A common misconception about Transformers is to believe that they're a sequence-processing architecture. They're not.
They're a set-processing architecture. Transformers are 100% order-agnostic (which was the big innovation compared to RNNs, back in late 2016 -- you compute the full matrix of pairwise token interactions instead of processing one token at a time).
The way you add order awareness in a Transformer is at the feature level. You literally add to your token embeddings a position embedding / encoding that corresponds to its place in a sequence. The architecture itself just treats the input tokens as a set.
Recent articles
- Olmo 3 is a fully open LLM - 22nd November 2025
- Nano Banana Pro aka gemini-3-pro-image-preview is the best available image generation model - 20th November 2025
- How I automate my Substack newsletter with content from my blog - 19th November 2025