The reason could be straightforward: the output of kvMN is built upon value (knowledge) embeddings and therefore information from key (context feature) embeddings does not directly contribute to it other than providing weights for the value.
kvMN acts in a similar yet inferior 12 way of setting (3) where only knowledge is used.