cs.IR · 2026-08-12 · No. 82

Information Retrieval, 2026-08-12.

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

01 — The papers

5 entries
  1. 01

    TimeRoute: Time-Aware Modality Routing and Diffusion for Multi-Modal Recommendation

    Pengyu Zhang, Yangqin Jiang, Klim Zaporojets, Congfeng Cao, Paul Groth

    cs.IR · cs.AI

    Multi-modal recommenders fuse collaborative signals with item modalities such as text, images, and audio, but the usefulness of each drifts over time and at different rates. For example, chocolate purchases typically guided by textual ingredient cues can shift toward visual packaging and ambient audio around Valentine's Day. This modality time-scale mismatch gives rise to two coupled challenges: (1) users require different modality...

    arxiv.org/abs/2608.10983 · PDF

  2. 02

    ENTLORE: A Graph-Grounded Benchmark for Latent Organizational Reasoning in Enterprise Question Answering

    Akrin Zheng, Alexander Wu, Alaia Liu

    cs.IR · cs.AI · cs.CL

    Enterprise question answering is framed as retrieving internal documents and generating grounded answers. Routine enterprise records, however, are work by-products in which required organizational relations remain implicit across heterogeneous sources. Existing benchmarks provide realistic multi-source evidence, but often materialize a predefined answer path and therefore test the composition of stated facts rather than recovery of a target...

    arxiv.org/abs/2608.10679 · PDF

  3. 03

    When Do Anchor-Based Pointwise LLM Rerankers Help? Retriever Quality, Statistical Scope, and Anchor Design

    Utshab Kumar Ghosh, Shubham Chatterjee

    cs.IR · cs.LG

    Anchor-based pointwise LLM reranking scores each candidate against a shared reference passage to recover cross-document context at pointwise cost. We study when this actually helps, using GCCP/PAGC as a representative method. Our study is reproduction-first. We use reproduction as a starting point for a controlled component-level stress test of anchor-based pointwise reranking. Our initial reimplementation, based only on the paper text,...

    arxiv.org/abs/2608.10528 · PDF

  4. 04

    Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging

    Linh Dieu Le, Tong Chen, Shazia Sadiq, Hongzhi Yin, Ming Jin, Junliang Yu

    cs.IR · cs.AI

    Large language model-based recommender systems are increasingly adopting slow-thinking models that generate step-by-step reasoning before making predictions, often achieving higher accuracy than fast-thinking models that predict directly. However, their reasoning traces are often unnecessarily verbose, increasing inference costs without commensurate accuracy gains. Existing training-based approaches to reasoning compression often incur...

    arxiv.org/abs/2608.10447 · PDF

  5. 05

    Post-Calibration Reliability Reranking of Relevance Decisions via Label-wise Monotone Projection

    Inwoo Tae, Yongjae Lee

    cs.IR · cs.LG

    Web search, product search, and question-answering retrieval systems often assign a relevance label and confidence score to each query-candidate pair. The relevance label describes how well a page, product, or passage matches the query, while the confidence often guides downstream use or fallback decisions. Post-hoc calibration is therefore needed because misaligned confidence can make systems over-trust wrong predictions or unnecessarily...

    arxiv.org/abs/2608.10406 · PDF

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