cs.IR · 2026-06-10 · No. 19

Information Retrieval, 2026-06-10.

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

    Generative Archetype-Grounded Item Representations for Sequential Recommendation

    Yifan Li, Jiahong Liu, Xinni Zhang, Hao Chen, Yankai Chen, Wenhao Yu, Jianting Chen, Irwin King

    cs.IR · cs.CL · cs.LG

    Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a critical bottleneck. While pre-trained large language models (LLMs) can provide rich semantic representations, existing approaches only rely on static encoding of fixed attributes, overlooking the crucial role of target audiences in defining item identity. Moreover,...

    arxiv.org/abs/2606.11023 · PDF

  2. 02

    Effective Reinforcement Learning for Agentic Search by Recycling Zero-Variance Queries During Training

    João Coelho, João Magalhães, Bruno Martins, Chenyan Xiong

    cs.IR · cs.AI

    The use of GRPO-style algorithms has become the standard strategy for training LLM search agents under outcome-only rewards. With these algorithms, a query contributes to parameter updates only when its rollout group mixes successes and failures; all-correct (too-easy) and all-incorrect (too-hard) groups are zero-variance and waste rollout cost. Existing approaches treat zero-variance as a static property and either discard or pre-filter such...

    arxiv.org/abs/2606.10709 · PDF

  3. 03

    STORM: Stepwise Token Optimization with Reward-Guided Beam Search

    Arthur Satouf, Giulio D'Erasmo, Yuxuan Zong, Habiboulaye Amadou Boubacar, Pablo Piantanida, Benjamin Piwowarski

    cs.IR · cs.AI

    Modern retrieval increasingly relies on dense and learned-sparse neural models that are effective but require encoding the entire corpus into a specialized index, rebuilt whenever the model changes. Lexical retrievers like BM25 stay efficient and transparent on a standard inverted index that need not change as models evolve, but suffer from vocabulary mismatch. LLM query rewriting can help, yet prompted rewriters emit well-formed but...

    arxiv.org/abs/2606.10621 · PDF

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