DIFFUSION GENERATIVE RETRIEVAL OF ORDERED SLATES

In generative retrieval, the model memorizes the index in its own parameters. In this setting, the recommender stores the catalog in its weights and emits each item directly as a tuple of semantic ID tokens, without an inference time candidate pool. Diffusion is a natural decoder when the entries of a list are chosen jointly. Earlier diffusion approaches operate along the identifier levels of a single item and assemble the slate from separate decodes. Diffusion Generative Retrieval of Ordered Slates (DiffROS) transposes this axis. It denoises a canvas that holds the full slate, with a slot for every position. At each identifier level, DiffROS predicts every slot in the context of all others. The standard offline objectives, however, are modular set functions. They score each item separately and add those scores across the slate. Ranking items by their marginals, or separate contributions, therefore already maximizes these objectives. Conditioned on retrieval, DiffROS orders the recovered items several times more sharply than beam search and the autoregressive slate decoder. A next item decoder read out by beam search recovers more of the target items, while DiffROS better orders the ones it recovers. At matched capacity and with a slate of one hundred, we found that DiffROS recovers close to three times as many targets as an autoregressive slate decoder while its decoding depth remains constant in slate size, with three forward passes rather than three hundred.