A continuous thought machine does not search per image: it rides one shared fixed-point curve, and its attention schedule is nearly generic
On ImageNet, the Continuous Thought Machine's internal state follows an attracting fixed-point curve that moves every tick, most of that motion is shared across images, and the attention schedule is largely generic: shuffling queries between images costs only about 7% of classification accuracy.