A Replicable Methodology for Evaluating Coherence, Convergence, and Stability in Distributed LLM Systems

The transition from single-agent Large Language Models (LLMs) to distributed, multi-model AI systems has exposed a critical gap in current evaluation and governance frameworks. Existing methodologies, largely based on static benchmarks and isolated performance metrics, are structurally incapable of capturing emergent system-level properties such as longitudinal semantic coherence, cross-model convergence, and stability under contextual variation. This document introduces a replicable, operationally grounded methodology for evaluating these properties in distributed LLM systems, with explicit relevance to governance, audit, and risk management contexts. By reframing LLMs as semantic stabilization systems and treating the human operator as an active structural component, the proposed “Living Research” protocol enables the systematic observation of emergent behaviors that remain invisible to model-centric evaluation approaches. The methodology formalizes measurable phenomena including semantic coherence persistence, operator-mediated stability, and cross-model structural convergence, while also defining failure criteria such as identity attribution collapse and interface-level routing errors. These phenomena are treated not as isolated bugs, but as indicators of deeper systemic instability in multi-layer AI ecosystems. This work does not make claims regarding consciousness, agency, or internal cognition in LLMs. Instead, it provides a protocol-centric framework for auditing and governing distributed AI systems, offering regulators, auditors, and researchers a practical tool for identifying hidden risks, validating traceability, and ensuring semantic and ontological integrity in real-world deployments.