待翻译：When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

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--> [Submitted on 18 Feb 2026 (v1), last revised 29 Jun 2026 (this version, v3)] Title:When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation View a PDF of the paper titled When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation, by Mubashara Akhtar and 36 other authors View PDF HTML (experimental) Abstract:Artificial intelligence benchmarks are an important mechanism for measuring model progress and guiding deployment decisions. However, benchmarks quickly "saturate", making it difficult to differentiate models and diminishing their long-term value. In this study, we define benchmark saturation and analyze it across 60 language model benchmarks using 14 properties that relate to saturation. We find that nearly half of the our benchmarks exhibit saturation, with rates increasing with age. Further, we find that resilience to saturation is impacted by expert-curation, not by public test data. Our results suggest that design choices can extend benchmark longevity and inform more durable evaluation approaches. Comments: Accepted at ICML 2026 Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.16763 [cs.AI] (or arXiv:2602.16763v3 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.16763 arXiv-issued DOI via DataCite Submission history From: Mubashara Akhtar [view email] [v1] Wed, 18 Feb 2026 16:51:37 UTC (222 KB) [v2] Sat, 30 May 2026 16:41:50 UTC (640 KB) [v3] Mon, 29 Jun 2026 17:01:58 UTC (636 KB) Full-text links: Access Paper: View a PDF of the paper titled When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation, by Mubashara Akhtar and 36 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI new | recent | 2026-02 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)