Researchers Tao Jiang, Minbo Gao, and Shaowei Cai have published a study on the Parhi-Nowak deep-RBV^2 architecture for Gaussian regression. They found that the number of parameters in this type of neural network grows quadratically with depth, which is a fundamental limit on how complex these networks can be. This result has implications for understanding the trade-offs between model size and performance in deep learning models. --- Why it matters: This research matters to AI engineers because it provides new insights into the fundamental limits of deep learning architectures, specifically the relationship between model complexity and depth. Understanding these limits is crucial for designing more efficient and effective neural networks. Source: https://arxiv.org/abs/2608.17434