A patrol robot drives the same route repeatedly; which of the changes between one visit and the next a supervisor should see depends on the surroundings, earlier moments, and local rules. We present a hybrid geometry-VLM pipeline for evaluating operational surveillance norms. LiDAR change detection and person tracking feed a spatio-temporal evidence graph of changed objects, people, and site areas; the graph indexes what is passed to a vision-language model. The model characterises what it is shown and judges it against the site’s norms, which range from explicit through relational to implicit. Developed at one site and evaluated at another, with reportable and non-reportable situations staged, it reaches an F1 of 0.70 at a precision of 0.66. A camera-only alternative nearly matches its recall but at an alert precision of 0.08; withdrawing the relational evidence costs 0.17 of F1. What a design must supply differs across the three norm categories.
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