A decoder-only model is given a prompt of 6 tokens and asked to write 4 more, ending with a 10-token sequence.
It generates the way the chapter describes: predict a token, append it, feed the whole sequence back in, predict the next. Nothing is carried over between passes — every pass rebuilds its attention grid from scratch.
Watch a single masked self-attention sublayer. In one pass over a sequence of tokens it computes one score for each (query position, key position) pair the mask allows: every position may compare itself against itself and against every position before it, and blocked pairs are never computed at all.
Counting the pass that reads the prompt and every pass after it, up to and including the one that produces the tenth token — how many attention scores does that one sublayer compute in total?
Answer with the exact whole number.