Research on frontier AI safety

Our research examines loss of control, deception and harmful manipulation in frontier AI models, as well as safety methods for models and agents.

Our evaluation approach

In-house closed-source evaluation methods

We develop closed-source tests to maintain evaluation quality and independence. This also helps address reliability challenges when models recognize they are being evaluated.

Findings and limitations

The evaluation process matters as much as the results. We examine what happens during each evaluation and acknowledge its limitations.

Independence and responsible disclosure

Each report identifies whether the evaluation was commissioned or independently initiated. Evaluated organizations may verify factual details but cannot change the conclusions.

Earlier research

Papers and benchmarks

Upcoming research