Alternatives To Transformers Trend 2026

In machine learning, alternatives to transformers include recurrent neural networks, state-space models, and convolutional architectures. These models process sequential data efficiently, often with lower computational costs. They are used in tasks like time-series forecasting, speech recognition, and lightweight NLP. Researchers, developers, and resource-constrained teams benefit from their speed and reduced memory demands.