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Résumé : This paper presents novel visual diagnostics for understanding and refining iterative optimization procedures in high-dimensional data analysis. Through innovative Latent Space Analysis & Embedding Visualization techniques, the approach provides a unique feedback mechanism, revealing how projections evolve and where optimization paths encounter complexities. This enables practitioners to diagnose and address intricacies within model transformations, ensuring robust and insightful data exploration.