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This paper analyzes how large language models internally process graph tokens in Graph Language Models (GLMs), finding a decoupling between activation-level saliency and graph-semantic utility. Graph sink tokens emerge as activation outliers but are not the primary carriers of graph structure, revealing limitations in current graph-token construction and alignment mechanisms.