BRIDGE: Bridging the Semantic Quantization Gap for Generative Recommendation
Refines a continuous state before generating recommendation identifiers, anchoring that state to semantic codebook clusters during training. Distills a teacher with item-neighborhood information and allocates more recurrent steps to early identifier levels.
First public: · Added to this list: November 9, 2026
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Continuous-state reasoning — Use vectors—lists of numbers—to carry intermediate reasoning from one step to the next, instead of selecting a text token at every step. Some methods use the model's hidden states; others combine several candidate token representations.
Recurrent reasoning — Repeat parts of the model or keep intermediate information in memory while working through a problem. Some methods also learn how many passes to run.