Massive Memorization with Hundreds of Trillions of Parameters for Sequential Transducer Generative Recommenders

ArXi:2510.22049v3 Announce Type: replace-cross Modern large-scale recommendation systems rely heavily on user interaction history sequences to enhance the model performance. The advent of large language models and sequential modeling techniques, particularly transformer-like architectures, has led to significant advancements recently (e.g., HSTU, SIM, and TWIN models