> The test works by inserting a semantically important "needle" frame at random positions in long videos, which the system must then find and analyze.

This seems to be somewhat unwise. Such an insertion would qualify as an anomaly. And if it's also trained that way, would you not train the model to find artificial frames where they don't belong?

Would it not have been better to find a set of videos where something specific (common, rare, surprising, etc) happens at some time and ask the model about that?