Recent research from the Swiss Federal Institute of Technology (EPFL) challenges the widely discussed Platonic Representation Hypothesis, which posited that advanced artificial intelligence systems naturally converge toward a shared internal understanding of reality. While the original 2024 MIT proposal suggested that models trained on diverse data types would independently develop aligned worldview representations, EPFL scientists demonstrate that these perceived similarities are largely artifacts of flawed measurement techniques. The study, led by Maria Brbic, Fabian Gröger, and Shuo Wen, was published on arXiv and will be presented at the 2026 International Conference on Machine Learning in Seoul. The team analyzed how concept similarity is measured within high-dimensional vector spaces, where AI models encode information. They found that standard similarity metrics artificially inflate alignment scores as model capacity increases, independent of actual shared learning. This distortion stems from high-dimensional geometry, where distance concentration causes unrelated data points to appear artificially close, creating a mathematical baseline that mimics convergence. Rather than adopting a universal, globally aligned representation of reality, modern AI systems exhibit stable local neighborhood relationships. Concepts such as vehicles or biological species consistently cluster together across different architectures, even when the broader geometric structure of the models diverges significantly. To better describe this phenomenon, the researchers propose the Aristotelian Representation Hypothesis, emphasizing relational categorization and contextual proximity over universal ideal forms. The findings carry direct implications for AI development, particularly in system alignment, multimodal integration, and model interpretability. The EPFL team has developed a corrected evaluation framework that filters out high-dimensional bias, revealing consistent local convergence across language, vision, and video models. This refined metric provides a more accurate basis for comparing architectures and tracking genuine shared learning. The original MIT authors responded constructively to the EPFL analysis, acknowledging the value of rigorous methodological refinement. The study does not dismiss AI convergence entirely but redirects focus toward precise structural alignment. Future research will examine which local patterns consistently emerge across systems and how these insights can improve safety, interoperability, and alignment in advanced AI pipelines. By distinguishing mathematical artifact from genuine shared representation, this work establishes a more robust foundation for evaluating how machines perceive and organize knowledge.