The LongEval lab focuses on the evaluation of information retrieval systems over time. Two datasets are provided that capture evolving search scenarios with changing documents, queries, and relevance assessments. Systems are assessed from a temporal perspective-that is, evaluating retrieval effectiveness as the data they operate on changes. In its third edition, LongEval featured two retrieval tasks: one in the area of ad-hoc web retrieval, and another focusing on scientific article retrieval. We present an overview of this year's tasks and datasets, as well as the participating systems. A total of 19 teams submitted their approaches, which we evaluated using nDCG and a variety of measures that quantify changes in retrieval effectiveness over time.
@article{arxiv.2509.17469,
title = {LongEval at CLEF 2025: Longitudinal Evaluation of IR Systems on Web and Scientific Data},
author = {Matteo Cancellieri and Alaa El-Ebshihy and Tobias Fink and Maik Fröbe and Petra Galuščáková and Gabriela Gonzalez-Saez and Lorraine Goeuriot and David Iommi and Jüri Keller and Petr Knoth and Philippe Mulhem and Florina Piroi and David Pride and Philipp Schaer},
journal= {arXiv preprint arXiv:2509.17469},
year = {2025}
}