Equation 14 · The Hardest Unsolved Problems in Open-Weight AI
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Every published leaderboard number implicitly assumes c 0 . The methods above show that assumption is not something the field can currently verify for a given open-weight release, only something it can occasionally catch violated in specific, published cases — which is a different and much weaker guarantee than a general bound on c .
Sources cited in the article section
- [13] Hugging Face Releases Open LLM Leaderboard 2: A Major Upgrade Featuring Tougher Benchmarks, Fairer Scoring, and Enhanced Community Collaboration ↗
- [12] It's Been a Wild Ride, Folks (End of the Open LLM Leaderboard) ↗
- [9] Detecting Pretraining Data from Large Language Models ↗
- [10] Rethinking Benchmark and Contamination for Language Models with Rephrased Samples ↗
- [11] On the Fragility of Benchmark Contamination Detection in Reasoning Models ↗
These citations give research context. Read each source to check which claims it supports.