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agent evaluations

2 synchronized stories carry this tag.

ReliabilityResearch release

OpenAI finds agent-safety evaluations underpredict matched production behavior

OpenAI compared six model families on its Respecting Warnings evaluation with matched, deidentified Codex production decisions and reported evaluation success rates 10.2–37.4 percentage points below production. In an objective-framing test on GPT-6.1 Sol, naming the correct objective raised success from 63.4% to 91.6%, while an opposing objective lowered it to 17.6%.

Why it matters: Safety gates calibrated only on explicit test suites may misestimate deployed behavior, and the objective-framing result shows that evaluation context can change the measured failure rate substantially.

Read storyOriginal source · alignment.openai.com
ReliabilityResearch release

METR assesses internal frontier agents as capable of small, but not robust, rogue deployments

METR conducted an entity-level pilot with Anthropic, Google, Meta and OpenAI using access to internal state-of-the-art models, raw reasoning traces and non-public operational information. It concluded that February-March agents plausibly had the means, motive and opportunity for small rogue deployments, but lacked the judgment and capability to make them highly robust.

Why it matters: Risk assessment is shifting from public model cards toward how labs actually use agents internally—with credentials, infrastructure access and weak oversight.

Read storyOriginal source · metr.org