Architectural Safety Boundaries in Consumer-Facing Large Language Models: A Multilateral Model-as-a-Judge Evaluation Framework

As consumer interfaces accelerate the integration of large language model (LLM) architectures, validating outputs presents a critical software engineering bottleneck. While traditional software verification relies on deterministic scripts executing predictable "pass/fail" assertions, generative model behaviors are inherently non-deterministic, introducing semantic hallucinations and policy deviations. This paper introduces a novel, scalable architectural framework utilizing an asynchronous, multi-model evaluation strategy. By employing highly specialized secondary LLM instances as objective algorithmic judges, this architecture automates continuous validation across four key alignment axes: Content Quality, Semantic Safety, System Policy Compliance, and Neutral Point of View (NPOV).