We also observed the same errors in the module's earlier version, 2.1.0. Although the VAE was the most effective approach for validity minimization and offered advantages such as a continuous radial organization, it has limitations. The generation process is computationally expensive, requiring inference via a million parameters in the decoder. In time complexity, random vector sampling and decoding is approximately 360 times slower than the fastest null generator, shuffle random. In space complexity, the decoder consumes approximately 5000 times the memory of the cheapest null generator, the index-token distribution random method (Table ). Furthermore, gradients dictate the generative potential of deep learning models. We generated SELFIES sets of size 10,000 at 994 evenly-spaced radii from 6.0 to 1000.0 with a uniqueness of 1.0 (fraction of generated unique strings). However, we expect the model to approach saturation by degeneracy at higher radii, decreasing uniqueness. This is because geometry (volume and surface area) and gradients (decoder output sensitivity to perturbations in decoded vector) limit the number of unique strings generated by sampling within or over hyper-spheres. We observe lower uniqueness at small radii (R < 6.0) and latent regions with small gradients, such as those at dramatically high radii. Resource, M.J.K.; Supervision, M.J.K., and R.V.C.G.; Project Administration, all authors;