  Deriving formal bounds on the expressivity of transformers, as well as
studying transformers that are constructed to implement known algorithms, are
both effective methods for better understanding the computational power of
transformers. Towards both ends, we introduce the temporal counting logic
$\textbf{K}_\text{t}$[#] alongside the RASP variant $\textbf{C-RASP}$. We show
they are equivalent to each other, and that together they are the best-known
lower bound on the formal expressivity of future-masked soft attention
transformers with unbounded input size. We prove this by showing all
$\textbf{K}_\text{t}$[#] formulas can be compiled into these transformers. As a
case study, we demonstrate on paper how to use $\textbf{C-RASP}$ to construct
simple transformer language models that, using greedy decoding, can only
generate sentences that have given properties formally specified in
$\textbf{K}_\text{t}$[#].
