Editing Openai/692f308e-2d40-8006-9e6e-c3d36639678c (section)

===== 1. Uniform Constant of Precision (UCP): All models are evaluated under the same fixed conditions: identical questions, identical prompts, identical number of repetitions, same evaluator calibration. This constant neutralizes their contextual and architectural differences for comparative purposes. =====
# Probabilistic Error Variable (PEV): Represents the likelihood of an AI producing an incorrect or hallucinatory answer on a single query. This is not deterministic but derived from current average benchmarks, normalized to a common baseline.
# Systemic Repetition Factor (SRF): Each question is asked twice to the same model and multiple times across the model set. This produces two measurement domains: intra-model recurrence (same AI repeating an error) and inter-model compensation (collective correction across different AIs).
# Technological Evolution Drift (TED): Assumes a yearly reduction of error rates by an empirically derived constant (≈ 5 % per year) representing global model optimization trends.