Paper recorded by Signals 4 on 2026-10-20 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-20 on arXiv · recorded by Signals 4 on 2026-10-21
Category: cs.AI · 人工智能 · first seen 2026-10-21
Generative retrieval (GR) casts search and recommendation as autoregressive generation of discrete semantic identifiers (SIDs). SID design largely determines GR quality, but its discrete, interdependent components (SID construction, collision handling, decoding, and training) cannot be optimized end-to-end with gradients and remain largely hand-designed. We introduce a framework that improves thes