16th October 2024
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
- The new GPT-5.6 family: Luna, Terra, Sol - 9th July 2026
- sqlite-utils 4.0, now with database schema migrations - 7th July 2026
- sqlite-utils 4.0rc2, mostly written by Claude Fable (for about $149.25) - 5th July 2026