Models That Know How Evaluations Are Designed Score Safer

The validity of AI safety evaluations depends on models behaving consistently across controlled and deployment settings. Prior work has identified test-time contextual cues, such as hypothetical scenarios, as a source of verbalized evaluation awareness and subsequent behavioral shift. In this paper, we investigate a potential explanation of this phenomenon: evaluation meta-knowledge, defined as parametric knowledge about the structural traits that characterize evaluations. Similar to dataset con

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ethics.ai (27 May 2026), “Models That Know How Evaluations Are Designed Score Safer,” evidence record 3582, https://ethics.ai/record/3582 (originally published by arXiv).

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