Temporal Leakage in Search-Engine Date-Filtered Web Retrieval: A Case Study from Retrospective Forecasting

2026-01-31

AI Summary

This study identifies and systematically categorizes pervasive temporal leakage mechanisms in web-based retrieval that critically undermine the validity of retroactive forecasting evaluations. Focusing on widely used search engine date filters (e.g., Google’s “before:” operator), the authors demonstrate that such methods introduce severe temporal contamination, leading to inflated performance estimates. Through a combination of manual auditing and automated analysis using a large language model (gpt-oss-120b), they find that 71% of retrieved documents exhibit some form of temporal leakage, with 41% directly revealing the answer. Consequently, the inclusion of these leaked documents artificially reduces the Brier score from 0.242 to 0.108, substantially distorting assessment reliability and exposing fundamental flaws in date-filtered retroactive evaluation protocols.

Technology Category

• Search and Optimization: Evaluation and Analysis
• Data Mining & Knowledge Management: Knowledge Acquisition from the Web
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

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: auditing Google Search with a before: filter, 71% of questions return at least one page containing strong post-cutoff leakage, and for 41%, at least one page directly reveals the answer. Using a large language model (LLM), gpt-oss-120b, to forecast with these leaky documents, we demonstrate an inflated prediction accuracy (Brier score 0.108 vs. 0.242 with leak-free documents). We characterize common leakage mechanisms, including updated articles, related-content modules, unreliable metadata/timestamps, and absence-based signals, and argue that date-restricted search is insufficient for temporal evaluation. We recommend stronger retrieval safeguards or evaluation on frozen, time-stamped web snapshots to ensure credible retrospective forecasting.

Problem

• Temporal Leakage
• Date-Filtered Retrieval
• Retrospective Forecasting
• Search Engine
• Evaluation Bias

Innovation

• temporal leakage
• date-filtered retrieval
• retrospective forecasting
• search engine auditing
• LLM evaluation