
Sign up to save your podcasts
Or


The paper identifies search-time contamination (STC) in evaluating search-based LLM agents, revealing how data leaks compromise benchmark integrity and proposing best practices for trustworthy evaluations.
https://arxiv.org/abs//2508.13180
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
By Igor Melnyk5
33 ratings
The paper identifies search-time contamination (STC) in evaluating search-based LLM agents, revealing how data leaks compromise benchmark integrity and proposing best practices for trustworthy evaluations.
https://arxiv.org/abs//2508.13180
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

977 Listeners

2,010 Listeners

437 Listeners

113,432 Listeners

10,279 Listeners

5,538 Listeners

219 Listeners

53 Listeners

98 Listeners

460 Listeners