The simple mental picture

A simple mental picture we have for an AI capability benchmark is to think of it as a sensor with a certain sensitivity within a certain range of capabilities. The sensitivity of a benchmark, i.e. it's ability to distinguish the capability of different models, is given by a curve like this:

The curve starts high (low sensitivity, high uncertainty), since for models with low capability all the tasks in the benchmark are too hard, and the benchmark can't distinguish between low and very low capability. Similarly all the tasks are too easy for a very capable model, and we lose the ability to differentiate again. In between is the range of capabilities the benchmark is sensitive to, and the sensitivity curve tells you how easy it is to distinguish small capability differences between models at different overall capability levels.

A good benchmark is very sensitive over a long range of capabilities, but there is a tradeoff. Say you want to make a benchmark with 1000 questions. You could make the questions all have roughly the same difficulty. That would make you very sensitive to capabilities close to that difficulty, but you would only [...]

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Outline:

(00:09) The simple mental picture

(02:06) Epoch Capability Index (ECI) as a toy model

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First published:
May 8th, 2026

Source:
https://www.lesswrong.com/posts/JzfcJMgfkhfRhwg4C/a-benchmark-is-a-sensor

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Narrated by TYPE III AUDIO.

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Images from the article:

Graph showing multiple colored parabolic curves plotted on coordinate axes.

Multiple sigmoid growth curves with scattered data points color-coded by category.

Graph showing U-shaped curve: Sensitivity decreases then increases with Capability.

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