Multi-Model AI Audit Finds Perfect Number Accuracy But Exposes Interpretation Gaps

A researcher running a personal AI agent measurement lab designed a structured 'fan-out' experiment in which five different AI models — including models from xAI, Google, and two open-weight families — each independently analyzed the same compact data digest using an identical prompt. A deterministic script verified every numeric claim across all five models against source data, finding that all 68 numeric citations passed recomputation with a maximum deviation of 1.68%, attributed to rounding rather than error. However, the researcher cautioned that perfect citation accuracy does not equal sound interpretation, as a model can reproduce numbers correctly while still drawing flawed conclusions. The more significant finding was that four of the five models independently flagged a zero-value data anomaly that the researcher's own sealed first-draft interpretation had overlooked. The experiment highlighted that multi-model fan-out is most valuable not for confirming consensus, but for catching blind spots that any single analyst — human or AI — might miss.