Transformer

Transformers represent inputs as tokens and use self-attention to let each token draw on information from other positions, with feed-forward layers refining the resulting representations. Encoder-only models such as BERT, autoregressive decoder-only language models, and encoder-decoder systems are adapted for tasks ranging from text generation and image recognition to forecasting and scientific simulation. Current approaches improve efficiency and capability through local or sparse attention, retrieval and memory, hierarchical processing, and hybrids with convolutional or state-space layers; evaluation spans language-modeling, long-context, vision, and domain-specific benchmarks. A central tension is whether architectural changes and larger models deliver reliable gains in generalization and reasoning while controlling training and inference cost, and theory and interpretability work continues to probe how Transformers learn and compute.