cs.IR · 2026-06-19 · No. 28

Information Retrieval, 2026-06-19.

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

    Structuring and Tokenizing Distributed User Interest Context for Generative Recommendation

    Ruizhong Qiu, Yinglong Xia, Dongqi Fu, Hanqing Zeng, Ren Chen, Xiangjun Fan, Hong Li, Hong Yan, Hanghang Tong

    cs.IR · cs.AI

    Generative recommendation is an emerging paradigm that has shown promise in industrial recommendation systems, aiming to predict users' next interactions from their historical behaviors. At the core of generative recommendation lies item tokenization, which bridges item semantics and recommendation models. However, existing methods often struggle to effectively organize and inject complex user-behavioral and item-semantic contexts into...

    arxiv.org/abs/2606.20554 · PDF

  2. 02

    ELVA: Exploring Ranking-Driven Universal Multimodal Retrieval

    Yuhan Liu, Pei Fu, Hang Li, Yukun Qi, Chao Jiang, Jingwen Fu, Zhen Liu, Bin Qin, Zhenbo Luo, Jian Luan, Jingmin Xin

    cs.IR · cs.AI

    Leveraging Multimodal Large Language Models (MLLMs) via contrastive learning has become a mainstream paradigm for improving the performance of Universal Multimodal Retrieval (UMR). However, previous works have ignored the grain blindness when adapting the contrastive paradigm into retrieval tasks. Grain blindness refers to the tendency of the model to overlook grain-level information contained in the query, which is crucial for effectively...

    arxiv.org/abs/2606.20280 · PDF

  3. 03

    ScholarQuest: A Taxonomy-Guided Benchmark for Agentic Academic Paper Search in Open Literature Environments

    Tingyue Pan, Mingyue Cheng, Daoyu Wang, Yitong Zhou, Jie Ouyang, Qi Liu, Enhong Chen

    cs.IR · cs.AI

    Academic paper search is a core step in scientific research, and LLM-based search agents are emerging as a promising paradigm for iterative, intent-driven literature exploration. However, existing benchmarks are insufficient for systematically evaluating agentic academic search under realistic open literature environments. We propose ScholarQuest, a large-scale, taxonomy-guided benchmark for agentic academic paper search. ScholarQuest is...

    arxiv.org/abs/2606.20235 · PDF

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