cs.IR · 2026-09-25 · No. 124

Information Retrieval, 2026-09-25.

3 new papers in cs.IR. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

    From Interests to Semantic IDs: Retrieval-Grounded Credit Assignment for Generative Recommendation

    Mengdan Zhu, Yufan Zhao, Yao Zhao, Sophie Di, Tao Di, Yulan Yan, Sridhar Iyer, Liang Zhao

    cs.IR · cs.AI

    Semantic IDs (SIDs) encode each catalog item as a short token sequence, enabling generative recommenders to predict the next item autoregressively. Reasoning-enhanced variants, an increasingly common extension, first generate a textual trace and then decode a next-item SID by beam search. Such recommenders are commonly trained with group-relative policy optimization under an exact-match SID reward, which is sparse in large catalogs. Two...

    arxiv.org/abs/2609.29983 · PDF

  2. 02

    Learning Better Reasoning for Generative Recommendation with Semantic IDs

    Mengdan Zhu, Yufan Zhao, Sophie Di, Yao Zhao, Tao Di, Yulan Yan, Sridhar Iyer, Liang Zhao

    cs.IR · cs.AI

    Generative recommendation reformulates item retrieval as sequence generation, allowing a unified model to directly generate the next item from a user's interaction history. Semantic IDs further make this paradigm effective and scalable by representing each item as discrete codes, enabling knowledge sharing among semantically related items. Recent studies introduce explicit reasoning before Semantic-ID generation, helping models summarize user...

    arxiv.org/abs/2609.29973 · PDF

  3. 03

    SEEK: Skill-Routed Evaluation with Evolvable Knowledge for Industrial Search

    Zhongxin Huang, Songyang Li, Renzhe Zhou, Feiran Zhu, Chenglei Dai, Zhen Xiao, Xuanping Li, Jingwei Zhuo

    cs.IR · cs.AI

    Search quality evaluation provides essential supervision and diagnostic signals for the development and iteration of industrial search systems. Although large language models (LLMs) offer a scalable alternative to manual assessment, reliable automatic evaluation remains challenging: users experience search results at the page level, while the applicable evaluation criteria are multi-dimensional and continuously evolving. Packing all...

    arxiv.org/abs/2609.29803 · PDF

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