I’m referring to the inference process for a device, not the training process…so after a device detection model has been created.
I assume the little orange Sense box in the panel is doing neither the training nor the inference, rather I assume it is just sensing the voltage/current (and getting samples from any smart plugs on the LAN), and sending the raw data to the cloud where the training and inference is performed, likely with some mechanism to compress the high sample rate data. I have no idea how long Sense would keep the high sample rate data.
I assume the power meter data that the Sense app (iOS, web, etc) gets is from the cloud only, but only a down sampled version of the data.
So you’ve witnessed “retroactive detection to a certain extent”; I think I’ve witnessed my example 1, where a device had actually already turned off, but Sense at the time hadn’t yet realized it, but later when I checked the app again for the same time range, Sense then correctly had the device as pulling no power. I don’t think I’ve witnessed my example 2 before.