  We present an active automata learning algorithm which learns a decomposition
of a finite state machine, based on projecting onto individual outputs. This is
dual to a recent compositional learning algorithm by Labbaf et al. (2023). When
projecting the outputs to a smaller set, the model itself is reduced in size.
By having several such projections, we do not lose any information and the full
system can be reconstructed. Depending on the structure of the system this
reduces the number of queries drastically, as shown by a preliminary evaluation
of the algorithm.
