Alemetria: A Research Protocol for Auditing Interpretational Behavior of Large Language Models via Multi-Model Triangulation / Алеметрия: исследовательский протокол аудита интерпретационного поведения больших языковых моделей

This preprint introduces Alemetria, a research protocol for auditing interpretational behavior in large language models (LLMs). The method shifts evaluation away from direct comparison of outputs and toward validation of interpretational comparability. It introduces a structured validation pipeline (B0-B4), a rule of calculation prohibition, and a distinction between valid and invalid regimes of inference. Alemetria enables the analysis of substantive agreement, semantic divergence, false consensus, interpretational suppression, and instability under prompt variation. The protocol is tested on a pilot corpus of 10 sociological stimuli using GPT, Llama, and YandexGPT. Earlier versions of the method were published as MMST/TVA preprints and focused on divergence metrics (ID, SD) and boundary conditions. The present version integrates these earlier developments into a single operational protocol. Related earlier preprints: 10.5281/zenodo.1881968910.5281/zenodo.1879943110.5281/zenodo.18865186