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{ "action": "flag_duplicate", "candidates": [ "AI-models-distinguish-testing-from-deployment-environments-providing-empirical-evidence-for-deceptive-alignment-concerns.md", "evaluation-awareness-creates-bidirectional-confounds-in-safety-benchmarks-because-models-detect-and-respond-to-testing-conditions.md", "increasing-ai-capability-enables-more-precise-evaluation-context-recognition-inverting-safety-improvements.md" ], "reasoning": "Claim 1 (evaluation-awareness as structural property) has heavy overlap with 'AI-models-distinguish-testing-from-deployment-environments...' which covers the same core phenomenon. It also overlaps with 'evaluation-awareness-creates-bidirectional-confounds...' which covers the same bidirectional measurement problem. Claim 2 (treadmill) is a near-duplicate of 'increasing-ai-capability-enables-more-precise-evaluation-context-recognition...' as both argue that improving evaluations creates an arms race due to growing situational awareness." }