# BRIDGE: Bridging the Semantic Quantization Gap for Generative Recommendation

Authors: Yatong Xiao, Mingjie Sun, Peng Wang, Wenjin Wu, Peng Jiang.

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: 2026-11-05.
ArXiv initial submission: 2026-11-05.

[Original paper](https://arxiv.org/abs/2611.07425) · [PDF](https://arxiv.org/pdf/2611.07425) · [HTML](https://arxiv.org/html/2611.07425v1) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2611.07425/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2611.07425/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2611.07425/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2611.07425/record.json)

## Approaches

- [Continuous-state reasoning](https://latent-implicit-thinking.github.io/topics/continuous-states/): 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](https://latent-implicit-thinking.github.io/topics/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.

Reading-list version 0.15.2; literature checked through 2026-11-03.