Researchers from the University of Edinburgh and Johns Hopkins University have found that there is no theoretical limit to how many languages a single neural network model can understand. The 'curse of multilinguality' refers to the idea that as more languages are added to a model, its performance degrades. However, the authors argue that this degradation is not due to any fundamental limitation in the model's architecture, but rather due to real-world data and training conditions. They formalized two conditions for 'perfect multilinguality', which they proved can be achieved with only a logarithmic increase in the model's dimensionality. --- Why it matters: This research matters because it provides new insights into the limitations of multilingual models and could inform the design of more efficient and effective language understanding systems. It also suggests that current empirical approaches to addressing the curse of multilinguality may be misguided, and that a reevaluation of data and training conditions is necessary. Source: https://arxiv.org/abs/2608.17088