Therefore, we investigate and benchmark three null models for their ability to generate invalid SELFIES. Since SELFIES is a token sequence and the VAE's decoder outputs token probability distributions per sequence position, we analogously explore random positionwise SELFIES generation for null models using the training dataset. These models test representational robustness because the authors claim that "each SELFIES corresponds to a valid molecule, even entirely random strings". We construct three generative null models that increasingly leverage SELFIES training distribution information.