Recent progress in AI has led to rapid saturation of most capability benchmarks - MMLU, RE-Bench, etc. Even much more sophisticated benchmarks such as ARC-AGI or FrontierMath see incredibly fast improvement, and all that while severe under-elicitation is still very salient.

As has been pointed out by many, general capability involves more than simple tasks such as this, that have a long history in the field of ML and are therefore easily saturated. Claude Plays Pokemon is a good example of something somewhat novel in terms of measuring progress, and thereby benefited from being an actually good proxy of model capability.

Taking inspiration from examples such as this, we considered domains of general capacity that are even further decoupled from existing exhaustive generators. We introduce BenchBench, the first standardized benchmark designed specifically to measure an AI model's bench-pressing capability.

Why Bench Press?

Bench pressing uniquely combines fundamental components of [...]

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Outline:

(01:07) Why Bench Press?

(01:29) Benchmark Methodology

(02:33) Preliminary Results

(03:38) Future Directions

The original text contained 1 footnote which was omitted from this narration.

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