cs.IR · 2026-06-25 · No. 34

Information Retrieval, 2026-06-25.

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

01 — The papers

4 entries
  1. 01

    AutoRelAnnotator: Calibrated Model Cascades for Cost-Efficient Relevance Evaluation in Sponsored Search

    Md Omar Faruk Rokon, Shasvat Desai, Hong Yao, Kuang-chih Lee

    cs.IR · cs.AI

    How can we generate high-quality relevance annotations at scale without the cost and delays of human labeling? Relevance annotations are the backbone of search ranking systems which is needed for training data preparation, NDCG evaluation, and root cause analysis. However, human annotation is slow and off-the-shelf LLMs suffer from accuracy on domain-specific tasks. We propose a calibrated model cascade, a systematic approach for...

    arxiv.org/abs/2606.25871 · PDF

  2. 02

    TheoremGraph: Bridging Formal and Informal Mathematics

    Simon Kurgan, Evan Wang, Eric Leonen, Sophie Szeto, Luke Alexander, Artemii Remizov, Jarod Alper, Giovanni...

    cs.IR · cs.AI · math.HO

    Mathematical knowledge is organized around statements and their dependencies, but this structure is exposed unevenly: informal papers cite mostly at the document level, while formal libraries record fine-grained dependencies over a much smaller body of mathematics. We introduce TheoremGraph, a unified statement-level dependency graph spanning both informal and formal mathematics. On the informal side, we parse 11.7M theorem-like environments...

    arxiv.org/abs/2606.25363 · PDF

  3. 03

    Extreme Meta-Classification for Large-Scale Zero-Shot Retrieval

    Sachin Yadav, Deepak Saini, Anirudh Buvanesh, Bhawna Paliwal, Kunal Dahiya, Siddarth Asokan, Yashoteja Prabhu, Jian...

    cs.IR · cs.LG

    We develop accurate and efficient solutions for large-scale retrieval tasks where novel (zero-shot) items can arrive continuously at a rapid pace. Conventional Siamese-style approaches embed both queries and items through a small encoder and retrieve the items lying closest to the query. While this approach allows efficient addition and retrieval of novel items, the small encoder lacks sufficient capacity for the necessary world knowledge in...

    arxiv.org/abs/2606.25237 · PDF

  4. 04

    TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems

    Qingyun Liu, Bo Yan, Yang Liu, Yuji Roh, Ekansh Sharma, Likang Yin, Emma Olowo, Min-hsuan Tsai, Yuxuan Li, Diego...

    cs.IR · cs.AI · cs.LG

    User modeling in industrial recommender systems typically produces dense embeddings, which suffer from representational constraints inherent to fixed-dimensional vectors. An emerging alternative for discrete user representation -- using LLMs to generate text-based user tokens -- captures topical co-occurrences rather than deep sequential behavior dynamics and produces outputs that are difficult to ground to item attributes. Meanwhile,...

    arxiv.org/abs/2606.25147 · PDF

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