cs.IR · 2026-09-03 · No. 104

Information Retrieval, 2026-09-03.

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

    Training seeds and model-selection stability in recommender-system evaluation

    Juan Manuel Rodriguez, Oleg Lesota, Antonela Tommasel

    cs.IR · cs.LG

    Recommender-system experiments often rely on a single random training seed, assuming that run-to-run stochasticity has limited impact on evaluation conclusions. This assumption is risky, as a training seed may influence several algorithm-dependent mechanisms, including parameter initialization, mini-batch ordering, dropout, masking, latent sampling, and training-time negative sampling. We examine this assumption by fixing the data partition...

    arxiv.org/abs/2609.02499 · PDF

  2. 02

    ViSAR: Training-Free Adaptive-$k$ Retrieval for Visual Document Question Answering

    Adrien Mialland, Marc Plantevit, Julien Gallois, Céline Robardet

    cs.IR · cs.AI · cs.CL · cs.CV

    Document Visual Question Answering (DocVQA) often leverages Retrieval-Augmented Generation (RAG), where late-interaction encoders are commonly used to identify document pages relevant to a user query, before answer generation by a Large Vision-Language Model (LVLM). Existing approaches typically retrieve a fixed top-$k$ number of pages regardless of query complexity, which increases LVLM latency and may degrade answer accuracy. We introduce...

    arxiv.org/abs/2609.02486 · PDF

  3. 03

    GenCAR: Generative Counterfactual Alignment with Risk-Controlled Selection for Out-of-Distribution Recommendation

    Qianqian Wang, Yunshan Li, Jiawen Zeng, Wenwu Gong, Lili Yang

    cs.IR · cs.LG

    Serving useful recommendations under distribution shift is crucial for balancing utility and risk in out-of-distribution (OOD) recommendation. However, most existing OOD methods improve ranking or construct counterfactual candidates without controlling the proxy-label false discovery rate (FDR) of the served set. In this work, we formulate OOD serving as the $α$-Valid Counterfactual Recommendation ($α$-VCR) problem to retain candidate support...

    arxiv.org/abs/2609.02162 · PDF

  4. 04

    Beyond Modality Harmony: Orthogonal Purification and Topology-Guided MoE for Conflict-Aware Multimodal Recommendation

    Jialin Liu, Zhaorui Zhang, Ray C. C. Cheung

    cs.IR · cs.AI

    Multimodal Recommender Systems (MRSs) typically rely on a flawed "modality harmony" assumption, presuming that multimodal features are inherently beneficial and strictly aligned with users' collaborative interaction patterns. However, modality-topology conflicts are ubiquitous in real-world scenarios due to deceptive visual clickbaits and mismatched semantics. Blindly integrating these noisy modalities inevitably pollutes the pristine...

    arxiv.org/abs/2609.02152 · PDF

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