arXiv (All)
open access
Separating Thinking from Spelling for Efficient Generative Recommendation
Generative recommenders retrieve items by writing their semantic IDs (SIDs) code by code under trie-constrained beam search, an approach now deployed on large web platforms. Standard autoregressive interfaces pay for the SID length K twice: a history of T items enters the backbone as TK code tokens,
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Learning Better Reasoning for Generative Recommendation with Semantic IDs#1078044
- Retrieval-Augmented Generation to Support Railways Engineering Tasks: A Case Study#1354940
- Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators#1324433
- Self-Evolving Memory for Generative Recommendation#1109586
- Iterative Critique-Refine Framework for Enhancing LLM Personalization#1326157
Record · ID 1448495
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive