Recurrent memory splits each write into two calls: whether to store a token, then how many slots to touch, lifting sparse-fact accuracy from 0.048 to 0.222
Recurrent-memory models compress history into a fixed set of slots, and earlier multi-slot memories wrote every token to the same number of slots, merging whether to store a token with how many slots to update. Factorized Write Routing separates the two: a learned gate first decides whether the current token is written, then a slot router chooses a fixed or token-adaptive number of slots, while queries still read all slots. Across ten seeds on two synthetic long-context tasks with four slots per write, the result reverses with information density: always writing reaches 0.472 long-context accuracy on dense Feature Recall versus 0.337 for the learned gate, while the learned gate reaches 0.222 on sparse-fact Passkey versus 0.048 for always writing.