A Generative Recommender Also Needs a Discrete Language for Memory

Generative recommendation provides a discrete language for items, typically through Semantic IDs (SIDs), but leaves user memory implicit in the interaction sequence. Even when the complete history is available, the model must repeatedly infer from raw events the predictive state that matters for future behavior. We argue that history and memory should not be conflated: interaction tokens record what happened, whereas memory should explicitly represent what those interactions imply about the future. This motivates an explicit predictive memory that exposes the future-relevant meaning of the interaction history. We instantiate this principle with DeYi○Φ. DeYi distills earlier interactions into a user-specific predictive state trained to preserve future-relevant information while the original interaction sequence remains available to the generator. Φ then maps these private continuous states into a shared discrete vocabulary according to their induced future-item predictions rather than latent-space proximity, giving recurring predictive states across users reusable symbolic identities. During generation, the resulting memory token is prepended to the full interaction sequence and also defines a context-dependent predictive route before the target item's native SID. Remarkably, adding only a single predictive memory token can outperform generation from the full history alone, showing that explicit memory provides information complementary to the event sequence itself. These results suggest a simple principle for generative recommendation: history provides evidence, while memory provides predictive abstraction. Code is available at https://anonymous.4open.science/r/DeYiPhi-1537.