Rebuild each forward into a persistent arena per graph shape class (one
for the talker, two for the code predictor prefill and step flavors that
alternate within a frame) so nodes keep stable addresses and the CUDA
graph cache replays its executable instead of reinstantiating. Pad the
talker attention window to 256 and fix the predictor window to the frame
cache size so decode shapes hold across steps, with the causal mask
carrying neg inf over the padded tail. Drop the per step ggml context
churn and the trailing sched resets: one talker step plus 15 predictor
micro steps per frame no longer pay a full build/alloc/free cycle each.
