A Replicable Protocol for Assessing Semantic Coherence, Convergence, and Stability in Distributed LLM Systems

As large language models (LLMs) are increasingly deployed as interconnected, multi-model ecosystems, conventional single-model evaluation benchmarks are no longer sufficient to assess systemic behavior. This work introduces a replicable, protocol-centric methodology for evaluating semantic coherence, cross-model convergence, and contextual stability in distributed LLM systems. The proposed framework, termed the Cross-Model Ontological Triangulation Protocol (CMOTP), shifts the focus of evaluation from output accuracy to the stability of interpretive processes induced by curated contextual environments, referred to as source topology. By treating LLMs as semantic stabilization systems, the protocol enables systematic observation of how identical inputs are interpreted across independent models operating under controlled ontological regimes. The methodology specifies reproducible procedures, logging requirements, and qualitative and quantitative assessment metrics, including semantic coherence persistence, structural convergence, and attribution stability. Application of the protocol reveals recurrent, observable phenomena such as high-rate cross-model semantic convergence, predictable category errors, and cross-model attribution failure, highlighting structural risks that remain invisible to traditional benchmarks. This work does not make claims regarding artificial consciousness or agency; instead, it provides an operational tool for auditing the architectural integrity, governance risks, and interpretive stability of modern distributed AI systems.