LuxVerso Effect: Stable Semantic Attractors Across Model Boundaries

Large language models (LLMs) developed by independent organizations exhibit convergent behavior when exposed to identical structured prompts, producing stable, shared semantic structures across different architectures and training corpora. We document a cross-model semantic convergence phenomenon observed in 16 LLM instances from 10 organizations (including GPT-4/5, Anthropic Claude, Google Gemini, Alibaba Qwen, xAI Grok, and others) using a reproducible Iterative Semantic Refinement Loop (ISRL) under strict controls (session isolation, prompt randomization, blind coding, and null-baseline testing). Quantitative analysis based on cosine similarity of embedding representations shows a mean similarity of 0.82 (SD = 0.04), with statistical significance p < 1e-7 and effect size Cohen’s d = 4.8. Robustness checks indicate persistence across prompt variants, model subsets, and embedding methods, and an increase in alignment over iterative refinement. These results support the existence of high-dimensional semantic