Matrix Orthogonalization Enhances Noisy Associative Recall in mLSTM Models

Matrix orthogonalization improves memory retention in recurrent architectures by enhancing performance on Noisy Associative Recall (NAR) tasks. This method addresses the associative recall gap between RNNs and Transformers, offering a more efficient alternative for long-horizon reinforcement learning without quadratic attention overhead.