework that transforms KGs into visually rendered representations as image inputs for MLLMs. We evaluate SeeKG under both training-free and supervised fine-tuning settings, where the experimental results show that SeeKG excels in understanding KG sub-graphs and achieves competitive performance even without training or demonstrations. Further fine-tuning on small-batch data reveals that it outperforms state-of-the-art LLM-based methods by substantial margins across multiple KG completion datasets.

E-commerce search systems typically separate query-item relevance matching from downstream personalized ranking. While effective for large-scale serving, this design leaves relevance levels dominated by objective query-item matching, forcing follow-up ranking to operate over candidate structures that may not reflect user behavioral preference. We define a new personalized behavioral relevance task, which predicts whether a relevance-stage candidate item is supported by the user’s behavioral evidence under current query intent. We develop a corresponding framework, BRIDGE, for Behavioral Relevance Inference with Dynamic Guidance from Evidence. It organizes multi-timescale behavioral context from online logs, obtains reliable supervision with multi-perspective annotation and adjudication, and trains an LLM to produce structured evidence-grounded decision rationales. It particularly incorporates sample-specific rubric rewards into GRPO to improve generated reasoning traces. Offline experiments validate that BRIDGE improves both prediction accuracy and reasoning quality. Online A/B tests show gains of 0.19% in active user-days and 0.67% in transaction-related user-days, both significant in statistics.