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This note investigates how reflexive contamination impacts the function of evaluation benchmarks in AI systems.

July 10, 2026Open Access

The Reflexive Contamination of Categorical Benchmarks: Use-induced erosion of evaluation infrastructure within capacity asymmetry

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This note investigates how reflexive contamination impacts the function of evaluation benchmarks in AI systems.
Analyzed mechanisms involved in benchmark erosion through empirical cases
Operationalized three proposed measurement protocols for evaluation
Examined the AI evaluation crisis using static and aggregate analysis
Identified the role of reflexive contamination in diminishing benchmark discriminative functions
Highlighted the implications of benchmark saturation and verification difficulties
Discussed documented weakening of established ranking signals like MMLU and HumanEval

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This note explores how reflexive contamination erodes evaluation benchmarks in AI, suggesting implications for systemic audits.

Ghjuvan Ortulanu (2026) studied this question.

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