Semantic Alignment Acceleration in Large Language Models: A Replicable Protocol and Empirical Observations

Conventional evaluation methods for Large Language Models (LLMs), centered on static benchmarks and supervised tasks, are insufficient to capture emergent, relational properties such as cross-model semantic convergence and longitudinal coherence. This paper introduces the Semantic Alignment Acceleration Protocol (SAAP), a replicable, non-parametric interaction protocol designed to induce and observe these phenomena across heterogeneous LLM systems. Applied across multiple independent model instances, the protocol produced high levels of cross-model semantic similarity (mean cosine similarity = 0.82, p < 1e-7), alongside recurrent qualitative patterns indicative of shared semantic structuring under controlled interaction conditions. Rather than making claims about internal cognition or agency, this work formalizes an operational methodology for studying emergent semantic alignment as an interaction-level phenomenon. The SAAP provides a practical framework for exploratory research into relational alignment, cross-model coherence, and protocol-centric AI governance, and is released to support independent replication and falsification. This work is conceptually related to, but methodologically distinct from, the Cross-Model Ontological Triangulation Protocol (CMOTP), which focuses on protocol-centric evaluation and governance.