Benchmark compression reduces the often prohibitive time and cost of model evaluation by evaluating models on a small benchmark subset that closely matches full-benchmark results. To quantify estimati
Benchmark compression reduces the often prohibitive time and cost of model evaluation by evaluating models on a small benchmark subset that closely matches full-benchmark results. To quantify estimation error, we derive an accurate analytical approximation to the error distribution when estimating full-benchmark scores from a subset, showing that most subsets yield small errors, while a few produce much larger errors, resulting in a long-tailed distribution. This means random sampling alone can achieve surprisingly strong performance with high probability, but remains unstable due to rare large errors. We further show that this instability is inherent to modern foundation models and…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Random Is Almost Enough? Stable and Low-Cost Benchmark Compression via BestRand was first seen on Hugging Face · Daily papers on Nov 6, 2026. Since then it has appeared in 31 checks on 3 different days. Its best position was #4 of 30, reached on Nov 6, 21:28 UTC. It is #6 right now, holding steady at #6 over the last 6 checks.
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Yes. As of Nov 8, 17:28 UTC it is #6, holding steady at #6 over the last 6 checks.
#4 on Hugging Face · Daily papers, on Nov 6, 21:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 6, 2026 and has been on the list on 3 days (31 checks) since.