Notably, performance on out of distribution data like those in RF100VL is super degraded

It worked really well zero-shot (comparatively to the foundation model field) achieving 13.3 average mAP, but counterintuitively performance degraded when provided visual examples to ground its detections from, and when provided textual instructions on how to find objects as additional context. So it seems it has some amount of object detection zero-shot training, probably on a few standard datasets, but isn't smart enough to incorporate additional context or its general world knowledge into those detection abilities

Here's a question for you: how do you reconcile that these stochastic mapping are starting to realize and comment on the fact that tests are being performed on them when processing data?

Extra points for that comment's author implying that people who don't like the wrong and smug movement are unintelligent and protecting their egos, thus personally proving its smugness

https://en.wikipedia.org/wiki/Air_well_(condenser)

Check out the passive section of the above