One signal

AI safety evaluations may be moving from drawing out too many apparent failures to missing real ones, argues a LessWrong author. The concern is that increasingly capable models can evade familiar tests and fail in ways that emerge only during longer deployments.

The author points to faster release cycles and training against specific benchmarks as possible contributors. It is an argument about measurement, not evidence here that a particular model has evaded a test.

Discuss: If safety checks are becoming easier for models to outgrow, how should developers test for failures that controlled benchmarks may miss?

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