Do independently trained monolingual language models converge on shared linguistic principles? To explore this question, we propose to analyze a suite of models trained separately on single languages but with identical architectures and budgets. We train sparse autoencoders (SAEs) on model activations to obtain interpretable latent features, then align them across languages using activation correlations. We do pairwise analyses to see if feature spaces show non-trivial convergence, and we identify universal features that consistently emerge across diverse models. Positive results will provide evidence that certain high-level regularities in language are rediscovered independently in machine learning systems.