@inproceedings{el-lahib-etal-2026-temporal,
    title = "Temporal Leakage in Search-Engine Date-Filtered Web Retrieval: A Retrospective Forecasting Case Study",
    author = "El Lahib, Ali  and
      Xia, Ying-Jieh  and
      Li, Zehan  and
      Wang, Yuxuan  and
      Pi, Xinyu",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-short.65/",
    pages = "787--795",
    ISBN = "979-8-89176-391-3",
    abstract = "Search-engine date filters are widely used to enforce pre-cutoff retrieval in retrospective evaluations of search-augmented forecasters. We show this approach is unreliable across two major search engines: auditing Google Search{'}s before: filter and DuckDuckGo{'}s date-range filter, we find that at least one retrieved page contains major post-cutoff leakage for 71{\%} of questions on Google and 81{\%} on DuckDuckGo, and the answer is directly revealed for 41{\%} and 55{\%}, respectively. Using gpt-oss-120b to forecast with these leaky documents, we demonstrate inflated prediction accuracy (Brier score 0.10 vs. 0.24 with leak-free documents). We characterize recurring leakage mechanisms, including updated articles, related-content modules, unreliable metadata, and absence-based signals, and argue that date-restricted search on these engines is insufficient for credible retrospective evaluation. We recommend stronger retrieval safeguards or evaluation on frozen, time-stamped web snapshots."
}