YOLO foraging model hits OOD wall

This is the right kind of paranoia for safety-critical vision: when the cost of a false positive is severe, raw softmax confidence is not a safety feature. The interesting part is not that YOLO can misclassify OOD inputs, but that the author treated latency, reject options, and deployment budget as first-class design constraints. Energy scores are the strongest idea here because they target the overconfident closed-set failure mode on OOD samples. A domain router plus specialist models is a better fit than one monolithic detector when the input space is narrow and the consequences of a wrong class are high, and the Hailo 8L constraint makes this a systems problem as much as an accuracy problem.