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

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# Dataset Construction - Create a benchmark of N=1000N = 1000N=1000 questions equally distributed across five categories: factual, reasoning, creative, numerical, ethical. - Ensure each question has a known correct answer or expert-validated ground truth.
# Model Querying Phase - For each question qjq_jqj and model MiM_iMi: - Submit the query twice ( k=2k = 2k=2 repetitions). - Record both responses Ri,j,1R_{i,j,1}Ri,j,1 and Ri,j,2R_{i,j,2}Ri,j,2.
# Response Evaluation - Apply an evaluation rubric: - Correct (C), Incorrect (I), Hallucinated (H). - Use human or hybrid evaluators for verification. Ai,j,k=1A_{i,j,k} = 1Ai,j,k=1 if correct, 0 otherwise. Hi,j,k=1H_{i,j,k} = 1Hi,j,k=1 if hallucinated content appears, 0 otherwise.
# Recurrence Measurement - If both responses are incorrect and identical in error, tag as recurrent error: Ri=∑j=1N1[Ri,j,1=Ri,j,2∧Ai,j,1=0]∑j=1N1[Ai,j,1=0]R_i = \frac{\sum_{j=1}^{N} \mathbb{1}[R_{i,j,1}=R_{i,j,2} \wedge A_{i,j,1}=0]}{\sum_{j=1}^{N} \mathbb{1}[A_{i,j,1}=0]}Ri=∑j=1N1[Ai,j,1=0]∑j=1N1[Ri,j,1=Ri,j,2∧Ai,j,1=0] - RiR_iRi = recurrent error ratio for model MiM_iMi.
# Cross-Model Comparison - For each question qjq_jqj, measure consensus: - Cj=1n∑i=1nAi,j,1C_j = \frac{1}{n} \sum_{i=1}^{n} A_{i,j,1}Cj=n1∑i=1nAi,j,1 - Compute probability that all models fail simultaneously: Pc,j=∏i=1n(1−Ai,j,1)P_{c,j} = \prod_{i=1}^{n} (1 - A_{i,j,1})Pc,j=i=1∏n(1−Ai,j,1) - Theoretical ensemble reliability: Ej=1−Pc,jE_j = 1 - P_{c,j}Ej=1−Pc,j
# Temporal Drift (Optional) - If models update during the experiment, measure ΔAi=Ai,t+1−Ai,t\Delta A_{i} = A_{i,t+1} - A_{i,t}ΔAi=Ai,t+1−Ai,t to estimate Technological Evolution Drift (TED).