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Open-world evaluations for measuring frontier AI capabilities

Sayash Kapoor · 16 Apr 2026

Read the piece on normaltech.ai ↗

3 korrents from this piece ↓

Sayash Kapoor did not write this page.

Every claim below was made in this piece, quoted word for word and numbered in the order the piece makes them, so you can read it there rather than take our word for it. The sentence above each quote is our reading of the claim, not their wording. Each quote was checked against a stored copy of the page at build time; where the two differ, the quote is the fact.

  1. Our reading

    App store operators should prepare for and police spam submissions, since they might soon see thousands of applications submitted autonomously by agents.

    Their wordsApp store operators should prepare for and police spam submissions, as they might soon see thousands of applications submitted autonomously using agents.

  2. Our reading

    Anything precise enough to benchmark is also precise enough to optimize for, and modern RL training increasingly resembles the shape of the benchmarks themselves.

    Their wordsBut whatever is precise enough to benchmark is also precise enough to optimize for, and modern RL training runs increasingly resemble the shape of the benchmarks themselves.

  3. Our reading

    Average performance on a benchmark suite is very different from the upper bound of what an AI agent can do.

    Their wordsAverage performance is very different from upper-bound capability elicitation. Trying to run dozens (or 100s) of tasks in a benchmark suite is only necessary to measure average performance.

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