New: RL environments scored against human competitors →

Verifiers

Verifiers need verification, too.

Even the measure of intelligence must prove itself.

Why evaluation needs
to be more reliable

59.4%

Of 138 difficult tasks reviewed by OpenAI, 59.4% had substantial test or task-description flaws. On 23 February 2026, OpenAI announced it would stop reporting SWE-bench Verified results. ↗

296

METR had four maintainers of scikit-learn, Sphinx and pytest review 296 AI-generated pull requests that had already passed the automated grader. Published 10 March 2026: maintainer merge rates ran 24.2 percentage points below the grader score. ↗

30%

On 8 July 2026, OpenAI audited SWE-Bench Pro (published by Scale AI), the benchmark it had recommended five months earlier as the replacement for SWE-bench Verified, and estimated that about 30% of its 731 public tasks are broken. An automated pipeline flagged 200; a parallel human annotation campaign flagged 249. OpenAI retracted the recommendation. ↗

Match the final state

Compare the final database with an annotated target state. An agent may reach that state through incorrect actions and still pass; each task also needs its own annotation. The approach below closes this gap.

Check process invariants

Encode business rules that must hold throughout the workflow. Tasks in a domain share these rules, and each step must cite its evidence.

How we keep scores honest

A score is only as good as the grader behind it.

01

Attacked before it ships

Every grader faces degenerate strategies before release. 400 cheating attempts on the Puzzle Benchmark; none got through.

Request the audit