Learning on dynamic graphs is difficult when changes in the underlying network are only partially observed. Acquiring current graph information incurs observation and computational costs, making complete updates impractical under limited resources. This paper focuses on budgeted task-aware acquisition on dynamic networks, where a model needs to decide which stale graph information to refresh for a downstream task. We propose Scout, a lightweight framework that learns the task value of querying each node from the maintained graph and observation history. Our evaluation covers one synthetic and 4 real-world dynamic networks, 2 downstream tasks, 9 acquisition baselines, and several query budgets. Scout achieves the highest mean downstream performance in 15 of the 21 benchmark-budget settings and ranks within the top two in 19 of them. Its average rank is 1.4, compared with 3.2 for the strongest baseline. On the same dynamic network, task-matched acquisition improves link-prediction AUC by 0.005-0.007 and node-classification accuracy by 0.007-0.017 over task-mismatched acquisition. These results show that useful graph observations depend on the downstream task and that limited observation budgets can be allocated effectively by learning directly from downstream utility.

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